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).

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.

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.

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.

IndicDialogue: A dataset of subtitles in 10 Indic languages for Indic language modeling.

The Languages of the Indian subcontinent are less represented in current NLP literature. To mitigate this gap, we present the IndicDialogue dataset, which contains subtitles and dialogues in 10 major Indic languages: Hindi, Bengali, Marathi, Telugu, Tamil, Urdu, Odia, Sindhi, Nepali, and Assamese. This dataset is sourced from OpenSubtitles.

Analysis of Internet of things implementation barriers in the cold supply chain: An integrated ISM-MICMAC and DEMATEL approach.

Integrating Internet of Things (IoT) technology inside the cold supply chain can enhance transparency, efficiency, and quality, optimize operating procedures, and increase productivity. The integration of the IoT in this complicated setting is hindered by specific barriers that require thorough examination. Prominent barriers to IoT implementation in a cold supply chain, which is the main objective, are identified using a two-stage model.

Crystallographic structure, antibacterial effect, and catalytic activities of fig extract mediated silver nanoparticles.

Silver nanoparticles (Ag NPs) play a pivotal role in the current research landscape due to their extensive applications in engineering, biotechnology, and industry. The aim is to use fig ( Linn. f.

Revealing the full potential of CsPbIBr perovskite solar cells: advancements towards enhanced performance.

Cesium lead iodide bromide (CsPbIBr) perovskite solar cells (PSCs) have improved stability compared to other perovskite compositions. However, they still face significant challenges due to their poor photovoltaic performance parameters, which limit the devices' power conversion efficiencies (PCEs). This study proposes a novel device design to tailor the potential of CsPbIBr PSCs by improving their optoelectronic properties.

A machine learning approach for vocal fold segmentation and disorder classification based on ensemble method.

In the healthcare domain, the essential task is to understand and classify diseases affecting the vocal folds (VFs). The accurate identification of VF disease is the key issue in this domain. Integrating VF segmentation and disease classification into a single system is challenging but important for precise diagnostics.