In recent years, advances in technology in several industries have resulted in massive data collections on the web. It raises worries about large data security and protection. The advent of Blockchain technology has caused a revolution in the security field for different applications. The distributed ledger is stored on each Blockchain node, which enhances security and data transparency. On the Blockchain network, illegal users are not authorized to undertake any fault transactions. In this article, we will discuss how Blockchain may be employed to secure the big data. We explain the problems

The explosive growth of mobile internet services and demand for data connectivity boosts the innovation and development in Radio Access Network (RAN) to define how next generation mobile networks will look like. Continuous improvement in existing RAN is crucial to meet very strict speed and latency requirements by different mobile applications with minimum investments. Exploiting the advancement in Machine Learning and AI-driven algorithms is essential to tackle these challenges in different functions within the RAN domain. In this paper we surveyed how to leverage different clustering

Lung cancer (LC) represents most of the cancer incidences in the world. There are many types of LC, but Lung Adenocarcinoma (LUAD) is the most common type. Although RNA-seq and microarray data provide a vast amount of gene expression data, most of the genes are insignificant to clinical diagnosis. Feature selection (FS) techniques overcome the high dimensionality and sparsity issues of the large-scale data. We propose a framework that applies an ensemble of feature selection techniques to identify genes highly correlated to LUAD. Utilizing LUAD RNA-seq data from the Cancer Genome Atlas (TCGA)

Food is an integral aspect of daily life in all cultures. It highly affects people's diets, eating behaviors, and overall health. People with poor eating habits are usually overweight or obese, which leads to chronic diseases such as diabetes and cardiovascular disease. Today, the classification of food images has several uses in managing medical conditions and dieting. Deep convolutional neural network (DCNN) architectures provide the foundation for the most recent food recognition models. However, DCNNs are computationally expensive due to high computation time and memory requirements. In

Blockchain is currently one of the most widely discussed inventions in the information and communication technology industry. It is a pillar of the fourth industrial revolution and it is a cryptographically demanding technology that is regarded as one of the most influential topics in academia. Many blockchain platforms currently utilize third-party cryptographic libraries that offer many cryptographic primitives in order to ensure users' protection against cyber-attacks. The Networking and Cryptography library (NaCl) is an open-source library for cryptographic primitives. NaCl is known to be

Customer churn poses a formidable challenge within the Telecom industry, as it can result in significant revenue losses. In this research, we conducted an extensive study aimed at developing a viable customer churn prediction method. Our method utilizes the Apriori algorithm's strength to identify the key causes of customer churn. In the pursuit of this goal, we utilized multiple machine learning predictive models. All of which were developed from the insights gleaned from the Apriori algorithm's feature extraction for churning customers. This extensive analysis encompassed a spectrum of

Epilepsy is a chronic nervous disorder, which disturbs the normal daily routine of an epileptic patient due to sudden seizure onset that may cause loss of consciousness. Seizures are periods of aberrant brain activity patterns. Early prediction of an epileptic seizure is critical for those who suffer from it as it will give them time to prepare for an incoming seizure and alert anyone in their close circle of contacts to aid them. This has been an active field of study, powered by the decreasing cost of non-invasive electroencephalogram (EEG) collecting equipment and the rapid evolution of

The global rise in Metabolic dysfunction-associated steatotic liver disease (MASLD)/Metabolic dysfunction-associated steatohepatitis (MASH) highlights the urgent necessity for noninvasive biomarkers to detect these conditions early. To address this, we endeavored to construct a diagnostic model for MASLD/MASH using a combination of bioinformatics, molecular/biochemical data, and machine learning techniques. Initially, bioinformatics analysis was employed to identify RNA molecules associated with MASLD/MASH pathogenesis and enriched in ferroptosis and exophagy. This analysis unveiled specific

This paper presents a comprehensive methodology for gender detection using hand palm images, leveraging image processing techniques and PySpark for scalable and efficient processing. The approach encompasses a meticulous image preprocessing pipeline, incorporating essential stages like grayscale conversion, the application ofthe Difference of Gaussians (DoG) filter, and adaptive histogram equalization. This approach not only refmes image features but also ensures scalability, accommodating large datasets seamlessly. After preprocessing of hand images, the VGG19 model is employed as a powerful

Database security has grown to a necessary level of importance today, characterized by the escalating demand for data storage. The surge in data storage requirements, propelled by technological advancements, has led to increased attacks targeting database security vulnerabilities. While multiple storage and protection methods have emerged in tandem with evolving technologies, the discovery of security vulnerabilities has led to new attack vectors. This study aims to repair this gap by securing and fortifying the security of stored data within databases. Fundamental to information security, the