Abstract Medical images possess significant importance in diagnostics when it comes to healthcare systems. These contain confidential and sensitive information such as patients’ X-rays, ultrasounds, computed tomography scans, brain images, magnetic resonance imaging. However, the low security of communication channels loopholes storage systems hospitals or medical centres put these at risk being accessed by unauthorized users who illegally exploit them for non-diagnostic purposes. In...

A brain tumour is one of the major reasons for death in humans, and it tenth most common type that affects people all ages. However, if detected early, treatable types tumours. Brain tumours are classified using biopsy, which not usually performed before definitive surgery. An image classification technique diseases important accelerating treatment process avoiding surgery errors from manual diagnosis by radiologists. The advancement technology machine learning (ML) can assist radiologists...

The Vision Transformer (ViT) architecture has been remarkably successful in image restoration. For a while, Convolutional Neural Networks (CNN) predominated most computer vision tasks. Now, both CNN and ViT are efficient approaches that demonstrate powerful capabilities to restore better version of an given low-quality format. In this study, the efficiency restoration is studied extensively. architectures classified for every task Seven tasks considered: Image Super-Resolution, Denoising,...

The evolution of wireless and mobile communication from 0G to the upcoming 5G gives rise data sharing through Internet. This transfer via open public networks are susceptible several types attacks. Encryption is a method that can protect information hackers hence confidential be secured cryptosystem. Due increased number cyber attacks, encryption has become an important component modern-day communication. In this article, new image algorithm presented using chaos theory dynamic substitution....

Visual selective image encryption can both improve the efficiency of algorithm and reduce frequency severity attacks against data.In this article, a new form is proposed based on keys derived from Deoxyribonucleic Acid (DNA) plaintext image.The scheme results in chaotic visual order to make ensure that robust secure various kinds attacks, initial conditions maps utilized are generated random DNA sequence as well via an SHA-512 hash function.To increase key space, three different single...

Vehicular ad hoc networks (VANETs) play an important role as enabling technology for future cooperative intelligent transportation systems (CITSs). Vehicles in VANETs share real-time information about their movement state, traffic situation, and road conditions. However, are susceptible to the cyberattacks that create life threatening situations and/or cause congestion. Intrusion detection (IDSs) rely on cooperation between vehicles detect intruders, were most suggested security solutions...

Satellite images have drawn increasing interest from a wide variety of users, including business and government, ever since their increased usage in important fields ranging weather, forestry agriculture to surface changes biodiversity monitoring. Recent updates the field also introduced various deep learning (DL) architectures satellite imagery as means extracting useful information. However, this new approach comes with its own issues, fact that many users utilize ready-made cloud services...

Early cancer identification is regarded as a challenging problem in prevention for the healthcare community. In addition, ensuring privacy-preserving data becomes more difficult with growing demand sharing these data. This study proposes novel non-invasive detection method using Deep Learning (DL). Initially, clinical collected over Internet via wireless channels diagnostic purposes. It paramount to secure personal against eavesdropping by unauthorized users that may exploit it personalized...

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...

Extensive research has been conducted on healthcare technology and service advancements during the last decade. The Internet of Medical Things (IoMT) demonstrated ability to connect various medical apparatus, sensors, specialists ensure best treatment in a distant location. Patient safety improved, prices have decreased dramatically, services become more approachable, operational efficiency industry increased. This paper offers recent review current future applications, security, market...

Abstract In many cases, images contain sensitive information and patterns that require secure processing to avoid risk. It can be accessed by unauthorized users who illegally exploit them threaten the safety of people’s life property. Protecting privacies has quickly become one biggest obstacles prevent further exploration image data. this paper, we propose a novel privacy-preserving scheme protect within images. The proposed approach combines deoxyribonucleic acid (DNA) sequencing code,...

Road detection technology plays an essential role in a variety of applications, such as urban planning, map updating, traffic monitoring and automatic vehicle navigation. Recently, there has been much development detecting roads high-resolution (HR) satellite images based on semantic segmentation. However, the objects being segmented are small size, not all information is equally important when making decision. This paper proposes novel approach to road segmentation edge detection. Our aims...

In the dynamic field of Industrial Internet Things (IIoT), networks are increasingly vulnerable to a diverse range cyberattacks. This vulnerability necessitates development advanced intrusion detection systems (IDSs). Addressing this need, our research contributes existing cybersecurity literature by introducing an optimized Intrusion Detection System based on Deep Transfer Learning (DTL), specifically tailored for heterogeneous IIoT networks. Our framework employs tri-layer architectural...

The Internet of Things (IoT) is a global network that connects large number smart devices. MQTT de facto standard, lightweight, and reliable protocol for machine-to-machine communication, widely adopted in IoT networks. Various devices within these networks are employed to handle sensitive information. However, the scale openness make them highly vulnerable security breaches attacks, such as eavesdropping, weak authentication, malicious payloads. Hence, there need advanced machine learning...

ChatGPT, a groundbreaking natural language processing technology released few months ago, has attracted significant attention due to its remarkable capabilities. This AI milestone urged researchers, industry, decision-makers, and governments examine this technology, including implications, threats, benefits. Despite the short period since release, several researchers have examined ChatGPT from different perspectives. paper presents comprehensive review of highlighting technical novelties...

Video streaming-based real-time vehicle identification and license plate recognition systems are challenging to design deploy in terms of processing on edge, dealing with low image resolution, high noise, identification. This paper addresses these issues by introducing a novel multi-stage, real-time, deep learning-based system. The system is based set algorithms that efficiently integrate two object detectors, an classifier, multi-object tracker recognize car models plates. information...