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Identification of Suspect Using Transformer and Cosine Similarity Model for Forensic Voice Comparison

Security and Privacy 2025年第3期8卷

作者： Kruthika, S.G. Nagavi, Trisiladevi C. Mahesha, P. Chethana, H.T. Ravi, Vinayakumar Al Mazroa, Alanoud Department of Computer Science and Engineering S. J. College of Engineering JSS Science and Technology University Karnataka Mysore India Department of Computer Science & Engineering Vidyavardhaka College of Engineering Mysore India Center for Artificial Intelligence Prince Mohammad Bin Fahd University Khobar Saudi Arabia Department of Information Systems College of Computer and Information Sciences Princess Nourah Bint Abdulrahman University Riyadh Saudi Arabia

Forensic voice comparison (FVC) is a crucial area within forensic science, focused on analyzing and comparing voice recordings to identify speakers. Traditional FVC methods often involve manual analysis and subjective interpretation, which can limit both efficiency and accuracy. In recent years, there has been increasing interest in leveraging advanced machine learning techniques to enhance the capabilities of FVC. This study explores the application of a semi-automated approach using the Transformer model in FVC, with the goal of improving the accuracy and efficiency of speaker identification. The research utilizes pre-processed forensic voice trace data in Free Lossless Audio Codec (.flac) format, along with suspect or mugshot voice samples collected by forensic experts. The Transformer model, specifically Wav2Vec 2.0, is employed for feature extraction and classification. The model processes these voice samples, using its encoder to extract relevant features and its decoder to classify the data based on cosine similarity. A threshold is applied to the cosine similarity index to determine the degree of match, with higher values indicating a stronger match. Experimental results demonstrate the effectiveness of this semi-automated Transformer-based approach for FVC. The similarity index achieved during testing ranged from 0.9970 to 0.9995, indicating the model's high accuracy in predicting speaker similarity. The identification process is further validated using performance metrics such as a confusion matrix, histogram, and scatter plot. This study highlights the potential of Transformer models to significantly advance the field of forensic voice comparison, offering more precise and reliable speaker identification. © 2025 John Wiley & Sons Ltd.

关键词： computer forensics

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Generative AI-Enabled Quantum Encryption Algorithm for Securing IoT-Based Healthcare Application Using Blockchain

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IEEE Internet of Things Journal 2025年

作者： Prajapat, Sunil Kumar, Pankaj Das, Ashok Kumar Muhammad, Ghulam Central University of Himachal Pradesh Srinivasa Ramanujan Department of Mathematics Dharamshala176215 India International Institute of Information Technology Center for Security Theory and Algorithmic Research Hyderabad500 032 India Korea University College of Informatics Department of Computer Science and Engineering Anam-ro Seoul145 Korea Republic of King Saud University College of Computer and Information Sciences Department of Computer Engineering Riyadh11543 Saudi Arabia

The integration of Artificial Intelligence (AI) with the Internet of Things (IoT) has transformed numerous domains through the AI of Things (AIoT). Nonetheless, AIoT encounters issues related to energy usage and carbon emissions as mobile technology continues to progress. Generative AI (GAI) possesses significant potential to mitigate carbon emissions associated with AIoT, owing to its higher reasoning and generative powers. Conventional security protocols frequently encounter issues with computational efficiency, latency, and overall security comprehensiveness. Blockchain technology, characterized by its decentralized and immutable properties, is a viable approach for improving electronic healthcare data transmission and node authentication in IoT networks. This research examines secure data transmission and node encryption in IoT systems, with a particular emphasis on data management. Conventional approaches encounter constraints in computational efficiency, latency, and comprehensive security. This study presents a novel protocol that combines GAI and blockchain technology with quantum encryption to enhance authentication and ensure secure data transmission. The algorithm comprises multiple consecutive processes, including the encoding and transmission of node requests, followed by the authentication process utilizing hash functions and digital signatures. The authentication approach utilizes a challenge-response technique, guaranteeing that only nodes with authentic credentials can advance. Thereafter, a dynamic key exchange protocol and quantum encryption method provide secure data delivery. The results indicate the procedure’s effectiveness in ensuring secure and regulated access to patient data, underscoring its significance in medical facilities. The system’s functionalities are augmented by a thorough evaluation employing machine learning. The findings indicate that the system exhibits an accuracy of 99.4%, precision of 99.10%, recall of 98.66%, F1-score of

关键词： Quantum computers

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Small Signal Stability-Guaranteed Moving Target Defense Against Load Redistribution Attack On IoT-based Smart Grid

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IEEE Internet of Things Journal 2025年第11期12卷 17413-17429页

作者： Wang, Bingdong Zhang, Zhenyong Wang, Mufeng Liu, Mengxiang Ye, Rui Deng, Ruilong Zhang, Xin Guizhou University State Key Laboratory of Public Big Data College of Computer Science and Technology Guiyang550025 China China Industrial Control Systems Cyber Emergency Response Team Beijing100040 China University of Sheffield Department of Automatic Control and System Engineering Sheffield United Kingdom Guizhou Power Grid Co. Ltd. Intelligent Operation Center China Zhejiang University State Key Laboratory of Industrial Control Technology College of Control Science and Engineering Hangzhou310027 China Huzhou Institute of Industrial Control Technology Huzhou313000 China

Moving target defense (MTD) is a promising approach to defend against load redistribution attacks on the internet-of-things (IoT)-based smart grid networks by probing the distorted state estimates with the distributed flexible AC transmission system. However, existing studies mainly focus on optimizing the performance of MTD and ignore the safety effect of it on the system’s operation. In this paper, we fill this gap by deeply analyzing the effect of MTD on the small signal stability and aim to alleviate the negative impact and guarantee its defending performance simultaneously. Firstly, the stability is formally described using the eigenvalue sensitivity. The relationship between the MTD-induced perturbation (MTDper) and the stability criteria is derived. Secondly, a new indicator is proposed to measure the effectiveness of MTDper. Thirdly, a constrained optimization problem is formulated to compute the bound of MTDper for guaranteeing the small signal stability. In addition, a surprising finding is that the stability margin can be improved and enhanced by optimizing the value of MTDper without losing the MTD’s effectiveness. Finally, we evaluate the performance of MTDper and its impact on the small signal stability with extensive simulations on the IEEE 30-bus, 39-bus, and 68-bus test power systems. © 2014 IEEE.

关键词： Constrained optimization

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Identifying Inflammatory Bowel Disease-Associated Gene Ontology Groups Using Biological Knowledge-Based Machine Learning

Identifying Inflammatory Bowel Disease-Associated Gene Ontol...

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18th International Joint Conference on Biomedical Engineering systems and Technologies, BIOSTEC 2025

作者： Ersoz, Nur Sebnem Bakir-Gungor, Burcu Yousef, Malik Department of Bioengineering Graduate School of Engineering and Science Abdullah Gul University Kayseri Turkey Department of Computer Engineering Faculty of Engineering Abdullah Gul University Kayseri38080 Turkey Department of Bioengineering Faculty of Life and Natural Sciences Abdullah Gul University Kayseri Turkey Department of Information Systems Zefat Academic College Zefat Israel Zefat Academic College Zefat Israel

Inflammatory bowel disease (IBD) is a chronic inflammatory disease. Complex pathogenesis behind disease formation and progression necessitated the development of new approaches to identify disease related genes and affected gene ontology (GO) terms. In this study, via exploiting GeNetOntology method, we have reanalysed a gene expression data including Crohn’s Disease (CD) and Ulcerative colitis (UC) patients and controls. In order to identify IBD related genes and affected GO terms, GeNetOntology uses GO hierarchy as the biological domain knowledge while performing gene expression data analysis based on machine learning (ML). In the training part of GeNetOntology, genes annotated with selected ontology terms have been utilized to perform a two-class classification task which generates an important set of ontologies as an output. IBD data samples were obtained from peripheral blood and colon tissue. In order to investigate the effect of different collection sites, IBD data have been analysed under different scenarios;i.e., all samples, only tissue samples and only blood samples. Experimental findings indicate that GeNetOntology can successfully determine significant disease-related ontology terms. Performance of the model slightly differs according to the sample source. Via analysing the differences/commonalities between affected gene ontologies under different scenarios, we attempt to enlighten IBD development mechanisms. © 2025 by SCITEPRESS– science and Technology Publications, Lda.

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Kalman-based Adaptive Moment Estimation Optimisation Algorithm to Enhance GPT in LLMs for Medical Sentiment Analysis of Patient Health-related Feedback

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IEEE Journal of Biomedical and Health informatics 2025年 PP卷 PP页

作者： Chen, Xingchi Ma, Wenxin Li, Dazhou Zhu, Fa Routray, Sidheswar Guduri, Manisha Margala, Martin The Pengcheng Laboratory Shenzhen518000 China The College of Computer Science and Technology Shenyang University of Chemical Technology Shenyang110142 China The College of Information Science and Technology College of Artificial Intelligence Nanjing Forestry University Nanjing China The Department of Computer Science and Engineering School of Technology Pandit Deendayal Energy University Gujarat Gandhinagar India The School of Computing and Informatics University of Louisiana LafayetteLA70503 United States

The progress in Natural Language Processing (NLP) using Large Language Models (LLMs) has greatly improved medical sentiment analysis of patient feedback extraction from health-related question and answer. However, using LLMs to analyze such data often requires significant training data and computational resources, resulting in considerable increases in training costs and durations, which is one of the primary issues in applying LLMs to real-world healthcare scenarios. To tackle these challenges, a novel optimization algorithm named KAdamEnGPT4LLM, based on Kalman filters and Adaptive Moment Estimation, is proposed to enhance training efficiency and reduce training costs of LLMs for analyzing patient feedback sentiment. Furthermore, the optimization algorithm KAdam-EnGPT4LLM is employed in training the LLM model GPT4ALL for medical sentiment analysis, resulting in the development of GPT4ALLMediSentAly-KAdam, which leds to faster convergence and more stable training specifically for medical questions and answer in the context of healthcare. The results show that our GPT4ALLMediSentAly-KAdam with the optimization algorithm KAdamEnGPT4LLM achieved better performance that include the best Accuracy, Recall, F1-score, and Runtime for both datasets, outperforming traditional fine-tuned LLMs such as the classic GPT4ALL, Ada, Babbage, Curie, and Duvinci. © 2013 IEEE.

关键词： Adaptive filters

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COPA: Comparing the Incomparable to Explore the Pareto Front

arXiv

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arXiv 2025年

作者： Javaloy, Adrián Vergari, Antonio Valera, Isabel School of Informatics University of Edinburgh Edinburgh United Kingdom Department of Computer Science Saarland University Saarbrücken Germany Max Planck Institute for Software Systems Saarbrücken Germany

In machine learning (ML), it is common to account for multiple objectives when, e.g., selecting a model to deploy. However, it is often unclear how one should compare, aggregate and, ultimately, trade-off these objectives, as they might be measured in different units or scales. For example, when deploying large language models (LLMs), we might not only care about their performance, but also their CO2 consumption. In this work, we investigate how objectives can be sensibly compared and aggregated to navigate their Pareto front. To do so, we propose to make incomparable objectives comparable via their CDFs, approximated by their relative rankings. This allows us to aggregate them while matching user-specific preferences, allowing practitioners to meaningfully navigate and search for models in the Pareto front. We demonstrate the potential impact of our methodology in diverse areas such as LLM selection, domain generalization, and AutoML benchmarking, where classical ways to aggregate and normalize objectives fail. © 2025, CC BY-NC-ND.

关键词： Contrastive Learning

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Data Driven Traffic Density Monitoring: A UAV and ResNet50 Deep Learning Framework

Data Driven Traffic Density Monitoring: A UAV and ResNet50 D...

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International Colloquium on Signal Processing & Its Applications, CSPA

作者： Saddaf Rubab Fida Hussain Dahri Ghulam E Mustafa Abro Nooritawati Md Tahir Muhammad Javed Department of Computer Engineering College of Computing and Informatics University of Sharjah Sharjah United Arab Emirates School of Computer Science and Engineering Southeast University Nanjing China Interdisciplinary Research Centre for Aviation and Space Exploration King Fahd University of Petroleum and Minerals (KFUPM) Saudi Arabia School of Electrical Engineering College of Engineering Universiti Teknologi MARA Shah Alam Selangor Malaysia Department of Cybersecurity International information Technology University Alamty Kazakhstan Department of Computer Science and Technology College of Computer Science Donghua University Shanghai China

ISBN: (数字)9798331522193

ISBN: (纸本)9798331522209

Urban traffic congestion necessitates innovative monitoring and control solutions. This study leverages UAV footage and deep learning techniques to classify and analyze traffic density levels autonomously. By employing a pre-trained ResN et50 model enhanced with additional layers, the research optimizes traffic density classification using UAV-captured data. A custom dataset of traffic scenarios in Dammam, Saudi Arabia, was developed, incorporating diverse conditions through advanced normalization and data augmentation techniques to improve resilience and robustness. The modified ResN et50 model demonstrated high classification accuracy across varied traffic scenarios, validating its reliability and scalability for real-world applications. Key findings include the superior adaptability of UAV-based monitoring for real-time urban traffic assessment and the enhanced classification precision achieved through model optimization. This research underscores the potential of integrating UAV technology with deep learning to advance urban traffic management and foster more efficient urban planning strategies.

关键词： Deep learning Scalability Heuristic algorithms Urban planning Autonomous aerial vehicles Real-time systems Monitoring Traffic congestion Resilience Residual neural networks

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An Empirical Study on Adaptive Learning Using Decision Trees for Student Performance Prediction

An Empirical Study on Adaptive Learning Using Decision Trees...

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Computational Intelligence Approaches and Applications (ICCIAA), International Conference on

作者： Asia Bataineh Ahlam Al-E'mari Rula Hajjaj Hazem Bani Abdoh Fuad Fataftah Computer Science Department Faculty of Information Technology University of Petra (UoP) Amman Jordan Department of Finance School of Business University of Jordan (UJ) Amman Jordan Department of Business Administration The Applied College Northern border University Arar Saudi Arabia Computer Science Department Faculty of Computer Science and Information Technology Jerash university Jerash Jordan Mutlimedia systems and Virtual Reality School of Computer science Universiti Sains Malaysia (USM) Penang Malaysia

ISBN: (数字)9798331523657

ISBN: (纸本)9798331523664

This study investigates the application of adaptive decision tree models for predicting student performance, focusing on their role in educational data mining and adaptive learning. A major challenge in student assessment is identifying at-risk learners and dynamically adjusting predictive models to accommodate evolving learning patterns. Traditional static models often fail to capture these variations, highlighting the need for adaptive approaches. This research develops an adaptive decision tree framework that integrates incremental learning and adaptive parameter tuning to improve predictive accuracy and stability. Using the Student Performance dataset from the UCI Machine Learning Repository, the study applies multiple decision tree variants, including C4.5, CART, Random Forest, and Gradient Boosted Trees. The dataset, consisting of demographic, social, and academic attributes of students, is preprocessed and split into 70 % training and 30 % testing sets. Models are evaluated using accuracy, precision, recall, and F1-score to assess their effectiveness. The results demonstrate that adaptive decision tree models significantly outperform static models, with the best adaptive model (Gradient Boosted Decision Trees) achieving an accuracy of 84% and an F1-score of 82%, compared to 77 % and 74%, respectively, for static counterparts. These findings highlight the potential of adaptive learning models to enhance personalized learning interventions and support data-driven curriculum design. This paper concludes by discussing the implications of adaptive decision tree applications in real-world educational settings and proposes future directions for improving predictive modeling in adaptive learning environments.

关键词： Adaptation models Adaptive learning Accuracy Incremental learning Predictive models Real-time systems Data models Decision trees Data mining Tuning

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Reliable Service Recommendation: A Multi-modal Adversarial Method for Personalized Recommendation under Uncertain Missing Modalities

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IEEE Transactions on Services Computing 2025年

作者： Chen, Junyang Yang, Ruohan Guo, Jingcai Wang, Huan Wu, Kaishun Zhang, Liangjie Shenzhen University College of Computer Science and Software Engineering China Huazhong Agricultural University College of Informatics Wuhan China The Hong Kong Polytechnic University Department of Computing Hong Kong DSA Thrust and IoT Thrust Center China

Personalized recommendation is of paramount importance in online content platforms like Kuai and Tencent. To ensure accurate recommendations, it is crucial to consider multi-modal information in both items and user-user/item interactions. While existing works on multimedia recommendation have made strides in leveraging multi-modal contents to enrich item representations, many of them overlook the practical scenario of multiple modality missing. As a result, the performance of recommendation systems can be significantly compromised in such cases. In this paper, we introduce a novel multi-modal adversarial method called MMAM, which aims to provide reliable personalized recommendation services even in the presence of uncertain missing modalities. The core idea behind MMAM is to design a generator that can effectively encode both user-user/item interactions and multi-modal contents, taking into account various missing cases. The generator is trained to learn transferable features from different combinations of missing modalities in order to deceive a discriminative classifier. Additionally, we propose a modal discriminator that can classify the missing cases of multi-modalities, further enhancing the capability of the model. Moreover, a well-equipped predictor utilizes the transferable features to predict potential user interests. To improve the prediction accuracy, we design a type discriminator that enhances the classification of link types. By employing a mini-max game between the generator and the discriminators, MMAM successfully obtains transferable features that encompass multi-modal contents, even when facing uncertain missing modalities. We conduct extensive experiments on industrial datasets, including Kuai and Tencent. Comparing with state-of-the-art approaches, MMAM achieves improvements in personalized recommendation tasks under uncertain missing modalities. MMAM holds promise for enhancing multi-modal personalized recommendations in real-world applications

关键词： Discriminators

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Leveraging Instruction-based Learning for Effective UAV Traffic Management in Dynamic Environment

Leveraging Instruction-based Learning for Effective UAV Traf...

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Multi-Agent systems for Collaborative Intelligence (ICMSCI), International Conference on

作者： R.S. Ramya Keren Lois Daniel S. Rajkumar Sruthi S Madhavan Akshya. J Mani Deepak Choudhry Department of Computer Science and Engineering Sri Ramakrishna Engineering College Coimbatore Department of Computer Science and Business Systems KGiSL Institute of Technology Coimbatore Department of Information Technology Sona College of Technology Salem Department of Artificial Intelligence and Data Science Nehru Institute of Engineering and Technology Coimbatore Department of Computational Intelligence SRM Institute of Science and Technology Chennai Department of Computing Technologies SRM Institute of Science and Technology Chennai

ISBN: (数字)9798331509828

ISBN: (纸本)9798331509835

With the popularization of UAVs (Unmanned Aerial Vehicles) in surveillance, delivery services, environmental monitoring, and increasingly other operational aspects, besides the need to take off and land, efficient traffic management becomes integral for assuring and securing further development and exploitation. Most of the existing traffic management systems for UAVs depend on traditional rule-based algorithms or simpler machine learning techniques, such as DQN or PPO. However, these systems also have enormous challenges: dynamic environments, high-density traffic, and robust decision-making over unpredictable weather and network conditions. In addition, the above traditional models usually experience difficulties in balancing exploration and exploitation, leading to suboptimal routing and enlarged collision risks in complicated urban airspaces. These challenges have driven us to develop a new proposal: a UA V traffic management framework based on Double Deep Q-Network (DDQN) combined with Instruction-Based Learning. In this approach, advanced learning for DDQN optimizes processes at decision-making points for UAVs. The model proposed herein, along with comprehensive simulations and quantitative analyses, demonstrated higher performance in terms of leadership and cumulative rewards while keeping lower training loss compared to the classical algorithms such as DQN, A3C, and PPO. At the same time, the DDQN model presented a better success rate (95%), as also reflected in the balanced precision-recall curve showing an area under the curve of 0.90, over others in the on-point prediction of UAVstate and safety margin of collision. Our experimental results demonstrate that the proposed DDQN-based framework significantly enhances UA V traffic management by optimizing energy consumption in complex airspaces and reducing the risk of collision.

关键词： Training Analytical models Adaptation models Statistical analysis Atmospheric modeling Computational modeling Decision making Predictive models Autonomous aerial vehicles Safety

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