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2023 IEEE International Conference on Consumer Electronics-Asia, ICCE-Asia 2023

作者： Kea, Kimleang Lee, Sanghyeon Kwak, Myeongjin Han, Youngsun Pukyong National University Department of Ai Convergence Busan Korea Republic of Kyungpook National University School of Computer Science and Engineering Daegu Korea Republic of

ISBN: (纸本)9798350344318

Foveated rendering emulates human vision by prioritizing sharpness at the center while introducing peripheral blur outside this central area. We explore its potential for enhancing object detection performance by integrating it into various object detection methods. Our investigation demonstrates that foveated rendering effectively reduces detection time and false alarm detections, thus showcasing its utility in improving object detection performance. Experimental results indicate a reduction in detection time of up to 20% compared with object detection using conventional rendering techniques. © 2023 IEEE.

关键词： Object detection

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Intelligent ai within a Metamodel Control System for Financial Crisis Management System 5

Intelligent AI within a Metamodel Control System for Financi...

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5th IEEE International Conference on Recent Trends in computer science and Technology, ICRTCST 2024

作者： Raajya Vardhini, A. Rawat, Ruchira Supreeth, B.R. Murugan, V.G. Krishna Reddy, P. Manikandan, G. V.S.B Engineering College Department of Computer Science and Engineering Karur India Graphic Era Deemed to be University Department of Computer Science & Engineering Uttarakhand Dehradun248002 India Oxford Degree and PG College Sir. M. Visveshwaraiah Layout Department of Computer Science Ullal Bangalore India Department of Management Studies Andhra Pradesh Madanapalle India St. Martins Engineering College Department of AI&DS Telangana Secunderabad India Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences Department of Electronics and Communication Engineering Thandalam India

ISBN: (纸本)9798350351378

In an effort to revolutionize the detection, management, and prevention of financial crises, this study investigates the use of Intelligent ai inside a Metamodel Control System for Crisis Management. Data collection and preprocessing, metamodeling, real-time monitoring and alerting, case studies, simulations, optimization, regulatory compliance, and ethical concerns are all part of the study's approach. The results show the strong performance of the metamodel, with high values for accuracy, precision, recall, and F1-score. Consistent with the concepts of adaptive control, real-time monitoring and alerting provide instantaneous early warnings of potential crises. The framework has been shown by case studies to be consistent with conventional economic models. The framework's potential to promote financial resilience and the educated decision-making process is underlined. Real-world applications, cutting-edge ai methods, ethical issues, and global cooperation are all potential next steps. More stable and prosperous economic futures are within reach, according to the insights gained from this study on the role technology plays in financial resilience. © 2024 IEEE.

关键词： Decision making

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General and Scalable Optimization for Robust ai 37

General and Scalable Optimization for Robust AI

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37th AAai Conference on Artificial Intelligence, AAai 2023

作者： Liu, Sijia Department of Computer Science & Engineering Michigan State University MI United States MIT-IBM Watson AI Lab IBM Research United States

ISBN: (纸本)9781577358800

Deep neural networks (DNNs) can easily be manipulated (by an adversary) to output drastically different predictions and can be done so in a controlled and directed way. This process is known as adversarial attack and is considered one of the major hurdles in using DNNs in high-stakes and real-world applications. Although developing methods to secure DNNs against adversaries is now a primary research focus, it suffers from limitations such as lack of optimization generality and lack of optimization scalability. My research highlights will offer a holistic understanding of optimization foundations for robust ai, peer into their emerging challenges, and present recent solutions developed by my research group. Copyright © 2023, Association for the Advancement of Artificial Intelligence (***). All rights reserved.

关键词： Deep neural networks

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Fast-Hubert: an Efficient Training Framework for Self-Supervised Speech Representation Learning

Fast-Hubert: an Efficient Training Framework for Self-Superv...

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2023 IEEE Automatic Speech Recognition and Understanding Workshop, ASRU 2023

作者： Yang, Guanrou Ma, Ziyang Zheng, Zhisheng Song, Yakun Niu, Zhikang Chen, Xie Shanghai Jiao Tong University MoE Key Lab of Artificial Intelligence Ai Institute X-LANCE Lab Department of Computer Science and Engineering China

ISBN: (纸本)9798350306897

Recent years have witnessed significant advancements in self-supervised learning (SSL) methods for speech-processing tasks. Various speech-based SSL models have been developed and present promising performance on a range of downstream tasks including speech recognition. However, existing speech-based SSL models face a common dilemma in terms of computational cost, which might hinder their potential application and in-depth academic research. To address this issue, we first analyze the computational cost of different modules during HuBERT pre-training and then introduce a stack of efficiency optimizations, which is named Fast-HuBERT in this paper. The proposed Fast-HuBERT can be trained in 1.1 days with 8 V100 GPUs on the Librispeech 960 h benchmark, without performance degradation, resulting in a 5.2x speedup, compared to the original implementation. Moreover, we explore two well-studied techniques in the Fast-HuBERT and demonstrate consistent improvements as reported in previous work.11The code for Fast-HuBERT training is available at https://***/yanghaha0908/FastHuBERT © 2023 IEEE.

关键词： Speech recognition

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A Comprehensive Survey on Naive Bayes Algorithm: Advantages, Limitations and Applications 4

A Comprehensive Survey on Naive Bayes Algorithm: Advantages,...

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4th International Conference on Smart Electronics and Communication, ICOSEC 2023

作者： Pajila, P. J. Beslin Sheena, B. Gracelin Gayathri, A. Aswini, J. Nalini, M. Siva Subramanian, R. Vel Tech Rangarajan Dr. Sagunthala R and D Institute of Science and Technology Dept of Computer Science and Engineering India Sathyabama Institute of Science and Technology India Dept of Cse India Saveetha School of Engineering Dept of Ai and Ml India S.A.Engineering College Dept of Cse India Rmk College of Engg and Tech Dept of Cse India

ISBN: (纸本)9798350300888

A well-known and frequently used machine learning method, the Naive Bayes algorithm has attracted a lot of interest in a number of fields. The Naive Bayes algorithm, along with its benefits, drawbacks, and practical applications, will be covering in this survey study. The Naive Bayes algorithm's fundamentals are first studied in the survey, with special attention paid to how heavily it relies on probability theory and the notion of feature independence. The Naive Bayes method is further examined, as well as its applicability to both continuous and discrete data. The study then looks into the benefits of the Naive Bayes method, emphasizing its tolerance to irrelevant characteristics, great computing efficiency, and capacity for handling high- dimensional data. The algorithm's interpretability and ability to work with different application domains are also highlighted. The survey article looks at various restrictions, such as the feature independence assumption and algorithm's sensitivity to missing data and its incapacity to recognize intricate correlations between variables. The survey article offers a variety of real-world situations where the Naive Bayes algorithm has been effectively used in order to give a thorough viewpoint. Text categorization, spam filtering, sentiment analysis, recommender systems, medical diagnosis, and financial fraud detection are some of these uses. This survey article provides academics, practitioners, and enthusiasts with an invaluable resource for comprehending the Naive Bayes algorithm's capabilities and concerns by compiling the algorithm's benefits, drawbacks, and applications. It serves as a starting point for further investigation and the creation of enhanced versions and modifications of the Naive Bayes algorithm. © 2023 IEEE.

关键词： Sentiment analysis

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Multi-Agent Reinforcement Learning with Epistemic Priors 9

Multi-Agent Reinforcement Learning with Epistemic Priors

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9th International Conference on Control, Decision and Information Technologies, CoDIT 2023

作者： Walker, Thayne T. Ide, Jaime S. Choi, Minkyu Guarino, Michael Alcedo, Kevin Applied Ai Team University of Denver Department of Computer Science United States Yale University Department of Psychiatry United States University of Texas at Austin Department of Electrical and Computer Engineering AustinTX United States

ISBN: (纸本)9798350311402

It is important for autonomous multi-agent teams to coordinate actions so that collaborative goals can be achieved efficiently without conflicts. Practical issues in multi-agent, real-time systems are limited sensing and communication capabilities. A significant number of multi-agent algorithms rely on accurate state information for all agents in order to effectively coordinate. In this paper, we propose an approach called Reinforcement Learning with Epistemic Priors (MARL-EP). MARL-EP incorporates a shared mental model between agents by leveraging epistemic estimation to infer portions of the obser-vation space which are unobservable. We show that MARL-EP allows a very high level of coordination to be achieved with severely impaired sensing and zero communication between agents. © 2023 IEEE.

关键词： Real time systems

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Scalable high-repetition-rate sub-half-cycle terahertz pulses from spatially indirect interband transitions

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Light(science & Applications) 2022年第6期11卷 1304-1311页

作者： Christian Meineke Michael Prager Johannes Hayes Qiannan Wen Lukas Zheyi Kastner Dieter Schuh Kilian Fritsch Oleg Pronin Markus Stein Felix Schafer Sangam Chatterjee Mackillo Kira Rupert Huber Dominique Bougeard Department of Physics University of Regensburg93040 RegensburgGermany Department of Electrical Engineering and Computer Science University of MichiganAnn ArborMl 48109USA Faculty of Electrical Engineering Helmut Schmidt University22043 HamburgGermany Institute of Experimental Physics I Justus Liebig University Giessen35392 GiessenGermany

Intense phase-locked terahertz(THz)pulses are the bedrock of THz lightwave electronics,where the carrier field creates a transient bias to control electrons on sub-cycle time *** applications such as THz scanning tunnelling microscopy or electronic devices operating at optical clock rates call for ultimately short,almost unipolar waveforms,at megahertz(MHz)repetition ***,we present a flexible and scalable scheme for the generation of strong phase-locked THz pulses based on shift currents in type-ll-aligned epitaxial semiconductor *** measured THz waveforms exhibit only 0.45 optical cycles at their centre frequency within the full width at half maximum of the intensity envelope,peak fields above 1.1 kVcm^(-1) and spectral components up to the mid-infrared,at a repetition rate of 4 *** only positive half-cycle of this waveform exceeds all negative half-cycles by almost four times,which is unexpected from shift currents *** detailed analysis reveals that local charging dynamics induces the pronounced positive THz-emission peak as electrons and holes approach charge neutrality after separation by the optical pump pulse,also enabling ultrabroadband *** unipolar emitters mark a milestone for flexibly scalable,next-generation high-repetition-rate sources of intense and strongly asymmetric electric field transients.

关键词： pump repetition cycle

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Smart Mood Detection: Real-Time Emotion Analysis for Human-computer Interaction Using Deep Learning

Smart Mood Detection: Real-Time Emotion Analysis for Human-C...

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2025 International Conference on Artificial Intelligence and Data engineering, aiDE 2025

作者： Chandrappa, S. Banerjee, Tapabrata Saha, Sohan Zaid, Mohd Aditya Pai, H. Guru Prasad, M.S. Rajesh, I.S. Dept. of Computer Science and Engineering Bengaluru India Bengaluru India MIT Art Design and Technology University Department of CSE Pune India Department of Computer Science and Engineering Dehradun India BMS Institute of Technology and Management Dept. of AI & ML Bengaluru India

ISBN: (纸本)9798331527518

A convolutional neural network (CNN)-based face emotion recognition system that analyzes human emotions in real-time, including happy, sorrow, rage, and surprise, is described. Existing approaches often struggle with accurately recognizing subtle or complex emotions due to limited feature extraction capabilities and the need for large, diverse training datasets. Additionally, they face challenges in adapting to real-world conditions, such as variations in lighting, angles, and occlusions. In order to improve the model's generalizability, the proposed research used a preprocessing pipeline that included gray scale conversion, scaling, normalization, and augmentation techniques including rotation and flipping on a labeled and balanced dataset of facial photos. Dropout and regularization techniques were used to reduce over fitting, and the CNN architecture was meticulously adjusted to maximize speed. High accuracy and resilience across emotion categories are demonstrated by preliminary results, indicating the model's potential for real-world uses in security surveillance, healthcare, and human-computer interaction. © 2025 IEEE.

关键词： Convolutional neural networks

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Demystifying the Applications of Internet of Medical Things (IoMT) for smart health care system

Demystifying the Applications of Internet of Medical Things ...

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Data, Information and Computing science (CDICS), International Conference on

作者： Ruqqaiya Begum Polisetty Swetha Koti Tejasvi M. A. Jabbar Dept. of Computer Science and Engineering Vardhaman College of Engineering Hyderabad India Dept. of Computer Science and Engineering(AI&ML) Vardhaman College of Engineering Hyderabad India

ISBN: (数字)9798350382778

ISBN: (纸本)9798350382785

As Internet of Things (IoT) devices become more commonplace and are used in more aspects of daily life, a bright future for people, data, and processes is emerging. Because of its potential to enhance patient care as well as its capacity to deliver more trustworthy clinical data, boost productivity, and save costs, In recent years, the Internet of Medical Things (IoMT) has a strategic goal for future e-healthcare. It is understandable why so many healthcare organization’s nowadays want to take advantage of what the IoMT has to offer. Its infrastructure includes interconnected medical equipment, software, and care systems and services. For everyone to understand how IoMT is enhancing the health care system with the aid of other technologies, we have described the implementation of IoMT in detail in this paper. Along with this, we have also discussed the different security concerns and difficulties the healthcare system is experiencing due to the IoMT’s information-gathering shortcomings.

关键词： Productivity Biomedical equipment Costs Smart healthcare Internet of Medical Things Software Security

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Hybrid Optimization Algorithm to Mitigate Phishing URL Attacks In Smart Cities 3

Hybrid Optimization Algorithm to Mitigate Phishing URL Attac...

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3rd International Conference on Innovative Practices in Technology and Management, ICIPTM 2023

作者： Sujatha, G. Ayyannan, Manimuthu Priya, S. Gayathri Arun, V. Arularasan, A.N. Kumar, M Jogendra Sri Venkateswara College of Engineering Department of Electronics and Communication Andhra Pradesh Tirupati India AI/ML and Data Expert San FranciscoCA United States R. M. D. Engineering College Department of Electronics and Communication Tamil Nadu Kavaraipettai India Madanapalle Institute of Technology Department of Computer Science &engineering Andhra Pradesh Madanapalle India Panimalar Engineering College Department of Artificial Intelligence &data Science Tamil Nadu Chennai India Koneru Lakshmaiah Education Foundation Department of Computer Science Andhra Pradesh Vaddeswaram India

ISBN: (纸本)9798350336238

To improve the detection of phishing sites, this proposal introduces feature weights for intelligent phishing site detection based on hybrid bio-inspired algorithms. The proposed approach uses Gray Wolf Optimization (GWO) and Firefly Algorithm (FF), which examines a wide range of website attributes, to more precisely identify phishing sites. Then employs Artificial Neural Network (ANN) to classify different website elements according to the importance of each component in differentiating between legitimate and phishing websites using bioinspired-based recommended site feature weights. According to experimental findings, the suggested hybrid bioinspired-based feature weighting greatly improved classification accuracy, true positive (TPR), and negative rates (TNR), as well as precision and F1 score. Phishing is an online crime that entails the gathering of private information like passwords, account numbers, and credit card numbers. Attackers use alluring URLs to entice phony website visitors. Recently, Artificial Intelligence-based phishing detection has seen some success, and in this study, ANN was used to detect phishing. This ANN classifier may make phishing websites simpler to spot. However, it has been shown that the effectiveness of detection can be increased by applying a genetic algorithm to improve feature selection. © 2023 IEEE.

关键词： Websites

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