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5th International Conference on Inventive Research in Computing Applications, ICIRCA 2023

作者： Supriya, MacHarla Srilatha, Kasanagottu Smitha, P.S. Oommen, Sujo Hemantha, C. Sharath, N. Geetanjali College of Engineering and Technology Computer Science and Engineering India Presidency University Computer Science and Engineering India School of Electrical and Electronics Engineering Reva University Bangalore India Bgs Institute of Technology Department of Mechanical Engineering Karnataka India

ISBN: (纸本)9798350321425

Food preservation is the practice of preparing food in such a way that its beneficial characteristics (sensory and nutritional) are maintained for as long as feasible after preparation. Water elimination and reduction of water content are the basis of the food preservation theory. Maintaining concentration at a particular level is crucial in the food manufacturing industry. Common processing equipment in the food industry is the Continuous Stirred Tank Reactor (CS TR). The CS TR's function is to keep the reactor's internal temperature and product concentration constant. This study aims to keep the product concentration constant using two different controllers. The first is a traditional Proportional Integral Derivative (PID) controller, and the second is a Reinforcement Learning (RL) controller based on Q-learning. A mathematical model of the CS TR is constructed, and the controllers are developed. The concentration of the product is set to the specific value and allows the controller to track it. Error measures such as Mean Squared Error (MSE), Mean Absolute Error (MAE), and Mean Absolute Percentage Error (MAPE) are used to assess the performance of each controller. The RL controller shows promising results in terms of error metrics, suggesting that it may be a viable option for maintaining concentration in food industries. © 2023 IEEE.

关键词： Reinforcement learning

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Big Data Security through Privacy - Preserving Data Mining (PPDM): A Decentralization Approach 2

Big Data Security through Privacy - Preserving Data Mining (...

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2nd International Conference on Electronics and Renewable Systems, ICEARS 2023

作者： Josphineleela, R. Kaliapp, S. Natrayan, L. Garg, Ashish Panimalar Engineering College Department of Computer Science Engineering Chennai600123 India Velammal Institute of Technology Department of Mechanical Engineering Chennai601204 India Saveetha School of Engineering Department of Mechanical Engineering Chennai602107 India Graphic Era Deemed to Be University Department of Computer Science and Engineering Dehradun248002 India

ISBN: (纸本)9798350346640

The increasing familiarity and improvements in data mining technologies bring serious security threats to the data of individuals. An emerging technology that provides better security and data security is PPDM: Privacy-Preserving Data Mining. The fundamental tenet of PPDM is to alter the data in such a manner that data mining techniques may be carried out successfully without affecting the privacy of sensitive data included in the data. The research article is focused on reducing the security and privacy risk that is created by data mining operations. During the process of data collection, Information processing, and publication of data there may be much security breached in the processing of the data in the data mining process. This paper focuses on the wide problems and is concerned with data mining, and finding a solution to ensure security and access through the data by diving the centralized data into various segments to ensure security by privacy-preserving methods. Through the process of finding various user interfaces related to data processing and issues related to the security of private information, this article will also show the vital role of PPDM in data mining and security. © 2023 IEEE.

关键词： Data mining

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Intelligent Virtual Laboratory Development and Implementation using the RASA Framework 7

Intelligent Virtual Laboratory Development and Implementatio...

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7th International Conference on Computing Methodologies and Communication, ICCMC 2023

作者： Josphineleela, R. Kaliappan, S. Natrayan, L. Bhatt, Upendra Mohan Department of Computer Science Engineering Panimalar Engineering College Poonamallee600123 India Department of Mechanical Engineering Velammal Institute of Technology Chennai601204 India Department of Mechanical Engineering Saveetha School of Engineering SIMATS 602107 India Department of Computer Science & Engineering Graphic Era Deemed to be University Dehradun248002 India

ISBN: (纸本)9781665464086

Virtual environments for learning with intelligent support are in high demand right now. This is due to the fact that smart e-learning environments must be built internally in order to increase students' hands-on abilities. Through inquiry and demonstration in a smart e-lab environment, these talents are linked to several disciplines of science and engineering. Students and instructors may make the transition from working in virtual environments to doing experiments using actual materials. Lab assistants often spend the bulk of their time making the same mistakes. As a result, this research looked at the technology, proposed approach, and implementation of an intelligent virtual C programming laboratory. This ensures that virtual assistants employ chatbots to fix student faults. The environment is based on his NLP (natural language processing) and machine learning models, with web application development using his MERN stack and virtual assistant production using the RASA, Artificial Intelligence (AI) framework. © 2023 IEEE.

关键词： Students

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IoT Enabled Water Flow Management and Monitoring System for Hydroelectric Power Generation

IoT Enabled Water Flow Management and Monitoring System for ...

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2024 International Conference on Decision Aid sciences and Applications, DASA 2024

作者： Shaikat, Abu Salman Tasnim, Rumana Hasan, Jahid Zonayed, Md Hussein, Molla Rashied Shithil, Shaekh Mohammad Al Amin, Abdullah Laurentian University of Sudbury Dept. of Bharti School of Engineering and Computer Science Ontario Canada World University of Bangladesh Dept. of Mechatronics Engineering Dhaka Bangladesh Dhaka University of Engineering & Technology Dept. of Mechanical Engineering Dhaka Bangladesh University of Asia Pacific Dept. of Computer Science & Engineering Dhaka Bangladesh University Technology Malaysia Dept. of Mechanical Engineering Kuala Lumpur Malaysia

ISBN: (纸本)9798350369106

Water energy sources play a crucial role, particularly in providing electrical energy through hydroelectric power. Both the water flow rate and the water head determine the potential energy available from water. The hydroelectric system's energy generation directly depends on the water flow rate. A lower flow rate means less water is available to generate energy, even if the head is high. It is essential to increase the potential of the water flow so that it can generate enough electricity. Traditionally, the flow rate is restricted due to the limited capacity of single penstock. This work addresses this issue by introducing multiple penstock-based water flow management systems. Also, conventional water flow monitoring systems often face limitations, like relying on manual measurement, as it only provides periodic data, making it difficult to respond quickly to changes in flow conditions. IoT-based flow sensors leverage IoT technology to monitor water flow and transmit flow data in real-time, enabling operators to detect fluctuating flow conditions and implement timely adjustments to processes, therefore enhancing the overall efficiency. This work aims to design and develop an IoT-enabled prototype hydroelectric power generation system using multiple water flows for enhancing power generation efficiency. The prototype indicates the use of multiple pipes as penstocks in a dam. Additionally, if generating a significant amount of electricity is not required, the water flow can be discontinued, thereby automatically closing each pipeline. This work uses flow sensors, Kitsware software, Arduino IoT Cloud, and an LCD display to generate flow measurement data every ten seconds from multiple pipelines. Moreover, the system automatically generates Excel data and graphically represents multiple water flows, enabling the user to compare the water flow data. The completed work resulted in a hydropower system demonstrating enhanced output and effective compatibility with IoT for

关键词： Penstocks

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Crafting chirality in three dimensions via a novel fabrication technique for bound states in the continuum metasurfaces

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Light(science & Applications) 2024年第3期13卷 390-392页

作者： Zaid Haddadin Anna My Nguyen Lisa V.Poulikakos Department of Electrical&Computer Engineering University of California San DiegoLa JollaCAUSA Department of Mechanical&Aerospace Engineering University of California San DiegoLa JollaCAUSA Program of Materials Science&Engineering University of California San DiegoLa JollaCAUSA

An additional deposition step was added to a multi-step electron beam lithographic fabrication process to unlock the height dimension as an accessible parameter for resonators comprising unit cells of quasi-bound stat... 详细信息

关键词： dimensions dimension intrinsic

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Optimizing Wind Turbine Placement for Enhanced Energy Production Using Modified Genetic Algorithm 7

Optimizing Wind Turbine Placement for Enhanced Energy Produc...

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7th International Conference on Contemporary Computing and Informatics, IC3I 2024

作者： Sudarshan, T.A. Srinivasulu, Ch. Thethi, H. Pal Bhutani, Vijay Subhashini, P. Ramesh, G. New Horizon College of Engineering Department of Mechanical Engineering Bangalore India Institute of Aeronautical Engineering Department of Computer Science and Engineering Telangana Hyderabad50043 India Lovely Professional University Phagwara India Amity University Amity School of Engineering and Technology Uttar Pradesh Greater Noida India Mlr Institute of Technology Department of Computer Science and Engineering Telangana Hyderabad India Griet Department of Computer Science and Engineering Bachupally Hyderabad India

ISBN: (纸本)9798350350067

The optimal site of wind farms is determined using an updated genetic algorithm approach, which maximizes output capacity while reducing the amount of turbines built and the area used by each farm. In particular, two examples are examined: One kind of wind is a homogeneous wind that blows in just one direction, while the other two are also possible. In the first scenario (a), 599 individuals were first split into 25 subpopulations and then evolved three thousand times. The evolution of case (b) takes place over three thousand generations, beginning with 600 individuals distributed over 19 subpopulations. Every design's fitness, overall power generation, power output efficiency, and ideal configurations are all part of the findings. The results show that the modified genetic algorithm provides much better results compared to the conventional Genetic Algorithm method. © 2024 IEEE.

关键词： Windmill

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Exploring the Benefits of Interactive Communication Media Algorithms for Networking Applications 3

Exploring the Benefits of Interactive Communication Media Al...

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3rd International Conference for Innovation in Technology, INOCON 2024

作者： Ahmed, Zahid Beemkumar, N. Ghosh, Debanjan Vivekananda Global University Department of Computer Science & Application Jaipur India School of Engineering and Technology Department of Mechanical Engineering Karnataka Bangalore India Arka Jain University Department of Computer Science & It Jharkhand Jamshedpur India

ISBN: (纸本)9798350381931

Interactive communication media algorithms are useful tools for creating efficient and effective networking applications. Such algorithms enable the application to accurately predict user behaviour and preferences, use natural language processing to automate conversations, and integrate with other systems. This paper investigates the benefits of using interactive communication media algorithms for developing networking applications, including improved user experience, scalability, and quicker response times. Additionally, this paper will address potential limitations, such as the need for significant data collection and availability of relevant computing resources. Finally, this paper will conclude with a discussion of the potential implications of further investigations into interactive communication media algorithms for networking applications. © 2024 IEEE.

关键词： Behavioral research

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A Novel Approach for Resume Ranking System Using Machine Learning 3rd

A Novel Approach for Resume Ranking System Using Machine Lea...

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3rd International Conference on Intelligent Systems and Sustainable Computing, ICISSC 2023

作者： Rajesh, B. Vignesh, G. Ram Sai Chamakura, Sai Kumar Karthik, N. Vani, V. Department of Computer Science and Engineering National Institute of Technology Puducherry Karaikal609609 India Department of Mechanical Engineering National Institute of Technology Karaikal Puducherry609609 India

ISBN: (纸本)9789819783540

Nowadays, many job ads are attracting a vast number of applications in less time on the internet. Screening and sorting these resumes manually is a very time-consuming process and costs a huge amount of money for recruiting organizations. This research paper suggests an automated method for matching resumes to open vacancies. To identify the most accurate machine learning approach, it investigates several of them, including Random Forest, K-Nearest Neighbors (KNN), Support Vector Machines (SVM), Decision Tree, Logistic Regression, Naïve Baye’s Classifier. Based on the findings of this research, Random Forest produces more accurate results for a given dataset. By using this automatic resume categorization method, the recruiting process will be revolutionized in terms of cost, time, and justice. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Adversarial machine learning

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Transfer Learning Approaches for Improved Thyroid Detection 5

Transfer Learning Approaches for Improved Thyroid Detection

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5th International Conference on Electronics and Sustainable Communication Systems, ICESC 2024

作者： Selvi, D. M. Kalai Joshi, Kireet Vani, V. Vekariya, Daxa Natrayan, L. Patil, Harshal R.M.D Engineering College Department of Computer Science and Engineering Chennai Tamil Nadu Tiruvallur601 206 India Graphic Era Deemed to Be University Department of Computer Science & Engineering Uttarakhand Dehradun248002 India Bangalore Institute of Technology Department of Information Science and Engineering Karnataka Bangalore560004 India Parul Institute of Engineering and Technology Parul University Department of Computer Science & Engineering Post Limda Gujarat 391760 India Saveetha School of Engineering Simats Department of Mechanical Engineering Tamil Nadu Chennai602105 India Computer Science and Engineering Maharashtra Pune412 115 India

ISBN: (纸本)9798350379945

Thyroid disease is a broad term for any medical disorder that impairs the thyroid gland's ability to generate enough hormones. It is not limited to any particular age group;it can strike anyone at any time. Detecting thyroid diseases usually includes blood tests, which can be difficult to interpret due to the large amount of data required for accurate predictions. To find the best algorithm for predicting thyroid risk, this study examines different Transfer Learning (TL) methods. The dataset utilized for this work was sourced from the machine learning repository at the University of California, Irvine. The distribution of thyroid class labels in this dataset is highly imbalanced. To address this, sampling techniques are used to balance the dataset. Transfer learning models like AlexNet, Inception, and MobileNet are employed. All models achieve an accuracy rate of 97%. Upon comparison, AlexNet demonstrates the highest accuracy among the three models. The research shows that proper preprocessing and data balancing can improve thyroid detection efficiency. © 2024 IEEE.

关键词： Federated learning

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Machine learning-based approaches for the diagnosis and detection of brain stroke 2nd

Machine learning-based approaches for the diagnosis and dete...

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2nd International Conference on Next-Generation Computing and Information Systems, ICNGCIS 2023

作者： Panchal, Trupti Ramasubramanian, Sugayaalini Kumar, Yogesh Department of Mechanical Engineering Pandit Deendayal Energy University Gujarat Gandhinagar India Department of Computer Science and Engineering Pandit Deendayal Energy University Gujarat Gandhinagar India

ISBN: (纸本)9781032738659

This paper presents an in-depth analysis on brain stroke prediction using machine learning based approaches for prediction and classification. We take on a wide range of data sources, including demographic data, medical records, lifestyle conditions, and clinical measures for a prediction model. We managed to determine significant patterns and risk factors related to stroke owing to the dataset utilised in this study. Feature selection techniques are utilised to identify the most relevant predictors, enhancing the efficiency and interpretability of the model. In addition, we study alternative data preparation methods, such as managing missing values, outlier identification, and feature scaling, to ensure data quality and model resilience. © 2025 The Author(s).

关键词： Forecasting

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