Attention-guided CenterNet deep learning approach for lung cancer detection.

Lung cancer remains a significant health concern worldwide, prompting ongoing research efforts to enhance early detection and diagnosis. Prior studies have identified key challenges in existing approaches, including limitations in feature extraction, interpretability, and computational efficiency. In response, this study introduces a novel deep learning (DL) framework, termed the Improved CenterNet approach, tailored specifically for lung cancer detection.

The role of blended learning in improving medical students' academic performance: evidence from Pakistan.

Objective: The study examines the role of blended learning in improving medical students' academic performance through self-regulatory learning and technological competence and identifies the moderating role of perceived institutional support in the relationships between self-regulatory learning, perceived teacher credibility, technological competencies, and academic performance.

Methods: The study was based on behavioral learning theory as a theoretical framework, and an adapted questionnaire was used to collect the data. In total, 275 medical students participated in the study, and the data was analyzed using structural equation modeling techniques with SmartPLS.

A secure blockchain framework for healthcare records management systems.

Electronic health records are one of the essential components of health organizations. In recent years, there have been increased concerns about privacy and reputation regarding the storage and use of patient information. In this regard, the information provided as a part of medical and health insurance, for instance, can be viewed as proof of social insurance and governance.

Ensemble Classification Model With CFS-IGWO-Based Feature Selection for Cancer Detection Using Microarray Data.

Cancer is the top cause of death worldwide, and machine learning (ML) has made an indelible mark on the field of early cancer detection, thereby lowering the death toll. ML-based model for cancer diagnosis is done using two forms of data: gene expression data and microarray data. The data on gene expression levels includes many dimensions.

Ancient and modern bone diagnosis: Towards a better understanding of chemical and structural feature alterations.

Chemical and structural alterations hold great importance in the field of diagenesis. Attenuated Total Reflectance - Fourier Transform Infrared Spectroscopy (ATR-FTIR) is a valuable method for examining bio-apatite composition changes. Infrared spectroscopy (IR) and X-ray diffraction (XRD) were employed to analyze both modern and archaeological bone specimens.

Emerging research trends in artificial intelligence for cancer diagnostic systems: A comprehensive review.

This review article offers a comprehensive analysis of current developments in the application of machine learning for cancer diagnostic systems. The effectiveness of machine learning approaches has become evident in improving the accuracy and speed of cancer detection, addressing the complexities of large and intricate medical datasets. This review aims to evaluate modern machine learning techniques employed in cancer diagnostics, covering various algorithms, including supervised and unsupervised learning, as well as deep learning and federated learning methodologies.

An efficient improved parrot optimizer for bladder cancer classification.

Bladder Cancer (BC) is a common disease that comes with a high risk of morbidity, death, and expense. Primary risk factors for BC include exposure to carcinogens in the workplace or the environment, particularly tobacco. There are several difficulties, such as the requirement for a qualified expert in BC classification.

Integrating Convolutional Neural Networks with Attention Mechanisms for Magnetic Resonance Imaging-Based Classification of Brain Tumors.

The application of magnetic resonance imaging (MRI) in the classification of brain tumors is constrained by the complex and time-consuming characteristics of traditional diagnostics procedures, mainly because of the need for a thorough assessment across several regions. Nevertheless, advancements in deep learning (DL) have facilitated the development of an automated system that improves the identification and assessment of medical images, effectively addressing these difficulties. Convolutional neural networks (CNNs) have emerged as steadfast tools for image classification and visual perception.

Advanced Optoelectronic Modeling and Optimization of HTL-Free FASnI/C60 Perovskite Solar Cell Architecture for Superior Performance.

In this study, a novel perovskite solar cell (PSC) architecture is presented that utilizes an HTL-free configuration with formamide tin iodide (FASnI) as the active layer and fullerene (C60) as the electron transport layer (ETL), which represents a pioneering approach within the field. The elimination of hole transport layers (HTLs) reduces complexity and cost in PSC heterojunction structures, resulting in a simplified and more cost-effective PSC structure. In this context, an HTL-free tin HC(NH)SnI-based PSC was simulated using the solar cell capacitance simulator (SCAPS) within a one-dimensional framework.

Multilayer cyberattacks identification and classification using machine learning in internet of blockchain (IoBC)-based energy networks.

The world's need for energy is rising due to factors like population growth, economic expansion, and technological breakthroughs. However, there are major consequences when gas and coal are burnt to meet this surge in energy needs. Although these fossil fuels are still essential for meeting energy demands, their combustion releases a large amount of carbon dioxide and other pollutants into the atmosphere.

Supervised Machine Learning-Based Prediction of Hydrogen Storage Classes Utilizing Dibenzyltoluene as an Organic Carrier.

Dibenzyltoluene (H0-DBT), a Liquid Organic Hydrogen Carrier (LOHC), presents an attractive solution for hydrogen storage due to its enhanced safety and ability to store hydrogen in a concentrated liquid form. The utilization of machine learning proves essential for accurately predicting hydrogen storage classes in H0-DBT across diverse experimental conditions. This study focuses on the classification of hydrogen storage data into three classes, low-class, medium-class and high-class, based on the hydrogen storage capacity values.

Identification of kidney stones in KUB X-ray images using VGG16 empowered with explainable artificial intelligence.

A kidney stone is a solid formation that can lead to kidney failure, severe pain, and reduced quality of life from urinary system blockages. While medical experts can interpret kidney-ureter-bladder (KUB) X-ray images, specific images pose challenges for human detection, requiring significant analysis time. Consequently, developing a detection system becomes crucial for accurately classifying KUB X-ray images.

Machine Learning-Based Approach for Identifying Research Gaps: COVID-19 as a Case Study.

Background: Research gaps refer to unanswered questions in the existing body of knowledge, either due to a lack of studies or inconclusive results. Research gaps are essential starting points and motivation in scientific research. Traditional methods for identifying research gaps, such as literature reviews and expert opinions, can be time consuming, labor intensive, and prone to bias.

Cyberattack patterns in blockchain-based communication networks for distributed renewable energy systems: A study on big datasets.

Blockchain-based reliable, resilient, and secure communication for Distributed Energy Resources (DERs) is essential in Smart Grid (SG). The Solana blockchain, due to its high stability, scalability, and throughput, along with low latency, is envisioned to enhance the reliability, resilience, and security of DERs in SGs. This paper presents big datasets focusing on SQL Injection, Spoofing, and Man-in-the-Middle (MitM) cyberattacks, which have been collected from Solana blockchain-based Industrial Wireless Sensor Networks (IWSNs) for events monitoring and control in DERs.

Enhancing photodynamic therapy efficacy through silica nanoparticle-mediated delivery of temoporfin for targeted in vitro breast cancer treatment.

Photodynamic therapy (PDT), an approach to cancer treatment, relies fundamentally on two key elements: a light source and a photosensitizing agent. A primary challenge in PDT is the efficient delivery of photosensitizers to the target tissue, hindered by the body's reticuloendothelial system (RES). Silica nanoparticles (SiNPs), known for their unique properties, emerge as ideal carriers in this context.

HLD-DDoSDN: High and low-rates dataset-based DDoS attacks against SDN.

Software Defined Network (SDN) has alleviated traditional network limitations but faces a significant challenge due to the risk of Distributed Denial of Service (DDoS) attacks against an SDN controller, with current detection methods lacking evaluation on unrealistic SDN datasets and standard DDoS attacks (i.e., high-rate DDoS attack).

Bad bosses and despotism at workplace: A systematic review of the despotic leadership literature.

Over the years, the concept of despotic leadership (DL) has emerged as a hot topic in academic and professional debates on leadership. To this end, this study aims to synthesize existing literature on despotic leadership in business management scholarship. We utilize a systematic literature review technique to systematically identify, select, and evaluate existing scholarly publications on despotic leadership to highlight emerging topics, theories, and the consequences of despotic leadership at the workplace.

Secure medical image transmission using deep neural network in e-health applications.

Recently, medical technologies have developed, and the diagnosis of diseases through medical images has become very important. Medical images often pass through the branches of the network from one end to the other. Hence, high-level security is required.

Co-Design Dedicated System for Efficient Object Tracking Using Swarm Intelligence-Oriented Search Strategies.

The template matching technique is one of the most applied methods to find patterns in images, in which a reduced-size image, called a target, is searched within another image that represents the overall environment. In this work, template matching is used via a co-design system. A hardware coprocessor is designed for the computationally demanding step of template matching, which is the calculation of the normalized cross-correlation coefficient.

Impact of Using the Internet of Medical Things on e-Healthcare Performance: Blockchain Assist in Improving Smart Contract.

Background: This paper explores the use of blockchain technology and smart contracts in the Internet of Medical Things (IoMT). It aims to identify the challenges and benefits of implementing smart contracts based on blockchain technology in the IoMT. It provides solutions and evaluates the IoMT uses in e-healthcare performance.

Hematologic Cancer Detection Using White Blood Cancerous Cells Empowered with Transfer Learning and Image Processing.

Lymphoma and leukemia are fatal syndromes of cancer that cause other diseases and affect all types of age groups including male and female, and disastrous and fatal blood cancer causes an increased savvier death ratio. Both lymphoma and leukemia are associated with the damage and rise of immature lymphocytes, monocytes, neutrophils, and eosinophil cells. So, in the health sector, the early prediction and treatment of blood cancer is a major issue for survival rates.