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Investigation of Heat exchanger performance with delta-wing tape inserts for high Reynolds Number using various machine learning techniques 14

Investigation of Heat exchanger performance with delta-wing ...

引用

14th International Conference on Computing Communication and Networking Technologies, ICCCNT 2023

作者： Rodrigues, Gizelle Srivastava, Neetu Pati, Peeta Basa Amrita Vishwa Vidyapeetham Amrita School of Engineering Dept. of Mathematics Bengaluru India Amrita Vishwa Vidyapeetham Amrita School of Computing Dept. of Computer Science and Engineering Bengaluru India

ISBN: (纸本)9798350335095

The present study utilizes the support vector regression (SVR) technique with a cubic kernel to forecast the performance of a double-pipe heat exchanger using T-W tape inserts with wing-width ratios of 0.31, 0.47, and 0.63. The SVR model that is established is applied to predict two key parameters, namely, the thermal performance (η) and the friction factor (f). Various scenarios are examined by considering the Reynolds number (Re) range from 5,500 to 14,500 and the wing-width ratio as inputs. The outcomes indicate that the SVR model accurately predicts the experimental data, as evidenced by the minimal mean square error (MSE) obtained when compared to the experimental values. Furthermore, the proposed SVR-based method predicts the thermal parameters with minimal variance. In addition, multiple regression models are utilized to evaluate the effectiveness of the proposed model, which shows that the recommended SVR technique is useful for predicting thermal parameters with minimal variance. It is noteworthy that the SVR model with the cubic kernel outperforms other modeling approaches in predicting the performance of the T-W double-pipe heat exchanger. © 2023 IEEE.

关键词： Mean square error

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PureConnect: A Localized Social Media System to Increase Awareness and Connectedness in Environmental Justice Communities 24

PureConnect: A Localized Social Media System to Increase Awa...

引用

11th Multidisciplinary International Social Networks Conference, MISNC 2024

作者： Hammad, Omar Rahman, Md Rezwanur Kanugo, Gopala Krishna Vasanth Clements, Nicholas Miller, Shelly Mishra, Shivakant Sullivan, Esther King Fahd University of Petroleum and Minerals Information and Computer Science Dept. Dhahran Saudi Arabia University of Colorado Boulder Computer Science Dept. BoulderCO United States University of Colorado Boulder Mechanical Engineering Dept. BoulderCO United States University of Colorado Denver Sociology Dept. DenverCO United States

ISBN: (纸本)9798400717550

Frequent disruptions like highway constructions are common now-a-days, often impacting environmental justice communities (communities with low socio-economic status with disproportionately high and adverse human health and environmental effects) that live nearby. Based on our interactions via focus groups with the members of four environmental justice communities impacted by a major highway construction, a common concern is a sense of uncertainty about project activities and loss of social connectedness, leading to increased stress, depression, anxiety and diminished wellbeing. This paper addresses this concern by developing a localized social media system called PureConnect with a goal to raise the level of awareness about the project and increase social connectedness among the community members. PureConnect has been designed using active engagement with four environmental justice communities affected by a major highway construction. It has been deployed in the real world among the members of the four environmental justice communities, and a detailed analysis of the data collected from this deployment as well as surveys show that PureConnect is potentially useful in improving community members' wellbeing and the members appreciate the functionalities it provides. © 2024 ACM.

关键词： Civic Engagement

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Emotional Behavior Trees for Empathetic Human-Automation Interaction 23

Emotional Behavior Trees for Empathetic Human-Automation Int...

引用

23rd Workshop "From Objects to Agents", WOA 2022

作者： Dell’Acqua, Pierangelo Costantini, Stefania Dept. of Science and Technology Linköping University Sweden Department of Information Engineering Computer Science and Mathematics University of L’Aquila Italy

For the tasks of improving caregiving in medicine and other sectors (i.e., teaching) and of constructing effective human-AI teams, agents should be endowed with an emotion recognition and management module, capable of empathy, and of modelling aspects of the Theory of Mind, in the sense of being able to reconstruct what what someone is thinking or feeling. In this paper, we propose an architecture for such a module, based upon an enhanced notion of Behavior Trees. We illustrate the effectiveness of the proposed architecture on a significant example, and on a wider case study. © 2022 Copyright for this paper by its authors. Use permitted under Creative Commons License Attribution 4.0 International (CC BY 4.0).

关键词： Forestry

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Post Quantum Blockchain Assisted Privacy Preserving Protocol for Internet of Medical Things

Post Quantum Blockchain Assisted Privacy Preserving Protocol...

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2023 IEEE World Conference on Applied Intelligence and Computing, AIC 2023

作者： Yadav, Dushyant Kumar Yadav, Dinesh Pal, Yogesh Chaudhary, Dharminder Sahu, Hemlal Manasa, A.S.L. Govt. J. Y. Chhattisgarh College Dept. of Mathematics Raipur India Shivalik College of Engineering Dept. of Computer Science and Engineering Raipur India Greater Noida India Amrita School of Computing Amrita Vishwa Vidyapeetham Dept. of Computer Science and Engineering Chennai India

ISBN: (纸本)9798350310061

Internet of Medical Things (IoMT) is an advanced ecosystem made with the help of Internet of Things technology, smart sensors, medical equipment, and connected end users. Internet of Medical Things is very helpful to enhance efficiency, accuracy, and qualities traditional healthcare system. A smart IoMT contains end users, heterogeneous network, and private data is transferred through public channel. Therefore, it becomes hard to achieve both security and privacy in the IoMT system. In this article, we have focused on designing privacy preserving authenticated key mechanism between two parties. We have proposed a blockchain assisted authenticated key exchange mechanism for IoMT systems supporting fog computing paradigm. The proposed privacy preserving authenticated key exchange protocol is based post quantum assumption 'ring learning with errors' and physical unclonable function (PUF), respectively. A detailed security analysis ensures that proposed protocol is secure against most of the existing attacks. This protocol is also secure against quantum attacks based on the assumption. © 2023 IEEE.

关键词： Authentication

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Investigation of Immersion Cooling Efficiency for Lithium-Ion Battery Pack with Machine Learning Validation 14

Investigation of Immersion Cooling Efficiency for Lithium-Io...

引用

14th International Conference on Computing Communication and Networking Technologies, ICCCNT 2023

作者： Shivaranjini, S. Srivastava, Neetu Pati, Peeta Basa Amrita Vishwa Vidyapeetham Amrita School of Engineering Dept. of Mathematics Bengaluru India Amrita Vishwa Vidyapeetham Amrita School of Computing Dept. of Computer Science and Engineering Bengaluru India

ISBN: (纸本)9798350335095

Battery thermal management systems play a critical role in improving their power capability, extending lifetime, and minimizing the probability of thermal runaway. These systems aim to maintain a regulated operating temperature by removing heat from the battery pack and homogenizing the temperature within and between the cells. Precisely predicting the outlet fluid temperature is crucial to enhance the efficiency and functionality of these systems through proper design and operation. In this study, we applied machine learning techniques to predict the outlet fluid temperature of immersion cooling for a lithium-ion battery pack. Nine regression models have been employed to accurately predict the outlet fluid temperature. The results indicate that the Light GBM model can significantly enhance the efficiency and performance of immersive cooling systems for Li-ion batteries. We validated our approach by comparing the predicted temperature distributions with experimental data with MAE and RMSE of 0.0136 and 0.0209, which demonstrated that our model effectively captures the heat transfer in the battery pack. © 2023 IEEE.

关键词： Lithium-ion batteries

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A Recommendation System for Crop Prediction Under Diverse Weather Conditions 9

A Recommendation System for Crop Prediction Under Diverse We...

引用

9th International Conference on Advanced Computing and Communication Systems, ICACCS 2023

作者： Shunmugalakshmi, P. Ram Kishore, V. Ramachandran, Pillai Pratham Santhi, S. Kalaiselvi, S. National Engineering College Dept. of Information Technology Tamil Nadu Kovilpatti628503 India National Engineering College Dept. of Computer Science and Engineering Tamil Nadu Kovilpatti628503 India

ISBN: (纸本)9798350397376

In order to develop strategic initiatives in agricultural belonging for trading protocol and tripling countrymen earnings, early and accurate agricultural output estimation is pivotal in computable and commercial decision at the territory plane. Crop production forecasting, which is done to foresee a greater crop output, is the impressive difficulty in the agronomic division. To help farmers increase agricultural yield, this study has collected and evaluated data on N, P, K, temperature, humidity, ph, rainfall and soil-moisture. The superior values of this work are to recommend the crop to cultivate, suggest the appropriate fertilizers to use and predict the disease of a crop. A python environment is used to perform initial preprocessing on the data. KNN classification is used for developing the recommendation system. The link between the count, nutrients, temperature, humidity, pH, rainfall, and crop, are visualized using bar graphs and scatter plots. The tensor and torch were used to forecast crop disease and also to suggest appropriate fertilizer based on the data. The application was developed using Python Flask. This Classification was compared with other algorithms such as SVM, DT , RF and K-means. © 2023 IEEE.

关键词： Support vector machines

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Convolutional Sequence to Sequence Learning for English-Khasi Neural Machine Translation 4

Convolutional Sequence to Sequence Learning for English-Khas...

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4th International Conference on Computing and Communication Systems, I3CS 2023

作者： Hujon, Aiusha Vellintihun Amitab, Khwairakpam Singh, Thoudam Doren St. Anthony's College Dept. of Computer Science Meghalaya Shillong India North Eastern Hill University Dept. of Information Technology Meghalaya Shillong India NIT Silchar Dept. of Computer Science and Engineering Assam India

ISBN: (纸本)9798350323771

The convolutional neural network is relatively prevalent for visual recognition tasks. Implementing CNN for neural machine translation is still challenging compared to the predominant approach such as recurrent neural networks and transformers. This paper presents an empirical study of the convolutional sequence to sequence learning model to translate English to Khasi. The approach uses convolution for both encoders and decoders with multi-step attention. The study also includes a model using a transfer learning approach on which the parent model is trained using the English-French dataset. The quantitative and qualitative results are significant thus suggesting that the convolutional approach for neural machine translation tasks can perform remarkably well as other tasks using convolution neural networks in the case of English-Khasi language pair. © 2023 IEEE.

关键词： Neural machine translation

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A Comprehensive Approach for Detecting and Classifying Loitering Events in Fishing Vessels Using Supervised and Semi-Supervised Learning 3

A Comprehensive Approach for Detecting and Classifying Loite...

引用

3rd IEEE Asian Conference on Innovation in Technology, ASIANCON 2023

作者： Sira, Rishi Kumar Tanjavur, Irthiqua Moin Guru Prasad, M.S. Rajan, Abraham Laxman Mohan Kumar, T.G. Dept. of Computer Science and Engg. Dehradun India Nagarjuna College of Engineering and Technology Dept. of Computer Science and Engg. Bengaluru India School of Computing and Information Technology Reva University Bengaluru India Nitte Meenakshi Institute of Technology Dept. of Information Science and Engg. Bengaluru India

ISBN: (纸本)9798350302288

Illegal, unreported, and unregulated (IUU) fishing practices pose significant threats to marine ecosystems and global fisheries sustainability. The detection and classification of loitering events, where fishing vessels spend an extended period in a specific area, are critical for identifying potential IUU activities. This research proposes a comprehensive approach that combines supervised and semi-supervised learning techniques to effectively detect and classify loitering events. By leveraging various machine learning algorithms, including logistic regression, Gaussian mixture models, support vector machines, and random forests, accurate predictions can be made to enhance surveillance and combat IUU fishing. © 2023 IEEE.

关键词： Logistic regression

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Stochastic Profit Optimization in Agile Supply Chains Using Monte Carlo Simulations and Markov Decision Processes 8

Stochastic Profit Optimization in Agile Supply Chains Using ...

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8th IEEE International Conference on Computational System and information Technology for Sustainable Solutions, CSITSS 2024

作者： Kagganty, Siddhanth N. Naik, Tulasi Ram K. Aradhya, Inchara N. Nishaanth, S. Muchikel, Nivya Sailaja, Y. R.V. College Of Engineering Dept. of Information Science Engineering Karnataka Bangalore India R.V. College Of Engineering Dept. of Industrial Engineering & Management Karnataka Bangalore India R.V. College Of Engineering Dept. of Electrical And Electronics Engineering Karnataka Bangalore India R.V. College Of Engineering Dept. of Mathematics KARNATAKA Bangalore India

ISBN: (纸本)9798331505462

Our paper presents a stochastic multi-objective optimization model {minimize cost, maximize profits} designed to manage supply and demand uncertainties in a two-tier supply chain. The model optimizes supplier selection and order allocation by incorporating key parameters such as defective rates, buyback agreements, and quantity discounts. Through Monte Carlo simulations and sensitivity analysis, the model evaluates the impacts of these parameters on supply chain performance, offering a resilient framework for making informed decisions. The research provides actionable strategies for enhancing operational efficiency, profitability, and customer satisfaction amidst supply chain uncertainties. © 2024 IEEE.

关键词： Sensitivity analysis

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Applicability of Deep Learning Model Trainings on Embedded GPU Devices: An Empirical Study

Applicability of Deep Learning Model Trainings on Embedded G...

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12th Mediterranean Conference on Embedded Computing, MECO 2023

作者： Chou, Po-Hsuan Wang, Chao Mei, Chih-Shuo National Taiwan Normal University Dept. of Computer Science and Information Engineering Taipei City Taiwan

ISBN: (纸本)9798350322910

The wide applications of deep learning techniques have motivated the inclusion of both embedded GPU devices and workstation GPU cards into contemporary Industrial Internet-of-Things (IIoT) systems. Due to substantial differences between the two types of GPUs, deep-learning model training in its current practice is run on GPU cards, and embedded GPU devices are used for inferences or partial model training at best. To supply with empirical evidence and aid the decision of deep learning workload placement, this paper reports a set of experiments on the timeliness and energy efficiency of each GPU type, running both Convolutional Neural Network (CNN) and Long Short-Term Memory (LSTM) model training. The results suggest that embedded GPUs did save the total energy cost despite the longer response time, but the amount of energy saving might not be significant in a practical sense. Further in this paper we report a case study for prognostics applications using LSTM. The results suggest that, by comparison, an embedded GPU may save about 90 percent of energy consumption at the cost of doubling the application response time. But neither the save in energy cost nor the increase in response time is significant enough to impact the application. These findings suggest that it may be feasible to place model training workload on either workstation GPU or embedded GPU. © 2023 IEEE.

关键词： Embedded systems

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