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2023 IEEE Renewable Energy and Sustainable E-Mobility Conference, RESEM 2023

作者： Swarnkar, Mamta Chopra, Manoj Dhote, Vijay Nigam, Nishesh Upadhyaya, Kaushiki Prajapati, Manju Ies College of Technology Department of Computer Science & Engineering Bhopal India

ISBN: (纸本)9798350311327

The use of artificial intelligence (AI) has the potential to revolutionize the development and generation of renewable energy. AI can be used in various stages of the renewable energy process, from site selection and design to operation and maintenance. By leveraging AI algorithms, renewable energy developers can optimize the efficiency and output of their renewable energy projects, resulting in increased energy production and cost savings. Additionally, AI can help predict and mitigate equipment failures, improve energy storage solutions, and enhance the integration of renewable energy sources into the grid. Despite the promising potential of AI in renewable energy development, challenges remain, including data availability, technical expertise, and regulatory hurdles. Nevertheless, the integration of AI in renewable energy generation is expected to continue to grow in the coming years, leading to a more sustainable and efficient energy future. © 2023 IEEE.

关键词： Renewable energy resources

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Red Deer Optimization with Artificial Intelligence Enabled Image Captioning System for Visually Impaired People

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computer Systems science & Engineering 2023年第8期46卷 1929-1945页

作者： Anwer Mustafa Hilal Fadwa Alrowais Fahd N.Al-Wesabi Radwa Marzouk Department of Computer and Self Development Preparatory Year DeanshipPrince Sattam bin Abdulaziz UniversityAlKharjSaudi Arabia Department of Computer Sciences College of Computer and Information SciencesPrincess Nourah bint Abdulrahman UniversityP.O.Box 84428Riyadh11671Saudi Arabia Department of Computer Science College of Science&Art at MahayilKing Khalid UniversityMahayilSaudi Arabia Department of Information Systems College of Computer and Information SciencesPrincess Nourah bint Abdulrahman UniversityP.O.Box 84428Riyadh11671Saudi Arabia Department of Mathematics Faculty of ScienceCairo UniversityGiza12613Egypt

The problem of producing a natural language description of an image for describing the visual content has gained more attention in natural language processing(NLP)and computer vision(CV).It can be driven by applications like image retrieval or indexing,virtual assistants,image understanding,and support of visually impaired people(VIP).Though the VIP uses other senses,touch and hearing,for recognizing objects and events,the quality of life of those persons is lower than the standard *** Image captioning generates captions that will be read loudly to the VIP,thereby realizing matters happening around *** article introduces a Red Deer Optimization with Artificial Intelligence Enabled Image Captioning System(RDOAI-ICS)for Visually Impaired *** presented RDOAI-ICS technique aids in generating image captions for *** presented RDOAIICS technique utilizes a neural architectural search network(NASNet)model to produce image ***,the RDOAI-ICS technique uses the radial basis function neural network(RBFNN)method to generate a textual *** enhance the performance of the RDOAI-ICS method,the parameter optimization process takes place using the RDO algorithm for NasNet and the butterfly optimization algorithm(BOA)for the RBFNN model,showing the novelty of the *** experimental evaluation of the RDOAI-ICS method can be tested using a benchmark *** outcomes show the enhancements of the RDOAI-ICS method over other recent Image captioning approaches.

关键词： Machine learning image captioning visually impaired people parameter tuning artificial intelligence metaheuristics

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Resource Scheduling using Enhanced Teaching–Learning-based Optimizer in Cloud Environment 2

Resource Scheduling using Enhanced Teaching–Learning-based ...

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2nd International Conference on Automation, Computing and Renewable Systems, ICACRS 2023

作者： Karunakaran, S. Khan, Nikhat Raza Porkodi, K. Hemalatha, S.M. Raj Balaji, D. Srimathi, S. Department of Computer Technology Kongu Engineering College Perundurai Erode India Department of Computer Science & engineering IES College of Technology Bhopal India Computer Science Rathinam College of Arts and Science Coimbatore India AI&DS M Kumarasamy college of engineering India Department of Computer Science Rathinam College of Arts and Science Coimbatore India Department of Bio Technology Saveetha School of Engineering SIMATS Tamilnadu Chennai India

ISBN: (纸本)9798350340235

Resource Scheduling (RS) in cloud environment is a significant task targetedat effective management andallocation of cloud resources in order to meet the changing user demands and applications while maximizing the system efficiency and utilization. Effective RS has gainedmore importance in the cloud environment for meeting the dynamic user demands, enhancing overall system performance, and optimizing resource utilization. In this research study, metaheuristicoptimizing techniques have proved efficiency in tackling the RS convolution issues. This study introduces a new Resource Scheduling using Enhanced Teaching–Learning-Based Optimizer (RS-ELTBO) algorithm in Cloud Environment. The primary goal of the RS -ELTBO technique lies in the process of optimizing resource allocationand performance metrics. Applying ETLBO for resource scheduling in cloud environments can lead to improved resource utilization, reduced costs, and enhanced overall performance. The ETLBO algorithm makes use of an objective function to determine the fitness of considerable resource allocation andemploys a teaching phase, learning phase, and exploration strategy to iteratively refine the solutions. The performance study of the RS -ELTBO system was tested on various aspects. The achieved values stated that the RS-ELTBO system reached improved scheduling results over other approaches. © 2023 IEEE.

关键词： Cloud Computing Metaheuristics Resource Scheduling Resource Utilization TLBO Algorithm

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Chasing Convex Functions with Long-term Constraints 41

Chasing Convex Functions with Long-term Constraints

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41st International Conference on Machine Learning, ICML 2024

作者： Lechowicz, Adam Christianson, Nicolas Sun, Bo Bashir, Noman Hajiesmaili, Mohammad Wierman, Adam Shenoy, Prashant Manning College of Information and Computer Sciences University of Massachusetts Amherst United States California Institute of Technology United States Cheriton School of Computer Science University of Waterloo ON Canada Computer Science & Artificial Intelligence Laboratory Massachusetts Institute of Technology United States

We introduce and study a family of online metric problems with long-term constraints. In these problems, an online player makes decisions xt in a metric space (X, d) to simultaneously minimize their hitting cost ft(xt) and switching cost as determined by the metric. Over the time horizon T, the player must satisfy a long-term demand constraint -t c(xt) ≥ 1, where c(xt) denotes the fraction of demand satisfied at time t. Such problems can find a wide array of applications to online resource allocation in sustainable energy/computing systems. We devise optimal competitive and learning-augmented algorithms for the case of bounded hitting cost gradients and weighted 1 metrics, and further show that our proposed algorithms perform well in numerical experiments. Copyright 2024 by the author(s)

关键词： Resource allocation

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Honey Badger Optimization Algorithm-Based RSU Deployment for Improving Network Coverage in VANETs 1

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6th International Conference on Microelectronics and Telecommunication Engineering, ICMETE 2022

作者： Sengathir, J. Deva Priya, M. Christy Jeba Malar, A. Sam Peter, S. Department of Information Technology CVR College of Engineering Mangalpally Vastunagar Telangana Hyderabad India Department of Computer Science and Engineering Sri Eshwar College of Engineering Tamilnadu Coimbatore India Department of Information Technology Sri Krishna College of Technology Tamilnadu Coimbatore India Department of Computer Science and Engineering Sri Krishna College of Technology Tamilnadu Coimbatore India

ISBN: (数字)9789811995125

ISBN: (纸本)9789811995118

Road Side Units (RSUs) installed in roadside, and intersections in Vehicular Ad hoc NETwork (VANET) play an anchor role in aggregating and exploring intelligent data associated with vehicle traffic. These RSUs help in exchanging information among vehicles and obtaining early warning messages to ensure safety driving of vehicles. However, determining the number of RSUs and position over which they must be deployed are vital due to high cost incurred in implementing and maintaining them in the network. This problem of determining the number of RSUs along with their positions of deployment is a multi-objective problem, since it necessitates maximized coverage of network with minimized number of RSUs in the network. In this paper, Honey Badger Optimization Algorithm-based RSU Deployment (HBOA-RSUD) scheme is proposed with a multi-objective fitness function for improving network coverage in VANETs. This HBOA-RSUD initially establishes a static model for determining the complexity involved during the deployment of RSUs in the urban road. Then, a multi-objective HBOA algorithm with sigmoid function is applied over individual discrete values of fitness for identifying the position of RSU deployment. It determines the new positions of RSUs for enhancing the performance of the population and convergence speed. Experimental results of HBOA-RSUD confirm a maximized throughput by 21.38%, maximized network coverage by 28.95%, minimized delay by 19.42% and reduced energy consumption by 21.98% for varying number of RSUs in contrast to the existing intelligent RSU deployment approaches. © 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Vehicular ad hoc networks

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Dynamic Insights: Well-being Activity Infused Fine-Tuning of Large Language Models

Dynamic Insights: Well-being Activity Infused Fine-Tuning of...

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2024 IEEE International Conference on Big Data, BigData 2024

作者： Mohammed, Hamza Haruna Kiss, Gabriel Serrano, J. Artur Lindseth, Frank Norwegian University of Science and Technology Faculty of Information Technology and Electrical Engineering Department of Computer Science Trondheim Norway Norwegian University of Science and Technology Faculty of Medicine and Health Sciences Department of Neuromedicine and Movement Science Trondheim Norway

ISBN: (纸本)9798350362480

The fast development of Large Language Models (LLMs) has made transformative applications in several fields attainable or possible. However, language models must often be more effective in specialized areas, especially health and prevention. This paper presents a novel method for fine-tuning LLMs using activity-related data to optimize them for the applicability of such models in health and wellbeing applications. We thus empirically evaluate this approach on the Cardiac Exercise Research corpus for fine-tuning the LLMs. The fine-tuning utilizes Quantized Low-Rank Adaptation (QLoRA) to ensure the models' size remains small while maintaining high performance and accuracy to keep the semantic understanding and relevance with health-related queries. Our results in answering domain-related prompts showed an improved user satisfaction and sentiment scores, providing strong confidence in the method's effectiveness. This study highlights the potential of domain-specific LLMs in advancing personalized healthcare. It instills a sense of optimism about the future of healthcare and the seamless integration of AI within health prevention and well-being domains. © 2024 IEEE.

关键词： Semantics

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Lighting the Smart Way: A Technological Odyssey in Urban Street Illumination 10

Lighting the Smart Way: A Technological Odyssey in Urban Str...

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10th International Conference on Advanced Computing and Communication Systems, ICACCS 2024

作者： Vinu, M.S. Priya, P Kanaga Thilakaveni, P. Hermina, J. M. Ashforn Prashanthini, K. Misba, M. Nehru Institute of Engineering and Technology Department of Computer Science and Engineering Coimbatore India Kpr Institute of Engineering and Technology Department of Computer Science and Engineering Coimbatore India Psg Polytechnic College Department of Computer Engineering Coimbatore India Bannari Amman Institute of Technology Department of Artificial Intelligence and Data Science Erode India Sri Ramakrishna Engineering College Department of Robotics and Automation Coimbatore India Bethlahem Institute of Engineering Department of Computer Science and Engineering Nagercoil Karungal India

ISBN: (纸本)9798350384369

The development of intelligent street light systems has ushered in a new era of efficiency and sustainability in urban infrastructure. The proposed work studies the integration of modern sensors and Internet of Things (IoT) technologies to increase the functionality of street lights. The major components include Light Dependent Resistors (LDR) for automatic light management, an MQ135 sensor for air quality monitoring, and a fog detection system employing a laser module and LDR. These sensors are integrated with the ESP8266, providing cloud integration for real-time data visualization and monitoring. Additionally, solar panels are employed to enhance energy efficiency. The proposed methodology digs into the precise implementation, data analysis, and the potential of this system to alter urban lighting infrastructure © 2024 IEEE.

关键词： Air quality

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Leveraging Hybrid LSTM-Attention-CatBoost Model for Complex Energy Behavior Forecasting 3

Leveraging Hybrid LSTM-Attention-CatBoost Model for Complex ...

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3rd International Conference on Smart Grids and Energy Systems, SGES 2024

作者： Lu, Lu Shen, Zhengdong Li, Shuting Wang, Weili Faculty of Science and Technology Beijing Normal University·Hong Kong Baptist University United International College Zhuhai China Department of Electronic and Information Engineering Nanjing University of Information Science and Technology Nanjing China School of Computer Science and Engineering Faculty of Innovation Engineering Macau University of Science and Technology China School of Computer Science South China Normal University Guangzhou China

ISBN: (纸本)9798331539634

We present an advanced energy prediction model that addresses energy imbalance by integrating Long Short-Term Memory (LSTM) with Attention Mechanism for time-series analysis and categorical boosting (CatBoost) for categorical feature optimization. This model accurately predicts the complex energy behaviors of prosumers who both generate and consume energy. Simulation results show that our model outperforms traditional methods with the lowest Mean Absolute Error (MAE) of 48.514. By achieving such a high level of accuracy, this model improves grid operation efficiency, offers a practical solution for smart grid management. © 2024 IEEE.

关键词： Attention Mechanism CatBoost Data Processing Energy Forecasting LSTM

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A semi-supervised deep neuro-fuzzy iterative learning system for automatic segmentation of hippocampus brain MRI

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Mathematical Biosciences and Engineering 2024年第12期21卷 7830-7853页

作者： Nisha, M. Kannan, T. Sivasankari, K. Department of Computer Science and Engineering Akshaya College of Engineering and Technology Tamil Nadu Coimbatore India Department of Mechanical Engineering Amrita College of Engineering and Technology Tamil Nadu Nagercoil India Department of Electronics and Engineering Akshaya College of Engineering and Technology Tamil Nadu Coimbatore India

The hippocampus is a small, yet intricate seahorse-shaped tiny structure located deep within the brain’s medial temporal lobe. It is a crucial component of the limbic system, which is responsible for regulating emotions, memory, and spatial navigation. This research focuses on automatic hippocampus segmentation from Magnetic Resonance (MR) images of a human head with high accuracy and fewer false positive and false negative rates. This segmentation technique is significantly faster than the manual segmentation methods used in clinics. Unlike the existing approaches such as UNet and Convolutional Neural Networks (CNN), the proposed algorithm generates an image that is similar to a real image by learning the distribution much more quickly by the semi-supervised iterative learning algorithm of the Deep Neuro-Fuzzy (DNF) technique. To assess its effectiveness, the proposed segmentation technique was evaluated on a large dataset of 18,900 images from Kaggle, and the results were compared with those of existing methods. Based on the analysis of results reported in the experimental section, the proposed scheme in the Semi-Supervised Deep Neuro-Fuzzy Iterative Learning System (SS-DNFIL) achieved a 0.97 Dice coefficient, a 0.93 Jaccard coefficient, a 0.95 sensitivity (true positive rate), a 0.97 specificity (true negative rate), a false positive value of 0.09 and a 0.08 false negative value when compared to existing approaches. Thus, the proposed segmentation techniques outperform the existing techniques and produce the desired result so that an accurate diagnosis is made at the earliest stage to save human lives and to increase their life span. © 2024 the Author(s),

关键词： Convolutional neural networks

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A New Chaotic-Based Analysis of Data Encryption and Decryption 18th

A New Chaotic-Based Analysis of Data Encryption and Decrypti...

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18th International Conference on Data science and Artificial Intelligence, ICDSAI 2022

作者： Johora, Fatema Tuj Alamin-Ul-Islam Yesmin, Farzana Rahman, Md. Mosfikur Department of Computer Science and Engineering Green University of Bangladesh Dhaka Bangladesh Department of Computer Science and Engineering Faculty of Science and Information Technology Daffodil International University Dhaka Bangladesh

ISBN: (纸本)9783031161773

Because the amount of exchange hypersensitive data via the Internet is growing at an exponential rate, network and data security have recently been the most pressing worry. On the subject of data security, many approaches are available, including "cryptography." When data is sent from the sender to the receiver, it is encrypted using an encryption method, and when it is received by the receiver, it is decrypted using a decryption algorithm to see the exact and true data that was sent by the sender previously. Data encryption can be done in a number of ways. With algorithms like AES, DES, and RSA for data encryption and decryption, this research proposes a novel CRSA (chaotic random seed algorithm) technique. CRSA was also compared to other algorithms to see how well they performed. The experimental data presented in this work are used to examine those algorithms as well as our new CRSA algorithm. Cryptography, encryption, decryption, random reed, millisecond, and data security are all terms used in this paper. © 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： Cryptography

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