Deep BiLSTM Attention Model for Spatial and Temporal Anomaly Detection in Video Surveillance.

Detection of anomalies in video surveillance plays a key role in ensuring the safety and security of public spaces. The number of surveillance cameras is growing, making it harder to monitor them manually. So, automated systems are needed.

Detection of Vitiligo Through Machine Learning and Computer-Aided Techniques: A Systematic Review.

Vitiligo is a chronic skin damage disease, triggered by differential melanocyte death. Vitiligo (0.5%-1% of the population) is one of the most severe skin conditions.

X-Ray Image-Based Real-Time COVID-19 Diagnosis Using Deep Neural Networks (CXR-DNNs).

On 11 February 2020, the prevalent outbreak of COVID-19, a coronavirus illness, was declared a global pandemic. Since then, nearly seven million people have died and over 765 million confirmed cases of COVID-19 have been reported. The goal of this study is to develop a diagnostic tool for detecting COVID-19 infections more efficiently.

Towards the Development of the Clinical Decision Support System for the Identification of Respiration Diseases via Lung Sound Classification Using 1D-CNN.

Respiratory disorders are commonly regarded as complex disorders to diagnose due to their multi-factorial nature, encompassing the interplay between hereditary variables, comorbidities, environmental exposures, and therapies, among other contributing factors. This study presents a Clinical Decision Support System (CDSS) for the early detection of respiratory disorders using a one-dimensional convolutional neural network (1D-CNN) model. The ICBHI 2017 Breathing Sound Database, which contains samples of different breathing sounds, was used in this research.

The dark side of enterprise social media and employee digital creativity: Communication visibility perspective.

The primary objective of this study is to evaluate the significances of excessive usage of enterprise social media (ESM) on individuals' digital creativity through ESM exhaustion. In addition, we intend to investigate the significance of ESM visibility as a moderator in the connection between excessive usage of ESM, ESM exhaustion, and digital creativity. The results presented in this study are derived from primary data collected from 718 Chinese workers who utilize ESM.

Composite Ensemble Learning Framework for Passive Drone Radio Frequency Fingerprinting in Sixth-Generation Networks.

The rapid evolution of drone technology has introduced unprecedented challenges in security, particularly concerning the threat of unconventional drone and swarm attacks. In order to deal with threats, drones need to be classified by intercepting their Radio Frequency (RF) signals. With the arrival of Sixth Generation (6G) networks, it is required to develop sophisticated methods to properly categorize drone signals in order to achieve optimal resource sharing, high-security levels, and mobility management.

Enhanced accuracy with Segmentation of Colorectal Polyp using NanoNetB, and Conditional Random Field Test-Time Augmentation.

Colonoscopy is a reliable diagnostic method to detect colorectal polyps early on and prevent colorectal cancer. The current examination techniques face a significant challenge of high missed rates, resulting in numerous undetected polyps and irregularities. Automated and real-time segmentation methods can help endoscopists to segment the shape and location of polyps from colonoscopy images in order to facilitate clinician's timely diagnosis and interventions.

Numerical investigation of entropy generation and Magnetohydronamic natural convection in a porous square cavity with four embedded cylinders.

In this study, we investigated buoyancy-induced convection in a permeable square hollow containing four embedded cylinders and subjected to a magnetic field using numerical methods. The finite element approach was used to solve the governing equations of the system as well as the initial and boundary conditions. We analyzed the effects of the emerging non-dimensional quantities on the flow pattern and thermal field, as well as entropy production, in relation to the thermophysical properties of the obstacles.

Integrating deep learning techniques for personalized learning pathways in higher education.

The rapid improvement of artificial intelligence (AI) in the educational domain has opened new possibilities for enhancing the learning experiences for students. This research discusses the critical need for personalized education in higher education by integrating deep learning (DL) techniques to create customized learning pathways for students. This research intends to bridge the gap between constant educational content and dynamic student needs.

Systematic literature review on the application of machine learning for the prediction of properties of different types of concrete.

Background: Concrete, a fundamental construction material, stands as a significant consumer of virgin resources, including sand, gravel, crushed stone, and fresh water. It exerts an immense demand, accounting for approximately 1.6 billion metric tons of Portland and modified Portland cement annually.

Feeling low: How social media influences employees' knowledge hidings and innovative behavior.

Prior research has identified both positive and negative consequences arising from the widespread integration of social media within the organizations. The present research suggests that upward social comparison (USC) in social media is related to psychological disengagement resulting in knowledge hiding and lower innovative behavior of individuals. It further suggests that mindfulness mitigates the impact of USC in social media.

Exploring how enterprise social media usage affects employee creativity: Based on self-determination theory.

The present research sheds light on how ESM (Enterprise Social media) usage (social, work) affects employee creativity through primary psychological standards (perceived relatedness, autonomy, and competence). Drawing on self-determination theory (SDT), current research also examines the mediating mechanism of basic psychological needs. Applying structural equation modeling (SEM), 415 samples were analyzed on AMSO 24.

Optimizing multi-objective task scheduling in fog computing with GA-PSO algorithm for big data application.

As the volume and velocity of Big Data continue to grow, traditional cloud computing approaches struggle to meet the demands of real-time processing and low latency. Fog computing, with its distributed network of edge devices, emerges as a compelling solution. However, efficient task scheduling in fog computing remains a challenge due to its inherently multi-objective nature, balancing factors like execution time, response time, and resource utilization.

Pangenomic analyses of tuberculosis strains to identify resistomes using computational approaches.

Objective: To locate resistomes in tuberculosis strains, to determine the severity of drug resistance, and to infer its implications with respect to high tuberculosis prevalence in a Third World setting.

Methods: The pangenomic study was conducted from October 2022 to January 2023 in Sir Syed University of Engineering and Technology, Karachi, and comprised 2012-22 data on multiple sequence alignment to assess the genetic evolution of tuberculosis strains. Antibiotic resistance drug classes were identified using the Canadian Antibiotic Resistance Database, which entailed multidrug-resistant and extremely drug-resistant strains.

A moderated mediation investigation of the influence of enterprise social media visibility on work stress.

Drawing upon the communication visibility theory, this study delves into the intricate dynamics of message transference and network translucence within the context of enterprise social media (ESM), unraveling their profound impact on information overload and social overload experienced by employees. Consequently, we examine the consequential relationship between these overwhelming cognitive burdens and the levels of work stress experienced by individuals in the organizational setting. Moreover, our study explores the moderating role of resilience, shedding light on how this intrinsic psychological trait can shape the connection among information overload, social overload, and work stress.

Development of AI-Augmented optimization technique for analysis & prediction of modal mix in road transportation.

Transport sector contribution to global emissions is a known fact, however, the mitigation path to achieve nationally determined goals for carbon reduction is often not specified, A simplified technique based on minimax optimization using Grey relational grade and Random forest narrows down on most contributing input variables from twelve road transport modes. This is a region-specific, scenario-based technique applied to north Punjab, Province of Pakistan that first categorizes modes based on their emission and then integrates with AI modeling using Deep Neural Network to develop sustainable trade-offs for carbon reduction. The output parameter translates the problem into a systematic iterative technique that predicts optimization options with different scenarios to bring out an environment-friendly transport mix.

IoT empowered smart cybersecurity framework for intrusion detection in internet of drones.

The emergence of drone-based innovative cyber security solutions integrated with the Internet of Things (IoT) has revolutionized navigational technologies with robust data communication services across multiple platforms. This advancement leverages machine learning and deep learning methods for future progress. In recent years, there has been a significant increase in the utilization of IoT-enabled drone data management technology.

Naringenin, a Functional Food Component, Improves Motor and Non-Motor Symptoms in Animal Model of Parkinsonism Induced by Rotenone.

Parkinson's disease (PD) and other age-related neurodegenerative ailments have a strong link to oxidative stress. Bioflavonoid naringenin has antioxidant properties. The effects of pre- and post-naringenin supplementation on a rotenone-induced PD model were examined in this work.

On the dynamic reconfigurable implementations of MISTY1 and KASUMI block ciphers.

Novel hardware architectures for dynamic reconfigurable implementation of 64-bit MISTY1 and KASUMI block ciphers are proposed to enhance the performance of cryptographic chips for secure IoT applications. The SRL32 primitive (Reconfigurable Look up Tables-RLUTs) and DPR (Dynamic Partial Reconfiguration) are employed to reconfigure single round MISTY1 / KASUMI algorithms on the run-time. The RLUT based architecture attains dynamic logic functionality without extra hardware resources by internally modifying the LUT contents.

An empirical investigation on actual usage of educational app: Based on quality dimensions and mobile self-efficacy.

The intention of this research is to analyze the actual use of educational app by Chinese students for learning purpose. Based on DeLone & McLean (D&M) this study proposes and tests a research model which analyzes the actual use of educational apps using data collected from the 357 Chinese students. Structural equation modeling (SEM) was applied to examine the hypothesis.

Support for tourism development in Pakistan: A study of road and transportation infrastructure development.

The local population is considered one of the main stakeholders in tourism development. Understanding whether and how road and transport infrastructure is related to local communities' overall attitude toward tourism development is crucial. Scholars have suggested that the overall positive attitude of a local community is associated with its tourism development.

A computer vision-based system for recognition and classification of Urdu sign language dataset.

Human beings rely heavily on social communication as one of the major aspects of communication. Language is the most effective means of verbal and nonverbal communication and association. To bridge the communication gap between deaf people communities, and non-deaf people, sign language is widely used.

Protective Performance of Coated Reinforcement in Coral Concrete under Dry/Wet Cycling.

The actual protective performance of the coated reinforcement in coral concrete was investigated by measuring the chloride ion diffusion coefficient, electrochemical analysis, and numerical simulation. The test results show that the corrosion rate of coated reinforcement in coral concrete under the action of wet and dry cycles is kept at a low level, and the Rp value is always greater than 250 kΩ·cm during the test period, which is in the uncorroded state and has good protection performance. Moreover, the chloride ion diffusion coefficient D is in accordance with the power function relationship with the wet and dry cycle time, and a time-varying model of chloride ion concentration on the surface of coral concrete is established.