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Effectiveness of Robot Motion Block on A-Star Algorithm for Robotic Path Planning 21

Effectiveness of Robot Motion Block on A-Star Algorithm for ...

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21st International Conference on New Trends in Intelligent Software Methodologies, Tools and Techniques, SoMeT 2022

作者： Kabir, Raihan Watanobe, Yutaka Naruse, Keitaro Islam, Rashedul Dept. of Computer Science and Engineering University of Aizu Fukushima Aizuwakamatsu Japan Dept. of Computer Science and Engineering University of Asia Pacific Dhaka Bangladesh Tsuruga Ikki-Machi Fukushima Aizu-Wakamatsu965-8580 Japan

ISBN: (纸本)9781643683164

Efficient path planning and minimization of path movement costs for collision-free faster robot movement are very important in the field of robot automation. Several path planning algorithms have been explored to fulfill these requirements. Among them, the A-star (A*) algorithm performs better than others because of its heuristic search guidance. However, the performance, effectiveness, and searching time complexity of this algorithm mostly depends on the robot motion block to search for the goal by avoiding obstacles. With this challenge kept in mind, this paper proposes an efficient robot motion block with different block sizes for the A∗ path planning algorithm. The proposed approach reduces robots' path cost and time complexity to find the goal position as well as avoid obstacles. In this proposed approach, grid-based maps are used where the robot's next move is decided by searching eight directions among the surrounding grid points. However, the proposed robot motion blocks size has a significant effect on path cost and time complexity of the A∗ path planning algorithm. For the experiment and to validate the efficiency of the proposed approach, an online benchmarked dataset is used. The proposed approach is applied on thousands of different grid maps with various obstacles, starting, and goal positions. The obtained results from the experiment show that the presented robot motion blocks reduce the robot's pathfinding time complexity and number of search nodes by maintaining a minimum path cost towards the goal position. © 2022 The authors and IOS Press. All rights reserved.

关键词： Motion planning

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Ensuring Fairness in Federated Learning Services: Innovative Approaches to Client Selection, Scheduling, and Rewards

Ensuring Fairness in Federated Learning Services: Innovative...

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International Conference on Distributed Computing Systems

作者： Meiying Zhang Huan Zhao Sheldon Ebron Ruitao Xie Kan Yang Dept. of Computer Science University of Memphis USA College of Computer Science and Software Engineering Shenzhen University China

ISBN: (数字)9798350386059

ISBN: (纸本)9798350386066

Federated Learning (FL) Services enable customers (requesters) to outsource their FL tasks to the FL service provider, who will recruit a group of clients with appropriate datasets to complete the FL task. For a given FL task, how to select appropriate clients fairly becomes a challenging problem due to budget restrictions and client heterogeneity. In this paper, we propose a new client selection, scheduling, and rewarding scheme to ensure fairness through a three-stage process: 1) multicriteria initial client pool selection, 2) data quality-oriented perround client scheduling, and 3) performance-based rewarding. Specifically, we first define a client selection metric with multiple criteria, such as client resources, data quality, and client behaviors. Then, we formulate the initial client pool selection problem into an optimization problem that aims to maximize the overall scores of selected clients within a given budget and propose a greedy algorithm to solve it. Furthermore, we formulate the per-round client selection problem into a data quality-oriented scheduling problem that aims to improve model quality and guarantee fairness. We propose a heuristic algorithm to divide the pool into several subsets such that the federated dataset in a subset is close to an independent and identical distribution (iid) while guaranteeing each client is selected at least once in a scheduling period. In addition, we propose a performance-based payment adjustment protocol with a bonus and punishment mechanism, such that the final payment reflects the actual performance of each selected client. Our fairness analysis and experimental results show that our scheme not only can guarantee fairness but also can improve the model quality especially when data are non-iid.

关键词： Measurement Greedy algorithms Analytical models Federated learning Heuristic algorithms data integrity data models

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Truthful Decentralized Blockchain Oracles

Truthful Decentralized Blockchain Oracles

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作者： Cai, Yuxi Irtija, Nafis Tsiropoulou, Eirini Eleni Veneris, Andreas Dept. of Electrical and Computer Engineering University of Toronto TorontoON Canada Dept. of Electrical and Computer Engineering University of New Mexico AlbuquerqueNM United States Dept. of Computer Science University of Toronto TorontoON Canada

Blockchain systems rely on oracles to bridge external information to the decentralized applications residing in the systems. Astraea protocols are decentralized oracle designs utilizing majority-voting mechanism to determine the oracle outcomes and/or rewards to voters. However, the voters are indifferent between voting through a single or multiple identities, as the potential rewards by the decentralized oracles grow linearly with the voters stakes. Additionally, the majority-voting mechanism may facilitate herd behaviors among the voters, as the voters are rewarded only if they are in agreement with the majority outcomes. In this paper, a novel oracle protocol is introduced by proposing a peer prediction-based scoring scheme along with non-linear staking rules, aiming at extracting subjective data truthfully. Specifically, an incentive compatible scoring scheme is designed so that voters uniquely maximize their expected score by honest reporting. The voters are rewarded when their report achieves a relatively high score compared to the rest of the voters, as opposed to the existing schemes, where a reward is only given when they agree to the majority. Furthermore, a non-linear stake scaling rule is proposed to discourage Sybil attacks. Detailed simulation results are presented to show the operation of the proposed oracle protocol and its improvement compared to indicative mechanisms proposed in the existing literature. © 2021 John Wiley & Sons, Ltd.

关键词： Blockchain

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A Dynamic Study on Energy Forecasts and The Potential of Renewable Energy Sources

A Dynamic Study on Energy Forecasts and The Potential of Ren...

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2022 IEEE Delhi Section Conference, DELCON 2022

作者： Noman, S.M. Salehin, Imrus Hasan, Mohammad Mahedy Baki-Ul-Islam Haque, Obaidul Haque, Ifranul Daffodil International University Dept. of Electrical and Electronic Engineering Dhaka Bangladesh Daffodil International University Dept. of Computer Science and Engineering Dhaka Bangladesh Pabna University of Science and Technology Dept. of Electrical and Electronic Engineering Pabna Bangladesh

ISBN: (纸本)9781665458832

With developing countries, energy demand has been rapid over the years. Bangladesh contributes just 5% of the entire energy ratio in a proportion of renewable energy, and desire to 10% of the year 2025. Bangladesh is based on imported fossil fuel, which is quite expensive and uses natural gas for about 65% of energy production. Bangladesh is fortunate to have a small amount of fossil fuel, i.e., natural gas assets, but these are not adequate to meet the desired robust growth for glorious achievements in the government and non-government sectors. The sustainable power source is a key segment for improvement and has just made significant track in succeeding the greater part of the nation's power request;both in Urban and Rural territories. The motivation behind this study is to analyse energy forecasting for upcoming days and the potential of alternative renewable energy sources to assimilate a comparison in the general diagram of the power utilization of Bangladesh from a global perspective. Anticipating the future demand and looking at the competent outcomes of renewable energy may make urgency among the developing countries to satisfy the high future need for clean energy. In this article, we have focused our attention on the examination of sustainable power sources and the potential outcomes for future energy. © 2022 IEEE.

关键词： Forecasting

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Fraud Detection and Analysis for Insurance Claim using Machine Learning

Fraud Detection and Analysis for Insurance Claim using Machi...

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2022 IEEE International Conference on Signal Processing, Informatics, Communication and Energy Systems, SPICES 2022

作者： Urunkar, Abhijeet Khot, Amruta Bhat, Rashmi Mudegol, Nandinee Walchand College of Engineering Dept. of Computer Science and Engineering Sangli India Walchand College of Engineering Dept. of Information Technology Sangli India St. John College of Engineering and Management Dept. of Computer Engineering Palghar India

ISBN: (纸本)9781665449403

Insurance Company working as commercial enterprise from last few years have been experiencing fraud cases for all type of claims. Amount claimed by fraudulent is significantly huge that may causes serious problems, hence along with government, different organization also working to detect and reduce such activities. Such frauds occurred in all areas of insurance claim with high severity such as insurance claimed towards auto sector is fraud that widely claimed and prominent type, which can be done by fake accident claim. So, we aim to develop a project that work on insurance claim data set to detect fraud and fake claims amount. The project implement machine learning algorithms to build model to label and classify claim. Also, to study comparative study of all machine learning algorithms used for classification using confusion matrix in term soft accuracy, precision, recall etc. For fraudulent transaction validation, machine learning model is built using PySpark Python Library. © 2022 IEEE.

关键词： Insurance

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Strong Baseline with Auto-encoder for Scale-Invariant Person Re-identification 6

Strong Baseline with Auto-encoder for Scale-Invariant Person...

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6th IEEE International Conference on Distributed Computing, VLSI, Electrical Circuits and Robotics, DISCOVER 2022

作者： Bilakeri, Shavantrevva Kotegar, Karunakar Manipal Institute of Technology Manipal Academy of Higher Education Dept. of Data Science and Computer Applications Karnataka Manipal India

ISBN: (数字)9781665487160

ISBN: (纸本)9781665487160

Person Re-identification associates the same person images captured by disjoint cameras. The different training strategies and precise hyper-parameter selection are required for the construction of an effective person re-identification. However, in the current work, the tests are carried out by trial and error, which takes a long time to reach an optimal solution. We experiment with various training strategies with the metric learning model serving as the baseline to address this issue. Existing person re-identification benchmarks have insufficient samples for training the ReID model. Also, the previous approaches fail when the same individual appears in varied sizes. To solve this issue, an auto-encoder module is used to create a single image in three distinct scales to enhance the sample size and tackle the scale variation problem of the person reidentification. In addition, a performance comparison is made between the baseline and the baseline with auto-encoder to demonstrate the influence of an auto-encoder as a data augmentation for the person re-identification. The adoption of auto-encoder module and bestperforming training techniques to a baseline have enhanced the rank1 and mAP on Market1501, DukeMTMC-reID datasets. © 2022 IEEE.

关键词： computer vision

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Cocoscan: AI-Powered Precision Diagnostics for Coconut Leaf Disease 4

Cocoscan: AI-Powered Precision Diagnostics for Coconut Leaf ...

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4th International Conference on Sentiment Analysis and Deep Learning, ICSADL 2025

作者： Santhi, S. Murugan, M. Nandhiha, V. Vivitha, S. Karthikeyan, D. KIT-Kalaignarkarunanidhi Institute of Technology Dept of Computer Science and Engineering Tamil Nadu India Centre for Plant Protection Studies Tamil Nadu Agricultural University Dept. of Agricultural Entomology Tamil Nadu Coimbatore India

ISBN: (数字)9798331523923

ISBN: (纸本)9798331523923

Coconut cultivation is essential in tropical areas, but diseases such as Leaf Spot, Leaf Blight, Grey Leaf Spot, Root Wilt, Stem Bleeding, and Basal Stem Rot can severely harm coconut trees. To reduce the effects of these illnesses and guarantee the highest possible crop yields, early disease detection is important. Conventional detection techniques, which depend on specialist knowledge and manual examination, are labor-intensive, prone to human error, and frequently out of reach for smallholder farmers. This research presents a web-based application that automatically detects and categories diseases of coconut leaves using deep learning, most especially the Inception V3 model. The number of epochs to guarantee comprehensive training, a batch size that strikes a compromise between training speed and memory consumption, and a learning rate optimised for efficient convergence are important model parameters. In order to accommodate the architecture of the model, the image input size is set at 299×299 pixels. Through the web interface, farmers and agricultural specialists can submit photos of coconut leaves. The Inception V3 model, which has been trained on a sizable dataset of both healthy and diseased leaves, analyses the photos and offers real-time feedback, including suggestions for treatment, early disease detection, and disease identification. The user-friendly design requires no specialized knowledge, allowing farmers to quickly receive accurate diagnoses and preventive measures, improving farm management. The system also integrates Explainable AI (XAI) techniques to provide visual explanations of predictions, ensuring transparency and trust in the model's outputs. © 2025 IEEE.

关键词： Information management

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Evaluating the Impact of dataset Variability on Malayalam Emotion Detection System

Evaluating the Impact of Dataset Variability on Malayalam Em...

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2025 International Conference on Emerging Technologies for Intelligent Systems, ETIS 2025

作者： Anuja, K. Reghu Raj, P.C. Remesh Babu, K.R. Govt. Engineering College A. P. J. Abdul Kalam Technological University Dept. of Computer Science and Engineering Kerala Palakkad India Govt. College of Engineering A. P. J. Abdul Kalam Technological University Dept. of Information Technology Kerala Palakkad India

ISBN: (纸本)9798331507541

Text emotion detection has become an increasingly important area of research, with applications ranging from sentiment analysis to enhanced human-computer interaction. The performance of emotion detection systems can, however, vary widely depending on the datasets used for training and evaluation. An emotion detection (ED) system is evaluated, comparing its performance across two datasets that differ in origin, structure, and emotional category, is presented in this paper. We employed a range of evaluation metrics, including accuracy, precision, recall, and F1-score, to analyze the system's performance. The results highlight the impact of dataset char-acteristics on the effectiveness of ED models, revealing notable differences between the two datasets. The performance of the ED system is significantly influenced by factors such as the diversity of emotions and the quality of annotations. This insight contributes to the development of more adaptable and reliable ED systems across a variety of domains. A discussion of future research directions is included at the end of the paper. © 2025 IEEE.

关键词： Emotion Recognition

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Advanced Temporal Attention Mechanism Based 5G Traffic Prediction Model for IoT Ecosystems

Advanced Temporal Attention Mechanism Based 5G Traffic Predi...

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IEEE Internatonal Conference on Mobile Adhoc and Sensor Systems (MASS)

作者： Dharani Sabari Samudrala Sambit Kumar Mishra Rajiv Senapati Dept. of Computer Science & Engineering SRM University Ap India

ISBN: (数字)9798350363999

ISBN: (纸本)9798350364002

Traffic prediction in5G is important for effective deployment and operation of Internet of Things (IoT) ecosystems. It enables resource management and optimization, guaranteeing that the network can handle unpredictable traffic volumes with-out experiencing traffic jams. This helps to ensure high quality of service and low latency for applications such as autonomous automobiles and virtual reality. Predictive traffic management further enhances user experience by keeping services consistent and reliable, particularly during busy hours. There are various approaches to traffic prediction in 5G networks, and each has advantages and disadvantages of its own. The choice of model will depend on how precise, adaptable, and computationally demanding the network must be. The model proposed in this paper integrates lightweight convolution with temporal attention to deliver accurate and efficient traffic prediction for 5G networks that may further be useful for developing IoT ecosystem.

关键词： Solid modeling Adaptation models Attention mechanisms 5G mobile communication Convolution Computational modeling Ecosystems Virtual reality Predictive models Internet of Things

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EMDA-Net: Earth Mover’s Distance (EMD) influenced Attention-aided Neural Network for Medical Image Classification

EMDA-Net: Earth Mover’s Distance (EMD) influenced Attention...

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IEEE International Conference on Cognitive Machine Intelligence (CogMI)

作者： Surya Majumder Kushaj Mallick Wrick Pal Somenath Chakraborty Ram Sarkar Dept. of Computer Science & Engineering Heritage Institute of Technology Kolkata India Dept. of Electrical Engineering Jadavpur University Kolkata India Dept. of Computer Science & Information Systems West Virginia University Institute of Technology West Virginia USA Dept. of Computer Science & Engineering Jadavpur University Kolkata India

ISBN: (数字)9798350386721

ISBN: (纸本)9798350386738

Medical image analysis methods need to be more generalized in order to meet the substantial problems associated with cancer detection and treatment, including skin, cervical, and breast cancer. In this work, we propose a novel lightweight convolutional neural network (CNN) architecture, named EMDA-Net (Earth Mover’s Distance (EMD) influenced Attention-aided Neural Network), specifically designed to meet the requirements of cancer subtype classification. It is built on the MobileNetV2 backbone and is equipped with hybrid statistical mechanism-aided attention mechanisms. Our model ensures high-performance while maintaining a lightweight design by leveraging the capabilities of the MobileNetV2 architecture and the attention mechanism. To improve similarity detection across data channels, we present a novel EMD influenced attention technique. We have examined datasets related to skin, cervical, and breast cancer for experimentation. Our model especially outperforms the state-of-the-art techniques, demonstrating its effectiveness in maximizing diagnosis precision for various cancer types across the datasets. The code of the proposed model will be made public at: https://***/q5DlN

关键词： Earth Attention mechanisms Codes Neural networks Skin Breast cancer Convolutional neural networks Medical diagnostic imaging Machine intelligence Image classification

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