Volume-8 , Issue-12 , Dec - 2020 Go Back

In today’s world, health is turning into the spinal cord of each individual`s way of life. Suitable health check-up for various health-related problems, every individual needs a specialist Doctor to treat them appropriately. To give ease in accessing the sources to the individuals about the doctors according to their requirements and for the doctors to get ease in accessing their patients and their data, by considering these challenges the idea of this work has been advanced with the use of recommendation system. The application will be used by both; the patients as well as the doctors. The patient will Sign-In for getting the required field of service from the specialist doctors that are available and, the doctors will Sign-In for accessing the patients that are to be treated via appointments made from the application used by the patient’s side.

Recommendation System

[1] Md. Abdul Majid, “Smart Doctors Appointment and Prescription System”, In the proceedings of IOSR Journal of Computer Engineering (IOSR-JCE), e-ISSN: 2278-0661, p- ISSN: 2278-8727, Volume 19, Issue 6, Ver. III (Nov.- Dec. 2017), PP 85-91.
[2] Chaitanya Kusurkar, “Android Application for Doctor’s Appointment”, In the proceedings of International Journal of Innovative Research in Computer and Communication Engineering, ISSN: 2320-9801, Vol. 2, Issue 1, January 2014.

Rohit Vaibhav Raut, Manavi Uttam Ghorpade, "Doctor’s Appointment Booking System Using Recommendation Model," International Journal of Computer Sciences and Engineering, Vol.8, Issue.12, pp.62-65, 2020.

Code smell refers to an anomaly in the source code that shows violation of basic design principles such as abstraction, hierarchy, encapsulation, modularity. In this research we are using SVM (support vector Machine) and decision Tree for code smell detection. In this research we improving the accuracy and time complexity of error in code with the help of Multi-Label classification.

CODE SMELLS, VECTOR MACHINE

[1] Thirupathi Guggulothu, Salman Abdul Moiz_Code Smell Detection using Multilabel Classi_cation Approach,School of Computer and Information Sciences, University of Hyderabad, Hyderabad-500 046, Telangana, India
[2] DT : a detection tool to automatically detect code smell in software project Xinghua Liu1, a and Cheng Zhang2, b 1 School of Computer Science and Technology?Anhui University, China 2 School of Computer Science and Technology?Anhui University, China a xinghua.liu@ahu.edu.cn?b cheng.zhang@ahu.edu.cn
[3] Information and Software Technology,Volume 108, April 2019, Pages 115-138 “Machine learning techniques for code smell detection: A systematic literature review and meta-analysis” Muhammad IlyasAzeemabFabioPalombadLinShiabQingWangabc,Laboratory for Internet Software Technologies, Institute of Software, Chinese Academy of Sciences, Beijing 100190, China
[4] “On the evaluation of code smells and detection tools” ,Thanis Paiva, Amanda Damasceno, Eduardo Figueiredo & Cláudio Sant’Anna ,Journal of Software Engineering Research and Development volume 5, Article number: 7 (2017)
[5]An experience report on using code smells detection tools Francesca Ar[5]Università of Milano Bicocca Department of Computer Science Milano, Italy arcelli@disco.unimib.it ,Andrea Morniroli, Raul Sormani, Alberto Tonello ,University of Milano Bicocca Department of ComputerScience Milano, Italy a.morniroli@campus.unimib.it
[6]https://becominghuman.ai/decision-trees-in-machine-learning-f362b296594a

Manpreet Kaur, Deepinder Kaur, "Improve the accuracy and time complexity of code smell detection using SVM and DECISION-TREE with MULTI-LABEL CLASSIFACTION," International Journal of Computer Sciences and Engineering, Vol.8, Issue.12, pp.66-69, 2020.

The data mining is the approach which can mine required information from the rough data. The prediction analysis is the approach which can predict future possibilities based on the current information. This review paper, is based on the diabetic prediction. The diabetic prediction technique has various steps like data pre-processing, feature extraction and classification. In this paper, various diabetic prediction techniques are reviewed and analyzed in terms of certain parameters.

Diabetic prediction, classification, feature extraction

[1] Dr.D.I.George Amalarethinam, N.Aswin Vignesh, “Prediction of Diabetes mellitus using Data Mining Techniques: A Survey”, International Journal of Applied Engineering Research, 2015.
[2] Ioannis Kavakiotis, Olga Tsave, Athanasios Salifoglou, Nicos Maglaveras,Ioannis Vlahavas , Ioanna Chouvarda, “Machine Learning and Data Mining Methods in Diabetes Research”, Computational and Structural Biotechnology Journal, 104–116, 2017.
[3] Francesco Mercaldo, Vittoria Nardone, Antonella Santone,“ Diabetes mellitus affected patients classification and diagnosis through machine learning techniques, International Conference on Knowledge based and International Information and Engineering System, KES, Marseille, France, 2017.
[4] Roxana Mirshahvalad, Nastaran Asadi Zanjani, “Diabetes Prediction Using Ensemble Perceptron Algorithm”, 2017, 9th International Conference on Computational Intelligence and Communication Networks
[5] Miss. Sneha Joshi, Prof. Megha Borse, “Detection and Prediction of Diabetes Mellitus Using Back-Propagation Neural Network”, International Conference on Micro-Electronics and Telecommunication Engineering, 2016.
[6] Deepti Sisodiaa, Dilip Singh Sisodiab, “ Prediction of Diabetes using Classification Algorithms ”, International Conference on Computational Intelligence and Data Science (ICCIDS 2018), 2018.
[7] Nongyao Nai-aruna, Rungruttikarn Moungmaia, “Comparison of Classifiers for the Risk of Diabetes Prediction”, 7th International Conference on Advances in Information Technology, 2015.
[8] Deeraj Shetty, Kishor Rit, Sohail Shaikh, Nikita Patil, “Diabetes disease prediction using data mining”, International Conference on Innovations in Information, Embedded and Communication Systems (ICIIECS) , 2017.
[9] Santosh Rani, Sandeep Kautish, “Association Clustering and Time Series Based Data Mining in Continuous Data for Diabetes Prediction”, Second International Conference on Intelligent Computing and Control Systems (ICICCS) 2018.
[10] Vrushali R. Balpande, Rakhi D. Wajgi, “Prediction and severity estimation of diabetes using data mining technique”, International Conference on Innovative Mechanisms for Industry Applications (ICIMIA), 2017.
[11] Girdhar Gopal Ladha, Ravi Kumar Singh Pippal, “A computation analysis to predict diabetes based on data mining: A review”, 3rd International Conference on Communication and Electronics Systems (ICCES), 2018.
[12] Messan Komi, Jun Li, Yongxin Zhai, Xianguo Zhang, “Application of data mining methods in diabetes prediction”, 2nd International Conference on Image, Vision and Computing (ICIVC), 2017.
[13] Zhongxian Xu, Zhiliang Wang, “A Risk Prediction Model for Type 2 Diabetes Based on Weighted Feature Selection of Random Forest and XGBoost Ensemble Classifier”, Eleventh International Conference on Advanced Computational Intelligence (ICACI), 2019.
[14] B.V. Baiju, D. John Aravindhar, “Disease Influence Measure Based Diabetic Prediction with Medical Data Set Using Data Mining”, 1st International Conference on Innovations in Information and Communication Technology (ICIICT), 2019.
[15] Wenqian Chen, Shuyu Chen, Hancui Zhang, Tianshu Wu, “A hybrid prediction model for type 2 diabetes using K-means and decision tree”, 8th IEEE International Conference on Software Engineering and Service Science (ICSESS), 2017.

Tejinder Sharma, Nitika Sharma, "Analysis of Various Diabetic Prediction Techniques," International Journal of Computer Sciences and Engineering, Vol.8, Issue.12, pp.70-73, 2020.

Our heart works nonstop throughout our life. Its failure means the death of the person. Diseases regarding cardiac system are the main cause of death in the whole world. Therefore it is compulsory to diagnose these types of abnormalities before the failure of the heart. For showing all the electrical actions of the cardiac system, the Electrocardiogram (ECG) signal is an easy and highly recommendable mean. Normally the physicians manually examine the ECG heartbeat to analyze the different types of Arrhythmia. But manually working on ECG graphs is not a satisfactory solution due to its non-stationary nature of ECG. Therefore, there is always a need of computer based systems to examine the ECG signals, which is helpful for physicians. For classification of ECG data, there are many techniques which are implemented by different researchers. This survey is focusing on the latest research papers in which machine learning and deep learning classification techniques are applied in different manners. The implemented machine learning techniques are Support Vector Machine, k-NN, Decision Tree, Neural Network, and Extreme Learning Machine. Convolutional Neural Network (CNN) is implemented in various researches, which is a deep learning technique. A CNN is defined as a deep feed-forward artificial neural network that can mine deep features from database automatically. Mostly works were evaluated on MIT-BIH arrhythmia database which is available publically. In this survey, the existing methods are compared according to qualitative factors like purpose of the work, implemented algorithms and results achieved.

Arrhythmia classification, Convolutional Neural Network, Electrocardiogram, Extreme Learning Machine, Support Vector Machine

[1] C. K. Roopa, B. S. Harish, “A Survey on various Machine Learning Approaches for ECG Analysis”, International Journal of Computer Applications, vol.163, issue.9, pp.25-33, 2017.
[2] S. Celin, K. Vasanth, “ECG Signal Classification Using Various Machine Learning Techniques”, Journal of Medical System, vol.42, issue.241, pp.1-11, 2018.
[3] Q. Yao, R. Wang, X. Fan, J. Liu, Y. Li, “Multi-class Arrhythmia detection from 12-lead varied-length ECG using Attention-based Time-Incremental Convolutional Neural Network”, Information Fusion vol.53, issue.2020, pp.174–182, 2019.
[4] Y. Kaya, H. Pehlivan, M. E. Tenekeci, “Effective ECG beat classification using higher order statistic features and genetic feature selection”, Biomedical Research, vol.28, issue.17, pp.7594-7603, 2017.
[5] A. Turnip, M. I. Rizqywan, D. E. Kusumandari, M. Turnip, P. Sihombing, “Classification of ECG signal with Support Vector Machine Method for Arrhythmia Detection”, Journal of Physics: Conf. Series, vol.970, issue.012012, pp.1-8, 2018.
[6] B. Pyakillya, N. Kazachenko, N. Mikhailovsky, “Deep Learning for ECG Classification”, Journal of Physics: Conference Series 913, 012004, pp.1-5, 2017.
[7] A. Isin, S. Ozdalili, “Cardiac arrhythmia detection using deep learning”, Procedia Computer Science, vol.120, issue.2017 pp.268-275, 2018.
[8] H. Lassoued, R. Ketata, “ECG Multi-Class Classification using Neural Network as Machine Learning Model”, IEEE, pp.473-478, 2018.
[9] M. Kachuee, S. Fazeli, M. Sarrafzadeh, “ECG Heartbeat Classification: A Deep Transferable Representation”, arXiv:1805.00794v2 [cs.CY], 2018.
[10] J. Li, Y. Si, T. Xu, S. Jiang, “Deep Convolutional Neural Network Based ECG Classification System Using Information Fusion and One-Hot Encoding Techniques”, Hindawi- Mathematical Problems in Engineering, vol.2018, Article ID.7354081, pp.1-10, 2018.
[11] Y. Ji, S. Zhang, W. Xiao, “Electrocardiogram Classification Based on Faster Regions with Convolutional Neural Network”, Sensors, vol.19, issue.2558, pp.1–18, 2019.
[12] J. H. Kim, S. Y. Seo, C. G. Song, K. S. Kim, “Assessment of Electrocardiogram Rhythms by GoogLeNet Deep Neural Network Architecture”, Hindawi-Journal of Healthcare Engineering, vol.2019, Article ID.2826901, pp.1-10, 2019.
[13] A. Rajkumar, M. Ganesan, R. Lavanya, “Arrhythmia classification on ECG using Deep Learning”, 5th International Conference on Advanced Computing & Communication Systems (ICACCS), pp.365-369, 2019.
[14] S. Nurmaini, R. U. Partan, W. Caesarendra, T. Dewi, M. N. Rahmatullah, A. Darmawahyuni, V. Bhayyu, F. Firdaus, “An Automated ECG Beat Classification System Using Deep Neural Networks with an Unsupervised Feature Extraction Technique”, Applied Sciences, vol.9, issue.2921, pp.1-17, 2019.
[15] A. Ullah, S. M. Anwar, M. Bilal, R. M. Mehmood, “Classification of Arrhythmia by Using Deep Learning with 2-D ECG Spectral Image Representation”, Remote Sensing, vol.12, issue.1685, 2020.
[16] F. Y. O. Abdalla, L. Wu, H. Ullah, G. Ren, A. Noor, H. Mkindu, Y. Zhao, “Deep convolutional neural network application to classify the ECG arrhythmia”, Signal, Image and Video Processing, vol.14, issue.7, 2020.
[17] T. M. Chen, C. H. Huang, E. S. C. Shih, Y. F. Hu, M. J. Hwang, “Detection and Classification of Cardiac Arrhythmias by a Challenge-Best Deep Learning Neural Network Model”, iScience, vol.23, issue.100886, pp.1-7, 2020.
[18] A. Diker, D. Avci, E. Avci, M. Gedikpinar, “A new technique for ECG signal classification genetic algorithm Wavelet Kernel extreme learning machine”, Optik-International Journal for Light and Electron, vol.180, pp.46-55, 2019.
[19] A. Diker, E. Avci, E. Tanyildizi, M. Gedikpinar, “A novel ECG signal classification method using DEA- ELM”, Medical Hypotheses, vol.136, issue.109515, pp.1-11, 2020.
[20] S. Dalal, V. P. Vishwakarma, “GA based KELM Optimization for ECG Classification”, Procedia Computer Science, vol.167, pp.580-588, 2020.
[21] P. P?awiak, U. R. Acharya, “Novel deep genetic ensemble of classifiers for arrhythmia detection using ECG signals”, Neural Computing and Applications, vol.32, issue.15, pp.11137–11161, 2020.
[22] F. I. Alarsan, M. Younes, “Analysis and classification of heart diseases using heartbeat features and machine learning algorithms”, Journal of Big Data, vol.6, issue.81, pp.1-15, 2019.
[23] M. Hadjem, F. N. Abdesselam, “An ECG T-wave Anomalies Detection Using a Lightweight Classification Model for Wireless Body Sensors”, HAL, Id: hal-01191622, 2015.
[24] M. G. Shankar, C. G. Babu, “An Exploration of ECG Signal Feature Selection and Classification using Machine Learning Techniques”, International Journal of Innovative Technology and Exploring Engineering (IJITEE), vol.9, issue-3, pp.797-804, 2020.
[25] S. H. Jambukia, V. K. Dabhi, H. B. Prajapati, “Classification of ECG signals using Machine Learning Techniques: A Survey”, International Conference on Advances in Computer Engineering and Applications (ICACEA), pp.714-721, 2015.
[26] S. Ansari, N. Farzaneh, M. Duda, K. Horan, H. B. Andersson, Z. Goldberger, D. Nallamothu, B. K. Najarian, “A Review of Automated Methods for Detection of Myocardial Ischemia and Infarction using Electrocardiogram and Electronic Health Records”, IEEE Reviews In Biomedical Engineering, vol.10, pp.264-298, 2017.
[27] R. Kher, “Signal Processing Techniques for Removing Noise from ECG Signals”, Journal of Biomedical Engineering and Research, vol.3, issue.101, pp.1-9, 2019.
[28] A. C. V. Maggio, M. P. Bonomini, E. L. Leber, P. D. Arini, “Quantification of Ventricular Repolarization Dispersion Using Digital Processing of the Surface ECG”, InTech, pp.181-206, 2012.
[29] J. Pan, W. J. Tompkins, “A Real-Time QRS Detection Algorithm”, IEEE Transactions on Biomedical Engineering, vol.BME-32, issue.3, pp.230-236, 1985.
[30] K. V. L. Narayana, A. B. Rao, “Noise Removal using Adaptive Noise Canceling, Analysis of ECG using Matlab”, International Journal of Engineering Science and Technology (IJEST), vol.3, issue.4, pp.2839-2844, 2011.
[31] K. O’shea, R. Nash, “An Introduction to Convolutional Neural Networks”, arXiv:1511.08458v2 [cs.NE], 2015.
[32] S. Albawi, T. A. Mohammed, S. Al-Zawi, “Understanding of a Convolutional Neural Network”, ICET2017 Antalya Turkey, 2017.

Shashank Yadav, Upendra Kumar, "A Review on Cardiac Abnormalities Classification using Electrocardiogram with Machine Learning and Deep Learning Classification Techniques," International Journal of Computer Sciences and Engineering, Vol.8, Issue.12, pp.74-84, 2020.

Information Technology can improve the technologies used in farming and is very useful in farm management, crop data management. There is much contribution from Information Technology in agricultural productivity. Various technologies or Computer-aided devices are used in the agriculture sector that leads to growth and overall performance in any field such as horticulture, forestry, etc. Information Technology is not only useful at the farmer level but also it supports the research related to this field and business that is running in the agriculture field like information to the Management level. This article covers the Information technology-based systems that are useful for the farmer community, mobile-based solutions, and Web-based services, automated systems, Agriculture database that may be useful for Scientific Research, Management Information System in Agriculture in India. In addition to it, Expert Systems, robots developed and designed especially for the agriculture sector, DSS (Decision Support System) in agriculture and its importance for Management level.

ICT, Web, Database, Experts System , Agribot

[1] S. Singh, S. Ahlawat and S. Sanwal, “Role of ICT in Agriculture: Policy Implications”, Oriental Journal of Computer Sciences and Technology, Vol. 10, Issue. 3, pp.691-697, 2017.
[2] K. Subhadra, "The Role of Emerging IT Technologies in Agriculture", International Journal of Computer Sciences and Engineering, Vol.8, Issue.3, pp.49-57, 2020.
[3] G. Malage, K. K. Patil, "RaitaSnehi - A Voice Based Farmer Information System", International Journal of Computer Sciences and Engineering, Vol.7, Issue.6, pp.347-352, 2019
[4] G. Vanitha, M. Kalpana, “Agro-Informatics”, New India Publishing Agency, India, pp. 57-58, 2011.

Manmohan Singh , "Information Technology in Agriculture," International Journal of Computer Sciences and Engineering, Vol.8, Issue.12, pp.85-87, 2020.

Vehicle Ad Hoc Network (VANET) is a type of wireless network composed of vehicles and roadside communication devices. The main goal of VANET is to provide seamless communication for people travelling on the road to collect and relay information from all nearby vehicles in the event of emergency situations such as severe traffic jams, collisions, lane shift, speed limit, hazard or road condition alerts, position alert services and in the event of climatic disasters, etc. Due to high mobility of vehicles data transfer between vehicles in short period requires smart, intelligent routing algorithms. Proposed work aims at design of a intelligent agent (Belief-Desire-Intention) based routing for vehicle-to-vehicle (V2V) communication in VANETs. Intelligent routing scheme operates as follows: gathering of routing parameters, generation of beliefs, development of desires and finalization of route. Presented routing scheme is analyzed and compared with existing routing algorithm for VANETs known as M-AODV+ (Modified AODV+) with respect to packet delivery ratio, network life time and control overhead.

Vehicular Ad hoc Network, Vehicle-to-Vehicle Communication, Cognitive agents

[1] S F. Cunha, L. Villas, A. Boukerche et al., “Data Communication in VANETs: Protocols, Applications and Challenges”, Elsevier Journal of Ad Hoc Networks, Vol. 44, pp. 90–103, 2016.
[2] Chukwu Jeremiah and Agwu Joy Nneka, “Issues and Possibilities in Vehicular Ad-hoc Networks (VANETs)”, In the Proceedings of the 2015 IEEE International Conference on Computing, Control, Networking, Electronics and Embedded Systems Engineering, Sudan, pp. 254-259, 2015.
[3] Jabbarpour M. R., Noor R. M., Khokhar R.H., Ke C.H., “Cross-layer Congestion Control Model for Urban Vehicular Environments”, Elsevier Journal of Network and Computer Applications, Vol. 44, pp. 1–16, 2014.
[4] S. Al-Sultan, Al-Doori M. M., Al-Bayatti A. H., Zedan H., “A Comprehensive Survey on Vehicular Ad-Hoc Network”, Elsevier Journal of Network and Computer Applications, Vol. 37, pp. 380–392, 2014.
[5] Melaouene Noussaiba, Romadi Rahal, “State of the art: VANETs applications and their RFID-based systems”, In the Proceedings of the 4th IEEE International Conference on Control, Decision and Information Technologies (CoDIT), Spain, pp. 0516-0520, 2017.
[6] Muhammad Rizwan Ghori, Kamal Z. Zamli, Nik Quosthoni, Muhammad Hisyam et.al, “Vehicular ad-hoc network (VANET): Review”, In the Proceedings of the IEEE International Conference on Innovative Research and Development (ICIRD), Thailand, pp.1-6, 2018.
[7] S.K. Bhoi, P.M. Khilar, M. Singh, R.R. Sahoo, R.R. Swain, “A routing protocol for urban vehicular ad hoc networks to support non-safety applications”, Journal of Digital Communications and Networks, Vol. 4, Issue 3, pp. 189-199, 2018.
[8] Shah SAA, Shiraz M., Nasir M.K., Noor R.M., “Unicast Routing Protocols for Urban Vehicular Networks: Review, Taxonomy and Open Research Issues”, Journal of Zhejiang University Science, Vol. 15, Issue 7, pp. 489–513, 2014.
[9] S. S. Manvi, M. S. Kakkasageri, “Issues in Mobile Ad hoc Networks for Vehicular Communication”, IETE Technical Review, Vol. 25, Issue 2, pp. 59-72, 2008.
[10] Kenneth C. Williams, “Introduction to Game Theory”, Text book of Oxford University Press, 2012.
[11] Sandeep Kumar, Medha Sharma, “Convergence of Artificial Intelligence, Emotional Intelligence, Neural Network and Evolutionary Computing”, International Journal of Advanced Research in Computer Science and Software Engineering (IJARCSSE), Vol. 2, Issue 3, pp.141-145, 2012.
[12] Pejman Tahmasebi, Ardeshir Hezarkhani, “A Hybrid Neural Networks-Fuzzy Logic-Genetic Algorithm for Grade Estimation”, Elsevier Journal of Computers & Geosciences, Vol. 42, pp. 18-27, 2012.
[13] Vidushi Sharma, Anoop Kumar Gupta, “A Comprehensive Study of Fuzzy Logic”, International Journal of Advanced Research in Computer Science and Software Engineering (IJARCSSE), Vol. 3, Issue 2, 2013.
[14] Rahul Malhotra, Narinder Singh, Yaduvir Singh, “Genetic Algorithms: Concepts, Design for Optimization of Process Controllers”, Journal of Computer and Information Science, Vol. 4, Issue 2, 2011.
[15] O. Urra, S. Ilarri, T. Delot, E. Mena, “Mobile Agents in Vehicular Networks: Taking a First Ride”, In the Proceedings of the 8th International Conference on Practical Applications of Agents and Multi-agent Systems (PAAMS 2010), Springer, Spain, pp. 118–124, 2010.
[16] M. S. Kakkasageri, S. S. Manvi, Hridya C. P., Vibha N. S., P. I. Basarkod, “A Survey on Cognitive Agent Architecture”, IETE Journal of Education, Vol. 53, Issue 1, pp. 21-37, 2012.
[17] K. L. Myers, N. Yorke-Smith, “A cognitive framework for delegation to an assistive user agent”, In the Proceedings of the Fall Symposium on Mixed-Initiative Problem Solving Assistants, Arlington, pp.1-6, 2005.
[18] H. C. Premkumar, V. R. Budyal and M. S. Kakkasageri, “Cognitive agent based stable routing protocol for vehicle-to-vehicle communication”, In the Proceedings of the 2016 IEEE Annual India Conference (INDICON), INDIA, pp. 1-5, 2016.
[19] Mahabaleshwar S. Kakkasageri, Mamata J. Sataraddi, , Poornima Chanal, Gururaj S. Kori, “BDI Agent based Routing Scheme in VANETs”, In the Proceedings of the 2017 IEEE International Conference on Wireless Communications Signal Processing and Networking (WiSPNET 2017), INDIA, pp. 129-133, 2017.
[20] M. S. Kakkasageri, S. S. Manvi, “Intelligent Information Dissemination in Vehicular Ad hoc Networks”, International Journal of Ad hoc, Sensor and Ubiquitous Computing (IJASUC), Vol. 2, Issue 1, pp.112-123, 2012.
[21] M. S. Kakkasageri, S. S. Manvi, “Regression based critical information aggregation and dissemination in VANETs: A cognitive agent approach”, Journal of Vehicular Communications, Elsevier, Vol. 1, Issue 4, pp. 168-180, 2014.
[22] Jan Wantoro, I Wayan Mustika, “M-AODV+: An Extension of AODV+ Routing Protocol for Supporting Vehicle-to-Vehicle Communication in Vehicular Ad hoc Networks”, In the Proceedings of the 2014 IEEE International Conference on Communication, Networks and Satellite (COMNETSAT), Indonesia, pp. 39-44, 2014.
[23] Celimuge Wu, Tsutomu Yoshinaga, Yusheng Ji, “V2R Communication Protocol Based on Game Theory Inspired Clustering”, In the Proceedings of the IEEE 86th Vehicular Technology Conference (VTC-Fall), Toronto,Canada, 2017.
[24] Jamal Toutouh, José García-Nieto, and Enrique Alba, “Intelligent OLSR Routing Protocol Optimization for VANETs”, IEEE Transactions on Vehicular Technology, Vol. 61, No. 4, pp. 1884-1894, 2012.
[25] Di Wu, Jing Cao, Yan Ling, Jiangchuan Liu, Limin Sun, “Routing Algorithm Based on Multi-Community Evolutionary Game for VANET”, Journal of Networks, Vol. 7, No. 7, pp. 1106-1115, 2012.

Mamata J. Sataraddi, Mahabaleshwar S. Kakkasageri, "Intelligent Routing Scheme for Vehicle-to-Vehicle Communication," International Journal of Computer Sciences and Engineering, Vol.8, Issue.12, pp.88-93, 2020.

Diseases are increasing rapidly now a days due to number of reasons. It will be very helpful to cure that disease if we predict occurrences of diseases in the early stages. Even though doctors and health centers collect data daily but most of them are not using machine learning and pattern matching techniques to extract the knowledge that can be very useful in prediction. We have chosen dataset of liver diseases to evaluate prediction algorithms in an effort to reduce burden on doctors. In our work, we have trained eight models Logistic Regression, Random Forest, XGBoost, KNN, Decision Trees, SVC, Gradient Boosting and Neural Network. The analysis compare all these models and choose the best model.

Data Mining, Classification, Decision Tree, Liver Disease

[1] Nahar, N. and Ara, F. “Liver disease prediction by using different decision tree techniques”. Int. J. Data Min. Knowl. Manag. Process, 8(2), pp.01-09, 2018.
[2] D. Sindhuja and R. J. Priyadarsini, “A survey on classification techniques in data mining for analyzing liver disease disorder”, International Journal of Computer Science and Mobile Computing, Vol.5, no.5, pp. 483-488, 2016.
[3] B. V. Ramana, M. R. P. Babu and N.B. Venkaeswarlu, “A Critical Study of Selected Classification Algorithms for Liver Disease Diagnosis”, International Journal of Database Management Systems (IJDMS), Vol.3, no.2, pp. 101-114, 2011.
[4] A.S.Aneeshkumar and C.J. Venkateswaran, “Estimating the Surveillance of Liver Disorder using Classification Algorithms”, International Journal of Computer Applications (0975 –8887) , Vol. 57, no. 6, pp. 39-42, 2012.
[5] G. Selvara and S. Janakiraman, “A Study of Textural Analysis Methods for the Diagnosis of Liver Disease from Abdominal Computed Tomography”, International Journal of Computer Applications (0975-8887), Vol. 74, no.11, PP.7-13, 2013.
[6] H. Sug, “ Improving the Prediction Accuracy of Liver Disorder Disease with Oversampling”, Applied Mathematics in Electrical and Computer Engineering, American-MATH 12/CEA12 proceedings of the 6th Applications and proceedings on the 2012 American Conference on Appied Mathematics, PP. 331-335, 2012.
[7] B. V. Ramanaland and M.S. P. Babu, “Liver Classification Using Modified Rotation Forest”, International Journal of Engineering Research and Development ISSN: 2278-067X, Vol. 1, no. 6, PP.17-24. 2012.
[8] C.K. Ghosk, F. Islam, E. Ahmed, D.K. Ghosh, A. Haque and Q.K. Islam, “Etiological and clinical patterns of Isolated Hepatomegaly” Journal of Hepato-Gastroenterology, vol.2, no. 1, PP. 1-4.
[9] R. Malathi, S. Ravichandran, “Dual Threshold Based Classification Technique (DTBCT) For Assessing Liver Abnormalities from Medical Images,” International Journal of Computer Sciences and Engineering, Vol.7, Issue.5, pp.1436-1439, 2019.

Surender Singh, Jyoti, "Condition Based Disease Detection Using Machine-Learning Algorithms Based Prediction," International Journal of Computer Sciences and Engineering, Vol.8, Issue.12, pp.94-97, 2020.