Automotive steering wheel switches technologies are evolving to give easy access to the several interior or exterior functions. This is worth a deep analysis for the current trends in order not to become unintuitive for the driver due to the increasing number of buttons. Through different technologies particularly the capacitive ones, range of innovative solutions have been developed like reconfigurable buttons on the steering wheel to offer commanding several functions twice or triple the number of allocated push buttons. In this paper, we address the problem in a different freely way to

The prosperous advancement in Medical Internet of Things (MIoT) technologies has hastened the development of healthcare systems. MIoT improves the traditional medical facilities through periodically monitor of patient's health records. Electronic Medical Records (EMRs) are sensitive private data and needs efficient secure and private schemes that interchange these EMRs between healthcare providers and patients. Most of the current privacy preserving schemes do not provide the desired privacy level and suffer from computation and communication overheads. The length of an IDentity-based Strong

The increasing sophistication of cyber attacks necessitates effective intrusion detection systems. We propose a novel intrusion detection method integrating deep learning with big data management using Apache Spark. Leveraging the comprehensive CSE-CIC-IDS2018 dataset, we apply extensive data preprocessing, including handling missing and unreliable values, duplicates, and redundant columns. In addition, implementation of a Random Forest based feature importance approach is derived to prioritize the most impactful Features. Furthermore, stratified k-fold cross-validation is used for a model

Cervical cancer (CC) is considered a significant global health threat to women therefore there is a need for personalized treatment strategy based on individual-specific gene expression patterns to enhance recovery and survival rates. Although a few studies have linked bisphosphoglycerate mutase (BPGM) expression with CC, its precise role in CC progression remains unclear. In this study, we conducted an integrated analysis for both bulk and single-cell RNA sequencing data to investigate the involvement of BPGM in CC. On the bulk RNA level, the Wilcoxon test result showed a significant

This paper proposes using entity2rec [1] which utilizes knowledge graph-based embeddings (node2vec) instead of traditional embedding layers in embedding based recommender systems. This opens the door to increasing the accuracy of some of the most implemented recommender systems running in production in many companies by just replacing the traditional embedding layer with node2vec graph embedding without the risk of completely migrating to newer SOTA systems and risking unexpected performance issues. Also, Graph embeddings will be able to incorporate user and item features which can help in

Nowadays events stream processing is one of the top demanding field(s) because of the business urgent need for ongoing real time analytics & decisions. Most business domains avail huge amount of data aiming to make use of each data point efficiently. Corporate(s) have a cloud of events vary from internal business transactions, social media feeds, IoT devices logs,.. etc. In this paper we would discuss state of the art event stream processing technologies using cloud of events by summarizing event stream processing definition, data flow architectures, common use cases, frameworks and

The exploration of sentiment analysis in multilingual contexts, particularly through the integration of deep learning techniques and knowledge graphs, represents a significant advance in language processing research. This study specifically concentrates on the Arabic language, addressing the challenges presented by its morphological complexity. While the primary focus is Arabic, the research also includes a comprehensive review of related work in other languages such as Bangla and Chinese. This contextualizes the challenges and solutions found in Arabic sentiment analysis within a broader

Skin cancer is the most annoying type of cancer diagnosis according to its fast spread to various body areas, so it was necessary to establish computer-assisted diagnostic support systems. State-of-the-art classifiers based on convolutional neural networks (CNNs) are used to classify images of skin cancer. This paper tries to get the most accurate model to classify and detect skin cancer types from seven different classes using deep learning techniques; ResNet-50, VGG-16, and the merged model of these two techniques through the concatenate function. The performance of the proposed model was

Because of the widespread availability of low-cost printers and scanners, document forgery has become extremely popular. Watermarks or signatures are used to protect important papers such as certificates, passports, and identification cards. Identifying the origins of printed documents is helpful for criminal investigations and also for authenticating digital versions of a document in today’s world. Source printer identification (SPI) has become increasingly popular for identifying frauds in printed documents. This paper provides a proposed algorithm for identifying the source printer and

The open Source Software (OSS) became the backbone of the most heavily used technologies, including operating systems, cloud computing, AI, Blockchain, Bigdata Systems, IoT, and many more. Although the OSS individual contributors are the primary power for developing the OSS projects, they do not contribute to the OSS project's decisionmaking as much as their contributions in the OSS Projects development. This paper proposes a framework to democratize the OSS Project's decision-making using a blockchain-related technology called Decentralized Autonomous Organization (DAO). Using DAO