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5th International Conference on Sustainable Communication Networks and Application, ICSCNA 2024

作者： Susmitha, Kota Bhargavi, Maridu Sri, Achyuta Mohitha Sai Reddy, Bogala Devi Prasaad Satyanarayana, Paladugu Siva Rao, K V Ranga Vignan's Foundation for Science Technology and Research Department of Computer Science and Engineering Guntur India Department of Computer Science and Engineering-Data Science Hyderabad India

ISBN: (纸本)9798331530013

Despite the various advances in medical technology, heart disease continues to rank among the leading causes of mortality in the world, killing millions each year. There is hope that the risks involved with heart disease can be reversed in time and the outcomes of patients improved with earlier prediction and diagnosis. However, the use of conventional methods of diagnostic techniques tends to be very resource-intensive and inaccessible to other more disadvantaged areas. The present work defines a model for machine learning in predicting heart disease and illustrates a more efficient and accessible solution. There is a risk for heart disease that is determined using a set of various clinical features. The algorithm used in the prediction of heart disease is XG Boost that are applied to the dataset. The method provides a reliable tool for the early detection of heart disease, thus helping healthcare providers to improve preventive care and treatment strategies. The results of this study will be helpful in advancing research to identify other avenues for prediction of heart disease. © 2024 IEEE.

关键词： early diagnosis Heart disease UCI (University California Irvine) Heart Disease dataset XG Boost

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NeuroProbe: A Performance Profiling Feed Forward Neural Network Simulator for Hardware Interaction Analysis

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SN computer science 2025年第5期6卷 1-22页

作者： Ayyappan, Binu Kumar, G. Santhosh Department of Computer Science Cochin University of Science and Technology Kochi India Department of Artificial Intelligence and Data Science Rajagiri School of Engineering & Technology Kochi India

NeuroProbe is a simple neural network simulator designed by authors specifically for educational purposes focusing on simulating inference phase on a computationally capable embedded hardware, aiming to provide a deeper understanding of hardware interactions and performance improvement. This simulator incorporates performance profiling capabilities, allowing students and educators to analyze and optimize the performance of a simulated trained neural network. By simulating the intricate hardware interactions, NeuroProbe enables users to gain insights into how various components impact the overall performance of neural networks. Using this simulator, learners can experiment with different configurations and architectures, identify potential bottlenecks, and explore optimization strategies to enhance neural network performance before deploying the model on an Edge Computing environment. It also enriches computer science education by enabling learners to gain deeper insights into hardware interactions and performance improvement. By identifying performance bottlenecks, learners can refine their understanding of neural networks and develop skills in optimizing their models for enhanced performance. In conclusion, NeuroProbe empowers users to bridge the gap between theoretical knowledge and practical implementation of neural network on an embedded device, fostering a more comprehensive understanding of neural networks and their hardware requirements. Currently, it is focused solely on single-threaded CPU-based computation enabling SSE, AVX. At present, NeuroProbe does not support SIMD extensions such as AVX-FMA, AVX-512, multi-threaded computation, and accelerators such as NPU, TPU, and GPU. © The Author(s), under exclusive licence to Springer Nature Singapore Pte Ltd. 2025.

关键词： Hardware–software interactions Neural network simulator NeuroProbe Performance bottlenecks Performance profiling Simulations

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Bi-clustering of Binary data via Neurodynamics-driven Binary Matrix Factorization 13

Bi-clustering of Binary Data via Neurodynamics-driven Binary...

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13th International Conference on Intelligent Control and Information Processing, ICICIP 2025

作者： Li, Hongzong Wang, Jun Department of Computer Science City University of HongKong Kowloon Hong Kong Department of Computer Science Department of Data Science City University of HongKong Kowloon Hong Kong

ISBN: (纸本)9798331516147

Bi-clustering, also known as co-clustering, is a powerful data analysis technique that simultaneously clusters rows and columns of a data matrix, revealing hidden patterns. In this paper, we propose a neurodynamics-driven binary matrix factorization approach for bi-clustering binary data. The proposed method utilizes multiple discrete Hopfield networks operating concurrently to explore local optimal solutions. Furthermore, a particle swarm optimization rule is iteratively applied to reinitialize the neuronal states for directing the search toward better solutions. Comparative evaluations across six benchmark datasets reveal that the proposed method outperforms five existing methods in terms of five internal and external indices. © 2025 IEEE.

关键词： Particle swarm optimization (PSO)

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Multi-Agent Deep Reinforcement Learning With Trajectory Prediction for Task Migration-Assisted Computation Offloading

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IEEE Transactions on Mobile Computing 2025年第7期24卷 5839-5856页

作者： Zhang, Xinyi Wang, Chunyang Zhu, Yanmin Cao, Jian Liu, Tong Shanghai Jiao Tong University Department of Computer Science and Engineering Shanghai200240 China East China Normal University School of Data Science and Engineering Shanghai200050 China Shanghai University School of Computer Engineering and Science Shanghai200444 China

Multi-access edge computing has become an effective paradigm to provide offloading services for computation-intensive and delay-sensitive tasks on vehicles. However, high mobility of vehicles usually incurs spatio-temporal load-imbalances among edge servers. Therefore, task migration is employed to maintain dynamic workload balancing by transmitting excessive tasks from overloaded to underloaded servers. Recent studies adopt deep reinforcement learning approaches to generate offloading and migration decisions based on current observations of systems. However, we argue that the migration direction is highly dependent on vehicular movements, and task migration towards the wrong direction could lead to additional delays. Therefore, we emphasize the importance of guiding task migration via exploring prospective trajectories of vehicles. We propose a Mobility-Aware Cooperative Multi-Agent (MCMA) deep reinforcement learning approach to make vehicle-by-vehicle decisions in multi-edge computation offloading scenarios. A two-stage decision framework is designed to solve the joint optimization problem of computation offloading and resource allocation. Additionally, an Informer-based multi-step vehicular trajectory prediction module is incorporated to enhance the capability of forecasting vehicular movements. Extensive experiments and analysis are conducted on synthetic and realistic scenarios, showing that our approach consistently outperforms both heuristic and DRL-based methods. The simulation scenarios and source codes are publicly available here. © 2002-2012 IEEE.

关键词： Computation offloading

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Predictive Analytics for Future Air Pollution Levels Based on Population Growth 9th

Predictive Analytics for Future Air Pollution Levels Based o...

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9th International Conference on computer science and Computational Intelligence, ICCSCI 2024

作者： Yeshe, Darel Wairooy, Irma Kartika Makalew, Brilly Andro Computer Science Department School of Computer Science Jakarta11480 Indonesia Mobile Application & Technology Program Computer Science Department School of Computer Science Jakarta11480 Indonesia

Air is very beneficial and crucial for every living creature on earth, hence it is very important to protect the air quality in order to avoid diseases. However, due to the increase in population, some human activities have been causing detrimental damage to the air quality. Some people have realized the potential impact of this problem and have done studies on predicting future air pollution. Some implemented machine learning models such as random forest regression, support vector machine, etc, while others have utilized deep learning models in their research.. This study will implement five different models, specifically linear regression, ridge regression, random forest regression, and multilayer perceptron regression. There will be two datasets used in this paper which will be merged and processed. There will be three key evaluation metrics being used in this paper namely root mean squared error, mean absolute error, and r-squared. The results from all of the models have concluded that more optimization and factors are needed in order to boost the final result of this study. © 2024 The Authors.

关键词： Adversarial machine learning

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Ordinal classification for knee osteoarthritis x-rays using vision transformers

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Multimedia Tools and Applications 2025年 1-28页

作者： Tariq, Tayyaba Suhail, Zobia Nawaz, Zubair Department of Computer Science University of the Punjab Allama Iqbal Campus Punjab Lahore54000 Pakistan Department of Data Science University of the Punjab Allama Iqbal Campus Punjab Lahore54000 Pakistan

Knee Osteoarthritis is a progressive and chronic knee joint disease that shows symptoms of pain, stiffness, and swelling and is diagnosed by knee radiographs. These radiographs are evaluated by radiologists using Kellgren and Lawrence’s (KL) grading scheme. According to this scheme, the knee radiograph is graded from KL grade 0 (i.e., Normal) to KL grade 4 (i.e., Severe) to assess the KOA severity. For an automated system, the first step is to localize knee joints from knee radiographs and then classify them according to the KL grading system. In this work, we have proposed vision-based transformers to localize knee joints. We also proposed a single-stage model to localize the knee and classify KOA using a single model. We also explored and compared the performance of various transformers using a multiclass image classification strategy. Since KL grades maintain intrinsic ordinal information of KL grades, we employed an ordinal classification-based strategy to improve automated knee osteoarthritis classification performance. A total of 10 models are trained and validated on multiple datasets, and visualizations are generated for the best model for better comprehension. Our model obtained state-of-the-art results for knee detection and ordinal X-ray classification for KOA. Analysis of Variance (ANOVA) tests also indicate that the ordinal classification approach significantly improves the performance of models over traditional image classification approaches. © The Author(s), under exclusive licence to Springer science+Business Media, LLC, part of Springer Nature 2025.

关键词： Arthroplasty

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Dynamic Distributed Model Compression for Efficient Decentralized Federated Learning and Incentive Provisioning in Edge Computing Networks

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IEEE Transactions on Mobile Computing 2025年第7期24卷 6293-6314页

作者： Asheralieva, Alia Niyato, Dusit Wei, Xuetao Southern University of Science and Technology Department of Computer Science and Engineering 1088 Xueyuan Avenue Shenzhen China Nanyang Technological University College of Computing and Data Science 50 Nanyang Avenue Singapore639798 Singapore

We study decentralized federated learning (DFL) in edge computing networks where edge nodes (ENs) collaboratively train their artificial intelligence (AI) models in a serverless manner without sharing local data. We consider the following critical DFL challenges: i) scarce bandwidth resources of ENs;ii) dynamic, heterogeneous edge environment;iii) incentive provisioning and complex tradeoffs between the DFL performance and training costs. To resolve these challenges, we develop a new model compression method where ENs utilize dynamic, non-identical compression rates to improve the communication efficiency of DFL under time-varying, heterogeneous resource constraints. We show that our method can be formulated as a graphical Markov potential game where ENs act as players deciding on their compression factors and the number of data samples used for model updates. Each EN is incentivized to participate in DFL through rewards based on the EN's contribution to training. We prove that our game has a dominant pure-strategy Nash equilibrium (NE) maximizing its potential function and propose a dynamic distributed compression algorithm in which each EN can find its dominant strategy independently. We show that this algorithm converges to the Pareto-optimal NE, representing the most efficient solution of our game enhancing the DFL performance with minimal costs. © 2002-2012 IEEE.

关键词： Federated learning

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Semënov Arithmetic, Affine VASS, and String Constraints 41

Semënov Arithmetic, Affine VASS, and String Constraints

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41st International Symposium on Theoretical Aspects of computer science, STACS 2024

作者： Draghici, Andrei Haase, Christoph Manea, Florin Department of Computer Science University of Oxford United Kingdom Computer Science Department Göttingen University Campus-Institut Data Science Germany

ISBN: (纸本)9783959773119

We study extensions of Semënov arithmetic, the first-order theory of the structure (N,+,2x). It is well-known that this theory becomes undecidable when extended with regular predicates over tuples of number strings, such as the Büchi V2-predicate. We therefore restrict ourselves to the existential theory of Semënov arithmetic and show that this theory is decidable in EXPSPACE when extended with arbitrary regular predicates over tuples of number strings. Our approach relies on a reduction to the language emptiness problem for a restricted class of affine vector addition systems with states, which we show decidable in EXPSPACE. As an application of our result, we settle an open problem from the literature and show decidability of a class of string constraints involving length constraints. © Andrei Draghici, Christoph Haase, and Florin Manea;licensed under Creative Commons License CC-BY 4.0.

关键词： Computability and decidability

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Spatio-Spectral Graph Neural Networks 38

Spatio-Spectral Graph Neural Networks

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38th Conference on Neural Information Processing Systems, NeurIPS 2024

作者： Geisler, Simon Kosmala, Arthur Herbst, Daniel Günnemann, Stephan Department of Computer Science Munich Data Science Institute Technical University of Munich Germany

Spatial Message Passing Graph Neural Networks (MPGNNs) are widely used for learning on graph-structured data. However, key limitations of -step MPGNNs are that their "receptive field" is typically limited to the -hop neighborhood of a node and that information exchange between distant nodes is limited by oversquashing. Motivated by these limitations, we propose Spatio-Spectral Graph Neural Networks (S2GNNs) - a new modeling paradigm for Graph Neural Networks (GNNs) that synergistically combines spatially and spectrally parametrized graph filters. Parameterizing filters partially in the frequency domain enables global yet efficient information propagation. We show that S2GNNs vanquish over-squashing and yield strictly tighter approximation-theoretic error bounds than MPGNNs. Further, rethinking graph convolutions at a fundamental level unlocks new design spaces. For example, S2GNNs allow for free positional encodings that make them strictly more expressive than the 1-Weisfeiler-Leman (WL) test. Moreover, to obtain general-purpose S2GNNs, we propose spectrally parametrized filters for directed graphs. S2GNNs outperform spatial MPGNNs, graph transformers, and graph rewirings, e.g., on the peptide long-range benchmark tasks, and are competitive with state-of-the-art sequence modeling. On a 40 GB GPU, S2GNNs scale to millions of nodes. © 2024 Neural information processing systems foundation. All rights reserved.

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Using CNN and Transformer Model for Unimodal Speech Emotion Recognition on MELD and IEMOCAP

Using CNN and Transformer Model for Unimodal Speech Emotion ...

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2025 International Conference on Advancement in data science, E-learning and Information System, ICADEIS 2025

作者： Aurelio, Clarence Chowanda, Andry Bina Nusantara University Binus Graduate Program - Master of Computer Science Computer Science Department Jakarta Indonesia School of Computer Science Bina Nusantara University Computer Science Department Jakarta Indonesia

ISBN: (纸本)9798331513320

Emotion Recognition is one field that is taking the world by storm in this current age. Multimodal emotion recognition has shown promising results however, previous studies shows that recognition using speech is a field has not performed that greatly. Previous research used popular methods such as CNN, RNN, and other machine learning methods has been used to achieved good results with the help of other modality, where if speech were to be used alone the results won't be as good. A factor that contributes to the result of the speech modality not performing as well is the fact of the dataset and the way it was processed. Therefore, this research looks into what makes speech one of the lowest performing modalities by preprocessing the dataset using a different method than previous researches as well as using a CNN model paired with a Transformer model. Results achieved from this research showed an improvement when using IEMOCAP by 8.09% and MELD by 1.06%. A comparison results of using the pre-processed data with previous studies model was also done to compare the results. In conclusion, this research has explored a different preprocessing method and deep learning model that has achieved results better than previous works with the same dataset. © 2025 IEEE.

关键词： Contrastive Learning

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