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22nd International Learning and Technology Conference, L and T 2025

作者： Sanaullah Honda, Hirotada Roy, Kaushik Schneider, Axel Wasmuth, Joachim Jungeblut, Thorsten University of Applied Sciences and Arts Dept. of Mathematics and Engineering Bielefeld Germany Toyo University Dept. of Information Networking Tokyo Japan A&T State University Dept. of Computer Science NC United States

ISBN: (纸本)9798331532215

The selection of optimal neural models in Spiking Neural Networks (SNNs) traditionally depends on a trial-and-error approach, which is both time-consuming and sometimes tends to suboptimal selection of the neural model. This research study explores the integration of Automated Machine Learning (AutoML) techniques into SNNs to simplify the process of designing an SNN model by automatically selecting the most optimal neural model. For example, in traditional neural networks, AutoML proves that it has the potential to significantly reduce the time and effort required to identify the best-performing architectures, by automating the selection and updating of loss functions and hyper-parametric values. To this end, we present a stand-alone framework for applying AutoML in SNNs, highlighting how this approach can improve accuracy, efficiency, and reliability in the selection process of optimal neural models, as these neural models are the core principle of designing SNN architecture. The proposed method not only accelerates the development of SNN models but also enhances their performance by systematically identifying optimal configurations that might be overlooked through manual methods. Therefore, to validate our approach, the proposed architecture was tested using different well-known benchmarks with different sets of neurons, ranging from 100 to 3000 neurons, and demonstrated state-of-the-art results in image classification tasks. © 2025 IEEE.

关键词： Neural network models

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Malicious Traffic Classification in WSN using Deep Learning Approaches 2

Malicious Traffic Classification in WSN using Deep Learning ...

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2nd IEEE International Conference on Communication, Security and Artificial Intelligence, ICCSAI 2023

作者： Gupta, Chhote Lal Prasad Rajassekharan, Dinesh Sharma, Dilip Kumar Elangovan, Mohanraj Myilsamy, Varatharaj Upreti, Kamal Bansal Institute of Engineering and Technology Dept. of Computer Science and Engineering Lucknow India Peninsula College Faculty of Computer Science Peninsula Malaysia Jaypee University of Engineering and Technology Department of Mathematics Madhya Pradesh Guna473226 India K. S. Rangasamy College of Technology Department of Artificial Intelligence and Data Science Tamil Nadu Tiruchengode637215 India V.S.B College of Engineering Technical Campus Department of Electrical and Electronics Engineering Tamil Nadu Coimbatore642109 India Department of Computer Science Delhi-NCR Ghaziabad India

ISBN: (纸本)9798350369960

Classifying malicious traffic in Wireless Sensor Networks (WSNs) is crucial for maintaining the network's security and dependability. Traditional security techniques are challenging to deploy in WSNs because they comprise tiny, resourceconstrained components with limited processing and energy capabilities. On the other hand, machine learning-based techniques, such as Deep Learning (DL) models like LSTMs, may be used to detect and categorize fraudulent traffic accurately. The classification of malicious traffic in WSNs is crucial because of security. To protect the network's integrity, data, and performance and ensure the system functions properly and securely for its intended use, hostile traffic categorization in WSNs is essential. Classifying malicious communication in a WSN using a Long Short-Term Memory (LSTM) is efficient. WSNs are susceptible to several security risks, such as malicious nodes or traffic that can impair network performance or endanger data integrity. In sequential data processing, LSTM is a Recurrent Neural Network (RNN) appropriate for identifying patterns in network traffic data. © 2023 IEEE.

关键词： data routing deep learning LSTM malicious transmission neural network Traffic data transmission WSN

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Deep learning detection approach for speech impairment children in Parkinson's disease 6

Deep learning detection approach for speech impairment child...

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6th International Congress on Human-computer Interaction, Optimization and Robotic Applications, HORA 2024

作者： Najm, Ihab Ahmed Abdulateef, Omar Gheni Ali, Ahmed Hussein Ahmed, Saadaldeen Rashid Ahmed, Mohsin A. Algburi, Sameer College of Education for Women Tikrit University Salahaldin Iraq College Of Literature University Of Samarra Salahaldin Iraq Dept. computer engineering technique Al Hikma University College Baghdad Iraq Artificial Intelligence Engineering Department College of Engineering Al-Ayen University Thi-Qar Iraq Computer Science Department Bayan University Kurdistan Erbil Iraq Computer Techniques Engineering Al-Ma’moon University College Al-Washash / Baghdad Iraq AL-KITAB UNIVERSITY KIRKUK36015 Iraq

ISBN: (纸本)9798350394634

Long-term neurological conditions that affect patients on a large scale, for instance, Parkinsonian disease (PD) represent a global strain in the life of patients and healthcare. Early detection and monitoring are the gates of successful treatment, these are the best ways that can be used to fight this disease and to stop its progress. The paper will be on the usefulness of voice pitch of tone of voice [rather than directly Wallace clear sounding as a marker of PD disease. The proposed paper looks forward to implementing a deep learning model to classify a varied arrangement of PD symptoms into a single class. As the two public datasets available for the data, the model is built with 61 fusing categories which there are 22 voice measures and 39 Mel Frequency Cepstral Coefficients (MFCC). The CNN-LSTM architecture that achieves a high accuracy rate of 97% was used. The brought forth model thus presents a viewing point for remote PD detection and monitoring which could be transformative to the clinical practice. © 2024 IEEE.

关键词： Long short-term memory

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Adapting Federated Learning to Dynamic and Non-Stationary Data: An Online Learning Approach

Adapting Federated Learning to Dynamic and Non-Stationary Da...

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International Conference on Artificial Intelligence in Information and Communication (ICAIIC)

作者： Jaemin Kim Donghyeon Hur Dongwook Won Sungrae Cho Dept. of Computer Science and Engineering Chung-Ang University Seoul Korea

ISBN: (数字)9798331507022

ISBN: (纸本)9798331507039

Online learning-based optimization represents a novel approach that integrates the principles of federated learning and online learning to effectively handle dynamic data distributions and continuous learning scenarios. This paper offers a comprehensive survey of the latest developments in online federated learning, focusing on optimizing communication efficiency in federated learning environments through online learning techniques. We delve into foundational concepts, key methodologies, challenges, and diverse applications of this emerg-ina field.

关键词： Surveys Privacy Federated learning Scalability Focusing Medical services Reliability Protection Optimization Industrial Internet of Things

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Outage Management of Power Distribution Systems with Electricity-Dependent Medically-Vulnerable Critical Loads 59

Outage Management of Power Distribution Systems with Electri...

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59th IEEE/IAS Industrial and Commercial Power Systems Technical Conference, I and CPS 2023

作者： Rostamzadeh, Mehdi Kapourchali, Mohammad Heidari Zhao, Long Aravinthan, Visvakumar Wichita State University Department of Electrical and Computer Engineering WichitaKS United States University of Alaska Anchorage Department of Electrical Engineering AnchorageAK United States South Dakota School of Mines and Technology Dept of Electrical Engineering and Computer Science Rapid CitySD United States

ISBN: (纸本)9798350396515

By deploying mobile power sources and using their network topology effectively, electric utilities can enhance resilience during power outages. This paper develops an outage management approach for power distribution systems in which loads equipped with electricity-dependent medical devices are considered as critical loads. Load criticality is determined by the distance of the load from shelters, the type of medical device, the socio-economic status of the customers, and the average age of the customers at medically vulnerable load points. The optimization model based on mixed-integer linear programming optimizes network reconfiguration, the installation of mobile power sources, and the curtailing of minimum critical loads. The model includes the constraints for a three-phase network configuration for practical grid representation. Optimal restoration schemes are obtained by maximizing restored weighted loads during restoration. In order to deal with uncertainty and imprecise judgments, a fuzzy modification of the analytical hierarchical process (FAHP) is used to prioritize the loads during restoration. The proposed method is tested on the modified IEEE 34-bus distribution test system under several scenarios. The outcome of this study can provide a strategy for power distribution grid operators to manage outages in the presence of loads with electricity-dependent medical devices. © 2023 IEEE.

关键词： Restoration

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Integrating Tradition and Technology: A Survey of AI Applications in the Design of Temple Architecture

Integrating Tradition and Technology: A Survey of AI Applica...

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Artificial Intelligence, Metaverse and Cybersecurity (ICAMAC), International Conference on

作者： J Meghana Harshavardana R Bellad Diya Pardhi Ishwari Magdum Chaitra Nagaraj dept. Computer Science Engineering PES University Bengaluru India

ISBN: (数字)9798350353488

ISBN: (纸本)9798350353495

This article will delve into the complicated inter-building of artificial intelligence and traditional skill of temple architecture of South India. It meticulously analyzes the historical roots of temple design, explaining in detail the subtle means and methods that have always been used in temple construction. Apart from detailed preparation and skilled craftsmanship, it is the cultural heritage that is demonstrated with these monumental creations. In the sequel, the paper sheds light on how AI technologies are transforming temple architecture through systems analysis and optimization, generative design and other methods. Living examples of Artificial Intelligence work do not only confirm the successful AI implementations but also illustrate the smooth link between tradition and novelty in the modern temple designs.

关键词： Surveys Technological innovation Metaverse Design methodology Cultural differences Artificial intelligence computer security System analysis and design Optimization Guidelines

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Single Object Tracking in Satellite Videos Through Deep Learning and Motion Detection Techniques

Single Object Tracking in Satellite Videos Through Deep Lear...

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Human-computer Interaction, Optimization and Robotic Applications (HORA), International Congress on

作者： Israa Hayder Fattah Alizadeh Dept. of Computer Science and Engineering University of Kurdistan Hawlere Erbil Iraq Dept. of Computer Science Toronto Metropolitan University Toronto Canada

ISBN: (数字)9798350394634

ISBN: (纸本)9798350394641

Single object tracking in satellite videos has recently gained a lot of attention in the field of computer vision. Although the satellite images are very informative, the small size of the objects and limited spatial and temporal resolution might make object tracking difficult and inaccurate. In this paper, we propose a method for accurately tracking small objects in satellite videos. In the initial phase, we applied the Fast Super-Resolution Convolutional Neural Network (FSRCNN), a deep learning-based super-resolution technique, to enhance the resolution of the frames. This procedure serves to sharpen the details and features of the objects, resulting in a clearer representation. The main contribution, however, lies in our incorporation of a robust motion detection technique into our framework, marking a significant step forward in this study. The motion detection approach combines various methods and operations with the primary goal of making moving objects distinctly visible and easily distinguishable from the complex image background. This enhancement significantly contributes to the precision and accuracy of the tracking system. We combined both approaches with Fully Convolutional Siamese Network (SiamFC) due to its robustness in tracking processes across frames in videos, capacity to handle changes in target appearance, and ability to deal with small datasets. The Satellite Video Single Object Tracking (SatSOT) and SV248A10-SOT (SV248S), two satellite video single object tracking datasets, were used to evaluate the proposed approach. When the techniques were applied, the SatSOT dataset showed an 11% precision score increase and a 10% success rate improvement. The SV248S dataset exhibited even more significant improvements, with precision scores increasing by 18% and success scores rising by 9%.

关键词： computer vision Satellites Target tracking Superresolution Motion detection Satellite images Object tracking

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Rank-Preference Consistency as the Appropriate Metric for Recommender Systems

arXiv

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arXiv 2024年

作者： Nguyen, Tung D. Uhlmann, Jeffrey Dept. of Electrical Engineering and Computer Science University of Missouri Colombia

In this paper we argue that conventional unitary-invariant measures of recommender system (RS) performance based on measuring differences between predicted ratings and actual user ratings fail to assess fundamental RS properties. More specifically, posing the optimization problem as one of predicting exact user ratings provides only an indirect suboptimal approximation for what RS applications typically need, which is an ability to accurately predict user preferences. We argue that scalar measures such as RMSE and MAE with respect to differences between actual and predicted ratings are only proxies for measuring RS ability to accurately estimate user preferences. We propose what we consider to be a measure that is more fundamentally appropriate for assessing RS performance, rank-preference consistency, which simply counts the number of prediction pairs that are inconsistent with the user’s expressed product preferences. For example, if an RS predicts the user will prefer product A over product B, but the user’s withheld ratings indicate s/he prefers product B over A, then rank-preference consistency has been violated. Our test results conclusively demonstrate that methods tailored to optimize arbitrary measures such as RMSE are not generally effective at accurately predicting user preferences. Thus, we conclude that conventional methods used for assessing RS performance are arbitrary and misleading. Copyright © 2024, The Authors. All rights reserved.

关键词： Recommender systems

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Predictive Modeling of Cryptocurrency Prices Using Machine Learning Algorithms

Predictive Modeling of Cryptocurrency Prices Using Machine L...

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International Conference on Computing and Networking Technology (ICCNT)

作者： Tanjim Mahmud Tahmina Akter Rishita Chakma Tikle Barua Mohammad Shahadat Hossain Karl Andersson Dept. of Computer Science and Engineering Rangamati Science and Technology University Rangamati Bangladesh Dept. of Computer Science and Engineering Port City International University Chittagong Bangladesh Dept. of Computer Science and Engineering University of Chittagong Chittagong Bangladesh Cybersecurity Laboratory Luleå University of Technology Luleå Sweden

ISBN: (数字)9798350370249

ISBN: (纸本)9798350370270

Cryptocurrencies, such as Bitcoin, Binance, Ethereum, FTX, and XRP, are decentralized digital assets known for their volatile nature and potential as investment instruments. Accurate price prediction is crucial for informed investment decisions. This study explores the feasibility of various modeling techniques on diverse data structures and features for predicting the prices of these cryptocurrencies. We utilize daily and high-frequency price data to classify and predict prices using deep learning and machine learning techniques, including LSTM, Bi-LSTM, GRU, linear regression, and SGD regression. Our findings indicate that daily price projections achieve an accuracy of 0.99, outperforming more complex deep learning and machine learning models. Compared to benchmark results, our approach demonstrates superior performance, with the highest scores achieved by the applied statistical methods and advanced algorithms. This research highlights the effectiveness of deep learning and machine learning models in cryptocurrency price forecasting, offering a foundation for further exploration in the field and emphasizing the significance of sample size in predictive modeling.

关键词： Deep learning Machine learning algorithms Accuracy Statistical analysis Computational modeling Linear regression Predictive models Prediction algorithms Long short term memory Investment

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TV-L1 Optical Flow based Hierarchical Transformer Network for Micro-Expression Recognition

TV-L1 Optical Flow based Hierarchical Transformer Network fo...

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Pervasive Computing and Social Networking (ICPCSN), International Conference on

作者： Ashish A Ankam Yashas M B Srujan V Srihari Desai M S Bhargavi Dept. of Computer Science and Engineering Bangalore Institute of Technology Bengaluru India

ISBN: (数字)9798331535193

ISBN: (纸本)9798331535209

Accurate interpretation of human emotions and micro-expressions is vital for advancing human-computer interaction, enhancing security systems, and supporting psychological research. Micro-expression analysis enables systems to detect subtle non-verbal cues, fostering empathetic and intuitive interactions in domains such as security, customer service, and mental health diagnostics. This study presents a novel approach to micro-expression recognition by integrating a Hierarchical Transformer Network (HTNet) with the TV-L1 optical flow algorithm. HTNet's hierarchical transformer architecture efficiently captures and processes temporal features, while the TV-L1 algorithm robustly detects subtle facial motion dynamics essential for micro-expression analysis. The proposed model is fine-tuned through hyperparameter optimization and evaluated using metrics like accuracy, Unweighted F1-score, Unweighted Average Recall, Matthews Correlation Coefficient, and Expected Calibration Error. Experiments conducted on the SAMM and CASME II datasets demonstrate the model's high precision, real-time capabilities, and adaptability to dynamic environments. This research bridges the gap between cutting-edge AI models and practical emotion recognition systems, offering valuable insights into human emotional subtleties. The findings highlight the transformative potential of AI in understanding and responding to human emotions, laying the foundation for future advancements in human-centric applications across diverse fields.

关键词： Human computer interaction Emotion recognition Adaptation models Heuristic algorithms Dynamics Transformers Calibration Security Artificial intelligence Optical flow

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