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Today, as education is very important for all human being, so it is necessary to analyze and improve the education system as technologies growing day by day, so use of latest technologies is very crucial to enhance the education system and academic performance of the student. Many researchers have been worked on predicting student performance and built predictive models to measure and predict students’ performance and found interesting results. This classification presents a review of works previously done by different authors on student performance by using different techniques. The aim of this work is to review the available study, to compare different models developed by different authors accordingly and to find out the best model from it. This study shows how different techniques used and produces result and which is best suitable technique. The various factors identified with the representation of machine learning algorithms based on methods and tools followed by their attributes and results respectively. This can help students, faculties, and institutions to increase the performance.

Student Performance, Machine learning algorithms, Tools

[1] Amjad Abu Saa, “Educational Data Mining & Students’ Performance Prediction”, International Journal of Advanced Computer Science and Applications(IJACSA), Vol. 7, No. 5, 2016.
[2] Brijesh Kumar Bhardwaj and Saurabh Pal, “Data Mining: A prediction for performance improvement using classification”, International Journal of Computer Science and Information Security(IJCSIS), Vol. 9, No. 4, 2011.
[3] Augusto Sandoval, Carlos Gonzalez, Rosa Alarcon, Karim Pichara, Maximiliano Montenegro, “Centralized student performance prediction in large courses based on low-cost variables in an institutional context”, The Internet and Higher Education, Vol. 37, pp.76-89, 2018, DOI:10.1016/j.iheduc.2018.02.002.
[4] Ioannis E. Livieris, Konstantina Drakopoulou, Vassilis T. Tampakas, Tassos A. Mikropoulos, Panagiotis Pintelas, “Predicting Secondary School Students’ Performance Utilizing a Semi-supervised Learning Approach”, Journal of Educational Computing Research, 2018, DOI: 10.1177/0735633117752614.
[5] Evandro B. Costa, Baldoino Fonseca, Marcelo Almeida Santana, Fabrísia Ferreira de Araújo, Joilson Rego, “Evaluating the effectiveness of educational data mining techniques for early prediction of students` academic failure in introductory programming courses”, Computers in Human Behavior, Vol.73, pp. 247-256, 2017, doi:10.1016/j.chb.2017.01.047.
[6] Parneet Kaur, Manpreet Singh, Gurpreet Singh Josan, “Classification and prediction based data mining algorithms to predict slow learners in education sector”, 3rd International Conference on Recent Trends in Computing(ICRTC-2015), Vol.57, pp. 500-508, 2015.
[7] Shiwani Rana, Roopali Garg, “Slow Learner Prediction using Multi-Variate Naïve Bayes Classification Algorithm”, International Journal of Engineering and Technology Innovation, Vol . 7, No. 1, pp. 11-23, 2017.
[8] A. T. M. Shakil Ahamed, Navid Tanzeem Mahmood, Rashedur M Rahman,“An intelligent system to predict academic performance based on different factors during adolescence”, Journal of Information and Telecommunication, Vol.1, No.2, pp.155-175, 2017.
[9] Ahmed Mueen, Bassam Zafar , Umar Manzoor, “Modeling and Predicting Students’ Academic Performance Using Data Mining Techniques”, I.J. Modern Education and Computer Science, Vol.11, pp. 36-42, 2016.
[10] Fadhilah Ahmad, Nur Hafieza Ismail , Azwa Abdul Aziz, “The Prediction of Students’ Academic Performance Using Classification Data Mining Techniques”, Applied Mathematical Sciences, Vol. 9, No. 129, pp. 6415 - 6426, 2015.
[11] Jie Xu, Kyeong Ho Moon, Mihaela van der Schaar, “A Machine Learning Approach for Tracking and Predicting Student Performance in Degree Programs”, IEEE Journal of Selected Topics in Signal Processing, Vol.11, No.5, pp. 742-753, 2017.
[12] M. Mayilvaganan, D. Kalpanadevi, “Comparison of Classification Techniques for predicting the performance of Students Academic Environment”, International Conference on Communication and Network Technologies (ICCNT). IEEE, pp.113-118, 2014, doi:10.1109/CNT.2014.7062736.
[13] M. Durairaj, C. Vijitha, “Educational Data mining for Prediction of Student Performance Using Clustering Algorithms”, (IJCSIT) International Journal of Computer Science and Information Technologies, Vol. 5, pp.5987-5991, 2014.
[14] S.Taruna, Mrinal Pandey, “An Empirical Analysis of Classification Techniques for Predicting Academic Performance”, 2014 IEEE International Advance Computing Conference (IACC), pp. 523-528, 2014, doi: 10.1109/IAdCC.2014.6779379.
[15] Olugbenga Adejo, Thomas Connolly, “An Integrated System Framework For Predicting Students’ Academic Performance In Higher Educational Institutions”, International Journal of Computer Science & Information Technology (IJCSIT), Vol 9, No. 3, 2017.
[16] Maria Koutina, Katia Lida Kermanidis, “Predicting Postgraduate Students’ Performance Using Machine Learning Techniques”, Springer, Berlin, Heidelberg, Vol. 364, pp.159-168, 2011.
[17] Ashkan Sharabiani, Fazle Karim, Anooshiravan Sharabiani, Mariya Atanasov, Houshang Darabi, ”An Enhanced Bayesian Network Model for Prediction of Students’ Academic Performance in Engineering Programs”, IEEE Global Engineering Education Conference (EDUCON), pp.832-837, 2014, doi: 10.1109/EDUCON.2014.6826192.
[18] Paulo Cortez, Alice Silva, “Using Data Mining To Predict Secondary School Student Performance” , EUROSIS, 2008.
[19] Qasem A. et al., “Mining Student Data Using Decision Trees”, The International Arab Conference on Information Technology (ACIT’2006), Nov. 2006, Jordan.
[20] M. Ramaswami, R. Bhaskaran , “A CHAID Based Performance Prediction Model in Educational Data Mining”, International Journal of Computer Science Issues(IJCSI), Vol. 7, Issue 1, No. 1, January 2010.
[21] Ali Daud, Naif Radi Aljohani, Rabeeh Ayaz Abbasi, Miltiadis D. Lytras, Farhat Abbas, Jalal S. Alowibdi, “Predicting Student Performance using Advanced Learning Analytics”, 2017 International World Wide Web Conference Committee (IW3C2), pp. 415-421, 2017.
[22] Shaobo Huang, Ning Fang, “Predicting student academic performance in an engineering dynamics course: A comparison of four types of predictive mathematical models”, Computers & Education Vol.61, pp.133–145, 2013.
[23] S. Kotsiantis et al., “Predicting Students’ Performance In Distance Learning Using Machine Learning Techniques”, Applied Artificial Intelligence, Vol.18, No.5, pp.411-426, 2004.
[24] Reynold A. Rustia et al., “Predicting Student’s Board Examination Performance using Classification Algorithms”, in the proceedings of the 2018 7th International Conference on Software and Computer Applications (ICSCA), pp.233-237, 2018, doi: 10.1145/3185089.3185101
[25] J.F. Superby et al., “Determination of factors influencing the achievement of the first-year university students using data mining methods” 8th international conference on intelligent tutoring systems, Educational Data Mining Workshop, Jhongli, Taiwan, 2006.
[26] Havan Agrawal, Harshil Mavani , “Student Performance Prediction using Machine Learning”, International Journal of Engineering Research & Technology (IJERT), Vol. 4, Issue 03, March-2015.
[27] Amirah Mohamed Shahiri et al., “A Review on Predicting Student’s Performance using Data Mining Techniques”, The Third Information Systems International Conference, Procedia Computer Science, Vol.72, pp. 414-422, 2015.

Zeba Parveen, Mohatesham Pasha Quadri, "Classification and Prediction of Student Academic Performance using Machine Learning: A Review," International Journal of Computer Sciences and Engineering, Vol.7, Issue.3, pp.607-614, 2019.

Software Defect Prediction is one of the important research areas of the software engineering. When developing new software from the existing prototype a software defect handling is one the major factor. In order to improve the quality of the software various data mining techniques are being used and applied to obtain predictions regarding the failure of particular software component by using the past datasets or logs consisting of various software measures related to the software defects. The main objective of the research was to rank & identify the most appropriate data mining classifier algorithms from the fifteen selected algorithms such as Lazy-IBK, Lazy-K Star, Function-SMO, Function-Multilayer Perceptron,Rules-ZeroR,Rules-OneR,Rules-PART,Tree-REP,Tree-Decision stump, J48, Naïve Bayes, BayesNet, Meta- AdaBoostM1,Misc-HyperPipes & Misc-VFI. In this particular research study firstly, 15 classifiers were applied to four datasets and the classification results were measured using 12 performance measures. Second, five MCDM methods (i.e., TOPSIS, GRA, VIKOR, PROMETHEE II, and ELECTRE III) were used to rank the classification algorithms based on their performances. So finally it can be concluded that the TOPSIS & VIKOR shows strong negative correlation which depicts that there is association between the two sets and the results were found in accordance. The best algorithm for software defect prediction datasets was found to be Lazy-IBK with highest overall score of 0.8023.

J48, IBK, TOPSIS, VIKOR, GRA, PROMETHEE II and ELECTRE III

[1] Akmel, Feidu & Birihanu, Ermiyas & Siraj, Bahir. (2018). “A Literature Review Study of Software Defect Prediction using Machine Learning Techniques”. International Journal of Emerging Research in Management and Technology. 6. 300. 10.23956/ijermt.v6i6.286.
[2] He, Peng, et al. "An empirical study on software defect prediction with a simplified metric set." Information and Software Technology 59 (2015): 170-190.
[3] Amit Kumar Jakhar, K. R. (2018). “Software Fault Prediction with Data Mining Techniques by Using”. International Journal on Electrical Engineering and Informatics - Volume 10, Number 3 , 447-465.
[4] Balogun, Abdullateef & O Bajeh, Amos & A Orie, Victor & W Yusuf-Asaju, Ayisat. (2018). “Software Defect Prediction Using Ensemble Learning: An ANP Based Evaluation Method”.
[5] Hammouri, Awni & Hammad, Mustafa & Alnabhan, Mohammad & Alsarayrah, Fatima. (2018). “Software Bug Prediction using Machine Learning Approach”. International Journal of Advanced Computer Science and Applications Vol. 9, No. 2,78-83.
[6] Deep Singh, Praman & Chug, Anuradha.(2017). “Software defect prediction analysis using machine learning algorithms”. 775-781. 10.1109/CONFLUENCE.2017.7943255.
[7] A.Parameswari. (2015). “Comparing Data Mining Techniques For Software Defect Prediction”. International Journal of Science and Engineering Research (IJ0SER),Vol 3 Issue 5 , 3221 5687, (P) 3221 568X.
[8] Saiqa A, Luiz F, and F. A, "Benchmarking Machine Learning Techniques for Software Defect Detection". International Journal of Software Engineering & Applications, vol. 6, pp. 11-23, May 2015.
[9] Dwivedi, V.K., & Singh, M.K. (2016). “Software Defect Prediction Using Data Mining Classification Approach”.
[10] Gang Kou, Y. L. (2012). “Evaluation of Classification Algorithms Using MCDM And Rank Correlation”. International Journal of Information Technology & Decision Making Vol. 11, No. 1 , 197-225.
[11] Ayse Tosun (2009) . AR1/Software defect prediction.The PROMISE Repository of Software Engineering Databases. School of Information Technology and Engineering, University of Ottawa, Canada . Creator : Software Research Laboratory (Softlab), Bogazici University, Istanbul, Turkey .Available: http://promise.site.uottawa.ca/SERepository.
[12] Tim Menzies (2004). CM1/Software defect prediction.The PROMISE Repository of Software Engineering Databases. School of Information Technology and Engineering, University of Ottawa, Canada . Creator : NASA. Available: http://promise.site.uottawa.ca/SERepository.
[13] Tim Menzies (2004). JM1/Software defect prediction. The PROMISE Repository of Software Engineering Databases. School of Information Technology and Engineering, University of Ottawa, Canada . Creator : NASA.Available: http://promise.site.uottawa.ca/SERepository.
[14] Tim Menzies (2004). KC1/Software defect prediction. The PROMISE Repository of Software Engineering Databases. School of Information Technology and Engineering, University of Ottawa, Canada .Creator : NASA.Available: http://promise.site.uottawa.ca/SERepository.

Ankit Mehta, Sandeep Upadhyay, "Performance Evaluation of Classification Algorithms Using MCDM and Rank Correlation Method Applied on Software Defect Prediction Datasets," International Journal of Computer Sciences and Engineering, Vol.7, Issue.3, pp.615-620, 2019.

The time spent by clients are very nearly at least two hours searching for papers that reduces the opportunity to make an internet searcher to improve and exactness in the outcomes. The Proposed work is to compose examine papers, utilizing a database of information related with the themes of programming, databases and working frameworks. Utilizing Clustering method the database is made for the required hunt. There are various grouping calculations, for example, progressive bunching, self-sorting out maps, K-means grouping, etc. In this paper, we propose a bunching calculation that look into the archives with common dialect contained and get the best expressions of their substance to frame a database information that the initial step to get the ideal learning. We actualized the framework utilizing the K-implies bunching calculation. Also the future work utilizes the web search tool to influence quests to order the data presented by the last client and seeking in the correct group.

Search Engine, Knowledge Base, Key Text Mining, Mining.

[1] Text Mining: The best in class and the difficulties. (Ok Hwee Tan Kent Ridge Digital Labs 21 HengMuiKeng Terrace Singapore 119613)
[2] week 14 Data mining-Clustering-Classification-Wrap-up.
[3] Survey of Text Mining: Clustering, Classification, and Retrieval, Second Edition.(Michael W. Berry and MaluCastellanos, Editors Jan 4, 2013).
[4] A Brief Survey of Text Mining. (Andreas Hotho KDE Group University of Kassel Andreas Nurnberger Information Retrieval Group School of Computer Science May 13, 2005).
[5] Integrated Clustering and Feature Selection Scheme for Text Documents
[6] Searching Research Papers Using Clustering and Text Mining (978-1-4673-6155-2/13/© 2013 IEEE ).
[7] A Text Clustering System dependent on k-implies Type Subspace Clustering and Ontology.(International Journal of Electrical and Computer Engineering 1:5 2006).
[8] K-implies like Algorithm for K-medoids and Its Performance, Department of Industrial and Management Engineering, POSTECH ―In Proceedings. Of CCS ‟07, pp. 598– 609, 2007.

Ch. Navya, D. VijayaLakshmi, "Clustering and Text Mining based on Search Engine," International Journal of Computer Sciences and Engineering, Vol.7, Issue.3, pp.621-623, 2019.

An Artificial Neural Network (ANN) is a data handling worldview that is propelled by the way organic sensory systems, for example, the mind, process data. The key component of this worldview is the novel structure of the data handling framework. It is made out of countless interconnected preparing components (neurons) working as one to take care of explicit issues. ANNs, similar to individuals, learn by precedent. An ANN is arranged for an explicit application, for example, design acknowledgment or information grouping, through a learning procedure. Learning in organic frameworks includes acclimations to the synaptic associations that exist between the neurons. This is valid for ANNs also. This paper gives review of Artificial Neural Network, working and preparing of ANN. It additionally clarify the application and favorable circumstances of ANN.

ANN(Artificial Neural Network), Neurons, design acknowledgment

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[2] Lippmann, R.P., 1987. A prologue to processing with neural nets. IEEE Accost. Discourse Signal Process. Mag., April: 4-22.
[3] N. Murata, S. Yoshizawa, and S. Amari, Learning bends, show determination and multifaceted nature of neural networks,‖ in Advances in Neural Information Processing Systems 5, S. Jose Hanson, J. D. Cowan, and C. Lee Giles, ed. San Mateo, CA: Morgan Kaufmann, 1993, pp. 607-614

Aditya Mineni, A.V.L. Prasuna, "A Study on Artificial Neural Networks and it`s Applications," International Journal of Computer Sciences and Engineering, Vol.7, Issue.3, pp.624-628, 2019.

Many occasions, in our city we see that the junk canisters or dustbins put at open spots are over-burden. It makes unhygienic conditions for individuals and in addition grotesqueness to that put leaving awful stench. To maintain a strategic distance from every single such circumstance we will execute a task called IoT Based Smart Garbage and Waste Collection canisters. These dustbins are interfaced with microcontroller based framework having IR remote frameworks alongside focal framework demonstrating current status of rubbish, on versatile internet browser with html page by Wi-Fi. Henceforth the status will be refreshed on to the html page. Significant piece of our undertaking relies on the working of the Wi-Fi module; basic for its execution. The principle point of this venture is to lessen HR and endeavors alongside the upgrade of a brilliant city vision.

Microcontroller ARM, IR Sensor (TSOP 1738), Wi-Fi Module

[1] Vikrant Bhor, Pankaj Morajkar, Maheshwar Gurav, Dishant Pandya4 "Brilliant Garbage Management System" International Journal of Engineering Research and Technology (IJERT) ISSN: 2278-0181 IJERTV4IS031175 Vol. 4 Issue 03, March-2015
[2] Insung Hong, Sunghoi Park, Beomseok Lee, Jaekeun Lee, Daebeom Jeong, and Sehyun Park, "IoT-Based Smart Garbage System for Efficient Food Waste Management", The Scientific World Journal Volume (2014), Article ID 646953
[3] Marian Look, "Junk Plant: India", earth911B.
[4] Basic Feature, "Strong waste Management Project by MCGM P.Suresh1J. Vijay Daniel2, Dr.V.Parthasarathy4" A best in class audit on the Internet of Things (IoT)" International Conference on Science, Engineering and Management Research (ICSEMR 2014)
[5] Arkady Zaslavsky, Dimitrios Georgakopoulos" Internet of Things: Challenges and State-of-the-workmanship arrangements in Internet-scale Sensor Information Management and Mobile Analytics" 2015 sixteenth IEEE International Conference on Mobile Data Management
[6]Theodoros.Anagnostopoulos1,Arkady.Zaslavsky 2,1, Alexey Medvedev1, Sergei Khoruzhnicov1" Top– k Query based Dynamic Scheduling for IoTenabled Smart City Waste Collection" 2015 sixteenth IEEE International Conference on Mobile Data Management.
[7] "City Garbage accumulation pointer utilizing RF (Zigbee) and GSM innovation".

Devulapally Shushrutha, G. Kasi Reddy, "A Study on IoT Based Smart Garbage and Waste in Smart City," International Journal of Computer Sciences and Engineering, Vol.7, Issue.3, pp.629-631, 2019.

Recent advancements in cellular networks have led to the demand of ‘any-where’ service - immaterial of the location of the user or the coverage area of the service provider. The increasing demand for providing subscribers with services beyond ones home service area necessitates the service providers to set up elaborate trust relationships and agreements amongst themselves, to ensure secured and reliable service to the genuine subscribers. This process however limits the ease and span of extending the services by a service provider. In addition, it is important that such services are provided without compromising the identity privacy of the subscriber. While identity privacy is an accepted requirement, vulnerability of the same remained across the generations. Newer threat like location tracking and comprehensive profiling - wherein data about movement, usage, etc., of a subscriber is amassed and linked to his/her identity to explore various attacks - has been identified. Much of the aforesaid limitations can be attributed to the trust model adopted by the cellular networks. In this article, we highlight the benefits of a trust model that may contribute towards reducing the complexities of a-priori agreements amongst service providers, for providing service beyond their territories in future mobile networks.

Roaming agreements, Authentication, Anonymity, IMSI, Privacy

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Hiten Choudhury, "Roaming Agreements vis-a`-vis Identity Privacy," International Journal of Computer Sciences and Engineering, Vol.7, Issue.3, pp.632-635, 2019.

The Internet of Things is a system of physical things embedded with sensors,softwares,electronics and connectivity to allow it to perform better by exchanging information with other connected devices. IOT(Internet of Things) technique is used in agriculture.In the existing work the leaf sensor senses the temperature difference level in leaf and sends it to PIC microcontroller. The sensors communicates remotely with a reader using backscatter bistatic standards. The drawback is that there is no way for monitoring the disease in crops. The sensors are used to find the unwanted plants , disease in the crops and maintain soil moisture. The data obtained from the field is stored in cloud by using wifi module. The information will be send to farmers through mobile Android application.

IOT,sensors,wifi module,Android application

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Muthuselvi M, Selva ganesh S, Sajeev A, Venkitesh S.V.R, Thangaraja S, "Smart Field Monitoring Using IoT," International Journal of Computer Sciences and Engineering, Vol.7, Issue.3, pp.636-640, 2019.

The main focus was to enhance the potency, quality, and speed to promote at intervals the software system development world. First it was Waterfall, next it absolutely was Agile and currently it’s DevOps. This is how today developers approach towards building the great products. DevOps is a way to develop software which consists of continuous development, continuous testing, continuous integration, continuous deployment, and continuous monitoring of the software throughout its development lifecycle. This is the way of process adopted by all the top companies to develop high-quality software and shorter development lifecycles. The goal of Continuous Delivery is to modify a relentless flow of changes into production via an automatic computer code line. The pipeline ought to give feedback to the team and visibility into the flow of changes to everybody concerned in delivering the new features. The delivery pipeline is countermined into some stages, as mentionedbelow.1.SourceCodeManagement 2.Continuous Integration 3.Static Code Analysis 4.Build the Artifact 5. Artifact Deployment.

Continous integration, Build, Artifacts, Log Monitoring, Test

[1] Akshaya H L, NisargaJagadish S, Vidya J, Veena K, “A Basic Introduction to DevOps Tools”, International Journal of Computer Science and Information Technologies, Vol. 6 (3) , 2015, 2349-2353 ISSN 0975-9646
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Mahadevi S. Namose, Shobha D. Patil, "Standard DevOps Pipeline," International Journal of Computer Sciences and Engineering, Vol.7, Issue.3, pp.641-646, 2019.

The effectiveness of player popularity as a proxy for ability, and the predictive power it would have in a model estimating a player`s market value is examined.

Multiple Linear Regression, k-means

[1]H. W. Altland, “Regression analysis: statistical modeling of a response variable,” Technometrics, vol. 41, no. 4, pp. 367–368, 1999.
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Fahad Hilal, Mohamad Saalim Wani, "Player popularity as a substitute for player ability in Premier league football using machine learning," International Journal of Computer Sciences and Engineering, Vol.7, Issue.3, pp.647-649, 2019.

In metropolitan cities traffic congestion became a severe issue due to large scale multiple-layer road networks. The multifaceted nature, heterogeneity of traffic framework and the enormous information challenge have turned out to be generous troubles. The current transportation systems deal with these issues with the requirement of qualified overall prediction accuracy. Checking, foreseeing and understanding traffic conditions in any city is a vital issue for city arranging. More recently, the development of new technology for traffic data processing using big data for accurate traffic prediction has shifted the spotlight to data-driven procedures. Different researchers build traffic forecasting systems using big data analytics in order to prevent traffic congestion and accident issues. However, most of the researchers focus on the prediction of individual road segments or intersections instead of the multilayer roads. This paper is an attempt to review the different techniques used by numerous researchers for traffic forecasting using big data analytics. The ultimate goal of this work is to set an updated compilation of prior literature around traffic prediction models so as to motivate and guide future research on this vibrant field.

Traffic Forecasting system, Big Data Analytics, Smart Transport System (STS)

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Mohd Azeem Ansari, T. Arundhathi, "Street Traffic Forecasting: Ongoing Advances and New Challenges," International Journal of Computer Sciences and Engineering, Vol.7, Issue.3, pp.650-656, 2019.