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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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Feature analysis and selection for BGP anomaly detection 28

Feature analysis and selection for BGP anomaly detection

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28th Conference on Innovation in Clouds, Internet and Networks, ICIN 2025

作者： Hakimi, Rifqy Reed, Martin J. School of Computer Science and Electronic Engineering University of Essex Colchester United Kingdom School of Electrical Engineering and Informatics Bandung Institute of Technology Bandung Indonesia

ISBN: (纸本)9798331542399

Automated incidence reporting requires accurate and timely anomaly detection. This paper considers this in the context of the Border Gateway Protocol (BGP). BGP is crucial for Internet routing but is vulnerable to attacks due to a lack of widespread authentication. While rare, BGP disruptions can be highly detrimental to Internet performance and security. Detecting BGP anomalies helps network operators protect their networks and improve Internet reliability. In this work, we investigate and develop anomaly detection for BGP using Machine-Learning. We extract relevant features of BGP control plane messages from RIPE RIS and RouteViews public datasets. After extracting features, we employ various feature selection algorithms to extract the most relevant features and explore balancing the datasets. Finally, we explore detection latency, an important operating parameter for automated anomaly detection. The results show that BGP anomalies can be detected in the order of seven minutes when using real attack data and that the observation point for the BGP data has a significant effect on anomaly detection. © 2025 IEEE.

关键词： Adversarial machine learning

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A fusion of machine learning and cryptography for fast data encryption through the encoding of high and moderate plaintext information blocks

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Multimedia Tools and Applications 2025年第8期84卷 5349-5375页

作者： Shafique, Arslan Mehmood, Abid Alawida, Moatsum Elhadef, Mourad Rehman, Mujeeb Ur School of Biomedical Engineering University of Glasgow Glasgow United Kingdom Department of Computer Sciences Abu Dhabi University Abu Dhabi United Arab Emirates Cyber Technology Institute School of Computer Science and Informatics De Montfort University Leicester United Kingdom

Within the domain of image encryption, an intrinsic trade-off emerges between computational complexity and the integrity of data transmission security. Protecting digital images often requires extensive mathematical operations for robust security. However, this computational burden makes real-time applications unfeasible. The proposed research addresses this challenge by leveraging machine learning algorithms to optimize efficiency while maintaining high security. This methodology involves categorizing image pixel blocks into three classes: high-information, moderate-information, and low-information blocks using a support vector machine (SVM). Encryption is selectively applied to high and moderate information blocks, leaving low-information blocks untouched, significantly reducing computational time. To evaluate the proposed methodology, parameters like precision, recall, and F1-score are used for the machine learning component, and security is assessed using metrics like correlation, peak signal-to-noise ratio, mean square error, entropy, energy, and contrast. The results are exceptional, with accuracy, entropy, correlation, and energy values all at 97.4%, 7.9991, 0.0001, and 0.0153, respectively. Furthermore, this encryption scheme is highly efficient, completed in less than one second, as validated by a MATLAB tool. These findings emphasize the potential for efficient and secure image encryption, crucial for secure data transmission in rea-time applications. © The Author(s) 2024.

关键词： Cryptography

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Knowledge augmented significant language model-based chatbot for explainable diabetes mellitus prediction 19

Knowledge augmented significant language model-based chatbot...

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

作者： Elfayoumi, Mazen AbouElazm, Mohamed Mohamed, Omar Abuhmed, Tamer El-Sappagh, Shaker Faculty of Computer Science and Engineering Galala University Egypt Faculty of Computer Science Goldsmith UOL United Kingdom College of Computing and Informatics Sungkyunkwan University Korea Republic of

ISBN: (纸本)9798331507817

This study proposes an innovative diabetes prediction chatbot that utilizes large language models (LLMs) to determine the likelihood of diabetes based on specific patient inputs. Unlike conventional machine learning models and in addition to providing precise, individualized, robust prediction of diabetes augmented by the percentage of its confidence, this chatbot provides detailed text-based explanations for its individual predictions and enhances user’s understanding by retrieving and presenting similar cases using case-based reasoning techniques. Utilizing key health indicators such as hypertension status, heart disease presence, smoking history, BMI, HbA1c level, and blood glucose levels, the chatbot not only predicts the presence of diabetes but also educates users about the underlying reasons for each prediction. The system's explanation module promotes transparency and trust in the predictive process. The proposed architecture integrates the retrieval augmented generation (RAG) technique with prompt engineering across multiple LLMs, including Llama 3.1, GPT-3.5, and Gemma2. This approach augments the chatbot’s responses with contextually relevant data from similar past cases, thereby enhancing both relevance and accuracy. Furthermore, RAG minimizes LLM hallucinations while enhancing them with up-to-date, precise, personalized medical information. The objective of this chatbot is to offer healthcare professionals and patients a dependable, educational instrument for early detection of diabetes supported by transparent AI principles. This will have a positive effect on preventative healthcare measures. © 2025 IEEE.

关键词： Prediction models

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Autonomous vehicle surveillance through fuzzy C-means segmentation and DeepSORT on aerial images

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PeerJ computer Science 2025年 11卷 1-23页

作者： Qureshi, Asifa Mehmood Alotaibi, Moneerah Alotaibi, Sultan Refa AlHammadi, Dina Abdulaziz Jamal, Muhammad Asif Jalal, Ahmad Lee, Bumshik Department of Computer Science Air University Islamabad Pakistan Department of Computer Science College of Science and Humanities Shaqra University Dawadmi Saudi Arabia Department of Information Systems College of Computer and Information Sciences Princess Nourah bint Abdulrahman University Riyadh Saudi Arabia Information and Communication Engineering Chosun University Gwangju Korea Republic of Department of Computer Science and Engineering College of Informatics Korea University Seoul Korea Republic of

The high mobility of uncrewed aerial vehicles (UAVs) has led to their usage in various computer vision applications, notably in intelligent traffic surveillance, where it enhances productivity and simplifies the process. Yet, there are still several challenges that must be resolved to automate these systems. One significant challenge is the accurate extraction of vehicle foregrounds in complex traffic scenarios. As a result, this article proposes a novel vehicle detection and tracking system for autonomous vehicle surveillance, which employs Fuzzy C-mean clustering to segment the aerial images. After segmentation, we employed the YOLOv4 deep learning algorithm, which is efficient in detecting small-sized objects in vehicle detection. Furthermore, an ID assignment and recovery algorithm based on Speed-Up Robust Feature (SURF) is used for multi-vehicle tracking across image frames. Vehicles are determined by counting in each image to estimate the traffic density at different time intervals. Finally, these vehicles were tracked using DeepSORT, which combines the Kalman filter with deep learning to produce accurate results. Furthermore, to understand the traffic flow direction, the path trajectories of each tracked vehicle is projected. Our proposed model demonstrates a noteworthy vehicle detection and tracking rate during experimental validation, attaining precision scores of 0.82 and 0.80 over UAVDT and KIT-AIS datasets for vehicle detection. For vehicle tracking, the precision is 0.87 over the UAVDT dataset and 0.83 for the KIT-AIS dataset. © 2025 Qureshi et al.

关键词： Image segmentation

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Quantum Linguistics in Patient-Clinical Trial Relations for Improved Healthcare

Quantum Linguistics in Patient-Clinical Trial Relations for ...

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2025 International Conference on Pervasive Computational Technologies, ICPCT 2025

作者： Arora, Chandani Sandhu, Ramandeep Islam, Sardar M. N. Lovely Professional University School of Computer Science & Engineering Phagwara India Victoria University Applied Informatics Research Melbourne Australia

ISBN: (纸本)9798331508685

The healthcare industry is undergoing a fundamental revolution with the integration of Quantum Natural Language Processing techniques for matching patient-clinical trials. This research envisions a paradigm where patients and clinical trials collaborate, changing the way healthcare is supplied and medical research is conducted. Apart from this, the research focuses on engaging patients in clinical trials. Patient involvement refers to the process by which patients and their healthcare providers collaborate to make decisions regarding their health. Within the framework of clinical studies, this may entail participants providing input on study designs or monitoring their data through wearable technology and smartphone apps. Quantum NLP has the potential to enhance patient outcomes and advance medical knowledge more quickly by enabling more precise and effective clinical trials. By setting the framework for future applications of quantum NLP, this study expands the conversation on the connection between quantum computing and healthcare. © 2025 IEEE.

关键词： Clinical research

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A Benchmark for Multi-Speaker Anonymization

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IEEE Transactions on Information Forensics and Security 2025年 20卷 3819-3833页

作者： Miao, Xiaoxiao Tao, Ruijie Zeng, Chang Wang, Xin Singapore Institute of Technology Singapore567739 Singapore National University of Singapore Electrical and Computer Engineering Department Queenstown 117583 Singapore National Institute of Informatics Chiyoda-ku Tokyo101-8340 Japan

Privacy-preserving voice protection approaches primarily suppress privacy-related information derived from paralinguistic attributes while preserving the linguistic content. Existing solutions focus particularly on single-speaker scenarios. However, they lack practicality for real-world applications, i.e., multi-speaker scenarios. In this paper, we present an initial attempt to provide a multi-speaker anonymization benchmark by defining the task and evaluation protocol, proposing benchmarking solutions, and discussing the privacy leakage of overlapping conversations. The proposed benchmark solutions are based on a cascaded system that integrates spectral-clustering-based speaker diarization and disentanglement-based speaker anonymization using a selection-based anonymizer. To improve utility, the benchmark solutions are further enhanced by two conversation-level speaker vector anonymization methods. The first method minimizes the differential similarity across speaker pairs in the original and anonymized conversations, which maintains original speaker relationships in the anonymized version. The other minimizes the aggregated similarity across anonymized speakers, which achieves better differentiation between speakers. Experiments conducted on both non-overlap simulated and real-world datasets demonstrate the effectiveness of the multi-speaker anonymization system with the proposed speaker ***, we analyzed overlapping speech regarding privacy leakage and provided potential solutions © 2005-2012 IEEE.

关键词： Anonymity

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Sobol Sequence Optimization for Hardware-Efficient Vector Symbolic Architectures

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IEEE Transactions on computer-Aided Design of Integrated Circuits and Systems 2025年第3期44卷 937-950页

作者： Aygun, Sercan Najafi, Hassan University of Louisiana at Lafayette School of Computing and Informatics LafayetteLA70503 United States Case Western Reserve University Electrical Computer and Systems Engineering Department ClevelandOH44106 United States

Hyperdimensional computing (HDC) is an emerging computing paradigm with significant promise for efficient and robust learning. In HDC, objects are encoded with high-dimensional vector symbolic sequences called hypervectors. The quality of hypervectors, defined by their distribution and independence, directly impacts the performance of HDC systems. Despite a large body of work on the processing parts of HDC systems, little to no attention has been paid to data encoding and the quality of hypervectors. Most prior studies have generated hypervectors using inherent random functions, such as MATLAB’s or Python’s random function. This work introduces an optimization technique for generating hypervectors by employing quasi-random sequences. These sequences have recently demonstrated their effectiveness in achieving accurate and low-discrepancy data encoding in stochastic computing systems. The study outlines the optimization steps for utilizing Sobol sequences to produce high-quality hypervectors in HDC systems. An optimization algorithm is proposed to select the most suitable Sobol sequences via indexes for generating minimally correlated hypervectors, particularly in applications related to symbol-oriented architectures. The performance of the proposed technique is evaluated in comparison to two traditional approaches of generating hypervectors based on linear-feedback shift registers and MATLAB random functions. The evaluation is conducted for three applications: 1) language;2) headline;and 3) medical image classification. Our experimental results demonstrate accuracy improvements of up to 10.79%, depending on the vector size. Additionally, the proposed encoding hardware exhibits reduced energy consumption and a superior area-delay product. © 2024 IEEE.

关键词： Stochastic systems

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A Two-Pronged Approach to Security of Discrete-Event Systems

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Journal of Systems Science & Complexity 2025年第1期38卷 178-209页

作者： MATSUI Shoma RUDIE Karen CAI Kai Department of Electrical and Computer Engineering&the Ingenuity Labs Research Institute Queen's UniversityKingston K7L 3N6Canada Department of Core Informatics Osaka Metropolitan UniversityOsaka 545-0051Japan

This paper investigates a security problem of simultaneously addressing two types of attacks:Eavesdropping and *** authors model the target system as a discrete-event system(DES)with subsets of concealable events and protectable events,in order to make the proposed methodology applicable to various practical systems and employ two existing works of DES security:Degree of opacity and state ***,the authors consider that all protectable events are observable,and some observable events are *** addition,protectable events cannot be protected once they are *** such a constraint,the goal is to figure out which events to conceal and which transitions to protect so that the prescribed requirements of degree of opacity and state protection are *** this work the authors decide which events to conceal as all transitions of a given event label are concealed or not *** proposed problem formulation also requires a solution to only involve absolutely necessary protectable events in order for the system to avoid superfluous protection *** authors first examine a general version of our security problem with an intuitive algorithm to compute acceptable solutions,and then present a special version which results in a reduced computation time compared to the general version.

关键词： Degree of opacity discrete-event systems security state protection

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Integrated terahertz transceiver front-end utilizing spatiotemporal modulation

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Journal of the Optical Society of America B: Optical Physics 2025年第5期42卷 1114-1122页

作者： Valizadeh, Mahsa Yousefi, Leila Miri, MirFaez School of Electrical and Computer Engineering College of Engineering University of Tehran Tehran*** Iran School of Engineering and Informatics University of Sussex FalmerBN1 9RH United Kingdom Department of Physics University of Tehran Tehran14395547 Iran

The paper proposes a method for developing spatiotemporally modulated structures operating at THz frequencies. In this technique, electromagnetic waves propagating inside microwave waveguides modulate the chemical potential of a graphene sheet both temporally and spatially. The modulated graphene sheet interconnects these waveguides with a plasmonic waveguide carrying a THz wave. This approach has been used to create an integrated device that encapsulates all components of a THz transceiver front-end in a single unit. The device leverages the extraordinary features of spatiotemporally modulated structures, such as nonreciprocal response and frequency mixing, to integrate all functionalities of a THz transceiver front-end. We have implemented a leaky-wave antenna, mixer, and duplexer with beam steering capabilities, all within a spatiotemporal graphene-based structure. The performance of the device is investigated both analytically and numerically in transmit and receive modes. Additionally, the modulating waveguides have undergone both electromagnetic and thermal numerical simulations to ensure they can handle the power required for efficient modulation of graphene without overheating, which could hinder their performance. © 2025 Optica Publishing Group. All rights, including for text and data mining (TDM), Artificial Intelligence (AI) training, and similar technologies, are reserved.

关键词： Terahertz waves

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