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检索条件"任意字段=International Multi-Conference in Computer Science and Computer Engineering"

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Epilepsy Seizure Detection based on EEG Signals using Machine Learning 3

Epilepsy Seizure Detection based on EEG Signals using Machin...

引用

3rd international conference on Intelligent and Innovative Technologies in Computing, Electrical and Electronics, IITCEE 2025

作者： Goel, Aadya School of Computer Science and Engineering Vellore Institute of Technology Vellore632014 India

ISBN: (纸本)9798331515911

Seizure detection is a critical aspect of epilepsy management, and accurate identification can significantly improve patient outcomes. This study explores the application of machine learning techniques, specifically the Random Forest classifier, for the detection of seizures using electroencephalogram (EEG) data. By employing a systematic approach that includes feature extraction and channel reduction, we enhanced the model's performance. Relevant features were extracted from EEG signals, including time-domain statistics and frequency-domain characteristics, which are vital for effective classification. The model was evaluated using standard classification metrics such as precision, recall, F1-score, and accuracy, yielding high performance, particularly in identifying non-seizure events. These findings highlight the potential of ensemble learning methods for real-time seizure monitoring and underscore the importance of this research in developing reliable diagnostic tools. Overall, our results demonstrate that machine learning approaches can significantly contribute to improved patient outcomes in epilepsy management, paving the way for future advancements in clinical applicability. © 2025 IEEE.

关键词： Electroencephalography

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SlidEar: Exploring eSense in-Ear Wearable for Voice-Assisted Smart Slide Supervision 17

SlidEar: Exploring eSense in-Ear Wearable for Voice-Assisted...

引用

17th international conference on COMmunication Systems and NETworkS, COMSNETS 2025

作者： Mondal, Susmita Zingre, Sameeran Ravishankar Chakraborty, Suchetana Indian Institute of Technology Department of Computer Science and Engineering Jodhpur India

ISBN: (纸本)9798331531195

In corporate settings, conferences, or classrooms, an orator relies on manual slide transitions, which can disrupt their presentation flow. Presentation devices can be inaccessible due to the physical limitations of individuals, reducing engagement with the audience. Traditionally used remote-controlled wireless presenters are hand-held devices that usually require a compatible connector slot for the presentation device and partially engage one hand of the orator. To ease the hand-free presentation of slides, we introduce SlidEar, a voice-assisted smart slide supervision successfully tested on eSense in-ear wearables. SlidEar leverages voice recognition fusion with wearable sensors to facilitate seamless slide operations. It allows real-time feedback on natural language commands during the presentation of slides. Compared with conventional methods, SlidEar enhances engagement and accessibility through hand-free in-ear placement and helps the orator focus on delivering their content without any disruption or distraction. The evaluation shows promising results that improve the presentation experience profusion. SlidEar significantly reduces errors and can be widely adopted in diverse presentation scenarios. © 2025 IEEE.

关键词： Speech recognition

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Performance Analysis of Hybrid MPI and OpenMP on Smith-Waterman Algorithm

Performance Analysis of Hybrid MPI and OpenMP on Smith-Water...

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international Symposium on Advanced Computing and Communication (ISACC)

作者： Keyur Ninama Jainamkumar Patel Girish K K Manideep Raya V S R S Reddy Biswajit Bhowmik Dept. of Computer Science and Engineering Maharshi Sushrut CAS Lab BRICS Laboratory National Institute of Technology Karnataka Mangalore Bharat Dept. of Computer Science and Engineering Ishwarchandra Vidyasagar AIT Lab BRICS Laboratory National Institute of Technology Karnataka Mangalore Bharat Dept. of Computer Science and Engineering Maharshi Kanad QC Lab BRICS Laboratory National Institute of Technology Karnataka Mangalore Bharat Dept. of Computer Science and Engineering BRICS Laboratory National Institute of Technology Karnataka Mangalore Bharat

ISBN: (数字)9798331523893

ISBN: (纸本)9798331523909

In the rapidly advancing field of bioinformatics, sequence alignment is a pivotal task for elucidating genetic statistics and evolutionary relationships. As the volume and complexity of biological data continue to grow, it becomes imperative to employ effective computational techniques to manage this expansion. The Smith-Waterman algorithm is a key tool for sequence alignment; however, its performance can be constrained by the substantial size of contemporary datasets. To overcome this limitation, this paper explores a hybrid parallelization strategy that integrates message passing interface (MPI) with open multi-processing (OpenMP). This approach aims to significantly enhance the algorithm’s efficiency by leveraging the strengths of both parallelization models. By optimizing the scalability and execution speed of the Smith-Waterman algorithm on advanced high-performance computing (HPC) systems, the hybrid technique not only improves performance but also enables more rapid and accurate biological data analysis.

关键词： Data analysis Scalability Message passing High performance computing Memory management Parallel processing Genetics Performance analysis Bioinformatics Intelligent systems

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Consumer Behavior in the Post-Pandemic Era: Insights from Machine Learning and AI-Driven Analytics 5

Consumer Behavior in the Post-Pandemic Era: Insights from Ma...

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5th IEEE international conference on Advances in Electrical, Computing, Communication and Sustainable Technologies, ICAECT 2025

作者： Nimma, Divya Anudeep, Ch. Sahu, Tripti Shah, Nusrat Hamid Shah, Lubna Hamid Bala, B. Kiran University of Southern Mississippi Ummc United States Department of Management studies Bhimavaram India Mima Institute of Management Maharashtra Pune India College of Engineering and Computer Science Jazan University Saudi Arabia College of Business Jazan University Department of Management and Marketing Saudi Arabia K.Ramakrishnan College of Engineering Department of Ai & Ds Trichy India

ISBN: (纸本)9798350356236

COVID-19 has been the disruptor in the world, and the society has been changed from one perspective of the consumer behavior in terms of digital platforms and their preferences for safety, convenience and sustainability. It is therefore very important for any business to acquaint itself with these changes so that may be in a position to effectively compete for the market within the current ever-changing business environment. Conventional consumer behavior models, on the other hand, rely on fragmented data sources and analytical tools that may not reflect the current post-pandemic consumption patterns. Such models are also incapable of incorporating multiple data types and can also fail to address changing social factors. In response, a multi-Modal Consumer Behavior Prediction Framework (MCBP) that considers a vast array of data sources is introduced. BERT and GNNs are used to give a detailed, real-time interpretation of consumer behaviour accomplished by the MCBP framework. It uses BERT to extract and analyze sentiment and topics of the textual data and GNNs to capture influence and peer effects on purchase decisions. The findings of the study show the effectiveness of the proposed framework with an accuracy of 99%. 5% and the implementation is done using Python. The high level of accuracy shown assures the success of the MCBP model in measuring and forecasting consumers' behaviors in the post-pandemic world. © 2025 IEEE.

关键词： Consumer behavior

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Low-Rank Tensors for multi-Dimensional Markov Models

Low-Rank Tensors for Multi-Dimensional Markov Models

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2025 IEEE international conference on Acoustics, Speech, and Signal Processing, ICASSP 2025

作者： Navarro, Madeline Rozada, Sergio Marques, Antonio G. Segarra, Santiago Dept. of Electrical and Computer Engineering Rice University HoustonTX United States Dept. of Signal Theory and Communications Rey Juan Carlos University Madrid Spain

ISBN: (纸本)9798350368741

This work presents a low-rank tensor model for multidimensional Markov chains. A common approach to simplify the dynamical behavior of a Markov chain is to impose low-rankness on the transition probability matrix. Inspired by the success of these matrix techniques, we present low-rank tensors for representing transition probabilities on multi-dimensional state spaces. Through tensor decomposition, we provide a connection between our method and classical probabilistic models. Moreover, our proposed model yields a parsimonious representation with fewer parameters than matrix-based approaches. Unlike these methods, which impose low-rankness uniformly across all states, our tensor method accounts for the multi-dimensionality of the state space. We also propose an optimization-based approach to estimate a Markov model as a low-rank tensor. Our optimization problem can be solved by the alternating direction method of multipliers (ADMM), which enjoys convergence to a stationary solution. We empirically demonstrate that our tensor model estimates Markov chains more efficiently than conventional techniques, requiring both fewer samples and parameters. We perform numerical simulations for both a synthetic low-rank Markov chain and a real-world example with New York City taxi data, showcasing the advantages of multi-dimensionality for modeling state spaces. © 2025 IEEE.

关键词： low-rank tensors Markov model tensor decomposition

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An Enhanced Intrusion Detection Model with FeedForward Neural Network Classifier 27

An Enhanced Intrusion Detection Model with FeedForward Neura...

引用

27th international conference on Advanced Communications Technology, ICACT 2025

作者： Amirjon, Asadov Gain, Mrityunjoy Oh Kim, Keon Hong, Choong Seon Department of Computer Science and Engineering Kyung Hee University Yongin Korea Republic of Department of Artificial Intelligence Kyung Hee University Yongin Korea Republic of

ISBN: (纸本)9791188428137

A major obstacle in the face of increasingly complex cyberattacks is network security. Proactive security measures require effective intrusion detection systems (IDS) that can precisely classify and categorize network threats. In order to improve network attack detection and classification, this paper proposes a reliable method utilizing a Feedforward Neural Network (FFNN) supplemented with Adaptive Synthetic (ADASYN) sampling. We created a model using the UNSWNB15 dataset that efficiently handles high-dimensional datasets by preprocessing data using a combination of polynomial feature transformation and one-hot encoding. The FFNN model is optimized for binary and multi-class classification tasks. It consists of layers of dense units with dropout and batch normalization. Our method’s efficacy is proven by rigorous training and validation procedures, where the model significantly increased its ability to handle class imbalances and improve classification accuracy. The synthesis of new training data by ADASYN was crucial in improving model performance, especially in underrepresented classes. Evaluation measures that highlight the potential of deep learning in network security applications are ROC-AUC scores and classification reports, which show a notable improvement in our IDS’s detection capabilities. The results show that advanced machine learning techniques can be used to enhance conventional intrusion detection systems and provide a means to build stronger network security designs. Copyright 2025 Global IT Research Institute (GIRI). All rights reserved.

关键词： Network intrusion

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multimodal Transformer Fusion for Sentiment Analysis using Audio, Text, and Visual Cues 1

Multimodal Transformer Fusion for Sentiment Analysis using A...

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1st international conference on Intelligent Systems and Computational Networks, ICISCN 2025

作者： Peerbasha, S. Habelalmateen, Mohammed I. Ramachandra, A.C. Revathi, P. Saravanan, T. Affiliated to Bharathidasan University Department of Computer Science Tiruchirapalli India The Islamic University College of Technical Engineering Department of Computers Techniques Engineering Najaf Iraq Nitte Meenakshi Institute of Technology Department of Electronics and Communication Engineering Bengaluru India Dhanalakshmi Srinivasan College of Engineering and Technology Department of Electronics and Communication Engineering Mamallapuram India New Prince Shri Bhavani College of Engineering and Technology Department of Electronics and Communication Engineering Chennai India

ISBN: (纸本)9798331529246

In modern era, the increased growth in social media platforms and technologies such as Artificial Intelligence (AI) have gained interest towards multimodal sentiment analysis that includes text, audio and visual cues for extraction of useful insights. Although these systems are capable of analyzing sentiment, but also faces certain challenges in synchronization of multimodal inputs, data fusion and reliability under various contexts. Hence, this research proposes an effective multimodal Transformer Fusion (MTF) which combines the strengths of various modalities to recognize human emotions. Initially, the data is collected from CMU-multimodal Opinion Sentiment and Emotion Intensity (CMU-MOSEI) dataset. Further, the data is preprocessed with Empirical Mode Decomposition (EMD) and Min-max normalization, stop words removal, and lemmatization to remove noise and improve the quality of sentiment analysis. Then, the features are extracted for different input modalities by using Mel-Frequency Cepstral Coefficients (MFCC), 3D Residual Network (ResNet 3D) and Distilled Bidirectional Encoder Representations from Transformers (DistilBERT). After that, extracted features are further fed into proposed MTF to classify the sentiment of a customer into positive, negative or neutral. From the results, the proposed MTF achieved outstanding results in F1 Score (80.76%) as well as Mean Absolute Error (0.376) when compared with the existing Tensor Fusion Network (TFN). © 2025 IEEE.

关键词： Empirical mode decomposition

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Nanophotonic Edge Computing System For Ultra-Low Latency Human-Machine Interface 38

Nanophotonic Edge Computing System For Ultra-Low Latency Hum...

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38th IEEE international conference on Micro Electro Mechanical Systems, MEMS 2025

作者： Ren, Zhihao Zhang, Zixuan Zhuge, Yangyang Xiao, Zian Xu, Siyu Zhou, Jingkai Lee, Chengkuo National University of Singapore Department of Electrical & Computer Engineering Singapore Singapore

ISBN: (纸本)9798331508890

This paper introduces a nanophotonic edge computing (NEC) system built on a bilayer AlN/Si waveguide platform, optimized for AI processing in wearable sensors. Utilizing AlN electro-optic micro-ring resonators (EOMRRs) for feature extraction and Si thermo-optic Mach-Zehnder interferometers (TOMZIs) for neural network computations, the NEC chip demonstrates high classification accuracy for gesture (96.77%) and gait (98.31%) analysis, ultra-low latency ( © 2025 IEEE.

关键词： Mach-Zehnder interferometers

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Research on optimization algorithm in grid engineering survey data acquisition and processing based on cloud computing 5

Research on optimization algorithm in grid engineering surve...

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5th international conference on Telecommunications, Optics, and computer science, TOCS 2024

作者： Zhang, Jiyong Sun, Xiaohu Huang, Bing Liu, Donghui Ren, Peixiang State Grid Economics and Technology Research Institute Co. Ltd. Beijing102209 China

ISBN: (纸本)9781510691667

With the increasing scale of power grids and the growing demand for intelligence, efficient and accurate collection and processing of grid engineering survey data has become critical. This study aims to explore the optimization application of cloud computing technology in grid engineering data collection and processing. We design and implement an optimization algorithm based on cloud computing, which integrates several technologies such as distributed computing, big data storage and management, cluster monitoring and security management. Through comparative experiments, our algorithm significantly outperforms traditional methods in terms of data processing speed, achieving at least a four-fold improvement;in terms of data accuracy, the error rate is controlled at a lower level of 0.2%-0.3%. In addition, our method demonstrates good scalability and cost-effectiveness. This study not only provides an efficient data processing solution for power grid engineering, but also lays a solid foundation for the intelligent and digital transformation of future power grids. © 2025 SPIE.

关键词： Cloud storage

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CLEAR: Towards Contextual LLM-Empowered Privacy Policy Analysis and Risk Generation for Large Language Model Applications 25

CLEAR: Towards Contextual LLM-Empowered Privacy Policy Analy...

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30th international conference on Intelligent User Interfaces, IUI 2025

作者： Chen, Chaoran Zhou, Daodao Ye, Yanfang Li, Toby Jia-Jun Yao, Yaxing Department of Computer Science and Engineering University of Notre Dame Notre DameIN United States Department of Computer Science Virginia Tech BlacksburgVA United States

ISBN: (纸本)9798400713064

The rise of end-user applications powered by large language models (LLMs), including both conversational interfaces and add-ons to existing graphical user interfaces (GUIs), introduces new privacy challenges. However, many users remain unaware of the risks. This paper explores methods to increase user awareness of privacy risks associated with LLMs in end-user applications. We conducted five co-design workshops to uncover user privacy concerns and their demand for contextual privacy information within LLMs. Based on these insights, we developed CLEAR (Contextual LLM-Empowered Privacy Policy Analysis and Risk Generation), a just-in-time contextual assistant designed to help users identify sensitive information, summarize relevant privacy policies, and highlight potential risks when sharing information with LLMs. We evaluated the usability and usefulness of CLEAR across two example domains: ChatGPT and the Gemini plugin in Gmail. Our findings demonstrated that CLEAR is easy to use and improves users' understanding of data practices and privacy risks. We also discussed LLM's duality in posing and mitigating privacy risks, offering design and policy implications. © 2025 Copyright held by the owner/author(s).

关键词： Differential privacy

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