Internet of Things Security Problems

The rapid development of"smart"devices leads to explosive growth of unprotected or partially protected home networks. These networks are easy prey for unauthorized access, the collection of personal information (including from surveillance cameras), interference in the operation of individual devices and the entire system as a whole. In addition, existing solutions for managing a smart house offer work in the cloud, which in turn reduces the availability of the system and simultaneously increases the risk of the unscrupulous use of personal information by the service provider (up to the sale of data to a third party). This article examines the existing access technologies, their weaknesses, and offers solutions to improve the overall security of the system with a local IoT gateway and virtual subnets.

Introduction
The Internet of Things (IoT) is essentially a construct where machines (cloud and data centerbased apps) and common devices (such as watches, toasters, thermostats, body monitors and cars) are connected to each other via the public Internet. Within the IoT, common devices are controlled and monitored remotely using wireless networks for the most part, while data flows between the cloud and traditional data centers for analysis and manipulation.
While this may suffice as an appropriate technical definition, it is hardly appropriate in respect to how the consumer must understand the IoT and specifically how it will directly affect their personal privacy. In this regard, the worst-case scenario is one where the consumer forfeits all of their privacy due to ignorance or complacency, and then has every detail of their personal lives made available to anyone who wants to pay for this information from the app provider or one of the many data brokers who will dominate the secondary market for IoT data. Many of these perpetrators will then target these same consumers with specific adverts and offers, as well as performing behavioral experimentation, usually without the consumer"s knowledge, much less specific consent. For further insights on these types of scenarios, see my previous privacy corner postings on data brokers and social experimentation.
The potential for such ubiquity (billions to trillions of devices) of IoT seems like a foregone conclusion at this point. But there are multi-dimensional privacy challenges which must be surmounted if this truly is going to become a reality. To get ahead of these challenges the privacy engineering community (via National Institute of Standards and Technology) is currently involved in intense discussions as to how to "engineer in" the right privacy regime, which will provide users (consumers) with direct control over a wide range of their own personal privacy settings as well as creating auditing and measuring schemes to ensure compliance with both user settings as well as regulatory mandates.
Privacy engineering is a very real challenge, and there are multiple paths in the IoT where a privacy regime must be monitored and maintained:  The device (data generator, data receiver and aggregation point).  The Internet (multi-directional data transport).  The cloud (data manipulation and aggregation point).  The machine (application services, big data repositories, analytics and more). Each path requires appropriate privacy protections to be engineered into it, with user control wherever appropriate (device, machine and others) while being maintained along its entire length (virtual and physical). High levels of encryption, redundancy and security will be necessitated to counter threats in flight as well as at the endpoints. There will also be regulatory controls and adherence monitoring, which must be facilitated along these same pathways. Most of these will fall under the auspices of FTC (US), Data Privacy Act (EU), and other regulatory bodies and statutes across the world.
In parallel with the need for comprehensive privacy, security and compliance capabilities, the IoT is entirely predicated on new business models, which disrupt conventional solutions. An enabler of this disruption is the cost model component, which dictates low inherent costs in the devices, and all other components of the value chain. These cost models will not be conducive to "out of band" controls via bolt on solutions. Engineering-in privacy as part of the device and other pathway structures will be the only path to success in which cost efficiencies are maintained while compliance is assured along the way.
IoT extends the "Zone of Privacy Vulnerability" for consumers to the innermost reaches of their lives. They will be monitored (and potentially manipulated) every second of every day, no matter where they are (unless completely off the grid) or what they are doing (awake or asleep). No longer is there a buffer zone in the form of endpoints such as PC"s, tablets or mobile phones. In the world of IoT, devices are attached to the consumer and embedded in everything around them, streaming data (and secrets) continuously to a variety of benign and potentially nefarious recipients and third parties. It is paramount that we take this into account as we develop a privacy strategy for IoT [1].

Default Passwords
All smart devices are built based on some operating system, sometimes these operating systems are based on Unix, Linux or sometimes they are so simple as they can just run some very specific actions by the way all operating systems has different user privileges and security levels, and this can be one of security potential in this way that manufactures who built these devices or who developed or customized these operating systems can set Default passwords, but now we should know what is the risk potential exactly ,in this article we mention two risk side of such situation.
Users mostly don"t change default passwords and it can be very dangerous even in not targeted attack but in random attacks also the y can be victim of hackers and easily they can lost their privacy and security.
And second, users don"t know about these different passwords, because mostly manufactures use some of these passwords as their backdoor or just for their service centers, by the way without telling clients such habits are prohibited by international privacy laws like article 8 of the European Convention on Human Rights, which was drafted and adopted by the Council of Europe in 1950 and meanwhile covers the whole European continent except for Belarus and Kosovo, protects the right to respect for private life: "Everyone has the right to respect for his private and family life, his home and his correspondence". Through the huge case-law of the European Court of Human Rights in Strasbourg, privacy has been defined and its protection has been established as a positive right of everyone. Data privacy laws are converging in the EU, helped by the National data protection authorities and the Data Protection Directive adopted in 1995. Article 17 of the International Covenant on Civil and Political Rights of the United Nations of 1966 also protects privacy: "No one shall be subjected to arbitrary or unlawful interference with his privacy, family, home or correspondence, nor to unlawful attacks on his honor and reputation. Everyone has the right to the protection of the law against such interference or attacks." And same instance on different regions and countries, but still such hidden passwords levels are available for example, A backdoor has been found in devices made by a Chinese tech firm specializing in VoIP product. Or According to the leaked information, FortiOS operating system, deployed on Fortinet"s FortiGate firewall networking equipment, includes an SSH backdoor that can be used to access its firewall equipment. Anyone with "Fortimanager_Access" username and a hashed version of the "FGTAbc11*xy+Qqz27" password string, which is hard coded into the firewall, can login into Fortinet's FortiGate firewall networking equipment. This issue affected all FortiOS versions from 4.3.0 to 4.3.16 and 5.0.0 to 5.0.7, which cover FortiOS builds from between November 2012 and July 2014, and of course many different other instances.

Limited Permission Access
Mostly companies don"t take full permission to clients, Some manufacturers or carriers may try to refuse you warranty service if you find a specific privilege access level to manage their devices. For example, on Mobile Devices we see such issue, iPhone are Jailed, Android devices are not Rooted and then clients cannot access to file systems and make changes in deep of system files or even install some application to give information about their devices. Then without security analyzing we cannot approve these manufactures don"t use clients data"s or even they don"t have any backdoor on such devices. specially smart devices are involved to our life 24/7 and even homes TV or even refrigerators and are on such risks.

Privacy for Sale
Some companies also collect and analyze information about users" "tweets, posts, comments, likes, shares, and recommendations." While many of these details were already available on the data companies" websites, the lawmakers used the letters as a chance to raise awareness about an industry that they said has largely "operated in the shadows." And now with growing IoT devices such companies can work easier because they have ear & eye everywhere, in our homes, in industry or even in very sensitive structures, today IoT devices are working everywhere and collecting data for manufactured companies are easy, and because of reasons that we wrote before even it is hard to detection. In this phase, we can see to side of risk, first side is non personal data"s, for example, the Czech Republic based security software vendor AVG Technologies recently updated its privacy policy. The objective of the changes, according to the company, was to explain in a more transparent manner to their users how it intends to use what it calls "non-personal information". The new privacy policy will take effect on 15 October 2015.The company defines "non-personal data" as data that cannot be linked to the identity of users in any way. The new privacy policy explains that the company might collect and sell this information to third parties) or many other samples.
Over the past years or so, a huge amount of attention has been paid to government snooping, and the bulk collection and storage of vast amounts of raw data in the name of national security. What most of you don"t know, or are just beginning to realize, is that a much greater and more immediate threat to your privacy is coming from thousands of companies you"ve probably never heard of, in the name of commerce. They"re called data brokers, and they are collecting, analyzing and packaging some of our most sensitive personal information and selling it as a commodity to each other, to advertisers, even the government, often without our direct knowledge. Much of this is the kind of harmless consumer marketing that"s been going on for decades. And as we mentioned even it is possible for them to sell personal data"s specially for spying or watching for example: released to the public on March 7, 2017, the first set of documents has been called "Year Zero" by Wikileaks. It is said to include details of the CIA"s global hacking program, its malware, and zeroday exploits for a number of devices. And in these devices even smart TV"s are available and the exploits are about remote voice sniffing or remote capturing.

Data Manipulation
Today, more than 92 percent of critical business records are generated, managed and stored electronically, creating efficiencies and cost-savings for businesses. Unfortunately, digital information can be easily deleted, altered and/or manipulated. For businesses, the burden of proof is on the company to ensure and attest to the accuracy and credibility of their electronic business records. This ability to prove the integrity of critical business records becomes especially important in litigation where executives are often called upon to support their claims of ownership of any discoverable records, as well as verify their history of creation and use. Electronic records have been proven to have been manipulated in cases ranging from stock options fraud to loan fraud to intellectual property disputes.
Some recent examples of actual cases surrounding the manipulation of electronic records include top executives at a successful technology company attempted to alter electronic records to hide a secret options-related slush fund to cover the tracks of their backdating options scheme.
A prominent real estate developer received an electronic version of a loan agreement to print and sign. Rather than just signing the document, he made subtle changes to it in order to make the terms of the loan more favorable to himself. The changes went undetected for a year until the loan was refinanced or in some other possible examples for example some companies can manipulate clients data"s like their power-supply counters, or any frauds [2].

Suggestion as Solutions
As first solution, we are suggesting using clouds. The cloud is a very broad concept, and it covers just about every possible sort of online service, but when businesses refer to cloud procurement, there are usually three models of cloud service under consideration, Software as a Service (SaaS), Platform as a Service (PaaS), and Infrastructure as a Service (IaaS). The comparison of cloud models is shown on Fig. 1.

Infrastructure as a Service
IaaS provides the infrastructure such as virtual machines and other resources like virtualmachine disk image library, block and file-based storage, firewalls, load balancers, IP addresses, virtual local area networks etc. Infrastructure as service or IaaS is the basic layer in cloud computing model. Platform as a Service PaaS or platform as a service model provides you computing platforms, which typically includes operating system, programming language execution environment, database, web server. It is a layer on top of IaaS as the second thing you demand after Infrastructure is platform.

Software as a Service
In a SaaS you are provided access to application services installed at a server. You don"t have to worry about installation, maintenance or coding of that software. You can access and operate the software with just your browser. You don"t have to download or install any kind of setup or OS, the software is just available for you to access and operate. The software maintenance or setup or help will be provided by SaaS provider company and you will only have to pay for your usage.

Fig. 1. Comparison of cloud models
In our solution data from IoT sensors to cloud should be encrypted, and using cloud as infrastructure to process data"s without knowing which data they are receiving then even PaaS is not suitable in this case because in PaaS, operating system, middleware and runtimes are also As the abilities and needs of devices proliferate, it is often not possible to have them communicate directly with systems. Some sensors and controllers don"t support energy-intensive protocols like Wi-Fi or Bluetooth. Some devices aggregate data so that it is overwhelming and invaluable in its raw form and they are all connecting to a variety of public and private networks.
An IoT gateway performs several critical functions from translating protocols to encrypting, processing, managing and filtering data. If you imagine an IoT ecosystem, a gateway sits between devices and sensors to communicate with the cloud.
IoT gateways help to bridge the gap between operations and IT infrastructure within a business. They do this by optimizing system performance through the operational data they gather and process in real-time in the field or at the network edge.
IoT gateways can perform a number of enhancements on the OT and IT silos:  High scalability-they are able to take intelligent data from the datacenter or cloud and push into the field or network edge.
 Lowering costs-end-point devices needn"t have as high processing power, memory or storage since the gateway does this all for them.
 Faster production-an accelerated and more advanced production line can decrease time-to-market significantly.
 Reduce telecommunications cost-less M2M communication means a smaller network and (WAN) traffic.
 Mitigate risks-gateways can isolate devices and sensors that aren"t performing before they cause bigger problems for the production line.

Adding a Layer of Security
As the number of devices and sensors grow, so does the number of communications that will take place over a combination of public and private networks. Communications between the "things", the gateway and the cloud therefore must be secure in order to prevent any data tampering or unrestricted access.
This will usually happen through a PKI infrastructure, whereby every "thing" that communicates is given an identity, that is, a pair of cryptographic keys (or Digital Certificate) which allows communication to be encrypted. This can be quite a handful to manage without the help of an IoT gateway.
Assuming you have a tool which manages all of your device certificates, you need the gateway to help mediate the on-boarding of devices (installation of certificates and provisioning of identity) How to Secure an IoT Gateway There are three key core principles of security-confidentiality, integrity and authentication. You will need to ensure that all communications between the gateway and devices are meeting each of the three principles while communication is happening in the internal and external networks.
It is also worth noting that the gateway is often the first to be attacked because of two reasons: It has a higher processing power, which it can use to run more intensive applications. More power means better software, but better software means more vulnerabilities for a hacker to exploit.
Because of its location as an Edge device between the internet and the intranet, the gateway is the point of entry for any threat vector (as well as a system"s first line of defense).
Our recommendations on securing an IoT gateway device involve three steps.
Step 1. Identity for the Gateway Device The first step would be to give your gateway device an identity (by using an X.509 Digital Certificate). Any external entities connecting to the gateway will now be able to verify the identity of the gateway which is now enabling HTTPs or NTLS protocols. Commands being issued to devices or sensors in the field are now coming from a trusted device.
Step 2. Enable "Strong" Identity for the Gateway Device Because your gateway device is vulnerable to physical tampering, private keys can be extracted and cloned leaving your gateway device vulnerable to spoofing or even man-in-themiddle (MITM) attacks.
In order to prevent this, you would have to use extra security measures, such as embedding a Trusted Platform Module (TPM) device into your Gateway, using a PUF (Physical Un-clonable Function). This would securely store the private keys of all Digital Certificates, making sure they never leave the gateway.
Step 3. Use the Gateway to Provision Identity to Your Ecosystem Now that you have enabled strong identity in your gateway device, you need to think about having strong identity for the devices and sensors in the field. Because some of these are likely unable to connect to the internet, provisioning identity through a Certificate Management Service without a Gateway will be difficult [3].
Instead, we can use the gateway as a trusted security mechanism to secure anything that is connected to the gateway (on the intranet). The gateway acts as a proxy between the platform (CA Services) and the devices in the field. As with the device itself you would expect this to happen using the standard PKI infrastructure, that is, an X.509 certificate through a private hierarchy. Now the gateway and devices are secure and therefore all the communication in your intranet is secure. Therefore, you have security, confidentiality and authentication, allowing your IoT ecosystem to be end-to-end secured using a PKI infrastructure [4].
Another solution can be like diagram as shown in Fig. 4.

Raspberry Pi
Internet Access point Web service DNS server DDNS client (optional) DHCP server Firewall IDS/IPS

Fig. 4. Virtual networks on one device
In this scenario on Raspberry Pi be should install at least these services: web service (e. g., Apache); DNS server (Bind); DDNS client (optional); access point (HostAPd); DHCP server; firewall (iptables); IDS/IPS (Snort).
Also for balancing load and make another layer for security, it can be expand and add a router to another scenario (Fig. 5).  In this scenario on RaspberryPi be should install at least these services: web service (Apache); access point (HostAPd); firewall (iptables); IDS/IPS (Snort). On AP we need install: DHCP and DNS.
And in this scenario we don"t need DDNS and also ESP8266 working as Access-point Repeater and it resend all UDP packets that received from other IoT devices who are connecting to that. In this way, Raspberry Pi can work as brain of this network and some IoT devices are connecting to Raspberry Pi directly and some others they are connecting to ESP8266, and ESP8266 repeating the Raspberry Pi wireless network and also it send all UDP Data"s to Raspberry Pi then directly or in-directly all IoT devices are connecting to Raspberry Pi and it can analyze all of these data"s and show results on Dashboard.


Virtual Friendly Device for Women Security

Crimes against women is becoming a daily routine in our country, nowadays. An increased demand persists in developing safety devices for ensuring safe and secure environment for women. The main aim is to develop a “Virtual Friendly” device, to safeguard themselves while in trouble. The proposed system developed consists of GPS, GSM modem, Microcontroller (LPC812), RF transceiver, Temperature Sensor, Voice Recognizer. GPS Receiver gets the location information from satellites in the form of latitude and longitude. The Microcontroller processes this information and this processed information is sent to the POLICE control room. The temperature sensor senses the body temperature continuously. SMS alert will be sent in case of low temperature which indicates emergency condition. The system can be activated by the user by themselves, when they are in need of self-defense. Features such as Alarm sound, voice recognition are added to the system to ensure optimum efficiency. The voice recognizer circuit is used to send command with voice by using the codes SAVE ME and HELP ME. The device is designed as a portable kit and easily operated, so that any women can easily carry with them anywhere and everywhere.

INTRODUCTION
No woman should be a victim of physical abuse. Women have to feel like they are not alone, but in the modern era women are feeling insecure. In corporate and IT sector, many women are working even in night shift. It has been recorded as the largest fall in the crime rate than other cities in India. It was just opposite in the capital of India, Delhi. Crime rate against women in Delhi was 17.6/100,000 females in 2000 (2,122 incidents) and 151.13/100,000 females in 2013 (11,449 incidents). The safety of women is very important in present days as crime actions against women are increasing drastically [1]. The fear has been caused by repeated case of violence toward women. Do not want to use the term 'harassment' instead it is sexual terrorism today. More than 800 women are harassed, raped, killed every day [7]. Schools and universities must compulsory teach the students to learn about physical education and art, schools do equip student with basic skills of life especially to safeguard of their own. All these issues forced to develop an security and safety system for women. The system is designed not only serve the purpose of providing security to women but also to face social challenges they never feel helpless [6]. The various problems arise can be overcome with the "all in one intelligent safety system for women security" [11]. The existing mobile application need lot of human interaction to operate and to send alert to police and family member by sending their location [3]. This application is not only used for cases like rapes and any perverts teasing girls but this also helps them from any bad condition or any health problem like fainting suddenly [10]. (c)ON WATCH: It allows the user to easily alert friends and emergency responders and police when needed with the GPS location.

(d)FAMILY LOCATOR:
This application notifies about the near and dear ones about the trouble .it provides useful information about the criminals in the neighbourhood tracking the most visited locations and so on [4].
(e)SENTINEL: It is a smartphone application used o serve as a virtual security guard for women the users can press a button once they feel they are being stalked or harassed [4].
(f)STREET SAFE: This application has a feature called "WALK WITH ME" which gets the details of the situation and says online until they ensure the users gets back home safe .In case the call is cut, the safety advisor connects the use to the local police for further help and guidance. In case of emergency situations, a feature called "SILENT ALARM" enable to get local help from the real-time location using the GPS and the physical description of the user.
The Internet of things (IOT) deals with the use of intelligently connecting devices and system to explore data gathered with sensors embedded machines actuators and other object in physical manner. IOT refers about the devices in network which sense, collection of data from around the world and then sharing the data over the internet where it can be utilized for various purposes and processed .The system propose is to provide safety and security for the women. As being in a developing nation and independent country, even today women's are not safe, so there must be some constructive measure for the women's security [8]. The proposed device is wearable one and easy to carry with more feature and functions, it also consist of audio recognizer which is more useful for physically challenged people [1], radio frequency identification as the name implies use of radio frequency to transfer data in wireless and non-contact style. The paper is organized as follows. Section I explained about the Introduction. Section II explains the related works on Women safety and security, Section III explores the proposed method, which includes both the hardware and software. Finally in last section result, conclusion and future works are explained.

LITERATURE REVIEW
GPS and GSM based Self Defense [1] deal with most of the critical issues faced by women, the design is proposed. This mechanism provides viewing the location of the victim in terms of latitude and longitude which can further be tracked using Google maps. This system helps to decrease the crime rate against women. These crimes can be brought to an end with the help of real time implementation of our proposed system. The microcontroller used is PIC16877A. It is interfaced with a push button, a GPS module, a GSM modem and a speech circuit (ISD1820PY). Internet of things plays [2] a major role in the system explained below. The parameters monitored are body temperature, skin resistance with an interrelationship process. The hardware components used are skin resistance accelerometer and force sensors. The device is used when the individual wants to go to a remote place and when they are in need of protection. The main objective for this work is to create a wearable IOT device for the security and shielding of women, girl children and the details of the rape and violence happening in the society against women.  [3] is a very important issue due to rising crimes against women these days. These existing devices use to read the human temperature and heartbeat to generate alarm in case of emergency. Every human may have different body temperature and heart beat pattern and thus keeping a fixed threshold for finding out emergency situation and then generating alarm is not correct way and this is where the existing devices are failing to correctly generate alarm in case of emergency. It deals to design a wearable women safety device that automatically reads and create patterns such as body temperature and pulse rate during running. If readings are higher than the normal readings then it will automatically call and message more than one person along with the location so that actions can be taken. The data is first collected by sensors in non-danger conditions to train the algorithm, after that data is used for testing the gauge accuracy. Thirdly, it deals with scenarios where there is no internet facility.
The implementation of wearable devices [4] is that women's are facing more troubles physically in countries like India due to lack of safety. Government also provides some safety system but it is necessary for each women to protect them self. In such case some additional equipment's are needed to protect themselves. The safety device is a wearable jacket which consists of a control button, when the victim presses the button information sent to the parent and nearly police station as location by a GPS and GSM modem. The jacket can also produce a non-lethal electric shock in emergency situation to attack or get rid of the attacker. Women Security System [5] identifies the surveys about the security system for women which allow immediate response in case of any harassment. The security system uses RFID technology. An RFID is embedded with the watch and RFID reader is embedded in mobile phone, when the user is in danger or critical situation, button is pressed in the watch. The RFID tags send information as radio frequency and received in mobile. The Processor processes the information and sends alert "HELP" and location 4 or 5 pre-defined contacts through GPS.
Women Security System [6] is very much benefitable. A one touch system using GSM & GPS to make women feel safe and secure. The system consists of raspberry pi, GSM, GPS and force sensor. When a woman is in danger or critical surrounding, a button is to be pressed on the device, suddenly the GPS track the location and with help of GSM The system sends message to the pre-defined number and police control room. The system provides the complete security to women's and kids. Smart Security Solution for Women based on Internet of things [7] is a device which automatically senses and rescues the victim. The hardware comprises of a wearable "Smart Band" which communicate with the smart phone via internet. An application is programmed with dates such as anger fear and anxiety, which is generated as a signal and transmitted to phone whenever an emergency signal is transmitted to the smart phone. The software generates a message (GPS) location to the pre-processed number or police station nearby. The Internet of Things [9] with the wearable devices is to analyze the factors that influence employees to use wearable devices at the workplace. The factors such as risk and trust are used to develop a conceptual model. Partial least square path and adaptive Neuro-Fuzzy inference modeling are used to validate and predicts these factors influencing employees to wear the device. Smart Solution for Women Safety [10] consists of flux sensor, vibration sensors, sensor for heart beat and tilt sensors are used for the purpose of safety. Location is detected using GPS and the same is send to the nearby police station and relatives which tend to monitor the health parameters of the woman.

PROPOSED METHODOLOGY
In the proposed methodology, women safety and security system is designed using the ARM processor. It has two inputs as two switches. By pressing the switch 1 it is enable to detect the victim location by using GPS and SMS is sent to the predefined mobile number by using GSM module. Another switch is recognized as panic switch, if the women get panic she can press the switch so that an alarming system is introduced. The security system is also featured with the temperature monitoring system, which would monitor the body temperature continuously and an SMS alert will be sent if it reaches below the threshold of 20 degree Celsius. Another additional feature such as voice recognition module is used in critical situations if the particular person is unable to press the switch by own. For the above function to work a voice code is already recorded and voice is recognized and an SMS alert is sent to predefined mobile numbers and control room. The block diagram, circuit diagram [ Fig 1, 2] explains about the transmission part of the wearable safety device. When Switch 1 is ON, the RF Transmitter transmits signal to the GPS and GSM Modem. When Switch 2 is ON the RF Transmitter transmits signal to alarm. The temperature sensor sense the temperature continuously, when it reaches below threshold, RF transmitter transmits signal to the GPS and GSM modem.
When voice recognizer [Fig 3, 4] recognize the code RF transmitter transmits the signal to the GPS and GSM modem. The transmitted signal from the Switch 1 is received in the receiver and the location is read and sent as alert message to police control room, When Signal from switch 2 is received the alarm rings for 10 seconds. Also when the temperature falls below the threshold, the location is sent as alert message to the control room GPS and GSM modem.

A. ADVANCED RISC MACHINE [ARM]
The Processor used is a low Cost ARM Processor. The LPC812 are an ARM Cortex-M0+ based, lowcost 32-bit MCU family operating at CPU frequencies of up to 30 MHz The LPC81x support up to 16 KB of flash memory and 4 KB of SRAM. The peripheral complement of the LPC81x includes a CRC engine, one I2C-bus interface, up to three USARTs, up to two SPI interfaces, one multi-rate timer, selfwake-up timer, and state-configurable timer, one comparator, function-configurable I/O ports through a switch matrix, an input pattern match engine, and up to 18 general-purpose I/O pins. The system consists of ARM Cortex-M0+ processor, running at frequencies of up to 30 MHz with Single -cycle multiplier and fast single-cycle I/O port. and ARM Cortex-M0+ built-in Nested Vectored Interrupt Controller (NVIC). The system tick timer, Serial Wire Debug (SWD) and JTAG boundary scan modes (BSDL) are supported. Micro Trace Buffer (MTB) are also supported. The ROM API support Boot loader, USART drivers, I2C drivers, Power profiles, Flash In-Application Programming (IAP) and In-System Programming (ISP). DHT11 temperature sensor features a temperature complex part with a calibrated digital signal output, it ensures high reliability and excellent long-term stability. This sensor includes a resistive-type temperature measurement component and an NTC temperature measurement component, and connects to a high performance 32-bit microcontroller, offering excellent quality, fast response, anti-interference ability and cost-effectiveness. It is used to monitor the temperature continuously. Four pins connected are mainly 1-vcc,2-Data,3-NC,4-Gnd.It is used for the sensing the body temperature an alert is sent when it reaches below the particular threshold limit. One capacitor valued 100nF can be added between VDD and GND for power filtering.

C. GSM MODULE [SIM600]
SIM800 is a quad-band GSM module that works on frequencies GSM 850MHz, EGSM 900MHz, DCS 1800MHz and PCS 1900MHz. The above module is used to send messages from the GPS module. With a tiny configuration of 24*24*3mm, SIM800 can meet almost all the space requirements in users applications, such as M2M, smart phone, PDA and other mobile devices. SIM800 has 68 SMT pads, and provides all hardware interfaces between the module , One full function UART port, and can be configured to serial ports.SIM800 is used with power technique SIM800 integrates TCP/IP protocol and extended TCP/IP AT commands which are very useful for data transfer applications.

D. Lumisense GPS Module [VP310]
The Global Positioning System (GPS) is a global navigation satellite system that provides location and time information in all weather conditions. The GPS operates independently of any telephonic or internet reception. The signal information is received by the GPS receiver in order to measure the user's correct position. Many GPS data is received in the form of around 11 satellites, out of this GPRMC data is being taken for the messaging.

Modem Data:
Supply voltage: 12v DC Interface: UART RS232 Optional T-TL Uart also available Precision: 5 meters, Automatic antenna switching function

E. Voice Recognizer [V3.1]
Voice recognizer module is used to send the input by voice and output is detected as message. The voice is already prerecorded in the module by using the voice code. The device works at an input voltage range of 4.5 -5 volts and will draw a current less than 40 MA. This module can work with 99% recognition accuracy if it is used under ideal conditions. The choice of microphone and the noise in the environment plays a vital role in affecting the performance of the module. Because of noise it is a microphone with good sensitivity and reduces the noise at the background. The prerecorded voice code such as "SAVE ME "HELP ME" is recorded and SMS alert is being sent.

F. RF TRANCEIVER [TRANSMITTER & RECEIVER]
The radio frequency (RF) transmission system employs Amplitude Shift Keying (ASK) with transmitter/receiver (Tx/Rx) pair operating at 433 MHZ. The transmitter module takes serial input and transmits these signals through RF. The transmitted signals are received by the receiver module placed away from the source of transmission. The RF transmitter and receiver may be used for the approximate of 100 meters. RF module uses radio frequency to send signals. These signals are transmitted at a particular frequency and a baud rate. A receiver can receive these signals only if it is configured for that frequency.

Specifications
Receiver Operating Voltage: 3V to 12V Receiver Operating current: 5.5mA

G. Switch [Push Button]
The push button is a simple type of switch that controls a action in that process. Normally Open (NO) Push Button is used its default state, makes no electrical contact with the circuit When the button is pressed down, the switch makes electrical contact and the circuit is now closed. In this Project switch1 is used to detect the location and another switch2 is used to alert by alarm.

H. BUZZER [3.3V]
Buzzer is an audio signaling device. The buzzer has two pins Pin1-Identified by (+) symbol or longer terminal lead. Can be powered by 6V DC Pin2 -Identified by short terminal lead. Typically connected to the ground of the circuit

A. EMBEDDED C
Embedded C is the most popular embedded software language in the world. Embedded C programming requires nonstandard extensions to the C language in order to support exotic features such as fixed-point arithmetic, multiple distinct memory banks, and basic I/O operations. Additionally, using C increases portability, since C code can be compiled for different types of processors. One of the more popular ones is the Keil compiler. Because of this, Embedded C is also sometimes known as KEIL. It is small and reasonably simpler to learn, understand, program and debug. C Compilers are available for almost all embedded devices in use today, and there is a large pool of experienced C programmers. Unlike assembly, C has advantage of processor-independence and is not specific to any particular

STEPS TO CREATE FILE
New µvision project Open a new tab in the keil software.
x Folder creation Create a separate folder for the new project and save the project.
x Device selection Click the device option and search for LPC812 Micro controller.
x Core Select the core option and tick.
x Start up Select the device start up option.
x Clock setup MSEL -> 0×04 PSEL -> P=2 Clock frequency required is 30 MHZ. Clock divider is used to reduce the frequency from 60MHZ to 30MHZ .
x Language Selection C file is selected and file name is added to the project file.
Program is written and stored in this C file, to get the output, HEX. File to be created. After creating the hex.File, the program will be executed. The operation or flow of execution is shown in figure 5. When Switch 1 is pressed, device is ON and RF signal is sent from the transmitter to receiver in the KIT 2. The program stops when the switch 1 is pressed again. The voice is detected in the voice recognizer and signal is sent to the KIT 2. Again when the Switch 2 is pressed the RF signal is sent to the KIT 2 to initiate the alarm circuit. Meanwhile temperature is continuously monitored and compared with threshold .when temperature lowers below the threshold signal is sent to the KIT 2, if the temperature is low there will be no action. The flow diagram for KIT2 is shown in figure 6. The Transmitted signal is received in the KIT 2.For switch 1, GPS reads the location and send this information as an alert message using GSM. For voice, the voice is recognized and the location is sent to the predefined number as message using GSM Modem.
Additionally alarm system is on if the receiver receives signal from the transmitter.  An SMS alert is sent to the predefined contact the input is by pressing the switch. The output is sent to the predefined contacts. The output is shown as "EMERGENCY SITUATION HELP ME and it is normally decoded as in the form of Latitude and Longitude parameters. [1333326.000, A, 1257.7700N, 080003.5286, E, 283.54, 130319, A*6A] and the detected location is identified by using GOOGLE MAPS. A buzzer output Alarming signal is used as a panic Switch A temperature sensor for sensing the body temperature and a limit if it mainly reaches below the threshold[20 C] .A Voice recognizer output signal is used to send the message with I'M Kidnapped Emergency Pls help me an output signal is shown in the predefined contact Number/Relatives Voice Recognizer is used to send the input by Voice as "HELP ME" and output is mainly sent to the predefined contact number and relatives. The output is sent to the relatives by "I'M KIDNAPPED EMERGENCY PLS HELP ME" as location is normally sent in the form of latitude and Longitude and the detected location is normally identified by using GOOGLE MAPS.

CONCLUSION AND FUTURE WORKS
The objective of devising the women safety and security system is proposed and designed in this paper. This small and light weight device can be easily carried by woman and young ones while travelling on road, work place and wherever they want. By using this alert device with self-defense mechanism of


extfieldsofstudy:
Computer Science
Mathematics

text:
Tweets Classification on the Base of Sentiments for US Airline Companies

The use of data from social networks such as Twitter has been increased during the last few years to improve political campaigns, quality of products and services, sentiment analysis, etc. Tweets classification based on user sentiments is a collaborative and important task for many organizations. This paper proposes a voting classifier (VC) to help sentiment analysis for such organizations. The VC is based on logistic regression (LR) and stochastic gradient descent classifier (SGDC) and uses a soft voting mechanism to make the final prediction. Tweets were classified into positive, negative and neutral classes based on the sentiments they contain. In addition, a variety of machine learning classifiers were evaluated using accuracy, precision, recall and F1 score as the performance metrics. The impact of feature extraction techniques, including term frequency (TF), term frequency-inverse document frequency (TF-IDF), and word2vec, on classification accuracy was investigated as well. Moreover, the performance of a deep long short-term memory (LSTM) network was analyzed on the selected dataset. The results show that the proposed VC performs better than that of other classifiers. The VC is able to achieve an accuracy of 0.789, and 0.791 with TF and TF-IDF feature extraction, respectively. The results demonstrate that ensemble classifiers achieve higher accuracy than non-ensemble classifiers. Experiments further proved that the performance of machine learning classifiers is better when TF-IDF is used as the feature extraction method. Word2vec feature extraction performs worse than TF and TF-IDF feature extraction. The LSTM achieves a lower accuracy than machine learning classifiers.

Introduction
Text mining is one of the distinguished fields of data mining which possesses the potential to extract useful information from raw data. In a world where 2.5 quintillion bytes of data are generated every day, text mining has become a key tool to retrieve meaningful data and organize them into profitable information [1,2]. Text classification is becoming a prominent field of research in text mining, especially after the inception and penetration of social platforms such as Facebook, Twitter, etc. People express their views on such platforms and their opinions serve as the guideline to design and govern the policies of various companies. For example, the tweets can be analyzed to find the sentiments of the users about a specific company or product, which helps to devise policies to increase the acceptance of products or improve user services. The wide use of such social platforms leads to generate many data that contain a variety of potential information.
The last few years have witnessed a growing interest in social network databases due to their richness and versatility. One iconic use of such data is to analyze user sentiments about a particular product or company. Such analysis of user sentiments from text is called "sentiment analysis' [3]. Sentiment analysis is a famous method that is used to extract people's reactions, opinions, reviews and feedback towards a specific product or service of a company. The user feedback on social platforms serves two broad purposes. First, the companies can model policies to attract new potential customers and revise the existing policies to increase the acceptance of their products/services based on sentiment analysis. For example, Rainie and Horrigan [4] pointed out that US presidential campaigns are planned according to the political reviews analyzed from Twitter data. In the same way, sentiment analysis is important for different companies to analyze customer reviews about products and make better decisions for the future [5,6]. Second, online reviews about various products and services have a significant influence on purchase trends [7]. Horrigan [8] pointed out that consumers are willing to pay more for a specific product which has a five-star rating than one that has a four-star rating.
Sentiment analysis can be divided into lexicon sentiment analysis technique, machine learning-based sentiment analysis, and hybrid methods [9], as shown in Figure 1. Lexicon sentiment analysis mainly works on the polarity of tokens (words) in a sentence. A lexicon is a dictionary or a container that contains a large set of standard words that are categorized based on the polarity score. However, most people use very informal words in reviews that are not present in lexicons. Therefore, researchers emphasize applying alternative techniques for sentiment detection in the text. Hence, the second category utilizes machine learning approaches for sentiment analysis. Models can be trained on a sample dataset and later can be used to perform predictions on a different dataset. The problem is formulated as a classification task, for example, a document can be represented by a set of features [5]. After that, these documents are labeled based on the polarity (i.e., positive, negative, or neutral), and converted into a feature matrix. In this way, machine learning approaches give better performance than that of lexicon-based method to detect sentiments [10].  The competition has been rising in every domain of life and airlines are no exception. They aim to generate more revenue by improving offered services and devising new schemes and policies for the future. Social networks play a very important role in such improvements, as the customer's reviews serve as the feedback to such companies. Customers' reviews are analyzed based on the expressions given in the reviews. The volume of such reviews is very high and it requires a large number of experts for analysis and classification. Thus, a variety of machine learning classifiers have been proposed which can help mitigate human effort to classify these reviews. However, improvements are still necessary to further increase the classification accuracy. This research proposes the use of a voting classifier to this end and aims to evaluate the performance of famous machine learning classifiers on a number of twitter datasets. This research serves the following key contributions: • Machine learning-based classifiers including calibrated classifier (CC), support vector classifier (SVC), AdaBoost (ADB), decision tree classifier (DTC), Gaussian naive Bayes (GNB), extra trees classifier (ETC), random forest (RF), logistic regression (LR), stochastic gradient descent classifier (SGDC), and gradient boosting machine (GBM) are trained on US airline twitter dataset.

•
A voting classifier (VC) is devised to perform tweets classification which is constituted by LR and SGDC.

•
Complete and partial pre-processing schemes are adopted to evaluate the impact of pre-processing on models' classification accuracy.

•
Tweets are classified as positive, negative, or neutral and the results are compared against the actual classification to evaluate models' performance.

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A deep learning long short-term memory (LSTM) network is implemented as well to analyze its performance on the selected dataset.
The rest of the paper is organized as follows. Section 2 describes a few pieces of research related to the current study. Section 3 gives an overview of the methodology adopted for the current research as well as a description of the dataset used for the experiment. Results are discussed in Section 4 while the conclusion is given in Section 5.

Literature Review
The area of text classification possesses a huge potential to analyze sentiments and many researchers have investigated the process of sentiment analysis by detecting emotions found in the text [11,12]. Others have proposed sentiment evaluation methods that are formulated by observing human responses to a certain experience [13]. The use of machine learning techniques including naïve Bayes (NB), maximum entropy (ME), and support vector machines (SVM) for sentiment classification has also been studied [14]. For example, the authors of [15] applied NB, ME, and SVM on the Internet Movie Database (IMDb), which consists of movie reviews expressed either with stars or in numerical values. The approach is evaluated using accuracy and recall measures. This work has served as a baseline for many authors and the same techniques have been utilized across different domains.
Similarly, the authors of [16] performed sentiment analysis on travelers' feedback about airlines. The authors found that the feature selection and over-sampling techniques are equally important to achieve refined results. Feature analysis is performed to select the best features which not only improves the overall performance of the model but reduces the training time as well. In addition, the skewed distribution of the classes found in most of the smaller datasets is reduced without causing over-fitting. The results of the research show the compelling evidence that the proposed model has a higher classification accuracy when predicting the three classes of positive, negative, and neutral. The authors of [17] followed a similar approach and performed a multi-class sentiment classification. A feature selection process is used to extract the important features that are later used to train a machine learning-based algorithm. The performance of DTC, NB, SVM, radial basis function neural network, and k nearest neighbor is tested with 10-fold cross-validation.
In another research [18], the authors used customers feedback to investigate different aspects such as loyalty, satisfaction, etc. The loyalty is determined through airline attributes, namely operational factors (punctuality, aircraft, and safety), attractive factors (food and beverages and the staff service), competitive factors (schedule, ticket prices, reputation, and flyer program), etc. The research concludes that the customer's higher satisfaction can be achieved through company reputation, staff service, frequent flyer program, aircraft, and punctuality. Kumar and Sebastian [19] presented a novel approach for the sentiment analysis of Twitter data. To uncover the sentiment, the authors extracted the opinion words (a combination of the adjectives along with the verbs and adverbs) in the tweets. The corpus-based method is used to find the semantic orientation of adjectives and the dictionary-based method to find the semantic orientation of verbs and adverbs. The overall tweet sentiment is then calculated using a linear equation that also incorporates emotion intensifiers. A score is calculated for the overall sentiment of the tweet and tweets are classified as positive, neutral and negative based on the calculated score.
The authors of [20] performed sentiment analysis using a machine learning technique. The polarity is found using TextBlob, SentiWordNet and word sense disambiguation (WSD) sentiment analyzers. TextBlob comes with the basic features of natural-language processing essentials, which are used for the polarity and subjectivity calculation of tweets. SentiWordNet is a publicly available analyzer for the English language that contains opinions extracted from a wordnet database. In addition, W-WSD has the ability to detect the correct word sense within a specified context. The authors of [21] presented a meta-heuristic method called CSK, which is based on cuckoo search (CS) and k-means (K). Since clustering plays a vital role in analyzing the viewpoints and sentiments in user tweets, the research proposes a method that is used to find the optimum cluster head from the twitter dataset. Experimental results show promising outcomes. The authors of [22] investigated the impact of multiple classifier systems on Turkish sentiment classification. The voting algorithm is used with NB, SVM, and bagging to evaluate their efficacy. The results demonstrate that the use of multiple classifiers elevates the performance of individual classifiers. The research approves that multiple classifier systems have more potential for sentiment classification.
In addition to the use of multiple classifiers for classification, employing various pre-processing techniques helps to improve the classification as well. For example, the authors of [23] proved that the selection of an appropriate pre-processing technique may produce enhanced classification performance. They investigated a variety of pre-processing techniques including term weighting, frequency cut, stemming, and stopword elimination to analyze their impact on machine learning-based classification methods. Their research shows that the combination of various pre-processing methods plays a decisive role in finding the best classification rates. They also studied the pre-processing techniques and their relevant impact on the feature space through visualization.
In the same fashion, the use of various feature extraction techniques has proven to improve classification accuracy. Text mining has many feature extraction methods but term frequency (TF), inverse document frequency (IDF), TF-IDF, word2vec and doc2vec are among the most commonly used feature extraction techniques [24]. The authors of [25] investigated the use of TF, IDF, and TF-IDF with linear classifiers including SVM, LR, and perceptron with a native language identification system. Experiments are carried out with ten-fold cross-validation on different languages. The TF-IDF is applied to n-gram words/characters/ parts-of-speech tags. The TF-IDF weighting on features proves to outperform other techniques when applied with uni-grams and bi-grams of words. Similarly, the authors of [26] analyzed the use of three feature extraction techniques with a neural network for the text analysis. TF-IDF along with its two modifications, namely latent semantic analysis (LSA) and linear discriminant analysis (LDA), is applied to evaluate the performance of each feature analysis technique. The experiment shows that TF-IDF helps the model to achieve higher accuracy with large dataset. For smaller datasets, the combination of TF-IDF and LSA is appropriate to achieve similar accuracy.
Machine learning techniques perform better for classification than that of traditional approaches. However, machine learning methods for classification problems commonly assume that the class values are unordered. However, in many practical applications, the class values exhibit a natural order, for example, when learning how to grade. The standard ordinal classification approach converts the class value into a numeric quantity, applies a regression learner to the transformed data and translates the output back into a discrete class value in a post-processing step. The authors of [27] presented a simple method that enables standard classification algorithms to make use of ordering information in class attributes. Tree induction methods and linear models are popular techniques for supervised learning tasks, both for the prediction of nominal classes and numeric values. For predicting numeric quantities, research has been conducted on combining two schemes into "model trees", i.e., trees that contain linear regression functions at the leaves. The authors of [28] presented an algorithm that performs classification using logistic regression instead of linear regression. A stage-wise fitting process is used to construct the logistic regression models that can select relevant attributes in the data in a natural way and shows how this approach can be used to build the logistic regression models at the leaves by incrementally refining those constructed at higher levels in the tree. In the current research, supervised learning algorithms are used, wherein some algorithms perform individually while others use ensemble learning techniques.

Materials and Methods
This section contains the description of the dataset used for sentiment analysis, its visualization, as well as the proposed methodology to perform the sentiment analysis on the selected dataset.

Data Description
In this study, the dataset from Kaggle was used, which contains tweets for six airlines of the United States (US). The dataset name is "twitter-airline-sentiment" and it contains a total of 14,640 records. Every record is labeled as positive, negative, or neutral according to the sentiment polarity. The selected dataset contains different features and its description is given in Table 1. Table 1. Feature description of selected dataset.

Airline Sentiment Confidence
A numeric feature representing the confidence level of classifying the tweet to one of the 3 classes.

Negative Reason
The reason behind considering this tweet as negative.

Negative Reason Confidence
The level of confidence in determining the negative reason behind a negative tweet. Airline Name of the airline Company.

Retweet Count
Number of retweets of a tweet. Text Original tweet posted by the user. Airline Sentiment Labels for tweets (positive, negative, neutral).

Data Visualization
The dataset is visualized to help understand its attributes. Figure 2 shows the most frequent reasons for customer complaints about the airline. The dataset visualization shows that the highest number of tweets are about "customer service issues". Figure 3 shows sentiment polarity for six airlines used as the standard to evaluate the performance of the selected classifiers.

Methodology
In the current research, different classifiers and feature extraction techniques were investigated. CC, LR, ETC, SVC, DTC, RF, ADB, GNB, SGDC, and GBM were evaluated on the selected dataset with term frequency (TF), term frequency-inverse document frequency (TF-IDF) and word2vec features. The phases followed during the experiments are described here briefly.
In the methodology steps of this research, pre-processing was carried out on the dataset. Different tools and libraries were utilized in this step, e.g., natural language toolkit. This study considered two strategies at the pre-processing level: • Complete pre-processing • Partial pre-processing In complete pre-processing, data cleaning was performed to improve the learning efficiency of machine learning models. Machine learning models show improved classification accuracy if the data are pre-processed. The pre-processing was done using the natural language toolkit of Python [29]. Tweets contain punctuation, stopwords, and the combination of lower-and uppercase words, which can affect the model learning capability. Two tweets are shown in Table 2 as a means to show the pre-processing steps followed in this study.  Figure 4 shows the sequence of the pr-processing steps followed for the selected twitter dataset. As a first step, punctuation has to be removed from tweets. The following punctuation was removed from text: []() \/ | , ; . '. In addition, twitter assigned @user to each user was also removed during this phase. Table 3 shows the tweets before and after punctuation removal.   Punctuation was removed from data because it does not contribute to text analysis in the study. Punctuation helps to make sentences readable but it impairs the models' ability to differentiate between punctuation and other characters [30]. In the next step, numeric values from the tweets were removed as they have no impact on text analysis. Removing numeric values decreases the complexity of training the models. Table 4 shows the output of before and after numbers were removed from tweets. After numeric removal, all text in the tweets was converted to lowercase. This step is important because text analysis is case sensitive. Yang and Zhang [30] stated that the probabilistic machine learning models count the occurrence of each word, which means that, e.g., "Good", and "good" are considered two different words if changing all text to lowercase is not performed. It could decrease the importance of more frequent terms in the text. Table 5 shows the example of before and after the tweets have been converted to lower case. Stemming is an important technique in pre-processing because removing affixes from words and converting them into their root form helps to increase the performance of the model [31]. For example, words may have many forms with essentially the same meaning in the text. For example, "goes" and "going" are modified forms of "go". Stemming converts these types words into their root form. Stemming was performed using Porter stemmer algorithms in current study [32]. Table 6 shows the sample of tweets before and after stemming. Table 6. Tweets before and after stemming.

Input Data
After Stemming plus youve added commercials to the experience tacky plus youve add commercial to the experience tacki i didnt today must mean i need to take another trip for months i didnt today must mean i need to take another trip for month The last step in the pre-processing phase is the removal of stopwords in the tweets. Stopwords have no analytic value for text analysis, so they need to be removed to reduce the complexity of input features. Table 7 shows the output of the tweets after stopwords were removed. Table 7. Output of tweets after stopwords removal.

Input Data
After Stopwords Removal plus youve add commercial to the experience tacki plus haveve add commercial experience tacki i didnt today must mean i need to take another trip for month today must mean need take another trip month 3.3.2. Partial Pre-processing Other than the complete pre-processing, this study considered the use of partial pre-processing as well, to analyze the impact of pre-processing steps on classifiers' accuracy. The partial pre-processing does not involve "stemming" and "stopwords removal". Thus, the pre-processing was carried out in the order given in Figure 5.  Steps followed in partial pre-processing.

Feature Extraction Methods
After the pre-processing phase, the corpus was divided into "training subset" and "testing subset". It was divided in the ratio of 3:1 for training and testing, respectively. Feature extraction methods were then applied to the training subset, as shown in Figure 6, which represents the adopted methodology.
Feature extraction techniques were applied to both training and testing data: on the training data to train the selected models and on the testing data when classification was performed. TF-IDF is a scoring measure widely used in information retrieval (IR) and summarization. TF-IDF is intended to reflect how relevant a term is in a given document. TF-IDF feature extraction considers TF and IDF. IDF rewards the tokens that are rare overall in a dataset. If a rare word appears in two documents, then it is more important to the meaning of each document. IDF weights a token t in a set of documents U and is computed as follows: where n(t) N is the frequency of t in U and N n(t) is the inverse frequency. Thus, the total TF-IDF weight for a token in a document is given as: TF-IDF is used with parameter "ngram_ range". TF-IDF is used to measure the importance weight of terms which give the weights of each term in the corpus. The term weighted matrix is the output of TF-IDF. With the TF-IDF vectorizer, the value increases proportionally to the count but is offset by the frequency of the word in the corpus. Table 8 shows the output of three sentences when TF-IDF technique is applied to the pre-processed form of these sentences. The sentences are: "good companies" "bad services" "I have seen good management"

Evaluation parameters
Accuracy Accuracy

Precision Precision
Recall Recall F1-score F1-score Figure 6. The methodology adopted for tweets classification. Similar to TF-IDF, the TF technique is used for feature extraction as well and is commonly applied in document classification where the (frequency) occurrence of each word is used as a feature for training a classifier. However, contrary to TF-IDF where more frequent words get smaller weight, the TF feature does not care if a word is common or not. The output of TF for the above-given sentences is shown in Table 9. Table 9. Output of TF on preprocessed data. This study also considers the use of word2vec as the feature extraction technique [33]. Word2vec is a famous two-layer neural net which produces the feature vectors from a text corpus. It utilizes the continuous bag-of-words (CBOW) or the skip-gram (SG) model for this purpose. This study employed SG because SG has been tested and shown good performance in NLP tasks [34,35]. The use of SG model aims at finding the word representations that are used to predict the adjacent word in a sentence. The SG model was considered in this study based on its suitability for small-to medium-sized datasets. Jang et al. [36] stated that the SG model is advantageous over CBOW when data size is not too large.

Classifiers Used for Tweet Classification
This section describes the necessary details for the machine learning classifiers used in this study for tweet classification.

Machine Learning Classifiers
Multiple classifiers were used in the current study. The DTC is one of the used classifiers. The DTC algorithm falls under the category of supervised learning and can be used to solve both regression and classification problems. In DTC, the major challenge is the identification of the attribute for the root node at each level [37]. This process is known as attribute selection. The two most popular attribute selection measures are "information gain" and "Gini index" [38]. To calculate Gini, this study considered the probability of finding each class after a node and then the sum of the square of those values was calculated and subtracted from 1. Thus, when a subset is pure (i.e., there is only one class in it), Gini will be 0, because the probability of finding that class is 1; indeed, it is concluded that we have reached a leaf. To calculate Gini value, the following equation is used: Besides Gini, information gain was also used for the selection of the best attribute. Whereas the Gini value gives the impurity of data in the dataset, information gain provides the purity of data in the dataset. There are two steps for calculating information gain for each attribute: Step 1. Calculate the entropy of the target.
Step 2. Calculate the entropy for every attribute.
Using information gain formula, entropy was subtracted from the entropy of target. Given a set of examples D, entropy is calculated using: where P r (c i ) is the probability of class c i in dataset D.
The entropy is used as the measure of impurity or disorder of a dataset ( or a measure of information in a tree). If an attribute A i is made with v values, this will partition D into v subsets D 1 , D 2 , ..., D v . If A i is used as the current root, the expected entropy is: Thus, information gain for selecting attribute A i to branch or partition the data is: The attribute with the highest gain was selected to branch/split the current tree in this study. The Gini value and information gain were used to construct the trees for all tree-based classifiers used in this study. SVM is another machine learning classifier utilized in the current study. It is a linear model for classification and regression problems. It can solve linear and non-linear problems and works well for many practical applications [39]. SVM creates a line or a hyper-plane which separates the data into classes. SVM has functions called kernels which take low-dimensional input space and transform it into a higher-dimensional space, i.e., it converts not separable problems to separable problems. It is mostly useful in non-linear separation problems. Simply put, it does some extremely complex data transformations and finds the process to separate the data based on the defined labels.
Two voting classifiers, namely LR and SGDC, were evaluated as well. Both LR and SGDC are able to estimate class probabilities on their outputs, i.e., they predict if the input is class-A with probability a and class-B with probability b. If a > b, then it outputs predicted class is A, otherwise B. In voting, the classifier sets the voting parameter to soft enable them in order to calculate their probability (also known as confidence score) individually and presents it to the voting classifier. Then, the voting classifier averages them and outputs the class with the highest probability. The GBM, on the other hand, trains many models in a gradual, additive and sequential manner. The major difference between ADB and GBM is how the two algorithms identify the shortcomings of weak learners (e.g., decision trees). The ADB model identifies the shortcomings by using high weight data points, while the GBM model performs the same by using gradients in the loss function y = ax + b + e, where e is the error term. The loss function is a measure indicating how good the model's coefficients are at fitting the underlying data. A logical understanding of loss function would depend on what we are trying to optimize.

Proposed Voting Classifier (LR + SGDC)
The voting classifier (VC) is an ensemble model that combines different base models to perform the classification through different voting schemes (e.g., soft voting and hard voting). It gets final results by aggregating the results from the classifiers. In this study, two classifiers, LR and SGDC, were ensembled through soft voting criteria for the final prediction of target class. SGDC is useful for big data, especially when there are redundancies in the dataset. It solves the classification problems by specifying a loss and penalty function [40]. It works similarly to regular gradient descent, except that it looks at only one sample at each step [41]. LR, on the other hand, derives the posterior class probability (PCP) p(Ct|v) implicitly to perform the binary classification. LR derives PCP through the sigmoid function σ by using a linear combination of the input [42]. VC can be expressed as: where ∑ n i LR i and ∑ n i SGDC i give n prediction probabilities for given samples. After each given probability for sample text, the probability passes through soft voting criteria, as shown in Figure 7.
The functioning of the VC can be described with the help of an example. Let the following be the probability scores of each class given by LR: The VC classifies it as the "positive" class for the given tweet using the maximum of the given probability. The tweet tested with the VC classifier also belongs to the "positive" class in the dataset.

Performance Evaluations Parameters
Different performance evaluation parameters have been utilized to analyze the performance of the classifiers. Four basic notations used in these parameters are as follows [43][44][45]: True These quantities are used to calculate accuracy, F1 score, recall, and precision of each classifier to evaluate its performance. Accuracy is defined as: Recall shows the completeness of a classifier and is calculated as: Precision is the exactness of the classifiers and involves TP to the sum of TP and FP. It is calculated using: The F1 score conveys the balance between the precision and the recall and is calculated as:

Results and Discussion
Experiment results are discussed with respect to various pre-processing steps utilized as well as the feature extraction techniques selected for this study.

Results with Complete Pre-processing
The current research utilized the selected machine learning classifiers with different hyper-parameters. These parameters were set empirically to achieve higher accuracy. CC, for example, performs best when it works with stochastic gradient descent. Similarly, the SVM classifier gives higher accuracy with a linear kernel. The accuracy results for all classifiers when used TF-IDF are displayed in Table 10. As shown in Figure 8, GNB gives the lowest accuracy when used with TF-IDF feature extraction technique and VC gives the best results with TF-IDF. VC gives the best results as it is an ensemble model that works with other classifiers.  Table 11 shows the classification accuracy of different classifiers when used with TF feature extraction method. Experimental results reveal that the SGDC classifier shows the best results when stopping criterion parameter value is set to "1e −3 " and max_ iter=1000. Similarly, VC gives higher accuracy than that of other classifiers when it works with LR and SGDC.  Figure 9 shows that SGDC performs better than other classifiers, even when the TF feature extraction method is utilized. There is a slight difference in the accuracy of RF on TF-IDF and TF techniques. However, at the same time, VC shows a very similar performance with both feature extraction techniques.  Table 12 shows the results for precision, recall, and F1 score of each class of tweets, as well as the average of three classes for all classifiers used in the study. In the current study, CC performs better when used with TF-IDF because it allows the calibration of the probabilities for a given model or to add support for probability prediction on the dataset. VC is the best classifier for tweets classification on the selected dataset when the TF-IDF feature extraction method is used. The average precision of VC is 82%, which is better than CC. LR examines the influence of various factors on a dichotomous outcome by estimating the probability of the event's occurrence [46]. It gives good results on the dataset with TF-IDF. The precision of LR is better on neutral and positive labels than VC. Additionally, ETC, RF, SVM, and SGDC perform better than the other classifiers in terms of precision, recall and F1 score. SVM shows better results with linear kernel and parameter c set to 2.0. DTC, GBM, ADB, and GNB do not perform well and have lower precision on the selected dataset. Table 12 shows the performance evaluation parameters for the selected classifiers with TF-IDF feature extraction method.
The results for performance parameters when selected classifiers make use of the TF feature extraction method are given in Table 13. The results demonstrate that CC, VC, LR, ETC, and RF perform better with both TF and TF-IDF feature extraction methods. On the other hand, the performance of GNB is severely degraded when used with TF feature extraction.  Table 14 shows the accuracy of selected classifiers when word2vec is used as the feature extraction method. The results show that the performance of all classifiers has been degraded with the exception of SVC and GNB. The accuracy of SVC has been slightly improved, while GNB's accuracy has been significantly elevated with word2vec features. Table 15 shows the comparison of classifiers' accuracy when used with various feature extraction methods. The results show that the accuracy of most classifiers is degraded with word2vec features. CC is able to achieve the highest accuracy of 0.780 with word2vec while many classifiers have a substantial decrease in accuracy. The average accuracy of all the classifiers with TF and TF-IDF is shown in Figure 10. The experimental results show that there is very little difference in accuracy when the feature extraction technique is changed from TF to TF-IDF; however, TF-IDF is better in terms of accuracy, precision, and other performance metrics.  Table 16 compares the results for classifiers' accuracy using complete pre-processing with the partial pre-processing results where stopwords removal and stemming has been discarded. The results exhibit that the partial pre-processing leads to reducing the overall accuracy of the classifiers. Complete pre-processing is of utmost importance to improve the performance of prediction. Stemming and stopwords removal help to mitigate the amount of meaningless data and reduce the data dimensionality [47]. Document features estimate the importance of specific terms in a document. Pre-processing helps to reduce the high-dimensional attributes and help feature extraction methods to learn only the important features. Hence, if the pre-processing is incomplete, the feature extraction may be improper, which leads to poor prediction of classifiers.

Results Using Long Short-Term Memory Classifier
A deep learning approach LSTM was employed as well to analyze its accuracy on the selected twitter dataset. Figure 11 shows the structure of the LSTM network used in this study.
Between the input layer and LSTM layer, an embedding layer was inserted, which creates word vectors from the input layer. Rectified linear unit (ReLU) was used as the activation function because of its better performance on text data [41,48]. A dropout layer was used as the regularization unit with a value of 0.5. Sigmoid was utilized at the end layer to produce the probability of each class [49]. This study used "Adam" optimizer as it has proven to show better performance in case of noisy data [50]. The LSTM is able to achieve an accuracy of 0.686, which is lower than those of most of the machine learning models investigated in this study. The poor performance of LSTM is due to the dataset used in the current study. Deep learning is a data-intensive approach that performs better when the dataset is large. Research shows various results both favorable and poor on the use of deep learning on smaller datasets. For example, Feng et al. [51] stated that deep learning-based methods perform poor with small datasets. However, Zampieri et al. [52] investigated the use of SVM, CNN, and bidirectional LSTM (BiLSTM) to predict the offensive language in social media. They found that BiLSTM and CNN can perform well even on relatively smaller datasets, and outperform traditional machine learning SVM. It is also possible that the specific architecture of the used LSTM model is not suitable for the selected dataset. Hence, the results cannot be conclusive without further investigation of LSTM and other deep learning techniques on more datasets.

Statistical Significance of Results
This study employed a T-test to validate if the difference between the results is statistically significant or not. Thus, the null hypothesis H o is that the difference between the classification accuracy is not significant while the alternative hypothesis H a claims that the difference between the accuracy is significant.
The T-test found that the null hypothesis cannot be rejected in favor of alternative hypotheses for TF and TF-IDF features. However, when the test was performed between the accuracy with TF-IDF and word2vec features, it favored the alternative hypothesis and stated that the difference in the accuracy is statistically significant.

Conclusions
This paper proposes a voting classifier that is based on logistic regression and stochastic gradient descent classifier. Soft voting is used to combine the probability of LR and SGDC. In addition, various machine learning-based text classification methods were investigated to perform sentiment analysis. The experiments were carried out on a twitter dataset which contains the reviews of travelers about US airlines. Three feature extraction methods, namely TF, TF-IDF, and word2vec, were investigated to analyze the impact on models' classification accuracy. The selected classifiers were used to classify the tweets into positive, negative and neutral classes. Precision, recall, and F1 score were used as performance metrics besides accuracy. The results demonstrate that TF-IDF feature extraction is more appropriate for tweet classification. The proposed voting classifier performs better with both feature extraction methods and achieves an accuracy of 0.789 and 0.791 with TF and TF-IDF, respectively. Ensemble classifiers show higher accuracy than the non-ensemble classifiers. A deep long short-term memory model was also implemented with TF-IDF feature extraction. The results show that LSTM does not perform well on the selected dataset. However, the results from LSTM are not conclusive, as research [52] shows strong evidence of superiority of bidirectional LSTM and CNN over machine learning classifiers. Thus, future work is intended to perform further experiments with more deep learning methods on the selected as well as additional datasets.


extfieldsofstudy:
Physics
Computer Science

text:
ECG Signal Classification Algorithm Based on Fusion Features

Cardiovascular disease is a common and frequently-occurring disease in clinic. Cardiovascular disease detection has become the primary concern of people’s health and the mortality is very high. In order to improve the accuracy and universality of the current ECG signal classification algorithm under multi-classification conditions, an ECG signal classification algorithm based on fusion features is proposed. The algorithm uses multi-layer Convolution Neural Network and Recurrent Neural Networks. The spatial and temporal features of ECG signals are extracted automatically. Finally, the accurate classification of ECG signals is realized by using multi-fusion features. In order to verify the accuracy of the algorithm, using MIT-BIH arrhythmia database as the standard test data, the classification accuracy of the algorithm is 99.42%. Compared with the ECG classification algorithm in document[1] [2], the algorithm proposed in this paper abandons the complexity of the traditional ECG algorithm in the process of data feature extraction and solving the problem of insufficient feature extraction of single convolution neural network for one-dimensional ECG signal leads to poor classification effect of small probability samples. The classification sensitivity of ECG classification is improved from 83.00% of single convolution network to 97.07% of class S (supraventricular abnormal beats) classification sensitivity, The classification sensitivity of Class F (Fused Rhythm) is increased from 74.48% to 97.24%, which significantly improves the classification sensitivity of small probability abnormal rhythms, and increases the universality of ECG classification algorithm in multi-class beat recognition.

Introduction
Artificial intelligence technology is very popular in recent years. With the increase of computational power, the conditions restricting deep learning have been solved, and deep learning technology has made rapid progress. The most extensive application of deep learning technology in medical treatment is undoubtedly medical diagnosis, among which ECG automatic diagnosis is one of the important tools for heart disease diagnosis. At present, most of the proposed ECG classification algorithms do not apply AAMI(The Association for the Advancement of Medical Instrumentation) standard, but arbitrarily select a number of MIT-BIH beats to classify. They are not universal and can not scientifically and effectively compare the merits and demerits of each algorithm. This is a very serious problem in the field of ECG classification.All the experiments in this paper are based on the classification criteria and evaluation criteria stipulated by AAMI standard.
In this paper, two kinds of deep neural network structures are proposed to perform multi-class experiments on MIT-BIH arrhythmia data. In model one, the network structure of three layers of onedimensional convolution layer, three layers of one-dimensional pooling layer and softmax output layer

Method
ECG signal classification is a multi-classification task for one-dimensional signal input. The input is one-dimensional ECG sequence signal = [ 1 , 2 , 3, … ],The output is the corresponding label = [ 1 , 2, 3 , … ], Each output tag corresponds to the category of the input ECG beat, and the cross-entropy is used as the objective function to optimize all samples in the training set.
p represents the probability value of the label which is determined by the network model. In this section, the detailed model structure of the proposed single convolution network based ECG classification algorithm and fusion feature based ECG classification algorithm, as well as the process of MIT-BIH ECG data preprocessing and model training and evaluation are given.

Network structure
In this paper, two kinds of neural network models for ECG signal classification are presented as shown in figure. 1 figure 1. The model consists of three layers of convolution, three layers of pooling, one layer of full connection and softmax output layer in series. A convolution layer is followed by a pool layer with three cycles. The input ECG signals are extracted by convolution network for low-level edge features and deep abstract features, and all features are sent to the final softmax output layer for classification judgment, and the probability values of each category of the samples are output. CNN-BILSTM model is shown in figure 2, the model is composed of CNN network and RNN network in parallel. The convolution network part is identical with the convolution part of model 1. The cycle part is slow in training because of the long length of single-beat ECG signal, therefore, the length of the input ECG signal is scaled and constructed by single-layer convolution and single-layer pooling structure. The eigenvectors of points are used to facilitate the training of circular networks. In view of the fact that bi-directional long-short term memory networks extract more information from each time node than unidirectional long-short term memory networks, so in the cycle part, BILSTM network is used to extract the time series feature of ECG signal, and the feature fusion layer is connected in the output layer of parallel network. The temporal and spatial features extracted from CNN and RNN networks are fused. Finally, the fused features are fed into the softmax output layer for classification judgment, and the probability values of each category of the samples are output. is easy to be compared horizontally. In order to verify the effectiveness and rationality of the proposed algorithm, the experimental data in this paper are also from MIT-BIH arrhythmia database [4], which consists of 48 annotated records, each of which takes about 30 minutes and consists of two lead signals. The sampling rate of the signal is 360HZ. Each record contains three files, atr, dat and hea. Atr is a tag file, which keeps the position and type of the heart beat labeled manually. Dat is a data file, which keeps the original data of the signal needed. Hea is a header file, which records the number, lead number, sampling rate, coding format and other information of the data. The algorithm proposed in this paper is validated by single lead data. Because the lead types used in 48 records are not identical, only 45 records contain complete lead II data, so this section of the experiment uses the 45 records of lead II heart beats to carry out experiments.

Introduction of ECG Standard Database and Data Preprocessing
In order to compare scientifically and effectively with other algorithms, the classifications of ECG classified in the experiment will be strictly in accordance with AAMI standards. ECG beats are divided into five categories: N (normal or bundle branch block), S (supraventricular abnormal beats), V (ventricular abnormal beats), F (fusion beats), Q (unclassified beats) and U (unidentified beats), The specific division is shown in Table 1 below: At the same time, AAMI standard also stipulates the evaluation criteria of arrhythmia detection algorithm, including the calculation method of algorithm accuracy (confusion matrix), and accuracy (Acc), sensitivity (Sen), true positive rate (Ppr) as a reference to measure classification performance. In medical diagnosis, it is expected that every disease can be accurately judged, so Sen in classification is a very important measurement index.
The original data of MIT-BIH has been processed by notch-filter and 0.1-100HZ band-pass filtering. Although there are some noise disturbances, because the deep neural network can extract the characteristics of the signal independently, in order to enhance the generalization ability of the model and enhance the robustness of the network, therefore, no filtering and denoising operations are performed on the original data. In the process of dealing with raw data, we need to use the WFDB toolkit provided by Physio Bank ATM to read every raw data ".dat" file and ".atr" annotation file. According to the position of R point labeled manually, the original ECG signal is effectively cut. The left side of R point is 100 and the right side is 150. The data of 250 points per beat is stored in the array. When the beat is cut, 250 points can basically contain all the main wave signals of an ECG beat. Next, according to the classification criteria stipulated by AAMI, all ECG rhythms are divided into five categories, among which ECG rhythm types which are not within the scope of the five categories are discarded. In order to accelerating the convergence speed of the network, preventing the disturbance of abnormal ECG data and avoiding unnecessary numerical problems, The original data after cutting need to be normalized by one step to reduce the influence of the change of signal distribution. Max-min normalization method is adopted which is commonly used. The formula is as follows: is the maximum amplitude of all signals and is the minimum amplitude of all signals. Finally, 45 ECG records and their corresponding tags are stored in an array file.

Training
The pretreated 103129 cardiac data were randomly disturbed and divided into training set and test set according to the ratio of 4 to1. The number of training set was 82500 and the test set was 20629 respectively. The input format of the network model data was 82500*250 matrix and the corresponding label was 1*82500 matrix.
For the two neural network models proposed in this paper, end-to-end supervised learning was adopted. In order to accelerate network attenuation, Adam Optimizer algorithm was used in both models, the batch-size is set to 128, and the learning rate was attenuated once every 10,000 steps, so that the model could converge to the optimal value. In the training process of CNN network, the model loss value converged to the minimum value when the training proceeded to 100000 steps, and the accuracy rate of training set (Acc) reached 99%. The cut heartbeat signal was entered into the BILSTM network in time sequence, and the hidden unit information output at each time point was saved. The time series characteristic matrix was formed by splicing the cut heartbeat signal into a matrix vector. At the fusion level, the space characteristic matrix of CNN network and the time series characteristic matrix of BILSTM were fused and spliced, and the loss value was calculated and weight parameters were updated by back propagation. When the model was trained to 50000 step, the loss value of the model tend to be stable and converged to the minimum value, and the Acc of the training set reaches 100%.
Because of the sample imbalance problem of MIT-BIH arrhythmia data classified according to AAMI standard, the test results of some small probability samples in the test set are poor, so data enhancement operation is carried out on the small probability samples. The small sample type heartbeat is randomly re-sampled using linear interpolation algorithm, and the frequency of this type heartbeat is expanded to between 60bpm and 120bpm. These ways not only increased the proportion of small probability samples in the overall training set, but also expanded heartbeat according to different frequencies. Finally, it increased the diversity of performance of single type in different people and prevented the problem of inaccurate recognition caused by the difference of individual heart rate in the prediction process, and enabled the model to learn more about the characteristics of heartbeat of small probability samples. The proposed network model was trained on the original data sample and the enhanced data sample respectively, and the test results of the CNN network model and the combined network model based on the fusion feature were compared under the two sample distributions.

Test
By saving the trained model network parameters and validating the model with test data, the confusion matrix under test data was outputted, and the accuracy (Acc), sensitivity (Se) and true positive rate (Pp), respectively were calculated. The predicted results of CNN network and combination network  [6]. The results are as Table 2: The test results show that the overall recognition accuracy of the combined network model based on fusion features reaches 98.92% before data enhancement, the sensitivity of S-type heartbeat is increased by 9.14% compared with CNN network, and the sensitivity of F-type is increased by 12.42%. After data enhancement, the overall recognition accuracy of the model is increased to 99.42%. Compared with the literature 5 algorithm which performs best in class S, the sensitivity of the model in class S is increased by 7.25%. Compared with the literature 6 algorithm which performs best in F class, the sensitivity of the model in F class is improved by 3.43%. It can be seen that the model is superior to all other algorithms in the overall recognition effect and the recognition effect of each class.

Result
In order to compare other algorithms horizontally, we refer to the recent literature [7] [8] [9] [10] on validating ECG classification on MIT-BIH datasets, including the research results based on traditional ECG feature classification and deep neural network classification, as shown in Table 3: From the table, it can be seen that the proposed ECG recognition algorithm based on fusion features reached to better recognition effects. Compared with other traditional algorithms, the proposed algorithm does not need complex feature engineering and has not undergone any complex pre-processing. It is simple to operate. Compared with the deep network, the model occupies less memory because of fewer layers, and the overall classification effect and the classification effect on each category are significantly improved compared with other algorithms in the literature.

Conclusion
By analysing and comparing the mature CNN network in the field of image recognition and the RNN network model which has advantages in time series information modeling, this paper proposes an ECG signal classification algorithm based on fusion features. Moreover, the recognition effect of MIT-BIH arrhythmia data set is validated. The comparative experiments show that the model improves the overall accuracy of classification and the classification effect of small probability class samples significantly, and the model is smaller. It has obvious advantages in memory proportion and real-time inference. This algorithm is suitable for off-line diagnosis on terminal equipment, because it is convenient and fast, and it has good recognition effect. It is of great significance in future telemedicine and real-time home monitoring.