Innovative deep learning classifiers for breast cancer detection through hybrid feature extraction techniques.

Breast cancer remains a major cause of mortality among women, where early and accurate detection is critical to improving survival rates. This study presents a hybrid classification approach for mammogram analysis by combining handcrafted statistical features and deep learning techniques. The methodology involves preprocessing with the Shearlet Transform, segmentation using Improved Otsu thresholding and Canny edge detection, followed by feature extraction through Gray Level Co-occurrence Matrix (GLCM), Gray Level Run Length Matrix (GLRLM), and 1st-order statistical descriptors.

Blockchain based electronic educational document management with role-based access control using machine learning model.

The emergence of digital technology has led to a significant increase in the importance of educational credential storage, exchange, and verification for organisations, enterprises, and universities. Academic record forgery, record misuse, credential data tampering, time-consuming verification procedures, ownership and control difficulties, and other problems plague the education sector. Machine learning (ML) and blockchain, two of the most disruptive methods, have replaced traditional techniques in the education sector with highly technological and efficient ways.

The impact of urbanization on land use land cover change using geographic information system and remote sensing: a case of Mizan Aman City Southwest Ethiopia.

Land use land cover change due to urbanization is the prime driving forces to environmental problem and land surface temperature. The gap of the study is the lack of awareness of stakeholders regarding the protection of native forests, fruit trees, and BEBEKA coffee plantations. Deforestation for urban functions, including timber production, construction materials, and firewood, adversely affects the environment.

Enhancing healthcare data security using RFE and CRHSM for big data.

Providing security to the medical big data stored in healthcare cloud systems is the most exciting and demanding task in the present day. Many researchers use cryptographic techniques to protect big data against malicious users/attacks in cloud environments. Still, they face the problems of high complexity in operations, increased time consumption, storage overhead, and lack of efficiency.

Quantum physical analysis of caffeine and nicotine in CCL4 and DMSO solvent using density functional theory.

This work used the 6-311++G(d, p) basis set in the DFT/B3LYP and DFT/CAM-B3LYP technique to build the molecular structures of the nicotine and caffeine molecules. The minimum energy gives stability to these molecules with their corresponding dipole moment. The optimized structure to compute Raman spectroscopy and UV-Vis in CCl4 and DMSO solvent, employing the basis set 6-311++G(d, p), the DFT/B3LYP and CAM-B3LYP hybrid function, with the C-PCM model.

Design and simulation of a highly efficient eco-friendly, non-toxic perovskite solar cell.

A highly efficient and nontoxic material methylammoniumtin(II) iodideperovskite solar cell is proposed. This proposed solar cell uses CHNHSnI as the absorber layer, TiO as an Electron transport layer (ETL), Indium tin oxide as a buffer layer, and Copper(I) oxide as the hole transport layer (HTL). The device is simulated using the SCAPS-1D simulation tool.

Integration of silver nanostructures in wireless sensor networks for enhanced biochemical sensing.

Integrating noble metal nanostructures, specifically silver nanoparticles, into sensor designs has proven to enhance sensor performance across key metrics, including response time, stability, and sensitivity. However, a critical gap remains in understanding the unique contributions of various synthesis parameters on these enhancements. This study addresses this gap by examining how factors such as temperature, growth time, and choice of capping agents influence nanostructure shape and size, optimizing sensor performance for diverse conditions.

Spectroscopic, quantum chemical, and topological calculations of the phenylephrine molecule using density functional theory.

In this work, Density Functional Theory (DFT) on Gaussian 09 W software was utilized to investigate the phenylephrine (PE) molecule (C9H13NO2). Firstly, the optimized structure of the PE molecule was obtained using B3LYP/6-311 + G (d, p) and CAM-B3LYP/6-311 + G (d, p) basis sets. The electron charge density is shown in Mulliken atomic charge as a bar chart and also as a color-filled map in Molecular Electrostatic Potential (MEP).

Securing healthcare medical image information using advance morphological component analysis, information hiding systems, and hybrid convolutional neural networks on IoMT.

Health care images contain a variety of imaging information that has specific features, which can make it challenging to assess and decide on the methods necessitated to safeguard the highly classified visuals from unauthorized exposure during transmission in a communication channel. As a result, this proposed approach utilizes a variety of techniques that will enhance the quality of textual healthcare images, communicate information securely, and interpret textual data from healthcare visuals without difficulty. Natural interference, primarily on the receiver side, reduces text-based healthcare image contrast, and numerous artifacts and adjacent picture element values impede diagnosis.

Face mask identification with enhanced cuckoo optimization and deep learning-based faster regional neural network.

A mask identification and social distance monitoring system using Unmanned Aerial Vehicles (UAV) in the outdoors has been proposed for a health establishment. The above approach performed surveillance of the surrounding area using cameras installed in UAVs and internet of things technologies, and the captured images seem useful for tracking the entire environment. However, innate images from unmanned aerial vehicles show an adaptable visual effect in an uncontrolled environment, making face-mask detection and recognition harder.

The Negative Impact of Social Media during COVID-19 Pandemic.

The coronavirus pandemic is a global pandemic of Coronavirus Disease 2019 (COVID-19) resulting from the Severe Acute Respiratory Syndrome Coronavirus 2 (SARS-CoV2). Despite its numerous benefits for sharing health information, social media has raised several concerns in terms of posing panic among the general population around the world during the COVID-19 pandemic. The main objective of this study was to investigate the negative impact of social media during the COVID-19 outbreak.

The Prevalence and Predictors of Depressive, Anxiety, and Stress Symptoms Among Tepi Town Residents During the COVID-19 Pandemic Lockdown in Ethiopia.

Background: As a result of the coronavirus disease 2019 (COVID-19) outbreak, many countries have imposed movement restrictions and implemented lockdowns. However, evidence from a variety of nations showed that the COVID-19 outbreak and its associated quarantine measures triggered a wide range of psychological problems, such as anxiety, depression, and stress in the general population. As a result, the purpose of this study was to determine the prevalence and predictors of depression, anxiety, and stress symptoms among Tepi town residents during the pandemic lockdown.

Deep Learning and Particle Swarm Optimisation-Based Techniques for Visually Impaired Humans' Text Recognition and Identification.

Blind people can benefit greatly from a system capable of localising and reading comprehension text embedded in natural scenes and providing useful information that boosts their self-esteem and autonomy in everyday situations. Regardless of the fact that existing optical character recognition programmes seem to be quick and effective, the majority of them are not able to correctly recognise text embedded in usual panorama images. The methodology described in this paper is to localise textual image regions and pre-process them using the naïve Bayesian algorithm.

Impact of SARS-CoV-2 in Online Education, Predicting and Contrasting Mental Stress of Young Students: A Machine Learning Approach.

The nation has already had to contend with such a major public health issue when this COVID-19 disease was propagated in March 2020. The corona's propagation does have an effect on all areas of people's lives. The hostile effect on education systems throughout all locations is indeed a diverse expansion impact.

A rare case of reversible acquired AA-type renal amyloidosis in a chronic filariasis patient receiving antifilarial therapy.

Lymphatic filariasis is a major health problem in India with a large number of patients tending to be asymptomatic. In the Southeast and South Asian regions, Wuchereria bancrofti is the most prevalent parasite, causing filariasis in 99.4% of cases.