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共 1624 条记录，以下是281-290 订阅

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Revolutionizing Public Transport: RFID-Enabled Occupancy Monitoring and Frequency Analysis System

Revolutionizing Public Transport: RFID-Enabled Occupancy Mon...

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2024 International Conference on Communication, Computing and Internet of Things, IC3IoT 2024

作者： Mageswaran, J. Uma Indumathi, G. Kalpana, A.V. Elangovan, G. Lalitha, S.D. Nalini, S. Sohanda, Mayank Kumar Dept. of Computer Science Engineering Amrita School of Computing Vengal Campus Tiruvallur DistTamil Nadu India Srm Institute Of Science And Technology Ramapuram India Srm Institute Of Science And Technology Department Of Data Science And Business Systems Kattankulathur India R.M.K Engineering College Department Of Computer Science And Engineering India Srm Institute Of Science And Technolog Department Of Computing Technologies Kattankulathur India

ISBN: (纸本)9798350352689

Public bus stops in India are becoming more crowded due to the country's fast population expansion. People wait a long time for buses to come, then suddenly congregate around them when they do, packing the buses with people and pushing them up into the footboards, which causes accidents. An additional concern of overcrowding is theft. All of this is the result of numerous bus stops not having adequate information on when busses would arrive. The proposed work paves a very important role for the passengers. The passengers will be provided with the information of the arrival of next bus, seat occupancy and the total passengers inside the bus. The information about the seat occupancy, in-passengers and out-passengers is done with the help of Radio Frequency Identification (RFID) tags. The arrival of the next bus is determined by calculating the duration of the buses that are near using RFID readers placed in different bus stations. The count and the frequency will be interfaced in the web application. The passengers can check the application to know the occupancy and today's society where people can catch the right bus at the station and at the right time. This will solve the problems like waiting in bus stops and wasting time and going in a crowded bus. © 2024 IEEE.

关键词： Buses

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An Effective Crop Recommendation using Ensemble Classifier

An Effective Crop Recommendation using Ensemble Classifier

引用

Intelligent data Communication Technologies and Internet of Things (IDCIoT), International Conference on

作者： Ashok Reddy Kandula Jai Srinivas Vadapalli Srinivasa Sai Shanmukha Mannava Sai Seetharam Modugumudi Irfan Mohammad Dept. of Artificial Intelligence & Data Science Seshadri Rao Gudlavalleru Engineering College Gudlavalleru India

ISBN: (数字)9798331527549

ISBN: (纸本)9798331527556

Modern expectations for efficiency and sustainability are sometimes not addressed by the existing approaches in the continually changing environment of agricultural activities. In order to fully address these difficulties, research investigates how precision farming practices may be integrated while utilizing technological improvements. Study attempts to estimate optimal crop choices by assessing important characteristics including area, rainfall, temperature, and district-specific data, giving farmers the power to make well-informed and data-driven decisions. Precision farming lowers risks related to climatic unpredictability, increases output, and minimizes resource waste. By using the strategy, encourage resilience and innovation in the agricultural industry while bridging the gap between current farming practices and the sustainable agriculture of the future. Employing advanced algorithms including LR, Ridge Regression, DTs, and classifiers, achieved a remarkable 96% accuracy rate. Additionally, the proposed system offers real-time alerts via email or SMS, delivering timely advice and actionable insights to enhance agricultural sustainability and productivity.

关键词： Productivity Technological innovation Temperature distribution Rain Crops Agriculture Real-time systems Sustainable development Farming Resilience

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Indoor Navigation System for KCT Using AR Techniques

Indoor Navigation System for KCT Using AR Techniques

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Advancements in Electrical, Electronics, Communication, Computing and Automation (ICAECA), International Conference on

作者： Anitha P Madhav S Nishanth M V GowriShankar S Sanjai Kumar S Jaidev M V Dept. of Computer Science and Engineering Kumaraguru College of Technology Coimbatore TamilNadu Dept. of Artificial Intelligence and Data Science Kumaraguru College of Technology Coimbatore TamilNadu

ISBN: (数字)9798331525439

ISBN: (纸本)9798331525446

Moving around in confusing indoor spaces like malls, airport, campuses is very difficult if the setting is unknown. This system gives real-time turn-by-turn guidance on mobile phones using Unity with AR Foundation and NavMesh. LiDAR cameras enable accurate 3D mapping, and a GPS, Wi-Fi and Bluetooth beacon hybrid positioning system enables accurate pinpoint positioning in GPS-denied environments. The study seeks to improve user experience by simple, real-time guidance and context information as AR visualization. The system improves user disorientation and speeds up navigation speed by superimposition of directional signs over the real-world environment. Major conclusions highlight the precision and consistency of the system, specifically in vast indoor environments. The power of the system is that it can substitute conventional maps and signs to make the process of navigating seamless. Scalability and leveraging AI for smart routing and natural interaction will be addressed in subsequent work, a revolution of AR navigation technology. The study opens up a possibility for indoor navigation solutions to become more intuitive and user-centric.

关键词： Three-dimensional displays Laser radar Accuracy Indoor navigation Routing Airports Real-time systems Augmented reality Wireless fidelity Global Positioning System

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Interactive Augmented Reality System for Real- Time Pre-Car Inspection and Maintenance Assistance

Interactive Augmented Reality System for Real- Time Pre-Car ...

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Advancements in Electrical, Electronics, Communication, Computing and Automation (ICAECA), International Conference on

作者： Anitha P DevaVirdha M Dinesh K Akilesh S Abishek R S Jayanitha Jayaprakash Jayadharani Dept. of Computer Science and Engineering Kumaraguru College of Technology Coimbatore TamilNadu Dept. of Artificial Intelligence and Data Science Kumaraguru College of Technology Coimbatore TamilNadu

ISBN: (数字)9798331525439

ISBN: (纸本)9798331525446

Vehicle checking along with upkeep are essential in order to assure safety as well as efficiency, but conventional examination techniques may prove to be cumbersome and need technical knowledge. In order to address this issue, an Interactive Augmented Reality (AR) System is proposed to facilitate pre-car real-time inspection and maintenance assistance. The system utilizes AR technology to monitor as well as examine numerous vehicle parts, such as the horn, dashboard lights, sunroof, cruise control, personal memory options, information display, gear system, air conditioning control, and audio control. With an interactive UI, every component is linked to a virtual button with visual and audio cues, as users receive information and instructions in real time. The system also includes comprehensive animated instructions for engine oil changes, providing accurate step-by-step guidance during required maintenance procedures on it. With the inclusion of real-time monitoring and interactive UI elements, the system enhances user understanding. Also, with audiovisual feedback, the system raises engagement, leading to better educated decisions when testing vehicles. The potential result is pretty much a streamlined inspection procedure, totally guaranteeing core vehicle systems remain in full operating condition prior to usage. This process doesn't only boost vehicle maintenance knowledge, it improves strength and performance, which makes it an effective tool for both work and personal use.

关键词： Visualization Manuals Inspection User interfaces Real-time systems User experience Maintenance Automobiles Augmented reality Automotive engineering

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Unified Model Agnostic Computation and Explainable AI for Enhanced Accuracy and Transparency in Medical Image Classification

Unified Model Agnostic Computation and Explainable AI for En...

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Smart Systems for applications in Electrical sciences (ICSSES), International Conference on

作者： Pavan P Kashyap Prakasha G Santosh Kumar S Sunil Kumar K N Raksha P R Shailesh Rastogi Department of CSE (Cyber Security) Sri Venkateshwara College of Engineering Bangalore Dept. of E&CE Sri Venkateshwara College of Engineering Bangalore India Dept. of CSE (Data Science) Sri Venkateshwara College of Engineering Bangalore India Dept. of ISE Sri Venkateshwara College of Engineering Bengaluru Dept. of CSE (Data Science) SVCE Bengaluru India

ISBN: (数字)9798331507671

ISBN: (纸本)9798331507688

The application of deep learning techniques in medical image classification has revolutionized healthcare diagnostics, but challenges remain in terms of model transparency and interpretability. Despite the high performance of deep learning models, their “black-box” nature often limits their clinical adoption, as healthcare professionals require an understanding of the rationale behind automated predictions. To address this, we propose a unified framework that integrates model-agnostic explainability techniques such as Shapley Additive Explanations (SHAP) and Local Interpretable Model-Agnostic Explanations (LIME) with deep learning models for medical image classification. This approach not only enhances the accuracy of the model by utilizing state-of-the-art convolutional neural network (CNN) architectures but also improves transparency by providing interpretable, humanunderstandable explanations for the model's decisions. The proposed framework is evaluated using various medical image datasets, including X-rays and MRIs, and is compared against traditional deep learning models without explainability methods. Results demonstrate that the integrated approach achieves superior classification accuracy while offering critical interpretability, making it more suitable for deployment in clinical settings. This work bridges the gap between highperformance deep learning models and the need for model transparency, promoting trust in AI-driven medical image analysis tools and enhancing their practical application in realworld healthcare scenarios.

关键词： Deep learning Analytical models Accuracy Explainable AI computational modeling Closed box Predictive models Convolutional neural networks Medical diagnostic imaging Image classification

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Impact of AI-Driven Automation on Job Displacement and Skill Development: A Societal Perspective

Impact of AI-Driven Automation on Job Displacement and Skill...

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2024 IEEE Silchar Subsection Annual Conference, SILCON 2024

作者： Balakumar, A. Sawant, Pravin D Nimma, Divya Khan, Shamim Ahmad Siddiqua, Ayasha Kalaiarasan, C. K.Ramakrishnan College of Engineering Department of Ece Trichy India Narayan Zantye College of Commerce Dept of Commerce Goa Bicholim India Computational Science University of Southern Mississippi Data Analyst in Ummc United States Glocal School of Science & Technology Glocal University UP India Jazan University Dept. of Information Technology and Security Jazan Saudi Arabia University of Madras Department of Commerce Ide Chennai India

ISBN: (纸本)9798331540821

As artificial intelligence (AI) continues to reshape industries, understanding its impact on job displacement and skill development is crucial. This research investigates these dynamics utilization Gradient Boosting Machines (GBM) for predictive analysis and Explainable AI (XAI) techniques to interpret model outcomes. in order to higher recognize those dynamics, this work employs Explainable AI (XAI) tools to examine model findings and Gradient Boosting Machines (GBM) for predictive evaluation. The study intends to expect developments in task displacement and pinpoint new ability calls for via analysing how AI-driven automation impacts employment features throughout several sectors. the prevailing examine offers whole insights into AI's impact on workforce dynamics considering, in comparison to prior research, it combines GBM for precise forecasting with XAI methodologies like SHAP values and characteristic importance rankings for transparent interpretation of models. The research makes use of a dataset that consists of process titles, AI effects, venture distributions, and AI Workload Ratios throughout industrial domains, and it uses Python as the implementation tool. employing XAI approaches, activity displacement chance is anticipated by means of GBM fashions and the motives using these projections are explained. Performance indicators including recall, accuracy, precision, and F1 score attest to the GBM algorithm's effectiveness in forecasting displacement from work. The examination of AI Workload Ratios, that assists in efficient staffing and adaptability, reveals that the effects of automation differ among disciplines. In order to minimise any adverse effects and maximize the potential that artificial intelligence (AI) technologies offer, this research underscores that artificial intelligence is transforming the structure of occupations and emphasizes the necessity for innovative methods in employment growth and policymaking. © 2024 IEEE.

关键词： Artificial Intelligence (AI) Explainable AI Gradient Boosting Machines Job displacement Skill development Workforce dynamics

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Causal Discovery for Rechargeable Battery Modeling Considering Group-Level DAG Constraints 50

Causal Discovery for Rechargeable Battery Modeling Consideri...

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50th Annual Conference of the IEEE Industrial Electronics Society, IECON 2024

作者： Wang, Yue Chen, Sihui Xu, Jiang Wan, Jiayu Liu, Yang Li, Xin Guangzhou China Dept. Mechanical and Energy Engineering Southern University of Science and Technology Shenzhen China Global Institute of Future Technology Shanghai Jiao Tong University Shanghai China Data Science Research Center Duke Kunshan University Kunshan China

ISBN: (纸本)9781665464543

Rechargeable battery has attracted great research popularity in recent years, and numerous data-driven solutions have been proposed for its modeling. While most research works in this domain focus on the statistical correlations among extracted variables, understanding their causal relationships is equally important to provide useful insights, thereby improving the operation in real-world scenarios. In order to reach this goal, causal discovery has been utilized, which learns a directed acyclic graph (DAG) structure, referred to as causal graph, from observation data to represent the causal relationships among variables with specific physical interpretations. However, existing approaches only consider the DAG characteristics between individual variables. It may easily lead to group-level causal conflicts if multiple variables are created from the same source factor, which is a common practice for modeling the degradation of rechargeable batteries. In order to tackle this challenge, a novel group-level DAG constraint is proposed in this paper as a continuous regularization term that discourages cycles among blocks as well as individual elements in the weighted adjacent matrix corresponding to the causal graph. It is efficiently solved using an augmented Lagrangian-based method. © 2024 IEEE.

关键词： Causal Discovery Group-Level DAG Rechargeable Battery

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Max-Min Fair 3D Trajectory Planning for Solar-Powered UAV-Assisted data Collection

Max-Min Fair 3D Trajectory Planning for Solar-Powered UAV-As...

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2022 IEEE/CIC International Conference on Communications in China, ICCC 2022

作者： Sun, Chao Xiong, Xinxuan Ni, Wei Ohtsuki, Tomoaki Wang, Xin Fudan University Dept. of Communication Science and Engineering Shanghai China Data61 Business Unit Sydney Australia Keio University Dept. of Information and Computer Science Yokohama Japan

ISBN: (数字)9781665484800

ISBN: (纸本)9781665484800

This paper presents a new solar-powered fixed-wing unmanned aerial vehicle (UAV)-assisted data collection technique, where a fixed-wing UAV harvests solar energy to fly and collect data from smart devices (SDs). The minimum of the data upload-ed from any of the SDs is maximized by optimizing the UAV's three-dimensional (3D) trajectory. The key idea is that we develop variable substitution and successive convex approximation (SCA) techniques to convexify the nonconvex data transmission, UAV energy consumption and mobility constraints, as well as the nonconvex energy harvesting constraint. The proposed algorithm guarantees a locally optimal solution satisfying the Karush-Kuhn-Tucker (KKT) conditions. Another important aspect is that an effective set of approximation parameters are identified for accurate approximation of the solar energy harvesting at relatively low altitudes, contributing to the design of efficient 3D trajectories balancing energy harvesting and data collection. Numerical results show that the 3D flight increases uploaded data by 59% as compared to the two-dimensional (2D) flight. © 2022 IEEE.

关键词： Unmanned aerial vehicles (UAV)

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Deep reinforcement learning applied to Monte Carlo power system reliability analysis

Deep reinforcement learning applied to Monte Carlo power sys...

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Power Tech Conference

作者： Øystein Rognes Solheim Boye Annfelt Høverstad Magnus Korpås Dept. of Data Science Statnett SF Dept. of Electric Power Engineering NTNU Oslo Norway Dept. of Data Science Statnett SF Dept. of Computer Science NTNU Trondheim Norway Dept. of Electric Power Engineering NTNU Trondheim Norway

This paper presents a proof of concept for a novel three-step method for using reinforcement learning in Monte Carlo simulations for power system reliability studies. Traditionally, an optimal power flow solver is used to compute the remedial actions necessary after a power system failure. Our new approach provides these remedial actions by the policy of a deep reinforcement learning agent. To train this agent, we construct a new power reliability reinforcement learning environment. After training the agent using the Twin Delayed Deep Deterministic Policy Gradient algorithm with a prioritized experience replay buffer, the agent is used within a time sequential composite power system Monte Carlo reliability simulation. We show that when the trained agent is used to find the remedial measures in these Monte Carlo simulations, we obtain near-optimal results on two standard power reliability test cases with favorable scaling of computation time. This work is a first step in using reinforcement learning in reliability analysis and establishes a foundation for future work.

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Malware Detection Using Dynamic Analysis

Malware Detection Using Dynamic Analysis

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2022 International Conference on data science, Agents and Artificial Intelligence, ICDSAAI 2022

作者： Kalaiselvi, Vkg Shannmugasundaram, Hariharan Subha, T. Theerej, C. Yokeshwaran, S. Sri Sairam Engineering College Dept. of Information Technology Chennai India Vardhaman College of Engineering Department of CSE Hyderabad India SRM Institute of Science and Technology Dept. of Computational Intelligence Chennai India

ISBN: (纸本)9798350333848

In the international cyberspace the main thing to be maintained by the experts is security. The physical security is provided by security companies and men. But the software and digital data protection is only provides by software's like antivirus. These codes are written by many malicious hackers all around the world.. The present technological antivirus are in static nature. This method of malware identification is out dated as the malware are coming in a modifying manner and many more malware's are being identified. This creates a problem of uselessness of a antivirus when it has no signature of a malware which is attacking the system. The machine learning algorithms can be very useful in building a dynamic malware analyzer. The prediction mechanism can be helpful in finding the malware's which are modified from its original form to another form to hide itself from antivirus (family). Then another method is to dynamically updating the signature database in the antivirus software. This can be done using getting the signature from all over the world and dynamically updating the software's signature database. When these two methods are integrated then it would be a good software with dynamic properties. © 2022 IEEE.

关键词： database systems

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