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Enhanced Distance-based Weighted K-Nearest Neighbor Algorithm for Data Classification

KSII Transactions on Internet and information systems 2025年第4期19卷 1097-1121页

作者： Amer, Ali A. Ravana, Sri Devi Habeeb, Riyaz Ahamed Ariyaluran Department of Information Systems Faculty of Computer Science & Information Technology Universiti Malaya Malaysia

The k-Nearest Neighbors (kNN) algorithm is one of the most widely used techniques for data classification. However, the imbalanced class is a key problem for its declining performance. Therefore, the kNN algorithm is kept updated to mitigate this problem’s effects. Following the pattern of the literature, our paper proposes a novel weighted kNN that aims to significantly reduce the negative consequences of this problem. An enhanced distance-based weighted KNN, EDWkNN, is developed to improve the overall KNN performance. For the test sample, the number of neighbors (k) is initially determined. Next, normalized weights are computed for these neighbors and assigned a value between 0 and 1. The weights of the nearest and farthest neighbors are 1 and 0, respectively. The weight values of the remaining neighbors range from 0 to 1, with the closest one having a heavier weight and the farthest neighbor having a lower weight value. In extreme circumstances, a few neighbors have equal distance from the test sample, leading to assigning uniform weights to each of these neighbors. Further, the EDWkNN considers both the magnitude of the distance and the proximity of neighbors. Comparing EDWkNN to its state-of-the-art rivals, its simplistic architecture and competitive performance demonstrate its uniqueness. In four experimental phases, a thorough assessment study is conducted utilizing four evaluation metrics (accuracy, precision, recall, and MAE) over forty-four datasets. The findings show that, on average and for individual k values, EDWkNN is substantially promising. Copyright © 2025 KSII.

关键词： Nearest neighbor search

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Optimizing Medical Image Analysis: A Performance Evaluation of YOLO-Based Segmentation Models

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International Journal of Advanced Computer Science and Applications 2025年第4期16卷 1167-1174页

作者： Alanazi, Haifa Department of Information Systems-Faculty of Computing and Information Technology Northern Border University Saudi Arabia

Instance segmentation is a critical component of medical image analysis, enabling tasks such as tissue and organ delineation, and disease detection. This paper provides a detailed comparative analysis of two fine-tuned one-stage object detection models, YOLOv11-seg and YOLOv9-seg, tailored for instance segmentation in medical imaging. Leveraging transfer learning, both models were initialized with pretrained weights and sub sequently fine-tuned on the NuInsSeg dataset, which comprises over 30,000 manually segmented nuclei across 665 image patches from various human and mouse organs. This approach facilitated faster convergence and improved generalization, particularly given the limited size and high complexity of the medical dataset. The models were evaluated against key performance metrics. The experimental results reveal that YOLOv11n-seg outperforms YOLOv9c-seg with a precision of 0.87, recall of 0.84, and mAP50 of 0.89, indicating superior segmentation quality and more accurate delineation of nuclei contours. This study highlights the robust performance and efficiency of YOLOv11n seg, demonstrating its superiority in medical image segmentation tasks, with notable advantages in both accuracy and real-time processing capabilities. © (2025), (Science and information Organization). All Rights Reserved.

关键词： Image segmentation

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Clustering Analysis of Physicians’ Performance Evaluation: A Comparison of Feature Selection Strategies to Support Medical Decision-Making

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International Journal of Advanced Computer Science and Applications 2025年第4期16卷 700-707页

作者： Ghazzawi, Amani Mustafa Almagrabi, Alaa Omran Namankani, Hanaa Mohammed Department of Information Systems Faculty of Computing and Information Technology King Abdulaziz University Jeddah. Saudi Arabia Department of Management Information Systems Faculty of Business Administration Taif University Taif Saudi Arabia Department of Information Systems Faculty of Computing and Information Technology King Abdulaziz University Jeddah Saudi Arabia

Evaluating physicians’ performance is one of the fundamental pillars of improving the quality of healthcare in medical institutions, as it contributes to measuring their ability to provide appropriate treatment, interact effectively with patients, and work within healthcare teams. This study aims to explore the impact of attribute selection on the accuracy of physician clustering using the K-Means algorithm, to improve physician performance assessment. Three datasets containing professional, medical, and administrative attributes were analyzed, such as age, nationality, job title, years of experience, number of operations, and evaluations from various entities. The optimal number of clusters was determined using the Elbow and Silhouette Score methods. The results showed that the original feature set and Lasso features performed best at k = 3, with a clear distinction between clusters. The "three-star" cluster performed well at k = 2 but lost some fine details. It was also shown that attribute selection directly affects the number and accuracy of clusters resulting from clustering, allowing for a clearer classification of physician categories. The study recommends using either original features or Lasso features to achieve more effective clustering, which supports improved recruitment, training, and management decision-making processes in healthcare organizations. © (2025), (Science and information Organization). All rights reserved.

关键词： Risk perception

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Dawwen: An Arabic Mental Health Mobile App Based on Natural Language Processing

International Journal of Interactive Mobile Technologies

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International Journal of Interactive Mobile Technologies 2025年第4期19卷 108-131页

作者： Wali, Arwa Almagrabi, Hana El-Feky, Sarah Jokhdar, Maryah Department of Information Systems Faculty of Computing and Information Technology King Abdulaziz University Jeddah Saudi Arabia

People are increasingly concerned about their mental health wellness. Scientific studies suggest that online counselling for anxiety and depression is just as effective as in-person treatment. Additionally, journaling interventions have shown promise for individuals dealing with mental and psychological issues. In recent years, a growing number of mobile applications have been developed to improve people’s mental wellness and emotional commu-nication. However, many of these applications are not available in Arabic but are available only in English or the native languages of their users, while other applications have feature limitations. This study presents a prototype of an interactive mental health mobile application, called Dawwen, to assist Arab individuals in freely expressing their feelings through online journaling, receiving recommendations for practices and activities, and locating nearby therapy centers based on their geolocation. Dawwen is augmented with an easy-to-use interface, a natural language processing (NLP) technique for analyzing the user input, and integrated features. The system is implemented using Android Studio and various cloud-based tools, making it adaptable for the integration of more advanced artificial intelligence (AI) technologies in the future. The System Usability Scale (SUS) scored an average of 88.25%, indicat-ing high user satisfaction with the app’s functionality and ease of use. The study highlights Dawwen’s effectiveness in improving mental health support for Arabic-speaking users, filling a critical gap in existing digital mental health resources. © 2025 by the authors of this article. Published under CC-BY.

关键词： Electronic health record

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Classification of human-written and AI-generated sentences using a hybrid CNN-GRU model optimized by the spotted hyena algorithm

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Alexandria Engineering Journal 2025年 126卷 116-130页

作者： Ragab, Mahmoud Ashary, Ehab Bahaudien Kateb, Faris Hakeem, Abeer Mosli, Rayan Albogami, Nasser N. Nooh, Sameer Information Technology Department Faculty of Computing and Information Technology King Abdulaziz University Jeddah21589 Saudi Arabia Electrical and Computer Engineering Department Faculty of Engineering King Abdulaziz University Jeddah21589 Saudi Arabia Information Systems Department Faculty of Computing and Information Technology King Abdulaziz University Jeddah21589 Saudi Arabia

The rapid advancement of artificial intelligence (AI) in generating human-like text poses significant challenges in distinguishing between human-written and AI-generated content. Recent advancements in natural language generation have significantly enhanced the quality and variety of AI-generated text, making it almost indistinguishable from human-written content. ChatGPT, a popular AI model, belongs to the generative pre-trained transformer family. While human content is created with a clear intent to convey meaning, AI-generated text aims to replicate human-like language. Classifying human-written and AI-generated sentences is crucial for addressing issues like fake news, plagiarism, and spamming. AI text often follows repetitive patterns, while human writing is more creative and original, making detection significant for combating misinformation. Therefore, this study proposes to classify human-written and AI-generated sentences using a hybrid CNN-GRU model optimized by the Spotted Hyena Algorithm (CHWAIG-DLSHO) approach. The approach involves preprocessing text data through tokenization, lemmatization, and data splitting, followed by word embedding using Latent Dirichlet Allocation (LDA). A hybrid convolutional neural network (CNN) and gated recurrent unit (GRU) model is employed for sentence classification. The spotted hyena optimizer (SHO) model is utilized to fine-tune the hyperparameters of the CNN-GRU model, enhancing its performance. The analysis of the CHWAIG-DLSHO method takes place utilizing AI vs. human text dataset. The performance validation of the CHWAIG-DLSHO method portrayed a superior accuracy value of 99.17 % over existing techniques. © 2025 The Authors

关键词： Convolutional neural networks

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Abnormal event detection in surveillance videos through LSTM auto-encoding and local minima assistance

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Discover Internet of Things 2025年第1期5卷 1-25页

作者： Sengonul, Erkan Samet, Refik Abu Al-Haija, Qasem Alqahtani, Ali Alsemmeari, Rayan A. Alghamdi, Bandar Alturki, Badraddin Alsulami, Abdulaziz A. Department of Computer Engineering Ankara University Ankara06100 Turkey Department of Cybersecurity Faculty of Computer & Information Technology Jordan University of Science and Technology PO Box 3030 Irbid22110 Jordan Department of Networks & Communications Engineering College of Computer Science & Information Systems Najran University Najran61441 Saudi Arabia Department of Information Technology Faculty of Computing and Information Technology King Abdulaziz University Jeddah21589 Saudi Arabia Department of Information Systems Faculty of Computing and Information Technology King Abdulaziz University Jeddah21589 Saudi Arabia

Abnormal event detection in video surveillance is critical for security, traffic management, and industrial monitoring applications. This paper introduces an innovative methodology for anomaly detection in video data, encompassing three primary stages: preprocessing, feature learning, and anomaly detection. We employ background subtraction and noise reduction during preprocessing to refine the data. The feature-learning stage involves training an LSTM autoencoder to capture the essential features of normal video sequences. For anomaly detection, we map video data to a lower-dimensional space (latent code) and compare it against the distribution of codes from normal sequences. We determine regularity scores and identify local minima points exceeding a specified threshold while scrutinizing shadows between adjacent maxima to confirm and pinpoint anomalies. When tested on the CUHK Avenue and UMN datasets, our methodology demonstrated performance with AUCs of 93.8% and 94.1%, respectively, outperforming several baseline models. Our results show the high precision of our method that can detect anomalies, highlighting its potential advantages that it can achieve for enhancing systems of surveillance. © The Author(s) 2025.

关键词： Noise abatement

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Robotic artistic writing — reproduction of signs drawn on the surface of plastically deformable materials

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IEEE Sensors Journal 2025年第10期25卷 18090-18105页

作者： He, Xin Zielinska, Teresa Matsumaru, Takafumi Dutta, Vibekananda Waseda University Graduate School of Information Production and Systems Fukuoka Kitakyushu Japan Warsaw University of Technology Faculty of Power and Aeronautical Engineering Warsaw Poland Warsaw University of Technology Faculty of Mechatronics Warsaw Poland

Existing surface shaping methods focus on hard materials with stable physical properties. This means that the approaches developed are insufficient for shaping soft materials. The article describes a cheap method of reproducing plastic deformations using a robotic manipulator. The shape recording and reproduction system consists of two RGB-D cameras, two containers with kinetic sand, and a manipulator with 6 degrees of freedom. Volunteers create templates by drawing on the surface of kinetic sand with a wooden stylus or finger. Some pressure is exerted while writing. The resulting shapes recorded by the RGB-D camera have the form of ribbon ditches (grooves). The obtained point cloud is processed to create a sand deformation model. In the first stage, a dedicated local smoothing technique is used. Then, special algorithms are implemented to create a description of the main curvatures and key dimensions of recorded signs. A spline-based approach is used. The method allows for the representation of various shapes in a unified form. In the final stage, modulated sinusoidal functions define the robot’s trajectory. The effects of the robot’s operation are recorded to assess the reproduction quality. The Point Cloud Structural Similarity Measure (PointSSIM) evaluates the results. Experimental research takes into account many different shapes. Copies of shapes created by humans and robots are compared with the originals. The outcomes show that the quality of reproduction achieved by humans and robots is comparable. The median-based curvature similarity measure obtained for the human was only 1.92% higher than the robot’s result, and the covariance-based geometric similarity measure was only 0.74% higher than the robot’s score. The system can be used to mass-produce souvenirs or special implants. © 2001-2012 IEEE.

关键词： Wooden containers

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An Adaptive Vehicle Location Prediction Using Machine Learning: A Case Study of Campus Shuttle Bus

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IEEE Access 2025年 13卷 78290-78302页

作者： Nandy, Tarak Kolandaisamy, Raenu Noor, Rafidah Md Bhattacharyya, Sananda UCSI University Institute of Computer Science and Digital Innovation Kuala Lumpur56000 Malaysia Universiti Malaya Faculty of Computer Science and Information Technology Department of Computer Systems and Technology Kuala Lumpur50603 Malaysia Faculty of Computer Science and Information Technology Kuala Lumpur50603 Malaysia Maldives Business School Department of Information Technology Male20175 Maldives

Vehicle location prediction and the use of vehicle location tracking are increasingly important topics of discussion among connected vehicle researchers. Location tracking for mobile users is essential due to the correlated services and to improve the quality of service;however, it is challenging. On the other hand, the stateless predictive model is unsuitable due to less accuracy and mismatch pattern analysis. This paper proposed a vehicle location prediction model using machine learning based on a case study of a campus shuttle bus scenario. Firstly, this paper comprehensively analyzes the recent research on vehicle location prediction models and provides a complete taxonomy. The proposed model uses the Support Vector Regression (SVR) based vehicle location predictive model in the implementation phase. In addition, a significant time-series predictive model, ARIMA, is configured and tested. Moreover, the seasonal ARIMA model is experimented with to predict the location of a mobile vehicle. The whole experiment is performed on a real dataset based on the university shuttle bus service. The results of the experiments, analysis, and discussions on the proposed model show the accuracy and effectiveness of their use on others. As a result, seasonal ARIMA outperformed ARIMA and SVR infractions. © 2013 IEEE.

关键词： Prediction models

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Energy and QoS-aware virtual machine placement approach for IaaS cloud datacenter

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Neural Computing and Applications 2025年第4期37卷 2211-2237页

作者： Elsedimy, E.I. Herajy, Mostafa Abohashish, Sara M. M. Department of Information Technology Management Faculty of Management Technology and Information Systems Port Said University Port Said Egypt Department of Mathematics and Computer Science Faculty of Science Port Said University Port Said Egypt Cyber Secuirty Department Faculty of Science Al-Esraa University College Baghdad Iraq

Virtualization technology enables cloud providers to abstract, hide, and manage the underlying physical resources of cloud data centers in a flexible and scalable manner. It allows placing multiple independent virtual machines (VMs) on a single server in order to improve resource utilization and energy efficiency. However, determining the optimal VM placement is crucial as it directly impacts load balancing, energy consumption, and performance degradation within the data center. Furthermore, deciding on VM placement based on a single factor is usually insufficient to improve data center performance because many factors must be considered, and ignoring them may be too expensive. This paper improves a new multi-objective VM placement (MVMP) algorithm using a quantum particle swarm optimization (QPSO) technique. We call it QPSO-MOVMP, and its objective is to find the Pareto optimal solution for the VM placement problem by balancing different goals. This algorithm generates Pareto optimal solutions that save power by minimizing the number of running physical machines, avoid performance degradation by maintaining service level agreement (SLA), and improve load balancing by keeping server loads at optimal utilization. The experimental results show that QPSO-MOVMP had superior performance in terms of power consumption and performance degradation compared to three other multi-objective algorithms and three conventional single-objective algorithms. Simulation results show that the proposed QPSO-MOVMP achieves a consumption of 2.4 × 104 watts in power. Furthermore, it outperformed the others, achieving a minimum of 12% SLA breaches while experiencing a significant surge in requests from VMs. Moreover, the proposed model generated Pareto solutions that had a better distribution than those derived from a comparative method. © The Author(s) 2025.

关键词： Particle swarm optimization (PSO)

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Comparative Study of Classification Algorithms Using Big Data Analytics for Flight Delays Prediction 5th

Comparative Study of Classification Algorithms Using Big Dat...

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5th Doctoral Symposium on Computational Intelligence, DoSCI 2024

作者： Asiri, Mona Hassan AL-Malaise AL-Ghamdi, Abdullah S. Fayoumi, Ayman G. Ragab, Mahmoud Department of Information Systems Collage of Computing and Information Technology King Abdulaziz University Jeddah Saudi Arabia Information Systems Department School of Engineering Computing and Design Dar Al-Hekma University Jeddah34801 Saudi Arabia Department of Information Systems Faculty of Computing and Information Technology King Abdulaziz University Jeddah21589 Saudi Arabia Information Technology Department Faculty of Computing and Information Technology King Abdulaziz University Jeddah21589 Saudi Arabia

ISBN: (纸本)9789819771776

Flight delays are the most pressing cause of concern in the airline sector because they can weaken and harm airlines, passengers, and airports. Flight delay prediction has become an essential step in decision-making processes within commercial aviation. Due to the developments of recent technologies, there are many prediction models for flight delays;however, more development of accurate prediction models is still needed. Machine learning, statistical models, and other techniques can be used to create quick and accurate flight delay prediction models. Furthermore, methods for feature selection can be developed to get rid of dimensionality issues. This study aimed to compare four classification algorithms using "big data analytics" to predict flight delays, a function that can improve the whole airline sector. The selected classification algorithms were the "Gaussian Naïve Bayes," "Gradient Boosted Tree" classifier, "Logistic Regression," and "Random Forest" classifier. Many assessment techniques were applied to each one of the suggested models to show the better performance of these models. The comparison used the resulting confusion matrix to summarize prediction results for the following classification problems: "F1 Score", "Accuracy," "Recall," "Precision," "Area Under ROC," and "Area Under PR." The assessment results showed the enhanced outcomes of the "Gradient Boosted Tree" Classifier with existing techniques, and all results of "Logistic Regression" and "Naïve Bayes" are equal. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Prediction models

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