Improving the efficiency of induction motor drive by flux and torque control: A hybrid LSE-RERNN approach.

A hybrid technique is proposed in this manuscript for the optimal design of an induction motor (IM) drive for the dynamic load profiles during torque and flux control. The proposed hybrid method combines a Ladder-Spherical-Evolution-Search-Algorithm (LSE) and a recalling-enhanced recurrent-neural network (RERNN), which is called an LSE-RERNN technique. The major objective of the proposed method is to minimize IM losses while maintaining control over speed and torque.

M2AI-CVD: Multi-modal AI approach cardiovascular risk prediction system using fundus images.

Cardiovascular diseases (CVD) represent a significant global health challenge, often remaining undetected until severe cardiac events, such as heart attacks or strokes, occur. In regions like Qatar, research focused on non-invasive CVD identification methods, such as retinal imaging and dual-energy X-ray absorptiometry (DXA), is limited. This study presents a groundbreaking system known as Multi-Modal Artificial Intelligence for Cardiovascular Disease (M2AI-CVD), designed to provide highly accurate predictions of CVD.

Characterization of new cellulose fiber extracted from second generation Bitter Albizia tree.

The present work examines the physical, thermal tensile, and chemical properties of wood skin fibers obtained from second generation Bitter Albizia (BA) tree skin. Chemical characterization of BA fibers showed the presence of various chemical contents such as cellulose of 74.89 wt.

Flamingo Jelly Fish search optimization-based routing with deep-learning enabled energy prediction in WSN data communication.

Nowadays, wireless sensor networks (WSN) have gained huge attention worldwide due to their wide applications in different domains. The limited amount of energy resources is considered as the main limitations of WSN, which generally affect the network life time. Hence, a dynamic clustering and routing model is designed to resolve this issue.

Investigation on thermochemical co-gasification of rice husk and groundnut shell in open core gasifier for the generation of producer gas: An optimization by central composite design.

Several energy-related strategies and scenarios have been suggested to address concerns about rising global temperatures. In addition to using renewable energy, the improvement in energy efficiency of conventional systems is also in focus. Policies are already in place in many countries, including India, to address the energy needs of rural and small-scale enterprises by gasifying locally available, diverse agricultural leftovers.

Hybrid optimization assisted channel selection of EEG for deep learning model-based classification of motor imagery task.

Objectives: To design and develop an approach named HC + SMA-SSA scheme for classifying motor imagery task.

Methods: The offered model employs a new method for classifying motor imagery task. Initially, down sampling is deployed to pre-process the incoming signal.

Sustainable utilization of biopolymers as green adhesive in soil improvement: a review.

Throughout history, soil improvement has relied on various additives, from ancient practices using lime and other traditional compounds to modern methods employing geosynthetics and microbial treatments. However, conventional soil admixtures, while effective, often carry significant environmental drawbacks, especially in the case of additives like cement. In response to these environmental concerns, there has been a growing interest in the use of biopolymers as a sustainable alternative for ground improvement.

Compact Flexible Planar Antennas for Biomedical Applications: Insight into Materials and Systems Design.

Planar antennas have become an integral component in modern biomedical instruments owing to their compact structure, cost effectiveness, and light weight. These antennas are crucial in realizing medical systems such as body area networks, remote health monitoring, and microwave imaging systems. Antennas intended for the above applications should be conformal and fabricated using lightweight materials that are suitable for wear on the human body.

Split injection strategy control map development through prediction-based calibration approach to improve the biodiesel-fuelled diesel engine characteristics.

Karanja oil blended with diesel acts as a good alternative for a CI engine due to its low emission and high oxygen content as compared to pure diesel which leads to the non-toxic and biodegradable nature of the fuel. The objective of this project is to enhance the performance and emission characteristics of a diesel engine that runs on biofuel by accurately calibrating its fuel injector control parameters. To achieve this goal, the project focuses on creating optimal split injector control maps using a novel global model-based calibration approach that considers the entire range of engine speed-load conditions.

Advancements in computer-assisted diagnosis of Alzheimer's disease: A comprehensive survey of neuroimaging methods and AI techniques for early detection.

Alzheimer's Disease (AD) is a brain disorder that causes the brain to shrink and eventually causes brain cells to die. This neurological condition progressively hampers cognitive and memory functions, along with the ability to carry out fundamental tasks over time. From the symptoms it is very difficult to detect during its early stage.

Efficient qualitative risk assessment of pipelines using relative risk score based on machine learning.

Pipelines are observed one of the economic modes of transport for transporting oil, gas, and water between various locations. Most of the countries in the world transport petroleum and other flammable products through underground pipelines. The underground and aboveground pipelines are facing various damages due to corrosion, dents, and ruptures due to the environment and operational fluid conditions.

A comprehensive review of analyzing the chest X-ray images to detect COVID-19 infections using deep learning techniques.

COVID-19, a highly infectious respiratory disease a used by SARS virus, has killed millions of people across many countries. To enhance quick and accurate diagnosis of COVID-19, chest X-ray (CXR) imaging methods were commonly utilized. Identifying the infection manually by radio imaging, on the other hand, was considered, extremely difficult due to the time commitment and significant risk of human error.

Learning without forgetting by leveraging transfer learning for detecting COVID-19 infection from CT images.

COVID-19, a global pandemic, has killed thousands in the last three years. Pathogenic laboratory testing is the gold standard but has a high false-negative rate, making alternate diagnostic procedures necessary to fight against it. Computer Tomography (CT) scans help diagnose and monitor COVID-19, especially in severe cases.

Cardiovascular diseases prediction by machine learning incorporation with deep learning.

It is yet unknown what causes cardiovascular disease (CVD), but we do know that it is associated with a high risk of death, as well as severe morbidity and disability. There is an urgent need for AI-based technologies that are able to promptly and reliably predict the future outcomes of individuals who have cardiovascular disease. The Internet of Things (IoT) is serving as a driving force behind the development of CVD prediction.

Integration of electronic waste management: a review of current global generation, health impact, and technologies for value recovery and its pertinent management technique.

The fast evolution of waste electrical and electronic equipment (WEEE) has developed into a prime environmental perturb in recent days. Today, electrical and electronic products merely become a needed part of people life's and professional lives. The whole process of e-waste contains an organized collection system, appropriate dismantling, and its treatment of recycling.

Mathematical modeling and statistical approach in tannery wastewater treatment.

The present work investigated the feasibility of photo-oxidation process for the removal of oil and chemical oxygen demand (COD) in Indian tannery wastewater with the support of mathematical modeling and statistical approach. The influence of process variables such as nano-catalyst dose and reaction time was analyzed on the removal of oil/grease and COD. The obtained results are discussed using the response surface methodology (RSM) design in detail.

Numerical simulation and parameter optimization of micromixer device using fuzzy logic technique.

The objective of this study is the design, simulation, and performance optimization of a micromixer device using the three input parameters of device structure, flow rate and diffusion coefficient of gold nanoparticles while the output parameters are concentration, velocity, pressure and time domain analysis. Each input parameter in the microfluidic chip influences the system output. The data were gathered through extensive study in order to optimize the diffusion control.

Automatic Segmentation of Psoriasis Skin Images Using Adaptive Chimp Optimization Algorithm-Based CNN.

Psoriasis is a severe skin disease that is surveyed outwardly by dermatologists. In recent years, computer vision is the major solution for diagnosing the psoriasis skin disease by segmenting the infected skin images. Besides, many researchers had presented efficient machine learning techniques for segmenting the psoriasis skin images.

Prediction of Equipment Effectiveness using Hybrid Moving Average-Adaptive Neuro Fuzzy Inference System (MA-ANFIS) for decision support in Bus Body Building Industry.

Managers are driven to accomplish significantly higher levels of operational performance due to the difficulty of today's dynamic production environment. Typically, the precision of production facilities and the efficiency of manufacturing systems are significant variables in productivity. Thus, predicting machine performance has become an inevitable challenge for production managers.

Deep learning based sentiment analysis and offensive language identification on multilingual code-mixed data.

Sentiment analysis is a process in Natural Language Processing that involves detecting and classifying emotions in texts. The emotion is focused on a specific thing, an object, an incident, or an individual. Although some tasks are concerned with detecting the existence of emotion in text, others are concerned with finding the polarities of the text, which is classified as positive, negative, or neutral.

Predicting phosphorus and nitrate loads by using SWAT model in Vamanapuram River Basin, Kerala, India.

Evaluations of probable environmental impacts of point and diffuse source pollution at regional sizes are essential to achieve sustainable development of natural resources such as land and water. This research focused on how nitrate and phosphorus load varied over time and space in the Vamanapuram River Basin (VRB). Phosphorus and nitrate loads have been evaluated in the VRB using the semi-distributed Soil and Water Assessment Tool (SWAT) hydrological model.

An integrated approach for an impact assessment of the tank water and groundwater quality in Coimbatore region of South India: implication from anthropogenic activities.

Groundwater in the Coimbatore region is very much essential for irrigation and human beings' day-to-day life activities. This research focuses on the impact of anthropogenic activities on the surface water quality of four tanks in Coimbatore city and its influences on disturbing the groundwater quality as well. Water quality is degrading as a result of encroachment and other anthropogenic activities.

A realistic perspective for CO triggered tuning of electrical conductivity.

To seek sustainable CO sequestration and conversion, an electrochemical cell has been investigated for carbon capture and utilization strategy (CCU). In this cell, atmospheric CO is captured under ambient conditions and incorporated into power generation using zinc nanopowder as the catalyst. As a result, a method was developed to tune the electronic property of zinc by passing CO.