iosr-JCE   Volume-25 ~ Issue-1 ~ Jan. - Feb. 2023

Abstract: Homogeneous Markov chain-based Markov Stationary Features (MSF) in image analysis, is getting popularity day by day. Because, it not only takes care the distribution of colors, just as the histogram method does, but also considers the spatial co-occurrence of histogram patterns. However, treating a giant database of images with a degree of heterogeneity, simple MSF techniques are not sufficient to distinguish the images. In our article, a Region Emphasized MSF (REMSF) based on non-homogeneous Markov Chain is proposed......

Key Word: Image retrieval, Markov stationary feature, Non-homogeneous Markov chain, MSF, REMSF, CBIR.

[1]. A. N. Tikle, C. Vaidya and P. Dahiwale. A Survey of Indexing Techniques for Large Scale Content-Based Image Retrieval. IEEE Int. Conf. on Electrical, Electronics, Signals, Communication and Optimization (EESCO). Visakhapatnam, India, Jan. 24-25, 2015, pp. 1-5.
[2]. K. Juneja et al. A Survey on Recent Image Indexing and Retrieval Techniques for Low-Level Feature Extraction in CBIR Systems. IEEE Int. Conf. on Computational Intelligence & Communication Technology (CICT). Ghaziabad, Pakistan, Feb. 13-14, 2015 , pp. 67-72.
[3]. Keyuri M. Zinzuvadia1, Bhavesh A. Tanawala, and Keyur N. Brahmbhatt. A Survey on Feature Based Image Retrieval Using Classification and Relevance Feedback Techniques. Int.l J. of Innovative Research in Computer and Communication Engineering. Jan. 2015, vol. 3, no. 1, pp. 508-513.
[4]. Mussarat Yasmin, Sajjad Mohsin, and Muhammad Sharif, "Intelligent Image Retrieval Techniques: A Survey. J. of Applied Research and Technology. Feb. 2014, vol. 12, no. 1 pp. 87-103.
[5]. G. Pass, and R Zabih. Histogram Refinement for Content-Based Image Retrieval. 3rd IEEE Workshop on. Applications of Computer Vision. Sarasota, Florida, Dec. 2-4, 1996, pp. 96-102.

Abstract: The academic performance of students in education is one of the important criteria for determining the quality of students going into the labor market. There are several factors affecting the academic performance of students in higher institutions of learning and these factors have seriously led to an increase in failure rates of students in their examinations. Some of the factors affecting the performance of students are the wrong time of giving orientation to students, poor guidance, poor communication skills, attitude and mentality of students towards their studies, joining of bad gangs, and wrong use of technology, just to mention a few. Academic performance of HND I students of Computer Science, Federal Polytechnic, (FEDPOFFA) were predicted using an artificial neural network model in terms of their total scores in the examination. Artificial neural networks are forecasting methods that are......

Keywords: Artificial Neural Network, regression, performance, algorithm.

[1]. Bappah A., Magaji M.A., and Usman B. (2021). Factors Affecting Students' Performance in Higher Institutions of Learning in Gombe State, Nigeria. International Journal of Research and Review, Vol., 8; Issue 3. www.ijrrjournal.com. E-ISSN: 2349-9788; P-ISSN: 2454-2237.[2]. Chima A.N. and Duroha A.E. (2019). Artificial Neural Network Application in Prediction – A Review. African Journal of Computing & ICT, Vol. 12, No 4, pp 75 85.
[3]. Lidia S., Ford L.,and Tokuro M. (2021). Machine Learning Algorithm to Predict Students' Performance: A Systematic Literature Review.TEM Journal, Volume 10, Issue 4, Pages 1919-1927, DOI:10.18421/TEM104-56.
[4]. Mohd Noor C.W., Mamat R.,Najafi G.,WanNik W.B., and Fadhil M. (2015). Application of Artificial Network for Prediction of Marine Diesel Engine Performance. 3rd International Conference of Mechanical Engineering Research (ICMER 2015). IOP Conf. Series: Materials Science and Engineering 100 (2015) 012023; doi:10.1088/1757-899X/100/1/012023.
[5]. Mustapha and Khan (2012). Factors Affecting Student's Academic Performance. Global Journal of Management and Business Research. Volume 12, Issue 9 Version 1.0. Online ISSN: 2249-4588 & Print ISSN: 0975-5853.

Abstract: Cloud computing security is a serious concern. There is a considerable requirement for infrastructure security at the network, host, application, and data levels. Data is related with each level, such as network, host, and application. This paper provides an overview and research on cloud computing, huge data storage, and various methods for cloud data security.

Keywords: Big data, storage, cloud computing, security

[1]. Glass Erin, (2022) https://www.digitalocean.com/community/tutorials/a-general-introduction-to-cloud-computing. Retrieved December, 2022.
[2]. Pansotra, E.A & Singh, E.S.P. (2015). Cloud Security Algorithms. International Journal of Security and its Applications, 9, 353-360.
[3]. Potey, M. M., Dhote, C & Sharma, D.H (2016). Homomorphic Encryption for security of Cloud Data. Procedia Computer Science, 79,175 – 181.
[4]. Li, Y., Gai., K., Qui L, M. & Zhao, H. (2017). Intelligent cryptography approach for secure distributed big data storage in cloud computing, Information Sciences, Volume 387,2017,Pages 103-115,ISSN 0020-0255,, https://doi.org/10.1016/j.ins.2016.09.005, (https://www.sciencedirect.com/science/article/pii/S0020025516307319)
[5]. Ghazali, Osman, 2017 Security Measurement as a Trust in Cloud Computing Service Selection and Monitoring. Journal of Advances in Information Technology Vol. 8, No. 2.