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.

Modified ensemble machine learning-based plant leaf disease detection model with optimized K-Means clustering.

In the farming sector, the automatic detection of plant leaf disease is considered a vital landmark. Farmers move long distances to consult pathologists to observe the disease, which is expensive and time-consuming. Moreover, detection of disease in a premature period is a difficult process in the existing model.

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).

Automated semantic lung segmentation in chest CT images using deep neural network.

Lung segmentation algorithms play a significant role in segmenting theinfected regions in the lungs. This work aims to develop a computationally efficient and robust deep learning model for lung segmentation using chest computed tomography (CT) images with DeepLabV3 + networks for two-class (background and lung field) and four-class (ground-glass opacities, background, consolidation, and lung field). In this work, we investigate the performance of the DeepLabV3 + network with five pretrained networks: Xception, ResNet-18, Inception-ResNet-v2, MobileNet-v2 and ResNet-50.

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.

AI Based Diagnosis of Pneumonia.

Pneumonia is a lung infection caused by bacteria, viruses and fungi. In this infection, the air sac (alveoli) of the lungs gets inflamed and breathing becomes difficult which causes mild to severe illness among people. They are diagnosed by performing chest X-ray, blood test, pulse oximetry.

Experimentation of dyes extracted from the peels of red banana and aloe vera as sensitizers for TiO-based dye-sensitized solar cells.

As an attempt of utilizing the peels to extract dye sensitizers for dye-sensitized solar cell (DSSC), the peels of red banana (fruit) and aloe vera (leaf) were used in this study. As far as we know, for the first time the peels of red banana and aloe vera are being used as sensitizer for DSSC. Acetone and ethanol were used as solvent in extracting dyes from these peels.

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.

COVID-19 vaccine sentiment analysis using public opinions on Twitter.

Twitter, as is well known, is one of the most active social media platforms, with millions of tweets posted every day, in which different people express their opinions on topics such as travel, economic concerns, political decisions, and so on. As a result, it is a useful source of knowledge. We offer Sentiment Analysis using Twitter Data for the research.

Content-Based Image Retrieval Using Colour, Gray, Advanced Texture, Shape Features, and Random Forest Classifier with Optimized Particle Swarm Optimization.

In this paper, a new approach for Content-Based Image Retrieval (CBIR) has been addressed by extracting colour, gray, advanced texture, and shape features for input query images. Contour-based shape feature extraction methods and image moment extraction techniques are used to extract the shape features and shape invariant features. The informative features are selected from extracted features and combined colour, gray, texture, and shape features by using PSO.

Role of heavy metals (copper (Cu), arsenic (As), cadmium (Cd), iron (Fe) and lithium (Li)) induced neurotoxicity.

Parkinson's disease (PD) is a neurodegenerative condition characterized by the dopamine (DA) neuronal loss in the substantia nigra. PD impairs motor controls symptoms such as tremor, rigidity, bradykinesia and postural imbalance gradually along with non-motor problems such as olfactory dysfunction, constipation, sleeping disorder. Though surplus of factors and mechanisms have been recognized, the precise PD etiopathogenesis is not yet implied.

Automated Detection of Infection in Diabetic Foot Ulcer Images Using Convolutional Neural Network.

A bacterial or bone infection in the feet causes diabetic foot infection (DFI), which results in reddish skin in the wound and surrounding area. DFI is the most prevalent and dangerous type of diabetic mellitus. It will mainly occur in people with heart disease, renal illness, or eye disease.

Analysis of a solar still with photovoltaic modules and electrical heater - Energy and exergy approach.

The availability of drinkable water, along with food and air, is a fundamental human necessity. Because of the presence of higher amounts of salt and pollution, direct use of water from sources such as lakes, sea, rivers, and subsurface water reservoirs is not normally suggested. Solar is still a basic technology that can use solar energy to transform accessible waste or brackish water into drinkable water.

Heat flow saturate of Ag/MgO-water hybrid nanofluid in heated trigonal enclosure with rotate cylindrical cavity by using Galerkin finite element.

MHD Natural convection, which is one of the principal types of convective heat transfer in numerous research of heat exchangers and geothermal energy systems, as well as nanofluids and hybrid nanofluids. This work focuses on the investigation of Natural convective heat transfer evaluation inside a porous triangular cavity filled with silver-magnesium oxide/water hybrid nanofluid [HO/Ag-MgO] under a consistent magnetic field. The laminar and incompressible nanofluid flow is taken to account while Darcy-Forchheimer model takes account of the advection inertia effect in the porous sheet.

Analysis of modified plug-in electric vehicle charger controller with grid support functionalities.

Power quality issues, which are mainly due to power electronic devices that are invariably used not only in domestic sector but also industries, still persist despite various mitigation strategies. The slow but steady invasion of Electric vehicles or Plug-in Electric Vehicles (PEVs) in recent years, in the automobile sector, adds woes to the power quality issues further. Majority of the charging systems presently available for charging PEVs are unidirectional and so supports Grid to Vehicle (G2V) mode only as the bidirectional integration of those vehicles into the grid is still a big challenge.

A brief comparative examination of tangent hyperbolic hybrid nanofluid through a extending surface: numerical Keller-Box scheme.

A novel hybrid nanofluid was explored in order to find an efficient heat-transmitting fluid to replace standard fluids and revolutionary nanofluids. By using tangent hyperbolic hybrid combination nanoliquid with non-Newtonian ethylene glycol (EG) as a basis fluid and a copper (Cu) and titanium dioxide (TiO) mixture, this work aims to investigate the viscoelastic elements of the thermal transferring process. Flow and thermal facts, such as a slippery extended surface with magnetohydrodynamic (MHD), suction/injection, form factor, Joule heating, and thermal radiation effects, including changing thermal conductivity, were also integrated.