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LiDAR-Based Climate Change Imaging in Geoscience Using Spatio Extreme Fuzzy Gradient Model

Remote Sensing in Earth systems sciences 2025年第2期8卷 465-472页

作者： Krushnasamy, V.S. Al-Omari, Omaia Sundaram, Ajith Varghese, Issac K. Muniyandy, Elangovan Rao, M. V. A. L. Narasimha Dept. of EIE Dayananda Sagar College of Engineering Bangalore India Information Systems Department College of Computer and Information Sciences Prince Sultan University Riyadh Saudi Arabia Amrita School of Business Amrita Vishwa Vidyapeetham Kochi India T A Pai Management Institute Manipal Academy of Higher Education Manipal India Department of Biosciences Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences Chennai 602105 India Applied Science Research Center Applied Science Private University Amman Jordan Department of MBA Koneru Lakshmaiah Education Foundation Andhra Pradesh Vaddeswaram Guntur 522302 India

In order to estimate high-resolution air temperatures, computationally demanding procedures are required because climate models do not provide the resolution required for urban climate studies. Data-driven methods, on the other hand, provide air temperature downscaling that is quicker and more precise. Lidar technology has become a vital tool for researching these atmospheric components because of its capacity to measure backscattered signals and investigate the atmosphere at extremely high spatial and temporal resolution. However, machine learning has become a potent technique for sophisticated data processing and analysis because of the complexity and diversity of lidar technology. This research proposes a novel technique in urban–rural interface analysis in remote sensing by machine learning (ML) model. Here the input is collected as urban–rural landscape data and processed for noise removal as well as normalisation. Then these data features have been extracted and classified using a spatio extreme fuzzy gradient model with reinforcement Markov vector perceptron neural networks. Experimental analysis has been carried out for various geographical regions in terms of training accuracy, average precision, recall, RMSE, and F-1 score. The proposed model attained an F-1 score of 92%, training accuracy of 98%, average precision of 95%, recall of 93%, and RMSE of 55%. © The Author(s), under exclusive licence to Springer Nature Switzerland AG 2025.

关键词： Lidar technology Machine learning model Reinforcement Markov vector Remote sensing Spatio extreme fuzzy gradient

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Dense Residual Networks for Gaze Mapping on Indian Roads

arXiv

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arXiv 2022年

作者： Kapoor, Chaitanya Kumar, Kshitij Vishnoi, Soumya Ramanathan, Sriram Dept. of Electrical and Electronics Engineering Bits Pilani Dept. of Computer Science and Information Systems Bits Pilani

In the recent past, greater accessibility to powerful computational resources has enabled progress in the field of Deep Learning and computer Vision to grow by leaps and bounds. This in consequence has lent progress to the domain of Autonomous Driving and Navigation systems. Most of the present research work has been focused on driving scenarios in the European or American roads. Our paper draws special attention to the Indian driving context. To this effect, we propose a novel architecture, DR-Gaze, which is used to map the driver's gaze onto the road. We compare our results with previous works and state-of-the-art results on the DGAZE dataset. Our code will be made publicly available upon acceptance of our paper. © 2022, CC BY.

关键词： Navigation systems

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FPGA-based Low Power Acceleration of HARK Sound Source Localization

FPGA-based Low Power Acceleration of HARK Sound Source Local...

引用

IEEE Cool Chips

作者： Zirui Lin Katsutoshi Itoyama Kazuhiro Nakadai Hideharu Amano Dept. of Systems and Control Engineering School of Engineering Tokyo Institute of Technology Tokyo Japan Honda Research Institute Japan Co. Ltd. Saitama Japan Dept. of Information and Computer Science Keio University Kanagawa Japan

ISBN: (数字)9798350384147

ISBN: (纸本)9798350384154

This paper presents an FPGA-based low-power acceleration of sound source localization in HARK, open-source software for robot audition. Due to the massive matrix operations, sound source localization in HARK takes substantial processing time in edge computing devices. To balance processing time and low power consumption, two functions in sound source localization that include many matrix operations are targeted and migrated on an FPGA SoC board called M-KUBOS. Compared to CPU-based computing on ARM Cortex A53, our implementation achieved a 2.0× speedup and 1.7× lower energy consumption.

关键词： Location awareness Program processors Power demand Parallel processing Energy efficiency Matrix decomposition Robots

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Discrete Shuffled Frog Leading Algorithm for Multiple-product Human-robot Collaborative Disassembly Line Balancing Problem

Discrete Shuffled Frog Leading Algorithm for Multiple-produc...

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2022 IEEE International Conference on systems, Man, and Cybernetics, SMC 2022

作者： Fan, ChenYang Wang, JiaCun Guo, XiWang Qin, ShuJin Zhou, MengChu Qi, Liang Engineering of Liaoning Petrochemical University School of Information and Control Fushun China Engineering of Monmouth University Dept. Computer Science and Software West Long Branch United States Liaoning Petrochemical University Fushun China Monmouth University West Long Branch United States College of Economics and Management of Shangqiu Normal University Shangqiu China Dept. Electrical and Computer Engineering of New Jersey Institute of Technology Newark United States Tech of Shandong University of Science and Technology Dept. Computer Intelligent Sci. & Qingdao China

ISBN: (数字)9781665452588

ISBN: (纸本)9781665452588

With the rapid development of recycling and remanufacturing technologies, disassembly line balancing problems (DLBP) have drawn great attention. Considering the limitation of disassembly by humans or robots alone, this paper focuses on human-robot collaborative disassembly lines. Specifically, this work proposes a multi-product human-robot collaborative disassembly line balancing model to tackle the inflexibility of single product disassembly and inconsistency in recovery values of different product components. Its objective is to maximize disassembly profit. As a commercially available solver, IBM's CPLEX is used to obtain the exact solution of DLBP and verify the correctness of the proposed mathematical model. A discrete shuffled frog leading algorithm is newly designed to solve the sizable problems. Experimental results show that the proposed algorithm has a fast convergence rate and can find solutions consistent with those with CPLEX but requires much less time than the latter, thus advancing the field of disassembly automation. © 2022 IEEE.

关键词： Robots

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Web Application-Based Enhanced Esophageal Disease Diagnosis in Low-Resource Settings

Web Application-Based Enhanced Esophageal Disease Diagnosis ...

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2024 IEEE International Conference on Biomedical engineering, computer and information Technology for Health, BECITHCON 2024

作者： Al Masum, Abdullah Limon, Zishad Hossain Islam, Md Ariful Rahman, Md Shahidur Khan, Mahbub Afridi, Sheikh Shemanto Ahmed, Md. Redwan Rahman, Shafiur Dept. of Information Technology Westcliff University CA92614 United States Dept. of Computer Science Westcliff University CA92614 United States Dept. of Business Administration International American University CA90010 United States Dept. of Engineering management Westcliff University CA92614 United States Dept. of Management Information System Pacific State University CA90010 United States Dept. of Computer Science and Engineering East West University Dhaka1212 Bangladesh Dept. of Software Engineering Daffodil International University Dhaka1216 Bangladesh

ISBN: (纸本)9798331534356

An essential part of the digestive system, the esophagus permits food and liquids to move easily from the throat to the stomach. Traditional diagnostic techniques, such as endoscopy and biopsy, are invasive and resource-intensive, making them less practical in resource-constrained environments. To address these challenges, we proposed utilizing a hybrid Deep Learning (DL) model called GAN-EfficientNet, which combines the power of Generative Adversarial Networks (GAN) and EfficientNet to effectively classify esophageal diseases. Our approach employs advanced image preprocessing techniques and data augmentation methods to enhance the diversity and balance of the dataset. Feature extraction using the Spatial Gray Level Dependence Method (SGLDM) and Principal Component Analysis (PCA) significantly reduces computational complexity without compromising accuracy. We explored both binary and multiclass classification methods, recognizing their crucial roles in various diagnostic scenarios. Binary classification distinguishes between normal and diseased states, essential for initial screening, while multiclass classification identifies specific esophageal diseases, aiding in personalized treatment plans. The GAN-EfficientNet model demonstrated exceptional performance, achieving an impressive F1 score of 99.44% for binary classification and 99.64% for multiclass classification. Additionally, we developed a web application that facilitates quick and precise disease diagnosis, particularly advantageous in areas with limited resources. This tool significantly improves patient outcomes through early detection, leading to reduced healthcare costs and less burden from advanced esophageal diseases. © 2024 IEEE.

关键词： Deep learning

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A CNN Based Approach to Classify the Folk Dances of Odisha

A CNN Based Approach to Classify the Folk Dances of Odisha

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2022 IEEE International Conference on Electronics, Computing and Communication Technologies, CONECCT 2022

作者： Samantaray, Swati Patra, Sudhansu Shekhar Mohanty, Amlan Barik, Lalbihari Barik, Akash Brahma, Aditya Narayan Kiit Deemed to Be University School of Humanities Bhubaneswar India Kiit Deemed to Be University School of Computer Applications Bhubaneswar India Iiit Department of Computer Science Bhubaneswar India King Abdulaziz University Faculty of Computing & Information Technology in Rabigh Department of Information Systems Saudi Arabia New Horizon College of Engineering Dept. of Computer Science Bengaluru India

ISBN: (数字)9781665497817

ISBN: (纸本)9781665497817

Folk dances are a primordial expression of ecstasy and bliss and serve as a mode of communication. They also facilitate in keeping the people connected to their traditions as well as ancestry. Furthermore, these dances help in preserving cultural unity and their tales can often reveal a lot about the periods these dances have developed through. Mahari, Gotipua and Odissi are such folk dance forms of the eastern state of Odisha. These folk dances are on the verge of extinction owing to the reducing number of practitioners, audience, absence of documentation, and digitalization. The absence of documentation has presented a barrier to the legitimacy of folk dance as an academic field of study. This research paper intends to focus on the classification of the folk dances of Odisha such as Mahari, Gotipua and Odissi which will help them to digitize and preserve. We propose deep learning-based methods for the classification and then it can be preserved. To validate the poses and hand motions, we offer a convolutional neural network model (CNN). © 2022 IEEE.

关键词： Neural network models

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Towards Eco-Sustainability and Green Analytics Model to Measure the Performance of Big Data systems

Towards Eco-Sustainability and Green Analytics Model to Meas...

引用

International Conference on information Management (ICIM)

作者： Yusmadi Yah Jusoh Rusli Abdullah Rozi Nor Haizan Nor Jamaiah Hj. Yahaya Shireen Muhammad Aishwharya Raani Arunachalam Dept. of Software Engineering and Information Systems Faculty of Computer Science and Information Technology Universiti Putra Malaysia Serdang Malaysia Faculty of Information Science and Technology Universiti Kebangsaan Malaysia Bangi Malaysia Faculty of Communication Visual Art and Computing Universiti Selangor (UNISEL) Selangor Malaysia

Many BDS is doomed to failure because of the missing knowledge on measuring the performance of BDS. The failure to identify the performance measurement and correct will make the problems worsen. This will complicate the efforts of fixing such problems and more importantly, will affect the quality of knowledge, insights, and results expected by the users in the future. In achieving durable performance, the organization needsto understand the factors that contribute to the performance of the eco-sustainability and green analytics metrics for BDS. Although the existence of its importance and awareness was largely neglected. Therefore, the eco-sustainability and green analytic measurement of the BDS is examined in this research. Measuring the BDS performance has the benefit of identifying problems and launching corrective actions before these problems happen and become worsen. The research will investigate the eco-sustainability and green analytic measures which will be proposed and used to capture performance for each process of BDS. Then, based on such measures and metrics, as well as existing performance concepts, frameworks, and models, a measurement model for BDS will be created. The model will be validated through expert evaluations and confirmatory studies with users and practitioners of BDS users. The research has a greater significance in leveraging existing performance concepts in BDS settings and the research proposes users' participation in the sustainability and continuous performance evaluation of theirBDS.

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Rain Predictive Model using Machine learning Techniques

Rain Predictive Model using Machine learning Techniques

引用

Multi-disciplinary Conference in Emerging Research Trends (IMCERT), International

作者： Muhammad Shahbaz Muneer Syed Muhammad Nabeel Mustafa Syeda Sundus Zehra Haniya Maqsood Dpertment of Computational Finance NED University of Engineering and Technology Karachi Pakistan Dept. of Computer Science & I.T NED University of Engineering and Technology Karachi Pakistan Dept. of Software Engineering Sir Syed Uuniversity of Engineering and Technology Karachi Pakistan Department of Computer and Information Systems Engineering NED University of Engineering and Technology Karachi Pakistan

Climate is rapidly changing around the world. Over time, there have been significant changes in the weather. Rainfall is now erratic due to climate change. The frequency of extreme weather events like droughts and floods has increased due to climate change, necessitating the need for more precise and timely rainfall forecasts. For strategic reasons including agriculture, water resource management, and architectural design, rain forecasting is crucial. The naturally occurring non-stationary component in the rainfall time series impairs model performance for practical hydrologists and drought risk assessors. We present a rain predicting model based on machine learning to address the forecasting issue. In our work, we predict the possibility of rain the next day on the basis of last 10 years' data. The variables that were calculated during the experiments were humidity, pressure, evaporation, sunshine, rainfall, and so on. Random Forest gave the 90% accuracy with 0.904 Area under Curve, highest out of all the algorithms. The model's performance will significantly aid in the rain forecast.

关键词： Climate change Rain Machine learning algorithms Droughts Weather forecasting Predictive models Prediction algorithms

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Effective Disease Recognition in Cucumbers: A Web-Based Application Using Transfer Learning Models

Effective Disease Recognition in Cucumbers: A Web-Based Appl...

引用

Robotics, Automation, Artificial-intelligence and Internet-of-Things (RAAICON), 2019 IEEE International Conference on

作者： Md Shahidur Rahman Asif Ahamed Md Nuruzzaman Pranto Md Ariful Islam Abdullah Al Masum Abdullah Al-Sakib S M Arafat Rahman Rezaul Haque Shafiur Rahman Dept. of Engineering Management Westcliff University CA USA College of Technology and Engineering Westcliff University CA USA Dept. of Business Administration International American University CA USA Dept. of Information Technology Westcliff University CA USA Dept. of Computer Science and Engineering East West University Dhaka Bangladesh Dept. of Software Engineering Daffodil International University Dhaka Bangladesh

ISBN: (数字)9798331534400

ISBN: (纸本)9798331534417

Cucumber farming plays a crucial role in Bangladesh's agricultural economy, significantly contributing to vegetable production. However, diseases like Downy Mildew, Bacterial Wilt, Anthracnose, and Belly Rot threaten crop quality and lead to financial losses. Effective management of these diseases is crucial for maintaining high crop yields and ensuring economic stability. Current disease identification methods are labor-intensive, costly, and dependent on expert knowledge. To address these challenges, this study presents an automated disease categorization system using advanced transfer learning models, including MobileNet, ShuffleNet, InceptionResNet-v2, AlexNet, and Ensemble Transfer Learning (ETL). Utilizing a dataset of 8,000 well-annotated images, enhanced through preprocessing techniques like GLRM feature extraction and InfoMAX-GAN feature selection, the ETL model achieved a remarkable accuracy of 99.79% and an F1-score of 99.78%. We also developed a user-friendly web tool for real-time diagnostics, providing invaluable support to farmers and agricultural specialists. This tool enables rapid disease detection, leading to increased productivity and a transformative approach to cucumber disease management. By reducing the reliance on chemical interventions, it promotes sustainable farming practices and safeguards human and animal health.

关键词： Deep learning Accuracy Animals Biological system modeling Transfer learning Feature extraction Real-time systems Chemicals Diseases Farming

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Blockchain-Enhanced Framework for Secure Third-Party Vendor Risk Management and Vigilant Security Controls

arXiv

引用

arXiv 2024年

作者： Gupta, Deepti Elluri, Lavanya Jain, Avi Moni, Shafika Showkat Aslan, Omer Dept. of Computer Information Systems Texas A&M University - Central Texas TX United States College of Business University of Tampa Florida United States Dept. of Electrical Engineering and Computer Science Embry-Riddle Aeronautical University Florida United States Department of Software Engineering Bandırma Onyedi Eylül University Bandırma Turkey

In an era of heightened digital interconnectedness, businesses increasingly rely on third-party vendors to enhance their operational capabilities. However, this growing dependency introduces significant security risks, making it crucial to develop a robust framework to mitigate potential vulnerabilities. This paper proposes a comprehensive secure framework for managing third-party vendor risk, integrating blockchain technology to ensure transparency, traceability, and immutability in vendor assessments and interactions. By leveraging blockchain, the framework enhances the integrity of vendor security audits, ensuring that vendor assessments remain up-to-date and tamper-proof. This proposed framework leverages smart contracts to reduce human error while ensuring real-time monitoring of compliance and security controls. By evaluating critical security controls—such as data encryption, access control mechanisms, multi-factor authentication, and zero-trust architecture—this approach strengthens an organization’s defense against emerging cyber threats. Additionally, continuous monitoring enabled by blockchain ensures the immutability and transparency of vendor compliance processes. In this paper, a case study on iHealth’s transition to AWS Cloud demonstrates the practical implementation of the framework, showing a significant reduction in vulnerabilities and marked improvement in incident response times. Through the adoption of this blockchain-enabled approach, organizations can mitigate vendor risks, streamline compliance, and enhance their overall security posture. Our findings highlight the importance of employing blockchain to enforce security controls and maintain compliance with healthcare regulations such as HIPAA. In this paper, we present a comprehensive set of security controls and demonstrate how blockchain technology enhances their effectiveness, ensuring greater transparency, accountability, and automation in vendor assessments. By reducing human error, enab

关键词： Blockchain

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