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5th International Conference on IoT Based Control Networks and Intelligent Systems, ICICNIS 2024

作者： Hariharasudhan, S. Varghese, Vineetha Malarvizhi, K. Santhoshkumar, S.P. Nalini, T. Vikram, D. Vel Tech Rangarajan Dr. Sagunthala R&D Institute of Science and Technology Department of Computer Science and Engineering Chennai Avadi India Rathinam Technical Campus Department of Computer Science and Engineering Coimbatore India Jct College of Engineering and Technology Department of Computer Science Al Business Systems Coimbatore India Vel Tech Rangarajan Dr. Sagunthala R&D Institute of Science and Technology School of Computing Department of Computer Science and Engineering Chennai Avadi India Saveetha Institute of Medical and Technical Sciences Saveetha School of Engineering Saveetha University Department of Computer Science and Engineering Chennai India Rathinam Technical Campus Department of Artificial Intelligence Data Science Coimbatore India

ISBN: (纸本)9798331518097

Authentication is a crucial step in the cyber security process that confirms user identities. Even though they are widely used, traditional password based techniques are frequently vulnerable to attacks like guessing and password theft. This study presents the Optimal Strong Password Authentication (OSPA) protocol, which uses USB sticks to provide safe user authentication in order to address these issues. The Multi-Level Hashing Algorithm (MLHA), which dynamically picks hash functions during authentication to generate many layers of hashed passwords, is a key component of OSPA's effectiveness. By preventing eavesdropping efforts and reducing the possibility of unwanted access through guessing attacks, this method strengthens security. This protocol provides strong authentication procedures, protecting confidential data and improving overall cyber security posture by integrating MLHA into the OSPA architecture. © 2024 IEEE.

关键词： Authentication Protocol

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External Knowledge Is Not Always Needed: An Adaptive Retrieval Augmented Generation Method 4

External Knowledge Is Not Always Needed: An Adaptive Retriev...

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4th International Symposium on Artificial Intelligence and Intelligent Manufacturing, AIIM 2024

作者： Guan, Wenbo Lu, Jiyu Feng, Qinyu Li, Xiaoqian Zhou, Jun Key Laboratory of Speech Acoustics and Content Understanding Institute of Acoustics Chinese Academy of Sciences Beijing100190 China Institute of Information Engineering Chinese Academy of Sciences Beijing100084 China School of Cyber Security University of Chinese Academy of Sciences Beijing100049 China Artificial Intelligence Technology Co. Ltd China Southern Power Grid Guangzhou510663 China School of Computing Science and Engineering South China University of Technology Guangzhou510006 China

ISBN: (纸本)9798331541729

Retrieval augmented generation (RAG), by integrating external knowledge with large language models (LLMs), has become a common practice to alleviate LLMs’ hallucination problem. The performance of RAG, however, depends on the capability of the adopted information retriever to a large extent. Specifically, a good information retriever can manage to obtain the most useful information from the external knowledge, which will then enhance the quality of generated content from LLMs and vice versa. Since the vanilla RAG will always leverage the retrieved information to assist with LLMs’ content generation despite of its usefulness, when combined with a retriever of limited capacity, therefore, the vanilla RAG will not benefit LLMs’ content generation sometimes but introduce additional noise, having a negative impact on the final performance. To address this problem, this paper proposes a novel adaptive RAG method which first uses LLMs to determine the usefulness of the retrieved information and then let LLMs to generate content on their own without the retrieved information if it is useless. Experimental results on four datasets demonstrate the effectiveness of the proposed adaptive RAG method with information retrievers of various capabilities, improving the performance of the vanilla RAG by an obvious margin. © 2024 IEEE.

关键词： Modeling languages

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MapExpert: Online HD Map Construction with Simple and Efficient Sparse Map Element Expert 39

MapExpert: Online HD Map Construction with Simple and Effici...

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39th Annual AAAI Conference on Artificial Intelligence, AAAI 2025

作者： Zhang, Dapeng Chen, Dayu Zhi, Peng Chen, Yinda Yuan, Zhenlong Li, Chenyang Sunjing Zhou, Rui Zhou, Qingguo School of Information Science & Engineering Lanzhou University China Robotics Technology Co. Ltd. China School of Information Science and Technology University of Science and Technology of China China Institute of Computing Technology Chinese Academy of Sciences China

ISBN: (纸本)157735897X

Constructing online High-Definition (HD) maps is crucial for the static environment perception of autonomous driving systems (ADS). Existing solutions typically attempt to detect vectorized HD map elements with unified models;however, these methods often overlook the distinct characteristics of different non-cubic map elements, making accurate distinction challenging. To address these issues, we introduce an expert-based online HD map method, termed MapExpert. MapExpert utilizes sparse experts, distributed by our routers, to describe various non-cubic map elements accurately. Additionally, we propose an auxiliary balance loss function to distribute the load evenly across experts. Furthermore, we theoretically analyze the limitations of prevalent bird’s-eye view (BEV) feature temporal fusion methods and introduce an efficient temporal fusion module called Learnable Weighted Moving Descent. This module effectively integrates relevant historical information into the final BEV features. Combined with an enhanced slice head branch, the proposed MapExpert achieves state-of-the-art performance and maintains good efficiency on both nuScenes and Argoverse2 datasets. Copyright © 2025, Association for the Advancement of Artificial Intelligence (***). All rights reserved.

关键词： Expert systems

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Applying Machine Learning & Knowledge Discovery to Intelligent Agent-Based Recommendation for Online Learning Systems

Applying Machine Learning & Knowledge Discovery to Intellige...

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2023 IEEE International Conference on Device Intelligence, computing and Communication Technologies, DICCT 2023

作者： Murugesan, S. Bharathiraja, N. Pradeepa, K. Ravindhar, N.V. Kumar, M. Vinoth Marappan, Raja Tagore Engineering College Department of It Chennai India Chitkara University Chitkara University Institute of Engineering and Technology Punjab India St. Michael College of Engineering and Technology Department of Computer Science and Engineering TN Sivaganga India Saveetha Engineering College Department of Cse Tamil Nadu India Dmi-St.Eugene University Dept. of Cse Zambia Sastra Deemed University School of Computing Thanjavur India

ISBN: (纸本)9781665474924

E-learning is one of the favorite jobs where all the learners can learn through different online sites to update their knowledge acquisition. Though online learning supports several benefits, there are many challenges to be considered, for example, the information resources, quality of the search, accessing the correct information, getting the search results within a reasonable computing time, etc. This research develops the new natural language processing (NLP) intelligent and online recommender using the learner's profile information and semantic analysis of the search history at different times. The intelligent clustering strategy is applied, so the system learns automatically, and the learner's characteristics are analyzed periodically. The simulation results of the proposed research are compared with the state-of-the-art techniques and provide the minimized error rates to the selection of courses and recommendations with the recommendation accuracy in (98.2%, 98.9%). The MAE lies between (0.12, 0.35) for the considered clusters. Compared to the other methods, the method works well for clusters of sizes 10, 20, 30, 40, and 50. © 2023 IEEE.

关键词： Knowledge acquisition

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A Deep Drive Analysis Framework on Emotions of Users in Attention Driven Systems

A Deep Drive Analysis Framework on Emotions of Users in Atte...

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International Conference on I-SMAC (IoT in Social, Mobile, Analytics and Cloud) (I-SMAC)

作者： S. Dharshini Ravula Akshita Suman Panigrahi Nadimpalli Sai Kiran Varma Senthil Kumar Thangavel Somasudaram K Abel Department of Computer Science and Engineering Amrita School of Computing Coimbatore Amrita Vishwa Vidyapeetham Tamil Nadu India Department of Computer Science and Engineering Amrita School of Computing Coimbatore Amrita Vishwa Vidyapeetham India Department of Mathematics Amrita School of Computing Amrita Vishwa Vidyapeetham Tamil Nadu India Department of Information Technology Faculty of Informatic and Design Cape Peninsula University of Technology South Africa

ISBN: (数字)9798350376425

ISBN: (纸本)9798350376432

One of the ways humans communicate is by using facial expressions. In psychology, the detection of emotions or facial expressions requires analysis and assessment of decisions in predicting a person’s emotions or group of people in communicating. Emotion is powerful in many ways because it can influence a decision and urge people to act. This emotion like anger, happiness, sadness, disgust, fear, surprise, and neutral detection is used for human behaviour and understanding, action of mental disorders, and synthetic human expressions. This can be used in healthcare, marketing, and manufacturing. Here the paper is based on marketing where we can detect the emotions of the people watching the ads. This emotion detection for the ads can be used for predictions like whether customers will like the product or not, whether the sales will increase or not, etc. The methodology used to predict human emotion through the face is CNN and MobileNetV2 with LSTM which is a pretrained model. The architecture involves pre-processing of the data, feature detection, face detection, and detection of emotion. There are various datasets used for detecting emotions like FER-2013, JAFFE, CK+, etc. Here the FER-2013 is used. The performance of the model is measured by using confusion matrix, recall, precision, f1-score, and accuracy.

关键词： Emotion recognition Mental disorders Feature detection Psychology Medical services Predictive models Manufacturing Convolutional neural networks Long short term memory Faces

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Efficient acne classification based on Resnet18 3

Efficient acne classification based on Resnet18

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3rd International Conference on Algorithms, Network, and Communication technology, ICANCT 2024

作者： Cheng, Shi Sun, Mingze Huang, Zhongyi Wang, Yushan Fu, Jiaxuan School of Engineering University of Manchester ManchesterM13 9PL United Kingdom School of Computing Science University of Glasgow GlasgowG12 8RZ United Kingdom School of Data Science and Big Data Nanjing Institute of Technology Nanjing210000 China The Affiliated High School Renmin University of China Beijing100083 China International Department Nanjing Jinling High School Nanjing21000 China

ISBN: (数字)9781510688902

ISBN: (纸本)9781510688896

In this study, we investigated the mechanical performance of lightweight composite slabs, specifically focusing on the application of an integrated face recognition and acne classification pipeline. We propose a comprehensive approach that commences with facial detection, proceeds to the acne region localization, and concludes with acne classification utilizing ResNet18 architecture. The aim is to develop a highly accurate and efficient system for acne classification, particularly beneficial for remote patients or those seeking a convenient self-assessment tool. Our dataset, though limited in size and diversity, is iteratively refined and augmented to mitigate data scarcity issues. Through experimental evaluations, we demonstrate that the combination of ResNet18 and Support Vector Machine (SVM) achieves an accuracy rate of 96%, significantly outperforming traditional manual feature extraction methods. Additionally, visualization techniques such as Principal Component Analysis (PCA) and t-Distributed Stochastic Neighbor Embedding (t-SNE) are employed to gain insights into the data distribution and the effectiveness of automatically extracted features. This study highlights the potential of deep learning for acne detection and underscores the importance of dataset refinement for enhancing model performance. © 2025 SPIE.

关键词： Support vector machines

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Generative Adversarial Networks (GANs) for Artistic Style Transfer in Images

Generative Adversarial Networks (GANs) for Artistic Style Tr...

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2024 International Conference on Distributed Systems, Computer Networks and Cybersecurity, ICDSCNC 2024

作者： Jamalpur, Bhavana Kiruthiga, S. Ranjitha, B. Lefty Joyson, J. Jayaram, K. Soundar, K. Ruba S R University Department of Computer Science and Artificial Intelligence India School of Computing Vel Tech Rangarajan Dr. Sagunthala R&d Institute of Science and Technology Department of Computer Science & Engineering Chennai India Mohan Babu University Department of Mathematics India Jp College of Engineering Department of Computer Science and Engineering Ayikudi India College of Engineering Adoor Department of Computer Science and Engineering Pathanamthitta India Department of Computer Science and Engineering Sivakasi India

ISBN: (纸本)9798350375442

Many in computer vision and computational imaging consider Generative Adversarial Networks (GANs) a big advance. Style transfer converts semantic visual content into creative styles. Recently, GANs for style transfer have gained popularity. Adversarial training of GANs to create realistic counterfeits has yielded encouraging results. High-quality style transfer results have been achieved using this method. Mode collapse is a common problem in classical GANs. This causes unsteady training and poor style transmission. Additionally, the GAN generator only supports one style. Therefore, numerous GANs must be trained to allow users to shift styles. This study examines artistic style transfer in photographs using depth extraction generative adversarial network (DE-GAN). This research aims to improve photo aesthetics by adding famous artworks' distinctive qualities. This study investigates DE-GANs' mechanics and how they modify text while keeping a style's creative qualities. By studying adversarial training, this work hopes to understand how DE-GAN achieves this dual goal. This research analyzes DE-GAN-based style transfer models to evaluate their effectiveness, identify their limits, and recommend areas for improvement. The experiments show that DE-GAN creates realistic and attractive stylized visuals. This study also discusses DE-GAN's practical applications, problems, and future possibilities in artistic style transfer. © 2024 IEEE.

关键词： Generative adversarial networks

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Retraction Note: Enhanced machine learning approach with orthogonal frequency division multiplexing to avoid congestion in wireless communication system

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Optical and Quantum Electronics 2024年第12期56卷 1-2页

作者： Saad Alqahtani, Abdulrahman Mubarakali, Azath Saravanan, M. Babu Changalasetty, Suresh Saroja Thota, Lalitha Parthasarathy, P. Sivakumar, B. College of Computing and Information Technology Department of Computer Science University of Bisha Bisha Kingdom of Saudi Arabia Department of Computer Science King Khalid University Abha Kingdom of Saudi Arabia Department of Computer Science and Engineering KPR Institute of Engineering and Technology Coimbatore India King Khalid University Abha Kingdom of Saudi Arabia Department of CSE Shadan Womens College of Engineering and Technology Hyderabad India Department of Electronics and Communication Engineering CMR Institute of Technology Bengaluru India Department of Computing Technologies School of Computing SRM Institute of Science and Technology Kattankulathur India

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AI algorithms for Autonomous Robotics Navigation 2

AI algorithms for Autonomous Robotics Navigation

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2nd International Conference Computational and Characterization Techniques in engineering and Sciences, IC3TES 2024

作者： Komala, C.R. Raja, J. Marotrao, Sarange Shreepad Neelima, Nalla Sujatha, R. Chidambarathanu, K. East Point College of Engineering and Technology Department of Information Science and Engineering Karnataka Bengaluru560049 India Vel Tech Rangarajan Dr. Sagunthala R&d Institute of Science and Technology Department of Computer Science and Engineering School of Computing Tamil Nadu Chennai600062 India Ajeenkya D Y Patil School of Engineering Department of Mechanical Engineering Maharashtra Pune412105 India R.V.R & J.C. College of Engineering Department of Information Technology Andhra Pradesh Chowdavaram522019 India Subbalakshmi Lakshmipathy College of Science Department of Computer Science Tamil Nadu Madurai625022 India R.M.K. Engineering College Department of Computer Science and Business Systems Tamil Nadu Chennai601206 India

ISBN: (纸本)9798350364699

Robotic systems depend much on autonomous navigation since it allows them to operate on their own in environments constantly changing and lacking clarity. The most evolved artificial intelligence (AI) techniques applied in self-driving robot navigation are briefly reviewed in this work. These approaches call for classical methodologies, machine learning, and deep learning as well. We examine the advantages and drawbacks of numerous approaches applied in route planning, location finding, map creation, obstacle avoidance. We investigate how neural networks, sensor fusion, and reinforcement learning may be applied to provide increased dependability and flexibility for guiding systems. Case examples illustrating the advantages and drawbacks of applying artificial intelligence (AI) in practical contexts including service robots, drones, and self-driving cars abound. Combining modern AI algorithms with conventional approaches indicates that it is feasible to create self-driving robots more dependable and efficient based on the increased tracking performance. © 2024 IEEE.

关键词： Reinforcement learning

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Webcam-Guided Gesture-Controlled Robotic Arm 3

Webcam-Guided Gesture-Controlled Robotic Arm

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3rd IEEE World Conference on Applied Intelligence and computing, AIC 2024

作者： Singh, Kanishk Joshi, Aditya Bhatt, Jaishankar Juyal, Amit Rawat, Manmohan Singh Graphic Era Deemed to Be University Department of Computer Applications Dehradun India Graphic Era Deemed to Be University Department of Computer Science and Engineering Dehradun India Graphic Era Hill University School of Computing Dehradun India Uttarakhand State Council for Science and Technology Dehradun India

ISBN: (纸本)9798350384598

This paper focuses on the design of a gesture-controlled robotic arm that is navigated with the help of a webcam and OpenCV-enabled real-time hand tracking. Embedded with OpenCV's hand tracking module, the system successfully identifies the hand landmarks from the live camera captured hand movement of the user. By the current state of each finger, the state is represented by a string format that includes the symbol and number (such as00010) that is transmitted to an Arduino Uno board, the robotic arm can move its fingers up or down. Control of the flows of the movement of the robotic arm is done using the Arduino Uno whereby the servo motors receive signals to move 0 or 180 degrees. This is mainly due to the efficiency, precision and short latency of the suggested system which makes it novel that gives the user a simple graphics interface to control robotic arms without necessity to know programming or have specific hardware. This technology provides an effective solution in robotic manipulation and presents the enormous potential to enhance HRI in different application areas. © 2024 IEEE.

关键词： Robotic arms

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