PSAP: Improving Accuracy of Students' Final Grade Prediction using ID3 and C4.5

PSAP: Improving Accuracy of Students' Final Grade Prediction using ID3 and C4.5
This study was aimed to increase the performance of the Predicting Student Academic Performance (PSAP) system, and the outcome is to develop a web application that can be used to analyze student performance during present semester. Development of the web-based application was based on the evolutionary prototyping model. The study also analyses the accuracy of the classifier that is constructed for the prediction features in the web application. Qualitative approaches by user evaluation questionnaire were used for this study. A number of few personnel expert users which are lecturers from Universiti Pendidikan Sultan Idris were chosen as respondents. Each respondent is instructed to answer a total of 27 questions regarding respondent’s background and web application design. The accuracy of the classifier for the prediction features is tested by using the confusion matrix by using the test set of 24 rows. The findings showed the views of respondents on the aspects of interface design, functionality, navigation, and reliability of the web-based application that is developed. The result also showed that accuracy for the classifier constructed by using ID3 classification model (C4.5) is 79.18% and the highest compared to Naïve Bayes and Generalized Linear classification model.

Introduction
This Nowadays, it is very common there would be one or two students will fail on a course. Failing to achieve good grade may result of student to extend the semester or dropping out university. Increase in rate of the student dropout may reflect badly at a university's reputation. According to Cherif et al. [1], the causes of the student failing can be categorized in a major area such as student-related factors, life, and socioeconomic issues and failure in the educational system.
Another cause of student failure is that lecturer is unable to keep up with all students that are taking the course. This is because there is a lot of students is taking the course and lecturers often don't pay much attention to on each individual performance in the class. Besides, the lecturer does not have the appropriate tools to predict students' academic performance and unable detect an early sign of student is going to fail the course. There have been various student retention programs that are being developed as efforts to reduce the rates of university dropout. In UPSI, there was a program called "Program Jejak Cemerlang" held by Faculty of Art, Computing and Creative Industry involving students that acquire less than 3.0 CGPS. This is one of many the attempts by the university to improve the student's motivation in achieving better CGPS for the upcoming semester. Besides that, another strategy in the attempt to detect potential student dropout is to develop a web-based application that would able to analyze student academic performance, predict their mark in the upcoming final exam and generate a report containing a list of possible candidates that have a high chance of failing the course. This early prediction allows lecturers to choose on which student they should pay more attention and be well prepared during the lecture. In our education system, assessments are methods or tools used by educators to assess, measure, and document the academic readiness, learning progress, skill acquisition or student education needs. This includes mid-term exam, group project, final exam, quizzes and other assessments that can be utilized to measure the academic performance of the student. These assessments also known as coursework will be recorded by lecturer and uploaded to the university's database. These data will be kept and will be reviewed to determine whether the students have all the requirements to graduate. The coursework will also determine the student's grade on the course. With the current technology of data mining, we can utilize the student's coursework data to predict the student's grade. This can be made possible by using the data mining process by compiling large data to identify patterns and establish relationships to solve problem through data analysis. Data mining tools allow us to predict future trends. We also can identify the patterns that eventually will lead students to fail a course.
Therefore, in this project, we will develop a system that allows lecturer to monitor their students' academic performance as well as to predict students' grade for the course before the student is undertaking the final exam. Besides that, the system will allow lecturer to manage the coursework information that is constructed for the course. In addition, lecturers will able to import students' coursework mark in order to construct the model for the prediction process.

Related Studies
The data mining has been applied to many fields, which include the educational field. Nghe et al. [2], Amirah et al [3] have provided an excellent review on how other studies have been applying data mining in the educational field especially on predicting student academic performance.
Osmanbegović and Suljic [4] have differentiate three supervised data mining algorithms that are used on preliminary estimation data to predict success in the course (pass or failure) and the performance of learning methods measured by their predicted accuracy, learning facilities and userfriendly features. The results show that Naïve Bayes producers outperform predictive performance and neural network methods. It has also been shown that a good classifier model should be accurate and understandable by the professor. Adhatrao et al. [5] developed a system that can predict student performance from the past result by using the concept of data mining techniques under Classification. They have analyzed data sets containing information about students, such as gender, X and XII grade test scores, scores and ranks in entrance examinations and results in the first year of the previous student group. Using the ID3 (Dichotomizer 3) and C4.5 algorithms for this data, they have foreseen the general achievement of new students and students who are accepted into future exams.
Nagy et al. [6] show how data mining can be used in education by developing a "Student Advisory Framework" that uses classifications and clusters to build smart systems. This system can be used to provide a variety of consultations to first-year university students to pursue a particular educational track in which they may succeed, aimed at reducing the high academic failures among these students.
Chen et al. [7] recommend a model that predicts student performance, based on the results of standardized examinations, including university entrance examinations, high school graduation examinations, and other influential factors. In this study, the approach to problems based on artificial neural networks (ANNs) with two nail-inspired meta-heuristic algorithms, Cuckoo Search (CS) and Cuckoo Optimization Algorithms (COA) is recommended. Specifically, they use previous exam results and other factors, such as the location of the secondary school students and the gender of the students as input variables, and predict student achievement.
Natek and Zwilling [8] in their study, ask whether it is possible to predict the success rate of students enrolling in their course? Are there any specific student characteristics, which can be attributed to the student success rate? Are the relevant student data for institutions of higher learning (HEI) where they can predict the success rate of the student? Unfortunately, data mining algorithms work well with large data sets, while student data, available for IPT, related courses are limited and included in the small dataset category. Their research results support the conclusion that data mining is generally not limited to large data sets as a majority but not all previous authors and investigations expect this discovery. The use of specialized data mining techniques for structured small data sets can also meet the usable results. Smaller data mining data from research is an example of the use of relevant data mining technology to develop HEI's knowledge management systems in the education domain.
A prediction model for student academic performance, for undergraduate and postgraduate students in the field of Computer and Electronics and Communication using the two selected classification methods developed by Hamsa et al. [9] by using Decision Tree and Fuzzy Genetic Algorithm. Parameters such as internal marks, session marks and entry scores are selected to do this work. Internal value is a combination of attendance value, the average value obtained from two session exams, and assignment scores. The entry value for the student level is a weighted score obtained from the test scores 10 and 12 and the entry value. In the case of a master's degree student, that includes the degree exam marks and entry marks. The resulting prediction model can be used to identify student performance for each subject. A systematic approach can be taken to improve performance over time.
Another research by Sweeney et al. [10] applied for data mining in the field of education. In their experiments, the Factorization Machine (FM), Random Forest (RF), and the Personalization Multi-Linear Regression model produced the lowest prediction error. By comparing feature interests across populations and across models, they reveal strong relationships between instructor characteristics and student performance. Research conduct by Sweeney et al. [10] found the main differences between transfer and non-transfer students. In the end, they found that the hybrid FM-RF method can be used to accurately predict values for new students and who re-take new and existing courses. The application of these techniques promises student-level planning, instructor intervention, and personal advice, all of which can improve retention and academic performance.
Adejo and Connolly [11] presented research on the use of basic and aggregate classifiers in predicting students' academic performance using UWS student data. Furthermore, the important variables needed for the prediction were identified using PCA. They showed that there is a correlation between the identified variables and that the improvements in the prediction model can be obtained using the generalization of the stack (stacking set) with the resulting effect of the increase in accuracy, the error rate reduced and the predictive value. A very precise model suggested by Chiheb et al. [12] propose a system whereby undergraduate students and postgraduate students can be classified according to their decisions and their performance can be predicted for years to come based on current results and their historical data. The system can also be used as an early warning tool for high school students and helps graduates to choose the appropriate master discipline for their studies. Livieris et al. [13] proposed new decision support software to predict students' performance in the final exams. The proposed software is based on a novel 2-step classification technique that performs better than any single learning algorithm studied. The main advantages of the presented tool are the simple and user-friendly interface and the possibility to be implemented on any platform under any operating system.

Methodology
This research contains three phases which are analysis phase, development phase, and evaluation phase. In the analysis phase, researcher doing observations of the early research and analyse the user 128 target. The Second phase is development phase where RDB (requirement-design-build) model will be use in this research. The last phase is evaluation phase.
Evaluations are consist of two, i.e. evaluation to know how the performance of the system and to find out if the predicting can be used to increase the understanding level of students academic performance.

Dataset Used for Prediction
Transcripts data for students who are undertaking the Software Engineering Program at Universiti Pendidikan Sultan Idris in the year 2015 were collected from the university's database management system and the totaling 123 students. The collected data was organized in a Microsoft Excel sheet. Each of the student records had the following attributes: Matric No, pre-University academic qualification type, (Cumulative Grade Point Average) CGPA, and the coursework mark excluding the final exam mark and finally the students' grade for the course. Table 1 shows the list of attributes of the data set.

RDBR Model
The Requirement, Design, Build and Refine (RDBR) model is best applied when detailed information related to input and output requirements of the system is not available. Normally, the RDBR model is used when a similar system does not yet exist or in case of a large and complex system where there is no manual process to determine the requirements. Since that the input output requirements of the PSAP application is not very clear and the application have very limited number of similar systems that have been developed, the most appropriate software development model that have been decided for the development of the PSAP web application is the RDBR model. Besides, RDBR model has benefit when used in developing a software project. Firstly, it increases user involvement in the software development which will lead the user to better understand what the users really want as final product. Other than that, defect can be detected on earlier phase which can reduces time taken and cost in order to complete the project. Any missing, confusing and difficult functionality would be identified and these functionalities can be modified, added or removed in order to meet the user requirement. 129 complicated detail. The aim of early construction of a prototype is to demonstrate the actual feeling of when the user is interacting with the web application.

Design
After the basic requirement is collected and analysed, a preliminary design of the prototype is generated as guideline in the construction of the prototype. The design is not very detail and would not be the final design. The design includes only the important aspect of the system which gives an idea of how the system operates to the user.

Build-Prototype
In build-Prototype phase, the first prototype of the system is constructed based on the preliminary design. Normally, the prototype built is a scaled-down system and demonstrates the resemblance of the functionality of the final product.

Evaluate
In the evaluate phase, the user will be presented with the prototype. The user will evaluate the prototype and identify the strength and the weakness, what should be added and removed. The feedbacks are collected and analysed. In the evaluation phase, any missing requirements that are not yet noticed will be identified.

Refine-Prototype
The feedbacks from the user are discussed at refining prototype phase and if the users are yet not satisfied with the current prototype, the requirement of the prototype will be refined based on the feedbacks. Thus, the phase will repeat the process of building a new prototype. The developer will do a quick design of the new prototype based on the refined requirements according to the feedback from the user evaluation.
Then, a new prototype is constructed based on the quick design. The new prototype will be represented to the user for evaluation and user feedback will be collected and analysed. The prototype will be refined until the prototype meets the user expectations. Whenever the user satisfied with the final prototype, then the stage will be proceeding to engineer the product.

Product
Once the user expectations are completely met, the final system will be constructed based on the final prototype. The final system is tested and evaluated thoroughly followed by the routine maintenance on regular basis for preventing large-scale failures and minimizing downtime.

PSAP Web-Based Application
For the purpose of this research, we developed a user-friendly web-based application, which is called PSAP (Predicting Student Academic Performance) that used for predicting student performance at the final examinations based on their CGPA on the pre-university academic qualification and total of coursework's mark. The following are the main features of the PSAP web-based application. Home Page : This module is optionally used by user to manage the course and coursework structures. Prediction : This module is used to accept student's information and then show the prediction result. Data Training : This module is used to import the data set of previous student's academic performance information including their coursework mark.
Firstly, the user/educator can use the uploaded data in the database of the web or they can upload their own data collected from their past courses in CSV file format. This function can be accessed on the "Data Training" section. The user can download the sample file as a reference of what the CSV file should contain.
Next, by scrolling down the pages, PSAP will display the decision tree constructed based on the data that are in the database of the web application. Last, user will be able to predict their student 130 performance by navigating to the "Prediction" section. User will have to select a course as a base for prediction. The course and coursework information can be edited in home page. Figure 2 - Figure 6 shows the PSAP's user interface. In Figure 5, the user then have to enter their student's academic information in order to view the prediction result in Figure 6.

Analysis
There are three classification model applied to the data set. Each model is evaluated by using confusion matrix and the result is shown in Figure 7. Figure 7 shown that C4.5 is highest accuracy compared to the other two model. While Figure 8 shows the decision tree model visualization.  Table 3 shows the result of confusion matrix evaluation of decision tree classifier. And since the decision tree model has the highest accuracy, it will be implemented to the system that is going to be developed. In attempt to increase the quality of the PSAP is to increase the number of rows of dataset in the databases. This will eventually decrease the number of cases of missing data thus increase the accuracy of the classifier model.
Other suggestions that are made are to add another column at the prediction result which is reasoning of the prediction result are made by the classifier. For instance, if the prediction result is "A" then the system will display a description of why the student will achieve that grade such as "Student have good past academic qualification and score excellent in coursework -Assignment 1".

Conclusion
As a conclusion, PSAP web-based application has benefit of allowing lecturer to monitor and predicting student grade. This helps lecturer to identify promising students and also provides them an opportunity to pay attention to and also improve those who would probably get lower grades. However, there are some improvement must be made in order to increase the level of user-friendliness of this application. Besides, improvement should be made to increase the level of user satisfaction of this application based on the user requirements.


extfieldsofstudy: Medicine Geography

text:
Updating the global occurrence of Culicoides imicola, a vector for emerging viral diseases

Culicoides imicola is the main vector transmitting viruses causing animal diseases such as Bluetongue, African Horse Sickness, and Schmallenberg. It has become widely distributed, with reports from South Africa to southern Europe, and from western Africa to southern China. This study presents a global compendium of Culicoides imicola occurrence between 1943 and 2018, reflecting the most recently compiled and harmonized global dataset derived from peer-reviewed literature. The procedures used in producing the data, as well as the geo-coding methods, database management and technical validation procedures are described. The study provides an updated and comprehensive global database of C. imicola occurrence, consisting of 1 039 geo-coded records from 50 countries. The datasets can be used for risk mapping of the diseases transmitted by C. imicola as well as to develop the global habitat suitability for the vector.

www.nature.com/scientificdata www.nature.com/scientificdata/ doubled. Thus, it is important and timely to expand the effort of Guichard et al. 14 and update the global Culicoides occurrence record. With these research gaps in mind, this study compiled the global occurrences of C. imicola based on the dataset provided by Guichard et al. 14 and literature published since 1 st January 2014 and created the largest currently available standardized up-to-date georeferenced global dataset for the vector, containing 1 039 occurrence records.

Methods
Literature search and data extraction. PubMed (http://www.ncbi.nlm.nih.gov) was searched using the term 'Culicoides imicola' OR 'Ceratopogonidae' . Automatic inclusion of all pseudonyms in the searches was guaranteed by using the Medical Subject Headings (MeSH) term technology of the PubMed citation archive (http:// www.nlm.nih.gov/mesh). The literature search was last updated on 14 th January 2019, which resulted in a collection of 1 920 articles. However, a geo-database of 649 occurrences of C. imicola compiled by Guichard et al. 14 14 for the period 1959 to 2014. The search retrieved a total of 380 articles published since 1 st January 2014 and the titles and abstracts of those articles were screened and those not fitting the criteria: 1) no mention of the vector species; and 2) data from experimental study were removed. After literature searching and initial selections, 150 eligible full-text articles were downloaded and examined in detail to filter those meeting the following criteria: 1) the coordinates of field sites were reported or could be retrieved from Google earth using the reported location information, and 2) occurrence of C. imicola was reported. Therefore, each entry was checked for site coordinates, occurrence of the target species, and other information if available. Subsequently, the geo-location of the vector was extracted from a total of 35 articles meeting all the criteria (Fig. 1). Each article was thoroughly reviewed and all important information was extracted: site location, site name, year of data collection and other information, and confirmed C. imicola occurrences within these articles were entered into the database. Occurrences were classified as confirmed when the article clearly stated the presence of the vector at a specific time in a specific location.
Geo-coding of data. The occurrence coordinates (longitude and latitude) of C. imicola was extracted from each articles and whenever the coordinates were not provided in the articles or supporting information of the articles, the study site name together with all contextual information as well as alternative spelling of site name was used to determine its coordinates using Google Earth (http://www.google.co.uk/intl/en_uk/earth). When two locations have the same name and different geolocations, both the location name and occurrence coordinates were provided. All data points were then linked to the FAO Global Administrative Unit Layer (GAUL) system (http:// www.fao.org/geonetwork) 112 by using a join attributes by location tool in QGIS Version 3.4 (https://qgis.org/).

Data Records
R (https://cran.r-project.org/), QGIS (https://qgis.org/), Mendeley Desktop http://www.mendeley.com/, and Microsoft Excel were the software packages used to manage, store and analyze the database. The dataset is saved in a comma-delimited (.csv), format and can be imported into a variety of Statistical and GIS software programs. The data records described in this paper are publicly and freely available on Figshare 113 . There are 2 589 entries (before technical validations) and 1 039 entries (after technical validations) with information in 8 columns (Table 1) in the dataset. The spatial thinning procedure was provided under technical validation section. In the data, the rows represent a single occurrence record (one or more C. imicola occurrences in the same unique location within a single calendar year). The fields contained in the database are described in Table 1.

technical Validation
To ensure the accuracy and validity of the occurrence records, a technical validation was performed. Firstly, a 5 km × 5 km resolution landcover raster was used to ensure all occurrences were positioned on a valid land pixel. Based on the reported coordinates some sites (n = 96) fell on water bodies. This was probably due to the precision of the longitude and latitude values since these sites were all in peri-coastal locations. Thus, from 2 589 occurrence points 96 were removed from the database.
Further, as the database was compiled from different sources and over many years, it was important to standardize the data entries such that identical locations which may have been geo-positioned slightly differently were given the same unique identifier. The present dataset is heavily clustered in Europe and Southern Africa, with a high degree of aggregation in Spain, Portugal, Italy and South Africa compared to elsewhere. Consequently, it was important to spatially thin the occurrence records. The spatial thinning was performed using R package spThin 114 with the use of the following parameters: "thin.par" (the distance between occurrence records in kilometers) and "reps" (the number of times to repeat the thinning process). In the thinning process, the distance (in kilometers) between occurrence records was set to 5 km (meaning if occurrence records lay within the same 5 km × 5 km pixel within a global grid only one record was retained) and the number of times to repeat the thinning process was set at 100. As a result, the 2 493 occurrence points were reduced down to 1 039.
The resulting database consists of 2 589 (before technical validations) and 1 039 (after technical validations) geo-positioned occurrences of C. imicola spanning 50 countries worldwide, disaggregated by continent, region, and country ( Table 2). The data before technical validations includes the 96 occurrence points that fell on water bodies as well. In Fig. 2 the global geographical distribution of C. imicola is displayed.

Usage Notes
The database described here can be used to investigate the spatial and temporal distribution of C. imicola. The data are most appropriate for applications at global and continental scales. It is known that C. imicola and the diseases transmitted by the vector were previously known to be a problem of Africa. However, due to the recent spread of the species to Europe and other parts of the world [115][116][117] , this data could support improved modelling of new locations at high-risk of experiencing the occurrence of the vector as well as the diseases transmitted by it.

Attribute
Column # Column name Unit Note www.nature.com/scientificdata www.nature.com/scientificdata/ The database after technical validations could be used to develop suitability and risk maps at global, continental, and regional scales. On the other hand, for local scale suitability and risk mapping, the database before technical validations could be used.
There are differences in the number of published studies and the availability of occurrence data by continent and region. Continental and regional biases in the density of occurrence records are apparent, and likely reflect differences in the level of surveillance. Due to the recent occurrence of Bluetongue and Schmallenberg viruses in Europe, substantial numbers of surveys have been conducted in Europe, and thus large numbers of recent occurrence records were from Europe. Many occurrence records were also obtained from Southern Africa. From 1 550 points thinned from the database during validations, 1 440 (92.9%) is from Southern Europe and Southern Africa. Thus, researchers using the technically unvalidated database would need to take into account geographical sampling bias.   www.nature.com/scientificdata www.nature.com/scientificdata/

Code availability
There is no custom code produced during the collection and validation of this dataset.


extfieldsofstudy:
Computer Science
Engineering

text:
Sample Drop Detection for Distant-speech Recognition with Asynchronous Devices Distributed in Space

In many applications of multi-microphone multi-device processing, the synchronization among different input channels can be affected by the lack of a common clock and isolated drops of samples. In this work, we address the issue of sample drop detection in the context of a conversational speech scenario, recorded by a set of microphones distributed in space. The goal is to design a neural-based model that given a short window in the time domain, detects whether one or more devices have been subjected to a sample drop event. The candidate time windows are selected from a set of large time intervals, possibly including a sample drop, and by using a preprocessing step. The latter is based on the application of normalized cross-correlation between signals acquired by different devices. The architecture of the neural network relies on a CNN-LSTM encoder, followed by multi-head attention. The experiments are conducted using both artificial and real data. Our proposed approach obtained F1 score of 88% on an evaluation set extracted from the CHiME-5 corpus. A comparable performance was found in a larger set of experiments conducted on a set of multi-channel artificial scenes.

INTRODUCTION
Distant multi-microphone conversational Automatic Speech Recognition (ASR) in real home environments has attracted a considerable interest in the research community in the past decade. There is a large number of ASR systems deployed successfully in applications which require robustness to different sources of degradation of the input signal. Moreover, distant-ASR introduces many new complex challenges to solve, mainly due to environmental acoustics and unpredictable noisy conditions, often represented by interfering speakers [1]. In order to improve the recognition performance, it is very common to adopt a multi-microphone multi-device setting which allows to observe the scene from different locations in space. For this purpose, the challenges and corpora such as CHiME, DIRHA, and AMI/AMIDA [2,3,4] were created to address a wide spectrum of research topics related to scenarios such as automatic transcription of speech in office and home environments, with spontaneous speech input, different noisy and reverberant conditions as well as microphone and device placement configurations [5,6]. In the specific case of the CHiME-5 challenge, we are in the presence of simultaneous recordings of different real conversational scenarios from multiple microphone arrays, distributed in rather large spaces. The recording sessions in this case are done by using 6 Kinect microphone arrays and binaural in-ear microphone pairs for each of the 4 speakers. The signals recorded by each Kinect microphone array are sample-synchronous within the device. However, the different devices can be subjected to asynchrony [7], due to both small clock speed variations and sample drop events. The latter aspect causes misalignment between different signals, creating additional hurdles for tasks such as voice activity detection, beamforming, and any other multi-channel processing that would require synchrony at sample or frame level [8,9]. It is also worth noting that such misalignment is additional to an intrinsic one that characterizes audio signals acquired by microphones largely spaced one to another, i.e., a shift due to propagation time delays related to the geometry of the problem. This can strongly depend on the spatial location of each sound source, which is typically represented by both active speakers and coherent noise sources, and is continuously changing in experimental scenarios such as CHiME-5. In principle, clock drift, sample drop, and sound source localization [10] could be jointly addressed with the aim of obtaining a rather accurate re-alignment between all signals. Under controlled conditions, e.g., using artificial multimicrophone audio datasets, this joint goal can be addressed, also thanks to fully available ground-truth information. On the contrary, in a real context the problem becomes highly challenging, and generally it is not supported by accurate ground-truth information, such as speaker location and drop duration itself. Just to give a rough idea about the relevance of the problem, in the case of the CHiME-5 challenge, the shift among different devices due to sample drops exceeds one second in many sessions, while the duration of each drop can range from a few milliseconds to more than hundred milliseconds.
As discussed in the following, in order to identify temporal intervals that can be affected by sample drop, one can apply standard cross-correlation techniques, which generally need to process rather large context windows (e.g., 10-15 seconds duration), in order both to accurately detect and to eventually quantify the drop duration. A crucial aspect is represented by the time accuracy with which the drop is detected. For this purpose, the analysis on a shorter context window is strongly required. The main focus of this paper is on the sample drop detection within a short context window, which is tackled by adopting a neural classifier. The design of the architecture has been conceived to respond to a two-fold problem. First, the model has to learn a latent representation of the signal that allows for the comparison between the signal from a device affected by a sample drop event, and the signals coming from the other devices. Secondly, the system has to attain robustness to the resulting time shift.
The paper is structured as follows. After a short overview of the relation to the prior works, Section 3 describes the binary classification task, cross-correlation based method and the neural solution. We then present the dataset, the conducted experiments and the relative results, in Section 4, followed by our conclusions.

RELATION TO PRIOR WORK
The relevance of the sample drop detection task in applications of multi-device multi-microphone processing is not restricted merely to the research related to CHiME-5. Nowadays, many ASR systems rely on multiple devices distributed in the environment, which transmit eventually the signals to the Cloud. Sometimes, these devices are low-cost and characterized by possible interrupts in their operational activity. The communication step itself can also be affected by packet loss, for instance in the case of limited internet access. Furthermore, there are two different research fields to which the general concept of the detection of missing data can relate. However, they both differ from our approach. According to the best of the authors knowledge, there is no similar prior work for the sample drop detection in distributed microphone arrays for distant-ASR.
A first group of studies is done for packet loss concealment algorithms, and the perceptual evaluation of the speech transmission quality in the communication networks. The term discontinuity defined as one of the three orthogonal speech quality dimensions [11], describes the isolated distortion introduced by packet loss due to the transmission errors or network delay. The predictive model designed with both handcrafted features and deep learning methods estimates the packet loss rate and explains the effect of the transmission errors on the speech quality [12,13].
Another related field is in the framework of missing feature techniques, where it is stated that a robust ASR system must acknowledge that some of the spectro-temporal regions are dominated by noise, and hence incomplete or missing. These techniques normally consider a first step for the estimation of a mask which divides the input signal into reliable and unreliable regions [14,15,16]. In order to deal with the unreliable regions normally the two marginalization and imputation methods are proposed [17,6].  Fig. 1. Signals and related spectrograms referred to recordings acquired by two asynchronous devices for CHiME-5. The signal of case b) is affected by a sample drop, which is highlighted by the shorter vowel sequence in the middle of the sentence (see blue rectangles) and by a different spectrographic alignment after that time instant.

CLASSIFICATION TASK
Let x(t) be a source signal, and y m,k (t) the corresponding signal acquired at time sample t by the m-th microphone of the device k, with m ∈ {1, · · · , M } and k ∈ {1, · · · , K}, where M = 4 and K = 6 in CHiME-5. Denote by Y m,k (·) the Short Time Fourier Transform (STFT) of the acquired signal. Given a context window of length , within a time interval of length T , with T , our goal is to decide whether a device has been subjected to a sample drop event, as depicted in Figure 1.
We denote by C(·, ·) a function which correlates or compares |Y m,hyp (·)| of a hypothesis device with the remaining reference devices |Y m,k (·)|, for all k = hyp. The binary classification task can be defined as follows: where F and Comb are a binary classifier and a combination strategy, e.g. averaging, respectively. Furthermore, we assume that the input to the function C(·, ·) is the log-magnitude spectrum 20 log 10 |Y m,k (·)|, in decibel.

Cross-correlation method
The normalized cross-correlation method adopted in this work, derives from a standard approach to pattern matching and feature detection often deployed in application fields based on image processing [18,19,20]. Given the log-magnitude spectrum 20 log 10 |Y m,k (.)|, it is possible to represent the signal in terms of spectrogram, whose portions are then processed as images, namely to find the best match shift between a pattern (see Figure 2) extracted from a signal of the device hyp, and a longer temporal sequence, related to the device ref , which is very likely covering the absolute time range of the former one. Due to different propagation time delays from active sound sources to the microphones of each device, and to minor drift effects due to clock mismatch, in general the two spectrographic representations are not synchronized in time. Under noisy and reverberant conditions, they can differ substantially, in particular in the case of distant devices. Nevertheless, a global similarity is often preserved in terms of temporal relationships among most significant speech contents (e.g., formants and voice onsets), which leads to a higher correlation value corresponding to the correct time shift. In the case of a sample drop that affects one sequence, this match drastically changes, due to an entire vertical slice of the spectrogram which has been lost in one of the two channels. However, this is the key aspect of the proposed method, since a new correlation peak is often found at a time shift that differs from the previous one (observed before the loss) by the exact duration of the loss. In order to increase the robustness of the method, we combine the analysis outputs obtained for each device by correlating the corresponding signal with each of the other K − 1 device signals. As a result, this processing produces K cumulative functions, one per device.
The described method is effective both to identify rather large intervals (e.g., 10-15 seconds) that are likely to include a possible drop, and to provide an estimate of its duration. However, in our preliminary work we observed some limitations when one aims to derive a more accurate location in time (e.g., less than one second resolution) of this drop. For this reason, in this work we explore the application of this normalized cross-correlation method for a preprocessing before the application of a neural classifier whose goal is to eventually decide between loss and no-loss, in a short context window of a few seconds.

Neural-based method
As mentioned in the last Section, the intervention of the neural model is carried out over a short context window, once the cross-correlation method has identified a set of candidate large time intervals.
The cross-correlation method works upon raw features like the log-magnitude spectrum, whereas deep learning approaches build intermediate abstractions of the features throughout a stack of neural layers [21]. These abstractions are usually more robust to low-level feature changes (like temporal shifts) and also facilitate a learnable preprocessing to solve a task of a high-abstraction level like classification. Hence we leverage the capacity of neural networks to process two separate log-magnitude spectrum sources, the hypothesis and the reference, and to classify whether there is sample loss or not in the hypothesis. To that end, we design a network that has two branches processing the raw spectral inputs, and relates them in the end with an attention mechanism. The proposed model is depicted in Figure 3. Given the hypothesis input and the reference input, both of length , we inject them into the two encoder branches. Each input branch is composed by a convolutional neural network (CNN) frontend, specially suitable to detect local temporal correlations in the spectral frames, and a long-short term memory (LSTM) [22] block that exploits specially the long-term sequential dependencies of the input sequence. The convolutional front-end also decimates the sequence lengths of the inputs by 4 through a couple of max-pooling layers that halve temporarily the feature maps right after each of the first two convolutional blocks. This type of model is known as siamese network [23], where both branches are constrained to share the same weights as they process the same type of input data. The optimization problem in the siamese network then leads to learning two separate embeddings which have positive inner products for sequences of the same class but negative inner products for those of different classes [24]. Once we obtained the two hidden representations of the signals, we forward them through the multi-head attention (MHA) component, which outputs a weighted sum of the reference computed by a compatibility function of the hypothesis to itself [25]. As a final step, we took the last time step values and forwarded it through a multi-layer perceptron (MLP), right before the application of a sigmoid function σ.
During training, the optimization procedure is not taking the multi-device scenario into account. The learning problem is reduced to the detection of the sample drop between pairs of hypotheses and references. Starting with the Equation 1 we define the function C to be a composition between a multi-head attention block and a linear layer. By setting F = σ, we have: During the test time, for each device j we forwarded five segment pairs using all devices k = j. The distinct output values of the network are then combined together by taking the average (or median) value, and leading to: (3)

EXPERIMENTS
The CHiME-5 corpus does not offer a reliable ground-truth for the sample drop detection task. Therefore, the overall training of the network has been carried out by using artificial data, and in two distinct stages. Concerning the evaluation of the performance of the model, we did not limit us to the mere usage of artificial data, and used also an evaluation set extracted from the real data.

Noisy-reverberated LibriSpeech
The first training round of the neural model was conducted using two different noisy and reverberated datasets, both derived from the LibriSpeech train-clean-100 corpus [26], containing 28539 speech utterances. Each utterance was filtered by using a different room Impulse Response (IR), characterized by a reverberation time typically ranging between 0.4 and 0.8 seconds. IRs were computed by using a modified version of the image method [27,28]. Moreover, the two datasets differ both due to a different environmental noise, and to a different sound propagation time shift. Overall, SNRs are generally in the range between 5 and 25 dB. Finally, a random sample loss event was simulated on one of the two versions, and from each of them segment pairs of hypothesis and reference were extracted.

Artificial multi-device mini-scenes
The artificial dataset incorporates 1182 mini-scenes that were generated using clean Librispeech signals, and an accurate and realistic simulation of multi-microphone acquisition in a noisy and reverberant environment, as described in [28]. Each scene refers to a different distribution of the six Kinect devices in space, with varying room size, location of speakers, and noise sources, as well as directivity of source, microphone polar pattern, and reflection coefficient of each wall. The mini-scenes were created without any sample drops. Each scene was then postprocessed to simulate the introduction of none, one, or more drops, for a total number of 880 drops. The data set was split into 782, 100 and 300 mini-scenes for drop detection training, development and evaluation, respectively. The scene duration ranges from 5 to 30 seconds. This material has then been organized to expose the neural model to a balanced set of drop/no-drop examples.

Real dataset
In order to evaluate the proposed model in a more realistic context, it is necessary to test the performance also on a real dataset. In this regard, it is worth noting that there exists no real corpus for studies on sample drop detection. The evaluation set was created by extracting 65 segments, from which 31 with sample drop, from three sessions of the train portion of the CHiME-5 corpus, namely sessions 03, 07, and 08. Some of these segments are characterized by a loss that was found through a very careful visual inspection of the related spectrograms, as outlined in Section 3.1.

Setting
The duration of the loss for the two different contaminated versions of LibriSpeech, was drawn from a left-truncated normal distribution N (600, 150), with a cut value of 50. The samples belonging to that interval were then eliminated from one of the segments. A one second context window containing this loss was isolated, by positioning the loss point randomly within the context window. The parameters of the pre-trained model on this data were then used for a further training step on the mini-scenes. The performance of the final model was tested on two separate evaluation sets belonging to both artificial and real data. The labels of the one-second-long segment pairs are distributed equally in both training and evaluation sets. Regarding the network's architecture, two different experiments were carried out. First, we concatenated h hyp and h ref defined in Equation 2 and applied the sigmoid function on the last time step. Then we added the attention block and operated in the same way for the binary output. We also experimented with the average activations over the time values, but it proved to be less effective. We trained the models for 20 epochs on minibatches of size 50 and 30 for the pre-trained and final models respectively. We used the Adam optimizer [29] with the default PyTorch [30] parameters and learning rate 5 · 10 −5 . Regarding the network structure, fully connected and convolutional layers comprise 512 units, with kernel length N = 5 in the case of convolutions. The LSTM layer contains 1024 cells. The model structure hence amounts to approximately 15 M of parameters in total.

Results
The results obtained from different experiments are reported in Table 1. A quick overview of the numbers reveals the importance of having a two-stage training procedure, since the pre-trained model on the contaminated LibriSpeech or the model trained directly on the mini-scenes without a pre-training phase have both a poor performance. The addition of the attention mechanism is also another important aspect. The difference of F1 score of the final model over mini-scenes is much smaller than the one over CHiME-5, in case of application of multi-head attention. Furthermore, the big gap between the result of the pre-trained model and others indicates the importance of having an appropriate data for multi-device case. For the Fast Fourier Transform (FFT) analysis frame length we tried 32 and 64 milliseconds (ms) with 50% overlap. The former choice leads to better results, specially in case of real data. During the test time we combined the output of the network for different pair devices by using major voting, averaging and the median value. The best choice resulted to be averaging and median for mini-scenes and CHiME-5, respectively. Table 1. Precision (P), Recall (R) and F1 Score [%] for the neural model pre-trained only on contaminated LibriSpeech (Pre-NN), and the final neural model (NN) with and without attention and pretraining step, on artificial multi-device mini-scenes and CHiME-5 corpus, using FFT analysis frame length of 32 ms.

CONCLUSIONS
In this paper, we investigated a possible approach for the detection of sample drops in the context of multi-microphone devices distributed in space, a very challenging technical issue that recently interested many researchers working on the CHiME-5 challenge.
The proposed approach consists in combining a normalized cross-correlation processing and a neural classifier, in order to detect short context windows that are characterized by a possible loss. Experimental results show that a classification performance of 88% F1 score is obtained both on simulated and on real evaluation data sets. In the near future, we plan to improve this performance along different research directions, such as cross-processing all the microphone signals acquired by the available devices.
Though our current main focus is on the CHiME-5 data set the proposed solution can be applied to other similar contexts, which are affected by loss of segments in the audio input signals. Our work also represents a preliminary step towards a joint combination of sample drop detection and quantification of the duration of the lost segment, which is a research issue under study. Moreover, we envisage a third processing step that includes a possible reconstruction of the lost information, based on exploiting redundant information available from higher-quality input channels not affected by the sample drop. For all of these foreseen directions, we also plan to verify soon a possible improvement in terms of recognition performance on the CHiME-5 task provided by the application of the proposed method and of the resulting array synchronization.


E-Andharakolu

: As age of human beings increase most of the people lose their eye sight nowadays, they face more problems in their daily routine life. One such example are persons with low visibility, who cannot operate mobiles in the emergency conditions whenever they need help from required persons, (requirement of doctor). Aged people with blindness find problem while walking, such as unable to view obstacle at a close distance in front of them which may inflict injuries to one-self. To overcome these such problems faced by low sight by old aged people, we have come up with solution which helps them to walk freely and fulfill their requirements using speech reorganization and intimate to the person by text message with the area where the patient is, and calling to specifies persons. This project informs the user through voice about the distance of a particular object ahead them through voice output. Along with this another feature is also added such as sensing the lighting condition in the room and illuminating an LED lamp automatically.

I. Introduction
This project aims at providing a proper navigation for individuals suffering from blurred vision and blindness, which happens due ageing factor. Many embedded systems have substantially different designs, structured modular design concept is adopted and the system is mainly composed of a Renesas microcontroller, LDR , LED driver, LED lamp, Ultrasonic sensor (HCSR04) and Bluetooth. An application created and stored in the Android based Smartphone, generates speech output depending upon the incoming messages transmitted via Bluetooth. This paper proposes the design and development of an e-Andharakolu that helps the visually impaired people.

II.
LITERATURE SURVEY 1) Paper [1] Title: Blind stick is an innovative stick designed for visually disabled people for improved navigation. This system uses ultrasonic sensor along with light and water sensing. The microcontroller used processes the data collected by the sensor and calculates whether the obstacle is close enough for the circuit to send a signal to the buzzer in order to produce the sound that indicates to the blind person about the obstacle. The system also sends out differently sounding buzzer when it detects water. Another add-on of this project is that allows the user to detect if the room that they enter is dark or there is sufficient light. The special feature of this system is that it helps the blind to find the blind stick if in case they loose it or also if they forgot where they left it which is implemented using a wireless RF based remote. 2) Paper [2] Title: A smart stick system for assisting blind people. The system used here is designed to act like an artificial vision and alarm unit. This system contains five different sensors ,which are an ultrasonic sensor, an IR sensor, a water sensor, a fire sensor and a light (LDR) sensor. The microcontroller (Arduino Uno R3) is used to receive the signals sent by the sensors and process those signals to short pulses that will be inputs to the Arduino pins where voice alarms, buzzers and vibrators are connected to. The system also uses GPS navigation that is separately in the mobile which is used to guide and help the blind user to reach or find new or unfamiliar places. 3) Paper [3] Title: Smart Stick for Blind People This system uses ultrasonic sensors to detect objects that are being approached by the blind. Since there are chances that they might get lost or are in a place where they are finding it difficult to get back home or to some familiar place, the system uses GPS and GSM modules to find the location In order to help the blind people for detecting object the proposed system make use of ultrasonic sensors to track the person. The system makes use of GPS and GSM modules to find the location. These modules are very useful since their family members will be able to track them very easily. 4) Paper [4] Title: An Intelligent Walking Stick for the Visually-Impaired People.
This system uses GPS and ultrasonic sensors to detect any obstacle and provide accurate location. The system uses four sensors with different attributes. All these four sensors are placed in such a way that two sensors are implemented at the front,one on the left and the other on the right, which will calculate distance and then send it to the Arduino module. After which the Arduino module will transmit the accurate distance to the application on the smartphone through the additional Bluetooth module used.Through the smartphone application the blind user will be able to hear the instruction through his/her headphone.

III.
PROBLEM STATEMENT The e-Andharakolu is an embedded system integrating the following : pair of ultrasonic sensor to detect obstacles in front of the blind from ground level height to head level height of the stick in the range of 400 cm ahead, infrared sensor to detect upward and downward stairs, Bluetooth HC05 to connect to the android application on the smartphone, a moisture sensor to detect water presence and also an RFID sensor. The sensors collect the real-time data and send it to the microcontroller for processing. After processing, the microcontroller invokes the right speech warming message through a Bluetooth earphone. The system is powered by a rechargeable battery. The GPS based blind device with user input interfacing get alert to the blind person when he reaches his destination by voice. This consists of microcontroller module, GPS Unit and a voice module to generate voice output.

IV.
METHODOLOGY Many embedded systems have substantially different designs according to their functions and utilities. In this project design, structured modular design concept is adopted and the system is mainly composed of a Renesas microcontroller, LDR, LED driver, LED lamp, Ultrasonic sensor (HCSR04) and Bluetooth.The microcontroller located at the center of the block diagram forms the control unit of the entire project. Once the entire unit (comprising of microcontroller and sensors) is worn by the patient the sensors begin to monitor the surrounding environment conditions .In this case the ultrasonic sensor and LDR form the input to the microcontroller. Based upon the program embedded within the controller an output is generated and transmitted to the Android based Smartphone via Bluetooth module. An application created and stored in the Android based Smartphone, generates speech output depending upon the incoming messages transmitted via Bluetooth. Ultrasonic sensor is utilized to locate the distance of any object from the person. The sensor on detecting the presence of the obstacle delivers an input voltage to the microcontroller which then alerts the patient via Android Smartphone through the use of Bluetooth module.

V. RESULT AND CONCLUSION
The main purpose of this project is to produce a prototype that can help the blind by detecting obstacles in front of the user and feeds warning back in the form of voice messages. The developed prototype gives good results in detecting obsatcles paced at distance in front of the user and also the obstacles.


extfieldsofstudy:
Computer Science
Mathematics

text:
Yelp Food Identification via Image Feature Extraction and Classification

Yelp has been one of the most popular local service search engine in US since 2004. It is powered by crowd-sourced text reviews and photo reviews. Restaurant customers and business owners upload photo images to Yelp, including reviewing or advertising either food, drinks, or inside and outside decorations. It is obviously not so effective that labels for food photos rely on human editors, which is an issue should be addressed by innovative machine learning approaches. In this paper, we present a simple but effective approach which can identify up to ten kinds of food via raw photos from the challenge dataset. We use 1) image pre-processing techniques, including filtering and image augmentation, 2) feature extraction via convolutional neural networks (CNN), and 3) three ways of classification algorithms. Then, we illustrate the classification accuracy by tuning parameters for augmentations, CNN, and classification. Our experimental results show this simple but effective approach to identify up to 10 food types from images.

INTRODUCTION
Nowadays people really love taking photos, especially when they are in a fancy restaurant, in addition that smart mobile phones today are well equipped with high-resolution cameras. So, it is not a surprise that you can see there would be thousands of pictures from someone's phone album after a year. However, labeling and searching by words for these photos becomes a real hassle. For 1 Wikipedia: https://en.wikipedia.org/wiki/Yelp 2 Yelp Data Challenge: https://www.yelp.com/dataset/challenge Permission to make digital or hard copies of part or all of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for third-party components of this work must be honored. For all other uses, contact the owner/author(s). E511'18, December 2018, Bloomington, IN © 2018 Copyright held by the owner/author(s). ACM ISBN 978-x-xxxx-xxxx-x/YY/MM. https://doi.org/10.1145/nnnnnnn.nnnnnnn example, imaging you are talking with a friend about your extraordinary experience of eating a lobster in Bloomington IN, turning your phone upside down just want to share this picture, oops, you can not find it. This is because you forgot to label this picture and did not remember which day you took this picture.
Not just for personal photo album management, this issue raises astonishing importance for companies like Yelp, which does showing and researching business reviews. Digital photos identification poses a hard problem for those companies which rely on users' uploaded photos. Since they serve millions of customers and they may have billions of photos [4]. It would be almost infeasible to edit photo labels by human editors. So, it would be promising that developing some automatically identification solution for user uploaded pictures.
More formally, in this work, we would like to build a model that can automatically classify a user uploaded food photo into a set of applicable categories, and the accuracy of prediction should be beyond average human guess. We first consider using pre-trained features provided by Yelp Data Challenge and feeding them to traditional machine learning algorithms like Convolutional Neural Network, Support Vector Machine, Gradient Boosting, with crossvalidation. But unfortunately, the classification result was way below human eyeball. After double check, it becomes evident that the main issue lies on the features. As Yelp is a user-generated content platform, many pictures in our training set are vague, offtopic or mislabeled.
Given so, the decision was to do it from scratch. We carefully select 30 pictures for each class, use argumentation methods to enlarge our train set, manually extract features, finally the classification result is satisfactory.
The rest of the paper is organized as follows. Section 2 discusses some related works, some of which either address similar issues or adopt similar solution methods that are related to the content of this work. Section 3 presents the overview of our solution, machine learning models, algorithms, and et al. Section 4 and 5 show the overview of the original datasets from Yelp and our experiments of showing the effectiveness of our implementation. Finally, this paper concludes the work and discusses some advantages and limitations of the work in Section 6.

RELATED WORK
In 2015, this work from Berkeley [6] revealed that fully convolutional networks for semantic segmentation adopting AlexNet [5], VGGNet [11], and Google Net [13] can improve and simplify the state-of-art learning and inference. So, we also adopt the CNN architecture as a solution of image feature extraction, details of which can be found in Section 3. [1], which is a simpel deep learning neural network for image classification for features relying on basic processing, such as PCA, binary hashing, and blockwise histogram. While our work does not rely on PCA, we use some image augmentation techniques which can improve accuracy.

Chan et al. proposes PCANet
While Convolutional Neural Network (CNN) is a promising approach to address many problems, Maggiori et al. [7] presented an end-to-end framework, which adopts fully CNN approach to train large set of imperfect image sets with small sets of correctly labeled data sets. They demostrated this CNN architecture and its effective to classification problems. In this paper, our approach to address Yelp photo classification is similar to the extent where we mix large amount of low-confidence labeled datasets with augmentation datasets. We also use CNN for image feature extractions, in addition, we use image augmentation with xgboost and SVM classification methods comparing with direct CNN. Figure 1 presents the overall architecture of our machine learning pipeline. Our structure consists of four major steps: raw image preprocessing, image augmentation, feature extraction and classification. The raw Yelp dataset decomposes into K different clusters based on specific users' labels being chosen. Clean and correct images are filtered out manually from each cluster. After being applied different augmentation methods such as flip, rotate, scale, gaussian noise and GAN, these filtered images are mixed with both new augmented images and previous images from K clusters to form a big training dataset. A CNN is then used to extract features from the training dataset and finally we apply different classification methods such as Xgboost, SVM and MLP on those extracted features to get our classification accuracy.

Image Preprocessing
3.2.1 Image Augmentation. Given the Yelp photos are from users' uploads, most images are not directly usable to feed into Machine Learning Models. Because user uploaded images are usually taken from phone cameras with some distortion, which can largely degrade feature extraction and the final accuracy of classifications. After necessary transformations beforehand and filtering duplicates, the clean visible images seize below 10% of the overall data sets. So, we use image augmentation techniques to enlarge our data sets in order to build accurate models. Some augmentation techniques used in our project are listed below: 1) Flip: Images are flipped both vertically and horizontally.
3) Scale: Because camera photos are usually take not far from objects, we scale images both outward and inward by only 10%. 4) Gaussian Noise: We place random black and white pixels over images to simulate Gaussian noise. 5) Generative Adversarial Networks (not used but ideally works): The overall idea behind GANs is that you have two models playing with each other. As Ian Goodfellow describes it: one is a counterfeiter trying to produce seemingly real data while the other is a cop trying to determine what the fake counterfeit data is while trying to not raise false positives on real data, and in this way we can train neural networks to generate plausible data using a zero sum game.
More specifically, in this work, we apply the above techniques and generate 32 times of augmented images.

Convolutional Neural Network
Convolutional Neural Network (CNNs or ConvNets) are a category of feed-forward neural networks that are commonly used in areas such as image recognition and classification. They take advantage of spatial coherence between nearby elements from inputs, which allows them to have fewer weights as some parameters are shared. CNNs are composed of a number of convolutional and subsampling layers followed by pooling layers and fully connected layers. In this way, CNNs can easily transform the origin picture's pixel values into final scores with relatively low computation cost based on fewer parameters in the hidden layer.

Image Feature Extraction
Keras [9] as an open source library provides a set of state-of-theart deep learning CNN models with pre-trained weights on Ima-geNet [8]. These pre-trained models such as VGG16 [11], ResNet50 [3] and InceptionV3 [12] can be used for image feature extraction.
In order to show the effective of our prepared images for augmentation, we study Silhouette coefficient in KMeans clustering algorithm. Figure 2 presents the Silhouette analysis results base on how many clusters can be selected from our prepared images for augmentation. In this figure, x-axis values are the number of clusters y-axis values are silhouette coefficient accordingly. The silhouette scores of VGG16 (blue bars) shows that using 10 clusters can obtain the highest scores, ResNet50 (red bars) shows also 10 clusters will get the highest scores and InceptionV3 (yellow bars) shows 8 will get the highest scores following by 10. The majority of models show their highest scores around 10 clusters , which means our ten types of food images are effective.
Due to the reason that VGG16 has less weight layers and perform relatively faster and more stable around 10 clusters, we utilize VGG16 as a CNN model to extract features from our original datasets and new augmented datasets. We restrict the input size of images as 64X64 to boost our training speed and the final output of features has a dimension of 2048.
The Silhouette Coefficient used to valida k clusters is defined as follow: b − a max(a, b) , in which the Silhouette Coefficient is calculated using the mean intra-cluster distance (a) and the mean nearest-cluster distance (b) for each sample. [10] 3.5 Image Classification 3.5.1 Xgboost. Gradient Boosting We tried gradient boost for this problem, specifically we used XGBoost, which is an implementation of gradient boosted decision trees designed for speed and performance. [2] 3.5.2 Support Vector Machine (SVM). Support Vector Machine (SVM) is a supervised machine learning algorithm which can be used for both classification or regression challenges. we plot each   We applied SVM with a linear kernel and RBF kernel on the data set. In order to get better performance, we also use grid search method to tune the C value.

Radial Basis Function kernel. Radial Basis Function kernel
(RBF) is a kernel function which extends to patterns that are not linearly separable by transformations of original data to tap into new space, defined as follow:

Multilayer Perceptron.
Neural network is a series of algorithms that endeavors to recognize underlying relationships in a set of data through a process that mimics the way the human brain operates. Neural networks can adapt to changing input so the network generates the best possible result without needing to redesign the output criteria.

Cross Validation.
Considering there is no ground truth dataset for testing, we use the training set splits for both training and testing. In our three classification methods, all tests are tuned to take 70% as training sets and 30% as testing sets.

DATASETS AND FEATURES
Yelp publishes over 280,000 pictures from over 2000 businesses, the dataset of which can be found on the Yelp Data Challenge Website [4]. This dataset consists of inside, outside, drink, and food photos. Yelp has labeled the above four categories, while subcategories for specific types of food are missing. Most photos are uploaded either by customers or business owners. These photos may have valid captions, which are correctly descriptions to corresponding photos. There are many photos which do not have any captions or have incorrect captions. For example, a restaurant which sells both burgers and sandwiches tends to write both "burger" and "sandwich" for burger photos and sandwich photos for better marketing.
In summary, there is no ground truth labeled image sets. Figure 3 shows a few representative sample photos. Most food images contain noisy background, including tables, plates, chefs, and etc. Some food images contain more than two kinds of food. So, the baseline accuracy of classification is lower than 40% because of in-perfect word captions from users.

Figure 3: Representative sample photos from Yelp
To isolate the scope of this work and increase the accuracy, initially, we select photos based on observations and keywords from rural user captions. According to our observations, the top ten most popular types are covered: 1) Burger, 2) Cake, 3) Chips, 4) Noodles, 5) Pizza, 6) Roll, 7) Salad, 8) Sandwich, 9) Steak, and 10) Wings. These ten categories are, comparatively, simpler and clearer to identify, so in this work we use them for the classification task.

Evaluation Metrics
The evaluation metrics for our experiment is the prediction accuracy. This accuracy comes from the fraction of data for which the classification algorithm correctly predicted if it can be labeled with the attribute or not. Specifically, for our photo dataset, we would be analyzing the accuracy for all the validation sets of raw k clusters' dataset, augmented dataset and mixing dataset. After manually selecting the images with correct labels, we come up with a very small but relatively clean dataset with 10 selected labels. Each of cluster contains 30 images. Due to the ineffectiveness of small training data, we apply image augmentation to every image and enhance one image into 32 augmented images. Therefore, the size of clean dataset increases from 10 × 30 to 10 × 30 × 32. For the image feature extraction, we used VGG16 as a CNN model to extract image to the dimension of 2048.

Experiments
For each method of classification, we utilized toolboxes for a simple and quick test. In SVM method, we chose RBF kernel with the relative parameter to do the non-linear transformation to the dataset. For MLP method, we created a five layer nerual network with two dropout layer and three activation layer. Considering the output layer result should be in the range of 0 to 10, we selected the activation function of final layer to be RELU. The first and the second activation layer are with RELU and sigmoid function. Between each activation layer, a drop out layer is applied to prevent overfitting and gradient boosting.
All the experiments except SVM were trained on GeForce GTX 960 GPUs. MLP took about 10 minutes to train 1000 epochs on each dataset and XGboost also took not more than 10 minutes. However, because our toolbox for SVM did not supply GPU computing, instead, we ran SVM on school's machine with 64 cores CPU. It took around 2 hours to train the raw dataset and over 6 hours to train the mixing dataset.

5.
3.1 Image Feature Extraction. The augmentation set has a total dimension of 9280 × 2048, the raw training set has a dimension of 4829 × 2048 and the mixing set has a dimension of 14109 × 2048.
After doing cross validation to each set, we got our testing sets which are in the dimension of 1856 × 2048 for aug, 966 × 2048 for raw and 2822 × 2048 for mix respectively.

Image Classification.
In order to avoid overfitting, we perform cross validation. It's very similar to train/test split, but creating more subsets. Meaning, we split our data into k subsets, and train on k-1 one of those subset. What we do is to hold the last subset for test. We perform such operation for each of the subsets. Table 1 shows the testing accuracy of each classification method. Clearly, before augmentation, our classification algorithms perform poorly and even way below human eyeball. While in our mixed data set, the Support Vector Machine achieves accuracy around 68.49%, pretty closes to our expectation.

DISCUSSIONS, CONCLUSIONS AND FUTURE WORK
As we tested k cluster validation with KMeans algorithms, the range of k varies from 4 to 12 by using three Image Model via transfer learning: VGG16, resnet20, and inceptionv3. All of them show effectiveness on our training sets. However, due the limitation of time and computing resources, we can only extract features with VGG16, even though incpetionv3 may produce better features. Even after manually select small sample images from the datasets and augmentation, there are still no ground truth datasets for training. This is mainly due to user uploaded pictures can be very vague and business owners tend to add more irrelevant captions for pictures as many as possible. All these facts can result in a less accurate design of our CNN for features extraction and classification, in which pooling and dropout layers may be not well studied.
The main contribution of this paper is, given low confidence about the quality of the training datasets, image augmentation shows its effectiveness, comparing the classification accuracy of before applying this technique and after. The training accuracy for 10 types of food can be as low as 28% in a multi-layer perceptron. However, after augmentation, the accuracy of classification can be as high as around 70%.
Furthermore, it would be interesting to see some more novelty augmentation methods in application, for example, "style transfer" can be used to augment data in situations where the available data set is unbalanced. Also, business-related metrics such as user comments, the numbers of likes, are supposed to be beneficial to the increasing the classification accuracy.


Development of a Path-loss Prediction Model Using Adaptive Neuro- fuzzy Inference System

The prediction of wireless communication signals is of paramount importance for proper network planning. The existing prediction models such as Okumura-Hata, Co-operative for Scientific and Technical Research (COST-231) and free space are less accurate for predicting path-loss values of wireless signals due to differences in propagation environments. Hence, this paper develops a path-loss model using Adaptive NeuroFuzzy Inference System (ANFIS) for accurate prediction of wireless High Speed Packet Access (HSPA) network signal in Ibadan, Nigeria. This is achieved by measuring the Received Signal Strength (RSS) from three Base Transmitting Stations (BTS) operating at 2100 MHz frequency in Ojo (longitude E 3’ 53.1060’, latitude N 7’27.2558’), Dugbe (longitude E 3’50.4361’, latitude N 7’ 23.0678’) and Challenge (longitude E 3’ 53.1060’, latitude N 7’ 21.258’) areas of Ibadan using the Drive Test. Ericson Test Equipment for Mobile System (TEMS) phone, Global Positioning System (GPS) and Computer System are used to obtain RSS data at different distances. Base station parameters such as the transmitting antenna height, receiving antenna height, carrier frequency and distance are used as input variables to train ANFIS to develop a model. These base station parameters are also used to investigate the suitability of Okumura-Hata, COST-231 and free space model. A five layer ANFIS structure is developed and trained using Least Square Error (LSE) and Gradient Descent (GD) method to adjust the consequent and premise parameters. The performance of the developed ANFIS model is evaluated using Mean Square Error (MSE) and Root Mean Square Error (RMSE) and compared with Okumura-Hata, COST 231 and free space. The results obtained for ANFIS give lower RMSE and MSE indicating the suitability of ANFIS model for path-loss prediction. The developed ANFIS model can be used for network planning and budgeting in these environments.

Introduction
Wireless communication is the transmission of signals over a wireless channel that has no physical connection between the transmitter and the receiver [1]. There is reduction in signal strength as the distance increases indicating the loss in signal along the path. To determine the actual path-loss, path-loss models have been developed for predicting the values. These models are mathematical expressions used to predict the radio signal characteristics of a place [2]. The models are categorized into three; random, deterministic and empirical models [1,7,9]. Random models generate predictions randomly without proper considerations of all the physical environmental phenomena which make it unreliable when applied to environment that has complex structure and many obstacles [9,12]. Deterministic models rely on ray optical laws for prediction and are computationally complex and time consuming [11,12]. Although these models seem to be accurate than other existing model but are difficult to implement especially in environments where there are many obstacles along the signal paths.
On the other hand, Empirical models are dependent on the environment where the measurement is taken. To make these models applicable to other environments, optimization approach must be carried out on the models for proper adaption [3,8,12,18,19]. The measured data is used as reference for comparison with optimized models. A major limitation of the models is large deviation when compared to the measured data. Therefore, this paper investigates the applicability of the existing models in Ibadan, Nigeria and the development of a new model using Adaptive Neuro-Fuzzy Inference System (ANFIS). The investigation is carried out using the drive test method. Drive test equipment such as Test Equipment for Mobile System (TEMS), a computer system and a Global Positioning System (GPS) are used to carry out the measurement along three different locations in Ibadan, Nigeria. Path-loss values obtained from measured RSS are used to determine the deviation from pathloss values of the existing models. An ANFIS structure was developed and trained with transmitting antenna height, receiving antenna height, carrier frequency and distance as the input variables. Least Square Error (LSE) and Gradient Descent (GD) method are used to adjust the consequent and premise parameters. The new model developed using ANFIS is compared to the measured path-loss values.

Related works
In [7], Fuzzy Logic (FL) approach was used for path-loss prediction in Mehuwala, India. A path-loss exponent was assigned to each propagation environment. The authors classified the propagation medium into several propagation environments. A path-loss exponent (n) was assigned to each propagation environment. The result was compared to Hata model using path-loss exponent. Path-loss increased at the rate of 26.7 dB per decade with distance for FL Model. The rate of increase of path-loss per decade with distance for Hata model is 114.8 dB. The authors concluded that FL Approach is more efficient in path-loss prediction than theoretical analysis. In [10], a model was developed for Hyderabad city using Artificial Neural Network (ANN). The authors trained the measured data using Feed Forward Neural Network (FFNN). Leverberg Marquart algorithm was used for the training. The result was presented and compared to Hata, COST-231 and Walfisc model. ANN results were found to be better than other models.
In [16], FL was used to estimate path-loss in Dehradum and Uttarakh environments at India. The authors assigned a mean path-loss exponent (n) to each of the environment such as free space terrain, flat area terrain, heavy terrain and light terrain. Data was taken in three Zones within 1km to 5km Transmitter-Receiver separation distance. Path-loss exponent (n) was calculated by linear regression such that the error between measured and estimated path-loss is minimized. Finally, FL model was compared to Hata, COST-231 and Electronic Communication Committee (ECC-33) models using path-loss exponents (n). FL method proved a different method in path-loss prediction. The author concluded that, theoretical analysis is said to be different from practical analysis and to achieve a better result, the two must be combined. In [17], Adaptive Neuro-Fuzzy Inference System approach was used for path-loss prediction at VHF band. A five layer ANFIS structure was trained with frequency and distance as the input variables. Performance of the developed ANFIS model was compared to COST-231, Egli and ECC-33 models. ANFIS was found to have better predictions in terms of RMSE, Spread Corrected RMSE (SC-RMSE) Mean Error (ME), and Standard Deviation Error (SDE). Artificial Intelligence (AI) techniques have experienced great success in the field of path-loss predictions in recent time. Majority of the researches conducted on path-loss predictions using AI techniques have been done at VHF and GSM frequency (900 MHz), very few have been done at UHF band indicating that AI techniques have not been majorly investigated at higher frequency. Based on this assertion and the peculiarity of the environment under consideration, it is therefore necessary to investigate the efficiency of ANFIS for its adaptability in Ibadan, Nigeria at 2100 MHz.

Determination of path-loss models
Path-loss models are mathematical expressions used in design and predictions of radio signal attenuation. These models determine path-loss by calculating the difference between the transmitted power and the received power at any distance [8]. These models are categorized into three; deterministic, random and Empirical model [9,13].

Deterministic model
These models are based on ray optical laws. They approximate reflection, diffraction and scattering of the signal using simple geometric equations. There must be adequate knowledge of the position of obstacles for this model to be accurate. However, the model has computational complexity which makes it inapplicable as a general tool for path-loss modeling except in indoor environments and rural areas where obstacles are very minimal [3,13,19].

Random models
These models are derived from randomness of the environment due to propagation mechanisms which make the wireless signal unpredictable [6,8]. As a result of this, statistical approach is adopted to predict the signal. The approach is known as random models.

Empirical models
Empirical models are based on real time measurement at a certain frequency range and a given distance, these models are limited to the geographical locations where the measurements are taken which makes their accuracy questionable when applied to other environments [2,11,18]. Therefore, optimization approach must be carried out for proper adaptation of these models to other environment. The optimized model is the used as a reference for comparison with the measured data

Applicability of path-loss models in Ibadan, Nigeria.
In this session, the applicability of some existing models for path-loss prediction in the environment under consideration is investigated using the base station parameters of the three BTSs ( 1 , 2 and 3 ) inIbadan. Details of the transmitting parameters are contained in Table 1.

Applicability Okumura-Hata Model
Okumura-Hata model developed empirical formulas valid over a frequency range of 150 MHz-1500 MHz [1,5]. Path-loss ' ' model described by Okumura-Hata model for urban environment is given by [1,16] as: where: ℎ is the receiving antenna height ℎ is the transmitting antenna height is the frequency in MHz, is the separation between the transmitter and the receiver, (ℎ ) is the correction factor mobile station.
Inserting the site parameters of 1 into equation (1) gives; Path-loss is obtained for 3 as;

Applicability of COST-231 Extension to Hata Model
COST 231 Extension to Hata Model operates over a frequency range of 500 MHz to 2000 MHz. Though, there is correction factor for adjustment. The ' ' model is given by [1,5] as: where: ℎ is the receiving antenna height ℎ is the transmitting antenna height is the frequency in MHz, is the separation between the transmitter and the receiver, (ℎ ) is the correction factor mobile station.
is the correction factors, and has a value of '3' for urban areas.
Inserting the site parameters of 1 in equation (5)

Free space models
Free space propagation assumes there is no obstruction along the path and the signal propagate on a straight line. The ' ' for free space propagation is given by [1,12] as: Where ( )is the separation between the transmitter and the receiver in meters (m) and 'f' is the frequency in MHz.

Inserting the site parameters, path-loss '
'is obtained for 1 , 2 and 3 as;

Methodology
The procedure for data collection involves Equipment Setup inside a vehicle. Information related to the BTS such as gains of the transmitting and receiving antennas, height of antennas and transmitted power are recorded for link budgeting.

Equipment Setup
Three BTSs in Ibadan, Nigeria at (longitude E 3' 53.1060' and latitude N 7'27.2558'), (longitude E 3'50.4361'and latitude N 7' 23.0678') and (longitude E 3' 53.1060' and latitude N 7' 21.258') that have omnidirectional antennas and transmit a continuous wave signal are used for the investigation. The mobile receiver used is a 3G Ericson (W995) TEMS Phone connected to a dual core computer system as shown in Fig. 1. The computer system is integrated with TEMS software capable of mapping Received Signal Strength (RSS) with Global Positioning System (GPS). During the Drive Test campaign, transmitting parameters such as transmitted power, antenna gain, antenna height and the carrier frequency are recorded for every base station. TEMS software is locked on each of the transmitting base station to record RSS and the coordinate of each test point in the direction of the sectors. The receiver is kept at height of 1.5 m. The vehicle is driven away from the base stations at an average speed of 40km/h. 10 Measurements samples of RSS are taken for each BTS between 100m and 1km. Seventy percent (70%) of the data is used for training and 30% for testing. Details of the measurement are presented in Table 2.   Dugbe  100  97  98  96  200  105  103  102  300  110  105  106  400  113  109  110  500  117  112  113  600  120  116  117  700  122  120  121  800  124  123  126  900  129  126  128  1000 133 129 136

Layer 1
This layer defines the membership grades for each set of input vectors. Usually, it is represented by a Gaussian function. The ℎ node of Layer 1has an output " 1, " defined by [15] as: where : ( , ) are called premise parameters that define the shape of the membership functions, , , are the input membership grades defined, determines the center of the corresponding membership function.

Layer 2 (Product layer)
Takes the product of the incoming membership grades with the output " 2, " of the ℎ node given by [15] as:

Layer 3 (Normalized layer)
The layer determines the ratio of the ith rule's firing strength to the sum of all rules' firing strength. The output ' 3, ' is defined as: for l= 1…16 (16) where; ̅̅̅̅ is the normalized firing strength, is the firing strength of the ℎ rule,

Layer 4
The layer computes the contribution of each rule to the overall output and provides output values resulting from the inference of the rules. The output ' 4, ' of the node is given as: where: ( , , , , ) are called consequent parameters, 1 , 2 , 1 and 2 are the input variables, ̅̅̅̅is the output of layer 3.

Layer 5 (Final Output)
This layer computes the overall output as a summation of all incoming signal. The output of the ith node of this layer ' 5, ' is given as: Where is the final part of the inference rule governing the path-loss defined as; In Training ANFIS for the path loss model development, the input variables are height of the transmitting antenna, height of the receiving antenna, frequency and distance represented as ( 1 , 2 , 3 , 4 ). Hybrid learning algorithm that combines LSE and GD method is used for the training. LSE are used to update the sets of consequent parameters while GD updates the premise parameters [14,15]. The training continues till error between the predicted and the measured values are minimized based on the relation. The performance of the developed model is compared to Okumura-Hata, COST-231 and free space model.

Results and discussion
Figs. 3. to 5. present path-loss versus distance for ANFIS model, Okumura-Hata model, COST-231 model, free space model and measured values for 1 (Ojo), 2 (Challenge) and 3 (Dugbe). Fig. 3. presents path-loss against distance for ANFIS, measured, Okumura-Hata, COST 231 and free space models for 1 . At 500 m from the base station, path-loss values obtained are 117,114, 122, 127 and 91 dB for ANFIS, measured, Okumura-Hata, COST-231 and free space. Fig. 4 depicts path-loss values against distance for ANFIS, measured, Okumura-Hata, COST-231 and free space models for 2 . At 500m from the base station, path-loss values obtained are 115, 112, 126, 131 and 93 dB for ANFIS, measured, Okumura-Hata, COST 231 and free space models, respectively. Fig. 5 shows path-loss values against distance for ANFIS, measured, Okumura-Hata, COST 231 and free space models for 3 . At 500 m from the base station, path-loss values obtained are 117, 113, 125, 131 and 93 dB for ANFIS, measured, Okumura-Hata, COST 231 and free space models, respectively.  Tables 3 and 4 for 1 to 3 . ANFIS models give the lowest AMSE and ARMSE indicating the best model to predict the path loss in these environments. This is due to reduction in error by ANFIS.

Conclusions
Adaptive Neuro-Fuzzy Inference Systems approach has been used to develop a path-loss model for HSPA network signal in Ibadan, Nigeria. RSS data are measured from three selected BTSed from three selected BTSs located at Ojo, Challenge and Dugbe areas of Ibadan using drive test. A five layer ANFIS structure is developed and trained using LSE and GD algorithm. HSPA base station parameters such as antennas height, carrier frequency, and separation of the transmitting and receiving antennas are used as input variables to train ANFIS. The development platform is MATLAB R2012a version on 64 bits' dual core computer system. The base station parameters are used to investigate the suitability of Okumura-Hata, COST-231 and free space model. The performance of the developed model is evaluated using RMSE and MSE and compared with Okumura-Hata, COST-231 and free space model. The results show that, errors related to ANFIS model is greatly reduced when compared with the existing models.