Enhancing security in financial transactions: a novel blockchain-based federated learning framework for detecting counterfeit data in fintech.

Fintech is an industry that uses technology to enhance and automate financial services. Fintech firms use software, mobile apps, and digital technologies to provide financial services that are faster, more efficient, and more accessible than those provided by traditional banks and financial institutions. Fintech companies take care of processes such as lending, payment processing, personal finance, and insurance, among other financial services.

High-Precision Skin Disease Diagnosis through Deep Learning on Dermoscopic Images.

Dermatological conditions are primarily prevalent in humans and are primarily caused by environmental and climatic fluctuations, as well as various other reasons. Timely identification is the most effective remedy to avert minor ailments from escalating into severe conditions. Diagnosing skin illnesses is consistently challenging for health practitioners.

A comprehensive survey of evolutionary algorithms and metaheuristics in brain EEG-based applications.

Electroencephalography (EEG) has emerged as a primary non-invasive and mobile modality for understanding the complex workings of the human brain, providing invaluable insights into cognitive processes, neurological disorders, and brain-computer interfaces. Nevertheless, the volume of EEG data, the presence of artifacts, the selection of optimal channels, and the need for feature extraction from EEG data present considerable challenges in achieving meaningful and distinguishing outcomes for machine learning algorithms utilized to process EEG data. Consequently, the demand for sophisticated optimization techniques has become imperative to overcome these hurdles effectively.

Does economic complexity help in achieving environmental sustainability? New empirical evidence from N-11 countries.

In view of the SDGs argued by UNO, it is vital to address the pressing issues regarding sustainable development. The aim of current study is to investigate the impact of economic complexity (ECC) on environmental sustainability. To achieve this aim, we sampled the 25 years of data of Next-11 countries over the period 1995 to 2019.

Multi-IsnadSet MIS for Sahih Muslim Hadith with chain of narrators, based on multiple ISNAD.

In the Islamic domain, Hadiths hold significant importance, standing as crucial texts following the Holy Quran. Each Hadith contains three main parts: the ISNAD (chain of narrators), TARAF (starting part, often from Prophet Muhammad), and MATN (Hadith content). ISNAD, a chain of narrators involved in transmitting that particular MATN.

Multi-model deep learning approach for segmentation of teeth and periapical lesions on pantomographs.

Introduction: The fields of medicine and dentistry are beginning to integrate artificial intelligence (AI) in diagnostics. This may reduce subjectivity and improve the accuracy of diagnoses and treatment planning. Current evidence on pathosis detection on pantomographs (PGs) indicates the presence or absence of disease in the entire radiographic image, with little evidence of the relation of periapical pathosis to the causative tooth.

Digital economy, innovation factor allocation and industrial structure transformation-A case study of the Yangtze River Delta city cluster in China.

The attainment of regional high-quality development necessitates the critical role of the digital economy in facilitating the transformation of industrial structures. This study intends to investigate the effect of the digital economy on industrial structure transformation from the perspective of innovation factor allocation using a panel dataset of 41 cities in the Yangtze River Delta region for the period from 2011 to 2020. This paper considers four dimensions to measure the level of industrial structure transformation i.

Towards classification and comprehensive analysis of AI-based COVID-19 diagnostic techniques: A survey.

The unpredictable pandemic came to light at the end of December 2019, known as the novel coronavirus, also termed COVID-19, identified by the World Health Organization (WHO). The virus first originated in Wuhan (China) and rapidly affected most of the world's population. This outbreak's impact is experienced worldwide because it causes high mortality risk, many cases, and economic falls.

A Hybrid Approach for Energy Consumption and Improvement in Sensor Network Lifespan in Wireless Sensor Networks.

In this paper, we propose an improved clustering algorithm for wireless sensor networks (WSNs) that aims to increase network lifetime and efficiency. We introduce an enhanced fuzzy spider monkey optimization technique and a hidden Markov model-based clustering algorithm for selecting cluster heads. Our approach considers factors such as network cluster head energy, cluster head density, and cluster head position.

Fully Automated Skull Stripping from Brain Magnetic Resonance Images Using Mask RCNN-Based Deep Learning Neural Networks.

This research comprises experiments with a deep learning framework for fully automating the skull stripping from brain magnetic resonance (MR) images. Conventional techniques for segmentation have progressed to the extent of Convolutional Neural Networks (CNN). We proposed and experimented with a contemporary variant of the deep learning framework based on mask region convolutional neural network (Mask-RCNN) for all anatomical orientations of brain MR images.

A Novel Markov Model-Based Traffic Density Estimation Technique for Intelligent Transportation System.

An intelligent transportation system (ITS) aims to improve traffic efficiency by integrating innovative sensing, control, and communications technologies. The industrial Internet of things (IIoT) and Industrial Revolution 4.0 recently merged to design the industrial Internet of things-intelligent transportation system (IIoT-ITS).

Comparative molecular dynamics simulations of thermal conductivities of aqueous and hydrocarbon nanofluids.

The addition of metal oxide nanoparticles to fluids has been used as a means of enhancing the thermal conductive properties of base fluids. This method formulates a heterogeneous fluid conferred by nanoparticles and can be used for high-end fluid heat-transfer applications, such as phase-change materials and fluids for internal combustion engines. These nanoparticles can enhance the properties of both polar and nonpolar fluids.

Analysis of Risk Factors in Global Software Development: A Cross-Continental Study Using Modified Firefly Algorithm.

In today's competitive world, software organizations are moving towards global software development (GSD). This became even more significant in times such as COVID-19 pandemic, where team members residing in different geographical locations and from different cultures had to work from home to carry on their tasks and responsibilities as travelling was restricted. These teams are distributed in nature and work on the same set of goals and objectives.

A benchmarking program to support software process improvement adaptation in a developing country, a Pakistan case.

Background: Standardization of software development processes using the software process improvement (SPI) frameworks like capability maturity model integration (CMMI) or the International Organization for Standardization (ISO) are vital for better performance and defect-free delivery of software engineering projects.

Problem: The studies from developing countries show that the organizations are unable to get the desired SPI benefits during early adaptation due to lack of benchmarking initiatives. These initiative needed bridging of the gap between major software engineering fields such as project performance, productivity, and SPI measurement metrics like effort, schedule, cost, productivity, quality, and customer satisfaction.

Wavelet-Based Fractal Analysis of rs-fMRI for Classification of Alzheimer's Disease.

The resting-state functional magnetic resonance imaging (rs-fMRI) modality has gained widespread acceptance as a promising method for analyzing a variety of neurological and psychiatric diseases. It is established that resting-state neuroimaging data exhibit fractal behavior, manifested in the form of slow-decaying auto-correlation and power-law scaling of the power spectrum across low-frequency components. With this property, the rs-fMRI signal can be broken down into fractal and nonfractal components.

Blockchain-Based Authentication and Trust Management Mechanism for Smart Cities.

Security has always been the main concern for the internet of things (IoT)-based systems. Blockchain, with its decentralized and distributed design, prevents the risks of the existing centralized methodologies. Conventional security and privacy architectures are inapplicable in the spectrum of IoT due to its resource constraints.

Energizing Intention to Visit Rural Destinations: How Social Media Disposition and Social Media Use Boost Tourism Through Information Publicity.

This study aimed to examine the impact of information publicity on the intention of tourists to visit rural destinations in developing countries. Based on the theory of planned behavior (TPB), we examined the indirect effect of information publicity on intention to visit subjective norms and further investigated the moderating effect of social media disposition and social media use. The study used data from a time-lagged design with three waves which supported the hypothesized model.

ECM-LSE: Prediction of Extracellular Matrix Proteins Using Deep Latent Space Encoding of k-Spaced Amino Acid Pairs.

Extracelluar matrix (ECM) proteins create complex networks of macromolecules which fill-in the extracellular spaces of living tissues. They provide structural support and play an important role in maintaining cellular functions. Identification of ECM proteins can play a vital role in studying various types of diseases.

Predicting treatment outcome of drug-susceptible tuberculosis patients using machine-learning models.

Tuberculosis (TB) is a deadly contagious disease and a serious global health problem. It is curable but due to its lengthy treatment process, a patient is likely to leave the treatment incomplete, leading to a more lethal, drug resistant form of disease. The World Health Organization (WHO) propagates Directly Observed Therapy Short-course (DOTS) as an effective way to stop the spread of TB in communities with a high burden.

Health informatics to optimize complex laboratory developed test configurations.

Background: Configuration of complex Laboratory Developed Tests (LDTs) is a time-consuming and complicated task, potentially leading to inconsistent LDTs in which features constraints remain unresolved and important features could remain unselected.

Objective: Our objective is to address these issues by presenting an automated, health informatics solution which autonomously optimizes feature selection in complex LDTs through Particle Swarm Optimization (PSO). The optimization goal is to minimize inconsistencies and configuration time, and maximize the number of selected features.