The integration and emerging adoption of machine learning (ML) algorithms in healthcare big data has revolutionized clinical decision-making, predictive analytics, and real-time medical diagnostics. However, the application of machine learning in healthcare big data faces computational challenges, particularly in efficiently processing and training on large-scale, high-velocity data generated by healthcare organizations worldwide. In response to these issues, this study critically reviews and examines current state-of-the-art advancements in machine learning algorithms and big data frameworks within healthcare analytics, with a particular emphasis on solutions addressing data volume and velocity. The reviewed literature is categorized into three key areas: (1) efficient techniques, arithmetic operations, and dimensionality reduction; (2) advanced and specialized processing hardware; and (3) clustering and parallel processing methods. Key research gaps and open challenges are identified based on the evaluation of the literature across these categories, and important future research directions are discussed in detail. Among the several proposed solutions are the utilization of federated learning and decentralized data processing, as well as efficient parallel processing through big data frameworks such as Apache Spark, neuromorphic computing, and multi-swarm large-scale optimization algorithms; these highlight the importance of interdisciplinary innovations in algorithm design, hardware efficiency, and distributed computing frameworks, which collectively contribute to faster, more accurate, and resource-efficient AI-driven healthcare big data analytics and applications. This research supports the UNSDG 3 (Good Health and Well-Being) and UNSDG 9 (Industry, Innovation and Infrastructure) by integration of machine learning in healthcare big data and promoting product innovation in the healthcare industry, respectively.

A content based video retrieval (CBVR)framework is built in this paper. One of the essential features of video retrieval process and CBVR is a color value. The discrete cosine transform (DCT) is used to extract a query video features to compare with the video features stored in our database. Average result of 0.6475 was obtained by using the DCT after implementing it to the database we created and collected, and on all categories. This technique was applied on our database of video, Check 100 database videos, 5 videos in each category.

The Internet played - and still - the key that continuously changing our ways of interaction with people. As a result, several electronic services had emerged allowing businesses to grow by effectively allowing wide and easy interaction with customers and other businesses. The security and privacy of information over the internet in general and in electronic services providers have been the focus of widely published studies and researches, such that several software and hardware based solutions or hybrid of both is required. E-banking services grown significantly in the last decade where all financial matters of customers and businesses can be done online, and therefore, e-banking security and privacy is important. In this paper, I propose the use of Whirlpool hash function to enhance the security of e-bank service providers by encrypting customer’s card sensitive information. In addition, based on the review of several articles, I found that Whirlpool outperformed several hashing functions and resists several well-known attacks.

The incorporation and growing uptake of machine learning (ML) algorithms in big data in healthcare has transformed real-time medical diagnostics, predictiv

In recent years, there are many authentication protocols that are used in the accessing of the sensitive and private data. However most of those methods have many weaknesses by which data can be extracted and used by unauthorized people this due to the use of a one level authentication that may face many attacks. This paper presents an authentication method that involves three levels of user authentication; the first two levels use two level passwords authentication together with a Personal Identification Number (third level) for every operation in Electronic Banking system. Hash functions -Secure Hash Algorithms- and Cyclic Redundancy check (32) for the generations of Personal Identification Number are used. This system eliminates the problem that is related with single level password authentication system and improves the security by using Personal Identification Number.Â