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Early Parkinson's Disease Detection and Classification using Machine Learning and Neutrosophic Sets

Neutrosophic Sets and systems 2025年 82卷 873-886页

作者： Oudah, Hasan H. Metwaly, Ahmed A. Eassa, Mohamed Abdelhafeez, Ahmed Nagm, Ahmad M. Salama, Ahmed S. College of Arts Department of Journalism Ahl Al Bayt University Karbala Iraq Department of Computer Science Faculty of Computers and Informatics Zagazig University Zagazig44519 Egypt Computer Science Department Faculty of Information System and Computer Science October 6 University Giza12585 Egypt Applied Science Research Center Applied Science Private University Amman Jordan Department of Computer Engineering and Electronics Cairo Higher Institute for Engineering Computer Science and Management New Cairo Egypt

Optimizing therapy and rehabilitation for Parkinson's disease (PD) requires early identification and precise evaluation of the illness's course. However, there is disagreement about the best way to use gait analysis to categorize the severity of motor symptoms and identify early-stage Parkinson's disease. The precision of machine learning (ML) models in identifying early and intermediate stages of Parkinson's disease was assessed in this study. Six ML models are used in this study for the prediction of PD. Different metrics are used to evaluate ML models such as accuracy, precision, recall, f1-score, and AUC score. Then we propose a multi-criteria decision-making methodology (MCDM) to evaluate the ML models and select the best one. Two MCDM methods are used such as CRITIC method to compute the criteria weights and the TOPSIS method to rank the alternatives. These methods are used under the bipolar neutrosophic sets (BNSs) to deal with uncertainty information. The results show the support vector machine is the best ML model for the prediction of PD. © 2025, University of New Mexico. All rights reserved.

关键词： Neurodegenerative diseases

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Detecting Fake Reviews in E-Commerce using Deep Learning Techniques 1

Detecting Fake Reviews in E-Commerce using Deep Learning Tec...

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1st International Conference on Intelligent systems and Computational Networks, ICISCN 2025

作者： Abd, Maysam Jalal Hussein, Mohsin Hasan University of Kerbala College of Computer Science and Information Technology Department of Computer Science Karbala Iraq

ISBN: (纸本)9798331529246

Users may now create and use large amounts of data saved online thanks to e-commerce systems. Modern shoppers examine online reviews before making purchases. Evaluations are essential for both people and organizations. People and organizations need evaluations. Customers use this information to buy. Spammers produce fake reviews to mislead consumers about internet products and services. No system controls reviews, thus anybody may post anonymously. This causes fake reviews. Consumers need ways to differentiate fake reviews from real ones. Long Short-Term Memory (LSTM) network and Convolutional Neural Network (CNN) are used alongside Random Forest (RF), Naive Bayes (NB), Logistic Regression (LR), and Support Vector Machine (SVM) in this study. We use the Deceptive Opinion Spam Corpus and Yelp Labeled datasets to do this. Experimental results showed that the Deceptive Opinion Spam Corpus LSTM classifier beat earlier approaches by 1.14% to 3.49%. The Yelp-annotated dataset showed 44.83% maximum recall. © 2025 IEEE.

关键词： Convolutional neural networks

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Evolutionary optimization of Yagi–Uda antenna design using grey wolf optimizer

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Neural Computing and Applications 2025年第6期37卷 4155-4183页

作者： Braik, Malik Sheta, Alaa Aljahdali, Sultan El-Hefnawi, Fatma Al-Hiary, Heba Elashmawi, Walaa H. Computer Science Department Al-Balqa Applied University Salt Jordan Department of Computer Science Southern Connecticut State University New HavenCT United States Computer Science Department Taif University Taif Saudi Arabia Microwave Department Electronics Research Institute Giza Egypt Department of Computer Information System Al-Balqa Applied University Salt Jordan Department of Computer Science Faculty of Computers & Informatics Suez Canal University Ismailia Egypt Department of Computer Science Faculty of Computer Science Misr International University Cairo Egypt

Meta-heuristic optimization algorithms have become widely used due to their outstanding features, such as gradient-free mechanisms, high flexibility, and great potential for avoiding local optimal solutions. This research explored the grey wolf optimizer (GWO) to find the ideal configuration for a six-element Yagi–Uda antenna. The GWO algorithm adjusted the lengths of the antenna wires and the spacings between them. The goal was to maximize the antenna’s ability to transmit signals (throughput gain). Optimal antenna selection relies on various parameters, including gain, bandwidth, impedance matching, frequency, side-lobe levels, etc. The optimization of a six-element Yagi–Uda antenna presents a challenging engineering design problem due to its multimodal and nonlinear nature. Achieving optimal performance hinges on the intricate interplay between the lengths of the constituent elements and the spacing configurations. To this end, a multiobjective function was adopted to design this antenna. The performance of several meta-heuristic algorithms, including genetic algorithms, biogeography-based optimization, simulated annealing, and grey wolf optimizer, was compared. The GWO-based approach has performed better than its competitors. This optimized antenna design based on GWO reported a gain of 14.21 decibel. Therefore, the GWO-based method optimizes antennas that can be further investigated for other antenna design problems. © The Author(s), under exclusive licence to Springer-Verlag London Ltd., part of Springer Nature 2024.

关键词： Simulated annealing

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Neutrosophic Sets and systems: A Decade of Scientific Contribution and Growth

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Neutrosophic Sets and systems 2025年 81卷 438-465页

作者： Saqlain, Muhammad Merigó, José M. Kumam, Poom Department of Mathematics King Mongkut’s University of Technology Thonburi Bangkok10140 Thailand School of Computer Science Faculty of Engineering and Information Technology University of Technology Sydney 81 Broadway UltimoNSW2007 Australia

Neutrosophic Sets and systems (NSS) has become an important Journal for neutrosophic theory and its applications in uncertainty modeling and decision sciences. In 2023, NSS celebrated its 10th anniversary, marking a decade of contributions to scientific literature. Motivated by this event, this paper provides a critical bibliometric analysis of NSS’s evolution as a leading academic journal in neutrosophy. This study explores the publication trends, citation impact, most productive authors, institutions, and global collaboration networks of NSS. The study maps bibliographic coupling, keyword co-occurrence and co-citation analysis using data from the Scopus database, secondary search using NSS webpage, and visualization tools like the VOSviewer. The findings identify Neutrosophic Sets, Decision-Making, and Fuzzy Sets as the most frequently studied keywords that highlight the increasing integration of neutrosophic approaches into multi-criteria decision-making (MCDM) and soft computing. The results indicate the most significant areas of research that are topology, soft set theory, hypersoft sets, and uncertainty modeling. The findings highlight the NSS increasing international collaboration, with contributions from America, Middle East, and Asia. Additionally, the analysis explores NSS’s alignment with sustainable development goals (SDGs), revealing notable contributions to good health and well-being (SDG 3), quality education (SDG 4), and peace, justice, and strong institutions (SDG 16). These contributions highlight how neutrosophic sets and systems is valuable across many researchers, from public health to governance to education. Overall, this paper serves as a fundamental study for researchers looking to explore emerging trends and novel applications of neutrosophic theories across diverse scientific and practical domains. © 2025, University of New Mexico. All rights reserved.

关键词： Bibliographies

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Predictive Analytics in Mobile Education: Evaluating Logistic Regression, Random Forest, and Gradient Boosting for Course Completion Forecasting

International Journal of Interactive Mobile Technologies

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International Journal of Interactive Mobile Technologies 2025年第5期19卷 210-232页

作者： Hayadi, B. Herawan Hariguna, Taqwa Information Technology Education Faculty of Teacher Training and Education Bina Bangsa University Banten 42124 Indonesia Department of Information System and Magister Computer Sciences Universitas Amikom Purwokerto Purwokerto Utara 53127 Indonesia

This study aimed to compare the effectiveness of three predictive algorithms—logistic regression, random forest, and GBM—in predicting course completion using user engagement data from online learning platforms. By analyzing engagement metrics such as session duration, session frequency, and quiz scores, the study sought to identify the most effective model for forecasting course completion, providing insights into which aspects of student behavior were most predictive of success. Logistic regression emerged as the best overall performer, achieving the highest accuracy (52.13%) and F1-Score (56.17%), indicating its balanced approach to predicting course completion and non-completion. Random forest and gradient boosting machines (GBM) showed strengths in specific areas;random forest maintained a good balance between precision and recall, while GBM excelled in recall, identifying students likely to complete courses but with lower precision, leading to more false positives. The findings have practical implications for educational technology, particularly in designing personalized learning paths and targeted interventions to support at-risk students. The study also acknowledged limitations, including the dataset’s focus on engagement metrics without demographic context and the potential for model-specific biases. Future research should explore additional predictive features, larger datasets, and more advanced algorithms to enhance the robustness and applicability of predictive models in real-time educational settings. © 2025 by the authors of this article.

关键词： Prediction models

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Mitigating Bias and Assessment Inconsistencies with BERT-Based Automated Short Answer Grading for the Indonesian Language

IAENG International Journal of Computer Science

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IAENG International Journal of computer science 2025年第3期52卷 533-545页

作者： Amalia, Amalia Lydia, Maya Silvi Muchtar, Muhammad Anggia Manik, Fuzy Yustika Sinu Gunawan, Dani Department of Computer Science Universitas Sumatera Utara Medan Indonesia Department of Information Technology Universitas Sumatera Utara Medan Indonesia

Automating the grading of short answers in Indonesian presents unique challenges, primarily due to the inherent variability in student responses and the limited linguistic resources available for fine-tuning models. This study aims to develop an automated grading system for short answers by employing a modified BERT model tailored explicitly for the Indonesian language. Our methodology involves translating the Stanford Question Answering Dataset (SQuAD 2.0) and augmenting it with domain-specific content from lecture materials, resulting in an additional 1,000 question-answer pairs. Furthermore, we optimize the model using the Optuna library for hyperparameter tuning. Experimental results show a minimum loss of 1.81 for the model, with optimized hyperparameters including a learning rate of 3.36 × 10-5, a training batch size of 8 per device, and two training epochs. The model achieves an F1-score of 69%, demonstrating a satisfactory level of accuracy comparable to or slightly exceeding typical performances on SQuAD 2.0 in English, which averages around 66%. Evaluations were conducted across various scenarios, including short-answer, long-context, and long-answer assessments. The results revealed that short answers achieved the highest cosine similarity and a perfect QWK score of 1. In contrast, long-context and long-answer scenarios yielded a QWK of 0.5, indicating a need for further improvement;however, the model shows promise for long answers with additional enhancements. This study demonstrates BERT’s potential to enhance equity and precision in grading Indonesian short answers, despite limitations in response completeness. © (2025), (International Association of Engineers). All rights reserved.

关键词： Linguistics

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Dual-Channel Multiplex Graph Neural Networks for Recommendation

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IEEE Transactions on Knowledge and Data Engineering 2025年第6期37卷 3327-3341页

作者： Li, Xiang Fu, Chaofan Zhao, Zhongying Zheng, Guangjie Huang, Chao Yu, Yanwei Dong, Junyu Ocean University of China Faculty of Information Science and Engineering Qingdao266005 China Shandong University of Science and Technology College of Computer Science and Engineering Qingdao266590 China Shanghai Jiao Tong University Department of Computer Science and Engineering Shanghai200240 China The University of Hong Kong Department of Computer Science Hong Kong

Effective recommender systems play a crucial role in accurately capturing user and item attributes that mirror individual preferences. Some existing recommendation techniques have started to shift their focus towards modeling various types of interactive relations between users and items in real-world recommendation scenarios, such as clicks, marking favorites, and purchases on online shopping platforms. Nevertheless, these approaches still grapple with two significant challenges: (1) Insufficient modeling and exploitation of the impact of various behavior patterns formed by multiplex relations between users and items on representation learning, and (2) ignoring the effect of different relations within behavior patterns on the target relation in recommender system scenarios. In this work, we introduce a novel recommendation framework, Dual-Channel Multiplex Graph Neural Network (DCMGNN), which addresses the aforementioned challenges. It incorporates an explicit behavior pattern representation learner to capture the behavior patterns composed of multiplex user-item interactive relations, and includes a relation chain representation learner and a relation chain-aware encoder to discover the impact of various auxiliary relations on the target relation, the dependencies between different relations, and mine the appropriate order of relations in a behavior pattern. Extensive experiments on three real-world datasets demonstrate that our DCMGNN surpasses various state-of-the-art recommendation methods. It outperforms the best baselines by 10.06% and 12.15% on average across all datasets in terms of Recall@10 and NDCG@10 respectively. The source code of our paper is available at https://***/lx970414/TKDE-DCMGNN. © 1989-2012 IEEE.

关键词： Graph neural networks

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Explainable ensemble deep learning-based model for brain tumor detection and classification

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Neural Computing and Applications 2025年第3期37卷 1289-1306页

作者： Hosny, Khalid M. Mohammed, Mahmoud A. Salama, Rania A. Elshewey, Ahmed M. Department of Information Technology Faculty of Computers and Information Zagazig University P.O.BOX 44519 Zagazig Egypt Department of Computer Science Faculty of Computers and Information Suez University P.O.BOX 43221 Suez Egypt Department of Information Technology Faculty of Computers and Information Suez University P.O.BOX 43221 Suez Egypt

Brain tumors are very dangerous as they cause death. A lot of people die every year because of brain tumors. Therefore, accurate classification and detection in the early stages can help in recovery. Various deep learning techniques have achieved good results in brain tumor classification. The traditional deep learning methods and training the neural network from scratch are time-consuming and can last for weeks of training. Therefore, in this work, we proposed an ensemble approach depending on transfer learning that utilizes pre-trained models of DenseNet121 and InceptionV3 to detect three forms of brain tumors: meningioma, glioma, and pituitary. While developing the ensemble model, some changes were made to the architecture of pre-trained models by replacing their classifiers (fully connected and SoftMax layers) with a new classifier to adopt the recent task. In addition, gradient-weighted class activation maps (Grad-CAM) are an explainable model to verify results and achieve high confidence. The suggested model was validated using a publicly available dataset and achieved 99.02% accuracy, 98.75% precision, 98.98% recall, and a 98.86% F1 score. The suggested approach outperformed others in detecting and classifying brain tumor MRI data, and verifying results using the explainable model achieved a high degree of trust. © The Author(s) 2024.

关键词： Transfer learning

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Blockchain and homomorphic encryption based system for secure supply chain network against counterfeit drugs

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Peer-to-Peer Networking and Applications 2025年第4期18卷 1-36页

作者： Agarwal, Udit Rishiwal, Vinay Yadav, Mano Yadav, Preeti Department of Computer Science and Information Technology MJP Rohilkhand University Bareilly India Department of Computer Science Bareilly College Bareilly India

Counterfeit medication is a critical global issue that poses severe risks to public health due to its harmful effects on the human body. The rise of online pharmacies has further expanded the market for counterfeit drugs, with over 50% of medicines sold online being fraudulent. These counterfeit drugs not only jeopardize the health and safety of individuals but also lead to significant revenue losses for legitimate pharmaceutical companies and substantial tax evasion costs for governments. Despite the implementation of various anti-counterfeiting strategies, many face challenges in ensuring reliable traceability, trust, and transparency regarding the origin of the drugs. Moreover, the lack of coordination between manufacturers, distributors, and wholesalers exacerbates the problem, leading to ineffective drug recall systems and a pervasive lack of trust. Blockchain technology presents a promising solution to counterfeiting and theft in pharmaceutical supply chains. This study proposes and implements a blockchain-based solution for combating counterfeit medications in India’s pharmaceutical supply chains. The proposed approach includes controlling supply chain operations using several procedures embedded in smart contracts running on the Ethereum blockchain, as well as storing information using the Interplanetary File system (IPFS), which improves scalability, reduces computing load, and allows for quick data retrieval with minimal processing. By leveraging homomorphic encryption (HE), regulatory authorities or auditors can securely validate that pharmaceuticals have followed the correct supply chain path while protecting sensitive information. The system’s scalability was determined by monitoring its performance as transaction volumes increased. The proposed system showed decreased latency and better throughput, indicating its suitability for application in real-world production environments. In addition to performance testing, the system’s security was assessed usi

关键词： Anti-Counterfeiting drug supply systems Blockchain technology Drug traceability Homomorphic encryption Secure pharma supply chain

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Dempster-shafer deep capsule attention model (DDCAM)

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Multimedia Tools and Applications 2025年 1-29页

作者： Mehravaran, Zahra Ghanizadeh, Ahmad Navid Hamidzadeh, Javad Harati, Ahad Mashhad Iran Department of Computer Science Saarland University Saarbrücken Germany Faculty of Computer Engineering and Information Technology Sadjad University Mashhad Iran

A capsule neural network faces significant challenges in achieving high accuracy on complex datasets due to its high computational complexity and limited ability to represent features. To overcome these limitations, this paper proposes a novel framework, Dempster-Shafer Deep Capsule Attention Model (DDCAM), that combines 3D convolution with attention mechanisms to improve feature representation. This model utilizes the Dempster-Shafer Theory to manage uncertainty and optimize feature selection, resulting in more reliable and accurate learning. In this architecture, dense capsule blocks are used to improve gradient flow and mitigate the effects of vanishing gradients. The DDCAM consistently outperforms state-of-the-art capsule networks on a wide range of datasets, especially complex datasets such as MNIST, CIFAR10, SVHN, and Fashion MNIST. DDCAM improves accuracy by 1.29% on CIFAR10 and 0.76% on SVHN compared to existing models, while achieving precision improvements of 1.22% and 1.08% on CIFAR10 and SVHN, respectively. Additionally, experiments conducted on the AffNIST dataset demonstrate the robustness of DDCAM in handling affine transformations, where it achieved significantly higher accuracy compared to existing models. These results illustrate the model’s enhanced ability to capture complex data relationships and improve both accuracy and precision, advancing capsule neural network performance. © The Author(s), under exclusive licence to Springer science+Business Media, LLC, part of Springer Nature 2025.

关键词： Affine transforms

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