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The ability to predict a student’s performance on a given concept is an important tool for the education institutions, as it allows them to understand the ability of students and derive important methods to enhance their knowledge levels. It is the responsibility of educational institutions to have an approximate prior knowledge of their students to predict their performance in future academics and to train them in various activities. It is used to identify bright students and also provides them an opportunity to pay attention to and improve the slow learners. For predicting the student academic performance a data mining technique under classification is used. I have analyzed the data set containing information about students, such as full name, Roll number, scores in board examinations of classes X and XII, Rank in Eamcet examinations, branch and admission type. ID3 and C4.5 classification algorithms are applied to predict the performance of newly admitted students in their future examinations. In this paper, the performance of ID3 and C4.5 algorithms are compared in terms of parameters like accuracy, error rate and the execution time and the experimental Results shown that C4.5 was found to be best in terms of execution time.

ID3, Classification, Prediction.

[1] Kalpesh Adhatrao, Aditya Gaykar, Amiraj Dhawan, Rohit Jha and Vipul Honrao “Predicting Students’ Performance using ID3 and C4.5 classification algorithms”, International Journal of Data Mining & Knowledge Management Process (IJDKP) Vol.3, No.5, pp.39-52,September 2013.
[2] Brijesh Kumar Bharadwaj and Saurabh Pal, “Mining Educational Data to Analyze Students Performance”, International Journal of Advances in Computer Science and Applications,Vol. 2(6) , pp. 63- 69, 2011.
[3] Surjeet Kumar Yadav, “Data Mining: A Prediction for Performance improvement of Engineering Students using Classification‖”, World of Computer Science and Information Technology Journal (WCSIT) ISSN: 2221- 0741 Vol. 2, No. 2, 51-56, 2012.
[4] Crist´obal Romero," Educational Data Mining: A Review of the State of the Art," IEEE Transactions On Systems, Man, and Cybernetics— Part C: Applications And Reviews, Vol. 40, No. 6, November 2010.
[5] Mining, H., Wenying, N. and Xu, L., (2009) “An improved decision tree classification algorithm based on ID3 and the application in score analysis”, Chinese -Control and Decision Conference (CCDC), pp 1876- 1879.
[6] Xiaoliang, Z., Jian, W., Hongcan Y., and Shangzhuo, W., (2009) “Research and Application of the improved Algorithm C4.5 on Decision Tree”, International Conference on Test and Measurement (ICTM), Vol. 2, pp 184-187.
[7] M. Mayilvaganan,D .Kalpanadevi," Comparison of Classification Techniques for predicting the Performance of Students Academic Environment," in International Conference on Communication and Network Technologies (ICCNT), 2014.
[8] R.S.J.D Baker and K.Yacef, “The State of Educational Data Mining in 2009: A Review and Future Visions” , Journal of Educational Data Mining, 1, Vol 1, No 1, 2009.

Kandepi Suneetha, "A Comparative Study on Student Academic Performance Prediction Using ID3 and C4.5 Classification Algorithms," International Journal of Computer Sciences and Engineering, Vol.8, Issue.4, pp.106-111, 2020.

In this paper focuses on a College Automation system is an Web Based project. Which is helpful for students as well as the Faculties, Library and Admin (Record Manager) In the existing system all the activities are done manually. It is very costly and time consuming. In our proposed system, Student can view college related details like assignment, notes, notices, view available books details, attendance, library status etc. Librarian can record new book details also issue book and renew book, etc. Faculty can upload assignment, mark, student attendance, etc. The proposed work has three modules: 1.Student 2.Admin 3.Staff. In Student module student need to register after the registration He/She need to be verify their email. Only after verification of email He/She can login into system. In Admin module, admin can handle and view the all record which store in database. This college automation system access anytime and anywhere, whenever user wants. College Automation System enables colleges to manage enrollment, students, faculty, attendance, fees, scheduling, assignments, grades and library of the institution. This system has easy interface. To overcome the problem of manual system we have developed this system.

Attendence system,Libaray status,asp.net,sql

[1] A.V. Pai, A.Krishna,”Web Based Student Attendence System” IJARSE, Vol No.5, Issue No.03, March 2012
[2] Geeta R.B., S.G.Totad, ”Student Information System” IJARCCE, Vol No.2, Issue 6, June 2013.
[3] Mohitha H V,Anandu Mohan, ”College Automaton And Management” IJIRCCE, Vol.5, Issue 5, May 2017.
[4] S. Patnaik, K. Singh, ”College Management System” IRJET, Vol No.3, Issue No.05, May 2016.
[5] S. Patnaik, K.K. Singh, R. Ranjan, N. Kumari "College Management System", International Research Journal of Engineering and Technology (IRJET), Vol. 3, Issue. 5, May 2016.
[6] S.J. Raut "Smart Class Learning Management System for School Education", Review Paper | Journal Paper (IJSRCSE), Vol. 6, Issue. 1, pp.9-13, January 2018.

S.S.Chavan, G.S. Vejare, S.G. Dsouza, N.M. Shivsharan, "Web Based College Automation System," International Journal of Computer Sciences and Engineering, Vol.8, Issue.4, pp.112-114, 2020.

In this paper focuses on a College Management system is an android application Based project. Which is helpful for students as well as the Faculties, Finance Department, Library and Admin (Record Manager). In the existing system all the activities are done manually. It is very costly and time consuming. In our proposed system, Student can view college related details like assignment, notes, notices, view available books details, give feedback or submit their issues etc. He/She view the detail of their Fee, etc. Librarian can record new book details also issue book and renew book, etc. Finance department can handle record like student fee details and salary of employee details, etc. Faculty can upload assignment, mark student attendance, etc. The proposed work has five modules: 1.Student 2.Faculty 3.Finance department 4.Librarian 5.Admin. In Student module student need to register after the registration He/She need to be verify their email. Only after verification of email He/She can login into system. In Librarian module, when Librarian add new book, that notification goes to all the subscriber of application access the system. In Admin module, admin can handle and view the all record which store in database. This college management system access anytime and anywhere, whenever user wants.

College management system, Android Operating System, Firebase database, Java, XML.

[1] Vishwakarma R Ganesh, "Android College Management System", International Journal of Advanced Research in Computer Engineering and Technology (IJARCET), Vol. 5, Issue. 4, April 2016.
[2] K. Datarkar, N. Hajare, N. Fulzele, S. Kawle, V. Suryavanshi, D. Radke "Online College Management System", International Journal of Computer Science and Mobile Computing, Vol. 5, Issue. 4, pg. 118-122, April 2016.
[3] M. Narendran, Ch. Venkata Rajesh, M. Venkata Siva Kumar, M. Venkatesh, G. Sree Ram Pavan "A Mobile Based College Application for Managing Records Based On Random Secure Procedure", Imperial Journal of Interdisciplinary Research (IJIR), Vol. 3, Issue. 9, 2017.
[4] A.J. kadam, A. Singh, K. Jagtap, S. Tankala "Mobile Web Based Android Application for College Management System", International Journal of Engineering and Computer Science, Vol. 6, Issue. 2, pg. 20206-20209, 2015.
[5] S. Patnaik, K.K. Singh, R. Ranjan, N. Kumari "College Management System", International Research Journal of Engineering and Technology (IRJET), Vol. 3, Issue. 5, May 2016.
[6] D. Rathore, A. Julwania, D.K. Dixit "AMS: Attendance Management System using Android Environment", Research Paper | Isroset-Journal (IJSRCSE), Vol. 4, Issue. 2, pp.20-25, April 2016.
[7] S.J. Raut "Smart Class Learning Management System for School Education", Review Paper | Journal Paper (IJSRCSE), Vol. 6, Issue. 1, pp.9-13, January 2018.

D.S. Pujare, M.S. Mir, S.M. Melasagare, "Android Based College Management System," International Journal of Computer Sciences and Engineering, Vol.8, Issue.4, pp.115-118, 2020.

As of today, almost everyone currently relocating their administrations into the cloud to offer an increasingly adaptable, open, versatile and omnipresent assistance. In any case, this additionally carries more introduction to security dangers, digital assaults and troubles in dependability and wellbeing. The proposed arrangement is to send a Honeypot in the Intrusion Detection and Prevention System (IDPS) model so as to ensure upgraded execution, extended degree of security in the Distributed computing condition and decrease in the threats to the Cloud condition - by concentrating on the issue of how the information is stored in the Cloud. The structure depicted utilizations both Anomaly Detection (AD) and Signature Detection (SD) in coordinated effort, to recognize various assaults and deny them access using the proposed IPS. The goal of this report is to feature, perceive and ensnare inward interlopers by the utilization of the Honeypot.

Intrusion detection system(IDS), intrusion prevention system(IPS), honeypot, anomaly detection(AD), signature detection(SD), firewall, insider threat, PC assault, network assault

[1] G. Aceto, A. Botta, W. de Donato and A. Pescapè, “Cloud Monitoring: definitions, issues and future directions”, 2012 IEEE 1st Int. Conf. on Cloud Networking (CLOUDNET), Paris, France, 2012, pp. 63-67.
[2] A. Malik and M. M. Nazir, “Security Framework for Cloud Computing Environment: A Review”, J. of Emerging Trends in Computing and Information Sciences, Vol. 3, No. 3, March 2012, pp. 390 – 394.
[3] K. Lee, “Security Threats in Cloud Computing Environments”, Int. J. of Security and its Applications, vol. 6, no. 4, Oct. 2012, pp. 25-32.
[4] S. Y. Ho, “Instrusion Detection – Systems for today and tomorrow”.
[5] S. M. Moorthy and M. Rajeswari, “Virtual Host based Intrusion Detection System for Cloud”, Int. J. of Eng. & Tech., Vol. 5, issue 6, Dec 2013/Jan 2014, p. 5024.
[6] H. M. Alsafi, W. M. Abduallah and A. K. Pathan, “IDPs: An Integrated Intrusion Handling Model for Cloud Computing Environment”, March 2012.
[7] C. Modi, D. Patel, B. Borisaniya, A. Patel and M. Rajarajan, “A survey on security issues and solutions at different layers of Cloud computing”, The J. of Supercomputing, vol. 63, issue 2, pp. 561 – 592.
[8] L. Spitzner, “The Value of Honeypots”, 10th Jan., 2003.
[9] N. F. Huang, C. Wang, I. J. Liao, C. W. Lin and C. N. Kao, “An OpenFlow-based collaborative intrusion prevention system for cloud networking”, 2015 IEEE International Conference on Communication Software and Networks (ICCSN), Chengdu, 2015, pp. 85-92, 607 June 2015.
[10] K. Shridhar and N. Gautam, “A Prevention of DDoS Attacks in Cloud Using Honeypot”, Int. J. of Science and Research (IJSR), vol. 3, issue 11, Nov. 2014, pp. 2378 – 2383.
[11] D. Winder, “How to use the cloud as a honeypot”, 2nd Oct., 2014.
[12] V. Sing, A. Kumar and D. Kumar, “An Advanced Hybrid Intrusion Detection System in Cloud Computing Environment”, Int. J. for Research in App. Sci. and Eng. Tech. (IJRASET), vol. 2, issue 6, June 2014, pp. 302 – 309.
[13] Jha, A., Johnson, D., Murari, K., Raju, M., Cherian, V., & Girikumar, Y.. OpenStack Beginner`s Guide (for Ubuntu - Precise). CSS Corp. Pvt. Lt, 2012.

Bhanushree V.K, Minavathi, "Cloud Packets Forensics through NIDS and NIPS with Honeypot," International Journal of Computer Sciences and Engineering, Vol.8, Issue.4, pp.119-122, 2020.

Drowsiness or fatigue is one of the major causes of road accidents. Numerous deadly mishaps can be forestalled if the drowsy drivers are cautioned in time. A variety of drowsiness detection techniques exist that monitor the state of the driver while driving and a warning alert is triggered if they do not concentrate on driving. In order to determine the state of the driver, various relevant features from facial expressions can be extracted such as yawning, eye closure, and head movements. This paper aims to study the existing techniques in order to enhance them or create a hybrid of them for a better result. The study highlights existing behavioural drowsiness detection techniques. Firstly, in this paper, we classify the existing techniques into three categories: behavioural, vehicular, and physiological parameters-based techniques. Our main focus is on the behavioural parameters. Secondly, implementation techniques for behavioural parameters used for drowsiness detection are reviewed in detail. In the end, the accuracy of each technique implemented is represented in a tabular format. The challenges faced along with the conclusion of the study may help researchers for finding further work in the relevant field.

Driver drowsiness, fatigue detection, supervised learning, classification, support vector machine (SVM), yawning, eye closure

[1] Anilkumar C.V, Mansoor Ahmed, Sahana R, Thejashwini R, Anisha P.S, "Design of Drowsiness, Heart Beat Detection System and Alertness Indicator for Driver Safety", In the Proceedings of the 2016 IEEE International Conference On Recent Trends In Electronics Information Communication Technology, India, pp-937-941, 2016.
[2] Ashish Kumar and Rusha Patra, "Driver Drowsiness Monitoring System using Visual Behaviour and Machine Learning", IEEE Journal, pp: 339-344, 2018.
[3] Bappaditya Mandal, Liyuan Li, Gang Sam Wang, and Jie Lin, "Towards Detection of Bus Driver Fatigue Based on Robust Visual Analysis of Eye State", IEEE Transactions on Intelligent Transportation Systems, Vol. 18, NO. 3, pp: 545-557, 2017.
[4] Belhassen Akrout and Walid Mahd, "Yawning detection by the analysis of variational descriptor for monitoring driver drowsiness", In the Proceedings of International Image Processing applications and Systems Conference, IEEE, pp:1-5, 2016.
[5] Feng You, Xiaolong Li, Yunbo Gong, Haiwei Wang, Hongyi Li, "A Real-time Driving Drowsiness Detection Algorithm With Individual Differences Consideration", IEEE Access, Vol.7, 2019.
[6] Jang Woon, Byung-Gil Han, Kwang-Ju Kim, Yun-Su Chung, Soo-In Lee,"Real-time Drowsiness Detection Algorithm for Driver State Monitoring Systems", pp-73-75, IEEE 2018.
[7] Jun-Juh Yan, Hang-Hong Kuo,Ying-Fan Lin, Teh-Lu Liao, "Real-time Driver Drowsiness Detection System Based on PERCLOS and Grayscale Image Processing",2016 International Symposium on Computer, Consumer and Control,IEEE, pp:243-246, 2016.
[8] Kangning Li, Shangshang Wang, Chang Du, Yuxin Huang, Xin Feng, Fengfeng Zhou, "Accurate Fatigue Detection Based on Multiple Facial Morphological Features", Hindawi, Journal of Sensors, Vol. 2019, pp:1-10,2019.
[9] Lei Zhao, Zenkai Wang, Xiaojin Wang, Qing Liu, "Driver drowsiness detection using facial dynamic fusion information and a DBN",IET Intell. Transp. Syst., 2018, Vol. 12 Iss. 2, pp. 127-133,2017.
[10] Marchel T. Tombeng, Hence Kandow, Stenly I. Adam, Argha Silitonga, Juve Korompis, "Android-Based Application To Detect Drowsiness When Driving Vehicle", In the Proceedings of 1st International Conference on Cybernetics and Intelligent System (ICORIS),Indonesia, pp:102-104, 2019.
[11] Melissa Yauri, Brian Meneses-Claudio and Natalia Vargas-Cuentas, "Design of a Vehicle Driver Drowsiness Detection System through Image Processing using Matlab", IEEE, 2018.
[12] Menchie Miranda, Alonica Villanueva, Brian Meneses-Claudio, Natalia Vargas-Cuentas, Avid Roman-Gonzalez" PORTABLE PREVENTION AND MONITORING OF DRIVER’S DROWSINESS FOCUSES TO EYELID MOVEMENT USING INTERNET OF THINGS",IEEE, 2018.
[13] Muhammad Tayab Khan, Hafeez Anwar et al., "Smart Real-Time Video Surveillance Platform for Drowsiness Detection Based on Eyelid Closure", Wireless Communications and Mobile Computing, Hindawi, Vol. 2019, pp: 1-9, 2019.
[14] Omar Rigane, Karim Abbes, Chokri Abdelmoula and Mohamed Masmoudi, “A Fuzzy Based Method for Driver Drowsiness Detection”, In the Proceedings of 2017 IEEE/ACS 14th International Conference on Computer Systems and Applications", pp: 143-147, 2017.
[15] Omar Wathiq and Bhavna D. Ambudkar, "Optimized Driver Safety through Driver Fatigue Detection Methods", In the Proceedings of International Conference on Trends in Electronics and Informatics, IEEE, pp:68-73, 2017.
[16] Rateb Jabbar, Khalifa Al-Khalifa, Mohamed Kharbeche, Wael Alhajyaseen, Mohsen Jafari, Shan Jiang, "Real-time Driver Drowsiness Detection for Android Application Using Deep Neural Networks Techniques", In the Proceedings of the 9th International Conference on Ambient Systems, Networks, and Technologies, Elsevier, Vol. 2018, pp- 400–407, 2018.
[17] Samra Naz,Aneeqa Ahmed, Qurat ul ain Mubarak, IrumNoshin," Intelligent Driver Safety System Using Fatigue Detection", ICACT2017, pp-89-93, 2017.
[18] Umit Budak,Varun Bajaj, Yaman Akbulut, Orhan Atilla, Abdulkadir Sengur, "An Effective Hybrid Model for EEG-Based Drowsiness Detection", IEEE Sensors Journal, Vol. 19,No. 17, pp:7624-7631, 2019.
[19] Wanghua Deng and Ruoxue WU,"Real-Time Driver-Drowsiness Detection System Using Facial Features", Preparation of Papers for IEEE Transactions and Journals, IEEE Access, “Unpublished”.
[20] ZhuoniJie, Marwa Mahmoud, Quentin Stafford-Fraser, Peter Robinson, Eduardo Dias and Lee Skrypchuk,” Analysis of yawning behaviour in spontaneous expressions of drowsy drivers”, In the Proceedings of 2018 13th IEEE International Conference on Automatic Face & Gesture Recognition, pp:571-576, 2018.

Manishi, Naveen Kumari, "A Comprehensive Study on Behavioural Parameters-Based Drowsiness Detection Techniques," International Journal of Computer Sciences and Engineering, Vol.8, Issue.4, pp.123-128, 2020.

Taxonomy is an essential process for gaining, sending, and classifying information, and also creating and using applications in several fields. To minimize humans, work to form the taxonomy learning from scratch and then increase the consistency of the taxonomy, now we suggest an approach to taxonomy learning, called TaxoFinder. TaxoFinder does three stages to construct a taxonomy automatically. Next, it distinguishes notions which are specific to the domain from a corpus of text. Later, it develops a graph describing how these definitions are connected at once depending on their co-occurrences. We will provide a technique for calculating strengths of associative between the concepts as the main method in TaxoFinder, which proves the strength and how tightly they have associated in the graphs, Using their similarities and spatial differences in sentences. Then lastly, have the TaxoFinder which uses a graph-analytical algorithm to trigger a taxonomy. TaxoFinder attempts to construct a taxonomy in such a way that to create a taxonomy, it enhances the associative strengths between the concepts in the graph. We test TaxoFinder on three separate domains using the gold standard evaluation: Mass-meetings emergency response, autism research and disorder domains. We evaluate TaxoFinder as the very effective subsumption method in this development, and it reveals that TaxoFinder was an efficient solution that successfully outperforms the subsumption process.

Knowledge searching, Taxonomy learning, Taxonomy, TaxoFinder, keyword phrases

[1] K. Meijer, F. Frasincar, and F. Hogenboom, “A semantic approach for extracting domain taxonomies from text,” Decision Support Syst., vol. 62, pp. 78–93, 2014.
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[5] X. Liu, Y. Song, S. Liu, and H. Wang, “Automatic taxonomy construction from keywords,” in Proc. 18th ACM SIGKDD Int. Conf. Knowl. Discovery Data Mining, 2012, pp. 1433–1441.
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[12] K. Dellschaft and S. Staab, “Strategies for the evaluation of ontology learning,” in Proc. Conf. Ontol. Learn. Population: Bridging Gap Between Text Knowl, 2008, pp. 253–272.
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[14] S. P. Ponzetto and M. Strube, “Taxonomy induction based on a collaboratively built knowledge repository,” Artif. Intell. , vol. 175, no. 9-10, pp. 1737–1756, Jun. 2011.
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Abhijeet Ashokrao Kadam, Shivputra Guruling Swami, "TaxoFinder A Graph-Based Technique for Taxonomy Learning," International Journal of Computer Sciences and Engineering, Vol.8, Issue.4, pp.129-132, 2020.

Plant identification system is helped to find unidentified plants. Plant identification is most difficult task with the existing classification algorithms. Many existing classifiers are present to identify the plant species with the help of leafs. With the various drawbacks, the system will not reach that much. In recent years, many applications belong to various domains and technologies are using the Deep Learning (DL) for rapid and better results. In this paper, the Novel Approach (NA) is introduced with the combination of CNN adopted with ensemble methods such as bagging and boosting. This paper addresses that the Convolutional Neural Network (CNN) with ensemble methods is better than Machine Learning methods to identify the plant by leaf. The ensemble methods are to improve the accuracy and sensitivity of plant identification model. The parameters such as sensitivity and accuracy are the two metrics to show the performance.

CNN, Bagging, Boosting, Novel Approach

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[2] Anami, B. S., Suvarna, S. N., & Govardhan, A. (2010). A combined color, texture and edge features based approach for identification and classification of indian medicinal plants. International Journal of Computer Applications,6(12), 45-51.
[3] A. Aakif, M. F. Khan, "Automatic classification of plants based on their leaves", Biosyst. Eng., vol. 139, pp. 66-75, Nov. 2015.
[4] Go¨eau, H., Bonnet, P., Joly, A.: Plant identification in an open-world (lifeclef 2016). In: CLEF working notes 2016. (2016)
[5] J. Wäldchen, P. Mäder, "Plant Species Identification Using Computer Vision Techniques: A Systematic Literature Review" in Arch. Comput. Methods Eng., pp. 1-37, Jan. 2017.

P. Siva Prasad, A. Senthilrajan, "An Ensemble Deep Learning Technique for Plant Identification," International Journal of Computer Sciences and Engineering, Vol.8, Issue.4, pp.133-135, 2020.

Due to recent mobile technology advances, consumers have 24* 7 accesses to social networks. With regard to knowledge gaps, the dissemination of misinformation is closely linked, particularly when the data is published slowly, often as unverified data. A significant investigation is done in online social media, particularly micro-blogging websites, automatically detect rumors. Recent research on the follow-up of disinformation in social media has explored such terminology. This article will present an overview of social media rumor detection research including various types of rumor classification available in order to recognize the rumor and class text. In this survey paper we will also highlight the features of classification algorithms like Naïve Bayes, Support Vector Machine, Logistic Regression and K-Nearest Neighbor.

Rumor detection, social networks, machine Learning, fake, NLP

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[14] S. Tschiatschek, A. Singla, M. Gomez Rodriguez, A. Merchant, and A. Krause, “Fake News Detection in Social Networks via Crowd Signals,” 2018.
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[16] Rajit Nair, Vaibhav Jain, Amit Bhagat, Ratish Agarwal, “An Efficient Approach for Sentiment Analysis Using Regression Analysis Technique,” International Journal of Computer Sciences and Engineering, Vol.7, Issue.3, pp.161-165, 2019..
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Ria Purohit, Nidhi Ruthia, Chetan Agrawal, "A Survey on Classification of Rumors on Social Media Using Machine Learning," International Journal of Computer Sciences and Engineering, Vol.8, Issue.4, pp.136-140, 2020.

Road traffic (RT) security is a major issue for government transport authorities& citizens. Road accidents (RAs) are unclear & accidents are unpredictable & their survey needs issues that affect them. RAs source problems are greater at an alarming rate. Adjusting traffic accidents on roads is a critical challenge. Towards provide benign driving recommendations, clear & suspicious consider of RT information is dangerous. The main of this paper is to consider RAs in India at the national, urban & state city levels. Exploration of RA situation at city & state level displays that there is a vast difference in fatalness threat across states & cities. The probability of fatality is greater than the average in India for ten people outside union territory and 35 Member States. While in these metropolitan cities RAs are slightly lower in India, almost 50 percent of the cities are facing greater fatality risks than their fossil counterparts. There is so an urgent want to identify a failing situation in injuries & road deaths& towards proceeds suitable action.

Road Traffic, Safe Driving, Data Mining Techniques, Literature Review

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Soniya Mudgal, Mahesh Parmar, "A Comprehensive Review on Road Traffic Fatal Accidents in India," International Journal of Computer Sciences and Engineering, Vol.8, Issue.4, pp.141-144, 2020.

Watershed algorithm is used in image processing primarily for segmentation purposes. The main aim of this technique is to segment the image, generally when two regions of interest are close to each other i.e, their edge touch. Watersheds use many of the concepts of edge-detection, thresholding & region growing and often produce stable segmentation results. Watershed segmentation algorithm is an attractive method when compared to other segmentation algorithm. The Watershed segmentation technique can be applied to binary image, gray scale image and textural images. In many field of image processing including medical image segmentation, the watershed transform has been widely used. This method yields a result of accurate segmentation by reducing the over segmentation effect. Watershed transform is applied on distance transform, gradient image ,and marker controlled image and check which among the three yield the best result.

Thresholding, Image segmentation, Distance Transform, Gradient Magnitude, Marker controlled watershed segmentation.

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Bimalini Meher, Chandra Sekhar Panda, "An Image Segmentation Approach Using Watershed Technique," International Journal of Computer Sciences and Engineering, Vol.8, Issue.4, pp.145-148, 2020.