B-UMCS: Blockchain-enabled Unified Medical Consultancy Service.

The advent of blockchain technology within the healthcare domain has signified a paradigm shift, transitioning from an emerging trend to an essential infrastructure component that ensures decentralization, transparency, integrity, and persistent availability. Despite its potential, the healthcare sector has not fully capitalized on the vast array of benefits blockchain technology offers. Most existing works utilized blockchain technology within a specific healthcare entity's services but not among several healthcare organizations.

A robust audio steganography technique based on image encryption using different chaotic maps.

The development of innovative methods for concealing critical data in multimedia files has exploded in information security in recent years. Cryptography and steganography cannot be used alone to protect data; rather, they can be combined and used in a single system. Audio steganography is among the most important information security techniques.

A Data-Centric Approach to improve performance of deep learning models.

The Artificial Intelligence has evolved and is now associated with Deep Learning, driven by availability of vast amount of data and computing power. Traditionally, researchers have adopted a Model-Centric Approach, focusing on developing new algorithms and models to enhance performance without altering the underlying data. However, Andrew Ng, a prominent figure in the AI community, has recently emphasized on better (quality) data rather than better models, which has given birth to Data Centric Approach, also known as Data Oriented technique.

Enhancing automated vehicle identification by integrating YOLO v8 and OCR techniques for high-precision license plate detection and recognition.

Vehicle identification systems are vital components that enable many aspects of contemporary life, such as safety, trade, transit, and law enforcement. They improve community and individual well-being by increasing vehicle management, security, and transparency. These tasks entail locating and extracting license plates from images or video frames using computer vision and machine learning techniques, followed by recognizing the letters or digits on the plates.

Securing Internet-of-Medical-Things networks using cancellable ECG recognition.

Reinforcement of the Internet of Medical Things (IoMT) network security has become extremely significant as these networks enable both patients and healthcare providers to communicate with each other by exchanging medical signals, data, and vital reports in a safe way. To ensure the safe transmission of sensitive information, robust and secure access mechanisms are paramount. Vulnerabilities in these networks, particularly at the access points, could expose patients to significant risks.

Securing web applications against XSS and SQLi attacks using a novel deep learning approach.

Modern web application development involves handling enormous amounts of sensitive and consequential data. Security is, therefore, a crucial component of developing web applications. A web application's security is concerned with safeguarding the data it processes.

Deep learning innovations in diagnosing diabetic retinopathy: The potential of transfer learning and the DiaCNN model.

Diabetic retinopathy (DR) is a significant cause of vision impairment, emphasizing the critical need for early detection and timely intervention to avert visual deterioration. Diagnosing DR is inherently complex, as it necessitates the meticulous examination of intricate retinal images by experienced specialists. This makes the early diagnosis of DR essential for effective treatment and prevention of eventual blindness.

Quad-port MIMO antenna with high isolation characteristics for sub 6-GHz 5G NR communication.

A four-port MIMO antenna with high isolation is presented. The antenna is primarily envisioned to cover the n48 band of Frequency Range-1 (FR-1) with TDD duplex mode. The engineered antenna has electrical dimensions of 90 × 90 × 1.

An efficient watermarking algorithm for digital audio data in security applications.

Transform-domain audio watermarking systems are more robust than time-domain systems. However, the main weakness of these systems is their high computational cost, especially for long-duration audio signals. Therefore, they are not desirable for real-time security applications where speed is a critical factor.

Automated diagnosis of EEG abnormalities with different classification techniques.

Automatic seizure detection and prediction using clinical Electroencephalograms (EEGs) are challenging tasks due to factors such as low Signal-to-Noise Ratios (SNRs), high variance in epileptic seizures among patients, and limited clinical data constraints. To overcome these challenges, this paper presents two approaches for EEG signal classification. One of these approaches depends on Machine Learning (ML) tools.

A comprehensive taxonomy on multimedia video forgery detection techniques: challenges and novel trends.

Thousands of videos are posted on websites and social media every day, including Twitter, Facebook, WhatsApp, Instagram, and YouTube. Newspapers, law enforcement publications, criminal investigations, surveillance systems, Banking, the museum, the military, imaging in medicine, insurance claims, and consumer photography are just a few examples of places where important visual data may be obtained. Thus, the emergence of powerful processing tools that can be easily made available online poses a huge threat to the authenticity of videos.

Integrating Digital Twins with IoT-Based Blockchain: Concept, Architecture, Challenges, and Future Scope.

In recent years, there have been concentrations on the Digital Twin from researchers and companies due to its advancement in IT, communication systems, Cloud Computing, Internet-of-Things (IoT), and Blockchain. The main concept of the DT is to provide a comprehensive tangible, and operational explanation of any element, asset, or system. However, it is an extremely dynamic taxonomy developing in complication during the life cycle that produces an enormous quantity of the engendered data and information from them.

Comparison of two newly suggested power, refrigeration, and hydrogen production, for moving towards sustainability schemes using improved solar-powered evolutionary algorithm optimization.

In the third millennium, developing countries will confront significant environmental problems such as ozone depletion, global warming, the shortage of fossil resources, and greenhouse gas emissions. This research looked at a multigenerational system that can generate clean hydrogen, fresh water, electricity, heat, and cooling. The system's components include Rankine and Brayton cycles, an Organic Rankine Cycle (ORC), flash desalination, an Alkaline electrolyzer, and a solar heliostat.

E2E-RDS: Efficient End-to-End Ransomware Detection System Based on Static-Based ML and Vision-Based DL Approaches.

Nowadays, ransomware is considered one of the most critical cyber-malware categories. In recent years various malware detection and classification approaches have been proposed to analyze and explore malicious software precisely. Malware originators implement innovative techniques to bypass existing security solutions.

Impact of Fireworks Industry Safety Measures and Prevention Management System on Human Error Mitigation Using a Machine Learning Approach.

In the fireworks industry (FI), many accidents and explosions frequently happen due to human error (HE). Human factors (HFs) always play a dynamic role in the incidence of accidents in workplace environments. Preventing HE is a main challenge for safety and precautions in the FI.

Simultaneous Super-Resolution and Classification of Lung Disease Scans.

Acute lower respiratory infection is a leading cause of death in developing countries. Hence, progress has been made for early detection and treatment. There is still a need for improved diagnostic and therapeutic strategies, particularly in resource-limited settings.

A Novel Design of Spike-Shaped Miniaturized 4 × 4 MIMO Antenna for Wireless UWB Network Applications Using Characteristic Mode Analysis.

In this article, a 4 × 4 miniaturized UWB-MIMO antenna with reduced isolation is designed and analyzed using a unique methodology known as characteristic mode analysis. To minimize the antenna's physical size and to improve the isolation, an arrangement of four symmetrical radiating elements is positioned orthogonally. The antenna dimension is 40 mm × 40 mm (0.

Vision Transformers in Image Restoration: A Survey.

The Vision Transformer (ViT) architecture has been remarkably successful in image restoration. For a while, Convolutional Neural Networks (CNN) predominated in most computer vision tasks. Now, both CNN and ViT are efficient approaches that demonstrate powerful capabilities to restore a better version of an image given in a low-quality format.

Hybrid Multilevel Thresholding Image Segmentation Approach for Brain MRI.

A brain tumor is an abnormal growth of tissues inside the skull that can interfere with the normal functioning of the neurological system and the body, and it is responsible for the deaths of many individuals every year. Magnetic Resonance Imaging (MRI) techniques are widely used for detection of brain cancers. Segmentation of brain MRI is a foundational process with numerous clinical applications in neurology, including quantitative analysis, operational planning, and functional imaging.

Studies of Performance of CsTiIBr (Where x = 0 to 6)-Based Mixed Halide Perovskite Solar Cell with CdS Electron Transport Layer.

The present research work represents the numerical study of the device performance of a lead-free CsTiIBr-based mixed halide perovskite solar cell (PSC), where x = 1 to 5. The open circuit voltage (V) and short circuit current (J) in a generic TCO/electron transport layer (ETL)/absorbing layer/hole transfer layer (HTL) structure are the key parameters for analyzing the device performance. The entire simulation was conducted by a SCAPS-1D (solar cell capacitance simulator- one dimensional) simulator.

Secure biometric systems based on bio-signals and DNA encryption of optical spectrograms.

Recently, biometrics has become widely used in applications to verify an individual's identity. To address security issues, biometrics presents an intriguing window of opportunity to enhance the usability and security of the Internet of Things (IoT) and other systems. It can be used to secure a variety of newly emerging IoT devices.

Investigations on Stub-Based UWB-MIMO Antennas to Enhance Isolation Using Characteristic Mode Analysis.

In this article, very compact 2 × 2 and 4 × 4 MIMO (Multiple-Input and Multiple output) antennas are designed with the help of Characteristics Mode Analysis to enhance isolation between the elements for UWB applications. The proposed antennas are designed with Characteristic Mode Analysis (CMA) to gain physical insight and also to analyze the dominant mode. To improve isolation and minimize the mutual coupling between radiating elements, elliptical shaped stubs are used.

A Novel Dynamic Bit Rate Analysis Technique for Adaptive Video Streaming over HTTP Support.

Recently, there has been an increase in research interest in the seamless streaming of video on top of Hypertext Transfer Protocol (HTTP) in cellular networks (3G/4G). The main challenges involved are the variation in available bit rates on the Internet caused by resource sharing and the dynamic nature of wireless communication channels. State-of-the-art techniques, such as Dynamic Adaptive Streaming over HTTP (DASH), support the streaming of stored video, but they suffer from the challenge of live video content due to fluctuating bit rate in the network.

Cancelable biometric system for IoT applications based on optical double random phase encoding.

The security issue is essential in the Internet-of-Things (IoT) environment. Biometrics play an important role in securing the emerging IoT devices, especially IoT robots. Biometric identification is an interesting candidate to improve IoT usability and security.

Android malware analysis in a nutshell.

This paper offers a comprehensive analysis model for android malware. The model presents the essential factors affecting the analysis results of android malware that are vision-based. Current android malware analysis and solutions might consider one or some of these factors while building their malware predictive systems.