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Sustainable Expert Systems (ICSES), International Conference on

作者： Garima Jain Amita Shukla Nitesh Kumar Bairwa Anamika Chaudhary Ashish Patel Ankush Jain Department of Computer Science and Buisness Systems Noida Institute of Engineering and Technology Greater Noida India Department of Artificial Intelligence Noida Institute of Engineering and Technology Greater Noida India Department of Data Science Noida Institute of Engineering and Technology Greater Noida India Department of Artificial Intelligence and Machine Learning Dronacharya Group of Institutions Greater Noida India Department of Computer Science and Enginnering Netaji Subhash University of Technology Delhi India

ISBN: (数字)9798331540364

ISBN: (纸本)9798331540371

This research presents the development and evaluation of SPEAR, an advanced voice-activated personal desktop assistant designed to address challenges in existing virtual assistant technology, such as limited language support, low recognition accuracy, and inadequate task automation. Despite recent advancements in Natural Language Processing (NLP) and machine learning, implementation in current systems remains subpar. SPEAR achieves an 88% overall command execution success rate, with a speech recognition accuracy of 92% and an average response time of 1.8 seconds, demonstrating efficiency in real-time interactions. Developed using Python and machine learning libraries, SPEAR supports 15 voice commands across multiple platforms, excelling in note creation (95% success), web searching (94%), and information retrieval (92%). Its natural language understanding score of 0.86 and context retention rate of 75% highlight its conversational capabilities. While the assistant performs well in messaging functionality (85% success) and currently supports three languages, expanding its multilingual capabilities is essential for broader user engagement. Metrics indicate a satisfaction score of 8.2/10 and an average of 12 daily interactions per user, showcasing its practical utility. Additionally, the system's text-to-speech quality (8.5/10) and error recovery rate (82 %) affirm its robustness. These findings contribute valuable insights for future research, focusing on enhancing multilingual support, broadening task categories, and improving context retention, ultimately positioning SPEAR to rival established industry leaders by offering a comprehensive suite of services.

关键词： Accuracy Personal voice assistants machine learning Information retrieval Natural language processing Real-time systems Robustness Text to speech Time factors Web search

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Exploring an IOT-Based Smart Charging Algorithm for Electric Vehicles Considering Multiprocessing 4

Exploring an IOT-Based Smart Charging Algorithm for Electric...

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4th International Conference on Distributed Sensing and Intelligent Systems, ICDSIS 2023

作者： William, P. Shashikala, K.S. Khalaf, Osamah Ibrahim Jebril, Iqbal H. Zaqaibeh, Belal Algburi, Sameer Department of Information Technology Sanjivani College of Engineering Savitribai Phule Pune University Pune India Department of Artificial intelligence and Machine Learning New Horizon College of Engineering Bengaluru560103 India Department of Solar Al-Nahrain Research Center for Renewable Energy Al-Nahrain University Jadriya Baghdad Iraq Al-Zaytoonah University of Jordan Queen Alia Airport St. 594 Amman11942 Jordan Faculty of Science and Information Technology Jadara University Jordan Al-Kitab University College of Engineering Techniques Kirkuk Iraq

ISBN: (纸本)9781839539954

The transportation industry as a whole is now undergoing a transition towards more environmentally friendly business practices, and electric vehicles (EVs) are an essential component of this industry-wide shift. An intelligent charging algorithm that makes use of multiprocessing strategies and is based on the Internet of Things (IoT) is now the subject of study as a potential solution to the problems associated with inefficient and wasteful charging of electric vehicles (EVs). The development and evaluation of a smart charging algorithm for electric vehicles using the Internet of Things (IoT) is going to be the focus of this project. This integration makes it possible to create real-time communication between electric cars (EVs), charging stations, and the grid, which is necessary in order to offer charging that is intelligent and adaptable. The growth of the electric vehicle sector will only increase the need of coming up with innovative answers to problems. The purpose of this study is to evaluate the effectiveness of the algorithm and to determine the extent to which it has the potential to revolutionize the process of charging electric cars while also improving grid management, energy efficiency, and the prospects for a sustainable future. © The Institution of engineering & technology 2023.

关键词： Charging stations

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Research on Majorization Algorithm and Application of Geotechnical engineering Structure Based on Granule Swarm Majorization 2

Research on Majorization Algorithm and Application of Geotec...

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2nd International Conference on Smart Technologies for Smart Nation, SmartTechCon 2023

作者： Gnana Soundari, A. Al-Fatlawy, Mohammed Hussien Omkar, Jamvant Arun Balaji, N. Suseela, D. Asha, V. Department of Computer Science and Engineering Saveetha School of Engineering SIMATS Tamil Nadu Chennai India College of Technical Engineering The Islamic University Najaf Iraq Department of Electronics & Communication IES College Of Technology MP Bhopal462044 India New Prince Shri Bhavani College of Engineering and Technology Tamil Nadu India Department of Artificial Intelligence and Machine Learning Bannari Amman Institute of Technology Tamil Nadu Sathyamangalam India Department of Master of Computer Applications New Horizon College of Engineering Bellandur Post Bengaluru560103 India

ISBN: (纸本)9798350305418

The traditional majorization design method makes too many restrictions on the objective function to be optimized and its constraints, which brings a lot of inconvenience to solving majorization problems in practical projects. A new stochastic Optimization algorithm based on swarm intelligence - granule swarm algorithm (PSO) was put forward in, and it has been widely concerned by researchers. When the algorithm is initialized, the granules are randomly divided into several sub-granule groups, each sub-granule group evolves independently according to a given strategy, and random migration and adaptive mutation of granules are carried out in the specified period of evolution to maintain the diversity of the entire population., to avoid premature convergence. The inverse analysis of geotechnical engineering majorization is essentially a typical majorization problem of complex nonlinear functions. The use of global majorization algorithm is an ideal way to solve this problem. low productivity. The new algorithm is applied to the elastic-plastic parameter inversion of geotechnical materials. The results show that compared with the conventional granule swarm majorization algorithm, the improved algorithm significantly improves the search efficiency of parameters, and the results that meet the accuracy requirements can be obtained with less iterations, which reduces the amount of calculation of elastic-plastic back analysis of geotechnical engineering. It is a feasible parameter inversion method. © 2023 IEEE.

关键词： Efficiency Geotechnical engineering Granule Swarm Particle swarm majorization Structural majorization

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Investigation of Red Fox Algorithm for Solving Task Assignment Problem in Heterogeneous Unmanned Aerial Vehicle Swarm

Investigation of Red Fox Algorithm for Solving Task Assignme...

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artificial intelligence For Internet of Things (AIIoT), International Conference on

作者： K. Manaswitha Titus Issac Salaja Silas J Sebastian Terance Division of Data Science and Cyber Security Karunya Institute of Technology and Sciences Coimbatore India Division of Computer Science and Engineering Karunya Institute of Technology and Sciences Coimbatore India Division of Artificial Intelligence and Machine Learning Karunya Institute of Technology and Sciences Coimbatore India

ISBN: (数字)9798350372120

ISBN: (纸本)9798350372137

In recent years, there have been significant advancements in Unmanned Aerial Vehicles (UAVs), leading to their integration into everyday life. UAVs are constrained by limited energy, communication, and localization. The various challenges in UAV research include localization, routing, navigation, and task assignment. Recently, UAV swarms have been employed across a wide range of applications. This study focuses on solving task assignment problems within UAV swarms, considering the unique multi-objective resource constraints of UAVs. The work explores the feasibility of adopting the Red Fox Algorithm to address the task assignment problem in UAV swarms. The investigation examines two primary input parameters: Task Energy Demand and distance. The evaluation includes energy utilization and shortest distance as performance metrics. Notably, the proposed approach outperforms the Particle Swarm Optimization Algorithm (PSO), resulting in a 17.47% lower energy utilization while maintaining a 17.83% reduction in the shortest distance travelled. By leveraging the Red Fox Algorithm, the work aims to enhance the efficiency and effectiveness of task allocation in UAV swarms, contributing to their broader adoption across various applications.

关键词： Location awareness Measurement Navigation Autonomous aerial vehicles Routing Resource management Internet of Things

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Innovative Horizons in Agricultural technology with TSA Based StrawberrySqueezeNet Classification Model

Innovative Horizons in Agricultural Technology with TSA Base...

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Data Science and Network Security (ICDSNS), International Conference on

作者： H M Manoj Priyanka Pramod Pawar R Krupa Piyush Kumar Pareek Deepak Kumar G B Vindhya Computer Science and Engineering BMS Institute of Technology and Management Bengaluru India PhD Information Technology University of the Cumberlands Mechanical Engineering Nitte Meenakshi Institute of Technology Bengaluru India Department of Artificial Intelligence & Machine Learning Nitte Meenakshi Institute of Technology Bengaluru India Channabasaveshwara Institute of technology Gubbi Tumkur

ISBN: (数字)9798350373110

ISBN: (纸本)9798350373127

A fruit plant with sufficient value is the strawberry plant. High concentrations of fibre, antioxidants, potassium, vitamin C, and folic acid can be found in strawberry fruit. Because bacteria, pests, and microbes frequently impact strawberry plants, cultivating strawberries is a simple operation. Digital image processing will be used to introduce strawberry disease initially and limit its spread in strawberry plants. To improve strawberry disease diagnosis, a multi-pronged strategy was used, starting with the application of a carefully selected dataset. The dataset was then enriched and diversified using data augmentation techniques, which made it easier to build a reliable and complex model. For classification, the StrawberrySqueezeNet architecture was used, utilising its inherent capacity to identify complex patterns in the data. For hyperparameter tuning, a Tangent Search technique was used to maximise the model's performance. In order to get higher classification accuracy, the iterative optimisation procedure dynamically adjusted parameters as it explored the hyperparameter space. The result of these efforts was an astounding accuracy of 98.3%, which beat the industry standards set by other models.

关键词： Accuracy Digital images Surveillance Data augmentation Data models Space exploration Reliability Tuning Standards Diseases

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Oxygen uptake response to switching stairs exercise by non-parametric modeling

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Control Theory and technology 2024年第2期22卷 315-325页

作者： Hairong Yu Chenyu Zhang Kai Cao Hamzah M.Alqudah Steven Weidong Su Key Laboratory of Child Development and Learning Science of Ministry of Education and the School of Biological Science and Medical Engineering Southeast UniversityNanjing210096JiangsuChina College of Artificial Intelligence and Big Data for Medical Science Shandong First Medical University and Shandong Academy of Medical SciencesJinan250117ShandongChina Faculty of Engineering and Information Technology University of Technology SydneyUltimoNSW2007Australia

Oxygen uptake plays a crucial role in the evaluation of endurance performance during exercise and is extensively utilized for metabolic assessment. This study records the oxygen uptake during the exercise phase (i.e., ascending or descending) of the stair exercise, utilizing an experimental dataset that includes ten participants and covers various exercise periods. Based on the designed experiment protocol, a non-parametric modeling method with kernel-based regularization is generally applied to estimate the oxygen uptake changes during the switching stairs exercise, which closely resembles daily life activities. The modeling results indicate the effectiveness of the non-parametric modeling approach when compared to fixed-order models in terms of accuracy, stability, and compatibility. The influence of exercise duration on estimated fitness reveals that the model of the phase-oxygen uptake system is not time-invariant related to respiratory metabolism regulation and muscle fatigue. Consequently, it allows us to study the humans’ conversion mechanism at different metabolic rates and facilitates the standardization and development of exercise prescriptions.

关键词： Non-parametric modeling Interval stair training exercise Kernel method.Cardiorespiratory response Oxygen uptake

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A Novel Namib Beetle Optimization Approach for Minimizing CO2 Emissions in Electric Vehicle Charging Integrated with Renewable Energy Sources

A Novel Namib Beetle Optimization Approach for Minimizing CO...

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Automation, Computing and Renewable Systems (ICACRS), International Conference on

作者： G. Adline Priya K. Kavithamani K. Anupriya S. Saranya S.Kaliappan M.Siva Ramkumar Department of Electrical and Electronics Engineering Karpagam College of Engineering Coimbatore Tamilnadu Department of Artificial Intelligence and Machine Learning Hindusthan College of Engineering and Technology Coimbatore Tamilnadu Department of Information and Technology Sri Krishna College of Technology Coimbatore Tamilnadu Department of Electrical and Electronics Engineering Kumaraguru College of Technology Coimbatore Dept of ECE SNS College of Technology India

ISBN: (数字)9798331532420

ISBN: (纸本)9798331532437

Integrating electric vehicles (EVs) with renewable energy sources (RESs) can lead to increased costs due to the need for advanced infrastructure and control systems, which may deter investment in clean technologies. Additionally, if the grid still relies on fossil fuels during periods of low renewable generation, the overall CO 2 emissions may not decrease significantly, undermining the environmental benefits of EV adoption. In an effort to eliminate these drawbacks, this manuscript develops an approach to implementing charging of EVs using RESs. Namib Beetle Optimization (NBO) is the proposed method of this research. The major aim of the proposed method is to minimize the cost of charging of EV and at the same time minimize CO2 emission. The proposed NBO is employed so as to control and maximize the charging and discharging rate of the EV. At that time the proposed technique is implemented in MATLAB and performance is compared with several other techniques. The proposed technique is better than all the previous techniques like IALO, GA, MWOA, JMVO, and BESA for countering the threats effectively. The results demonstrate that the proposed method significantly outperforms traditional methods, effectively reducing costs of EV charging as $7,500 and CO 2 emissions as 60.5ppm.

关键词： Photovoltaic systems Renewable energy sources Schedules Costs Supply and demand Prediction algorithms Electric vehicle charging Sustainable development Carbon emissions Optimization

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A Hazelnut Tree Search Algorithm for Enhancing Large-Scale Electric Vehicle Charging on the Power Grid

A Hazelnut Tree Search Algorithm for Enhancing Large-Scale E...

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Automation, Computing and Renewable Systems (ICACRS), International Conference on

作者： K. Kavithamani G. Adline Priya S. Saranya K. Anupriya M.Siva Ramkumar S.Kaliappan Department of Electrical and Electronics Engineering Karpagam College of Engineering Coimbatore Tamilnadu Department of Information and Technology Sri Krishna College of Technology Coimbatore Tamilnadu Department of Artificial Intelligence and Machine Learning Hindusthan College of Engineering and Technology Coimbatore Tamilnadu Dept of ECE SNS College of Technology India Department of Electrical and Electronics Engineering Kumaraguru College of Technology Coimbatore

ISBN: (数字)9798331532420

ISBN: (纸本)9798331532437

Electric vehicle (EV) adoption on a large scale would drastically change patterns of electricity demand, posing both opportunities and challenges for modern power systems. However, the increased and often unpredictable demand from simultaneous EV charging can strain grid infrastructure, leading to potential overloads and instability. This paper proposes the Hazelnut Tree Search Algorithm (HTSA) as an effective optimization method for large-scale EV charging. The objective of the proposed method is to minimize electricity costs and improve grid efficiency in the EV charging. By optimizing charging schedules, HTSA enhances grid stability and improves load balancing throughout the power grid (PG). By then, the proposed method is implemented in MATLAB platform and compared to other existing methods such as Ant Colony Optimization (ACO), artificial Bee Colony (ABC), Particle Swarm Optimization (PSO), Genetic Algorithm (GA), and NSGA-II. Results show that HTSA reduces electricity costs to 4.3 INR/kWh, and achieves 96% of higher efficiency compared to other existing methods. This superior performance demonstrates HTSA’s potential for optimizing EV charging strategies while minimizing grid disruptions and enhancing the integration of renewable energy sources (RES).

关键词： Electric potential Schedules Renewable energy sources Costs Electricity Power system stability Electric vehicle charging Sustainable development Genetic algorithms Strain

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Intrusion Detection in IoT Environment Using Stochastic Gradient Descent with Warm Restarts and Gated Recurrent Unit

Intrusion Detection in IoT Environment Using Stochastic Grad...

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Data Science and Network Security (ICDSNS), International Conference on

作者： Revatthy Krishnamurthy Haider Alabdeli Rajani N Vankudoth Ramesh Bura Vijay Kumar Department of Artificial Intelligence & Machine Learning Rajalakshmi Engineering College Chennai India Department of Computers Techniques Engineering College of Technical Engineering The Islamic University Najaf Iraq Dept. of ECE Nitte Meenakshi Institute of Technology Bengaluru India Department of CSE-DS CVR College of Engineering Mangalpalle India School of Computer Science and Artificial Intelligence SR University Warangal India

ISBN: (数字)9798350373110

ISBN: (纸本)9798350373127

The rapid growth of Internet of Things (IoT) has attracted the cybercriminals which resulted in increase of cyber-attacks on IoT devices. If the cyber-attacks are undetected for a longer time, it causes serious service interruptions, identity protection threats. To handle these issues, this research proposes a novel intrusion detection system by employing Stochastic Gradient Descent with warm Restarts and Gated Recurrent Unit (SGDR-GRU) method. The data is normalized to obtain common scaled data by using z-score normalization technique and the feature selection is performed by Stochastic Gradient Descent with warm Restarts (SGDR). The SGDR is an optimization technique that escapes local minima, reduces overfitting and improves the performance by adjusting learning rate. Finally, classification of normal and anomaly data is carried out by Gated Recurrent Unit (GRU) that deals with gradient descent problem and captures long-range dependencies. The performance of the proposed model is evaluated on NSL-KDD dataset and achieved high accuracy 99.12%, precision 99.45%, recall 99.23% in identifying intrusion over IoT devices. The proposed method surpassed existing intrusion detection models such as stacked-deep polynomial network (SDPN), Hybrid Chicken Swarm Genetic Algorithm (HCSGA).

关键词： Performance evaluation Accuracy Intrusion detection Stochastic processes Transforms Logic gates Feature extraction Internet of Things Optimization Cyberattack

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Multi-UAV Multi-RIS QoS-Aware Aerial Communication Systems Using DRL and PSO

Multi-UAV Multi-RIS QoS-Aware Aerial Communication Systems U...

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IEEE International Conference on Communications (ICC)

作者： Marwan Dhuheir Aiman Erbad Ala Al-Fuqaha Mohsen Guizani Division of Information and Computing Technology College of Science and Engineering Hamad Bin Khalifa University Qatar Foundation Doha Qatar Machine Learning Department Mohamed Bin Zayed University of Artificial Intelligence (MBZUAI) Abu Dhabi UAE

ISBN: (数字)9781728190549

ISBN: (纸本)9781728190556

Recently, Unmanned Aerial Vehicles (UAVs) have attracted the attention of researchers in academia and industry for providing wireless services to ground users in diverse scenarios like festivals, large sporting events, natural and man-made disasters due to their advantages in terms of versatility and maneuverability. However, the limited resources of UAV s (e.g., energy budget and different service requirements) can pose challenges for adopting UAV s for such applications. Our system model considers a UAV swarm that navigates an area, providing wireless communication to ground users with RIS support to improve the coverage of the UAV s. In this work, we introduce an optimization model with the aim of maximizing the throughput and UAVs coverage through optimal path planning of UAVs and multi-RIS phase configurations. The formulated optimization is challenging to solve using standard linear programming techniques, limiting its applicability in real-time decision-making. Therefore, we introduce a two-step solution using deep reinforcement learning and particle swarm optimization. We conduct extensive simulations and compare our approach to two competitive solutions presented in the recent literature. Our simulation results demonstrate that our adopted approach is 20 % better than the brute-force approach and 30% better than the baseline solution in terms of QoS.

关键词： Wireless communication Simulation Quality of service Autonomous aerial vehicles Throughput Real-time systems Path planning

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