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.

Abstract: In recent years, parking of automobiles has become a tedious task owing to the growing number of vehicles. A crucial and still unsolved difficulty for such systems is how to effectively and efficiently detect and localize parking spots indicated by regular line segments around the vehicle. In actuality, a variety of negative circumstances, such as the variety of ground textures, changing lighting conditions, and unpredictable shadows cast by neighboring trees, make vision-based parking-slot recognition far more difficult than it appears. Hence, obtaining data on parking slots is a requirement for the development of parking slot detectors. We suggested a deep learning-based parking-slot-marking detection solution in this research. The detecting procedure entails creating a mask of the marking-points with the help of Single shot detector Resnet. Around 4000 surround-view photos were used from regular parking lots to create this collection.

Keywords: Tensor Flow object detection API, SSD Resnet, Deep Learning, COCO Dataset, Parking Slot.

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Abstract: The task of localizing text in scene images is a challenging task due to varying possible orientations, fonts, and backgrounds. Methods that can model a richer context of text instances outperform models that only consider limited feature representation of the text instance. TextFuseNet is one such method that uses three levels of hierarchical feature representations - character-level, word-level, and global-level features of text instances present in the images and uses them to be fused with text proposal's feature maps to make richer fused features. To further improve the richness of the context of a text instance, we use efficient attention to model the long-range dependencies across the feature maps in several locations of the network. We conduct experiments on the SynthText dataset and achieved a 9.3% improvement in average precision with attention added after the third and fourth ResNet blocks, after each FPN layer, and within the global segmentation mask compared to the baseline of no attention added to the TextFuseNet architecture. Hence demonstrating the effectiveness of adding efficient attention to the TextFuseNet framework

Keywords: Efficient Attention, Text Localization, Feature Fusing, TextFuseNet, Mask R-CNN, SynthText dataset

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Abstract: In the fields of engineering, technology, commerce, industry economics there is need to handle massive amounts of data. The capacity to analyze massive amounts of data derived from a variety of data sets continues to be a significant obstacle. Coping with a significant volume of data presents challenges for a wide variety of companies. They will be incapable to manage, alter, process, distribute, or retrieve vast volumes of data using standard software applications. As a result of the conventional software tools' high cost and extensive length of time required for data acquisition. The concept known as "big processing" refers to the management of programs that deal with very big datasets. These types of programs spend the majority of their runtime moving data from their data store to the computational node in some kind of a computer system. The primary obstacles faced by.....

Keywords:- Map reduction, big data processing, Data efficiency techniques etc.

[1]. A.Moturi, C., & K. Maiyo, S. (2012). Use of Mapreduce for Data Mining and Data Optimization on a Web Portal. International Journal of Computer Applications, 56(7), 39–43. https://doi.org/10.5120/8906-2945
[2]. Abdullah M Alghamdi, & Fahad Alghamdi. (2019). Enhancing Performance of Educational Data Using Big Data and Hadoop. International Journal of Applied Engineering Research, 14(19), 3814–3819.
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Abstract: This paper presents a system for detecting faulty solder joints on printed circuit boards created by Automatic Welding Robot. The system consists of the Raspberry Pi 4 Model B, Raspberry Pi High-Quality Camera, Raspberry Pi 16mm Telephoto lens, and Touch screen 13.3″ LCD V2 (H). Firstly, use the camera to take pictures of the printed circuit boards made from the automatic soldering robot. Next, use the labelImg tool to label the defective and missing solder joints. Use these images of solder joints to train and test the Yolov6 network to detect faulty and missing solder joints by the Google colab.....

Keywords:- Yolov6, training, testing, solder joints, detect, defect, missing.

[1]. Qianru Zhang, Meng Zhang, ChinthakaGamanayake, Chau Yuen, Zehao Geng, Hirunima Jayasekara, Chia-wei Woo, Jenny Low, Xiang Liu, Yong Liang Guan, Deep learning based solder joint defect detection on industrial printed circuit board X-ray images, Complex & Intelligent Systems (2022) 8:1525–1537, doi: https://doi.org/10.1007/s40747-021-00600-w.
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Abstract: Data conveyed by the contraptions and the clients in current circumstances is high in volume and variable in structure. Everything considered named as Big Data, it is trying to store and oversee using typical managing instruments. Standard structures store data on real servers or cloud achieving more crucial cost and space multifaceted nature. Generally, affiliations like to send their servers for data storage, yet as the volume of data increases it becomes activating for the relationship to examine the establishment required and the cost related with it, this also presents flexibility issues. The issue related with the relationship of data can be overseen by using establishments like Cloud, which outfit close to huge storage close by relationship, for instance, data security because of which data owners don't have to zero in fundamentally on it and can focus in on their standard endeavors......

Keywords:- Storage, Big, Data

[1]. Li, Yang, and Yike Guo. "Wiki-health: from quantified self to self-understanding." Future Generation Computer Systems 56 (2016): 333-359.
[2]. Huang, Qunying, et al. "Cloud computing for geosciences: deployment of GEOSS clearinghouse on Amazon's EC2." Proceedings of the ACM SIGSPATIAL international workshop on high performance and distributed geographic information systems. 2010.Sharma, Sushil, et al. "Image Steganography using Two's Complement." International Journal of Computer Applications 145.10 (2016): 39-41
[3]. Krish, K. R., et al. "On efficient hierarchical storage for big data processing." 2016 16th IEEE/ACM International Symposium on Cluster, Cloud and Grid Computing (CCGrid). IEEE, 2016.
[4]. Feller, Eugen, Lavanya Ramakrishnan, and Christine Morin. "Performance and energy efficiency of big data applications in cloud environments: A Hadoop case study." Journal of Parallel and Distributed Computing 79 (2015): 80-89.
[5]. Prasad, Bakshi Rohit, and Sonali Agarwal. "Comparative study of big data computing and storage tools: a review." International Journal of Database Theory and Application 9.1 (2016): 45-66.

Abstract: The field of study known as "the Internet of Things" (IoT) is vast and intricate. The hardware, software, and protocols used to build such systems are typically quite varied. As a result, and because of the rising volume of hastily developed Internet of Things solutions and services, the vast majority of these deployments are riddled with vulnerabilities. Recent events have shed even more emphasis on these security concerns.

Keywords:- Internet of Things, Security, privacy and trust, Wireless communications, Artificial intelligence, Decentralized architecture

[1]. Cheng, Peng & Chen, Youjia & Ding, Ming & Chen, Zhuo & Liu, Sige & Chen, Yi-Ping. (2023). Deep Reinforcement Learning for Online Resource Allocation in IoT networks: Technology, Development, and Future Challenges. IEEE Communications Magazine.
[2]. Aqeel, Muhammad & Ali, Fahad & Iqbal, Muhammad waseem& Rana, Toqir& Arif, Muhammad &Auwul, Md. (2022). A Review of Security and Privacy Concerns in the Internet of Things (IoT). Journal of Sensors. 2022. 1-20. 10.1155/2022/5724168.
[3]. Alam, Tanweer & Gupta, Ruchi. (2022). Federated Learning and Its Role in the Privacy Preservation of IoT Devices. Future Internet. 14. 246. 10.3390/fi14090246.
[4]. Nalini, T. & Krishna, T. (2022). Analysis on Security in IoT Devices—An Overview. 10.1002/9781119769026.ch2.
[5]. Abid, Muhammad. (2022). IoT Security Challenges and Mitigations: An Introduction.

Abstract: In today's digital age, data is more valuable than ever before. Businesses, governments, and individuals all rely on data to operate, and a data breach can have devastating consequences. That's why data encryption is so important. Data encryption is the process of converting data into a form that can only be read by authorized users. This is done by using an encryption algorithm and a key. The algorithm is a mathematical formula that scrambles the data, and the key is used to unscramble it......

Keywords:- Data, Encryption, Storage, Device

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Abstract: Quantum computing has emerged as a promising paradigm that potentially revolutionizes optimization in various fields of computer science and engineering. This abstract presents a comprehensive exploration of quantum computing algorithms tailored for optimization problems. Beginning with an overview of classical optimisation methods and their limitations, we delve into the principles of quantum mechanics that underpin quantum algorithms. Various quantum algorithms, including Grover's algorithm, quantum annealing, and quantum approximate optimization algorithm (QAOA), are analysed in terms of their applicability and efficiency for solving optimization problems.......

Keywords:- Quantum Computing; Optimization Problems; Algorithms; Computer Science; Engineering

[1]. Ajagekar, A., & You, F. (2019). Quantum computing for energy systems optimization: Challenges and opportunities. Energy, 179, 76-89. [2]. Ajagekar, A., & You, F. (2022). New frontiers of quantum computing in chemical engineering. Korean Journal of Chemical Engineering, 39(4), 811-820.
[3]. Brown, C., & Wilson, E. (2024). Quantum computing algorithms: A roadmap for implementation in computer science and engineering. Computers & Electrical Engineering, 42, 56-71.
[4]. Chen, L., & Wang, Q. (2024). Exploring the efficiency of quantum algorithms for optimization problems in computer science and engineering. International Journal of Quantum Computing, 5(2), 67-82.