Applying the defense model to strengthen information security with artificial intelligence in computer networks of the financial services sector.

The increasing digitization of the Financial Services Sector (FSS) has significantly improved operational efficiency but has also exposed institutions to sophisticated Cyber Threat Intelligence (CTI) such as Advanced Persistent Threats (APT), zero-day exploits, and high-volume Denial-of-Service (DoS) attacks. Traditional Intrusion Detection Systems (IDS), including signature-based and anomaly-based approaches, suffer from high False Positive Rates (FPR) and lack the adaptability required for modern threat landscapes. This study aims to develop and evaluate an Artificial Intelligence-Enhanced Defense-in-Depth (AI-E-DiD) designed to provide real-time, adaptive, and scalable cybersecurity prevention for financial networks.

A non-sub-sampled shearlet transform-based deep learning sub band enhancement and fusion method for multi-modal images.

Multi-Modal Medical Image Fusion (MMMIF) has become increasingly important in clinical applications, as it enables the integration of complementary information from different imaging modalities to support more accurate diagnosis and treatment planning. The primary objective of Medical Image Fusion (MIF) is to generate a fused image that retains the most informative features from the Source Images (SI), thereby enhancing the reliability of clinical decision-making systems. However, due to inherent limitations in individual imaging modalities-such as poor spatial resolution in functional images or low contrast in anatomical scans-fused images can suffer from information degradation or distortion.

An online tool based on the Internet of Things and intelligent blockchain technology for data privacy and security in rural and agricultural development.

The standard implementation of the Internet of Things (IoT) has renovated numerous sectors, supporting agriculture with modern technological development. Termed Agriculture-Internet of Things (Agri-IoT), this combination has helped in Smart Farming (SF) using wireless sensors that record real-time data improvement sustainable agriculture practices like irrigation, pest control, and overall field operations. So far, Agri-IoT research faces challenges, mainly focusing on data security and management, which are vulnerabilities in existing centralized solutions.

GShC-Net: Hybrid deep learning with DCTLAP feature extraction for brain tumor detection.

A brain tumor is an abnormal cell growth in a brain, which is not detected early. Initial detection of brain tumors is extremely critical for treatment planning as well as the survival of a patient. Brain tumors come in different forms, have unique properties, and require tailored therapies.

Optimizing the early diagnosis of neurological disorders through the application of machine learning for predictive analytics in medical imaging.

Early diagnosis of Neurological Disorders (ND) such as Alzheimer's disease (AD) and Brain Tumors (BT) can be highly challenging since these diseases cause minor changes in the brain's anatomy. Magnetic Resonance Imaging (MRI) is a vital tool for diagnosing and visualizing these ND; however, standard techniques contingent upon human analysis can be inaccurate, require a long-time, and detect early-stage symptoms necessary for effective treatment. Spatial Feature Extraction (FE) has been improved by Convolutional Neural Networks (CNN) and hybrid models, both of which are changes in Deep Learning (DL).

Parkinson's disease prediction using improved crayfish optimization based hybrid deep learning.

BackgroundPredicting the course of Parkinson's disease is essential for prompt diagnosis and treatment, which may enhance patient outcomes.ObjectiveThis study presents a novel method for Parkinson's disease prediction using freely accessible resources. The suggested approach starts with band-pass filter data preprocessing and uses Empirical Mode Decomposition (EMD) for feature extraction.

Multi-skin disease classification using hybrid deep learning model.

Among the many cancers that people face today, skin cancer is among the deadliest and most dangerous. As a result, improving patients' chances of survival requires skin cancer to be identified and classified early. Therefore, it is critical to assist radiologists in detecting skin cancer through the development of Computer Aided Diagnosis (CAD) techniques.

Deep Learning-Assisted Computer-Aided Diagnosis System for Early Detection of Lung Cancer.

Purpose: The largest cause of cancer-related fatalities worldwide is lung cancer. The dimensions and positioning of the primary tumor, the presence of lesions, the type of lung cancer like malignant or benign, and the good mental health diagnosis all play a part in the diagnosis of the disease.

Methods: Three processes should be used by standard computer-assisted diagnosis (CAD) systems to detect lung cancer: preprocessing, feature extraction, and classification.

Federated learning based fire detection method using local MobileNet.

Fire is a dangerous disaster that causes human, ecological, and financial ramifications. Forest fires have increased significantly in recent years due to natural and artificial climatic factors. Therefore, accurate and early prediction of fires is essential.

An efficient enhanced stacked auto encoder assisted optimized deep neural network for forecasting Dry Eye Disease.

Meibomian Gland Dysfunction (MGD) and Dry Eye Disease (DED) comprise two of the most significant eye diseases, impacting millions of sufferers worldwide. Several etiological factors influence the early symptoms of DED. Early diagnosis and treatment of erectile dysfunction may significantly improve the Quality of Life (QoL) for people.

Effective clinical decision support implementation using a multi filter and wrapper optimisation model for Internet of Things based healthcare data.

Feature Selection (FS) is essential in the Internet of Things (IoT)-based Clinical Decision Support Systems (CDSS) to improve the accuracy and efficiency of the system. With the increasing number of sensors and devices used in healthcare, the volume of data generated is vast and complex. Relevant FS from this data is crucial in reducing computational overhead, improving the system's interpretability, and enhancing the Decision-Making System (DMS) quality.

Multi-dimensional dense attention network for pixel-wise segmentation of optic disc in colour fundus images.

Background: Segmentation of retinal fragments like blood vessels, Optic Disc (OD), and Optic Cup (OC) enables the early detection of different retinal pathologies like Diabetic Retinopathy (DR), Glaucoma, etc.

Objective: Accurate segmentation of OD remains challenging due to blurred boundaries, vessel occlusion, and other distractions and limitations. These days, deep learning is rapidly progressing in the segmentation of image pixels, and a number of network models have been proposed for end-to-end image segmentation.

Magnetic Resonance Imaging Images Based Brain Tumor Extraction, Segmentation and Detection Using Convolutional Neural Network and VGC 16 Model.

Objectives: In this paper, we look at how to design and build a system to find tumors using 2 Convolutional Neural Network (CNN) models. With the help of digital image processing and deep Learning, we can make a system that automatically diagnoses and finds different diseases and abnormalities. The tumor detection system may include image enhancement, segmentation, data enhancement, feature extraction, and classification.

CarbDisMut: database on neutral and disease-causing mutations in human carbohydrate-binding proteins.

Protein-carbohydrate interactions are involved in several cellular and biological functions. Integrating structure and function of carbohydrate-binding proteins with disease-causing mutations help to understand the molecular basis of diseases. Although databases are available for protein-carbohydrate complexes based on structure, binding affinity and function, no specific database for mutations in human carbohydrate-binding proteins is reported in the literature.

Multi class robust brain tumor with hybrid classification using DTA algorithm.

Analyzing brain tumours is important for prompt diagnosis and efficient patient care. The morphology of tumours, which includes their size, location, texture, and heteromorphic appearance in medical pictures, makes them difficult to analyse. A unique two-phase deep learning-based framework is suggested in this respect to recognise and classify brain cancers in magnetic resonance images (MRIs).

Metal and Polymer Based Composites Manufactured Using Additive Manufacturing-A Brief Review.

This review examines the mechanical performance of metal- and polymer-based composites fabricated using additive manufacturing (AM) techniques. Composite materials have significantly influenced various industries due to their exceptional reliability and effectiveness. As technology advances, new types of composite reinforcements, such as novel chemical-based and bio-based, and new fabrication techniques are utilized to develop high-performance composite materials.

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.

Challenges, strategies and opportunities for wind farm incorporated power systems: a review with bibliographic coupling analysis.

Wind power is a rapidly developing energy source. Many nations use wind power to meet a considerable amount of their energy needs. Moreover, the technology of wind power has evolved over the period of time.

FMTM-feature-map-based transform model for brain image segmentation in tumor detection.

The segmentation of brain images is a leading quantitative measure for detecting physiological changes and for analysing structural functions. Based on trends and dimensions of brain, the images indicate heterogeneity. Accurate brain tumour segmentation remains a critical challenge despite the persistent efforts of researchers were owing to a variety of obstacles.

An efficient modular framework for automatic LIONC classification of MedIMG using unified medical language.

Handwritten prescriptions and radiological reports: doctors use handwritten prescriptions and radiological reports to give drugs to patients who have illnesses, injuries, or other problems. Clinical text data, like physician prescription visuals and radiology reports, should be labelled with specific information such as disease type, features, and anatomical location for more effective use. The semantic annotation of vast collections of biological and biomedical texts, like scientific papers, medical reports, and general practitioner observations, has lately been examined by doctors and scientists.

A critical review of an additive manufacturing role in Covid-19 epidemic.

In 2019, a massive and deadly coronavirus pandemic known as the COVID-19 pandemic has swept through more than 180 nations, causing a massive strain on already overtaxed health systems around the globe. Global demand for medical equipment has put a strain on traditional manufacturing methods, resulting in the need for an efficient, low-cost, and speedy mode of production. Additive manufacturing, or 3D printing, has been used by manufacturers to bridge the gap and enhance the production of medical products.

PCA-MutPred: Prediction of Binding Free Energy Change Upon Missense Mutation in Protein-carbohydrate Complexes.

Protein-carbohydrate interactions play an important role in several biological processes. The mutation of amino acid residues in carbohydrate-binding proteins may alter the binding affinity, affect the functions and lead to diseases. Elucidating the factors influencing the binding affinity change (ΔΔG) of protein-carbohydrate complexes upon mutation is a challenging task.