Chromosome Image Classification Using Edge Fusion Attention Network.

Identification of chromosome pairs is important for karyotype generation and genetic disease prediction. Conventional methods tend to fail with structural variations of the chromosomes and imprecise boundaries. To solve these issues, we propose a deep learning architecture, Edge Fusion Attention Network (EFANet), for chromosome classification.

Enhancing privacy in IoT-based healthcare using provable partitioned secure blockchain principle and encryption.

The Internet of Things (IoT) has attained significant interest recently, particularly in the medical field due to the quick development of IoT devices. Medical related data contains a significant volume of personal information, and it is crucial to maintain privacy. As medical information becomes increasingly electronic in the era of big data, securely and accurately storing medical information is critical.

Automated weed and crop recognition and classification model using deep transfer learning with optimization algorithm.

Weeds and crops contribute to a endless resistance for similar assets, which leads to potential declines in crop production and enlarged agricultural expenses. Conventional models of weed control like extensive pesticide use, appear with the hassle of environmental pollution and advancing weed battle. As the need for organic agricultural and pollutant-free products increases, there is a crucial need for revolutionary solutions.

An elegant intellectual engine towards automation of blockchain smart contract vulnerability detection.

To prevent vulnerabilities and ensure app security, smart contract vulnerability detection identifies flaws in blockchain code. To overcome the limitations of traditional detection methods, this study introduces a novel approach that combines Explainable Artificial Intelligence (XAI) with Deep Learning (DL) to detect vulnerabilities in smart contracts. The proposed intellectual engine operates in multiple stages.

A hybrid deep learning model EfficientNet with GRU for breast cancer detection from histopathology images.

Breast cancer remains a significant global health challenge among women, with histopathological image analysis playing a critical role in early detection. However, existing diagnostic models often struggle to extract complex patterns from high-resolution tissue images, limiting their diagnostic accuracy and generalization. This study aims to develop a high-performance deep learning framework for accurate classification of breast cancer in histopathology images, addressing limitations in feature extraction and spatial dependency modeling.

Performance improvement of DC motor control system using PID controller with Kookaburra and Red Panda optimization algorithm.

In recent years, DC motors have found widespread use in numerous industrial applications. Precise speed control is required and would be achieved via the implementation of a Proportional-Integral-Derivative (PID) controller in the system. The tuning of controller gain has received significant attention, and conventional metaheuristic algorithms have been utilized for tuning.

A novel enhanced FBR and low SAR MIMO antenna with limited ground plane for wearable devices.

This study explores a novel approach to mitigate the challenge of a small ground plane inducing substantial back lobes in high front-to-back ratio (FBR) microstrip patch antennas. Introducing a combined technique employing rectangular strip and connecting strip methods, this research aims to counteract backward radiation by generating supplementary magnetic currents through an inductive effect. The hybrid method effectively reduces the backward radiation from the original patch.

Experimental investigation to enhancing the energy efficiency of a solar-powered Visi cooler.

Refrigeration methods in secluded regions are a major issue for sustaining the quality of perishables like vaccines and food. Traditional refrigeration systems, including kerosene and gas-powered units, often suffer from interruptions in the supply of fuel. Additionally, they do not satisfy the stringent criteria set by the World Health Organization (WHO) Performance, Quality and Safety (PQS) system requirements.

A novel attention based vision transformer optimized with hybrid optimization algorithm for turmeric leaf disease detection.

Turmeric leaf disease detection is essential to maintain the crop health and optimize the yield. Through early identification disease can be controlled and its relevant economic losses can be avoided. However, the existing methods for leaf disease detection exhibit limitations in extracting complex leaf features which leads to lower classification accuracy.

Experimental investigation of flexible eight-port asymmetric fed MIMO antenna with narrow-super-widebandn-s characteristics for future applications including internet of things.

In this work, an eight-port MIMOn-s antenna with dual-band operation is presented. The proposed work involves several wireless applications with FR1 and FR2 millimeter-wave bands. Also, a need for conformal antenna with capability of reduced SAR can cater the need for applications in flexible electronics.

Synergistic feature selection and distributed classification framework for high-dimensional medical data analysis.

Feature selection and classification efficiency and accuracy are key to improving decision-making regarding medical data analysis. Since the medical datasets are large and complex, they give rise to certain problematic issues such as computational complexity, limited memory space, and a lesser number of correct classifications. In order to overcome these drawbacks, the new integrated algorithm is presented here: Synergistic Kruskal-RFE Selector and Distributed Multi-Kernel Classification Framework (SKR-DMKCF).

Topology synthesis of coupled inductor based four port DC-DC converter for multi-input PV-battery application with autonomous mode selection.

Multiport converters are the most reliable and integral component for latest renewable source integration with multiple inputs. This article is one among the kind, which proposes a novel Coupled Inductor based Four Port topology Multiport Converter (CI-FP-MPC) for integrating multiple PV sources with different voltages. The adoption of coupled inductor contributes an increased voltage gain with reduced stress on the switches and diodes.

Enhanced IoMT security framework using group teaching optimized auto-encoder for intrusion detection.

Providing security to Internet of Medical Things (IoMT) is significant worldwide problem for future generations its implementation to be successful. The traditional security methodologies developed for IoMT struggles with the specific issues of high false positives and lower detection rate. Therefore, the proposed work aims to develop a ground-breaking intrusion detection model, named as, Group Teaching Optimized Probabilistic Deep Auto-Encoder (GTPDA) for increasing the security of IoMT networks.

Towards Explainable Detection of Alzheimer's Disease: A Fusion of Deep Convolutional Neural Network and Enhanced Weighted Fuzzy C-Mean.

Background: Alzheimer's disease (AD) is a progressive neurodegenerative disorder characterized by cognitive decline, posing a significant challenge for individuals and society. Early detection and treatment are essential for effective disease management.

Objective: The objective of this research is to develop a novel and interpretable deep learning model for rapid and accurate Alzheimer's disease detection, incorporating Explainable Artificial Intelligence (XAI) techniques.

Performance evaluation and chemical oxygen demand removal of tannery wastewater through the aerobic-anaerobic route.

With characterized for complex and maximum substance (suspended solids, broke up oil, a mixture of inorganic and chromium sulfides), tannery wastewater was subjected to a treatment process on removal of chemical oxygen demand (COD) via upstream anaerobic sludge blanket reactor where we found reduced departure efficiencies and that process limits were affected by the assortments in regular stacking rates, closeness of chromium, and sulfides. Hence, a combination of the aerobic-anaerobic hybrid reactor was set up for sequential treatment to determine possible COD reduction. This study investigated the biological degradation of tannery wastewater in a laboratory-scale sequential up-flow aerobic-anaerobic reactor.

Micro and nano plastics in food: A review on the strategies for identification, isolation, and mitigation through photocatalysis, and health risk assessment.

Over the past few years, it has become increasingly evident that microplastic pollutant heavily contaminates water sources, posing a potential threat to both human and wildlife. These plastic pollutants do not get degraded efficiently by natural processes and the existing traditional treatment methods are incapable of fully eradicating them. In this regard, degradation of microplastic contaminants through photocatalytic methods has emerged as a powerful technique.

Improved graph neural network-based green anaconda optimization for segmenting and classifying the lung cancer.

Normal lung cells incur genetic damage over time, which causes unchecked cell growth and ultimately leads to lung cancer. Nearly 85% of lung cancer cases are caused by smoking, but there exists factual evidence that beta-carotene supplements and arsenic in water may raise the risk of developing the illness. Asbestos, polycyclic aromatic hydrocarbons, arsenic, radon gas, nickel, chromium and hereditary factors represent various lung cancer-causing agents.

Identifying cancer risks using spectral subset feature selection based on multi-layer perception neural network for premature treatment.

Recently, human beings have been affected mainly by dreadful cancer diseases. Predicting cancer risk levels is a major challenge in biomedical research for feature selection and classification at the margins. To resolve this problem, we propose a Subset Clustering-Based Feature Selection using a Multi-Layer Perception Neural Network (SCFS-MLPNN).

Comparative assessment of groundwater quality indices of Kannur District, Kerala, India using multivariate statistical approaches and GIS.

The aim of the study was to determine the groundwater characteristics of rural and industrial zones in the Kannur region. In 2011, 25 groundwater data were collected from the centre for water resource development management (CWRDM), and in 2019, 25 groundwater samples from rural and near-industrial areas were collected and analysed for major anions (HCO-, CO, Cl, NO- and SO), and cations (TH, Ca, Mg, Na, K and Fe) using APHA standards. To better understand the link between water quality parameters, multivariate statistical analysis approaches such as principal component analysis (PCA), hierarchical cluster analysis (HCA), correlation matrix analysis (CMA), and Pearson correlation bivariate one-tailed analysis (PCBOTA) were used to analyse the inter-relationship of data.

The Combined Effect of Glass Fiber Mesh and Steel Fiber on Two-Layered Preplaced Aggregate Concrete against Drop Weight Impact.

Buildings and other infrastructure should be designed to withstand impact loads in terrorist attacks or industrial catastrophes. Fibrous concrete is utilized in a variety of ways in the construction of structures to resist impact loads. Preplaced aggregate fibrous concrete (PAFC) has a different production method than traditional fibrous concrete.

Crystal structure, Hirshfeld surface and frontier mol-ecular orbital analysis of 10-benzyl-9-(4-hydroxy-3-meth-oxy-phen-yl)-3,3,6,6-tetra-methyl-3,4,6,7,9,10-hexa-hydro-acridine-1,8(2,5)-dione.

In the fused ring system of the title mol-ecule, CHNO, the conformation of the central di-hydro-pyridine ring is inter-mediate between boat and envelope with the N and the opposite C atoms lying out of the basal plane. The conformations of terminal rings are close to envelope, with the atoms substituted by two methyl groups as the flaps. In the crystal, the mol-ecules are linked by O-H⋯O hydrogen bonds into helical chains.

Effectiveness of Nonuniform Heat Generation (Sink) and Thermal Characterization of a Carreau Fluid Flowing across a Nonlinear Elongating Cylinder: A Numerical Study.

During thermal radiation treatments, heat therapies, and examination procedures like scans and X-rays, the cylindrical blood vessels may get stretched; meanwhile, the blood flow through those blood vessels may get affected due to temperature variations around them. To overcome this issue, this work was framed to explore the impact of heat transmission in a Carreau fluid flow (CFF) through a stretching cylinder in terms of the nonlinear stretching rate and irregular heat source/sink. Temperature-dependent thermal conductivity and thermal radiation are taken into consideration in this study.

Mathematical approach for segmenting chromosome clusters in metaspread images.

Karyotyping is an examination that helps in detecting chromosomal abnormalities. Chromosome analysis is a very challenging task which requires various steps to obtain a karyotype. The challenges associated with chromosome analysis are overlapping and touching of chromosomes.