A Multi-Class Intrusion Detection System for DDoS Attacks in IoT Networks Using Deep Learning and Transformers.

The rapid proliferation of Internet of Things (IoT) devices has significantly increased vulnerability to Distributed Denial of Service (DDoS) attacks, which can severely disrupt network operations. DDoS attacks in IoT networks disrupt communication and compromise service availability, causing severe operational and economic losses. In this paper, we present a Deep Learning (DL)-based Intrusion Detection System (IDS) tailored for IoT environments.

Financial frictions and stock return: A novel least minus more frictional factor for asset pricing models in emerging economies.

The primary objective of this study is to empirically evaluate the role of various levels of financial friction in explaining stock returns through different asset pricing models. This study enhances asset pricing model estimates by incorporating diverse levels of financial friction by introducing a novel least minus more frictional asset pricing factor specifically constructed for emerging economies. The empirical analysis is conducted using data from a sample including five countries: China, India, Pakistan, Bangladesh, and Sri Lanka.

Advanced cloud intrusion detection framework using graph based features transformers and contrastive learning.

This paper presents a modular and scalable intrusion detection framework that combines graph-based feature extraction, Transformer-based autoencoding, and contrastive learning to improve detection accuracy in cloud environments. Network flows are modeled as graphs to capture relational patterns among IP addresses and services, and a Graph Neural Network (GNN) is used to extract structured embeddings. These embeddings are refined through a Transformer-based autoencoder to preserve contextual information, while contrastive learning enforces clear class separation during classification.

UMEDNet: a multimodal approach for emotion detection in the Urdu language.

Emotion detection is a critical component of interaction between human and computer systems, more especially affective computing, and health screening. Integrating video, speech, and text information provides better coverage of the basic and derived affective states with improved estimation of verbal and non-verbal behavior. However, there is a lack of systematic preferences and models for the detection of emotions in low-resource languages such as Urdu.

WG-Storm: a resource-aware scheduler for distributed stream processing engines.

Stream processing engines (SPEs) allow applications to process a large amount of data in real-time. However, to schedule big data applications; the SPEs create several challenges regarding resource utilisation, dynamic configurations, heterogeneous environment, resource awareness, load balancing, As the volume of data increases over time, it also poses a challenge to predict the resource and application requirements for processing. All these factors play an important role, they can cause problems in achieving maximum throughput due to inefficiency in any of them.

Vision-based approach to knee osteoarthritis and Parkinson's disease detection utilizing human gait patterns.

Recently, the number of cases of musculoskeletal and neurological disorders, such as knee osteoarthritis (KOA) and Parkinson's disease (PD), has significantly increased. Numerous clinical methods have been proposed in research to diagnose these disorders; however, a current trend in diagnosis is through human gait patterns. Several researchers proposed different methods in this area, including gait detection utilizing sensor-based data and vision-based systems that include both marker-based and marker-free techniques.

Estimation of finite population mean in a complex survey sampling.

Accurate estimation of the finite population mean is a fundamental challenge in survey sampling, especially when dealing with large or complex populations. Traditional methods like simple random sampling may not always provide reliable or efficient estimates in such cases. Motivated by this, the current study explores complex sampling techniques to improve the precision and accuracy of mean estimators.

Shortening surgical time in high myopia treatment: a randomized controlled trial comparing non-OVD and OVD techniques in ICL implantation.

Introduction: High myopia correction is essential for improving visual acuity and quality of life. Traditional intraocular collamer lens (ICL) implantation using an ophthalmic viscosurgical device (OVD) is effective but often results in prolonged surgical times, impacting patient comfort and recovery. This study investigates a novel non-OVD ICL implantation technique aimed at reducing surgical duration and improving patient outcomes.

Estimation of the distribution function of a finite population utilizing auxiliary information in the context of non-response within complex survey sampling.

This study focuses on estimating a finite population cumulative distribution function (CDF) using two-stage and three-stage cluster sampling under non-response. This work is then extended to estimate the finite population CDF under non-response using stratified two-stage and three-stage cluster sampling. We propose two distinct families of CDF estimators, specifically designed for these complex surveys, namely classical ratio/product-type and exponential ratio/product-type.

Anomaly recognition in surveillance based on feature optimizer using deep learning.

Surveillance systems are integral to ensuring public safety by detecting unusual incidents, yet existing methods often struggle with accuracy and robustness. This study introduces an advanced framework for anomaly recognition in surveillance, leveraging deep learning to address these challenges and achieve significant improvements over current techniques. The framework begins with preprocessing input images using histogram equalization to enhance feature visibility.

Behavioral biases and Fintech adoption: Investigating the role of financial literacy.

This paper studies the influence of behavioral biases on Fintech adoption. Additionally, the role of financial literacy in adaptation of Fintech services is evaluated. Primary data from customers in the banking sector is gathered using a structured questionnaire.

Cognitive biases, Robo advisor and investment decision psychology: An investor's perspective from New York stock exchange.

Investment decision making is a systematic process that becomes complex due to cognitive biases and risk perceptions. For rational investment decision making, the investors in the New York Stock Exchange (NYSE) tend to seek automated investment solutions through Robo Advisors and the volume of such trades is mounting. However, there is dearth of studies that depict the role of cognitive biases, Robo Advisor and risk perception in investment decision psychology.

Analyzing the patterns of adverse drug reactions due to anti-infectives from large-scale nationwide database in Thailand.

Background: Adverse drug reactions (ADRs), which can occur in any drug class and are one of the leading causes of morbidity and hospitalization around the world, remain a public health concern. This study aimed to explore the distribution and patterns of anti-infective-induced ADRs in Thailand.

Research Design And Methods: The national database of anti-infective-induced ADRs from January 2012 to December 2021 in the 77 provinces of Thailand.

Correction: Javaid et al. WebGIS-Based Real-Time Surveillance and Response System for Vector-Borne Infectious Diseases. 2023, , 3740.

In the published publication [...

Herding behavior, disposition effect, and investment decisions: A multi-mediation analysis of risk perception and dividend policy.

The traditional perspective of finance believes that volatility in the stock market is due to market efficiency. However, the perspective of modern behavioral finance is more concerned with the emotional and psychological factors associated with an investor. This research aims to identify the multi-mediating mechanism of risk perception and dividend policy in the Pakistan Stock Exchange (PSX).

Genre-aware user profiling using duration count matrices: A novel approach to enhancing content recommendation systems.

Recommender systems play a vital role in enhancing the user experience and facilitating content discovery on online platforms. However, conventional approaches often struggle to capture users' evolving preferences over time, leading to suboptimal performance as recommended videos frequently do not align with users' interests. To address this issue, this study introduces an innovative method that leverages watch-time duration to analyze long-term user behavior and generate personalized recommendations.

The conditionality of COVID-19 vaccine acceptance: A qualitative study in Pakistan.

Understanding both the motivations for vaccination and the causes of vaccine reluctance is necessary for the present worldwide immunization campaigns against the COVID-19 pandemic. The intention of the article is to compile local perspectives and misconceptions about vaccination choices. The intention of this study is to assemble what is usually recognized in cultural context as conspiracies and post-traumatic phase affects in decision making.

Social media as a workplace panopticon: The development and validation of social media monitoring by workplace contacts scale.

The monitoring of employees' private social network accounts by employers and colleagues has become increasingly prevalent, yet research in this area remains limited. To address this gap, the present study developed and validated a scale to measure social media monitoring by workplace contacts (SMMWC). The scale, comprising fifteen items, was developed using Hinkin's (1998) approach to scale development and has four dimensions based on the concept of panoptic effect by Foucault (1977) and Botan (1996).

Stages prediction of Alzheimer's disease with shallow 2D and 3D CNNs from intelligently selected neuroimaging data.

Detection of Alzheimer's Disease (AD) is critical for successful diagnosis and treatment, involving the common practice of screening for Mild Cognitive Impairment (MCI). However, the progressive nature of AD makes it challenging to identify its causal factors. Modern diagnostic workflows for AD use cognitive tests, neurological examinations, and biomarker-based methods, e.

Analysis of time-fractional cancer-tumor immunotherapy model using modified He-Laplace algorithm.

Cancer encompasses various diseases characterized by the uncontrolled growth of abnormal cells, which can invade healthy tissues and spread throughout the body, making it the second leading cause of death worldwide. This study presents a fractional cancer treatment model with immunotherapy to enhance understanding of cancer's mathematical framework and behavior. The model comprises fractional differential equations analyzed using the Caputo-fractional derivative, aiming to control cancer growth while considering cell population metrics.

Dynamical analysis of fractional hepatitis B model with Gaussian uncertainties using extended residual power series algorithm.

Hepatitis B virus (HBV) is a significant global health concern, causing acute and chronic liver diseases, including cirrhosis and hepatocellular carcinoma. This manuscript extends existing mathematical models for HBV by introducing a treatment compartment to improve understanding, diagnosis, and treatment strategies. A stability analysis is conducted for disease-free equilibrium and to address the inherent uncertainties in parameter values, Gaussian fuzzy numbers are incorporated, resulting in a more realistic predictive framework.

Epidemiology of reported serious adverse drug reactions due to anti-infectives using nationwide database of Thailand.

Serious Adverse Drug Reactions (ADRs) can cause a longer stay, which can result in fatal outcomes. Understanding the prognostic factors for the serious ADRs play a vital role in developing appropriate serious ADR prevention strategies. This study aimed to analyze nationwide database in Thailand to identify predisposing factors associated with the serious ADRs, explore drug exposure, distribution of serious ADRs, types of ADRs, and classify the determinants of serious ADR due to anti-infective in Thailand.

Optimized machine learning framework for cardiovascular disease diagnosis: a novel ethical perspective.

Alignment of advanced cutting-edge technologies such as Artificial Intelligence (AI) has emerged as a significant driving force to achieve greater precision and timeliness in identifying cardiovascular diseases (CVDs). However, it is difficult to achieve high accuracy and reliability in CVD diagnostics due to complex clinical data and the selection and modeling process of useful features. Therefore, this paper studies advanced AI-based feature selection techniques and the application of AI technologies in the CVD classification.

Threats and Mitigation Strategies for Electroencephalography-Based Person Authentication.

This work is aimed at investigating the potential risks linked to electroencephalography (EEG)-based person authentication and providing solutions to mitigate these issues. Authenticating a person by EEG involves verifying the legitimacy of the signals used for user identification. EEG signals serve as a biometric modality for authentication and verification.

NLP for Analyzing Electronic Health Records and Clinical Notes in Cancer Research: A Review.

This review examines the application of natural language processing (NLP) techniques in cancer research using electronic health records (EHRs) and clinical notes. It addresses gaps in existing literature by providing a broader perspective than previous studies focused on specific cancer types or applications. A comprehensive literature search in the Scopus database identified 94 relevant studies published between 2019 and 2024.