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Improving Oil Spill Detection in Marine Environments Through Deep Learning Approaches 4

Improving Oil Spill Detection in Marine Environments Through...

4th International Conference on Advances in Electrical, computing, Communication and Sustainable Technologies, ICAECT 2024

作者： Shanmukh, M. Phani Priya, S. Baghavathi Madeswaran, Tamilselvi Amrita Vishwa Vidyapeetham Amrita School of Computing Department of Computer Science and Engineering Chennai India University of Technology and Applied Sciences Department of Information Technology Nizwa Oman

ISBN: (纸本)9798350343670

Oil spills represent a major environmental threat, particularly in marine ecosystems. Timely detection and segmentation of oil spills from satellite imagery is crucial for directing rapid response and remediation *** research concludes with an insights by developing a deep learning approach for automated detection and segmentation of oil spills from synthetic aperture radar (SAR) *** research implements and compares three algorithms PSPNET,DeepLapV3 and fully convolutional networks (FCN) with U-Net for semantic segmentation of SAR *** the networks are trained on a dataset of SAR images with pixel-wise labels indicating oil spill regions. This research also evaluates the segmentation performance using common metrics like intersection-over-union (IoU) and pixel-wise accuracy. Our results indicate that both FCN and U-Net can effectively detect and delineate oil spill regions, with U-Net demonstrating slightly higher IoU scores and with and accuracy of 95%. The deep learning models generalize well to unseen SAR images. Overall, this work demonstrates the feasibility of deep convolutional neural networks for automated analysis of SAR imagery to enable rapid oil spill response at marine environment. © 2024 IEEE.

关键词： Deep Learning Algorithms Oil Spill Detection Rapid Oil Spill Response Semantic Segmentation Synthetic Aperture Radar (SAR) Imagery

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q-Difference Recurrence Relations of Aleph Function with Generalization to nth Derivative

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International Journal of applied and Computational Mathematics 2024年第2期10卷 1-14页

作者： Bhat, Mohammad Younus Maqbool, Humaira Bhat, Altaf A. Department of Mathematical Sciences Islamic University of Science and Technology Kashmir Awantipora India Mathematics and Computing Skills Preparatory Studies Centre University of Technology and Applied Sciences Salalah Oman

Sharma and Jain studied the basic analogue of Meijer’s G-function by using the methods of q-calculus and constructed the q-difference operators and their lie algebra. In this article, we employ the same technique for basic anlogue of the Aleph function defined by Dutta et al. and thus on account of general nature of q-analogue of Aleph function,the q-difference recurrence relations of the Aleph function are obtained and an enumerous number of new and known results are special cases of our main results. Also the recurrence relation of I-function and Fox-H function are deduced as special cases by suitable substitutions. Furthermore the generalisation to nth partial derivatives and the required relations are also discussed which are quite general in nature and capable of producing a number of new and known results. © The Author(s), under exclusive licence to Springer Nature India Private Limited 2024.

关键词： 133D05 20C20 42C40 Aleph function q-Analogue q-Calculus q-Gamma function Recurrence relations

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Strengthening-softening transition and maximum strength in Schwarz nanocrystals

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Nano Materials Science 2024年第3期6卷 320-328页

作者： Hanzheng Xing Jiaxi Jiang Yujia Wang Yongpan Zeng Xiaoyan Li Center for Advanced Mechanics and Materials Applied Mechanics LaboratoryDepartment of Engineering MechanicsTsinghua UniversityBeijing100084China Institute of High Performance Computing(IHPC) Agency for ScienceTechnology and Research(A*STAR)Singapore138632Singapore

Recently,a Schwarz crystal structure with curved grain boundaries(GBs)constrained by twin-boundary(TB)networks was discovered in nanocrystalline Cu through experiments and atomistic *** Cu with nanosized Schwarz crystals exhibited high strength and excellent thermal ***,the grainsize effect and associated deformation mechanisms of Schwarz nanocrystals remain ***,we performed large-scale atomistic simulations to investigate the deformation behaviors and grain-size effect of nanocrystalline Cu with Schwarz *** simulations showed that similar to regular nanocrystals,Schwarz nanocrystals exhibit a strengthening-softening transition with decreasing grain *** critical grain size in Schwarz nanocrystals is smaller than that in regular nanocrystals,leading to a maximum strength higher than that of regular *** simulations revealed that the softening in Schwarz nanocrystals mainly originates from TB migration(or detwinning)and annihilation of GBs,rather than GB-mediated processes(including GB migration,sliding and diffusion)dominating the softening in regular *** analyses of simulation data further showed that compared with those in regular nanocrystals,the GB-mediated processes in Schwarz nanocrystals are suppressed,which is related to the low volume fraction of amorphous-like GBs and constraints of TB *** smaller critical grain size arises from the suppression of GB-mediated processes.

关键词： Schwarz nanocrystal Curved grain boundary Atomistic simulation Grain size effect Maximum strength

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Enhancing Gastrointestinal Image Classification: A Fusion of CNN and Vision Trasformers 2

Enhancing Gastrointestinal Image Classification: A Fusion of...

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2nd IEEE International Conference on Contemporary computing and Communications, InC4 2024

作者： Ganesh, Bachu Annamalai, R. Jayapal, Senthil Amrita School of Computing Amrita Vishwa Vidyapeetham Department of Computer Science and Engineering Chennai India Collage of Computing and Information Science University of Technology and Applied Sciences Ibra Oman

ISBN: (纸本)9798350383652

In order to meet the pressing demand for early diagnosis in healthcare, we proposed a unique hybrid architecture in this study that is intended for the categorization of gastrointestinal (GI) illnesses. With fewer trainable parameters, faster training times, and less storage needed for model weights, our suggested architecture prioritizes efficiency achieves an impressive 94% accuracy. To effectively diagnose GI disorders, this design combines convolutional neural networks (CNNs) with Transformers. The creation of this hybrid architecture, which boasts excellent accuracy in illness categorization, decreased storage requirements, and efficiency in training, is one of the research's accomplishments. Our proposed model demonstrates its flexibility for many uses, encouraging simple implementation and expandability. Extensive tests performed on a large dataset of endoscopic images highlight our hybrid architecture's better performance over current techniques. With this study, diagnostics for gastrointestinal diseases have advanced significantly, opening the door to future diagnostic systems that will be more precise and effective. The proposed architecture has outperformed the traditional pre-trained models both in classification accuracy and time to train the model with no weights. © 2024 IEEE.

关键词： Endoscopy

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An Empirical Investigation on Employing Machine Learning for Balancing Home Agent Loads in Next Generation IP Mobility 2

An Empirical Investigation on Employing Machine Learning for...

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2nd International Conference on Machine Learning and Data Engineering, ICMLDE 2023

作者： Khatri, Anshu Mathi, Senthilkumar Ashiq Sheriff, A. Deepthika, R. Naveen Raaghavendran, G. Ramalingam, Venkadeshan Department of Computer Science and Engineering Amrita School of Computing Amrita Vishwa Vidyapeetham Coimbatore India Faculty in Information Technology Department University of Technology and Applied Sciences-Shinas Oman

Mobile IPv6 is a key technology to enable the mobility of devices on next-generation internet protocol networks. Home agents provide simple services to registered mobile nodes. In addition, the use of multiple domestic operators for load-balancing devices faces problems of synchronization and signalling overhead. Thus, most of the existing load balancing methods present a significant delay in the transmission of data packets. The machine learning approach provides an efficient hardware storage solution for balancing the load of the home agent by improving the state-work schedule. It provides a method for dynamic load balancing based on the research-consumption system. Thus, the present paper investigates the use of machine learning, specifically reinforcement learning, to enhance home agent load balancing in next-generation IP traffic. The reinforcement learning-based home agent decisions, guided by mobile node speed and registration queries, reduce latency, improve network performance, and provide dynamic solutions. The proposed method significantly reduces the delay for packet delivery by balancing the home agent load with the incorporation of reward parameters. The empirical investigation found a remarkable improvement in network reliability, as manifested by a 2.31% increase in packet delivery ratio compared to systems with existing methods. © 2024 Elsevier B.V.. All rights reserved.

关键词： Reinforcement learning

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AI Integrated Healthcare System for Disease Predictions and Virtual Consultations 3

AI Integrated Healthcare System for Disease Predictions and ...

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

作者： Wilfred Blessing, N.R. Gunasekaran, Hemalatha Hariharan, B. Al Jabri, Jaber Saleh Salim University of Technology and Applied Sciences College of Computing and Information Sciences Ibri Oman Srm Institute of Science and Technology Department of Computational Intelligence Tamil Nadu Kattankulathur India

ISBN: (纸本)9798331509675

AI-based Disease Prediction and Online Consultation System aims to redesign the way people experience healthcare. The deep growth of machine learning opened doors for new opportunities, such as this system that carefully inputs and validates patients' symptoms, and subsequently predicts possible diseases. In addition, it fosters remote consultation in a user-friendly manner over the internet and erases the barriers of distance and mobility. It is expected that this combination of data-driven forecasting and logic will enhance the treatment accuracy in terms of increased availability of healthcare services. AI-based Disease Prediction and Online Consultation System is a practical showcase on how to transform equity in healthcare. The increasing change in technology can give us possibilities for the utmost usage of this system. In this regard, the complex predictive analytics associated with the system may in effect change how HEIs (health education institutions) practice healthcare management. Patients will be offered a practice which supports the idea of delivering the right information on time and stress the universal right of access to healthcare. Its transformational power has far more prospects than it seems at first. At the core it sets the landscape and, indeed, the stage for the most equal, efficient and inclusive healthcare ecosystem that could ever exist. © 2025 IEEE.

关键词： Electronic health record

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A New Approach to Road Incident Detection Leveraging Live Traffic Data: An Empirical Investigation 2

A New Approach to Road Incident Detection Leveraging Live Tr...

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2nd International Conference on Machine Learning and Data Engineering, ICMLDE 2023

作者： Kumar Gannina, Aswin Ram Jaffarullah, Aadhil Ahamed Reddy, Tiyyagura Mohit Subba Reddy, Sabbella Manoj Vikas, Ambati Sai Mathi, Senthilkumar Ramalingam, Venkadeshan Department of Computer Science and Engineering Amrita School of Computing Amrita Vishwa Vidyapeetham Coimbatore India Faculty in Information Technology Department University of Technology and Applied Sciences-Shinas Oman

Accidents can have a significant impact on road safety and the efficiency of transport. With the ever-increasing number of vehicles on the road, it is crucial to quickly detect and respond to such events to decrease the impact. The current paper presents a road accident and incident detection solution utilizing traffic API monitoring and continuously analyzing real-time traffic data from various sources like GPS-enabled devices. Consequently, the proposed approach can identify abnormal patterns in the movement of vehicles, such as unexpected road closures or an accident. It numerically analyzes the traffic pattern and identifies incidents and accidents using live traffic data collected from the map service. When the approach identifies any abnormality, it can immediately let the user know, allowing them to act accordingly. It can facilitate authorities in identifying accident-prone areas and deploy safety measures. This can help law enforcement agencies to identify drivers at risk of causing accidents and implement education and enforcement programs targeted towards them. Hence, the proposed approach can aid in reducing the negative impact of traffic incidents, and the public can respond to these incidents more preparedly. © 2024 Elsevier B.V.. All rights reserved.

关键词： Accidents

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Digital Maturity Assessment Model (DMAM): assimilation of Design Science Research (DSR) and Capability Maturity Model Integration (CMMI)

Digital Transformation and Society

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Digital Transformation and Society 2025年第2期4卷 128-152页

作者： Omol, Edwin Juma Mburu, Lucy Waruguru Abuonji, Paul Anyango Department of Software Development and Information Systems (SDIS) School of Technology KCA University Nairobi Kenya Chair of Department - Networks and Applied Computing (NAC) School of Technology KCA University Nairobi Kenya KCA University Nairobi Kenya

Purpose: This study introduces the Digital Maturity Assessment Model (DMAM), a model tailored to assess the digital maturity of SMEs, tracing its development from addressing business challenges to establishing a comparative analysis framework grounded in Resource Dependence Theory (RDT). Design/methodology/approach: DMAM is based on positivist philosophy and objectivist epistemology, supported by Design Science Research (DSR) and Capability Maturity Model Integration (CMMI). The methodology involves iterative development, from problem identification to creating a practical solution for assessing SMEs' digital maturity and guiding digitalization efforts. Findings: DMAM offers a clear and specific methodology, distinguishing itself by addressing the unique needs of SMEs, particularly resource-dependent ones. The model’s development fills critical gaps in existing literature and provides a practical artifact for SMEs' digitalization. Originality/value: DMAM is original in its focus on the specific needs of resource-dependent SMEs, offering actionable recommendations and addressing shortcomings in existing models. It serves as a foundational framework for SMEs' digital transformation, making a significant contribution to the digital maturity assessment literature. © 2024, Edwin Juma Omol, Lucy Waruguru Mburu and Paul Anyango Abuonji.

关键词： Capability Maturity Model Integration (CMMI) Design Science Research (DSR) Digital Maturity Assessment Model (DMAM) Goal Question Metric (GQM) Proof-of-Concept (POC) & Minimum Viable Product (MVP) Resource Dependence Theory (RDT)

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A Comprehensive Survey of Techniques for Lung Cancer Diagnosis and Prediction 7

A Comprehensive Survey of Techniques for Lung Cancer Diagnos...

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7th Edition Global Conference on Wireless and Optical Technologies, GCWOT 2024

作者： Jamil, Danish Shah, Syed Mehr Ali Al-Jarwani, Fatma Mahfoodh Mohammed Sir Syed University of Engineering & Technology Department of Software Engineering Karachi Pakistan College of Computing and Information Sciences University of Technology and Applied Sciences Musandam Oman

ISBN: (纸本)9798331534271

Despite significant advancements, lung cancer remains a formidable global health challenge, necessitating effective diagnostic and prognostic methodologies. This survey paper examines current literature to identify and evaluate methods and models utilized for lung cancer diagnosis and prognosis, aiming to enhance early detection strategies for improved patient outcomes. The research addresses critical questions: What are the current methods and models employed for lung cancer diagnosis and prognosis, their strengths, limitations, and potential for future improvement? It investigates specific applications of deep learning architectures such as CNN, GoogleNet, VGG-16, U-Net, and machine learning algorithms like XGBoost, SVM, KNN, ANN, Random Forest, and hybrid models in lung cancer detection. Methodologically, a systematic review across key databases using relevant keywords was conducted to synthesize findings. The study highlights the strengths and limitations of existing approaches and identifies gaps in model interpretability, real-world validation, and integration of diverse techniques. Conclusions underscore the transformative potential of advanced computational methods in enhancing lung cancer diagnosis and prognosis, proposing avenues for future research to address current challenges and improve patient care. © 2024 IEEE.

关键词： Lung cancer

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A roulette wheel-based pruning method to simplify cumbersome deep neural networks

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Neural computing and Applications 2024年第22期36卷 13915-13933页

作者： Chan, Kit Yan Yiu, Ka Fai Cedric Guo, Shan Jiang, Huimin School of Electrical Engineering Computing and Mathematics Sciences Curtin University Bentley Australia Department of Applied Maths The Hong Kong Polytechnic University Hong Kong School of Business Macau University of Science and Technology China

Deep neural networks (DNNs) have been applied in many pattern recognition or object detection applications. DNNs generally consist of millions or even billions of parameters. These demanding computational storage and requirements impede deployments of DNNs in resource-limited devices, such as mobile devices, micro-controllers. Simplification techniques such as pruning have commonly been used to slim DNN sizes. Pruning approaches generally quantify the importance of each component such as network weight. Weight values or weight gradients in training are commonly used as the importance metric. Small weights are pruned and large weights are kept. However, small weights are possible to be connected with significant weights which have impact to DNN outputs. DNN accuracy can be degraded significantly after the pruning process. This paper proposes a roulette wheel-like pruning algorithm, in order to simplify a trained DNN while keeping the DNN accuracy. The proposed algorithm generates a branch of pruned DNNs which are generated by a roulette wheel operator. Similar to the roulette wheel selection in genetic algorithms, small weights are more likely to be pruned but they can be kept;large weights are more likely to be kept but they can be pruned. The slimmest DNN with the best accuracy is selected from the branch. The performance of the proposed pruning algorithm is evaluated by two deterministic datasets and four non-deterministic datasets. Experimental results show that the proposed pruning algorithm generates simpler DNNs while DNN accuracy can be kept, compared to several existing pruning approaches. © The Author(s) 2024.

关键词： Deep neural networks

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