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Intricacy of CNN and RL with Substantiation of CNN Model in Simulated Self-Driving Environment

Intricacy of CNN and RL with Substantiation of CNN Model in ...

Cognitive Computing in Engineering, Communications, sciences and Biomedical Health Informatics (IC3ECSBHI), International Conference on

作者： Trilok Kumar Saini Akanksha Maurya Devanshu Dhyani Anju Saini Defence Electronics Applications Laboratory DRDO Dehradun India Dept. of Computer Science and Engg. Graphic Era Deemed to be University Dehradun India Dept. of Mathematics Graphic Era Deemed to be University Dehradun India

ISBN: (数字)9798331518523

ISBN: (纸本)9798331518530

Machine Learning (ML) has emerged as a technological revolution that has incarnated advancements across various domains. Incorporating machine learning in technology opens a new paradigm for technology innovation. The convolutional Neural Networks (CNNs) paradigm of machine learning technique has a remarkable capacity to automatically extract significant characteristics from inputs. The key attributes of CNN make it indispensable in disciplines like pattern recognition and computer vision. This paper explores the architecture and functioning of CNNs, emphasizing their unique attributes which contribute to their superior performance in complex tasks. Building on the foundation of CNNs, the study also explores Reinforcement Learning (RL), a technique that empowers agents to learn optimal policies through interaction with an environment. By implementing the CNN technique, this paper investigates the potential of the CNN model in autonomous systems, particularly in self-driving vehicles. A CNN model is employed to demonstrate the effectiveness of these techniques. The results highlight the model's ability and the improvements to accurately classify and predict outcomes, underscoring the practical applicability of CNNs in real-world scenarios.

关键词： computer vision Technological innovation Computational modeling Biological system modeling Neural networks Reinforcement learning Predictive models Pattern recognition Convolutional neural networks Accidents

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Enhanced Healthcare Through Metaverse Integration: Explainable AI and Blockchain for Trust, Security, and Immersive Experiences

Enhanced Healthcare Through Metaverse Integration: Explainab...

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Computational Intelligence Approaches and applications (ICCIAA), International Conference on

作者： Bilal Ibrahim Sowan Hazem Qattous Mohammad Fasha Ahmad Altarawneh Dept. of Bus. Intell. & Data Analytics University of Petra Amman Jordan Dept. of Software Engineering Princess Sumaya University for Technology Amman Jordan Dept. of Computer Science Brunel University of London London UK

ISBN: (数字)9798331523657

ISBN: (纸本)9798331523664

The metaverse, a dynamic blend of virtual and augmented reality, holds extraordinary promise for reshaping the healthcare landscape. By integrating advanced technologies such as virtual reality (VR), artificial intelligence (AI), blockchain, and digital twins, the metaverse introduces transformative opportunities in patient care, medical education, and healthcare operations. These tools foster immersive, personalized, and data-driven environments, significantly improving diagnostic precision, therapeutic strategies, and training methodologies. This paper provides a framework to utilize this potential while navigating key concerns, including systems scale, user customization, and ethical adequacy. In that case, the framework's goal should be eliminating existing limitations and barriers within healthcare systems by utilizing state-of-the-art technologies with real-world applications. The framework involves virtual consultation sites that enable provider-patient interactions, intelligent virtual agents with specific care recommendations, blockchain integration for data integrity, and dynamic VR segments that are functional in delivering healthcare education and training. The study focuses on scenario-based modeling and the use of pilot deployment in various healthcare settings to test for practicability and feasibility. The research also emphasizes that ethical issues and inclusion during the design and implementation of the innovations should also consider society's values regarding the medical field and promote equal access to healthcare. This framework proves a comprehensive blueprint for continuous progress and places the metaverse as one cornerstone in the global healthcare system.

关键词： Training Ethics Technological innovation Solid modeling Metaverse Medical services Blockchains Digital twins Security Medical diagnostic imaging

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TWIRL: A Steady Device to Detect the Malicious Fragment Using Various Machine Learning Approaches

TWIRL: A Steady Device to Detect the Malicious Fragment Usin...

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International Conference on Inventive Computation Technologies (ICICT)

作者： Poojarini Mitra Suseta Datta Subhasree Bhattacharjee Pritam Kundu Dept. of CSE - Data Science Techno Main Salt Lake Kolkata India Dept. of Computer Application Narula Institute of Technology Kolkata India Dept. of AI Cloud Tata Consultancy Services Ltd. Kolkata India

ISBN: (数字)9798331512248

ISBN: (纸本)9798331512255

Malware has become a major worry as the use of platforms in sensitive applications such as banking has grown. The arduousness of apprehending such malware engenders a range of challenges due to the inhibited resources available and the circumscribed skills supplied to the utilizer, but it also offers up incipient possibilities in the needed information associated with each entreaty. Malware detection predicated on machine learning algorithms is widely used to forfend and bulwark unique data such as personal portal passwords, personal information, and online transactions. Machine learning classifiers are successful at detecting and ceasing malware assaults. This paper describes some machine learning algorithms that are trained to distinguish between legitimate setup files and malware samples. Some error values have been quantified based on malware samples and legitimate setup files, and 99.99% of endeavor precision has been computed, primarily predicated on the discombobulation matrix of a finely geared-up approach. To make this project more user-friendly, a tool has been constructed as an interactive and dynamic web page. In the raw dataset, attributes that distinguish malware domains from regular domains have been used as examples. The malware sample in the body of the message, including the logo and other legitimate content, is designed to appear to be heading for a fraudulent company.

关键词： Radio frequency Machine learning algorithms Accuracy data visualization Web pages Tag clouds Malware Tokenization Random forests Portals

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The Utilization of Google Cloud in the Healthcare Industry

The Utilization of Google Cloud in the Healthcare Industry

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

作者： Rishikesh Kapse Palash Gourshettiwar Dept. of Artificial Intelligence and Data Science Faculty of Engineering and Technology DMIHER (DU) Sawangi(M) Wardha Maharashtra Dept. of Computer Science And Medical Engineering Faculty of Engineering and Technology DMIHER (DU) Sawangi(M) Wardha Maharashtra

ISBN: (数字)9798331505745

ISBN: (纸本)9798331505752

This essay's primary focus is on Google Cloud's use in healthcare and how it affects data management, teamwork, and cost-effectiveness. With a focus on Google Cloud, which improves the processing, storing, and accessibility of medical data, the study examines the trends, advantages, and persistent challenges that come with the increasing usage of cloud computing technology. These consist of remote patient monitoring, “artificial intelligence,” and “machine learning” applications in diagnostics, diagnostics, and personalized medicine. data privacy, security issues, and concerns with the HIPAA incident are among the problems that are discussed along with all the significant elements that could facilitate the cloud migration of healthcare.

关键词： Industries Cloud computing data privacy Technological innovation Medical services Machine learning Teamwork Security Predictive analytics Medical diagnostic imaging

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AI Enhanced Anesthesia Dosing: Evaluating ML Models

AI Enhanced Anesthesia Dosing: Evaluating ML Models

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Emerging Systems and Intelligent Computing (ESIC), International Conference on

作者： Deepshikha Patangia Amrita Panda Satya Ranjan Dash Paola Barra M Disha Dept. of Anaesthesiology Kalinga Institute of Medical Sciences Bhubaneswar India School of Computer Applications KIIT University India Department of Science and Technology University of Naples "Parthenope" Italy

ISBN: (数字)9798331522100

ISBN: (纸本)9798331522117

Artificial intelligence (AI) is revolutionizing health-care, particularly in the field of anesthesiology. This paper evaluates the application of AI-enhanced models in optimizing anesthesia dosing to improve patient outcomes and operational efficiency. By integrating machine learning (ML) techniques, we analyze structured and unstructured patient data to predict anesthesia needs, automate drug delivery, and monitor vital signs in real time. The study examines several ML models, such as Support Vector Machines, Decision Trees, and Gradient Boosting, assessing their accuracy in predicting anesthetic requirements. The results show that AI can effectively reduce human error, enhance decision-making, and improve patient safety during perioperative care. The paper discusses the system architecture, data collection methods, and model performance, emphasizing the future potential of AI in automating anesthesia processes.

关键词： Support vector machines Analytical models Accuracy Computational modeling Predictive models Anesthesia Drug delivery data models Artificial intelligence Monitoring

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Deep Learning in Autonomous Robotics

Deep Learning in Autonomous Robotics

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Interdisciplinary Approaches in Technology and Management for Social Innovation (IATMSI), IEEE Conference on

作者： Anand Singh Bisen Sami Anand S. Sujatha Tholkapiyan. M Yogendra Kumar Madhuri Sharma Dept. of CSE & IT Vikrant University Gwalior India Dept. of Computer Science and Engineering Lovely Professional University Punjab India Dept. of Civil Engineering K.Ramakrishnan College Trichy India Dept. of Civil Engineering Chennai Institute of Technology Tamil Nadu India Dept. of Computer Eng. & Applications GLA University Mathura India Dept. of Life Sciences CHRI Campus Gwalior India

ISBN: (数字)9798331521691

ISBN: (纸本)9798331521707

Deep learning has become a cornerstone in the development of autonomous robotics, enabling machines to detect, understand, and interact with their environment with precision and efficiency. By leveraging advanced deep neural networks, autonomous robots can process and interpret a wide range of sensory data, including visual, auditory, and tactile inputs. This ability is crucial for tasks like object recognition, scene understanding, and decision-making in complex, dynamic environments. Deep learning algorithms, such as convolutional neural networks (CNNs) and recurrent neural networks (RNNs), provide visual perception, allowing robots to navigate and operate in sophisticated settings. Additionally, reinforcement learning techniques teach robots to make real-time decisions and adjust their actions accordingly. These technological advancements have led to significant progress in applications such as autonomous vehicles, surgical robots, industrial automation, and unmanned aerial vehicles (UAVs). This abstract highlight the various applications and benefits of deep learning in autonomous robotics, emphasizing its role in enhancing the performance and adaptability of robotic systems across diverse fields.

关键词： Deep learning Visualization Technological innovation Service robots Robot sensing systems Sensors Convolutional neural networks Vehicle dynamics Robots Visual perception

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Blockchain Integration for Robust Intellectual Property Protection in New Product Development (NPD) 3

Blockchain Integration for Robust Intellectual Property Prot...

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3rd International Conference on Electronics and Renewable Systems, ICEARS 2025

作者： Chinnasamy, P. Subashini, B. Nijanthan, Nijanthan Madasamy Raja, G. Swetha, Devulapally Sreenivasulu Reddy, L. School of Computing Kalasalingam Academy of Research and Education Department of Computer Science and Engineering Tamilnadu India School of Computing Srmist Department of Data Science and Business Systems Kattankulathur India Paavai Engineering College Tamilnadu Namakkal India Paavai Engineering College Tamilnadu Namakkal India Vaagdevi Engineering College Dept. of Computer Science and Engineering Warangal Dist. Telangana Bolli Kunta506005 India School of Advanced Sciences Kalasalingam Academy of Research and Education Department of Mathematics Tamilnadu Krishnankoil India

ISBN: (纸本)9798331509675

The rapid advancement of Blockchain technology has led to its widespread application in several domains of digital activities, such as e-government concerns and monetary security. This article presents a smart contract-based Blockchain solution for protecting intellectual property rights in new product development. The proposed solution effectively tackles the challenges associated with the confidentiality of personally identifiable information, efficiency in storing transaction-related information, as well as cooperation and monitoring in IPR. Initially, we create a mathematical model based on smart contract enabled Blockchain and intelligent contracts. Subsequently, we develop algorithms for Blockchain generation, and intelligent contracts. The system is suitable for protecting IPR in a blockchain network, by implementation of distributed identity authentication and real-time extensible storage. These features enhance the security and expandability of circuit copyright protection based on blockchain technology. The experimental results demonstrate that the suggested algorithm has decreased the cost for communication and enhanced the effectiveness of storing information and administration. Furthermore, it demonstrates resilience against many common attacks such as double-spending assaults, while maintaining low cost and overhead. Additionally, it exhibits a superior level of security in comparison to three other competing algorithms. © 2025 IEEE.

关键词： Smart contract

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Deep Learning Driven - Pest Classification and Sustainable Agriculture

Deep Learning Driven - Pest Classification and Sustainable A...

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Electronics and Renewable Systems (ICEARS), International Conference on

作者： M Sirish Kumar R Usha B Rama Kantha Reddy S Ramachandra P Jaya Prakash B Swetha Dept. of Data Science School of Computing Mohan Babu University India CSE Madanapalle Institute of Technology and Science Madanapalle CSE (AI and ML) Sri Venkateswara College of Engineering Tirupati CSE Mother Theresa Institute of Engineering and Technology Palamaner Department of IT Sri Venkateswara College of Engineering Tirupati Dept. of Master of Computer Applications Mohan Babu University India

ISBN: (数字)9798331509675

ISBN: (纸本)9798331509682

This study introduces a system designed to identify pests in crops and classify them as either beneficial or harmful. The project begins by providing a comprehensive overview of various pest identification methods, analyzing their strengths and limitations. To address the problem, a categorization approach is proposed that incorporates convolutional neural networks (CNN) to build the pest identification and classification model. The model was trained using a dataset containing images of different pest species. Extensive testing was conducted on a large dataset, and the system's performance was validated against multiple traditional classification models. The proposed framework achieved a classification accuracy of 90%, outperforming conventional methods. This research highlights agriculture's vital role in the economy and underscores the potential of advanced technologies in supporting sustainable farming practices.

关键词： Deep learning Accuracy Insects System performance Transfer learning Crops Pesticides Agriculture Convolutional neural networks Testing

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DEAL-YOLO: Drone-based Efficient Animal Localization using YOLO

arXiv

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arXiv 2025年

作者： Naidu, Aditya Prashant Gosalia, Hem Gakhar, Ishaan Rathore, Shaurya Singh Didwania, Krish Verma, Ujjwal Dept. of Computer Science and Engineering Manipal Institute of Technology Manipal Academy of Higher Education Karnataka Manipal India Dept. of Mechatronics Engineering Manipal Institute of Technology Manipal Academy of Higher Education Karnataka Manipal India Dept. of Information and Communication Technology Manipal Institute of Technology Manipal Academy of Higher Education Karnataka Manipal India Dept. of Data Science and Computer Applications Manipal Institute of Technology Manipal Academy of Higher Education Karnataka Manipal India Dept. of Electronics and Communication Engineering Manipal Institute of Technology Manipal Academy of Higher Education Karnataka Manipal India

Although advances in deep learning and aerial surveillance technology are improving wildlife conservation efforts, complex and erratic environmental conditions still pose a problem, requiring innovative solutions for cost-effective small animal detection. This work introduces DEAL-YOLO, a novel approach that improves small object detection in Unmanned Aerial Vehicle (UAV) images by using multi-objective loss functions like Wise IoU (WIoU) and Normalized Wasserstein Distance (NWD), which prioritize pixels near the centre of the bounding box, ensuring smoother localization and reducing abrupt deviations. Additionally, the model is optimized through efficient feature extraction with Linear Deformable (LD) convolutions, enhancing accuracy while maintaining computational efficiency. The Scaled Sequence Feature Fusion (SSFF) module enhances object detection by effectively capturing inter-scale relationships, improving feature representation, and boosting metrics through optimized multiscale fusion. Comparison with baseline models reveals high efficacy with up to 69.5% fewer parameters compared to vanilla Yolov8-N, highlighting the robustness of the proposed modifications. Through this approach, our paper aims to facilitate the detection of endangered species, animal population analysis, habitat monitoring, biodiversity research, and various other applications that enrich wildlife conservation efforts. DEAL-YOLO employs a two-stage inference paradigm for object detection, refining selected regions to improve localization and confidence. This approach enhances performance, especially for small instances with low objectness scores. © 2025, CC BY.

关键词： Unmanned aerial vehicles (UAV)

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A Big data Analytic and IoT-Based Approach for Addressing Water Scarcity in Jordan

A Big Data Analytic and IoT-Based Approach for Addressing Wa...

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Computational Intelligence Approaches and applications (ICCIAA), International Conference on

作者： Abdelraouf Ishtaiwi Nuha El-Khalili May Alnashashibi Mohammad Abu Arqoub Dept. of Data Science and Artificial Intelligence University of Petra Amman Jordan Department of Software Engineering University of Petra Amman Jordan Department of Computer Science University of Petra Amman Jordan

ISBN: (数字)9798331523657

ISBN: (纸本)9798331523664

Water scarcity critically threatens agricultural sus-tainability in Jordan, necessitating innovative technological solutions. This study presents a comprehensive framework that integrates Internet of Things (IoT) sensors with Big data Analytics to optimize water usage in agriculture. Employing a multi-stage approach, we conducted real-time data acquisition from diverse farms, processed the data using clustering algorithms, and implemented intelligent decision-making systems. Our analysis focused on key parameters such as soil moisture, temperature, rainfall, and wind speed across various farming locations. The methodology encompassed extensive field data collection, advanced analytics, and the deployment of smart irrigation systems. Results demonstrated a significant 25 % reduction in water consumption while maintaining optimal crop yields. Additionally, clustering identified three distinct farm categories, each requiring tailored water management strategies. This research contributes to the field of smart agriculture by providing practical, scalable solutions for regions facing severe water scarcity. The findings highlight the potential of advanced technologies to enhance agricultural sustainability and address critical water management challenges.

关键词： Temperature sensors Smart agriculture Irrigation Wind speed Soil moisture Big data Real-time systems Internet of Things Sustainable development Water resources

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