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Exploring the Role of Sleep Quality, Obsessive–Compulsive Disorder, Fear of Missing Out, and Psychological Resilience on Problematic Instagram Use

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Journal of Technology in Behavioral Science 2024年 1-11页

作者： Yilmaz, Ramazan Avci, Ummuhan Karaoglan Yilmaz, Fatma Gizem Department of Computer Technology & Information Systems Faculty of Science Bartin University Bartin Turkey Department of Management Information Systems Faculty of Economics and Administrative Sciences Bartin University Bartin Turkey

This study addresses a gap in the literature regarding the relationships between sleep quality, obsessive–compulsive disorder (OCD), fear of missing out (FoMO), psychological resilience, and problematic Instagram use (PIU) among university students. By examining these interrelated factors, the research aims to clarify the mechanisms behind Instagram addiction, support effective interventions, and contribute to academic knowledge. This study examined university students’ sleep quality, PIU, OCD, FoMO, and psychological resilience, as well as the structural relationships between these variables. The study encompassed a sample of 407 Turkish university students with an average age of 21.78. The age range of the participants was between 18 and 35 years;43% of them were female, and 57% were male. This study was conducted according to the correlational research method, and pathway analysis was used to conduct the research. Research findings show that OCD and FoMO positively affect PIU. In other words, as OCD and FoMO increase, Instagram addiction also increases. Psychological resilience has an indirect effect on PIU. OCD plays a mediating role between psychological resilience and PIU and between psychological resilience and FoMO. Therefore, as psychological resilience increases, PIU and FoMO decrease. A positive correlation was found between OCD and FoMO. As the OCD level increases, the FoMO level of the individuals also increases. Another study finding was that OCD, FoMO, psychological resilience, and PIU did not significantly affect sleep quality. Future research may focus on exploring cause-effect relationships among these variables alongside implementing school-based programs to enhance students’ psychological resilience through training, planning, and follow-up studies. © The Author(s), under exclusive licence to Springer Nature Switzerland AG 2024.

关键词： Fear of missing out (FoMO) Obsessive–compulsive disorder Problematic Instagram use Psychological resilience Sleep quality

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Analyzing and Mitigating Attacks in IoT Smart Home Using a Threat Modeling Approach-Based STRIDE

International Journal of Interactive Mobile Technologies

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International Journal of Interactive Mobile Technologies 2025年第2期19卷 126-142页

作者： Ouaissa, Mariya Ouaissa, Mariyam Computer Systems Engineering Laboratory Cadi Ayyad University Marrakech Morocco Laboratory of Information Technologies Chouaib Doukkali University El Jadida Morocco

The Internet of Things (IoT) is a network of interconnected devices that enables data exchange. It is widely used in areas such as healthcare, aviation, agriculture, energy, and home automation. Despite its rapid growth and the massive adoption of connected devices, IoT presents significant security risks. Traditional threat modeling approaches are insufficient to address these risks. Architecture-based modeling is recommended, as it considers the entire system and helps in understanding potential threats. Threat modeling is a systematic technique used to identify and evaluate potential threats that could compromise the security of a system. The main objec-tive is to understand the vulnerabilities of a system in order to design appropriate security measures to mitigate them. This paper aims to analyze and mitigate specific IoT smart home threats using the STRIDE threat modeling framework, which systematically identifies potential vulnerabilities at the development level. By applying STRIDE, which stands for Spoofing, Tampering, Repudiation, information Disclosure, Denial of Service, and Elevation of Privilege, we focused on addressing key security threats, including denial of service (DoS), phishing, and man-in-the-middle (MitM) attacks. Our findings demonstrate that the proposed mitigation strategies are effective in countering these threats, providing a robust security layer for IoT smart homes. Through this study, we highlight the importance of architecture-based threat modeling to enhance security within the IoT ecosystem and offer practical solutions that strengthen IoT smart home resilience. The outcomes of the STRIDE-based analysis and the effectiveness of the mitigation techniques are detailed, offering empirical evidence to support our approach. © 2025 by the authors of this article. Published under CC-BY.

关键词： Phishing

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Detection and Classification of Hemorrhages in Retinal Images

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computer systems Science & Engineering 2023年第2期44卷 1601-1616页

作者： Ghassan Ahmed Ali Thamer Mitib Ahmad Al Sariera Muhammad Akram Adel Sulaiman Fekry Olayah College of Computer Science and Information Systems Najran UniversityNajran61441Saudi Arabia Department of Studies in Computer Science University of MysoreMysoreIndia

Damage of the blood vessels in retina due to diabetes is called diabetic retinopathy(DR).Hemorrhages is theﬁrst clinically visible symptoms of *** paper presents a new technique to extract and classify the hemorrhages in fundus *** normal objects such as blood vessels,fovea and optic disc inside retinal images are masked to distinguish them from *** masking blood vessels,thresholding that separates blood vessels and background intensity followed by a newﬁlter to extract the border of vessels based on orienta-tions of vessels are *** masking optic disc,the image is divided into sub-images then the brightest window with maximum variance in intensity is *** the candidate dark regions are extracted based on adaptive thresholding and top-hat morphological *** are extracted from each candidate region based on ophthalmologist selection such as color and size and pattern recognition techniques such as texture and wavelet *** different types of Support Vector Machine(SVM),Linear SVM,Quadratic SVM and Cubic SVM classiﬁer are applied to classify the candidate dark regions as either hemor-rhages or *** efﬁcacy of the proposed method is demonstrated using the standard benchmark DIARETDB1 database and by comparing the results with methods in *** performance of the method is measured based on average sensitivity,speciﬁcity,F-score and *** results show the Linear SVM classiﬁer gives better results than Cubic SVM and Quadratic SVM with respect to sensitivity and accuracy and with respect to speciﬁcity Quadratic SVM gives better result as compared to other SVMs.

关键词： Diabetic retinopathy hemorrhages adaptive thresholding support vector machine

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Drone-Based Public Surveillance Using 3D Point Clouds and Neuro-Fuzzy Classifier

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computers, Materials & Continua 2025年第3期82卷 4759-4776页

作者： Yawar Abbas Aisha Ahmed Alarfaj Ebtisam Abdullah Alabdulqader Asaad Algarni Ahmad Jalal Hui Liu Faculty of Computing and AI Air UniversityIslamabad44000Pakistan Department of Information Systems College of Computer and Information SciencesPrincess Nourah bint Abdulrahman UniversityRiyadh11671Saudi Arabia Department of Information Technology College of Computer and Information SciencesKing Saud UniversityRiyadh12372Saudi Arabia Department of Computer Sciences Faculty of Computing and Information TechnologyNorthern Border UniversityRafha91911Saudi Arabia Department of Computer Science and Engineering College of InformaticsKorea UniversitySeoul02841Republic of Korea Cognitive Systems Lab University of BremenBremen28359Germany

Human Activity Recognition(HAR)in drone-captured videos has become popular because of the interest in various fields such as video surveillance,sports analysis,and human-robot ***,recognizing actions from such videos poses the following challenges:variations of human motion,the complexity of backdrops,motion blurs,occlusions,and restricted camera *** research presents a human activity recognition system to address these challenges by working with drones’red-green-blue(RGB)*** first step in the proposed system involves partitioning videos into frames and then using bilateral filtering to improve the quality of object foregrounds while reducing background interference before converting from RGB to grayscale *** YOLO(You Only Look Once)algorithm detects and extracts humans from each frame,obtaining their skeletons for further *** joint angles,displacement and velocity,histogram of oriented gradients(HOG),3D points,and geodesic Distance are *** features are optimized using Quadratic Discriminant Analysis(QDA)and utilized in a Neuro-Fuzzy Classifier(NFC)for activity ***-world evaluations on the Drone-Action,Unmanned Aerial Vehicle(UAV)-Gesture,and Okutama-Action datasets substantiate the proposed system’s superiority in accuracy rates over existing *** particular,the system obtains recognition rates of 93%for drone action,97%for UAV gestures,and 81%for Okutama-action,demonstrating the system’s reliability and ability to learn human activity from drone videos.

关键词： Activity recognition geodesic distance pattern recognition neuro fuzzy classifier

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Deepfake detection using convolutional vision transformers and convolutional neural networks

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Neural Computing and Applications 2024年第31期36卷 19759-19775页

作者： Soudy, Ahmed Hatem Sayed, Omnia Tag-Elser, Hala Ragab, Rewaa Mohsen, Sohaila Mostafa, Tarek Abohany, Amr A. Slim, Salwa O. Department of Computer Science Faculty of Computers and Artificial Intelligence Helwan University Cairo Egypt Department of Information Systems Faculty of Computers and Information Kafrelsheikh University Kafrelsheikh Egypt

Deepfake technology has rapidly advanced in recent years, creating highly realistic fake videos that can be difficult to distinguish from real ones. The rise of social media platforms and online forums has exacerbated the challenges of detecting misinformation and malicious content. This study leverages many papers on artificial intelligence techniques to address deepfake detection. This research proposes a deep learning (DL)-based method for detecting deepfakes. The system comprises three components: preprocessing, detection, and prediction. Preprocessing includes frame extraction, face detection, alignment, and feature cropping. Convolutional neural networks (CNNs) are employed in the eye and nose feature detection phase. A CNN combined with a vision transformer is also used for face detection. The prediction component employs a majority voting approach, merging results from the three models applied to different features, leading to three individual predictions. The model is trained on various face images using FaceForensics++ and DFDC datasets. Multiple performance metrics, including accuracy, precision, F1, and recall, are used to assess the proposed model’s performance. The experimental results indicate the potential and strengths of the proposed CNN that achieved enhanced performance with an accuracy of 97%, while the CViT-based model achieved 85% using the FaceForences++ dataset and demonstrated significant improvements in deepfake detection compared to recent studies, affirming the potential of the suggested framework for detecting deepfakes on social media. This study contributes to a broader understanding of CNN-based DL methods for deepfake detection. © The Author(s) 2024.

关键词： Face recognition

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Sepsis Prediction Using CNNBDLSTM and Temporal Derivatives Feature Extraction in the IoT Medical Environment

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computers, Materials & Continua 2024年第4期79卷 1157-1185页

作者： Sapiah Sakri Shakila Basheer Zuhaira Muhammad Zain Nurul Halimatul Asmak Ismail Dua’Abdellatef Nassar Manal Abdullah Alohali Mais Ayman Alharaki Department of Information Systems College of Computer and Information SciencesPrincess Nourah Bint Abdulrahman UniversityRiyadh11671Kingdom of Saudi Arabia Department of Computer Science and Information Technology Applied CollegePrincess Nourah Bint Abdulrahman UniversityRiyadh11671Kingdom of Saudi Arabia

Background:Sepsis,a potentially fatal inflammatory disease triggered by infection,carries significant healthimplications *** detection is crucial as sepsis can rapidly escalate if left *** in deep learning(DL)offer powerful tools to address this ***:Thus,this study proposeda hybrid CNNBDLSTM,a combination of a convolutional neural network(CNN)with a bi-directional long shorttermmemory(BDLSTM)model to predict sepsis *** the proposed model provides a robustframework that capitalizes on the complementary strengths of both architectures,resulting in more accurate andtimelier ***:The sepsis prediction method proposed here utilizes temporal feature extraction todelineate six distinct time frames before the onset of *** time frames adhere to the sepsis-3 standardrequirement,which incorporates 12-h observation windows preceding sepsis *** models were trained usingthe Medical information Mart for Intensive Care III(MIMIC-III)dataset,which sourced 61,522 patients with 40clinical variables obtained from the IoT medical *** confusion matrix,the area under the receiveroperating characteristic curve(AUCROC)curve,the accuracy,the precision,the F1-score,and the recall weredeployed to evaluate ***:The CNNBDLSTMmodel demonstrated superior performance comparedto the benchmark and other models,achieving an AUCROC of 99.74%and an accuracy of 99.15%one hour beforesepsis *** results indicate that the CNNBDLSTM model is highly effective in predicting sepsis onset,particularly within a close proximity of one ***:The results could assist practitioners in increasingthe potential survival of the patient one hour before sepsis onset.

关键词： Temporal derivatives hybrid deep learning predicting sepsis onset MIMIC III machine learning(ML) deep learning

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Impact of Feature Selection and Interactions on Accuracy of Classical Machine Learning Algorithms: A Driver Behavior Case Study 2

Impact of Feature Selection and Interactions on Accuracy of ...

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2nd International Conference of Intelligent Methods, systems and Applications, IMSA 2024

作者： Gamal, Rabab Al-Qutt, Mirvat Khaled, Heba Fayez, Mahmoud Ghoniemy, Said Ain Shams University Faculty of Computer and Information Sciences Computer Systems Dept Cairo Egypt

ISBN: (纸本)9798350362633

This study aims to investigate how the number of features and their interrelationships impact the accuracy of classical machine learning algorithms, focusing on driver behavior as a case study. As sensor data becomes increasingly available in modern vehicles, there is a growing interest in understanding driver behavior patterns for various applications, including safety and personalized services. However, the specific influence of different features and their interactions on the accuracy of machine learning models in predicting driver behavior remains unclear. To address this gap, the study utilizes a comprehensive dataset called UAH-DriveSet. Multiple subsets of the dataset are created by gradually adding or removing features, allowing for an examination of the impact of feature quantity. Additionally, feature engineering techniques are applied to explore the relationships and interactions between the features. The study employs a set of classical machine learning algorithms, including logistic regression, decision trees, random forests, support vector machines, naive Bayes, k-nearest neighbors, linear discriminant analysis, and AdaBoost, to model driver behavior based on various feature subsets. The performance of each algorithm is evaluated using accuracy and F1-score as the evaluation metric. The findings of the study reveal that the number and selection of features that were extracted after the preprocessing of the data set, play a crucial role in determining the accuracy of machine learning models for predicting driver behavior. The preprocessing is carried out using an autoencoder model for dimension reduction, and Singular value decomposition to get the most effective column to be converted to the alphabet using k-means, then extracting the motifs representing the new feature. © 2024 IEEE.

关键词： Random forests

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Bitcoin price prediction using LSTM autoencoder regularized by false nearest neighbor loss

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Soft Computing 2024年第21期28卷 12827-12834页

作者： Raj, Chahat Chattopadhyay, Manojit Department of Computer Science George Mason University Fairfax United States Information Systems Indian Institute of Management Raipur Chhattisgarh India

We implement deep learning for predicting bitcoin closing prices. Identifying two new determiners, we propose a novel LSTM Autoencoder using Mean Squared Error (MSE) loss which is regularized by False Nearest Neighbor (FNN) algorithm. The method results in reduced error rates when compared to traditional forecasting algorithms and is statistically validated. This research contributes by developing a robust algorithm that accurately determines the fluctuation directions in bitcoin prices and results in values closer to the actual prices. © The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature 2024.

关键词： Costs

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Smart Healthcare Activity Recognition Using Statistical Regression and Intelligent Learning

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computers, Materials & Continua 2024年第1期78卷 1189-1205页

作者： K.Akilandeswari Nithya Rekha Sivakumar Hend Khalid Alkahtani Shakila Basheer Sara Abdelwahab Ghorashi Department of Computer Sciences Government Arts College(Autonomous)Salem636007India Department of Computer Sciences College of Computer and Information SciencesPrincess Nourah Bint Abdulrahman UniversityRiyadh11671Saudi Arabia Department of Information Systems College of Computer and Information SciencesPrincess Nourah Bint Abdulrahman UniversityRiyadh11671Saudi Arabia

In this present time,Human Activity Recognition(HAR)has been of considerable aid in the case of health monitoring and *** exploitation of machine learning with an intelligent agent in the area of health informatics gathered using HAR augments the decision-making quality and *** many research works conducted on Smart Healthcare Monitoring,there remain a certain number of pitfalls such as time,overhead,and falsification involved during ***,this paper proposes a Statistical Partial Regression and Support Vector Intelligent Agent Learning(SPR-SVIAL)for Smart Healthcare *** first,the Statistical Partial Regression Feature Extraction model is used for data preprocessing along with the dimensionality-reduced features extraction ***,the input dataset the continuous beat-to-beat heart data,triaxial accelerometer data,and psychological characteristics were acquired from IoT wearable *** attain highly accurate Smart Healthcare Monitoring with less time,Partial Least Square helps extract the dimensionality-reduced *** that,with these resulting features,SVIAL is proposed for Smart Healthcare Monitoring with the help of Machine Learning and Intelligent Agents to minimize both analysis falsification and *** evaluation is carried out for factors such as time,overhead,and false positive rate accuracy concerning several *** quantitatively analyzed results indicate the better performance of our proposed SPR-SVIAL method when compared with two state-of-the-art methods.

关键词： Internet of Things smart health care monitoring human activity recognition intelligent agent learning statistical partial regression support vector

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Water potability classification based on hybrid stacked model and feature selection

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Environmental Science and Pollution Research 2025年第13期32卷 7933-7949页

作者： Elshewey, Ahmed M. Youssef, Rasha Y. El-Bakry, Hazem M. Osman, Ahmed M. Department of Computer Science Faculty of Computers and Information Suez University P.O. Box: 43221 Suez Egypt Department of Information Systems Faculty of Computers and Information Suez University P.O. Box: 43221 Suez Egypt Department of Information Systems Faculty of Computers and Information Mansoura University P.O. Box: 35516 Mansoura35516 Egypt

Clean water requires accurate water quality categorization. A water potability (WP) dataset with pH, hardness, solids, chloramines, sulfate, conductivity, and other metrics for 3276 water bodies was used in this paper. After median imputation for missing values, normalization for feature scaling, and class imbalance correction using SMOTE, the Kaggle public dataset was prepared. With binary particle swarm optimization (BPSO) and binary whale optimization algorithm (BWAO), feature selection (FS) was used to determine the most important features for classification. A subset of seven essential characteristics is selected with the lowest average error of 0.3745 by the BPSO. Random forest (RF), gradient boosting (GB), support vector machine (SVM), Extra Tree (ET), decision tree (DT), and XGBoost are tested for WP prediction. The ET classifier ranked first, with 70.63% accuracy and 71.17% F1-score. Predictive performance was improved by stacking random forest, extra trees, and XGBoost base learners with Logistic Regression meta-learner. The stacking model improved with 69.53% accuracy, 70.23% F1-score, and 77.62% AUC. We found that stacking uses high-performing models to create a strong and balanced categorization framework. This paper shows that ensemble learning can improve WP categorization and that stacking may be a feasible way for measuring and managing water quality. © The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature 2025.

关键词： Particle swarm optimization (PSO)

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