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EFS-XGBoost: A robust framework for precision classification of COVID-19 cases

Intelligent Data Analysis 2025年第1期29卷 171-185页

作者： Abidi, Mustufa Haider Khare, Neelu D, Preethi Alkhalefah, Hisham Umer, Usama Advanced Manufacturing Institute King Saud University Riyadh Saudi Arabia School of Computer Science Engineering and Information Systems Vellore Institute of Technology Tamil Nadu Vellore India Department of Computer Science and Engineering Faculty of Engineering and Technology SRM Institute of Science and Technology Ramapuram Chennai India

The emergence of the novel COVID-19 virus has had a profound impact on global healthcare systems and economies, underscoring the imperative need for the development of precise and expeditious diagnostic tools. Machine learning techniques have emerged as a promising avenue for augmenting the capabilities of medical professionals in disease diagnosis and classification. In this research, the EFS-XGBoost classifier model, a robust approach for the classification of patients afflicted with COVID-19 is proposed. The key innovation in the proposed model lies in the Ensemble-based Feature Selection (EFS) strategy, which enables the judicious selection of relevant features from the expansive COVID-19 dataset. Subsequently, the power of the eXtreme Gradient Boosting (XGBoost) classifier to make precise distinctions among COVID-19-infected patients is *** EFS methodology amalgamates five distinctive feature selection techniques, encompassing correlation-based, chi-squared, information gain, symmetric uncertainty-based, and gain ratio approaches. To evaluate the effectiveness of the model, comprehensive experiments were conducted using a COVID-19 dataset procured from Kaggle, and the implementation was executed using Python programming. The performance of the proposed EFS-XGBoost model was gauged by employing well-established metrics that measure classification accuracy, including accuracy, precision, recall, and the F1-Score. Furthermore, an in-depth comparative analysis was conducted by considering the performance of the XGBoost classifier under various scenarios: employing all features within the dataset without any feature selection technique, and utilizing each feature selection technique in isolation. The meticulous evaluation reveals that the proposed EFS-XGBoost model excels in performance, achieving an astounding accuracy rate of 99.8%, surpassing the efficacy of other prevailing feature selection techniques. This research not only advances the field of COVI

关键词： classification COVID-19 ensemble-based feature selection machine learning XGBoost

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Review of dehazing techniques: challenges and future trends

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multimedia Tools and Applications 2025年第3期84卷 1103-1131页

作者： Ayoub, Abeer El-Shafai, Walid El-Samie, Fathi E. Abd Hamad, Ehab K. I. EL-Rabaie, El-Sayed M. Department of Electronics and Electrical Communication Engineering Faculty of Electronic Engineering Menoufia University Menouf32952 Egypt Security Engineering Lab Computer Science Department Prince Sultan University Riyadh11586 Saudi Arabia Department of Information Technology College of Computer and Information Sciences Princess Nourah Bint Abdulrahman University Riyadh11671 Saudi Arabia Electrical Engineering Department Faculty of Engineering Aswan University Aswan81542 Egypt

The phenomenon of atmospheric haze arises due to the scattering of light by minute particles suspended in the atmosphere. This optical effect gives rise to visual degradation in images and videos. The degradation is primarily influenced by two key factors: atmospheric attenuation and scattered light. Scattered light causes an image to be veiled in a whitish veil, while attenuation diminishes the image inherent contrast. Efforts to enhance image and video quality necessitate the development of dehazing techniques capable of mitigating the adverse impact of haze. This scholarly endeavor presents a comprehensive survey of recent advancements in the domain of dehazing techniques, encompassing both conventional methodologies and those founded on machine learning principles. Traditional dehazing techniques leverage a haze model to deduce a dehazed rendition of an image or frame. In contrast, learning-based techniques employ sophisticated mechanisms such as Convolutional Neural Networks (CNNs) and different deep Generative Adversarial Networks (GANs) to create models that can discern dehazed representations by learning intricate parameters like transmission maps, atmospheric light conditions, or their combined effects. Furthermore, some learning-based approaches facilitate the direct generation of dehazed outputs from hazy inputs by assimilating the non-linear mapping between the two. This review study delves into a comprehensive examination of datasets utilized within learning-based dehazing methodologies, elucidating their characteristics and relevance. Furthermore, a systematic exposition of the merits and demerits inherent in distinct dehazing techniques is presented. The discourse culminates in the synthesis of the primary quandaries and challenges confronted by prevailing dehazing techniques. The assessment of dehazed image and frame quality is facilitated through the application of rigorous evaluation metrics, a discussion of which is incorporated. To provide empiri

关键词： Generative adversarial networks

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Contemporary Impact of Cyberbullying on Youngsters: An Appraisal of Psychological, Emotional, Physical Health, and Academic Implications 19

Contemporary Impact of Cyberbullying on Youngsters: An Appra...

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19th International Conference on Ubiquitous information Management and Communication, IMCOM 2025

作者： Faith, Fatokun B. Long, Zalizah Awang Hamid, Suraya Malaysian Institute of Information Technology Universiti Kuala Lumpur Kuala Lumpur Malaysia Department of Information Systems Faculty of Computer Science IT Universiti Malaya Kuala Lumpur Malaysia

ISBN: (纸本)9798331507817

Cyberbullying is now a serious issue in contemporary times. The impact of cyberbullying on youngsters is of enormous concern in the public health sector as well as in the cybersecurity field. Several researchers have discussed the impact of cyberbullying from diverse perspectives, however, there is still a large gap in finding the paramount impact of cyberbullying in contemporary times, as well as providing solutions to eradicate this menace, especially as it deals with the negative effects of technology which is emerging daily. This study reviews via a scoping evidence approach suggesting the impact of cyberbullying on youngsters, focusing on the psychological, emotional, physical health, and academic implications. Findings from studies revealed that most youngsters face more of the psychological and emotional consequences of cyberbullying as compared to others discussed in the literature. Interestingly, there were also revelations of how cyberbullying had negative influences on the physical health and academic achievements of youngsters. These findings thus instigate the need for novel approaches that can help curb the rampancy of cyberbullying as well as ensure that decision-makers and stakeholders in the respective institutions put these impacts of cyberbullying into consideration when designing cybersecurity policies to ensure that victims of cyberbullying are assisted to recover, put in place technological solutions that can track and punish offenders and also reduce the rate of cyberbullying victimization. © 2025 IEEE.

关键词： Electronic health record

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Secure Channel Estimation Using Norm Estimation Model for 5G Next Generation Wireless Networks

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computers, Materials & Continua 2025年第1期82卷 1151-1169页

作者： Khalil Ullah Song Jian Muhammad Naeem Ul Hassan Suliman Khan Mohammad Babar Arshad Ahmad Shafiq Ahmad Faculty of Information Engineering and Automation Kunming University of Science and TechnologyKunming650500China Department of Computer Science Abbottabad University of Science and TechnologyHavelianAbbottabad22500Pakistan Department of Computing and Electronics Engineering Middle East CollegeMuscat124Oman Department of CS&IT Pak-Austria Fachhochschule Institute of Applied Sciences and Technology MangHaripur22621Pakistan Industrial Engineering Department College of EngineeringKing Saud UniversityRiyadh11421Saudi Arabia

The emergence of next generation networks(NextG),including 5G and beyond,is reshaping the technological landscape of cellular and mobile *** networks are sufficiently scaled to interconnect billions of users and *** in academia and industry are focusing on technological advancements to achieve highspeed transmission,cell planning,and latency reduction to facilitate emerging applications such as virtual reality,the metaverse,smart cities,smart health,and autonomous *** continuously improves its network functionality to support these *** input multiple output(MIMO)technology offers spectral efficiency,dependability,and overall performance in *** article proposes a secure channel estimation technique in MIMO topology using a norm-estimation model to provide comprehensive insights into protecting NextG network components against adversarial *** technique aims to create long-lasting and secure NextG networks using this extended *** viability of MIMO applications and modern AI-driven methodologies to combat cybersecurity threats are explored in this ***,the proposed model demonstrates high performance in terms of reliability and accuracy,with a 20%reduction in the MalOut-RealOut-Diff metric compared to existing state-of-the-art techniques.

关键词： Next generation networks massive mimo communication network artificial intelligence 5G adversarial attacks channel estimation information security

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Epileptic seizure detection in EEG signals via an enhanced hybrid CNN with an integrated attention mechanism

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Mathematical Biosciences and Engineering 2025年第1期22卷 73-105页

作者： Mekruksavanich, Sakorn Phaphan, Wikanda Jitpattanakul, Anuchit Department of Computer Engineering School of Information and Communication Technology University of Phayao Phayao56000 Thailand Department of Applied Statistics Faculty of Applied Science King Mongkut’s University of Technology North Bangkok Bangkok10800 Thailand Department of Mathematics Faculty of Applied Science King Mongkut’s University of Technology North Bangkok Bangkok10800 Thailand Intelligent and Nonlinear Dynamic Innovations Research Center Science and Technology Research Institute King Mongkut’s University of Technology North Bangkok Bangkok10800 Thailand

Epileptic seizures, a prevalent neurological condition, necessitate precise and prompt identification for optimal care. Nevertheless, the intricate characteristics of electroencephalography (EEG) signals, noise, and the want for real-time analysis require enhancement in the creation of dependable detection approaches. Despite advances in machine learning and deep learning, capturing the intricate spatial and temporal patterns in EEG data remains challenging. This study introduced a novel deep learning framework combining a convolutional neural network (CNN), bidirectional gated recurrent unit (BiGRU), and convolutional block attention module (CBAM). The CNN extracts spatial features, the BiGRU captures long-term temporal dependencies, and the CBAM emphasizes critical spatial and temporal regions, creating a hybrid architecture optimized for EEG pattern recognition. Evaluation of a public EEG dataset revealed superior performance compared to existing methods. The model achieved 99.00% accuracy in binary classification, 96.20% in three-class tasks, 92.00% in four-class scenarios, and 89.00% in five-class classification. High sensitivity (89.00–99.00%) and specificity (89.63–99.00%) across all tasks highlighted the model’s robust ability to identify diverse EEG patterns. This approach supports healthcare professionals in diagnosing epileptic seizures accurately and promptly, improving patient outcomes and quality of life. © 2025 American Institute of Mathematical sciences. All rights reserved.

关键词： Electroencephalography

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Video classification for efficient data storage using deep learning: a comparison of sequential and simultaneous feature extraction methods

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multimedia Tools and Applications 2025年第6期84卷 3071-3094页

作者： Dang Thi, Phuc Nguyen Dang, Hoang Thi Doan Huu, Phuc Dau Sy, Hieu Department of Computer Science Faculty of Information Technology Industrial University of Ho Chi Minh City 12 Nguyen Van Bao Ho Chi Minh City7000 Viet Nam Department of Applied Physics Faculty of Applied Science University of Technology - Viet Nam National University HCMC To Hien Thanh Ho Chi Minh City7000 Viet Nam

Optimizing camera information storage is a critical issue due to the increasing data volume and a large number of daily surveillance videos. In this study, we propose a deep learning-based system for efficient data storage. Videos captured by cameras are classified into four categories: no action, normal action, human action, and dangerous action. Videos without action or with normal action are stored temporarily and then deleted to save storage space. Videos with human action are stored for easy retrieval, while videos with dangerous action are promptly alerted to users. In the paper, we propose two approaches using deep learning models to address the video classification problem. The first approach is a separate approach, where pretrained CNN models extract features from video frame images. These features are then passed through RNN, Transformer models to extract relationships between them. The goal of this approach is to delve into extracting features of objects in the video. The proposed models include VGG16, InceptionV3 combined with LSTM, BiLSTM, Attention, and Vision Transformer. The next approach combines CNN and LSTM layers simultaneously through models like ConvLSTM and LRCN. This approach aims to help the model simultaneously extract object features and their relationships, with the goal of reducing model size, accelerating the training process, and increasing object recognition speed when deployed in the system. In Approach 1, we construct and refine network architectures such as VGG16+LSTM, VGG16+Attention+LSTM, VGG16+BiLSTM, VGG16+ViT, InceptionV3+LSTM, InceptionV3+Attention+LSTM, InceptionV3+BiLSTM. In Approach 2, we build a new network architecture based on the ConvLSTM and LRCN model. The training dataset, collected from real surveillance cameras, comprises 3315 videos labeled into four classes: no action (1018 videos), actions involving people (832 videos), dangerous actions (751 videos), and normal actions (714 videos). Experimental results show t

关键词： Deep learning

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An Intelligent Optimization Technique Of Automatic Speech Recognition For Smart Homes 13

An Intelligent Optimization Technique Of Automatic Speech Re...

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13th International Conference on Research in ComputIng at Feminine, RIF 2024

作者： Aly, Rabab Hamed M. Hussein, Aziza I. The Higher Institute for Management and Information Technology Minya Egypt Electrical and Computer Engineering Department College of Engineering Effat University Jeddah Saudi Arabia Computer Science Department Faculty of Computer Science Nahda University Beni Suef Egypt

The creation of new approaches to the design and configuration of smart buildings relies heavily on AI tools and Machine Learning (ML) algorithms, particularly optimization techniques. The widespread use of electronic devices has sparked a strong desire to incorporate the Internet of Things (IoT) into houses, leading to the development of smart homes. As networked gadgets proliferate rapidly, this phenomenon is characterized by rapid proliferation. In smart buildings, smart cities, smart grids, and smart homes, interconnected electronic devices are becoming more popular. The objective of this paper is to enhance the functionality of home automation systems through the performance of speech recognition using the Bat-Salp Swarm Optimization (BSSO). This paper investigates the notion of (BSSO), a data analysis methodology that facilitates the automated construction of analytical models. The implementation of BSSO provides an enhancement to the feature selection process in speech recognition, providing an approximation solution that improves the accuracy of system decisions. The use of the BSSO technique improves the precision of the voice recognition system and also incorporates an Artificial Neural Network (ANN) for the classification part. The findings substantiated the efficacy of the employed methodology. © 2024 Copyright for this paper by its authors.

关键词： Bat Optimization Algorithm Salp Optimization Algorithm Smart homes Speech recognition

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Beyond administrative reports: a deep learning framework for classifying and monitoring crime and accidents leveraging large-scale online news

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Neural Computing and Applications 2025年第10期37卷 7183-7205页

作者： Tuarob, Suppawong Tatiyamaneekul, Phonarnun Pongpaichet, Siripen Tawichsri, Tanisa Noraset, Thanapon Faculty of Information and Communication Technology Mahidol University Nakhon Pathom Thailand Department of Computer Science University College London London United Kingdom Puey Ungphakorn Institute for Economic Research Bank of Thailand Bangkok Thailand

The escalating prevalence of violent crimes and accidents underscores the urgent need for efficient and timely monitoring systems. Traditional methods reliant on administrative reports often suffer from significant delays. This paper proposes CRIMSON, a novel framework that leverages large-scale online news to provide real-time insights into crime and accident trends. CRIMSON utilizes a multi-label classification technique that leverages a fine-tuned, pre-trained, cross-lingual language model to accurately categorize news articles. Our experimental results, conducted on a substantial dataset of Thai news articles, demonstrate superior performance, achieving an average F1 score of 86%. Beyond classification, CRIMSON aggregates categorized news into real-time statistics, revealing strong correlations between news-reported incidents and official crime data. This study pioneers online news as a reliable and timely crime and accident monitoring source, offering valuable insights for law enforcement, policymakers, and researchers. © The Author(s) 2025.

关键词： Deep learning

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CMV2U-Net: A U-shaped network with edge-weighted features for detecting and localizing image splicing

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Journal of Forensic sciences 2025年第0期70卷 2025 Apr 3页

作者： Akram, Arslan Jaffar, Muhammad Arfan Rashid, Javed Boulaaras, Salah Mahmoud Faheem, Muhammad Faculty of Computer Science and Information Technology The Superior University Lahore Pakistan Information Technology Services University of Okara Okara Pakistan Department of Mathematics College of Science Qassim University Buraydah Saudi Arabia VTT Technical Research Centre of Finland Espoo Finland

The practice of cutting and pasting portions of one image into another, known as “image splicing,” is commonplace in the field of image manipulation. Image splicing detection using deep learning has been a hot research topic for the past few years. However, there are two problems with the way deep learning is currently implemented: first, it is not good enough for feature fusion, and second, it uses only simple models for feature extraction and encoding, which makes the models vulnerable to overfitting. To tackle these problems, this research proposes CMV2U-Net, an edge-weighted U-shaped network-based image splicing forgery localization approach. An initial step is the development of a feature extraction module that can process two streams of input images simultaneously, allowing for the simultaneous extraction of semantically connected and semantically agnostic features. One characteristic is that a hierarchical fusion approach has been devised to prevent data loss in shallow features that are either semantically related or semantically irrelevant. This approach implements a channel attention mechanism to monitor manipulation trajectories involving multiple levels. Extensive trials on numerous public datasets prove that CMV2U-Net provides high AUC and F₁ in localizing tampered regions, outperforming state-of-the-art techniques. Noise, Gaussian blur, and JPEG compression are post-processing threats that CMV2U-Net has successfully resisted. © 2025 American Academy of Forensic sciences.

关键词： digital forensics feature fusion image authentication image forgery localization image splicing detection U-shaped network

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A Comparative Study of Deep Learning and Modern Machine Learning Methods for Predicting Australia's Precipitation

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International Journal of Advanced computer science and Applications 2025年第4期16卷 943-966页

作者： Farman, Hira Mastoi, Qurat-Ul-Ain Abbas, Qaiser Ahmad, Saad Alshahrani, Abdulaziz Jan, Salman Syed, Toqeer Ali Department of Computer Science Iqra University Karachi Institute of Economics and Technology Karachi74600 Pakistan School of Computing and Creative Technologies University of the West of England Bristol United Kingdom Faculty of Computer and Information Systems Islamic University of Madinah Madinah42351 Saudi Arabia Faculty of Computer Science IQRA University Karachi Pakistan Faculty of Computer Studies Arab Open University 512 United Kingdom Islamic University of Madinah CO 42351 Madinah Saudi Arabia

Floods are chaotic weather patterns that cause irreversible and devastating harm to people's lives, crops, and the socioeconomic system. It causes extensive property damage, animal mortality, and even human fatalities. To mitigate the risk of flooding, it is imperative to create an early warning system that can accurately forecast the amount of rain that will fall tomorrow. Rainfall forecasting is essential to the lives of people and is absolutely important everywhere in the world. The rainfall prediction model reduces risk and helps to prevent further human deaths. Statistics cannot reliably forecast rainfall since the atmosphere is dynamic. Due to the preceding factors, this study uses machine learning and deep learning techniques to estimate precipitation. The purpose of this study is to develop and evaluate a prediction model for forecasting rainfall of 5 cities of Australia (Darwin, Sydney, Perth airport, Melbourne, Brisbane). The Dataset was gathered from the national meteo-rological organization of Australia is the Australian Government Bureau of Meteorology, also known as the BOM. To monitor and forecast meteorological conditions, climatic trends, and natural calamities like cyclones, storms, floods, the Bureau of Meteorology is essential. The dataset includes 14, 5460 size, 23 features detailed city-specific monthly averages for Australia from 2008 to 2017(10 years). An effective rainfall forecasting was produced by integration of a number of Machine Learning and Deep Learning techniques, including Random Forest model (RF), Decision Tree (DT) and Gradient Boosting classifier (GBC), Artificial Neural Network (ANN), and Recurrent Neural Network (RNN). The models were trained to forecast rainfall, reducing the potential impact of floods. Results indicate that combining neural networks and Random Forests provides the most accurate predictions. © (2025), (science and information Organization). All rights reserved.

关键词： Rain

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