Abstract Deep learning‐based applications for disease detection are essential tools experts to effectively diagnose diseases at different stages. In this article, a new approach based on an evidence fusion theory is proposed, allowing the combination of set deep learning classifiers provide more accurate results. The main contribution work application Dempster–Shafer five pre trained convolutional neural networks including VGG16, Xception, InceptionV3, ResNet50, and DenseNet201 diagnosis...

The Internet of Things (IoT) is prone to malware assaults due its simple installation and autonomous operating qualities. IoT devices have become the most tempting targets well-known vulnerabilities such as weak, guessable, or hard-coded passwords, a lack secure update procedures, unsecured network connections. Traditional static detection analysis methods been shown be unsatisfactory solutions understanding behavior for mitigation prevention. Deep learning models made huge strides in realm...

Abstract By the start of 2020, novel coronavirus (COVID‐19) had been declared a worldwide pandemic, and because its infectiousness severity, several strands research have focused on combatting ongoing spread. One potential solution to detecting COVID‐19 rapidly effectively is by analyzing chest X‐ray images using Deep Learning (DL) models. Convolutional Neural Networks (CNNs) presented as particularly efficient techniques for early diagnosis, but most still include limitations. In this...

Due to the sharp increase in global industrial production, as well over-exploitation of land and sea resources, quality drinking water has deteriorated considerably. Furthermore, nowadays, many supply systems serving growing human populations suffer from shortages since rivers, lakes, aquifers are drying up because climate change. To cope with these serious threats, smart management great demand ensure vigorous control quantity water. Indeed, monitoring is essential today it allows real-time...

Alzheimer's disease is a severe brain disorder that causes harm in various areas and leads to memory damage. The limited availability of labeled medical data poses significant challenge for accurate detection. There critical need effective methods improve the accuracy detection, considering scarcity data, complexity disease, constraints related privacy. To address this challenge, our study leverages power Big Data form pre-trained Convolutional Neural Networks (CNNs) within framework...

Sinus diseases are inflammations or infections of the sinuses that significantly impact patient quality life. They cause nasal congestion, facial pain, headaches, thick discharge, and a reduced sense smell. However, accurately diagnosing these is challenging due to multiple factors, including inadequate adherence pre-diagnostic protocols. By leveraging latest developments in Artificial Intelligence (AI), there exists substantial opportunity improve precision effectiveness classification...

The synergy between the Internet of Things (IoT) and big data technologies has resulted in great development multiple smart applications varied fields such as energy management, environmental monitoring, elderly healthcare, etc. Due to increasing demand for applications, opting a flexible scalable software architecture that supports accelerates these is dire need nowadays. As an effective solution continuously maintain, upgrade, scale IoT-based microservices paradigm been adopted...

Deep learning-based disease diagnosis applications are essential for accurate at various stages. However, using personal data exposes traditional centralized learning systems to privacy concerns. On the other hand, by positioning processing resources closer device and enabling more effective analyses, a distributed computing paradigm has potential revolutionize diagnosis. Scalable architectures analytics also crucial in healthcare, where results must have low latency high dependability...

Having access to safe water and using it properly is crucial for human well-being, sustainable development, environmental conservation. Nonetheless, the increasing disparity between demands natural freshwater resources causing scarcity, negatively impacting agricultural industrial efficiency, giving rise numerous social economic issues. Understanding managing causes of scarcity quality degradation are essential steps toward more management use. In this context, continuous Internet Things...

By the start of 2020, novel coronavirus disease (COVID-19) has been declared a worldwide pandemic. Because severity this infectious disease, several kinds research have focused on combatting its ongoing spread. One potential solution to detect COVID-19 is by analyzing chest X-ray images using Deep Learning (DL) models. In context, Convolutional Neural Networks (CNNs) are presented as efficient techniques for early diagnosis. study, we propose randomly initialized CNN architecture recognition...

Recently, Convolutional Neural Networks (CNNs) have made a great performance for remote sensing image classification. Plant recognition using CNNs is one of the active deep learning research topics due to its added-value in different related fields, especially environmental conservation and natural areas preservation. Automatic plants protected helps surveillance process these zones ensures sustainability their ecosystems. In this work, we propose an Enhanced Randomly Initialized Network...

The Internet of Things (IoT) has been introduced as a breakthrough technology that integrates intelligence into everyday objects, enabling high levels connectivity between them. As the IoT networks grow and expand, they become more susceptible to cybersecurity attacks. A significant challenge in current intrusion detection systems for includes handling imbalanced datasets where labeled data are scarce, particularly new rare types cyber Existing literature often fails detect such...

With the rapid rise of Internet Things (IoT), ensuring security IoT devices has become essential. One primary challenges in this field is that new types attacks often have significantly fewer samples than more common attacks, leading to unbalanced datasets. Existing research on detecting intrusions these labeled datasets primarily employs Convolutional Neural Networks (CNNs) or conventional Machine Learning (ML) models, which result incomplete detection, especially for attacks. To handle...

Graph Mamba, a powerful graph embedding technique, has emerged as cornerstone in various domains, including bioinformatics, social networks, and recommendation systems. This survey represents the first comprehensive study devoted to address critical gaps understanding its applications, challenges, future potential. We start by offering detailed explanation of original Mamba architecture, highlighting key components underlying mechanisms. Subsequently, we explore most recent modifications...

Deep learning-based disease diagnosis applications are essential for accurate at various stages. However, using personal data exposes traditional centralized learning systems to privacy concerns. On the other hand, by positioning processing resources closer device and enabling more effective analyses, a distributed computing paradigm has potential revolutionize diagnosis. Scalable architectures analytics also crucial in healthcare, where results must have low latency high dependability...