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Multi-Agent Systems for Collaborative Intelligence (ICMSCI), International Conference on

作者： R.S. Ramya Keren Lois Daniel S. Rajkumar Sruthi S Madhavan Akshya. J Mani Deepak Choudhry Department of Computer Science and Engineering Sri Ramakrishna Engineering College Coimbatore Department of Computer Science and Business Systems KGiSL Institute of Technology Coimbatore Department of Information Technology Sona College of Technology Salem Department of Artificial Intelligence and Data Science Nehru Institute of Engineering and Technology Coimbatore Department of Computational Intelligence SRM Institute of Science and Technology Chennai Department of Computing Technologies SRM Institute of Science and Technology Chennai

ISBN: (数字)9798331509828

ISBN: (纸本)9798331509835

With the popularization of UAVs (Unmanned Aerial Vehicles) in surveillance, delivery services, environmental monitoring, and increasingly other operational aspects, besides the need to take off and land, efficient traffic management becomes integral for assuring and securing further development and exploitation. Most of the existing traffic management systems for UAVs depend on traditional rule-based algorithms or simpler machine learning techniques, such as DQN or PPO. However, these systems also have enormous challenges: dynamic environments, high-density traffic, and robust decision-making over unpredictable weather and network conditions. In addition, the above traditional models usually experience difficulties in balancing exploration and exploitation, leading to suboptimal routing and enlarged collision risks in complicated urban airspaces. These challenges have driven us to develop a new proposal: a UA V traffic management framework based on Double Deep Q-Network (DDQN) combined with Instruction-Based Learning. In this approach, advanced learning for DDQN optimizes processes at decision-making points for UAVs. The model proposed herein, along with comprehensive simulations and quantitative analyses, demonstrated higher performance in terms of leadership and cumulative rewards while keeping lower training loss compared to the classical algorithms such as DQN, A3C, and PPO. At the same time, the DDQN model presented a better success rate (95%), as also reflected in the balanced precision-recall curve showing an area under the curve of 0.90, over others in the on-point prediction of UAVstate and safety margin of collision. Our experimental results demonstrate that the proposed DDQN-based framework significantly enhances UA V traffic management by optimizing energy consumption in complex airspaces and reducing the risk of collision.

关键词： Training Analytical models Adaptation models Statistical analysis Atmospheric modeling Computational modeling Decision making Predictive models Autonomous aerial vehicles Safety

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Discovering Innovative Applications for Real-Time Data Analysis with Machine Learning 3

Discovering Innovative Applications for Real-Time Data Analy...

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3rd International Conference on Smart Generation computing, Communication and Networking, SMART GENCON 2023

作者： Jadhav, Prranjali Raja Praveen, K.N. Aggarwal, Prateek Gautam, Sandeep Naval, Preeti Nirmala, D. Vishwakarma Institute of Information Technology Department of Artificial Intelligence & Data Science Pune India Computer Science and Engineering Faculty of Engineering and Technology Karnataka Bangalore India Centre of Interdisciplinary Research in Business and Technology Chitkara University Institute of Engineering and Technology Chitkara University Punjab India Vivekananda Global University Department of Computer Science & Engineering Jaipur India Maharishi School of Engineering and Technology Maharishi University of Information Technology Uttar Pradesh India Prince Shri Venkateshwara Padmavathy Engineering College Department of Electronics and Communication Engineering Chennai127 India

ISBN: (纸本)9798350319125

This paper investigates the capability for discovering modern programs for actual-time information analysis the use of system learning. We will take a look at the technology and strategies used in system getting to know and talk their adaptive potential to interpret and analyze dynamically changing facts inputs. Especially, we can examine existing technological methods to understand the capacity of AI to discover creative solutions to complicated challenges. We will also explore capability techniques to contain gadget gaining knowledge of into present real-time records analysis platforms and architectures. Finally, we are able to speak the programs of machine studying in real-time records evaluation and its potential to allow fast and correct decision making. This paper gives a theoretical framework to apprehend and harness the power of device gaining knowledge of to investigate and interpret real-time facts in modern approaches. © 2023 IEEE.

关键词： Information analysis

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A Deep Study on Encroachment Recognition Model through W-S-N and I-O-T 4

A Deep Study on Encroachment Recognition Model through W-S-N...

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4th IEEE International Conference on Advance computing and Innovative Technologies in engineering, ICACITE 2024

作者： Dixit, Krishna Kant Singh, Satyendra Kiran, P. Ravi Al-Attabi, Kassem Sinha, Aashna Angadi, Sanjeev Kumar Gla University Department of Electrical Engineering Mathura India Chitkara University Chitkara Centre for Research and Development Himachal Pradesh 174103 India Cmr College of Engineering & Technology Department of Ece India The Islamic University Najaf Iraq Uttaranchal University Divison of Research and Innovation School of Applied and Life Sciences Uttarakhand Dehradun India Nutan College of Engineering and Research Computer Science and Engineering Pune Maharashtra Talegaon Dabhade India

ISBN: (纸本)9798350360165

The burgeoning challenge of Internet of Things (IoT) technology heralds' transformative capability for agriculture, mainly in enhancing the safety and operational performance of huge-scale farmlands. This paper introduces an revolutionary IoT-based framework that synergizes Global System for Mobile Communication (GSM) and Global Positioning System (GPS) modules to forge a whole, actual-time tracking and intrusion detection answer tailored for agricultural domain names. Our proposed framework is designed to address the dual demanding situations of optimizing farm management practices while ensuring robust safety in competition to capacity intrusions. By deploying a network of brand-new sensors across farmlands, our system enables the seamless collection, evaluation, and transmission of essential environmental statistics, thereby enabling farmers to make knowledgeable alternatives all of sudden. Furthermore, the combination of GSM and GPS technology enhances the tool's functionality to without delay alert farmers about unusual sports activities, making sure immediate reaction to protection breaches. The pilot deployment of our framework demonstrates its effectiveness in no longer best substantially improving tracking accuracy however moreover in bolstering the safety of agricultural lands towards unauthorized get right of entry to. © 2024 IEEE.

关键词： Farms

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Adaptive Frequency Hopping for 5G New Radio mMTC Security

Adaptive Frequency Hopping for 5G New Radio mMTC Security

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2023 IEEE International Conference on Industrial Technology, ICIT 2023

作者： Chan, Wai Ming Kwon, Hyuck M. Chou, Remi A. Love, David J. Fahmy, Sonia Hussain, Syed Rafiul Kim, Sang Wu Valk, Chris Vander Brinton, Christopher G. Marojevic, Vuk Pham, Khanh D. Kim, Taejoon The University of Kansas Department of Electrical Engineering and Computer Science LawrenceKS66045 United States Wichita State University Department of Electrical and Computer Engineering WichitaKS67260 United States School of Computing Wichita State University WichitaKS67260 United States Elmore Family School of Electrical and Computer Engineering Purdue University West LafayetteIN47907 United States Purdue University Department of Computer Science West LafayetteIN47907 United States The Pennsylvania State University Department of Computer Science and Engineering State CollegePA16802 United States Iowa State University Department of Electrical and Computer Engineering AmesIA50011 United States Raytheon Bbn CambridgeMA02138 United States Mississippi State University Department of Ece StarkvilleMS39762 United States Air Force Research Laboratory AlbuquerqueNM87123 United States

ISBN: (纸本)9798350336504

3GPP standards (e.g., LTE and 5G New Radio (NR)) have revolutionized commercial broadband access. Unfortunately, the openness of 3GPP specifications and high-level of adaptability make fifth-generation (5G) networks increasingly sensitive to unsophisticated attacks in the network. While 5G NR has introduced frequency hopping (FH) for massive machine-type communications (mMTC), there has not been a unified approach to designing the FH patterns in practical 5G systems. In this paper, we investigate the multi-user FH design problem at the physical resource grid of the 5G communication networks. We present various criteria for FH pattern design and propose two algorithms that are implementable with low complexity. The first scheme utilizes known interference patterns to design a set of user equipment (UE) FH patterns that are immune to interfering attacks. The second scheme leverages interference statistics and minimizes the expected number of hits by the interferer. Through numerical simulations and analytical modeling, we demonstrate the efficacy of the proposed schemes, which outperform the uniform FH pattern and achieve near-optimal performance. © 2023 IEEE.

关键词： Frequency hopping

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Comparative Analysis of Unified Payment Interface (UPI) in Tamil Nadu Using Machine Learning Techniques

Comparative Analysis of Unified Payment Interface (UPI) in T...

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Communication, computer sciences and engineering (IC3SE), International Conference on

作者： C.N. Vanitha Malathy Sathyamoorthy Rajesh Kumar Dhanaraj P. Sivakumar Department of Computer Science and Engineering Kongu Engineering College Erode Department of IT KPR Institute of Engineering and Technology Coimbatore Symbiosis Institute of Computer Studies and Research (SICSR) Symbiosis International (Deemed University) Pune India Department of Computer Technology Kongu Engineering College Erode

ISBN: (数字)9798350366846

ISBN: (纸本)9798350366853

Nowadays, the process of transferring money in physical mode is eradicated and moved to the next level based on online UPI payments. The proposed system will integrate machine learning techniques to identify the impact on UPI transactions. The transaction details are classified based on the patterns by utilizing advanced machine learning techniques and produce the accurate output. The user behavior, timestamp of the transaction and the size of the transaction are considered as important factors in the proposed system. The proposed approach deploys an effective framework for classification which will help the stakeholders to respond based on the constantly changing market needs. This approach is effectively used for predicting the implications of UPI transactions based on the changes in the market needs in an effective way.

关键词： Accuracy Machine learning Stakeholders

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3D Eye-to-Hand Coordination for Uninstructed Robot Grasp Planning 12

3D Eye-to-Hand Coordination for Uninstructed Robot Grasp Pla...

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12th International Conference on CYBER Technology in Automation, Control, and Intelligent Systems, CYBER 2022

作者： Liang, Peidong Huang, Chengxi Fan, Zhendong Zhang, Chentao Zhang, Ruizhe Zhao, Lijun -HIT Research Institute of Engineering and Technology China Harbin Institute of Technology China Department of Computer Science and Technology Tsinghua University Beijing China

ISBN: (数字)9781665472678

ISBN: (纸本)9781665472678

Robot grasping is widely explored in factories, where most objects are instructed with rigid grasp planning. However, for disordered and cluttered objects, it is rarely applied due to difficult object recognition and robot trajectory constraints, which may bring low-cost performance. Normally, the shape and pose of an object, which can be represented as point cloud are preliminary feedback for robot grasping strategy. Therefore, the point cloud segmentation of disordered and clustered objects is critical for grasping manipulation tasks with hand eye coordination. This paper presents an algorithm for locating and dividing different parts by point cloud image in chaotic environment, to determine the reasonable grasping position of parts and the trajectory planning of manipulator. With the experiment on a eye-to-hand robot platform, the results show that the proposed algorithm is robust to different parts stacked and has a relatively better recognition performance. © 2022 IEEE.

关键词： Object recognition

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Impact Assessment and Mitigation Strategies for Plug-in Electric Vehicles on Distribution Networks: A Probabilistic Approach

Impact Assessment and Mitigation Strategies for Plug-in Elec...

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International Conference on Contemporary computing and Informatics (IC3I)

作者： Vijilius Helena Raj M Siva Swetha Reddy H Pal Thethi G Ramesh Q. Mohammad Munugapati Bhavana Department of Applied Sciences New Horizon College of Engineering Bangalore Computer Science Software Engineering Institute of Aeronautical Engineering Hyderabad Telangana Lovely Professional University Phagwara India Department of Computer Science and Engineering GRIET Bachupally Hyderabad India Hilla University College Babylon Iraq Department of Computer Science and Engineering MLR Institute of Technology Hyderabad Telangana India

ISBN: (数字)9798350350067

ISBN: (纸本)9798350350074

The rapid depletion of fossil fuel reserves coupled with the ever-increasing demand for electrical power has prompted the search for new energy sources. Electric vehicles (EVs) are one potential answer because of the technology that allows for more efficient transportation while simultaneously reducing fossil fuel usage and emissions of greenhouse chemicals. Depending on the power system’s capability, structure, and architecture, several studies have shown that the distribution network may be negatively affected by the widespread interconnection of electric vehicles. Consequently, it is important to determine the effects of this additional load on the network so that measures can be taken to prevent threats to the system’s stability, dependability, and security, especially because the Indian federal and state governments have introduced incentives to increase sales of this type of vehicle.

关键词： Semantic Web Prevention and mitigation Transportation Distribution networks Power system stability Transformers Probabilistic logic Fossil fuels Security Protection

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Prediction of Cardiac Arrest using Generalized Soft computing Techniques

Prediction of Cardiac Arrest using Generalized Soft Computin...

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International Conference on computing for Sustainable Global Development (INDIACom)

作者： Radhanath Hota Sachikant Dash Sujogya Mishra Subhalaxmi Das S Lenka G Pradhan Department of Computer Science Engineering GIET University Gunupur Odisha India Department of Computer Science Engineering Gandhi Institute for Education and Technology Gunupur Odisha India Department of Mathematics Odisha University of Technology and Research Bhubaneswar India Dept. of Computer Science and Engineering Odisha University of Technology and Research Bhubaneswar India Department of Computer Science and Application Utkal University Bhubaneswar India

Cardiac-related diseases always lead to fatal consequences around the globe. This research includes vague data analysis of the data related to cardiac anomalies. This study proposes a method to develop a dimension reduction model using vague data related to cardiac diseases using rough set theory (RST). The overall research is divided into two different sections. The first section of this study proposes analyses of the raw cardiac-related data using RST, and the second section calculates the performance accuracy using Principal Component Analysis (PCA) applied to the universal data set; the performance measures also include a combination of two Universal data sets. The above claims are verified using statistical tests

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Successive Cancellation Sampling Decoder: An Attempt to Analyze List Decoding Theoretically

arXiv

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arXiv 2024年

作者： Wang, Hsin-Po Guruswami, Venkatesan Department of Electrical Engineering and Computer Science Simons Institute The Theory of Computing University of California Berkeley BerkeleyCA United States

Successive cancellation list (SCL) decoders of polar codes excel in practical performance but pose challenges for theoretical analysis. Existing works either limit their scope to erasure channels or address general channels without taking advantage of soft information. In this paper, we propose the successive cancellation sampling (SCS) decoder. SCS hires iid "agents" to sample codewords using posterior probabilities. This makes it fully parallel and amenable for some theoretical analysis. As an example, when comparing SCS with a agents to any list decoder with list size , we can prove that the error probability of the former is at most /ae more than that of the latter. In this paper, we also describe how to adjust the "temperature" of agents. Warmer agents are less likely to sample the same codewords and hence can further reduce error probability. Copyright © 2024, The Authors. All rights reserved.

关键词： Decoding

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Climate Impact Prediction: Whale-Optimized Conv-XGBoost with Remote Sensing and Sociological Data

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Remote Sensing in Earth Systems sciences 2024年第4期7卷 443-456页

作者： Jayakarthik, R. Chandrashekhara K.T Sampath, O. Kumar, Deepak Biban, Laxmi Maroor, Jnaneshwar Pai Malluvalasa, S. N. Lakshmi Department of Computer Science Saveetha College of Liberal Arts and Sciences SIMATS Chennai India Department of Information Science and Engineering BMS Institute of Technology and Management Bangalore India Department of CSE RGM College of Engineering and Technology Nandyal India Department of Applied Sciences Manav Rachna International Institute of Research and Studies Faridabad India Department of Botany Pt. C.L.S. Govt. College Haryana Karnal India Department of Business Administration Nitte Institute of Professional Education Nitte (Deemed to be University) Mangaluru Karnataka India Department of Artificial Intelligence & amp Data Science Koneru Lakshmaiah Education Foundation Vaddeswaram Guntur District Andhra Pradesh India

Climate change poses significant challenges globally, impacting ecosystems, economies, and societies. Accurate prediction of climate impacts is crucial for developing effective mitigation strategies and informed policy decisions. Traditional predictive models often struggle to capture the complex interactions between environmental and socio-economic factors, necessitating advanced approaches that leverage diverse data sources and sophisticated algorithms. This study presents a robust predictive framework integrating convolutional neural networks (CNN) and XGBoost, optimized by the whale optimization algorithm (WOA). The proposed model leverages high-resolution satellite imagery and socio-economic data, combining spatial feature extraction with advanced machine learning techniques to enhance prediction accuracy and efficiency. We evaluate the model’s performance against baseline models and various optimization techniques, including particle swarm optimization (PSO) and genetic algorithm (GA). The integrated CNN and XGBoost model optimized by WOA significantly outperforms these benchmarks, achieving an accuracy of 98%, with substantial improvements in precision, recall, F1-score, and redundant phrases, and focuses directly on the core points to enhance clarity such as mean squared error (MSE), area under the curve (AUC), specificity, sensitivity, mean absolute error (MAE), and R². The results highlight the efficacy of the proposed approach in capturing complex environmental and socio-economic interactions, providing a powerful tool for climate impact assessment and decision-making. © The Author(s), under exclusive licence to Springer Nature Switzerland AG 2024.

关键词： Climate Impact Prediction CNN Convolutional Neural Networks GA Genetic Algorithm Machine Learning Model Optimization Particle Swarm Optimization PSO Spatial Feature Extraction Whale Optimization Algorithm WOA XGBoost

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