Dual Multi Scale Attention Network Optimized With Archerfish Hunting Optimization Algorithm for Diabetics Prediction.

Diabetes is a chronic disease that occurs when the body cannot regulate blood sugar levels. Nowadays, the screening tests for diabetes are developed using multivariate regression methods. An increasing amount of data is automatically collected to provide an opportunity for creating challenging and accurate prediction modes that are updated constantly with the help of machine learning techniques.

Evolutionary bioinformatics with veiled biological database for health care operations.

The tremendous growth of biological data processing systems in the realm of health care applications has made real-time information accessible to everyone with no processing lags. Bioinformatics is even integrated into most wireless technology applications to account for all physical characteristics. The planned model focuses on evolutionary bioinformatics for medical sensor applications in health care.

Investigation into the impact of acetylene on performance and emission characteristics of a compression ignition engine using a blended biodiesel of ethanol and tamanu oil.

Due to the significant increase in transportation, traditional fossil fuels utilised in internal combustion engines will only be accessible for a limited duration. Additionally, the harmful pollutants produced by these fuels, including CO, NO, unburned hydrocarbons, smoke, and a small amount of particulate matter, have a severe negative impact on the environment. Although biodiesel proves efficient without necessitating engine modifications, its performance is hindered by its higher viscosity.

A comprehensive experimental study of eco-friendly hybrid polymer composites using pistachio shell powder and Aquilaria agallocha Roxb.

This study investigates the effects of incorporating pistachio shell powder and a mixture of Aquilaria agallocha Roxb (AAR) resin with epoxy on the mechanical, dynamic mechanical, thermal, and biodegradability properties of an epoxy composite. Filler loadings ranged from 10 to 35% by volume, in 5% increments. Scanning electron microscopy (SEM) revealed a uniform distribution of the hybrid polymer materials, particularly at 30% natural resin content, enhancing the load-bearing capacity of the composites.

Emerging therapeutic and diagnostic strategies for coronary artery disease: Current trends and future perspectives.

Coronary vascular disease (CVD) is the general term used to cover conditions like narrowed blood vessels that may cause stroke or heart attack. Coronary artery disease (CAD) is one of the CVD and it is the most severe disease worldwide. The traditional treatment for CAD includes Coronary Artery Bypass Graft Surgery (CABG) and Percutaneous Coronary Intervention (PCI).

Tracking perishable foods in the supply chain using chain of things technology.

Modern food supply chains are intrinsically sophisticated due to their multi-participant and multi-echelon structure, which are challenging to handle high turbulent business environment. The development of Perishable Food Supply Chains (PFSC) has to be strong enough to manage any type of disruptions in the food industry. At the same time, the food processing industry must also take responsibility for the social and environmental consequences of their deeds.

An efficient colorectal cancer detection network using atrous convolution with coordinate attention transformer and histopathological images.

The second most common type of malignant tumor worldwide is colorectal cancer. Histopathology image analysis offers crucial data for the clinical diagnosis of colorectal cancer. Currently, deep learning techniques are applied to enhance cancer classification and tumor localization in histopathological image analysis.

Directive transportation in smart cities with line connectivity at distinctive points using mode control algorithm.

This article examines the operational functionality of intelligent transport systems to enhance smart cities by reducing traffic congestion. Given the increasing populations of smart cities, there is a growing demand for public transit systems to address the issue of traffic congestion. Therefore, the suggested system is developed using a few parametric design models, which combine point-to-point protocol and mode control optimization.

Enhancing natural disaster analysis and waste classification: a novel VGG-FL approach.

The study of natural disasters is a crucial field that involves analyzing the occurrence, impact, and aftermath of various natural hazards that can cause significant harm to communities and the environment. Efficient waste management and environmental protection require proper classification of waste. Analyzing natural disasters and categorizing waste can be a time-consuming task, and conventional methods often struggle with it.

Extraction and characterization of microcrystalline cellulose from Vachellia nilotica plant leaves: A biomass waste to wealth approach.

Biobased waste utilization is an intriguing area of research and an ecologically conscious approach. Plant-based materials can be used to render cellulose, which is an eco-friendly material that can be used in numerous aspects. In the current investigation, cellulose was extracted from the leaves of the Vachellia nilotica plant via acid hydrolysis.

Pictorial depiction on controlling crowd in smart conurbations using Internet of Things with switching algorithms.

The proliferation of smart conurbations entails an efficient system design for managing all the crowds in public places. Multitude controlling procedures are carried out for controlling compact areas where more number of peoples is present at several groups. Therefore for controlling purpose the proposed method aims to design a pictorial representation using Internet of Things (IoT).

A big data scheme for heart disease classification in map reduce using jellyfish search flow regime optimization enabled Spinalnet.

Background: The disease related to the heart is serious and can lead to death. Precise heart disease prediction is imperative for the effective treatment of cardiac patients. This can be attained by machine learning (ML) techniques using healthcare data.

An innovative breast cancer detection framework using multiscale dilated densenet with attention mechanism.

Cancer-related deadly diseases affect both developed and underdeveloped nations worldwide. Effective network learning is crucial to more reliably identify and categorize breast carcinoma in vast and unbalanced image datasets. The absence of early cancer symptoms makes the early identification process challenging.

An efficient cardio vascular disease prediction using multi-scale weighted feature fusion-based convolutional neural network with residual gated recurrent unit.

The cardiovascular disease (CVD) is the dangerous disease in the world. Most of the people around the world are affected by this dangerous CVD. In under-developed countries, the prediction of CVD remains the toughest job and it takes more time and cost.

IoT-based external attacks aware secure healthcare framework using blockchain and SB-RNN-NVS-FU techniques.

Background: In recent times, there has been widespread deployment of Internet of Things (IoT) applications, particularly in the healthcare sector, where computations involving user-specific data are carried out on cloud servers. However, the network nodes in IoT healthcare are vulnerable to an increased level of security threats.

Objective: This paper introduces a secure Electronic Health Record (EHR) framework with a focus on IoT.

HARNet in deep learning approach-a systematic survey.

A comprehensive examination of human action recognition (HAR) methodologies situated at the convergence of deep learning and computer vision is the subject of this article. We examine the progression from handcrafted feature-based approaches to end-to-end learning, with a particular focus on the significance of large-scale datasets. By classifying research paradigms, such as temporal modelling and spatial features, our proposed taxonomy illuminates the merits and drawbacks of each.

Dynamic resource allocation in 5G networks using hybrid RL-CNN model for optimized latency and quality of service.

The rapid deployment of 5G networks necessitates innovative solutions for efficient and dynamic resource allocation. Current strategies, although effective to some extent, lack real-time adaptability and scalability in complex, dynamically-changing environments. This paper introduces the Dynamic Resource Allocator using RL-CNN (DRARLCNN), a novel machine learning model addressing these shortcomings.

Enhancing Lung Nodule Classification: A Novel CViEBi-CBGWO Approach with Integrated Image Preprocessing.

Cancer detection and accurate classification pose significant challenges for medical professionals, as it is described as a lethal illness. Diagnosing the malignant lung nodules in its initial stage significantly enhances the recovery and survival rates. Therefore, a novel model named convolutional vision Elman bidirectional-based crossover boosted grey wolf optimization (CViEBi-CBGWO) has been proposed to enhance classification accuracy.

Archimedes optimisation algorithm quantum dilated convolutional neural network for road extraction in remote sensing images.

Roads are closely intertwined with human existence, and the process of extracting road networks has emerged as the most prominent task in remote sensing (RS). The automated road interpretation process of remote sensing images (RSI) efficiently acquires road network data at a reduced expense in comparison to the traditional visual interpretation of RSI. However the manifestation of RSI is completely distinct because of the great difference in length, width, material, and shape of road networks in dissimilar areas.

Privacy-Preserving Breast Cancer Classification: A Federated Transfer Learning Approach.

Breast cancer is deadly cancer causing a considerable number of fatalities among women in worldwide. To enhance patient outcomes as well as survival rates, early and accurate detection is crucial. Machine learning techniques, particularly deep learning, have demonstrated impressive success in various image recognition tasks, including breast cancer classification.

Optimized self-attention based cycle-consistent generative adversarial network adopted melanoma classification from dermoscopic images.

Skin is the exposed part of the human body that constantly protected from UV rays, heat, light, dust, and other hazardous radiation. One of the most dangerous illnesses that affect people is skin cancer. A type of skin cancer called melanoma starts in the melanocytes, which regulate the colour in human skin.