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A Reinforcement Learning Approach for Routing in Marine Communication Network of Fishing Vessels

SN computer science 2025年第1期6卷 1-12页

作者： Surendran, Simi Montresor, Alberto Vinodini Ramesh, Maneesha Department of Computer Science and Engineering Amrita School of Computing Amrita Vishwa Vidyapeetham Amritapuri Kollam India Department of Information Engineering and Computer Science University of Trento Trento Italy Amrita Center for Wireless Networks & Applications (AmritaWNA) Amrita School of Engineering Amrita Vishwa Vidyapeetham Amritapuri Kollam India

The lack of affordable communication facilities to the shore remains a fundamental problem for fishermen engaged in deep-sea fishing. The Offshore Communication Network (OCN) is a wireless network of fishing vessels, whose goal is to provide Internet over the ocean. However, the dynamic nature of OCNs characterized by extreme weather, the difficulty of deploying additional infrastructure, wave-induced vessel movements, and high mobility causes significant challenges for traditional routing protocols. This paper proposes OCN-AR, a Q-learning-based adaptive routing strategy for ocean networks. The quality of the learning process relies on the reward function, which has been carefully designed to incorporate the most important features, including real-time forecasts of connectivity quality, path probability, link availability duration, and distance to the destination. The routing performance is evaluated through extensive simulations conducted under diverse conditions, including varying mobility scenarios, transmission rates, vessel rocking intensities, and node densities, and is compared against traditional protocols. The results demonstrate that OCN-AR significantly outperforms existing routing approaches, making it a reliable solution for maritime communication. © The Author(s), under exclusive licence to Springer Nature Singapore Pte Ltd. 2025.

关键词： Fishing vessel network Marine communication network Reinforcement learning Routing protocol

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Analysis of Various Hyperparameters for Predicting Hypertension using Machine Learning Algorithms 8

Analysis of Various Hyperparameters for Predicting Hypertens...

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8th International Conference on Electronics, Communication and Aerospace Technology, ICECA 2024

作者： Duggal, Shallu Sood, Shivani Singh, Amanpreet Singh, Harjeet Malarvel, Muthukumaran Lovely Professional University School of Computer Applications Phagwara144411 India Chitkara Institute of Engineering and Technology Chitkara University Punjab 140401 India Department of Computer Science and Engineering India

ISBN: (纸本)9798350367904

Predicting hypertension, or high blood pressure, is crucial because it's a risky condition that frequently doesn't exhibit any obvious symptoms or indicators until major problems arise. By using predictive models for early identification, the risk of uncontrolled hypertension-related cardiovascular events, strokes, and organ damage can be decreased to take quick action. The study aims to predict the diagnosis of hypertension using three machine learning techniques: Logistic Regression, Gaussian Naive Bayes, and Support Vector Machine(SVM). With the focus on improving accuracy and reliability, the various hyperparameters of each individual algorithm are tuned using the cross validation and grid search approaches. After extensive testing and parameter modification, the SVM algorithm was found to be the most accurate predictor, which achieved a significant accuracy of 9 7. 0 0 %. The best hyper-parameters achieved for SVM are c value 4 and kernel as 'rbf'. The models' performance is evaluated using accuracy, specificity, sensitivity, F1-score, or AUC (Area Under the Curve). This study facilitates proactive healthcare measures by offering insightful information to determine the best algorithms for the early prediction of hypertension. © 2024 IEEE.

关键词： Prediction models

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Hybrid Image Captioning Model

Hybrid Image Captioning Model

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2022 OPJU International Technology Conference on Emerging Technologies for Sustainable Development, OTCON 2022

作者： Panigrahi, Lipismita Panigrahi, Raghab Ranjan Chandra, Saroj Kumar Kiit University Department of School of Computer Applications Bhubaneswar India Soa University Department of Computer Science and Engineering Bhubaneswar India Opju University Department of Computer Science and Engineering Raigarh India

ISBN: (纸本)9781665492942

Image captioning is implemented using Deep learning and NLP (Natural Language Processing) resulting in producing a description of an image. The proposed model generates a caption for an image using a Convolutional Neural Network (CNN) together with a Recurrent Neural Network (RNN) and area of attention. Previously, the image names were used as keys to map the images with descriptions. In order to achieve high performance, in the proposed model the image caption is based on the relationship between the areas of a picture (attention model), the words used in the caption, and the state of an RNN language model. The approach of progressive loading is employed for the loading of the image dataset. Further, for encoding the image dataset into a feature vector, VGG16 a pre-trained CNN is used. The extracted feature vector is given as input to the RNN model. These image encodings are output to a specific type of RNN model known as Long Short-Term Memory (LSTM) networks. Subsequently, the LSTM works on decoding the feature vector and predicts the sequence of words, resulting in the generation of descriptions or captions. The training performance is measured using one of the model's quantitative analysis metrics known as BLEU. © 2023 IEEE.

关键词： Long short-term memory

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Future Database Technologies for Big Data Analytics

Future Database Technologies for Big Data Analytics

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2023 International Conference on Intelligent Systems for Communication, IoT and Security, ICISCoIS 2023

作者： Rani, Geeta Sharma, Taruna Sharma, Avinash Chitkara University Institute of Engineering and Technology Dept of Computer Applications Punjab India Maharishi Markandeshwar Engineering College Department of Computer Science and Engineering Mullana India

ISBN: (纸本)9798350335835

Data is valuable asset for organizations but it cannot be used directly for business decision making. It needs to be processed in a form that is easily understandable and can to be used by decision makers. The database technology is continuously changing as the data is growing at a rapid date and business requirements change with time. With the advent of IOT, social media and other technology advancements, data become more complex in terms of volume, variety and veracity. Traditional technology can't be used for handling this data due to its complex and dynamic nature of Big Data. So more advanced and sophisticated tools are essential to handle complex nature of data. In this paper the various database tools are discussed and analysed by different vendors which are suitable for big data Analytics. © 2023 IEEE.

关键词： Big data

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Machine Learning Classifiers for Predicting the Risk of Cardiovascular Disease in Healthcare 4.0 3

Machine Learning Classifiers for Predicting the Risk of Card...

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3rd International Conference on Communication, Control, and Intelligent Systems, CCIS 2024

作者： Kumari, Palak Kumar, Ashish Chatterjee, Kakali Master of Computer Applications Patna Women's College Patna India National Institute of Technology Patna Dept. of Computer Science and Engineering Patna India

ISBN: (纸本)9798331528201

Cardiovascular disease (CVD) generic phrase used to describe heart or blood vessel illness. Conditions that impact the body's, brain's, or heart's blood flow. Reduced blood flow can result from factors such as blood clots, leading to heart attack, angina, or stroke. Other potential meanings of CVD. These models offer key benefits like clear interpretability and the ability to analyze complex patterns, significantly enhancing the accuracy of cardiovascular disease risk predictions. The demerits of using SVM, LR, K-NN, and MLP for CVD prediction include inter-pretability issues, overfitting risks, data requirements, feature selection challenges, scalability concerns, algorithm complexity, and class imbalance. Collaboration between machine learning experts and healthcare professionals is essential for developing accurate and reliable CVD prediction models that enhance patient care. CVD is a significant health threat, often leading to fatal complications. Early detection is crucial for effective intervention. This study employs SVM (accuracy is 0.84%), LR (accuracy is 0.86%), K-NN (accuracy is 0.94%), and MLP (accuracy is 0.79%) machine learning techniques to enhance CVD detection by addressing outliers and null values, contributing to advancing automatic detection initiatives. © 2024 IEEE.

关键词： Nearest neighbor search

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A Bio-Inspired Virtual Machine Placement Toward Sustainable Cloud Resource Management

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IEEE Systems Journal 2023年第3期17卷 3894-3905页

作者： Singh, Ashutosh Kumar Swain, Smruti Rekha Saxena, Deepika Lee, Chung-Nan National Institute of Technology Department of Computer Applications Kurukshetra136119 India Goethe University Department of Computer Science Frankfurt60323 Germany National Sun Yat-sen University Department of Computer Science and Engineering Kaohsiung804 Taiwan

Virtual machine placement (VMP) is the process of selecting the most appropriate physical machine (PM) to place users' requested virtual machine (VM) in large cloud data centers. Several methods have been framed to deal with this problem. However, the current solutions only consider limited resource types, resulting in an unbalanced load that activates unnecessary PMs inside the data center. In this article, we suggest a flower pollination-based nondominated sorting optimization (FP-NSO) algorithm that maximizes resource usage and minimizes energy consumption and carbon emission of the data center. Multiple resource-constraint metrics are associated with our algorithm that assists in finding the most suitable PMs for deploying VMs in a cloud environment. The VMP is carried out by employing the combined concept of flower pollination optimization and nondominated sorting technique-based genetic algorithm (NSGA-II). The algorithm is evaluated using the Google cluster dataset. The performance metrics like resource utilization, power consumption, and carbon emission values are computed for static and dynamic scenarios. The obtained results are compared with existing approaches. There is a significant reduction in power consumption, carbon emission, and execution time up to 16.69%, 48.60%, and 75.87%, respectively, and an improvement in resource utilization is up to 78.18%. © 2007-2012 IEEE.

关键词： Cloud computing

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An Efficient Approach for Semantic Segmentation of Salt Domes in Seismic Images Using Improved UNET Architecture

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Journal of The Institution of Engineers (India): Series B 2023年第3期104卷 569-578页

作者： Bodapati, Jyostna Devi Sajja, RamaKrishna Naralasetti, Veeranjaneyulu Department of Advanced Computer Science and Engineering Vignan’s Foundation for Science Technology and Research Vadlamudi India IAENG Life Member Andhra Pradesh Guntur India Department of Information Technology and Computer Applications Vignan’s Foundation for Science Technology and Research Vadlamudi India

Many areas of Earth’s surface with large accumulations of gas and oil even have huge deposits of salt under the surface. Exploring such deposits helps many countries to increase the storage capacity of their Petroleum reserves and explore new ones. But finding such deposits is a herculean task. Expert seismic imaging requires human interpretation of salt bodies. But this leads to very biased and highly variable translations. So the idea behind this paper is to build an approach that accurately and automatically identifies if the seismic image contains any region of salt deposit or not. If a surface is found to have salt deposits, then it may contain the accumulations of oil or gas and even the salt domes or caverns can be used as a storage site for already available petroleum or oil. Since semantic segmentation classifies every pixel in the given image to its class label, this can be used to segment the salt deposits from the provided seismic images. In this paper, we introduce a variation of UNet, a popular segmentation model, for seismic image segmentation. We have added a batch normalization layer following every convolution layer as a deeper network helps extract better features which turned out to be true. Some interesting findings of this work are that the augmentation works well as this avoids over-fitting of the model. Sharpening as a post-processing technique has come up with a considerable amount of rise in the performance. Here, we prefer to use the metric as Intersection over Union (IoU) instead of accuracy as it is not as affected by the class imbalances that are inherent in foreground/background segmentation tasks. With the proposed methodology, we achieve an averaged IoU of 85.6 which is far better compared to the IoU achieved with the Segnet approach which stands at 77. © 2023, The Institution of Engineers (India).

关键词： Image enhancement

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Heart disease Classification Utilising Machine Learning Techniques 3

Heart disease Classification Utilising Machine Learning Tech...

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3rd International Conference for Advancement in Technology, ICONAT 2024

作者： Dhondiyal, Shiv Ashish Verma, Himanshu Kumar, Rohit Sawan, Vikash Uniyal, Ishita Graphic Era Deemed to Be University Dept. of Computer Science and Engineering Dehradun India Haridwar University Roorkee Dept. of Computer Science and Engineering India GLA University Department of Computer Engineering & Applications Mathura India Era Hill University Department of Electrical Engineering Graphic India

ISBN: (纸本)9798350354171

Heart diseases comprise a extensive Various heart conditions and are commonly referred to as cardio vascular diseases. These encompass issues with the rhythm of the heart, blood vessel diseases, and congenital cardiac defects. It has been the world's biggest cause of death for the last few decades. Finding a reliable and accurate way to automate the process of early disease diagnosis and efficient disease management is urgently needed. Data science facilitates the medical sciences in analysing vast quantities of data. Researchers utilize various Data Mining and Machine Learning methods to evaluate extensive datasets in order to accurately forecast heart illness. This study examines the supervised learning models of Random Forest, K-NN, and SVM to provide a comparative analysis of their efficiency. It is discovered that, when compared to other algorithms, SVM offers the highest accuracy at 0.994. © 2024 IEEE.

关键词： Random forests

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A metaheuristic virtual machine placement framework toward power efficiency of sustainable cloud environment

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Soft Computing 2023年第7期27卷 3817-3828页

作者： Singh, Ashutosh Kumar Swain, Smruti Rekha Lee, Chung Nan Department of Computer Applications National Institute of Technology Haryana Kurukshetra136119 India Department of Computer Science and Engineering National Sun Yat-sen University Kaohsiung804201 Taiwan

The primary aim of Virtual Machine Placement (VMP) is the mapping of Virtual Machines (VMs) to Physical Machines (PMs), such that the PMs may be utilized to their maximum efficiency, where the already active VMs are not to be interrupted. It provides a list of live VM migrations that must be accomplished to get the optimum solution and reduces energy consumption significantly. The inefficient VMP leads to wastage of resources and excessive energy consumption and increases the overall operational cost of the data center. A Metaheuristic Virtual Machine Placement Framework towards the Power Efficiency of Sustainable Cloud Environment (MV-PESC) approach is suggested to address the issues mentioned above. An Extended Flower Pollination Optimization algorithm is suggested, which combines the concept of the Random Fit algorithm and the Flower Pollination Optimization algorithm. The proposed work’s performance is evaluated using actual workload traces of the benchmark Google Cluster Data set. The obtained results are compared with various state-of-the-art and demonstrate a notable reduction in power consumption, the number of active PMs, and execution time up to 64.89%, 35%, and 21.12%, respectively. © 2022, The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature.

关键词： Energy utilization

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Predicting Future Automobile Ownership Trends in Uzbekistan Using Linear Regression 2nd

Predicting Future Automobile Ownership Trends in Uzbekistan ...

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2nd International Conference on Cyber Intelligence and Information Retrieval, CIIR 2023

作者： Ather, Danish Singh, Malikhan Arora, Kavita Pathak, Sonal Qurashi, Suhail Javed Singh, Anupam Amity University Tashkent Uzbekistan Computer Science and Engineering College of Engineering & Technology Mathura India School of Computer Applications Manav Rachna International Institute of Research and Studies Haryana Faridabad India Department of Computer Science and Engineering Graphic Era Hill University Dehradun India

ISBN: (纸本)9789819735938

The rapid urbanization and economic growth of Uzbekistan over the past decades have engendered significant changes in automobile ownership trends. Understanding these trends is crucial for infrastructure planning, sustainable transportation strategies, and environmental considerations. This Paper seeks to employ Machine Learning (ML) techniques to forecast the number of cars per 100 households in Uzbekistan by 2025, based on historical data from 2010, 2015, and 2020. The dataset, derived from official statistics provided by the State Committee of the Republic of Uzbekistan on Statistics, shows a remarkable increase from 21 cars per 100 households in 2010 to 48 by 2020. Leveraging regression models, we aim to predict the expected growth for 2025. Preliminary analyses hint at a continuing upward trend, impacted potentially by factors such as Population growth, GDP growth, urban migration, and increasing global connectivity. The findings from this research will not only serve as a prediction tool but will also shed light on the socio-economic factors influencing car ownership, providing valuable insights to policymakers and urban planners. While the limited data points present a challenge for complex model utilization, the study underscores the potential of ML in forecasting and the need for more comprehensive data collection in future studies. The predictive insights drawn from this research can significantly aid in shaping transportation policies, mitigating potential environmental impacts, and planning urban infrastructures in Uzbekistan. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.

关键词： Forecasting

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