Knowledge distillation approach for skin cancer classification on lightweight deep learning model.

Over the past decade, there has been a global increase in the incidence of skin cancers. Skin cancer has serious consequences if left untreated, potentially leading to more advanced cancer stages. In recent years, deep learning based convolutional neural network have emerged as powerful tools for skin cancer detection.

SmartSkin-XAI: An Interpretable Deep Learning Approach for Enhanced Skin Cancer Diagnosis in Smart Healthcare.

Skin cancer, particularly melanoma, poses significant challenges due to the heterogeneity of skin images and the demand for accurate and interpretable diagnostic systems. Early detection and effective management are crucial for improving patient outcomes. Traditional AI models often struggle with balancing accuracy and interpretability, which are critical for clinical adoption.

Key degradation mechanisms of perovskite solar cells and strategies for enhanced stability: issues and prospects.

Perovskite materials have garnered significant attention within a very short period of time by achieving competitive efficiency. In addition, this material demonstrated intriguing optoelectronic properties and versatile applications. Although they have confirmed amazing efficiency in solar cells at the laboratory scale, mass commercial manufacturing of perovskite solar cells (PSCs) is still a problem due to their poor longevity.

Solving the maximum cut problem using Harris Hawk Optimization algorithm.

The objective of the max-cut problem is to cut any graph in such a way that the total weight of the edges that are cut off is maximum in both subsets of vertices that are divided due to the cut of the edges. Although it is an elementary graph partitioning problem, it is one of the most challenging combinatorial optimization-based problems, and tons of application areas make this problem highly admissible. Due to its admissibility, the problem is solved using the Harris Hawk Optimization algorithm (HHO).

Motivational orientation and perception of active learning instruction by pre-service language teachers.

This study investigates the impact of active learning instruction on the motivational orientation of pre-service language teachers. The data were collected by using the AGQ-R and the StRIP questionnaire, and analyzed through repeated measures of MANOVAs and correlation coefficient. Pre-service language teachers reported a higher approach goal orientation emphasizing the desire to succeed rather than avoidance goal orientation, i.

Analysis of jute-glass fiber reinforced epoxy hybrid composite.

This study investigated a composite material combining epoxy with hybrid jute (J) and glass (G) fibers. A straightforward and effective fabrication method was employed, utilizing five layers with various reinforcement materials. To identify the optimal combination, a comprehensive series of tests were conducted using a range of characterization instruments, including Scanning Electron Microscopy (SEM), Universal Testing Machine (UTM), pendulum impact tester, density measurement, specific gravity evaluation, water absorption, and swelling thickness tests.

Perceptions and perspectives towards safe food handling and its practices: a case study at Jahangirnagar University.

Background: Foodborne illness is a significant public health concern, particularly in developing countries like Bangladesh. Young adults, aged 18-26 (including undergraduates and recent graduates), are especially vulnerable to the onset of unhealthy eating habits and nutritional imbalances as they begin living independently, often away from their families. This research aims to identify the risk factors associated with the knowledge, attitudes, and practices related to safe food handling among residential university students.

Monosaccharide coating modulate the intracellular trafficking of gold nanoparticles in dendritic cells.

Dendritic cells (DCs) have emerged as a promising target for drug delivery and immune modulation due to their pivotal role in initiating the adaptive immune response. Gold nanoparticles (AuNPs) have garnered interest as a platform for targeted drug delivery due to their biocompatibility, low toxicity and precise control over size, morphology and surface functionalization. Our investigation aimed to elucidate the intracellular uptake and trafficking of AuNPs coated with different combinations of monosaccharides (mannose, galactose, and fucose) in DCs.

Segmentation for mammography classification utilizing deep convolutional neural network.

Background: Mammography for the diagnosis of early breast cancer (BC) relies heavily on the identification of breast masses. However, in the early stages, it might be challenging to ascertain whether a breast mass is benign or malignant. Consequently, many deep learning (DL)-based computer-aided diagnosis (CAD) approaches for BC classification have been developed.

Computer vision algorithms in healthcare: Recent advancements and future challenges.

Computer vision has emerged as a promising technology with numerous applications in healthcare. This systematic review provides an overview of advancements and challenges associated with computer vision in healthcare. The review highlights the application areas where computer vision has made significant strides, including medical imaging, surgical assistance, remote patient monitoring, and telehealth.

A lightweight deep learning method to identify different types of cervical cancer.

Cervical cancer is the second most common cancer in women's bodies after breast cancer. Cervical cancer develops from dysplasia or cervical intraepithelial neoplasm (CIN), the early stage of the disease, and is characterized by the aberrant growth of cells in the cervix lining. It is primarily caused by Human Papillomavirus (HPV) infection, which spreads through sexual activity.

Fusing global context with multiscale context for enhanced breast cancer classification.

Breast cancer is the second most common type of cancer among women. Prompt detection of breast cancer can impede its advancement to more advanced phases, thereby elevating the probability of favorable treatment consequences. Histopathological images are commonly used for breast cancer classification due to their detailed cellular information.

Exploring Internet of Things adoption challenges in manufacturing firms: A Delphi Fuzzy Analytical Hierarchy Process approach.

Innovation is key to gaining a sustainable edge in an increasingly competitive global manufacturing landscape. For Bangladesh's manufacturing sector to survive and thrive in today's cutthroat business environment, adopting transformative technologies such as the Internet of Things (IoT) is not a luxury but a necessity. This article tackles the formidable task of identifying and comprehensively evaluating the impediments to IoT adoption in the Bangladeshi manufacturing industry.

Ensemble of hybrid model based technique for early detecting of depression based on SVM and neural networks.

The prevalence of depression has increased dramatically over the last several decades: it is frequently overlooked and can have a significant impact on both physical and mental health. Therefore, it is crucial to develop an automated detection system that can instantly identify whether a person is depressed. Currently, machine learning (ML) and artificial neural networks (ANNs) are among the most promising approaches for developing automated computer-based systems to predict several mental health issues, such as depression.

A comprehensive dental dataset of six classes for deep learning based object detection study.

This article presents a dental dataset for the improvement of research on deep learning-based detection and classification of dental diseases. The dataset is consisted of 232 panoramic dental radiographs, categorized into six major classes: healthy teeth, caries, impacted teeth, infections, fractured teeth, and broken-down crowns/roots (BDC/BDR). The images were collected from three renowned private clinics in Dhaka, Bangladesh, with the help of an experienced dental practitioner who ensured the confidentiality of patients and high-quality data acquisition using a 64-megapixel Android phone camera.

tRF-BERT: A transformative approach to aspect-based sentiment analysis in the bengali language.

In recent years, the surge in reviews and comments on newspapers and social media has made sentiment analysis a focal point of interest for researchers. Sentiment analysis is also gaining popularity in the Bengali language. However, Aspect-Based Sentiment Analysis is considered a difficult task in the Bengali language due to the shortage of perfectly labeled datasets and the complex variations in the Bengali language.

Role of Low Amount of Iron Intake from Groundwater for Prevention of Anemia in Children: A Cross-Sectional Study in Rural Bangladesh.

In Bangladesh, groundwater, the principal source of drinking water, contains predominantly high levels of iron. Drinking groundwater is associated with good iron status in populations. Against this backdrop, iron supplementation is often associated with side effects, which reduces its intake compliance.

Human papillomavirus type-specific distribution in cervical intraepithelial neoplasia and cancer in The Gambia prior to HPV immunization programme: a baseline for monitoring the quadrivalent vaccine.

Background: Cervical cancer is the leading cause of cancer deaths in Gambian women. Current estimates indicate that 286 women are annually diagnosed with cervical cancer with a fatality rate of 70%. In an attempt to address this, in 2019 the quadrivalent HPV vaccine was incorporated into the Gambia's Expanded Programme on Immunisation.

DenseNet201Plus: Cost-effective transfer-learning architecture for rapid leaf disease identification with attention mechanisms.

Plant leaf diseases are a significant concern in agriculture due to their detrimental impact on crop productivity and food security. Effective disease management depends on the early and accurate detection and diagnosis of these conditions, facilitating timely intervention and mitigation strategies. In this study, we address the pressing need for accurate and efficient methods for detecting leaf diseases by introducing a new architecture called DenseNet201Plus.

An extensive investigation of convolutional neural network designs for the diagnosis of lumpy skin disease in dairy cows.

Cow diseases are a major source of concern for people. Some diseases in animals that are discovered in their early stages can be treated while they are still treatable. If lumpy skin disease (LSD) is not properly treated, it can result in significant financial losses for the farm animal industry.

Ergonomic design of computer laboratory furniture: Mismatch analysis utilizing anthropometric data of university students.

Many studies have shown that ergonomically designed furniture improves productivity and well-being. As computers have become a part of students' academic lives, they will continue to grow in the future. We propose anthropometric-based furniture dimensions that are suitable for university students to improve computer laboratory ergonomics.