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Annual IEEE Systems Conference

作者： Rafeeq Muhammad T. Maheswara Reddy Yenumula Tanner Mergist Tytiana James Ali Harimi Kasem Khalil Ashok Kumar School of Computing and Informatics University of Louisiana at Lafayette Lafayette USA Electrical and Computer Engineering University of Mississippi Mississippi USA

ISBN: (数字)9798331508180

ISBN: (纸本)9798331508197

Classifying heartbeat sounds is a critical step in improving cardiovascular diagnostics, especially as reliance on traditional stethoscope assessments can be limited by human subjectivity. In this study, we explored the application of machine learning techniques to the PhysioNet Heartbeat Sound Database, aiming to differentiate normal from abnormal heart sounds with both accuracy and consistency. Our approach integrates unsupervised K-means clustering, which identifies natural groupings within the data, with supervised classification models to validate these groupings and enhance predictive power. Key acoustic features-such as pitch, RMS energy, MFCCs, and formants-were extracted to provide a comprehensive representation of the heart sounds. To streamline classification, we applied the InfoGain algorithm, selecting the 50 most significant features from an initial set of 238. This process ensured that the models focused on the most relevant information, improving efficiency and interpretability. We evaluated four classifiers-SVM (SMO), Decision Tree (J48), Naive Bayes, and k-NN (IBK)-using a 10-fold cross-validation approach. SMO and J48 demonstrated strong performance, achieving accuracies of 97.4% and 96.02%, respectively, with high Kappa statistics, showcasing their reliability in distinguishing heart sound categories. These results highlight the potential of machine learning in addressing critical healthcare challenges, particularly when labeled data is scarce. Future work will focus on expanding the dataset, incorporating neural network architectures, and exploring additional clustering methods to further improve generalizability and diagnostic accuracy.

关键词： Heart Support vector machines Accuracy Heart beat Databases Machine learning Stethoscope Feature extraction Acoustics Decision trees

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ExPDrug: Integration of an interpretable neural network and knowledge graph for pathway-based drug repurposing

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computers in Biology and Medicine 2025年 187卷 109729-109729页

作者： Kim, Junku Jang, Hojoong Park, Youngjun Jung, Inuk Jo, Kyuri Department of Computer Engineering Chungbuk National University Cheongju Korea Republic of Department of Medical Informatics University Medical Center Göttingen Göttingen Germany School of Computer Science and Engineering Kyungpook National University Daegu Korea Republic of

Precision medicine aims to provide personalized therapies by analyzing patient molecular profiles, often focusing on gene expression data. However, effectively linking these data to actionable drug discovery for clinical application remains challenging. In this paper, we introduce ExPDrug, a neural network (NN) model that integrates biological pathways from transcriptomic data with a biomedical knowledge graph to facilitate pathway-based drug repurposing. ExPDrug enhances disease phenotype prediction by capturing the complex relationships between genes and pathways. Using layer-wise relevance propagation (LRP), the model interprets the contribution of each pathway using relevance scores applied in a random walk-with-restart (RWR) algorithm to prioritize potential drug candidates in the biomedical network. ExPDrug outperforms existing methods in predicting phenotypes for the three diseases and identifying drug candidates, as supported by the literature. This model offers a transformative approach for advancing precision medicine by linking transcriptomic insights directly to clinical drug repurposing, thereby potentially improving treatment strategies for complex diseases. © 2025 Elsevier Ltd

关键词： Knowledge graph

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Heart Sound Classification Using Weka for Machine Learning

Heart Sound Classification Using Weka for Machine Learning

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IEEE Southeastcon

作者： Tanner Mergist T. Maheswara Reddy Yenumula Rafeeq Muhammad Tytiana James Ali Harimi Kasem Khalil Ashok Kumar School of Computing and Informatics University of Louisiana at Lafayette Lafayette USA Electrical and Computer Engineering University of Mississippi Mississippi USA

ISBN: (数字)9798331504847

ISBN: (纸本)9798331504854

The accurate and efficient classification of heart sounds represents a crucial challenge in cardiac care, with significant implications for early diagnosis and timely intervention. This study investigates the application of machine learning techniques, utilizing the Weka framework, to address this problem and used PhysioNet dataset, which comprises 3,240 heart sound recordings, and extracted 238 features to capture the essential spectral and temporal characteristics of heartbeats. Considering the high dimensionality of the dataset, various feature selection methods were evaluated, including information gain ranking, Principal Component Analysis (PCA), and Recursive Feature Elimination (RFE). Ultimately, the InfoGain algorithm was applied, selecting the 50 most significant features from the initial set of 238. The performance of five machine learning models—Multilayer Perceptron, Random Forest, Decision Tree, k-Nearest Neighbor (k-NN), and Support Vector Machine (SVM) — on both the full and reduced feature sets, using 80-20 split and 10-fold cross-validation was evaluated. The results demonstrate that the Random Forest model achieved the highest accuracy of 92.1% on the full feature set, while the k-NN model exhibited superior computational efficiency on the reduced set, with an accuracy of 89.4% and significantly faster training times. These findings highlight the trade-offs between model complexity and efficiency, providing valuable insights for clinicians and researchers in selecting appropriate algorithms for real-world heart sound classification applications.

关键词： Heart Deep learning Training Support vector machines Accuracy Machine learning algorithms Computational modeling Feature extraction Real-time systems Computational efficiency

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Enhancing Multimodal Rumor Detection with Statistical Image Features and Modal Alignment via Contrastive Learning 21st

Enhancing Multimodal Rumor Detection with Statistical Image...

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21st Pacific Rim International Conference on Artificial Intelligence, PRICAI 2024

作者： Zhou, Chenyu Li, Xiuhong Li, Zhe Chen, Fan Sheng, Jiabao Chen, Bin Wang, Haoyu School of Computer Science and Technology Xinjiang University Xinjiang China Department of Electrical and Electronic Engineering The Hong Kong Polytechnic University Hong Kong Department of Electrical Engineering Stanford University CA United States Department of Health Technology and Informatics The Hong Kong Polytechnic University Hong Kong School of Mechanical Engineering University of Shanghai for Science and Technology Shanghai China School of Electronic and Electrical Engineering Shanghai University of Engineering Science Shanghai China

ISBN: (纸本)9789819601219

The swift proliferation of multimodal rumors on social media, particularly those with manipulated images and complex intermodal interactions, significantly challenges current detection methods. In response, we utilize statistical image features, including mean and variance, to capture spatial attributes effectively and improve the detection of image-tampered tweets. To tackle complex intermodal correlations, we introduce a contrastive learning approach that aligns features across modalities efficiently. Additionally, we introduce a cross-attention fusion module (CAFM) that enhances the integration of image and text modalities, thereby improving multimodal rumor detection performance. In conclusion, we propose the cross-attention fusion network (ConCAFN), leveraging contrastive learning for robust multimodal rumor detection. Extensive experiments on two real-world datasets confirm the model’s enhanced capability to detect multimodal rumors accurately, demonstrating our methods’ effectiveness. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Contrastive Learning

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Speech Emotion Recognition Using ML Models and Audio Features

Speech Emotion Recognition Using ML Models and Audio Feature...

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Annual IEEE Systems Conference

作者： Tytiana James T. Maheswara Reddy Yenumula Tanner Mergist Rafeeq Muhammad Ali Harimi Kasem Khalil Ashok Kumar School of Computing and Informatics University of Louisiana at Lafayette Lafayette USA Electrical and Computer Engineering University of Mississippi Mississippi USA

ISBN: (数字)9798331508180

ISBN: (纸本)9798331508197

The ability to recognize and respond to human emotions has become a crucial component in enhancing humancomputer interaction, with the development of speech emotion recognition technologies playing a pivotal role in this advancement. While deep learning models have driven significant progress in the field of speech emotion recognition, traditional machine learning algorithms offer a practical alternative, balancing performance with lower computational requirements. This study presents a comprehensive approach to speech emotion recognition using the Berlin Emotional Speech Database, classifying seven emotional states: anger, sadness, anxiety or fear, disgust, boredom, happiness, and neutrality. The study employs a range of acoustic features, including pitch, RMS energy, MFCCs, and formants, which are combined with 14 statistical descriptors and extracted using tools like OpenSMILE. Preprocessing steps, such as normalization, noise reduction, and silence removal, are applied to enhance the quality and reliability of the data. The performance of traditional machine learning models, including Support Vector Machine, Random Forest, and $k$ -Nearest Neighbors, is evaluated on the processed dataset. The results demonstrate the effectiveness of these traditional models, with Support Vector Machine achieving the highest classification accuracy of 90.65 %, followed by Random Forest and $k$ -Nearest Neighbors. The results of this study highlight the capacity of traditional machine learning techniques to effectively capture the complexities of emotional expression, while circumventing the computational burden associated with deep learning models. The practical relevance of this research extends to real-time applications across various domains, including healthcare, virtual assistants, and customer service, where the demand for efficient and reliable emotion recognition systems is paramount.

关键词： Support vector machines Deep learning Emotion recognition Accuracy Computational modeling Virtual assistants Anxiety disorders Speech recognition Feature extraction Random forests

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Gendering Robots in Human-Robot Interaction: An Interdisciplinary Approach 20

Gendering Robots in Human-Robot Interaction: An Interdiscipl...

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20th Annual ACM/IEEE International Conference on Human-Robot Interaction, HRI 2025

作者： Akalin, Neziha Arnelid, Maria Harrison, Katherine School of Engineering Jönköping University Dept. of Computer Science and Informatics Jönköping Sweden Linköping University Unit of Gender Studies Dept. of Thematic Studies Linköping Sweden

ISBN: (纸本)9798350378931

This paper emphasizes the importance of interdisciplinary collaboration in exploring the concept of robot gender within Human-Robot Interaction (HRI). It draws on a case study of the authors' own collaboration, where interdisciplinary discussions on the nature of gender informed the design of a multimethod study. This approach allowed us to avoid assigning a binary gender to Pepper robot, while still encouraging participants to reflect on their own gendering practices during interactions with robots. Additionally, we invite readers to consider alternative ways of conceptualizing robot gender in HRI - specifically, as fluid and performative rather than binary, which relies on stereotypical cues. After describing how our discussions on gender influenced and reshaped the study design, we offer practical advice on fostering interdisciplinary collaborations. These suggestions focus on communication strategies, mindset, and the practical setup of collaborative studies. We hope that these recommendations will inspire other researchers to continue exploring new and interdisciplinary ways of approaching robot gender in HRI. © 2025 IEEE.

关键词： Social robots

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Cost Optimization for Serverless Edge Computing with Budget Constraints using Deep Reinforcement Learning

arXiv

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arXiv 2025年

作者： Chen, Chen Guan, Peiyuan Chen, Ziru Taherkordi, Amir Hou, Fen Cai, Lin X. Department of Computer Science and Technology University of Cambridge United Kingdom Department of Informatics University of Oslo Norway Department of Electrical and Computer Engineering Illinois Institute of Technology United States Department of Electrical and Computer Engineering University of Macau China

Serverless computing adopts a pay-as-you-go billing model where applications are executed in stateless and short-lived containers triggered by events, resulting in a reduction of monetary costs and resource utilization. However, existing platforms do not provide an upper bound for the billing model which makes the overall cost unpredictable, precluding many organizations from managing their budgets. Due to the diverse ranges of serverless functions and the heterogeneous capacity of edge devices, it is challenging to receive near-optimal solutions for deployment cost in a polynomial time. In this paper, we investigated the function scheduling problem with a budget constraint for serverless computing in wireless networks. Users and IoT devices are sending requests to edge nodes, improving the latency perceived by users. We propose two online scheduling algorithms based on reinforcement learning, incorporating several important characteristics of serverless functions. Via extensive simulations, we justify the superiority of the proposed algorithm by comparing with an ILP solver (Midaco). Our results indicate that the proposed algorithms efficiently approximate the results of Midaco within a factor of 1.03 while our decision-making time is 5 orders of magnitude less than that of Midaco. © 2025, CC BY.

关键词： Budget control

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Covert Communications in Active-IOS Aided Uplink NOMA Systems With Full-Duplex Receiver

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IEEE Transactions on Vehicular Technology 2025年第5期74卷 8419-8424页

作者： Kang, Xueyu Qi, Nan Lv, Lu Boulogeorgos, Alexandros-Apostolos A. Tsiftsis, Theodoros A. Liu, Hongwu Shandong Jiaotong University School of Information Science and Electrical Engineering Jinan250357 China Nanjing University of Aeronautics and Astronautics Key Laboratory of Dynamic Cognitive System of Electromagnetic Spectrum Space Nanjing210016 China Xidian University State Key Laboratory of Integrated Services Networks Xi'an710071 China Xidian University School of Telecommunications Engineering Xi'an710071 China University of Western Macedonia Department of Electrical and Computer Engineering Kozani50100 Greece University of Thessaly Department of Informatics and Telecommunications Lamia35100 Greece University of Nottingham Ningbo China Department of Electrical and Electronic Engineering Ningbo315100 China

In this paper, an active intelligent omni-surface (A-IOS) is deployed to aid uplink transmissions in a non-orthogonal multiple access (NOMA) system. In order to shelter the covert signal embedded in the superposition transmissions, a multi-antenna full-duplex (FD) receiver is utilized at the base-station to recover signal in addition to jamming the warden. With the aim of maximizing the covert rate, the FD transmit and receive beamforming, A-IOS refraction and reflection beamforming, NOMA transmit power, and FD jamming power are jointly optimized. To tackle the non-convex covert rate maximization problem subject to the highly coupled system parameters, an alternating optimization algorithm is designed to iteratively solve the decoupled sub-problems of optimizing the system parameters. The optimal solutions for the sub-problems of the NOMA transmit power and FD jamming power optimizations are derived in closed-form. To tackle the rank-one constrained non-convex fractional programming of the A-IOS beamforming and FD beamforming, a penalized Dinkelbach transformation approach is proposed to resort to the optimal solutions via semidefinite programming. Numerical results clarify that the deployment of the A-IOS significantly improves the covert rate compared with the passive-IOS aided uplink NOMA system. It is also found that the proposed scheme provides better covert communication performance with the optimized NOMA transmit power and FD jamming power compared with the benchmark schemes. © 1967-2012 IEEE.

关键词： Jamming

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Feature Analysis and Selection for BGP Anomaly Detection

Feature Analysis and Selection for BGP Anomaly Detection

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International Conference on Intelligence in Next Generation Networks (ICIN)

作者： Rifqy Hakimi Martin J. Reed 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: (数字)9798331542399

ISBN: (纸本)9798331542405

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.

关键词： Technological innovation Soft sensors Machine learning Feature extraction Routing Border Gateway Protocol Security Reliability Anomaly detection Faces

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Performance and Implementation Comparison of Knuth-Morris-Pratt and Boyer-Moore String Search Algorithms

Performance and Implementation Comparison of Knuth-Morris-Pr...

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Advanced Innovations in Smart Cities (ICAISC), International Conference on

作者： Taj Saleh Fatma Corut Ergin Malek Malkawi Reda Alhajj Department of Computer Engineering Marmara University Istanbul Turkey Department of Computer Engineering Istanbul Medipol University Istanbul Turkey Department of Computer Science University of Calgary Alberta Canada Department of Heath Informatics University of Southern Denmark Odense Denmark

ISBN: (数字)9798331506995

ISBN: (纸本)9798331507008

String search algorithms play an important role in many research areas such as data mining and bioinformatics. While there exist a number of algorithms that handles the topic, we are exploring the the Knuth-Morris-Pratt (KMP) and Boyer-Moore algorithms due to their efficiency and versatility. In this work, we compared the algorithms in terms of characteristics, performance and implementation details. We also tested both the algorithms with various patterns and texts that differs in size. We also analyzed the performance of the algorithms on 4 different processors to understand the technological advancements effects on their performance. Our findings suggest that the BM algorithm perform better with large texts and patterns, while the KMP algorithm is better suited for smaller ones. Also, while newer processor generally exhibit improved performance, the significance of these enhancements may vary. Thus, we should rather be looking specific architectural advancements within generations rather than focusing solely on the generational gap.

关键词： Technological innovation Program processors Smart cities Focusing Data mining Bioinformatics Pattern matching

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