Volume-10 , Issue-2 , Feb - 2022 Go Back

Polyps in the small bowel have a chance of developing into cancerous tumors. As a result, it is important to recognize and treat such polyps at an initial stages. This would significantly boost the patient`s chance of survival. Due to the rapid advancement of technology, wireless capsule endoscopy is regarded as a medical breakthrough. This allows for easy, painless, and inexpensive observation of the interior body, which is not visible to the naked eye. Simultaneously, the wireless capsule endoscopy`s low-quality images are considered as its primary weakness. As a result, certain forms of polyps cannot be diagnosed from this wireless endoscopic imaging, even by a highly qualified physician. As a result, computer-aided polyp identification remains an ongoing challenge. This research introduces a novel segmentation algorithm for this purpose. The purpose of this research is to present a modified convolutional neural network (CNN) algorithm for wireless capsule endoscopy image segmentation that is based on dropout and the stochastic gradient descent optimizer. To increase feature extraction accuracy while decreasing time costs, this work analyses the CNN structure, the over fitting problem, and the combination of dropout and the SGD optimizer with the CNN. Additionally, this novel innovation was assessed using many polyp databases and its experimental results were compared to those of previously developed polyp segmentation techniques. The results demonstrate that our enhanced CNN outperformed state-of-the-art techniques.

Wreless capsule endoscopy, CNN, Stochastic Gradient Descent Optimizer, Polyp detection, Image processing

[1] JAEYONG KANG, “Ensemble of Instance Segmentation Models for Polyp Segmentation in Colonoscopy Images”, date of publication February 21, 2019, date of current version March 12, 2019 in IEEE Translations.
[2] MING LIU et al, “Colonic Polyp Detection in Endoscopic Videos With Single Shot Detection Based Deep Convolutional Neural Network”, date of publication June 5, 2019, date of current version June 20, 2019 in IEEE Translations.
[3] . JAEYONG KANG et al, “Ensemble of Instance Segmentation Models for Polyp Segmentation in Colonoscopy Images”, 2019, date of current version March 12, 2019, in IEEE Translations.
[4] Hemin Ali Qadir et al, Improving Automatic Polyp Detection Using CNN by Exploiting Temporal Dependency in Colonoscopy Video, GENERIC COLORIZED JOURNAL, VOL. XX, NO. XX, XXXX 2018.
[5] HEMIN ALI QADIR, “A Framework With a Fully Convolutional Neural Network for Semi-Automatic Colon Polyp Annotation”, ACCESS.2019.2954675.
[6] Akshay M Godkhindi et al, “Automated Detection of Polyps in CT Colonography images using Deep Learning Algorithms in Colon Cancer Diagnosis”, IEEE International Conference on Energy, Communication, Data Analytics and Soft Computing (ICECDS-2017).
[7] Saad Albawi et al, “Understanding of a convolutional neural network”, IEEE 2017 International Conference on Engineering and Technology (ICET).
[8] Nadia Jmour et al, “Convolutional neural networks for image classification”, 2018 International Conference on Advanced Systems and Electric Technologies (IC_ASET).
[9] Nilanjan Dey, “Wireless Capsule Gastrointestinal Endoscopy: Direction-of-Arrival Estimation Based Localization Survey”, IEEE Reviews in Biomedical Engineering ( Volume: 10 ).
[10] Eng Gee Lim, “Moveable wireless capsule endoscopy”, IEEE 2013 International SoC Design Conference (ISOCC).
[11] Pengfei Zhang et al, “A method for the generation of small intestine map based on endoscopic Micro-Ball”, 2012 IEEE Biomedical Circuits and Systems Conference (BioCAS).

S. Sunitha, S.S. Sujatha, "WCE Images Polyp Segmentation System Using Convolutional Neural Network (CNN) With Stochastic Gradient Descent Optimizer," International Journal of Computer Sciences and Engineering, Vol.10, Issue.2, pp.1-6, 2022.

Machine Learning (ML) is a field of Artificial Intelligence, which applies the principles of statistics to predict the outcome of process by utilizing the historical data. Each ML model is built on a specific Data Set and programming language, however the knowledge obtained by the model is restricted to that particular Dataset. Web Service Composition (WSC) process of combining the available web services (WS) and arriving at a new web service based on the required inputs and preconditions. The paper presents an approach to represent a ML model as web service and automatically composing them with other ML models to utilize the knowledge gained on different data sets. The MLWSC approach consists of two stages. In stage one, semantic networks are created among the ML services to form a network. In stage two, MLWSC compositions are constructed based on the requirement of the user.

Web Service Composition, Machine Learning, Data Sets

[1] Rik Eshuis, Freddy Lecue, and Nikolay Mehandjiev, “Flexible Construction of Complex Service Compositions from Reusable Semantic Knowledge”, In the Proceedings of IEEE 19th International Conference on Web Services, pp.631-632, 2012.
[2] Paolo Traverso and Marco Pistore, “Automated Composition of Semantic Web Services into Executable Processes”, In the Proceedings Third International Semantic Web Conference, Hiroshima, Japan, pp.380-394, 2004.
[3] Mohan H G, Chetan K R, “Semantic Based Automatic Web Service Composition”, International Journal of Applied Research and Studies, Vol.3, Issue.6, 2014.
[4] Mohan H G and Devaraj F V, “A Survey on Semantic Based Automatic Web Service Compositions”, European Journal of Advances in Engineering and Technology, pp.73-79, 2014.
[5] Chatti Subbalakshmi, Rishi Sayal , H. S. Saini, “S-REST: A design of Secured Protocol for Implementation of RESTful Webservices”. International Journal of Computer Sciences and Engineering Vol.7(1), Jan 2019.
[6] Felix Mohr, Marcel Wever, Eyke Hullermeier, Amin Faez, “Towards the Automated Composition of Machine Learning Services”, In the IEEE International Conference on Services Computing, 2018.
[7] Igor Lavrov and Jenny Domashova, “Constructor of compositions of machine learning models for solving classification problems”, In the Postproceedings of the 10th Annual International Conference on Biologically Inspired Cognitive Architectures, BICA 2019.
[8] Kailash Chander Bhardwaj and R K Sharma, “Machine Learning In Efficient And Effective Web Service Discovery”, Journal of Web Engineering, Vol. 14, No.3&4, pp.196-214, 2015.
[9] Y. Yang, X. Li, Z. Liu, and W. Ke, “RM2PT: A tool for automated prototype generation from requirements model”, In the Proceedings of International Conference on Software Engineering, May 2019.
[10] L. Zeng, B. Benatallah, M. Dumas, J. Kalagnanam, and Q. Z. Sheng, “Quality driven web services composition”, In the Proceedings of the 12th international conference on World Wide Web. ACM, pp.411–421, 2003.
[11] F. Mohr, A. Jungmann, and H. Kleine Buning, “Automated online service composition”, In the Proceedings of the International Conference on Services Computing, IEEE, pp.57-64, 2015.
[12] Berardi, D. Calvanese, G. De Giacomo, M. Lenzerini, and M. Mecella, “Automatic Composition of e-services that export their behavior”, In the Proceedings of the International Conference on Service-Oriented Computing. Springer, pp.43-58, 2003.
[13] Freddy Lécué and Alain Léger, “Semantic Web Service Composition through a Match making of Domain”, In the Proceedings of IEEE 4th European Conference on Web Services, pp.171-180, 2006.
[14] Yilong Yang, Nafees Qamar, Peng Liu, Katarina Grolinger, Weiru Wang, Zhi Li, Zhifang Liao, “ServeNet: A Deep Neural Network for Web Services Classification”, In the 12th International Conferences on Web Services, China, Oct. 2020.
[15] Jing Zhang, Yang Chen, Yilong Yang, Changran Lei, Deqiang Wang, “ServeNet-LT: A Normalized Multi-head Deep Neural Network for Long-tailed Web Services Classification”, In the IEEE International Conferences on Web Services, Sep. 2021.

Mohan H.G., Nandish M., Devaraj F.V., "Automatic Composition of Machine Learning Models as Web Services across Data Sets," International Journal of Computer Sciences and Engineering, Vol.10, Issue.2, pp.7-10, 2022.

In today’s environment technologies play a vital role. Nowadays college and school students require new and smart technology like smartphones which supports to get information related to applying for leave or OD, information about placement, attendance percentage etc. Though there are online tools like Google classroom which supports students and teachers by some facilities like to access lecture notes, quizzes, assignments, etc. But Google classroom does not support features like accessing information about their leave/OD, attendance percentage, no due, semester results, and other details.Maintaining the records of the university/school is becoming an aberration. The authorities/admin spend adequate time to maintain this records of the students .Not only the authorities but, even students spend most of their time for doing their regular and basic activities.This project provides a better way which eradicates the usage of paper .A mobile app which has all the facilitates through we can access of all academic-related details of the students and teachers.

Mobile Application, Database management of the users

[1] Almahdi Alshareef, Ahmed Alkilany "Toward a Student Information System for Sebha University, Libya",Fifth international conference on Innovative Computing Technology (INTECH)-pp- 34-39 ,2015
[2] S.R.Bharamagoudar,”web based students information management system”International Journal of Advanced Research in Computer and Communication Engineering,Vol. 2, Issue 6,pp-2342-2348, 2013
[3] HananA.Al--Sheddi,HebaA.Kurdi “Enhanced TSFS Algorithm for Secure Database Encryption” Science and Information Conference . -pp328-335 2013
[4] Md.Milon Islam,Md.Kamrul Hasan, Md Masum Billah, and Md.Manik Uddin “Development of Smartphone- rested Pupil Attendance System”, IEEE Region 10 Humanitarian Technology Conference (R10-HTC) Dhaka, Bangladesh, pp- 21-23 ,Dec 2017
[5] Ritesh Ramchandra Landage ,”Students Information Management System”JETIR Vol. 7, Issue 3,pp-2097-2100, 2020
[6] Sreedhar,M.and Venkatesh. “Impulse Radio Ultra-Wide Band Predicated Mobile Adhoc Network Routing Performance Analysis”, American Journal of Applied Lores pp-361-366, 2013.
[7] Li Qian, Jun Hu, Shuying Liu “SQL Injection Attack and Prevention Technology” International Conference on Estimation, Discovery and Information Fusion (ICEDIF) pp-303-307, 2015

Dhivya S., Merlin Jancy, Arul Kumaran, Arun pandian, Bhuvaneswaran, "Education Workspace Application (EWA) Mobile App for Managing the Students Information Developed By Java," International Journal of Computer Sciences and Engineering, Vol.10, Issue.2, pp.11-16, 2022.

Our idea is to design an application that will make each and every place, hour safer for women and children again. For their security and safety purpose, our government has provided security through rules and regulations in the society and there are many smart security systems existed. In this project we are designing a mobile application that can be used by women for help in emergencies as it contains an emergency alert button when it is pressed then it will automatically connect a call to the emergency contacts it also sends the live location of the user through normal message. This application also contains a voice alert through which the user can raise a voice and spell “help” 1 time so that automatically the message and the location will be shared to the emergency contacts which was registered.

Women safety app; voice-powered safety application; mobile apps; women security; safety gadgets

[1] Abhijit Paradkar, Deepak Sharma Associate Professor, "All in one Intelligent Safety System for Women Security", International Journal of Computer Applications, Vol. 130 – No.11, pp.33-40, 2015.
[2] S Shambhavi , M Nagaraja, "Smart Electronic System for Women Safety", International Journal Of Innovative Research In Electrical, Electronics, Instrumentation And Control Engineering, Vol. 4, Issue 3, pp.27-28, 2016.
[3] Jismi Thomas, Maneesha K J, Nambissan Shruthi Vijayan, Prof. Divya R, "TOUCH ME NOT-A Women Safety Device", International Research Journal of Engineering and Technology (IRJET), Vol. 05 Issue: 03, pp.1055-1059, 2018.
[4] B.Umadevi, Dr.P.Eswaran, Dr.N.Manoharan, "Womens Security Solution Using: Iot", International Journal of Pure and Applied Mathematics, Vol. 119 No. 10, pp.1871-1874, 2018.
[5] Sunil Punjabi, Suvarna Chaure, Ujwala Ravale, Deepti Reddy, “Smart Intelligent System for Women and Child Security”, IEEE 9th Annual Information Technology, Electronics and Mobile Communication Conference, Vancouver, pp.451-454, 2018.
[6] S. A. Bankar, Kedar Basatwar, Priti Divekar, Parbani Sinha, Harsh Gupta, “Foot Device for Women Security”, 2nd International Conference on Intelligent Computing and Control System, pp.345-347, 2018.
[7] Madhura Mahajan, K.Reddy, M.Rajput, “Design and implementation of a rescue system for safety of women”, International Conference on Wireless Communications, Signal Processing and Networking ,Chennai, India, pp.1955-1959, 2016.
[8] G.C. Harikiran, K.Menasinkai, S.Shirol, “Smart Security solution for women based on Internet of Things(IOT)”, International Conference on Electrical, Electronics and Optimization Techniques, Chennai, India, pp.3551-3554, 2016.
[9] Sharifa Rania Mahmud, Jannatul Maowa & Ferry Wahyu Wibowo, “Women Empowerment: One Stop Solution for Women,” International Conferences on Information Technology, Information System and Electrical Engineering, pp.485-488, 2017.
[10] A.Helen, M. Fathima Fathila, R.Rijwana, Kalaiselvi V.K.G, “A Smart Watch for Women Security based on IoT Concept”, 2nd International Conference on Computing and Communications Technologies(ICCCT), Chennai, India, pp.23-24, 2017.

S. Dhivya, R. Ramnath, C. Redhanya, S. Sasi, S.L. Sathiya Priya, "Voice Recognition Powered Women’s Safety App," International Journal of Computer Sciences and Engineering, Vol.10, Issue.2, pp.17-21, 2022.

Due to the growing demand for crop output, quality, and the usage of high-quality greenhouses, modern greenhouses are becoming larger and more complex in their monitoring and control systems. The rise in greenhouse size has increased the necessity for real-time monitoring of certain parameters. This has added complexity to their management and maintenance. The purpose of this paper is to describe a wireless sensor network-based system for monitoring and controlling greenhouses. Because tiny nodes are utilised in WSN, they are spatially scattered, battery-powered, and energy-limited; as a result, nodes may die, resulting in communication failure. Thus, to address this shortcoming, we propose a routing protocol that balances resource usage across all sensor nodes, thereby extending the sensor node`s lifetime. As a result, a potential greenhouse capable of measuring a variety of factors has been created.

Greenhouse, routing protocol, and wireless sensor network

[1] Ian F. Akyildiz ,Weilian Su, Yogesh Sankarashubramaniam, Erdal Cayirci, A survey on sensor networks, IEEE Communication magazine, 2018.
[2] Ahmed A. Ahmed, Hongchi Shi and Yi Shang, A survey on network protocols for wireless sensor networks,IEEE conferences,2017.
[3] AbdulWahid Ali, Parmanand, Energy Efficiency in routing protocol and data collection approaches for WSN: A survey,ICCCA2015,IEEE 2016.
[4] R. Jaferi ,Azad Zahoory, F. dabiri, Majid. s, Wireless sensor networks for health monitoring,Department of computer science.
[5] M. Pejanovic, Zhilbert Tafa, Goran Dimic, V.Milutinovic, A survey of military applications of wireless sensor network, Mediterranean conference on embedded computing,2015.
[6] Yongxian Song, Juanli Ma, Xianjin Zhang and Yuan Feng, Design of Wireless Sensor Network-Based Greenhouse Environment Monitoring and Automatic Control System,journal of networks, May 2014.
[7] P.Krishnaveni, Dr.J.Sutha ,Analysis of Routing protocols for Wireless sensor networks,IJEATAE, Volume 2, Issue 11,November 2014.
[8] Ru-anLi,XuefengSha and Kai Lin,2014 , Smart greenhouse: A real time Mobile intelligent monitoring system based on WSN,IEEE,2015.
[9] ManijehKeshtgari, AmeneDeljoo, A Wireless Sensor Network Solution for Precision Agriculture Based on ZigBee Technology, Wireless Sensor Network, 2014.
[10] 10. Luis OstizUrdiain, Carlos Pita Romero,JeroenDoggen, Tim Dams, Patrick Van Houtven, Wireless Sensor Network Protocol for Smart Parking Application Experimental Study on the Arduino Platform, AMBIENT 2014.
[11] Ganesh Bhosale, prof SandeepRaskar, Prof RavindraDuche, Distributed clustering approach in wireless sensor network by using K-means algorithm,PISER 16.

Tanu Saxena, Narendra Pal Singh, "Advancement of the Greenhouse with the Usage of a Wireless Sensor Network with Optimised Routing," International Journal of Computer Sciences and Engineering, Vol.10, Issue.2, pp.22-25, 2022.

Stock market prediction is the act of trying to determine the future value of a company stock or other financial instrument traded on a financial exchange. The successful prediction of a stock`s future price will maximize investor’s gains. In this paper we analyze a machine learning model to predict stock market price, where existing algorithm integrates Particle swarm optimization (PSO) and least square support vector machine (LS-SVM) are identified in which, the PSO algorithm is employed to optimize LS-SVM to predict the daily stock prices. The proposed model is based on the study of stocks historical data and technical indicators. PSO algorithm selects best free parameters combination for LS-SVM to avoid over-fitting and local minima problems and improve prediction accuracy. The proposed model was also applied and evaluated using thirteen benchmark financials datasets and compared with artificial neural network with Levenberg-Marquardt (LM) algorithm. The obtained results showed that the proposed model has better prediction accuracy and the potential of PSO algorithm in optimizing LS-SVM.

Least Square Support Vector Machine, Particle Swarm Optimization, Technical Indicators and Stock Price prediction.

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[2] Leng, X. and Miller, H.-G. : “Input dimension reduction for load forecasting based on support vector machines”, IEEE International Conference on Electric Utility Deregulation, Restructuring and Power Technologies (DRPT2004), 2004.
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[6] ANDRÉSM.,GENARODAZA,S.,CARLOSD.,GERMÁN C.: “Parameter Selection In Least Squares-Support Vector Machines Regression Oriented, Using Generalized Cross- Validation” , Dyna, year 79, Nro. 171, pp. 23-30.Medellin, February, 2012.
[7] Carlos A. Coello, Gary B. Lamont, David A. van Veldhuizen: “Evolutionary Algorithms for Solving Multi-Objective Problems”,Springer,2007.
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[12] Omar S. Soliman 2 and Mustafa Abdul Salam3, LSSVM-ABC Algorithm for Stock Price prediction Osman Hegazy , International Journal of Computer Trends and Technology (IJCTT) – volume 7 number 2– Jan 2014

Shubh Lodhi, Amit Kumar Agrawal, Shivani Dubey, "Analysis of Stock Market Prediction by using PSO Algorithm Optimizing LS-SVM," International Journal of Computer Sciences and Engineering, Vol.10, Issue.2, pp.26-30, 2022.

In this advanced technology most of the modern hospitals are adopting Clinical Decision Support System (CDSS) model for the diagnosis and management of most of the medical related problems. The system plays a vital role in medical decisions. In the present study, we are developing a CDSS which helps the physician to take better medical decision on the diagnosis of Chronic Obstructive Pulmonary Disease (COPD). The system also helps to take appropriate decision on treatment and management strategies for patients who are suffering from COPD. COPD is an increased inflammatory immune response to the lungs to particles and gases, from cigarette smoke, neutrophils. COPD is considered as a long term dysfunction, disease but its natural history as it occurs at intervals by periods of acute deterioration or exacerbations. Patients with COPD can have a sign of relief and be positive in today’s generation because new medical therapies with alternate remedies. Any disease requires well-planned management strategies. In this paper we have designed a CDSS for treatment and management for COPD.

COPD, CDSS, Treatment & Management strategies

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Sudhir Anakal, Sandhya P., "Clinical Decision Support System for Treatment and Management strategies of COPD," International Journal of Computer Sciences and Engineering, Vol.10, Issue.2, pp.31-34, 2022.

Finding the patterns from the huge collection of datasets is considered as one of the primary application of machine learning. Machine learning has already proved itself in transportation field and can be used in various other fields such as manufacturing, healthcare, investigation of crimes etc. Great advancement in technologies and societies has led to advancement in crimes and also the damage caused by them. It becomes even more difficult to prevent when the population in any area is concentrated and changes are rapid. That’s why in many cities various crime prevention measures have been adopted as a part of smart city development. However, crimes can happen anywhere the need only is to determine the pattern of their occurrences which in turn can reduce the crime percentage. In order to provide society a better living crime investigation or analysis is considered as important application of machine learning. In this paper a survey has been done on analysis of crime and their prediction using machine learning techniques.

Machine Learning, Crime prediction, pattern extraction, Decision tree, KNN, SVM

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[5] Chao Yangt, Shiyuan Chet, Xueting Cao, Yeqing Sun, Ajith Abraham, “A Rough-Fuzzy C-Means Using Information Entropy For Discretized Violent Crimes Data” 13th International Conference On Hybrid Intelligent Systems, 2013.
[6] Saleha Farheen, Monika Raghuwanshi, "Performance of Machine Learning Techniques in the Prevention of Financial Frauds", International Journal of Computer Sciences and Engineering, Vol.9, Issue.1, pp.27-29, 2021.
[7] Chao Yang, Hongbo Liu, Yeqing Sun, Ajith Abraham, “Multi-Knowledge Extraction From Violent Crime Datasets Using Swarm Rough Algorithm”, 12th International Conference On Hybrid Intelligent Systems (His), 2012.
[8] Jieling Jin, Yuanchang Deng, “A Comparative Study On Traffic Violation Level Prediction Using Different Models” , 4th International Conference On Transportation Information And Safety (Ictis), 2017.
[9] Anshu Sharma, Shilpa Sharma, “An Intelligent Analysis Of Web Crime Data Using Data Mining”, International Journal Of Engineering And Innovative Technology (Ijeit) 2(3), 2012.
[10] K K Sindhu and B B Meshram, “Digital Forensics And Cybercrime Data Mining”, Journal Of Information Security, 3, 196-201, 2012.
[11] Sachin Kumar and Durga Toshniwal, “A Data Mining Approach To Characterize Road Accident Locations” , Journal Of Modern Transportation 24(1) pp.6272 , 2016.
[12] Sachin Kumar and Durga Toshniwa, “A Data Mining Framework To Analyze Road Accident Data” Journal Of Big Data, 2015.
[13] Neetu Singh, Tripti Ajariya, Shailesh Raghuvanshi, Neha Singh, "Crime Analysis and Prediction Model Using Data Mining and Machine Learning Techniques: Comparative Analysis", International Journal of Computer Sciences and Engineering, Vol.9, Issue.6, pp.97-104, 2021.
[14] A. Bogomolov, B. Lepri, J. Staiano, N. Oliver, F. Pianesi, and A. Pentland, "Once upon a crime: towards crime prediction from demographics and mobile data," Proc. of the 16th Intl. Conf. on Multimodal Interaction, pp. 427-434, 2014.
[15] R. Iqbal, M. A. A. Murad, A. Mustapha, P. H. Shariat Panahy, and N. Khanahmadliravi, "An experimental study of classification algorithms for crime prediction," Indian J. of Sci. and Technol., vol. 6, no. 3, pp. 4219- 4225, Mar. 2013.
[16] T. Beshah and S. Hill, "Mining road traffic accident data to improve safety: role of road-related factors on accident severity in Ethiopia," Proc. of Artificial Intell. for Develop. (AID 2010), pp. 14-19, 2010.
[17] H. Chen, W. Chung, J. J. Xu, G. Wang, Y. Qin, and M. Chau, "Crime data mining: a general framework and some examples," IEEE Computer, vol. 37, no. 4, pp. 50-56, Apr. 2004.
[18] T. Beshah and S. Hill, "Mining road traffic accident data to improve safety: role of road-related factors on accident severity in Ethiopia," Proc. of Artificial Intell. for Develop. (AID 2010), pp. 14-19, 2010.
[19] M. Al Boni and M. S. Gerber, "Area-specific crime prediction models," 15th IEEE Intl. Conf. on Mach. Learn. and Appl., Anaheim, CA, USA, Dec. 2016.
[20] Q. Zhang, P. Yuan, Q. Zhou, and Z. Yang, "Mixed spatial-temporal characteristics based crime hot spots prediction," IEEE 20th Intl. Conf. on Comput. Supported Cooperative Work in Des. (CSCWD), Nanchang, China, May 2016.
[21] N. Mahmud, K. Ibn Zinnah, Y. Ar Rahman, and N. Ahmed, "CRIMECAST: a crime prediction and strategy direction service," IEEE 19th Intl. Conf. on Comput. and Inform. Technol., Dhaka, Bangladesh, Dec. 2016.
[22] Y. L. Lin, L. C. Yu, and T. Y. Chen, "Using machine learning to assist crime prevention," IEEE 6th Intl. Congr. on Advanced Appl. Inform. (IIAIAAI), Hamamatsu, Japan, Jul. 2017.
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[24] H. W. Kang, H. B. Kang, "Prediction of crime occurrence from multimodal data using deep learning," PLoS ONE, vol. 12, no. 4, Apr. 2017.
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[26] X. Zhao and J. Tang, "Exploring transfer learning for crime prediction," IEEE Intl. Conf. on Data Mining Workshop (ICDMW), New Orleans, LA, USA, Nov. 2017.
[27] R. Marchant, S. Haan, G. Clancey, and S. Cripps, "Applying machine learning to criminology: semi parametric spatial demographic Bayesian regression," Security Inform., vol. 7, no. 1, Dec. 2018.
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[30] E. Cesario, C. Catlett, and D. Talia, "Forecasting crimes using autoregressive models," IEEE 14th Intl. Conf. on Dependable, Auton. and Secure Comput., Auckland, New Zealand, Aug. 2016.
[31] M. A. Tayebi, U. Glässer, and P. L. Brantingham, "Learning where to inspect: location learning for crime prediction," IEEE Intl. Conf. on Intel. and Security Inform. (ISI), Baltimore, MD, USA, May 2015.
[32] L. G. A. Alves, H. V. Ribeiro, and F. A. Rodrigues, "Crime prediction through urban metrics and statistical learning," Physica A, vol. 505, pp. 435-443, 2018.
[33] N. Baloian, E. Bassaletti, M. Fernández, O. Figueroa, P. Fuentes. R. Manasevich, M. Orchard. S. Peñafiel, J. A. Pino, and M. Vergara, "Crime prediction using patterns and context," IEEE 21st Intl. Conf. on Comput. Supported Cooperative Work in Des. (CSCWD), Wellington, New Zealand, Apr. 2017.
[34] M. V. Barnadas, Machine learning applied to crime prediction, Thesis, Universitat Politècnica de Catalunya, Barcelona, Spain, Sep. 2016.

Ashish Kumar, Kaptan Singh, Amit Saxena, "A Survey on Analysis of Crime Detection Techniques Using Machine Learning," International Journal of Computer Sciences and Engineering, Vol.10, Issue.2, pp.35-40, 2022.

Eye tracking system has a suitable design which controls any devices which has digital screen with the eyeball movement and gesture without any help of required hardware. This technical concept has a potential to abolish and replace the standard mouse with the human eyes as a new way to interact and communicate with computer and also intended to replace standard computer screen pointing devices for the use of disable and handicapped people or as an alternative for using mouse which is very easy to use for faster input process. The system makes use of a PC webcam in order to detect eye movement. The system continuously scans camera input for pattern similar to the eye. Once the eye is detected, the system locks it as an object. The eye moment image is captured and transmitted by Raspberry Pi 3 model b and Microcontroller in order to process with OpenCV to derive the coordinator of the eyeball. The approach we described and defined is a real-time, non-intrusive, quick and cost-effective method of tracking facial features with the help of IR sensors.

human computer interaction (HCL), Eyeball movement, OpenCV, Python, Raspberry Pi

[1]“Eyeball based Cursor Movement Control”Sivasangari.A, Deepa.D, Anandhi.T, Anitha Ponraj and Roobini.M.S, International Conference on Communication and Signal Processing, July 28 - 30, 2020, India.
[2]. EYE BALL CURSOR DETECTION USING IMAGE PROCESSING ,Siddhesh Shirsath1, Suraj Tiwari2, Rushikesh Kalyane3, Malhari Shinde4 1,2,3,4 UG Students of Computer Engineering, Sinhgad Institute of Technology, Maharashtra, India IJARIIE-ISSN(O)-2395-4396 Vol-7 Issue-3 2021
[3]. Eyeball Movement based Cursor Control using Raspberry Pi and OpenCV , J. Sreedevi, M. Shreya Reddy, B. Satyanarayana International Journal of Innovative Technology and Exploring Engineering (IJITEE) ISSN: 2278-3075, Volume-9 Issue-7, May
[4]A Wearable Gestural Interface:www.pranavmistry.com/projects/sixthsense/ (05.04.2017)
[5] “Pupil center coordinates detection using the circular Hough transform technique” https://ieeexplore.ieee.org/document/7248041/ (2015)
[6] John J. Magee, MargritBetke, James Gips, Matthew R. Scott, and Benjamin
N.Waber“A Human-Computer Interface Using Symmetry Between Eyes to Detect Gaze Direction” IEEE Trans, Vol. 38, no.6,pp.1248-1259, Nov (2008).
[7] SunitaBarve, DhavalDholakiya, Shashank Gupta, DhananjayDhatrak, “Facial Feature Based Method For Real Time Face Detection and Tracking I-CURSOR”, International Journal of EnggResearchand App., Vol. 2, pp. 1406-1410, Apr (2012).
[8] Yu-Tzu Lin Ruei-Yan Lin Yu-Chih Lin Greg C Lee“Real-time eye-gaze estimation using a low-resolution webcam”, Springer, pp.543-568, Aug (2012).
[9] Samuel Epstein-Eric MissimerMargritBetke “Using Kernels for avideo-based mouse-replacement interface”, Springer link, Nov (2012)
[10] Hossain, Zakir, Md Maruf Hossain Shuvo, and Prionjit Sarker. "Hardware and software implementation of real time electrooculogram (EOG) acquisition system to control computer cursor with eyeball movement." In 2017 4th International Conference on Advances in Electrical Engineering (ICAEE), pp. 132-137. IEEE, 2017

Shaikh Adiba Kashish, Nidhi Ghodele, Hashmi Syed Waquas, Nabeela Tanzeel, "Cursor Control through Eye movement," International Journal of Computer Sciences and Engineering, Vol.10, Issue.2, pp.41-44, 2022.

There are two types of monsoons or rainfall seasons in India: summer rainfall from October to March and winter rainfall from April to September. Rainfall plays a vital role in the cultivation, cropping, drinking and other purpose of human beings. Generally, in India, most of times the water source is from rain. In this paper, we are fitted isotonic regression model, linear regression, additive regression, Rep tree and simple linear regression by using machine learning models and are estimated using WEKA software for rainfall as dependent variable and time as an independent variable. The best model for the data is chosen using various accuracy measures like Absolute Mean Error, Root Mean Squared Error, Relative absolute error and Root Relative squared error.

Rainfall, Isotonic Regression, Rep tree, RMSE

[1] P.E.NailHomani, “Time series Analysis model for Rainfall data in Jordan case study for using Time series Analysis”, American Journal of environmental sciences vol. 5 no5, pp. 599-604, 2009.
[2] MostataDastorani, Mohammad Mirzawad, Mohammad TaghiDastorani, and Syed Javad Sadatinejad,“Comparative study among different time series models applied to monthly rainfall forecasting in semi-arid climate condition”, Natural Hazards, vol. 81, pp.1811–1827, 2016.
[3] N. Hasan, N. C. Nath and R. I. Rasel, “A support vector regression model for forecasting rainfall”, 2015 2nd International Conference on Electrical Information and Communication Technologies (EICT), Khulna, Bangladesh, pp. 554-559, 2015. doi: 10.1109/EICT.2015.7392014.
[4] Kin C. Luk, J.E. Ball and A. Sharma,“An application of Artificial Neural Networks for rainfall forecasting”, mathematical and computer modelling, vol 33, pp. 683-693, 2001.
[5] N.Q.Hung, M.S. Babel, S. Weejakul and N.K. Tripathi,“An Artificial Neural Network model for rainfall forecasting in Bangkok”, Thailand, Hydrology and earth sciences, vol. 13, pp.1413-1425, 2009.
[6] M. P. Darji, V. K. Dabhi and H. B. Prajapati, "Rainfall forecasting using neural network: A survey, “Proceedings of the International Conference on Advances in Computer Engineering and Applications, Ghaziabad, India, pp.706-713, 2015, doi: 10.1109/ICACEA.2015.7164782.
[7] B. Hari Mallikarjuna Reddy, S. Venkatramana Reddy, and B. Sarojamma, “Data Mining Techniques for estimation of wind speed using WEKA”, International Journal of Computer Sciences and Engineering(IJCSE), 9(9), 49-53. 2021.
DOI: https://doi.org/10.26438/ijcse/v9i9.4851
[8] S. Damodharan, S. Venkatramana Reddy, B.Sarojamma, “Quantile Regression Models for Rainfall Data”, International Journal of Computer Sciences and Engineering(IJCSE), 9(9), 83-85, 2021.
DOI: https://doi.org/10.26438/ijcse/v9i9.8385
[9] Data website: URL:http://www.imd.gov.in/Welcome%20To%20IMD/Welcome.php.

K. Anil Kumar, S. Venkatramana Reddy, B. Sarojamma, "Data Mining Techniques for Rainfall Data Using WEKA," International Journal of Computer Sciences and Engineering, Vol.10, Issue.2, pp.45-48, 2022.