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RAIL: Recursive A* and Imitation Learning-Based Approach for Collision-Free Path Estimation

RAIL: Recursive A* and Imitation Learning-Based Approach for...

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International Conference on Electronics, Materials engineering and Nano-Technology (IEMENTech)

作者： Subhadip Chandra Kamlesh Kumar Dubey Gaurav Agarwal Anumoy Pathak Department of CSE (AIML) IEM Centre of Excellence for Data Science Institute of Engineering and Management University of Engineering & Management Kolkata India Department of Applied Sciences and Humanities Invertis University Bareilly UP India Department of Computer Science & Engineering Invertis University Bareilly UP India Department of CSBS Institute of Engineering and Management University of Engineering &Management Kolkata India

ISBN: (数字)9798331520762

ISBN: (纸本)9798331520779

This paper discusses collision-free path planning for autonomous vehicles, a crucial aspect of their development. The authors propose a method to detect objects of different sizes, both dynamic and static, and compare algorithms for path planning and avoidance of collision. They use traditional path-finding algorithms like A* Algorithm, which include heuristics and Rapidly Exploring Random Tree (RRT) algorithms for real-time analysis. The authors discuss the challenges of ensuring path-planning algorithms scale to large environments and operate in real-time, and the need for future work on improving the robustness of path-planning algorithms in dynamic and unpredictable scenarios. The methodology involves creating a dataset, analyzing images, and training neural networks to imitate the collision-free path- finding algorithm. Static obstacles change their coordinates with time, making working in dynamic environments difficult. Recursive A* and Imitation Learning (RAIL) based approaches analyze obstacles frame by frame, making them static at a particular time-instant. The Generative Adversarial Network (GAN) and Inverse Reinforcement Learning are used to train an imitation learning model for faster trajectory finding in autonomous vehicles. Future development could involve using the Internet of Things (IoT) to share the trained trajectory-finding model results.

关键词： Rails Machine learning algorithms Heuristic algorithms Imitation learning Generative adversarial networks Real-time systems Robustness Internet of Things Vehicle dynamics Autonomous vehicles

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Estimation of Response Time with Neural Networks in Cloud Computing System

Estimation of Response Time with Neural Networks in Cloud Co...

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Advances in computer science, Electrical, Electronics, and Communication Technologies (CE2CT), International Conference on

作者： Rajesh More Sanjna Suresh Sathish Kumar Veerappampalayam Easwaramoorthy Haider Ali Rizvi Kannan Kunnathully Sahil K. Gupta Departmen of AIML Cloudwerx Wordwide Hyderabad Department of Computer Science and Engineering Amrita Vishwa Vidyapeetham Coimbatore School of Engineering and Technology Sunway University Selangor Darul Ehsan Malaysia Department of Artificial Intelligence and Machine Learning Yogananda School of Artificial Intelligence Shoolini University Department of Research Veltech University Chennai Department of Computer Science and Engineering Tula's Institute Dehradun India

ISBN: (数字)9798331518578

ISBN: (纸本)9798331518585

Resource allotment, responsiveness, connection, research of unexplored assets, related resource modeling, and asset scheduling are just a few of the numerous factors and concerns that need to be considered to enhance cloud computing efficiency. The functionality of the cloud has a significant impact on the propagation of cloud architecture. Consequently, it is important to determine the components that influence cloud function to analyze it. Response duration is the primary aspect that significantly affects cloud computation functioning, according to the research. The response duration assistance is among the numerous different variables that may be used to organize resource applications. Artificial neural networks (ANNs) were employed as a technique for evaluating the parameter of pursuit, and a fork-join framework or an architecture with demand partitioning was selected as the queuing paradigm. As a result, this study suggests a load-managing method depending on cloud demand response duration using the ARIMA Estimation of Response Duration Method. The fundamental concept is to employ ARIMA methods to estimate the future response duration, providing a more efficient method of allocating resources with limited values. The results of the investigation demonstrated that the estimations were more precise compared to those that had been recently established. The results of the study indicate that estimation is an outstanding path for load balance and has possibility and value for load balance with displayed response duration. Additionally, the suggested method makes it feasible to acquire estimations for a wider range of cloud structure functioning signs compared to earlier feasible and also to extend the evaluation of the cloud platform to the instance of a structure with a non-Poisson data flow and non-exponential assistance duration.

关键词： computer science Cloud computing Processor scheduling Estimation computer architecture Artificial neural networks Load management Scheduling Demand response Time factors

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Enhanced Demand-Side Management for Electric Vehicle Charging Stations Using Pyramidal Dilation Attention Neural Networks

Enhanced Demand-Side Management for Electric Vehicle Chargin...

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Mobile Computing and Sustainable Informatics (ICMCSI), International Conference on

作者： N. Venkateswaran Ramprabu J Ramaprabha Marimuthu Nagella Venkata Ramana Mahesh Kotha Balasubbareddy Mallala Department of Master of Business Administration Panimalar Engineering College Chennai Tamil Nadu India Department of Electrical and Electronics Engineering Kumaraguru College of Technology Coimbatore Tamil Nadu India Department of Computer Science and Engineering SRM Institute of Science and Technology Chennai Tamil Nadu India Department of Management Studies Madanapalle Institute of Technology & Science Madanapalle Andhra Pradesh India Department of Computer Science and Engineering (AIML) CMR Technical Campus Hyderabad Telangana India Department of Electrical and Electronics Engineering Chaitanya Bharathi Institute of Technology Hyderabad Telangana India

ISBN: (数字)9798331522667

ISBN: (纸本)9798331522674

With the improvement in EV's popularity, controlling the load demand of EVCS's has become an essential task to keep the grid balanced and efficient. Conventional DSM techniques tend to rely on historic patterns to predict charging trends, hence cannot serve as viable solutions to peak load problems. In order to improve charging demand forecasting, this research presents a Pyramidal Dilation Attention Convolutional Neural Network (PDACNN) for the DSM of EV charging stations. By using precise forecasting and ideal charging schedule, this study aims to increase the effectiveness of DSM in EV charging stations. The PDACNN is used to predict the charging load for EV stations, allowing for optimized scheduling of charging sessions that align with grid capacity and energy availability. By then, the proposed method is implemented on MATLAB platform and evaluated with other existing methods such as Artificial Neural Network (ANN), Deep Reinforcement Learning (DRL), and Genetic Algorithm (GA). The proposed PDACNN method achieves an RMSE of 0.0753 and an accuracy of 98.9%, demonstrating its effectiveness in EV charging load forecasting and demand-side management.

关键词： Training Accuracy Demand side management Scalability Transportation Electric vehicle charging Mathematical models Convolutional neural networks Vehicle dynamics Genetic algorithms

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Effective Summarization Techniques for Legal Document Analysis

Effective Summarization Techniques for Legal Document Analys...

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Sentiment Analysis and Deep Learning (ICSADL), International Conference on

作者： Neelima Gurrapu J S V Gopala Krishna V T Ram Pavan Kumar M Raghu Dhumpati Jeevana Sujitha Mantena Kiran Kumar Reddy Penubaka Department of computer science and Artificial intelligence SR University Warangal India Department of CSE Sir C R Reddy College of Engineering Eluru India Department of Computer Science Kakaraparti Bhavanarayana College Vijayawada India Department of CSE Bahrain Polytechnic College Bahrain Department of CSE SRKR Engineering College Bhimavaram India Department of CSE-AIML MLR Institute of Technology Hyderabad India

ISBN: (数字)9798331523923

ISBN: (纸本)9798331523930

Summarizing legal texts is a critical task in natural language processing (NLP), which addresses the growing demands for efficient processing of large legal documents. Due to the complexity and structured nature of legal texts, the development of models capable of generating accurate and concise summaries presents significant challenges. This study examines the use of three state-of-the-art transformer-based models for summarizing legal texts. Each model is evaluated for its ability to capture nuanced semantics and preserve the contextual integrity of legal documents. The research uses a specialized dataset containing different types of legal documents, including summaries of case law, statutes and regulatory texts. Preprocessing steps include tokenization, normalization, and removal of non-informative content, ensuring that model inputs reflect the structure and verbosity contained in legal texts. Performance benchmarking is done using standard metrics such as ROUGE, BLEU, and BERT Score, along with domain-specific criteria to assess factual consistency and relevance. Experimental consequences exhibit that BART-base excels in generating fluent and human-like summaries, LED-base proves effective for longer documents due to its prolonged context-handling ability, and T5 showcases its versatility in domain edition. A specified analysis famous the strengths and limitations of every version in dealing with domain-unique demanding situations consisting of felony terminology, quotation references, and logical reasoning.

关键词： Training Measurement Adaptation models Analytical models Accuracy Law Light emitting diodes Transformers Tokenization Complexity theory

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Speech Measuring Following Semiconductor Feature for Acoustic Using Biodegradable Material

Speech Measuring Following Semiconductor Feature for Acousti...

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

作者： Shrikant Upadhyay Rakesh Kumar Binod Kumar Barkha Kumari Ankita Lakra Sampurna Mandal Department of Electronics and Communication Engineeing MLR Insitute of Technology Hyderabad India Wipro Engineering Edge Hyderabad India Facultyt of CSE & IT Jharkhand Rai University Ranchi India Department of Computer Science and Engineering (AI & AIML) GH Raisoni College of Engineering and Management Pune Faculty of Computing and IT Usha Martin Univerity Ranchi India Department of Computer Science and Engineering-Artifical Intelligence Brainware University Kolkata India

ISBN: (数字)9798331527549

ISBN: (纸本)9798331527556

This research paper primarily focuses to identify the various feature removal presence used for addresser recognition and validation. The current template i.e. classical and typical are considered to examine its role in different acoustic surroundings and also try to measure feature removal in acoustic condition considering factors like jitters, noise, echo etc. which truly a factor that affect the speech performance and degrades the overall performance of any system. Effort has been put to inspect the attainment of various feature removal approach with the help of classifier considering different structures. The behavior of different feature extraction removal simulated using Hindi samples and it will be analysed in terms of efficiency and error rate. The result may be useful for suitable for existing as well as any proposed model by changing its classifiers. Speech of different ages as well groups are used to analysed and implementation using 16-bit TMS 320 C5X semiconductor devices with the feature extraction and algorithm may improve the overall efficiency of the proposed system. Bio-degradable material can also help to reduce the acoustic level at different of speaker identifications. The feature of semiconductor helps to understand and also add the improvements in proper selection of feature extraction that consume less time in evaluating the algorithm and provide better efficiency in different acoustic conditions. Biodegradable material has not used so far in various studies and biodegradable paly a very important role in acoustic condition as we are dealing with acoustic condition and it is very important to take care of acoustic to come up with better efficiency.

关键词： Semiconductor device modeling Biodegradable materials Semiconductor device measurement Error analysis Biological system modeling Noise Feature extraction Acoustics Mel frequency cepstral coefficient Standards

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Exploring Time Series Techniques in Production Function Modeling: ARIMA and VECM Applications

Exploring Time Series Techniques in Production Function Mode...

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International Conference on ent Computing and Control Systems (ICICCS)

作者： Mohammed Ali Shaik Abdul Rahim V Subhalakshmi Dr R Ravi Kumar Raghunadh Pasunuri Dhanraj Verma School of Computer Science & Artificial Intelligence SR University Warangal Telangana India Department of ECE Malla Reddy Engineering College for Women Secunderabad India Department of CSE(AI&ML) Vidya Jyothi Institute of Technology Department of CSE CMR College of Engineering & Technology Hyderabad India Dept Of CSE AIML Malla Reddy Engg. College PIET- AIML Parul University Vadodara Gujarat

ISBN: (数字)9798331512088

ISBN: (纸本)9798331512095

This paper uses both used ARIMA and VECM models to analyze the production function to determine the interactions between GDP, capital and labor inputs in this analysis. The paper explores short-term dynamics as well as equilibrium behavior of these variables using annual data for the period 1984–2023. The coefficients estimated by the ARIMA (2,0,0) model indicate that current GDP depends on its past levels, and both capital and labor, although they have positive signs, are statistically insignificant. Moreover, Johsen cointegration estimates unfold one co-integrating vector; VECM result also confirms the positive association between capital and GDP but an unexpected negative relation with labor. The above study’s findings reaffirm the status of capital accumulation as a foundation for developing states’ growth policy priorities; the results of labor analysis are inconclusive and may indicate issues such as difficulty in measuring labor or the possibility of structural changes. The policy implications are for the continuous long term orientations, reform of the labor market, improving human capital, and expanding economic diversification. The paper recognizes certain limitations in the type of data and model used and recommends that future research consider non linearities, technological progress to transfer technological improvement to the production function as well as examining sectoral differences for a better understanding of growth paths in fast growing economies.

关键词： Analytical models Economic indicators Sensitivity analysis Biological system modeling Computational modeling Time series analysis Stability criteria Production Vectors Thermal stability

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Two-Stage Dual Boosting for Alzheimer's Disease Prediction Optimizing Accuracy with Feature Selection 2

Two-Stage Dual Boosting for Alzheimer's Disease Prediction O...

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2nd International Conference on Machine Learning and Autonomous Systems, ICMLAS 2025

作者： Nagabansa, Manaswini Padmapriya, M. Kumar, V T Ram Pavan Ganji, Damodar Penubaka, Kiran Kumar Reddy Battarusetty, Lakshmana Rao Gandhi Institute of Engineering and Technology University Department of CSA Odisha Gunupur India SRM Institute of Science and Technology Department of CSE Tamil Nadu India Kakaraparti Bhavanarayana College Department of Computer Science Vijayawada India Sir C R Reddy College of Engineering Department of CSE Eluru India Institute of Technology Department of CSE-AIML MLR Hyderabad India NBKR Institute of Science and Technology Department of CSE Tirupathi India

ISBN: (纸本)9798331505745

Alzheimer's disease (AD) is a difficult task to manage since it is chronic and difficult to diagnose in the initial stages. Forecasting of this diseases progress is very important in terms of treatment and management at the right time. This work presents the 2DB model, a stacked ensemble of AdaBoost and XGBoost classifiers, to improve the accuracy of the AD diagnosis. The dataset adopted includes extensive health, lifestyle, and cognitive factors collected from Kaggle. The work includes an extensive data pre-processing step where issues such as missing values, and normalization, were also tackled alongside SMOTE to deal with the class imbalances. Feature selection is done through Analysis of Variance (ANOVA) to select the relevant predictors. Two-Stage Dual Boosting (2DB) combines the weight distribution of AdaBoost that reassigns weights to all the misclassified instances with XGBoost's gradient boosting method for classification, where logistic regression is the base classifier for meta-classification. The proposed model was assessed with quantitative measurements such as accuracy, precision, recall, and F1-score to its maximum accuracy of 94.6%. The performance shows that the proposed Two-Stage Dual Boosting (2DB) model's generalization significantly outperforms traditional classifiers while providing accurate predictions of Alzheimer's disease with better robustness and interpretability. The results of this work show the possibility of increasing the diagnostic accuracy of the junction hybrid machine learning models and have important potential for clinical usage in early diagnosis. © 2025 IEEE.

关键词： Logistic regression

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Two-Stage Dual Boosting for Alzheimer's Disease Prediction Optimizing Accuracy with Feature Selection

Two-Stage Dual Boosting for Alzheimer's Disease Prediction O...

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Machine Learning and Autonomous Systems (ICMLAS), International Conference on

作者： Manaswini Nagabansa M Padmapriya V T Ram Pavan Kumar M Damodar Ganji Kiran Kumar Reddy Penubaka Lakshmana Rao Battarusetty Department of CSA Gandhi Institute of Engineering and Technology University Gunupur Odisha India Department of CSE SRM Institute of Science and Technology Tamil Nadu India Department of Computer Science Kakaraparti Bhavanarayana College Vijayawada India Department of CSE Sir C R Reddy College of Engineering Eluru India Department of CSE-AIML MLR Institute of Technology Hyderabad India Department of CSE NBKR Institute of Science and Technology Tirupathi India

ISBN: (数字)9798331505745

ISBN: (纸本)9798331505752

关键词： Accuracy Stacking Redundancy Predictive models Feature extraction Boosting Prediction algorithms Robustness Alzheimer's disease Analysis of variance

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A Strong and Adaptable Watermarking Methodology for Relational Databases

A Strong and Adaptable Watermarking Methodology for Relation...

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Intelligent Control, Computing and Communications (IC3), International Conference on

作者： Dharmapuri Siri S. Sangeetha P. Kalaiselvi Devayanai V C Haassan M.M. AlJawahry Ritin Behl Department of CSE Gokaraju Rangaraju Institute of Engineering and Technology Hyderabad Telangana India Department of CSE CMR College of Engineering & Technology Hyderabad Telangana Department of Computer Science Engineering Sethu Institute of Technology Kariapatti Tamil Nadu India Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences Chennai Tamil Nadu India Department of computers Techniques engineering College of technical engineering The Islamic University Najaf Iraq Department of CSE (AIML) ABES Engineering College Ghaziabad

ISBN: (数字)9798331527495

ISBN: (纸本)9798331527501

Watermarking technique refers to discreet embedding of an imprint to establish possession of the plagiarism. The current databases watermarking methods lack the ability to self-adapt to various kinds of information & exhibit inadequate robustness. The article introduces a novel and resilient initiative for watermarking databases. The plan has the capability of adapting the watermarking technique & variables to suit the particular features of quantitative & textual information. We have successfully demonstrated the effectiveness and reliability of our approach through both theoretical analysis & empirical validation, particularly in regards of its resilience & minimal information deformation. This feature renders it appropriate for objectives such as safeguarding plagiarism, identifying owners, or tracking potential traitors.

关键词： Deformation Plagiarism Watermarking Relational databases Reliability theory Robustness Object recognition Resilience Intelligent control

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RhinoGuardNet: An Integrated Deep Learning System for Monitoring, Movement Prediction, and Threat Detection in Javan Rhino Conservation

RhinoGuardNet: An Integrated Deep Learning System for Monito...

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Machine Learning and Autonomous Systems (ICMLAS), International Conference on

作者： P. Ebby Darney A. Vegi Fernando R. Santhana Krishnan J. Relin Francis Raj V. Lavanya V. Vinoth Kumar Department of Electrical and Electronics Engineering Rajarajeswari College of Engineering Bangalore India Department of AIML (CS Cluster) Dayananda Sagar University Bangalore India Department of Electronics and Communication Engineering SCAD College of Engineering and Technology Cheranmhadevi India Department of Electronics and Communication Engineering Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences Chennai India Department of Computer Science and Engineering Velammal College of Engineering and Technology Madurai India Department of Electronics and Communication Engineering Vel Tech Rangarajan Dr.Sagunthala R&D Institute of Science and Technology Avadi Chennai India

ISBN: (数字)9798331505745

ISBN: (纸本)9798331505752

The Javan rhinoceros (Rhinoceros sondaicus), one of the most critically endangered species, faces numerous threats such as poaching, habitat destruction, and limited monitoring capabilities. Traditional conservation methods are often ineffective due to the vast and difficult-to-access nature of rhino habitats. This paper proposes a Hybrid Deep Learning (HDL) framework, combining CNNs, RNNs, and GNNs, to address these challenges and enhance conservation efforts. The proposed system offers a comprehensive solution for real-time rhino monitoring, poacher detection, and habitat preservation. The framework integrates CNNs for image-based monitoring, enabling the detection of rhinos, poachers, and habitat changes using camera trap images, drone footage, and satellite images. RNNs, specifically LSTM networks, are employed to analyze bioacoustic recordings from forest microphones, capturing long-term dependencies in sound data to detect rhino vocalizations and poaching sounds like gunshots or chainsaws. In addition, GNNs are utilized to process GPS tracking data to model spatial-temporal relationships and predict rhino movement, optimizing anti-poaching patrol routes and identifying high-risk zones for poaching. The proposed framework has been assessed using various evaluation metrics such as mAP for object detection, F1-Score for audio classification, and RMSE for movement prediction. The system is designed to be deployed in the field on a cloud-based platform, enabling real-time monitoring and decision-making. Edge computing on drones and sensor devices ensures low-latency processing and immediate action in response to detected threats. This model is expected to significantly enhance Javan rhino conservation by providing accurate, real-time data and actionable insights.

关键词： Deep learning Tracking Habitats Real-time systems Data models Threat assessment Satellite images Recording Monitoring Drones

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