The demand for electric vehicles (EVs) is growing rapidly. This requires an ecosystem that meets the user’s needs while preserving security. The rich data obtained from electric vehicle stations are powered by the Internet of Things (IoT) ecosystem. This is achieved through us of electric vehicle charging station management systems (EVCSMSs). However, the risks associated with cyber-attacks on IoT systems are also increasing at the same pace. To help in finding malicious traffic, intrusion detection systems (IDSs) play a vital role in traditional IT systems. This paper proposes a classifier

Most artificial intelligence (AI) research and innovations have concentrated in high-income countries, where imaging data, IT infrastructures and clinical expertise are plentiful. However, slower progress has been made in limited-resource environments where medical imaging is needed. For example, in Sub-Saharan Africa, the rate of perinatal mortality is very high due to limited access to antenatal screening. In these countries, AI models could be implemented to help clinicians acquire fetal ultrasound planes for the diagnosis of fetal abnormalities. So far, deep learning models have been

The purpose of this study is to investigate the gradual incorporation of deep learning in the dental healthcare system, offering an easy and efficient diagnosis. For that, an electronic search was conducted in the Institute of Electrical and Electronics Engineers (IEEE) Xplore, ScienceDirect, Journal of Dentistry, Health Informatics Journal, and other credible resources. The studies varied with their tools and techniques used for the diagnosis while coping with the rapid deep-learning evolving base, with different types of conducting tools and analysis for the data. An inclusion criterion was

Wireless sensor networks (WSNs) have garnered much attention in the last decades. Nowadays, the network contains sensors that have been expanded into a more extensive network than the internet. Cost is one of the issues of WSNs, and this cost may be in the form of bandwidth, computational cost, deployment cost, or sensors’ battery (sensor life). This paper proposes a dual-level sensor selection (DLSS) model used to reduce the number of sensors forming WSNs. The sensor reduction process is performed at two consecutive levels. First, a combination of the Fisher score method and ANOVA test at the

Big data is a vast amount of structured and unstructured data that must be dealt with on a regular basis. Dimensionality reduction is the process of converting a huge set of data into data with tiny dimensions so that equal information may be expressed easily. These tactics are frequently utilized to improve classification or regression challenges while dealing with machine learning issues. To achieve dimensionality reduction for huge data sets, this paper offers a hybrid particle swarm optimization-rough set PSO-RS and Mayfly algorithm-rough set MA-RS. A novel hybrid strategy based on the

Active Directory Domain is a Microsoft service that allows and facilitates the centralized administration of all workstations and servers in any environment. Due to the wide use and adoption of this service, it has become a target for many attackers. Active Directory attacks have evolved through years. The attacks target different functions and features provided by Active Directory. In this paper, we provide insights on the criticality, impact, and detection of Active Directory attacks. We review the different Active Directory attacks. We introduce the steps of the Active Directory attack and

Type 2 diabetes mellitus (T2D) remains a critical health concern, particularly in its early disease stages such as prediabetes. Understanding these early stages is paramount for improving patient outcomes. Multiomics data integration tools offer promise in unraveling the underlying mechanisms of T2D. The advent of high-throughput technology and the increasing availability of multiomics data has led to the development of several statistical and network-based integration methods. However, the performance of such methods varies, requiring their output evaluation in an unbiased manner. Here, we

Circulatory failure, also known as shock, is a critical condition that can have serious consequences for one's health. Early detection and timely intervention are crucial for improving patient outcomes. Machine learning (ML) models have shown promise in predicting circulatory failure based on clinical data. In our study, we examined different machine learning (ML) models to predict circulatory failure in patients who were admitted to the intensive care unit (ICU) with suspected circulatory problems. The ML model we developed used various algorithms like random forest, LG, XGB, Decision Tree

This paper proposes Parallelized Linear Time-Variant Acceleration Coefficients and Inertial Weight of Particle Swarm Optimization algorithm (PLTVACIW-PSO). Its designed has introduced the benefits of Parallel computing into the combined power of TVAC (Time-Variant Acceleration Coefficients) and IW (Inertial Weight). Proposed algorithm has been tested against linear, non-linear, traditional, and multiswarm based optimization algorithms. An experimental study is performed in two stages to assess the proposed PLTVACIW-PSO. Phase I uses 12 recognized Standard Benchmarks methods to evaluate the

The emergence of very sophisticated stem therapy or regenerative medicine can be attributed to the identification and extraction of pluripotent ESCs. This breakthrough resolved the ethical dilemma linked to embryonic stem cells and facilitated the initiation of clinical trials and subsequent rapid progress in the subsequent years. This chapter explores the prospect of stem cell therapy as a treatment for Parkinson's disease (PD), a degenerative neurological condition. The text commences by providing an overview of stem cells, clarifying their distinct regenerative characteristics and their