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arXiv 2024年

作者： Yang, Yaoqi Du, Hongyang Sun, Geng Xiong, Zehui Niyato, Dusit Han, Zhu The College of Communications Engineering Army Engineering University PLA Nanjing210000 China The School of Computer Science and Engineering NTU Singapore The College of Computer Science and Technology Jilin University Changchun130012 China The College of Computing and Data Science Nanyang Technological University 639798 Singapore The Information Systems Technology and Design Pillar Singapore University of Technology and Design Singapore The Department of Electrical and Computer Engineering The University of Houston HoustonTX77004 United States

Game theory offers a powerful framework for analyzing strategic interactions among decision-makers, providing tools to model, analyze, and predict their behavior. However, implementing game theory can be challenging due to difficulties in deriving solutions, understanding interactions, and ensuring optimal performance. Traditional non-AI and discriminative AI approaches have made valuable contributions but struggle with limitations in handling large-scale games and dynamic scenarios. In this context, generative AI emerges as a promising solution because of its superior data analysis and generation capabilities. This paper comprehensively summarizes the challenges, solutions, and outlooks of combining generative AI with game theory. We start with reviewing the limitations of traditional non-AI and discriminative AI approaches in employing game theory, and then highlight the necessity and advantages of integrating generative AI. Next, we explore the applications of generative AI in various stages of the game theory lifecycle, including model formulation, solution derivation, and strategy improvement. Additionally, from game theory viewpoint, we propose a generative AI-enabled framework for optimizing machine learning model performance against false data injection attacks, supported by a case study to demonstrate its effectiveness. Finally, we outline future research directions for generative AI-enabled game theory, paving the way for its further advancements and development. © 2024, CC BY.

关键词： Game theory

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Emotion Recognition System via Facial Expressions and Speech Using Machine Learning and Deep Learning Techniques

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SN Computer science 2023年第4期4卷 363页

作者： Chaudhari, Aayushi Bhatt, Chintan Nguyen, Thanh Thi Patel, Nisarg Chavda, Kirtan Sarda, Kalind U & P U. Patel Department of Computer Engineering Chandubhai S. Patel Institute of Technology Charotar University of Science And Technology (CHARUSAT) Gujarat Changa 388421 India Department of Computer Science and Engineering School of Technology Pandit Deendayal Energy University Knowledge Corridor Gujarat Gandhinagar 382007 India School of Information Technology Faculty of Science Engineering and Built Environment Deakin University Victoria 3216 Australia Bishop University 2600 College St Sherbrooke J1M 1Z7 QC Canada Crest Data Systems Private Limited Gandhinagar Highway Makarba Gujarat Ahmedabad 380051 India Programmer Analyst Meditab Software Pvt Ltd Kalasagar Mall Ghatlodiya Gujarat Ahmedabad 380061 India

Patients in hospitals frequently exhibit psychological issues such as sadness, pessimism, eccentricity, and anxiety. However, hospitals normally lack tools and facilities to continuously monitor the psychological health of patients. It is desirable to identify depression in patients so that it can be managed by instantly providing better therapy. This can be possible by advances in machine learning for image processing with notable applications in the domain of emotion recognition using facial expressions. In this paper, we have proposed two different methods, i.e. facial expression detection and voice analysis, to predict emotions. For facial expression recognition, we have used two approaches, one is the use of Gabor filters for feature extraction with support vector machine for classification and another is using convolutional neural network (CNN). For voice analysis, we extracted mel-frequency cepstral coefficients from speech data and, based on those features, predicted the emotions of the speech using a CNN model. Experimental results show that our proposed emotion recognition methods obtained high accuracy and thus could be potentially deployed to real-world applications. © 2023, The Author(s), under exclusive licence to Springer Nature Singapore Pte Ltd.

关键词： CNN Deep learning Expressions Facial emotion Machine learning Speech SVM

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English Offensive Text Detection Using CNN Based Bi-GRU Model

English Offensive Text Detection Using CNN Based Bi-GRU Mode...

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information and Communication Technology (ICICT), International Conference on

作者： Tonmoy Roy Md Robiul Islam Asif Ahammad Miazee Anika Antara Al Amin Sunjim Hossain Data Analytics & Information Systems Utah State University Utah United States Computer Science William & Mary Virginia United States Computer Science Maharishi International University Iowa United States Electrical and Electronics Engineering Brac University Dhaka Bangladesh Computer Science Uttara University Dhaka Bangladesh Computer Science Northern University Dhaka Bangladesh

ISBN: (数字)9798331508227

ISBN: (纸本)9798331508234

Over the years, the number of users of social media has increased drastically. People frequently share their thoughts through social platforms, and this leads to an increase in hate content. In this virtual community, individuals share their views, express their feelings, and post photos, videos, blogs, and more. Social networking sites like Facebook and Twitter provide platforms to share vast amounts of content with a single click. However, these platforms do not impose restrictions on the uploaded content, which may include abusive language and explicit images unsuitable for social media. To resolve this issue, a new idea must be implemented to divide the inappropriate content. Numerous studies have been done to automate the process. In this paper, we propose a new Bi-GRU-CNN model to classify whether the text is offensive or not. The combination of the Bi-GRU and CNN models outperforms the existing models.

关键词： Image resolution Social networking (online) Blogs Text detection information and communication technology Videos

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Iot Based Smart Assistant Cane For The Visually Impaired

Iot Based Smart Assistant Cane For The Visually Impaired

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International Conference on Communication, Computing and Internet of Things (IC3IoT)

作者： A Harini C Buvana M Harshini S Keerthana D Prabhu S K Umamaheswaran Department of Electronics and Communication Engineering Sri Sairam Engineering College Chennai Tamil Nadu Department of Information Technology Sri Sairam Engineering College Chennai Tamil Nadu Department of Artificial intelligence and Data Science Sri Sairam Engineering College Chennai Tamil Nadu Department of Computer Science and Business Systems Sri Sairam Engineering College Chennai Tamil Nadu Department of Mathematics Sri Sairam Engineering College Chennai Tamil Nadu

ISBN: (数字)9798350352689

ISBN: (纸本)9798350352696

This paper presents the design and implementation of a Smart Cane for the visually impaired individuals, incorporating several sensors and features to improve their mobility and safety. The device includes an ultrasonic sensor for obstacle detection, an MPU-6050 device for detecting falls and tilts, a GPS module for navigation, an RTC module for medicine reminders, and a fitness tracking module for monitoring health parameters. Additionally, a Bluetooth headset with a microphone is provided to communicate with the voice assistant in noisy areas. The Smart Cane triggers a buzzer for help and sends a notification to the caretaker in case of a fall or crash. The device is powered by a LIPO battery and is designed to improve the quality of life of visually impaired individuals by providing them with enhanced mobility and safety features.

关键词： Navigation Personal voice assistants Sensor phenomena and characterization Safety Telecommunication computing Noise measurement Intelligent sensors

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English offensive text detection using CNN based Bi-GRU model

arXiv

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arXiv 2024年

作者： Roy, Tonmoy Islam, Md Robiul Miazee, Asif Ahammad Antara, Anika Amin, Al Hossain, Sunjim Data Analytics & Information Systems Utah State University Utah United States Computer Science William & Mary VA United States Computer Science Maharishi International University Iowa United States Electrical and Electronics Engineering Brac University Dhaka Bangladesh Computer Science Uttara University Dhaka Bangladesh Computer Science Northern University Dhaka Bangladesh

关键词： Tweets

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Robotics in Healthcare: Artificial Intelligence-Driven Application for Medical Diagnosis and Treatment

Robotics in Healthcare: Artificial Intelligence-Driven Appli...

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Innovative Computing, Intelligent Communication and Smart Electrical systems (ICSES), International Conference on

作者： Dhanya D A. Devendran S. Balaji Vaishali M R. Sujatha K.V. Sreelekha Department of Artificial Intelligence and Data Science Mar Ephraem College of Engineering and Technology Kanyakumari Tamil Nadu India School of Business Woxsen University Hyderabad Telangana India Department of Information Technology Sri Manakula Vinayagar Engineering College Puducherry India Deportmet of Computer Science and Business Systems Sri Eshwar College of Engineering Coimbatore Tamil Nadu India Department of Computer Science SubbalakshmiLakshmipathy College of Science Madurai Tamil Nadu India Department of Computer Science and Engineering Hindusthan College of Engineering and Technology Coimbatore Tamil Nadu India

ISBN: (数字)9798331543624

ISBN: (纸本)9798331543631

The rapid development of deep learning methods presents a potentially game-changing opportunity in the realm of education, particularly in the promotion of student involvement and the comprehension of the subject matter. To exemplify learning gestures and resolve educational challenges, this exploratory study investigates the operation of deep learning algorithms to determine the interests of students. Our deep learning model can provide direct predictions on the areas of interest for individual students by analyzing enormous volumes of educational data. These data include pupil relations, performance standards, and behavioral patterns of students. By aligning instructional tactics with the preferences of students, this strategy not only makes it easier for students to become accustomed to newly presented educational material but also encourages active learning. The research reveals that deep learninghelps capture the intricacies of student engagement. It also provides preceptors with valuable insights that may be used to cultivate a learning landscape that is more engaging and investigative. The results of our research highlight the possibility that deep learning will be used to change educational procedures to make them more adaptable and sensitive to the various needs of students. In this paper, the practice of using deep learning for interest identification in education is discussed, along with its methodology, perpetrators, and counteraccusations

关键词： Deep learning Education Surgery Predictive models Robot sensing systems Prediction algorithms Real-time systems Pupils Medical diagnostic imaging Standards

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A Lightweight Fine-Grained Scheme for Distinguishing the Hotness of Warm data to Reduce Segment Cleaning Overhead

SSRN

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SSRN 2025年

作者： Yang, Lihua Wang, Fang Lin, Weizhao Zhang, Wei Xiao, Yang Tan, Zhipeng Li, Jiaxin Lu, Kai College of Computer Science and Technology National University of Defense Technology Yanwachi Street Changsha410073 China Wuhan National Laboratory for Optoelectronics Key Laboratory of Information Storage System Engineering Research Center of Data Storage Systems and Technology Ministry of Education of China Huazhong University of Science and Technology Luoyu Road Wuhan430074 China

With the widespread adoption of flash memory, the Flash Friendly File System (F2FS) designed to flash memory characteristics has become widely-used in large data centers. However, F2FS encounters from significant cleaning overheads due to its logging scheme writes. We observe that warm data in F2FS account for a substantial proportion, at least 80%. Nevertheless, the mixed storage of warm data with varying hotness exacerbates segment cleaning challenges. To address this issue, we propose a scheme called M2H, which involves a fine-grained management of warm data hotness identified by the K-means clustering algorithm. M2H determines hotness by considering factors such as file block update distance, most recently used distance, and workload characteristics. M2H facilitates {M}ulti-log delayed writing and {M}odified segment cleaning based on {H}otness. To reduce costs associated with distinguishing data hotness at the file block level, we employ Mini Batch K-means and refer to this variant as M2H_MBK. Moreover, for servers equipped with GPUs, the clustering process can be offloaded to the GPU, known as M2H_GPU. We conduct a comprehensive comparison of traditional F2FS, M2H, M2H_MBK, and M2H_GPU on a real platform. Results show that compared to traditional F2FS, M2H_GPU reduces the number of segment cleanings by 54.41% to 97.93%. © 2025, The Authors. All rights reserved.

关键词： Flash memory

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Domain-adaptive state of health prediction of vehicle batteries powered by deep learning

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Cell Reports Physical science 2025年第5期6卷

作者： Li, Minghe Fei, Zicheng Yang, Luoxiao Zhang, Zijun Tsui, Kwok-Leung Department of Data Science College of Computing City University of Hong Kong Hong Kong Hong Kong School of Future Science and Engineering Soochow University Suzhou China College of Automation and Information Engineering Xi'an University of Technology Xi'an China Department of Industrial Manufacturing and Systems Engineering at the University of Texas-Arlington Arlington TX United States

State of health (SOH) estimation of battery packs in electric vehicles (EVs) is essential for transportation electrification safety and reliability. The noise and complexity of EV battery pack data hinder the effectiveness of various data-driven SOH estimation methods using lab data. To address these challenges and achieve more effective data-driven EV battery pack SOH predictions, this study develops a comprehensive deep-learning-based SOH modeling framework for EV batteries. The framework begins with a two-stage mode decomposition (TSMD) method designed to effectively identify neat SOH degradation patterns better representing noisy field data. Next, an endogenous and exogenous multibranch network structure with a hierarchically fused attention mechanism (EEMB-HiFA) is developed for real-time prediction of EV battery pack SOH. Computational experiments leveraging datasets from seven EVs are conducted to validate the accuracy and adaptiveness of the proposed EEMB-HiFA. The results show that the EEMB-HiFA can achieve a 96.49% improvement in accuracy compared to strong benchmarks considered. © 2025 The Author(s)

关键词： data feature fusion data-driven model deep learning domain adaptation electric vehicles lithium-ion battery signal denoising state of health

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Mobility and Behavior based Trustable Routing in Mobile Wireless Sensor Network

Mobility and Behavior based Trustable Routing in Mobile Wire...

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International Conference on Computing Methodologies and Communication (ICCMC)

作者： B. Praveen Kumar J. Umamageswaran A.V. Kalpana R. Dhanalakshmi Department of Computer Science and Engineering KL University Vijayawada Andhra Pradesh India Department of Information Technology RMK Engineering College Chennai Tamil Nadu India Department of Data Science and Business Systems SRM Institute of Science and Technology Chennai Tamil Nadu India Department of Computer Science and Engineering Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences Chennai Tamil Nadu India

Mobile wireless sensor networks (MWSNs) allow the sensor nodes to move freely and transmit to each other without needing a fixed infrastructure. Usually, the routing process is very complex, and it becomes even more complicated as the sensor nodes are low-power, cost-effective mobile devices with minimum resources. MWSNs raises deployed in security-critical applications due to their resource-constrained features; they are prone to several security attacks that critically affect data transmission. The most significant innovation of trust is that it evades untrustable nodes by activating several detection routes to detect and obtain node trust, enhancing security quickly. Mobility and Behavior based Trustable Routing (MBTR) is proposed to solve these issues. This approach measures sensor node mobility based on the Received Signal Strength (RSS) between the two sensor nodes. The trust manager computes the node trust ability by the node behavior. Here, the node behavior is checked through the node energy, supportive account, and received packet account. The trust manager selects the security algorithm based on distance during data transmission. The simulation examination demonstrates lesser delay and lesser packet loss in the network. Furthermore, it improves the network throughput in the MWSN.

关键词： Wireless communication Wireless sensor networks Computational modeling Routing Throughput Behavioral sciences Delays

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Personalized Federated Learning with Collaborative Aggregation Networks for Multi-Site Brain Disorder Diagnosis

Personalized Federated Learning with Collaborative Aggregati...

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Industrial Automation, Robotics and Control engineering (IARCE), International Conference on

作者： Qian Si Yang Li School of Cyber Science and Technology Beihang University Beijing China Department of Automation Science and Electrical Engineering Beijing Advanced Innovation Center for Big Data and Brain Computing State Key Laboratory of Virtual Reality Technology and Systems Advanced Institute of Information Technology Peking University Beihang University Beijing China

ISBN: (数字)9798350380323

ISBN: (纸本)9798350380330

In multi-site brain disease diagnosis studies, traditional centralized training methods necessitate sharing medical data, posing significant privacy risks. Federated learning (FL) offers a privacy-preserving solution by enabling global model training through aggregating locally trained models from multiple data centers without sharing raw data. However, current FL approaches rely on a server-based network topology, where central server failure disrupts training. Additionally, data heterogeneity across sites often slows convergence and reduces accuracy. To overcome these issues, we introduce a decentralized personalized federated learning collaborative aggregation network (pFedCAN). This framework has two core components: (1) separating local models into shared and personalized layers, and (2) forming a collaborative aggregation network via similarity detection in the shared layers. Specifically, each center trains its local model, then separates it into shared and personalized layers. The shared layer is exchanged with other centers, while the personalized layer remains local. data centers analyze similarities in received shared layers to build a collaborative network, where shared layers from similar centers are aggregated to refine the model. This approach flexibly adapts to varying levels of data heterogeneity, enhancing model training efficiency. Validation on public datasets, ABIDE I and ADHD, shows that the proposed method outperforms current leading techniques.

关键词： Training Adaptation models data centers Federated learning Service robots Collaboration Brain modeling data models Servers Medical diagnostic imaging

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