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Data Mining
S.Aamina Sadiya - Assistant Professor, PG Department of Computer Science, Islamiah Women’s Arts and Science College for women, Vaniyambadi.
Pages: 1-7 | First Published: 05 May 2025
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Abstract 

Data mining is a critical process of extracting meaningful patterns, relationships, and knowledge from large datasets. With the explosion of data generated in various domains, data mining helps organizations make informed decisions and gain competitive advantages. This paper presents an in-depth study of data mining, including its techniques, challenges, applications, and future trends. The aim is to provide scholars and researchers with a strong foundation and insight into this rapidly evolving field.

References

1. Han, J., Kamber, M., & Pei, J. (2011). Data Mining: Concepts and Techniques. Elsevier. 

2. Fayyad, U., Piatetsky-Shapiro, G., & Smyth, P. (1996). From Data Mining to Knowledge Discovery in Databases. 

3. Witten, I. H., Frank, E., & Hall, M. A. (2011). Data Mining: Practical Machine Learning Tools and Techniques. 

4. Aggarwal, C. C. (2015). Data Mining: The Textbook. Springer. 

5. Kotsiantis, S. B., Zaharakis, I., & Pintelas, P. (2006). Machine Learning: A Review of Classification and Combining Techniques.

A Study on Smart Contract Device on Big Data Analytics
Dr. M. Duraisamy - Assistant Professor, Department of Computer Science, Government Arts and Science College, Tirupattur
Pages: 8-14 | First Published: 05 May 2025
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Abstract 

Understanding the diverse potential domains of IoT applications and the associated research challenges is crucial as the Internet of Things (IoT) evolves as the next phase of the Internet's progression. IoT is expected to play a role in various fields such as smart cities, healthcare, smart agriculture, logistics, retail, smart living, and smart environments. Despite notable advancements in enabling technologies for IoT, numerous unresolved issues persist. The dependence of BitComet on a range of technologies makes encountering research challenges inevitable. IoT, being pervasive and impacting almost every aspect of our lives, is a significant research area, particularly in linked fields like computer science and information technology. This broad influence is opening up new research opportunities. This presentation not only delves into upcoming applications and research issues but also scrutinizes recent advancements in IoT technology. Keywords: Internet of Things; IoT applications; IoT challenges; future technologies; smart cities; smart; smart agriculture; smart living.

References

[1] R.Vignesh and 2A.Samydurai ans1 Student, 2Associate Professor Security on Internet of Things (IOT) with Challenges and Countermeasures in 2017 IJEDR | Volume 5, Issue 1 | ISSN: 2321-9939. 

[2] N. Kibitz, “Elliptic curve cryptosystems,” Mathematics of Computation, vol. 48, 203-209, 1987. 

[3] J.-Y. Lee, W.-C.Lin, and Y.-H. Huang, “A lightweight Authentication protocol for internet of things,” in Int’l Symposium On Next-Generation Electronics (ISNE), 1-2, 2014. 

[4] Y. Xin and D. Wang, “An Item-Level Access Control Framework For Inter System Security in the Internet of Things,” in Applied Mechanics and Materials, 1430-1432, 2014. 

[5] B. Angloromani, P. N. McHale, N. R. Prasad, and R. Prasad, “Capability-based access control delegation model on the Federated IoT network,” in Int’l Symposium on Wireless Personal Multimedia Communications (WPMC), 604 608, 2012 

[6] S. V. Zanjal and G. R. Talmale, “Medicine reminder and monitoring System for secure health using IOT,” Procedia Computer Science, vol. 78, pp. 471–476, 2016.

[7] R. Jain, “A Congestion Control System Based on VANET for Small Length Roads”, Annals of Emerging Technologies in Computing (AETiC), vol. 2, no. 1, pp. 17–21, 2018, DOI: 10.33166/AETiC.2018.01.003. 

[8] S. Soomro, M. H. Miraz, A. Prasanth, M. Abdullah, “Artificial Intelligence Enabled IoT: Traffic Congestion Reduction in Smart Cities,” IET 2018 Smart Cities Symposium, pp. 81–86, 2018, DOI: 10.1049/cp.2018.1381. 

[9] Mahmud, S. H., Assan, L. And Islam, R. 2018. “Potentials of Internet of Things (IoT) in Malaysian Construction Industry”, Annals of Emerging Technologies in Computing (AETiC), Print ISSN: 2516-0281, Online ISSN: 2516-029X, pp. 44-52, Vol. 2, No. 1, International Association of Educators and Researchers (IAER), DOI: 10.33166/AETiC.2018.04.004. 

[10] Mano, Y., Faical B. S., Nakamura L., Gomes, P. G. Libralon, R. Meneguete, G. Filho, G. Giancristofaro, G. Pessin, B. Krishnamachari, And Jo Ueyama. 2015. Exploiting IoT technologies for enhancing Health Smart Homes through patient identification and emotion recognition. Computer Communications, 89.90, (178-190). DOI: 10.1016/j.comcom.2016.03.010.

A Framework Design to Backup and Recover the Cloud Computing Data
Dr. A.Priya - Assistant Professor & Head, PG Department of Computer Science, Government Arts and Science College, Tirupattur
Pages: 15-25 | First Published: 05 May 2025
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Abstract 

Electronic data has been created today in large quantities requiring data recovery services organization’s work may experience the various type of disasters whether it was natural or man-made, which may result in huge loss of data. In an experimental evaluation, the encryption and spatial scrambling performance and the average response time have been estimated in terms of the data file size. The Data user verifies the document with the proof and decrypts the encrypted file if verification is correct. Finally, it describes a prototype system configuration for several practical network applications, including the hybrid utilization of cloud computing facilities and environments which are already commercialized.

References

[1] Vahid Ashktorab and Seyed Reza Taghizadeh, Security Threats and Countermeasuresin Cloud Computingǁ, International Journal of Application or Innovation in Engineering and Management (IJAIEM), Volume 1, Issue 2, October 2018. 

[2] Cloud Security Alliances, ―Top Threats to Cloud Computing V1.0ǁ, Cloud Security Alliances, Version 1, Page No. 3, March 2017. 

[3] Wiiliam R Claycomb and Alex Nicoll,InsiderThreatstoNewResearchChallengesǁ,CERT. Wayne A. Janssen, Cloud Hooks: Security and Privacy Issues in Cloud Computing , 44th Hawaii International Conference on System Sciences, January 2015. 

[4] Michael Armbrust, Armando Fox, Rean Griffith, Anthony D. Joseph, Randy Katz, Andy Konwinski, Gunho Lee, David Patterson, Ariel Rabkin, Ion Stoica, and MateiZahria, A view of Cloud Computingǁ, Communications of the ACM, Volume 53, Issue 4, April 2016. 

[5] E. Kirda, C. Kruegel and G. Vigna, Cross- Site Scripting Prevention with Dynamic Data Tainting and Static Analysisǁ, Proceeding of the Network and Distributed System. 2014. Mayas Publication 24 Emperor Journal of Applied Scientific Research 

[6] Shengmei Luo, Zhaoji Lin, Xiaohua Chen, Zhuolin Yang and JianyongChen,Virtualization Security for Cloud Computing Servicesǁ,International Conference on Cloud and Service Computing, December 2011. 

[7] Albert B Jeng, Chien Chen Tseng, Der-Feng Tseng and Jiunn-Chin Wang, A Study of CAPTCHA and its Application to user Authenticationǁ, Proceedingof2ndInternationalConferenceonComputational Collective Intelligence: Technologies and Applications, 2010. 

[8] A. Liu, Y. Yuan and A Stavrou, ǁSQLProb:A Proxybased Architecture toward Preventing SQL Injection Attacksǁ, SAC, March 2009. 

[9] D. Gollmann, Securing Web Applicationsǁ, Information Security Technical Report, Volume 13, Issue 1, 2008 153. 

[10] ZouheirTrabelsi, Hamza Rahmani, Kamel Kaouech and Mounir Frikha, Malicious Sniffing System Detection Platformǁ, Proceedings of the 2004 International Symposium on Applications and the Internet, 2004. 

[11] Flavio Lombardi and Roberto di Pietro, Secure Virtualization for Cloud Computingǁ, Journal of Network and Computer Applications, Academic Press Ltd. London, UK, Volume 34, Issue 4, July 2011. 

[12] Hanqian Wu, Yi Ding, Winer C. and Li Yao,ǁNetwork Security for Virtual Machine in Cloud Computingǁ, 5th International Conference Information Technology, Seoul, December 2010. 

[13] SAVVIS, Securing the Cloud A Review of Cloud ComputingSecurity Implications and Best Practicesǁ, VMWARE WHITE PAPER, SAVVIS.

[14] Ruiping Lua and Kin Choong Yow, Mitigating DDoS Attacks with Transparent and Intelligent Fast-Flux Swarm Networkǁ, IEEE Network, Volume 25, Number 4, August 2011. 

[15] Ramaiah, Y. Govinda, and G. VijayaKumari. "Efficient public key homomorphic encryption over integer plaintexts." Information Securityand Intelligence Control (ISIC), 2012 International Conference on. IEEE, 2012.

Heart Attack Prediction Using Fuzzy C-Means: A Comparative Study with Neural Networks
G. Sharmila, R. Sangeetha -Assistant Professor, PG Department of Computer Science, Islamiah Women’s Arts and Science college, Vaniyambadi, Tamil Nadu, India.
Pages: 26-39 | First Published: 05 May 2025
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Abstract 

Fuzzy logic has emerged as a powerful tool in the medical domain, offering effective solutions for complex diagnostic tasks. It has been widely used in detecting critical conditions such as breast cancer, lung cancer, prostate cancer, and heart disease. This study presents an unsupervised classification model for early prediction of heart attacks using the Fuzzy C-Means (FCM) algorithm. The system analyzes patient medical records, utilizing 13 key attributes as input to assess heart attack risk. A dataset comprising 297 patient records was used to evaluate the model’s performance, resulting in a classification accuracy of 100%. When compared to traditional neural network models like back propagation and adaptive linear networks, the FCM-based approach demonstrated superior efficiency and cost-effectiveness. The model was developed using MATLAB’s Fuzzy Logic Toolbox and aims to support physicians in making more accurate and timely diagnoses of heart-related conditions.

References

[1]. Kawamoto, K., Houlihan, C.A., Balas, E.A., Lobach, D.F., 2005. “Improving clinical practice using clinical decision support systems: Clinical decision support system: Risk level prediction of heart disease using weighted fuzzy rules 39 a systematic review of trials to identify features critical to success”. BMJ 330, 765. 

[2].Shanthakumar B.Patil,Y.S,Kumaraswamy”Intelligent and Effective Heart Attack Prediction System Using Data Mining and Artificial Neural Network”. European Journal of Scientific Research Vol. 31, No. 04, 2009, 642-656 . 

[3]. Carolas Ordonez “Assosiation Rule Discovery With the Train and Test Approach for Heart Disease Prediction” IEEE Transactions on Information Technology in Biomedicine, Vol. 10, No. 2, April 2006. 

[4]. Sellappan Palaniappan,Rafiah Awang, “Intelligent Heart Disease Prediction System Using Data Mining Techniques” IEEE Conference, 2008,pp 108-115. 

[5]. K.Srinivas ,B.Kavihta Rani ,Dr. A.Govrdhan “Applications of Data Mining Techniques in Healthcare and Prediction of Heart Attacks” K.Srinivas et al. / (IJCSE) International Journal on Computer Science and Engineering Vol. 02, No. 02, 2010, 250-255. 

[6]. Markos G. Tsipouras, Themis P. Exarchos, Dimitrios I. Fotiadis, Anna P. Kotsia, Konstantinos V. Vakalis,Katerina K. Naka, and Lampros K. Michalis” Automated Diagnosis of Coronary Artery Disease Based on Data Mining and Fuzzy Modeling “IEEE Transactions on Information Technology in Biomedicine, Vol. 12, No. 4, July 2008 . 

[7]. Dan Li, Hong Gu, Liyong Zhang,” A fuzzy c-means clustering algorithm based on nearest-neighbor intervals for incomplete data”, Expert Systems with Applications 37 (2010) 6942–6947 

[8]. Yang, M.-S., Hwang, P.-Y., Chen, D.-H., 2004. Fuzzy clustering algorithms for mixed feature variables. Fuzzy Sets Systems 141, 301–317. 

[9]. Ross, Timothy J., 2004. Fuzzy Logic with Engineering Applications, John Wiley & Sons, Second Edition. 

[10]. Lanhai L. Comparison of conventional and fuzzy land classification and evaluation techniques in Oxfordshire England. Int Agric Eng J 1998;7:1–12. 

[11]. Sadeghi, S., Barzi, A., Sadeghi, N., & King, B. (2006). A Bayesian model for triage decision support. International Journal of Medical Informatics, 75(5)` 

[12]. Yan, H.-M., Jiang, Y.-T., Zheng, J., Peng, C.-L., & Li, Q.-H. (2006). A multilayer perceptron-based medical decision support system for heart disease diagnosis. Expert Systems with Applications, 30(2), 272–281.

A Structured Review and Comparative Study of Big Data Processing Frameworks: Hadoop, Spark, and Flink
C. Firza Afreen - Assistant Professor PG Department of Computer Science Islamiah Women’s Arts and Science College Vaniyambadi
Pages: 40-47 | First Published: 05 May 2025
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Abstract 

Big Data technology has become very important nowadays to deal with vast and complex datasets and to extract meaningful data. In order to manage data effectively, strong data processing frameworks are very important. This paper provides a Structured Review and Comparative Study of three famous big data processing frameworks, Apache Hadoop, Spark and Flink. The three selected frameworks have been analyzed in terms of its architecture, features, benefits, limitations and real-world use cases. Comparative analysis is conducted based on factors such as Processing Model, Performance and Latency, Fault Tolerance, Data Handling, Scalability, APIs, Ease of Use, Libraries, Community Adoption. The study highlights the strengths which is associated with each framework. Hadoop’s batch-processing reliability, Spark’s in-memory speed and Flink’s true stream processing capabilities are discussed. The work done in this paper aims to guide the researchers and practitioners in selecting the best suitable frameworks according to their use-case requirements.

References

[1] M. Parsian, Data Algorithms with Spark: Recipes and Design Patterns for Scaling Up. 1st ed. Sebastopol, CA, USA: O’Reilly Media, 2022. ISBN: 978-1 4920-8238-5. 

[2] H. Karau, A. Konwinski, P. Wendell, and M. Zaharia, Learning Spark: Lightning-Fast Big Data Analysis. 1st ed. Sebastopol, CA, USA: O’Reilly Media, 2015. ISBN: 978-1-4493-5862-4. 

[3] S. Ryza, U. Laserson, S. Owen, and J. Wills, Advanced Analytics with Spark: Patterns for Learning from Data at Scale. 2nd ed. Sebastopol, CA, USA: O’Reilly Media, 2017. ISBN: 978-1-4919-7294-6. 

[4] F. Hueske and V. Kalavri, Stream Processing with Apache Flink. 1st ed. Sebastopol, CA, USA: O’Reilly Media, 2019. ISBN: 978-1-4919-7429-2. 

[5] M. Guller, Big Data Analytics with Spark: A Practitioner’s Guide to Using Spark for Large Scale Data Analysis. 1st ed. New York, NY, USA: Apress, 2016. ISBN: 978-1-4842-0964-6. 

[6] V. Ankam, Big Data Analytics: Real-Time Analytics Using Apache Spark and Hadoop. 1st ed. Birmingham, UK: Packt Publishing, 2016. ISBN: 978-1 78588-469-6.

[7]V. S. Agneeswaran, Big Data Analytics Beyond Hadoop: Real-Time Applications with Storm, Spark, and More Hadoop Alternatives. 1st ed. Upper Saddle River, NJ, USA: Pearson Education, 2014. ISBN: 978-0133838251. 

[8]N. Marz and J. Warren, Big Data: Principles and Best Practices of Scalable Real Time Data Systems. 1st ed. New Delhi, India: Wiley India, 2015. ISBN: 978 9351198062.

AI with Deep and Machine Learning in Natural Language Processing
R.Padmalatha - Assistant Professor in Department of Computer Science, Islamiah Women’s Arts and Science College, Vaniyambadi
Pages: 48-53 | First Published: 05 May 2025
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Abstract 

Artificial Intelligence (AI) is the field of science and engineering focused on creating machines that can perform tasks requiring intelligence, particularly through sophisticated computer programs. While it shares similarities with efforts to mimic human intelligence using computers, AI does not need to be limited to biological models or methods observable in nature. Although there is no universally agreed-upon definition of AI, it is generally understood as the study of computational processes that enable perception, reasoning, and action. In today's world, the volume of data generated by both humans and machines greatly exceeds our capacity to process and interpret it effectively, making AI essential for handling complex decision-making. AI underpins all forms of machine learning and is poised to shape the future of advanced decision systems. This paper explores the key features of artificial intelligence, including its introduction, various definitions, historical development, practical applications, growth, and notable achievements. Keywords- Machine Learning, Deep Learning, Neural Networks,Natural Language Processing and Knowledge Base System.

References

1. http://en.wikibooks.org/wiki/Computer_Science:Artificial_Intelligence 

2. http://www.howstuffworks.com/arificialintelligence 

3. http:// www.google.co.in 

4. http://www.library.thinkquest.org 

5. https://www.javatpoint.com/application-of-ai

A Structured Review of Machine Learning
Salma A - Assistant Professor & Head PG Department of Computer Science Islamiah Women’s Arts and Science College Vaniyambadi
Pages: 54-62 | First Published: 05 May 2025
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Abstract 

Machine learning (ML) is a game-changer because it lets computers learn on their own from experience, instead of us telling them every single rule. Basically, ML tries to figure out a smart way to turn raw information (like a picture) into the right answer (like "this is a cat"). A big part of this is finding the most important bits of information, called "features" (like a cat's pointy ears or whiskers). What's really cool is that a newer part of ML, called representation learning, helps computers automatically discover these important features, so we don't have to painstakingly find them ourselves.

References 

[1] https://www.researchgate.net 

[2] https://www.science.org 

[3] https://link.springer.com 

[4] https://www.geeksforgeeks.org 

[5] Ivry_rev-libre.pdf

The Transformative Impact of the Internet of Things: Opportunities and Hurdles
J.Pavithra - Assistant Professor, PG Department of Computer Science Islamiah Women's Arts and Science college, Vaniyambadi
Pages: 63-69 | First Published: 05 May 2025
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Abstract

The Internet of Things (IoT) has rapidly emerged as a foundational technological paradigm, connecting an ever-expanding array of physical objects to the digital realm. This interconnectedness is profoundly transforming industries, economies, and daily life by enabling unprecedented data collection, sophisticated automation, and innovative service delivery. This paper explores the transformative impact of IoT, highlighting numerous opportunities it presents across sectors such as smart cities, healthcare, agriculture, and industrial automation, leading to enhanced efficiency, improved quality of life, and novel insights. Concurrently, the pervasive adoption of IoT introduces significant hurdles that demand urgent attention. Critical challenges include pervasive security vulnerabilities, complex privacy concerns arising from vast data collection, persistent issues of interoperability and standardization, and the immense scalability requirements. By critically examining both the vast potential and the inherent obstacles, this paper aims to provide a balanced perspective on the IoT landscape, emphasizing the necessity of robust solutions to navigate its complexities and fully realize its revolutionary promise.

References

[1] C. Perera, "Internet of Things Research and Teaching: Vision and Mission," Ph.D. dissertation, Cardiff University, Cardiff, UK, 2023.

[2] N. Ahmed et al. Internet of things (IoT) for smart precision agriculture and farming in rural areas IEEE Internet Things J. (2018) 

[3]M. Shafique;T.Theocharides; C.S Bouganis; M.A Hanif;F.Khalid ;R.Hafiz ; S. Rehman. An Overview of Next-Generation Architectures for Machine Learning:Roadmap, Opportunities and Challenges in the IoT Era.InProceedingsoftheDesign,AutomationandTestinEuropeConference(DAT E),Dresden,Germany,19–23March2018;pp.827–832 

[4]J. Granjal et al. Security for the internet of things: a survey of existing protocols and open research issues IEEE Commun. Surveys Tutor. (2015) 

[5]Dave, Deep Manishkumar&Mittapally, Bharath. (2024). Data Integration and Interoperability in IOT: Challenges, Strategies and Future Direction. INTERNATIONAL JOURNAL OF COMPUTER ENGINEERING & TECHNOLOGY. 15. 45-60.