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2nd International Conference on Distributed Computing and Optimization Techniques, ICDCOT 2024

作者： Kelin, A. R. Darshika Sudhakar, K. Surekha, Pothireddy Palanivel, R. Al-Jawahry, Hassan M. Karunya Institute of Technology and Sciences Data Science and Cyber Security Karunya Nagar Coimbatore India Department Electronics and Communication Engineering Telangana Hyderabad India Griet Department of Computer Science and Engineering Telangana Hyderabad India Nitte Meenakshi Institute of Technology Department of Ai and Ds Bengaluru India The Islamic University Najaf Iraq

ISBN: (纸本)9798350382952

In general, high power electronic converters such as Multilevel Inverters (mlI), that find their use in many applications, create difficulties of augmented switch count, capacitor flow current and reverse current flowing from inductive loads. above problems, in turn, result in faults, without which mlI reliability is unattainable. Therefore, fault classification and tolerance strategies are of paramount contribute to keeping reliability of inverters. Herein we present a deep learning (DL)-based fault classification approach for 15-level CHB Multi-level Inverter. proposed system includes pre-processing based on Adaptive Hilbert-Huang filtering, segmentation by a Partial Differential Equation (PDE), feature extraction utilizing Scale-Invariant Feature Transform (SIFT), and fault signal classification being performed by a Convolutional Neural Network (CNN) trained with a Honey Badger Optimization Algorithm (HBOA). MATLAB was applied to our method of identifying a set of four complex fault types for purpose of measurement. results of HBOA-optimized CNN have demonstrated highest accuracy rate of 99.91%. © 2024 IEEE.

关键词： Electric inverters

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An Efficient Method for Smart Parking System Using Adaptive CNN Ensemble Approach

An Efficient Method for Smart Parking System Using Adaptive ...

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International Conference on Inventive Research in Computing Applications (ICIRCA)

作者： R. Ganesh B. Srinivasa Rao Sreemayi Goriparthi Priyank Udaybhai Trivedi Neerav Nishant P Naresh Kumar Reddy Computer Science and Engineering (AI&ML) Institute of Aeronautical Engineering Hyderabad Telangana India Computer Science and Engineering Gokaraju Rangaraju Institute of Engineering and Technology Hyderabad Telangana India Department of Computer Science and Engineering (AI&ML) Guru Nanak Institute of Technology Ibrahimpatnam Telanagana India Institute of Infrastructure Technology Research and Management Maninagar Ahmedabad Gujarat India Department of Computer Science and Engineering School of Engineering Babu Banarasi Das University Lucknow Uttar Pradesh India Department of Freshman Mohan Babu University Tirupathi India

Every big city across the world has a similar parking problem: it's expensive and hard to find. There is an immediate need for creative parking solutions to fulfill the anticipated parking demand. Using encrypted wireless networks and sensor data, this article suggests an advanced new parking system called Smart Parking. Smart Parking provides a safe and smart parking environment for its customers. The allocated parking spot is secure and discrete. The parking guidance system works well and is easy to use. Every step of the parking procedure will be offered continuously. Smart parking is an advanced parking management system from the perspective of the administration. The suggested method involves a series of steps, including preprocessing, DHE for contrast enhancement, CHT for feature extraction, and Adaptive CNN Ensemble for model training. The proposed method has better results than the other two models (CNN and SVM).

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Design of Dual-Stack, Tunneling, and Translation Approaches for Blockchain-IPv6

Design of Dual-Stack, Tunneling, and Translation Approaches ...

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International Conference on Intelligent Systems and Sustainable Computing, ICISSC 2021

作者： Mantena, Srihari Varma Jayasundar, S. Sharma, Dilip Kumar Veerappan, J. Bennet, M. Anto Sengan, Sudhakar Rangasam, Rajasekar Department of Computer Science and Engineering SRKR Engineering College Bhimavaram India Department of Computer Science and Engineering Idhaya Engineering College for Women Tamil Nadu Chinnasalem India Department of Mathematics Jaypee University of Engineering and Technology Madhya Pradesh Guna India Department of Electronics and Communication Engineering Saveetha School of Engineering Tamil Nadu Chennai India Department of Electronics and Communication Engineering Vel Tech Rangarajan Dr Sagunthala R&D Institute of Science and Technology Tamil Nadu Chennai India Department of Computer Science and Engineering PSN College of Engineering and Technology Tamil Nadu Tirunelveli India Department of Computer Science and Engineering-AI & ML School Engineering Malla Reddy University Hyderabad India

ISBN: (纸本)9789811900105

The most current edition of the Internet protocol is IPv6 (IP). Because once compared to IPv4 and IPv6 has a series of benefits of more efficient packet processing. With each day, IPv4 addresses should become obsolete. Migration from IPv4 to IPv6 is now required. This study uses tunneling, dual-stack, and translation methods to transition from IPv4 to IPv6. Using such techniques, we could send a limited time from an IPv4 user to an IPv6 client. IPv4 packets on an IPv4 network and IPv6 packets on an IPv6 network are transmitted using a dual-stacked method. The proposed tunneling method uses the same channel for data transfer in IPv6 and IPv4 packets. For IPv4 to IPv6 transmission, a translation method has also been implemented. The dual-stack, tunneling, and translation method has high inter-communication versatility, efficiency, and consistency. Only with a wire shark’s help, the procedure of this channel be examined (a packet analyzer). © 2022, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Blockchain

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Improved Sail Fish Optimizer for Energy Efficient Routing in Wireless Sensor Network

Improved Sail Fish Optimizer for Energy Efficient Routing in...

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2023 International Conference on Integrated Intelligence and Communication Systems, ICIICS 2023

作者： Aluvala, Srinivas Reddy, Geetha Al-Jawahry, Hassan M. Ramadan, Ghazi Mohamad Pareek, Piyush Kumar SR University School of Computer Science & Artificial Intelligence Warangal India CSE Malla Reddy Engineering College for Women Hyderabad India The Islamic University Najaf Iraq Ahl Al Bayt University College of Mlt Karbala Iraq Nitte Meenakshi Institute of Technology Department of Ai and Ml Bengaluru India

ISBN: (纸本)9798350315455

A Wireless Sensor Network (WSN) is a collection of nodes fitted with small sensors and transceiver elements that are utilized for sensing, tracking, and data collection in a variety of situations. Sensor energy is considered a significant resource due to the difficulty in providing battery a backup to sensors when they are deployed in an uncontrolled or wild area. Integrating clustering & routing strategies are the optimal techniques for saving sensor node energy. In this research, the Improved Sail Fish Optimizer (ISFO) is proposed for the energy efficient routing path selection in WSN to improve the network lifetime. The ISFO algorithm is utilized to identify an accurate routing to sink node for data transmission. The performance of proposed algorithm is evaluated with performance metrics of Packet Delivery Ratio (PDR), Throughput, energy consumption and delay. The proposed algorithm attained high PDR of 96%, high throughput of 97%, less energy consumption of 0.4J and less delay of 3ms which is superior than other existing algorithms. © 2023 IEEE.

关键词： Clustering Improved Sail Fish Optimizer Routing protocol Sensor Nodes and Wireless Sensor Network

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LEARNING CONTINUOUS NORMALIZING FLOWS FOR FASTER CONVERGENCE TO TARGET DISTRIBUTION VIA ASCENT REGULARIZATIONS 11

LEARNING CONTINUOUS NORMALIZING FLOWS FOR FASTER CONVERGENCE...

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11th International Conference on Learning Representations, ICLR 2023

作者： Chen, Shuangshuang Ding, Sihao Karayiannidis, Yiannis Björkman, Mårten School of Electrical Engineering and Computer Science Royal Institute of Technology Sweden AI Lab Volvo Car Corporation Sweden Department of Automatic Control Lund University Sweden

Normalizing flows (NFs) have been shown to be advantageous in modeling complex distributions and improving sampling efficiency for unbiased *** this work, we propose a new class of continuous NFs, ascent continuous normalizing flows (ACNFs), that makes a base distribution converge faster to a target *** solving such a flow is non-trivial and barely possible, we propose a practical implementation to learn flexibly parametric ACNFs via ascent regularization and apply it in two learning cases: maximum likelihood learning for density estimation and minimizing reverse KL divergence for unbiased sampling and variational *** learned ACNFs demonstrate faster convergence towards the target distributions, therefore, achieving better density estimations, unbiased sampling and variational approximation at lower computational ***, the flows show to stabilize themselves to mitigate performance deterioration and are less sensitive to the choice of training flow length T. © 2023 11th International Conference on Learning Representations, ICLR 2023. All rights reserved.

关键词： Maximum likelihood estimation

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Machine Learning-based Secure Communication Framework for Connected Autonomous Vehicles

Machine Learning-based Secure Communication Framework for Co...

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2023 IEEE Globecom Workshops, GC Wkshps 2023

作者： Jivani, Dirgha Gajjar, Harshal Gupta, Rajesh Jadav, Nilesh Kumar Tanwar, Sudeep Bhatia, Jitendra Trivedi, Harshal Institute of Technology Nirma University Department of Computer Science and Engineering Gujarat Ahmedabad India Tusker Ai Pvt. Ltd It Support and Services Ahmedabad380015 India

ISBN: (纸本)9798350370218

As connected autonomous vehicles (CAVs) gain popularity, new challenges emerge in ensuring secure network connectivity. In this study, we present a machine learning (ml)-based secure communication system for autonomous vehicles. Our main goal is to distinguish between harmful and non-malicious message transfers in order to strengthen communication channel security. We used a unique framework to bin float values to make them more suitable for classification. We then fed these values into a classifier model. The framework utilizes a dataset comprising 63 features and 450,000 records. Among various ml models evaluated, random forest (RF) emerged superior in classifying CAVs messages. The RF model, trained on this dataset, achieved an impressive accuracy of 94.26%. This underscores the potential of ml techniques, especially RF, in fortifying the security of networked autonomous vehicles. This study contributes to ongoing efforts to establish secure and reliable communication systems for CAV networks. © 2023 IEEE.

关键词： Connected Autonomous Vehicles Machine Learning Secure Message Exchange Security

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Attendance System via Internet of Things, Blockchain and Artificial Intelligence Technology: Literature Review 37th

Attendance System via Internet of Things, Blockchain and Art...

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37th International Conference on Advanced Information Networking and Applications, aiNA 2023

作者： Abidemi, Sarumi Usman Mubarak, Auwalu Saleh Akanni, Olukayode Ameen, Zubaida Said Cacciagrano, Diletta Al-turjman, Fadi Artifiical Intelligence Engineering Department AI and Robotics Institue Near East University Mersin 10 Nicosia Turkey Computer Science Division School of Science and Technology Camerino University Camerino62032 Italy Research Center for AI and IoT Faculty of Engineering University of Kyrenia Mersin 10 Kyrenia Turkey

ISBN: (纸本)9783031286933

Many researchers have make efforts on creating several attendance systems to keep track of student attendance in school which is also part of academic curriculum that will have great impact in student academic performance. There have been different technologies such as RFID, face recognition, biometric, web-based attendance system etc. To capture attendance in a way that’s better than the traditional method of pen and paper or roll call system. Making this attendance record available on blockchain network and applying artificial intelligence algorithm for prediction or classification in correlation with the data captured from student attendance purpose. Several articles, papers and journals has been reviewed from different research plethora of research publishers’ database through internet such as Google scholar, IEEExplore, Springerslink etc. This will make it easy for comparing several attendance systems with respect to internet of things, blockchain and artificial intelligence to address the research gap in this aspect. © 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： Blockchain

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Integrating Recurrent Neural Networks in Virtual Reality-Based Learning Environments for Dynamic Educational Content Generation 13

Integrating Recurrent Neural Networks in Virtual Reality-Bas...

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13th IEEE International Conference on Communication Systems and Network Technologies, CSNT 2024

作者： Paliwal, Sachit Bhadauria, Shraddha Sasikala, P. Ramya, M. Bala, Kiran B. Amity University Online Uttar Pradesh Noida India Gla University Online Uttar Pradesh Mathura India Department of Computer Science Bangalore India Panimalar Engineering College Department of It Chennai India K. Ramakrishnan College of Engineering Department of Ai & Ds Trichy India

ISBN: (纸本)9798350305463

The integration of Recurrent Neural Networks (RNNs) in virtual reality (VR)-based learning environments represents a cutting-edge approach to revolutionizing educational content generation The study begins by establishing the need for personalized and responsive educational content in the context of evolving learning paradigms. RNNs, known for their ability to capture sequential dependencies and patterns, are introduced as a powerful tool for modeling dynamic aspects of educational content. The integration of RNNs in VR environments is motivated by the desire to create interactive and evolving learning scenarios that adapt to individual learner preferences and progress. The research methodology involves the development of a prototype VR-based learning environment that incorporates RNNs for real-time content generation. The RNNs are trained on diverse datasets, allowing the system to learn and adapt to the varying needs and learning styles of users. The VR setting provides an immersive and engaging platform for learners, fostering a deeper understanding of complex concepts through interactive experiences. Key outcomes of the study include insights into the effectiveness of RNNs in dynamically generating educational content. The accuracy of the proposed model is 98.32% which is higher than existing methods like CNN (Convolutional Neural Network), CNN-LSTM (Long shirt Term Memory, GRU (Gated Recurrent Units). © 2024 IEEE.

关键词： Virtual reality

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Thinking Machines: A Survey of LLM based Reasoning Strategies

arXiv

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arXiv 2025年

作者： Bandyopadhyay, Dibyanayan Bhattacharjee, Soham Ekbal, Asif Department of Computer Science and Engineering IIT Patna India School of AI and Data Science IIT Jodhpur India

Large Language Models (LLMs) are highly proficient in language-based tasks. Their language capabilities have positioned them at the forefront of the future AGI (Artificial General Intelligence) race. However, on closer inspection, Valmeekam et al. (2024);Zečević et al. (2023);Wu et al. (2024) highlight a significant gap between their language proficiency and reasoning abilities. Reasoning in LLMs and Vision Language Models (VLMs) aims to bridge this gap by enabling these models to think and re-evaluate their actions and responses. Reasoning is an essential capability for complex problem-solving and a necessary step toward establishing trust in Artificial Intelligence (ai). This will make ai suitable for deployment in sensitive domains, such as healthcare, banking, law, defense, security etc. In recent times, with the advent of powerful reasoning models like Openai O1 and DeepSeek R1, reasoning endowment has become a critical research topic in LLMs. In this paper, we provide a detailed overview and comparison of existing reasoning techniques and present a systematic survey of reasoning-imbued language models. We also study current challenges and present our findings. © 2025, CC BY.

关键词： Problem solving

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Deep CNN based brain tumor detection in intelligent systems

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International Journal of Intelligent Networks 2024年第1期5卷 30-37页

作者： Gupta, Brij B. Gaurav, Akshat Arya, Varsha International Center for AI and Cyber Security Research and Innovations & Department of Computer Science and Information Engineering Asia University Taiwan Taichung413 Taiwan Ronin Institute MontclairNJ United States Department of Electrical and Computer Engineering Lebanese American University Beirut1102 Lebanon Symbiosis International University Pune India School of Computing Skyline University College P.O. Box 1797 Sharjah United Arab Emirates Department of Business Administration Asia University Taiwan Taiwan Dehradun India

The early detection of brain tumor is crucial for effective treatment and improved patient prognosis in Industrial Information Systems. This research introduces a novel computational model employing a three-layer Convolutional Neural Network (CNN) for the identification of brain tumors in Industrial Information Systems. Leveraging advanced computational techniques, this proposed model can autonomously detect intricate patterns and features from medical imaging data, resulting in more accurate and expedited diagnoses. With an impressive 90 % precision rate, our model demonstrates the potential to serve as a valuable tool for medical professionals working in the field of neuroimaging. By presenting a dependable and precise computational model, this study contributes to the advancement of brain tumor identification within the domain of medical imaging. We anticipate that our methodology will aid healthcare providers in making more accurate diagnoses, thereby leading to enhanced patient outcomes. Potential avenues for future research encompass refining the model's fundamental architecture and exploring real-time therapeutic applications. © 2024 The Authors

关键词： Deep learning

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