Chromosome Abnormality Detection Using Visual Geometric Transformer and Mantis Search Optimization.

Chromosomes, which carry vital genetic material, have a distinctive thread-like appearance located within the cell nucleus. The process of examining these structures known as karyotyping is fundamental for identifying genetic abnormalities. Although several techniques have been developed for this purpose, many existing methods are limited by inefficiencies, particularly in terms of processing time and accurate feature extraction.

A novel DLDRM: Deep learning-based flood disaster risk management framework by multimodal social media data.

The impacted community and humanitarian organizations have used social media platforms extensively over the past 10 years to disseminate information during a disaster. Even though numerous researches have been conducted in recent times to categorize useful and non-informational posts on social media, the majority of these studies are unimodal, that is, they separately employed documented or pictorial information to improve deep learning (DL) approaches. In this research, a multimodal DL approach will be created by integrating the complementary data offered by the text and visual Twitter posts made by members of the affected community discussing the same occurrence.

Hybrid deep learning model for image de-noising and de-mosaicking with adaptive Gannet optimization algorithm.

Image reconstruction is a critical step in various applications, such as art restoration, medical image processing, and agriculture, but it faces challenges due to noise and mosaic artefacts. In this research, a novel approach is introduced for de-noising and de-mosaicking images to enhance image reconstruction quality. The proposed model consists of three main steps: detail layer extraction, image de-noising using an Efficient Generative Adversarial Network (E-GAN), and de-mosaicking using an Adaptive Gannet-based Residual DenseNet (AG_DenseResNet).

A Joint Multimodal User Authentication-based Privacy Preservation with Disease Prediction Framework in Modern Healthcare System Using Multi-Scale Cross Attention-based ResNet.

Background/introduction: The disease prediction process plays a crucial part in a person's life "to lead a healthy life." The sudden spread of the data mining approach has generated the disease forecasting system. Secure transfer of medical data and effective storage is the major difficulty faced by recent healthcare management.

A novel dilated weighted recurrent neural network (RNN)-based smart contract for secure sharing of big data in Ethereum blockchain using hybrid encryption schemes.

Background: With the enhanced data amount being created, it is significant to various organizations and their processing, and managing big data becomes a significant challenge for the managers of the data. The development of inexpensive and new computing systems and cloud computing sectors gave qualified industries to gather and retrieve the data very precisely however securely delivering data across the network with fewer overheads is a demanding work. In the decentralized framework, the big data sharing puts a burden on the internal nodes among the receiver and sender and also creates the congestion in network.

The Impact of Hunched Posture on the Elasticity of the Upper and Middle Trapezius Muscles During Sedentary Activity.

Prolonged sitting is often associated with poor posture, hunched shoulder posture (HSP), which may alter the mechanical properties of key muscles such as the upper trapezius (UT) and middle trapezius (MT). This study aimed to investigate the effect of HSP on the elasticity of the UT and MT muscles during a sitting task and to gain insight into the musculoskeletal risks associated with postural errors while sitting. Thirty-four asymptomatic individuals participated in this study between the ages of 25 and 40, who led sedentary lifestyles with 7 h of sedentary activity per day.

Prostate cancer prediction through a hybrid deep learning method applied to histopathological image.

Background: Prostate Cancer (PCa) is a severe disease that affects males globally. The Gleason grading system is a widely recognized method for diagnosing the aggressiveness of PCa using histopathological images. This system evaluates prostate tissue to determine the severity of the disease and guide treatment decisions.

Cognitive neurodynamic approaches to adaptive signal processing in wireless sensor networks.

In recent years, Wireless Sensor Networks (WSN) have become vital because of their versatility in numerous applications. Nevertheless, the attain problems like inherent noise, and limited node computation capabilities, result in reduced sensor node lifespan as well as enhanced power consumption. To tackle such problems, this study develops a Modified-Distributed Arithmetic-Offset Binary Coding-based Adaptive Finite Impulse Response (MDA-OBC based AFIR) framework.

An optimal deep learning approach for breast cancer detection and classification with pre-trained CNN-based feature learning mechanism.

Breast cancer (BC) is the most dominant kind of cancer, which grows continuously and serves as the second highest cause of death for women worldwide. Early BC prediction helps decrease the BC mortality rate and improve treatment plans. Ultrasound is a popular and widely used imaging technique to detect BC at an earlier stage.

Coinciding Diabetic Retinopathy and Diabetic Macular Edema Grading With Rat Swarm Optimization Algorithm for Enhanced Capsule Generation Adversarial Network.

In the worldwide working-age population, visual disability and blindness are common conditions caused by diabetic retinopathy (DR) and diabetic macular edema (DME). Nowadays, due to diabetes, many people are affected by eye-related issues. Among these, DR and DME are the two foremost eye diseases, the severity of which may lead to some eye-related problems and blindness.

Brain tumor classification for MRI images using dual-discriminator conditional generative adversarial network.

This research focuses on improving the detection and classification of brain tumors using a method called Brain Tumor Classification using Dual-Discriminator Conditional Generative Adversarial Network (DDCGAN) for MRI images. The proposed system is implemented in the MATLAB programming language. In this study, images of the brain are taken from a dataset and processed to remove noise and enhance image quality.

Automatic nutrient estimator: distributing nutrient solution in hydroponic plants based on plant growth.

Background: The primary objective is to address the specific needs of plants at different growth stages by delivering precise nutrient concentrations tailored to their developmental requirements. Challenges such as uneven nutrient distribution, fluctuations in pH and electrical conductivity, and inadequate nutrient delivery pose potential hindrances to achieving optimal plant health and yield in hydroponic systems. By overcoming these challenges, the hydroponic farming community aims to enhance the accuracy of nutrient dosing, streamline automation processes, and minimize resource wastage.

Enhancing drug discovery in schizophrenia: a deep learning approach for accurate drug-target interaction prediction - DrugSchizoNet.

Drug discovery relies on the precise prognosis of drug-target interactions (DTI). Due to their ability to learn from raw data, deep learning (DL) methods have displayed outstanding performance over traditional approaches. However, challenges such as imbalanced data, noise, poor generalization, high cost, and time-consuming processes hinder progress in this field.

Type 1 and Type 2 Diabetes Measurement Using Human Face Skin Region.

Aim: Analyse the diabetes mellitus (DM) of a person through the facial skin region using vision diabetology. Diabetes mellitus is caused by persistent high blood glucose levels and related complications, which show variation in facial skin regions due to reduced blood flow in the facial arteries. .

Synthesis, Structural, Spectroscopic, Fluorescence, and Dielectric Studies of Bis-(4-Aminopyridine)-Zinc(II) Acetate: A Metal-Organic Crystal.

The advancement of crystalline growth and characterization tools allows us to investigate novel nonlinear optical substances suitable for photonic applications. Bis-(4-aminopyridine)-zinc(II) acetate (B4AZA), a metal-organic crystal was produced in this study using the slow evaporation procedure at room temperature. Analytical studies such as X-ray crystallography, Fourier transform infrared (FT-IR), UV-visible (UV-Vis), fluorescence, second harmonic generation (SHG), and dielectric tests were used to characterize the as-grown B4AZA crystals.

Deep convolutional neural network based hyperspectral brain tissue classification.

Background: Hyperspectral brain tissue imaging has been recently utilized in medical research aiming to study brain science and obtain various biological phenomena of the different tissue types. However, processing high-dimensional data of hyperspectral images (HSI) is challenging due to the minimum availability of training samples.

Objective: To overcome this challenge, this study proposes applying a 3D-CNN (convolution neural network) model to process spatial and temporal features and thus improve performance of tumor image classification.

Ant Colony Optimization-Enabled CNN Deep Learning Technique for Accurate Detection of Cervical Cancer.

Cancer is characterized by abnormal cell growth and proliferation, which are both diagnostic indicators of the disease. When cancerous cells enter one organ, there is a risk that they may spread to adjacent tissues and eventually to other organs. Cancer of the cervix of the uterus often initially manifests itself in the uterine cervix, which is located at the very bottom of the uterus.

Emerging Trends in Porogens toward Material Fabrication: Recent Progresses and Challenges.

Fabrication of tailor-made materials requires meticulous planning, use of technical equipments, major components and suitable additives that influence the end application. Most of the processes of separation/transport/adsorption have environmental applications that demands a material to be with measurable porous nature, stability (mechanical, thermal) and morphology. Researchers say that a vital role is played by porogens in this regard.

An efficient and low complex model for optimal RBM features with weighted score-based ensemble multi-disease prediction.

Multi-disease prediction is regarded as the capacity to simultaneously identify various diseases that are expected to be affected an individual at a certain period. These multiple diseases are seemed to be at various progression levels and need to be detected in the patient at the time of clinical visits. Diverse studies in the literature have included the predictive models for particular diseases yet, it is unable to notice humans with multiple diseases since humans are mostly suffered not only from a single disease but also from multiple diseases.

An Efficient Signal Processing Algorithm for Detecting Abnormalities in EEG Signal Using CNN.

Electroencephalography (EEG) is crucial for epilepsy detection; however, detecting abnormalities takes experience and knowledge. The electroencephalogram (EEG) is a technology that measures brain motion and represents the brain's function. EEG is an effective instrument for deciphering the brain's complicated activity.

Classification of Electrocardiography Hybrid Convolutional Neural Network-Long Short Term Memory with Fully Connected Layer.

Electrocardiography (ECG) is a technique for observing and recording the electrical activity of the human heart. The usage of an ECG signal is common among clinical professionals in the collection of time data for the examination of any rhythmic conditions associated with a subject. The investigation was carried out in order to computerize the assignment by exhibiting the issue using encoder-decoder techniques, creating the information that was simply typical of it, and utilising misfortune appropriation to anticipate standard or anomalous information.

An ensemble approach for healthcare application and diagnosis using natural language processing.

Integration of healthcare records into a single application is still a challenging process There are additional issues when data becomes heterogeneous, and its application based on users does not appear to be the same. Hence, we propose an application called MEDSHARE which is a web-based application that integrates the data from various sources and helps the patient to access all their health records in a single point of source. Apart just from the collection of data, this portal enables the process of diagnosis using Natural language processing.

Fuzzy segmentation and black widow-based optimal SVM for skin disease classification.

The skin, which has seven layers, is the main human organ and external barrier. According to the World Health Organization (WHO), skin cancer is the fourth leading cause of non-fatal disease risk. In medicinal fields, skin disease classification is a major challenging issue due to inaccurate outputs, overfitting, larger computational cost, and so on.