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3rd International Conference on Advancement in electronics and Communication engineering, AECE 2023

作者： Krishnamoorthy, R. Kaliyamurthie, K.P. Ahamed, B.S.H. Shayeez Harathi, Nimmala Selvan, R.Senthamil Bharath Institute of Higher Education and Research Department of Computer Science and Engineering Chennai India Madanapalle Institute of Technology and Science Department of Computer Science and Engineering Madanapalli India Department of Electronics and Communication Engineering Tirupati India Annamacharya Institute of Technology and Sciences Department of Electronics and Communication Engineering Tirupati India

ISBN: (纸本)9798350330724

Predicting consumer behaviour elucidates demographics, tastes, and underlying patterns. By keeping tabs on consumer activity, businesses may learn more about their wants and requirements, allowing them to provide better suggestions and increase conversion rates. Numerous factors affect consumer choices, including customer economics, buyer segmentation, and product quality. The most pressing problem that needs fixing is extracting actionable insights from these massive data sets to predict customers' actions. Author presented the multi objective evolutionary method to considerably improve the precision of consumption-related predictions as part of a cutting-edge quantitative research approach for predicting and analyzing customers' consumption habits. As the foundation for the whole prediction model, the data is first compiled based on customer preference and behaviours. As part of the data preparation, min-max normalization eliminates extraneous or irrelevant information. The author uses the Word2vec model for extraction of features, and we adopt boosting ant colony optimization (BACO) for feature selection. Multi-objective evolutionary algorithms (MOEAs) are used to make the predictions. The performance of the proposed system is evaluated, and its metrics are compared with those of well-established methods. The results indicate that the proposed multi objective evolutionary algorithm (MOEA) technique outperforms traditional Machine Learning (ML), excessive gradient boosting (XGB), Artificial intelligence (AI), and naive bayes (HNB) algorithms in various performance metrics. These metrics include accuracy (96 per cent), prediction quality (97per cent), accuracy (98 %), Recall (94 %), F1 score (99 %), and forecast time fifty seconds). Therefore, the results indicate that the regression model is viable and can be maintained over time. The proposed system for predicting consumption behaviour has shown its effectiveness in enhancing profitability. © 2023 IEEE.

关键词： Machine learning

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Smart Aeroponic Farms with IoT-Enabled Efficient Automation and Monitoring 2

Smart Aeroponic Farms with IoT-Enabled Efficient Automation ...

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2nd International Conference on Artificial Intelligence and Machine Learning Applications, AIMLA 2024

作者： Jeyabharath, R. Tamilvani, P. Karthikeyan, G. Vijayakumar, P. Rohini, J. Mohammadha Hussaini, M. Kavin Kumar, K. K.S.R. College of Engineering Department of Electrical and Electronics Engineering Tiruchengode India M.P.Nachimuthu M.Jaganathan Engineering College Department OfElectrical and Electronics Engineering Erode India Sona College of Technology Department of Electrical and Electronics Engineering Salem India Kongu Engineering College Master of Computer Applications Erode Perundurai India Erode Sengunthar Engineering College Department of Computer Science and Engineering Thuduppathi638057 India Government College of Engineering Department of Electrical and Electronics Engineering Erode India Kongu Engineering College Department of Electronics and Communication Engineering Erode India

ISBN: (数字)9798350349221

ISBN: (纸本)9798350349221

The Internet of Things (IoT) has enabled the adoption of automated aeroponics equipment, which has transformed our understanding of horticulture. Aeroponics is a method of vegetative production that does not use soil. Plant cultivation takes place in an airborne or vapour environment. The use of Internet of Things (IoT)-enabled aeroponics devices allows farms to autonomously monitor and manage variables such as temperature, lighting, humidity, and fertiliser concentrations. This allows for real-time adjustments to be made to optimize plant growth and yield. The IoT-enabled automated aeroponics system utilizes sensors and actuators to collect data on environmental conditions and automate the delivery of nutrients and water to the plants. Additionally, the system can be programmed to send alerts and notifications to farmers in case of any irregularities or issues that require attention. This level of automation not only increases efficiency and productivity but also reduces the need for manual labor and minimizes resource wastage. The IoT-enabled automated aeroponics systems have the potential to revolutionize farming by providing a more sustainable and efficient method of crop production. The computer system uses environmental elements to evaluate the results of aeroponics studies. The decision was made to use an automated aeroponics methodology to develop maize plants. According to the findings of this study, applying this method could potentially reduce labour expenses while increasing the value of the product. © 2024 IEEE.

关键词： Internet of things

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Employee Attrition Prediction based on Light Gradient Boosting Machine with Bayesian Optimization 1

Employee Attrition Prediction based on Light Gradient Boosti...

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1st International Conference on Intelligent Systems and Computational Networks, ICISCN 2025

作者： Pramodhini, R. Obbayed, Saif Manju, K. Swarnalakshmi, G. Purushotham, N. Nitte Meenakshi Institute of Technology Department of Electronics and Communication Engineering Bengaluru India The Islamic University of Al Diwaniyah College of Technical Engineering Department of Computers Techniques Engineering Al Diwaniyah Iraq Sona College of Technology Department of Electronics and Communication Engineering Salem India New Prince Shri Bhavani College of Engineering and Technology Department of Mechanical Engineering Chennai India Mohan Babu University Department of Computer Science and Engineering Tirupati India

ISBN: (纸本)9798331529246

In recent days, many organizations struggle to retain the valuable employees with severe consequences of Employee Attrition (EA) rate. The existing Random Forest (RF) was utilized to predict the turnover rate of employees in corporate industries. However, the existing model struggled with interpretability and computation cost issues. To overcome these issues, Light Gradient Boosting Machine with Bayesian Optimization (LightGBM-BO) is proposed. The proposed LightGBM-BO reduced computational complexity with gradient boosting and improved interpretability with hyperparameter tuning. Initially, the data is considered and preprocessed with min-max normalization to rescale the data into significant range. Then, features are extracted with Principal Component Analysis (PCA) by utilizing covariance matrix. After that, Recursive Feature Elimination (RFE) is incorporated to select optimal features based on feature importance. Finally, the EA prediction is done with proposed LightGBM-BO and the performance is improved with hyperparameter tuning. From the results, the proposed LightGBM-BO attained better results when compared to existing eXtreme Gradient Boosting (XGBoost) including accuracy (94.93%) and precision (92.84%) respectively. © 2025 IEEE.

关键词： Adaptive boosting

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Biometric Web based Vote Cast using Blocks 3

Biometric Web based Vote Cast using Blocks

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3rd International Conference on Innovative Mechanisms for Industry Applications, ICIMIA 2023

作者： Abinaya, M. Priya, P.Kaviya Mirza, Mohsina Anbumani, A. Sathya, R. Sethuram, M. Sri Eshwar College of Engineering Department of Computer Science and Engineering Coimbatore India P.A College of Engineering and Technology Department of Computer Science and Engineering Coimbatore India Global College of Engineering and Technology Department of Electrical and Electronic Engineering India Paavai College of Engineering Department of Computer Science and Engineering Coimbatore India Kongunadu College of Engineering and Technology Department of Information Technology Trichy India Hindusthan College of Engineering and Technology Department of Electronics and Communication Engineering Coimbatore India

ISBN: (纸本)9798350343632

Web based vote casting is a form of voting in which the voter is able to cast their votes through any web portal wherever in the world. In the Biometric method, voter can cast the votes using their finger impression. In this method the voter can selects the login portal which is particularly designed for an election commission. Each person needs to register the election commission website, using the registration details substantiate themselves before the machine authorize the user to sight the ballot visible on the screen. The voter is then allowed to pick their selected candidate and then cast the votes which would be passed to the election server for the refining process. In 'E-Voting' method a voter can cast their votes through online without any difficulty. If all the entries provided by the user are precise user can login the system using the user ID and password voter can cast their votes through online wherever in the world. If an information given by the voter is not correct then the entry will be discarded and it shows the error message. This technique is mainly implemented to reduce the difficulty of the election commission and voter. It is a user-friendly approach, also it avoids the unwanted crowds during the election period. Here, this study has used immutable ledger technology that is a blockchain based application, which provides the security and also increase the efficiency of the voting process. © 2023 IEEE.

关键词： Blockchain Electronic Voting (E-Voting) Personnel computer (PC)

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Development of Novel-Epoxy-Based Transformer Bushing Insulator for High Breakdown Voltage (BDV) 3

Development of Novel-Epoxy-Based Transformer Bushing Insulat...

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3rd International Conference on Emerging Trends in Electrical, Control, and Telecommunication engineering, ETECTE 2024

作者： Siddique, Abubakar Akram, Laraib Aslam, Waseem Mishal, Areeba Shahid, Muhammad Usama Ahmed, Haseeb Department of Electrical and Biomedical Engineering Khwaja Fareed University of Engineering and Information Technology Rahim Yar Khan Pakistan Department of Electrical Electronics & Computer Engineering University of Sargodha Sargodha Pakistan

ISBN: (纸本)9798331504861

In today’s trends, nanocomposites have garnered significant interest due to their potential to enhance dielectric properties and be used as insulators. Incorporating nanoparticles into the base epoxy makes achieving highly efficient dielectric materials feasible. However, the low electric field stress management capability and insufficient relative permittivity of typical epoxy provide a hindrance. Efforts have been made to overcome these limitations by adding nanosized fillers with high relative permittivity to the epoxy to enhance the dielectric properties. This research study focuses on the development of Novel-Epoxy-Based Transformer Bushing Insulator using nanomaterial Al2O3 and SiO2. The epoxy resin served as the base material, with nano-fillers added to improve its dielectric properties. The hardener was added to initiate the curing process and create a highly crosslinked structure. Surfactants were introduced to improve the interaction between the epoxy resin and nanoparticles. The nano-silica and nano-alumina particles were combined in different ratio configurations to create the nanocomposites by using the preparation method;Sol-Gel and Green Synthesis, which were subsequently tested for their dielectric properties. The proposed sample is the nanocomposite made by the green synthesis method and with the combination of 0.3 g + 0.3 g of the nanoparticle. Results show improved dielectric strength of the proposed nanocomposites compared to conventional porcelain materials. This paper presents an environment friendly approach for developing epoxy-based transformer bushing insulators, offering enhanced performance. ©2024 IEEE.

关键词： Permittivity

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Autotrack: a framework for query-based vehicle tracking and retrieval from CCTV footages using machine learning at the edge

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International Journal of Information technology (Singapore) 2023年第7期15卷 3827-3837页

作者： Sreedhar, Sreela Philip, Abin Oommen Sreeja, M.U. Department of Computer Science and Engineering Toc H Institute of Science and Technology Ernakulam India Department of Electronics Cochin University of Science and Technology Kochi India Department of Computer Science and Engineering-Cybersecurity Indian Institute of Information Technology Kottayam (IIIT Kottayam) Kottayam India

Automobile theft and hit and run incidents have become ever rampant than before urging the need for automated tracking systems. However, prompt detection of the vehicle involved in incident and recovery of the stolen vehicle is crucial as these are immediately forged to reduce the chances of getting caught. Hence, the proposed model puts forth a query-based framework to intelligently detect and track vehicles from CCTV surveillance footages using queries involving custom input features such as model, make, colour and license plate number. The model uses a combination of techniques for detecting the vehicle make and model, tracking vehicle, and for identifying the license plate number based on You Only Look Once (YOLO), Kalman filters, and Tesseract respectively. Further, the model performs vehicle detection and tracking from multiple surveillance videos which can ensure that the suspicious vehicle which is reported of either theft, license plate forgery or any unusual activity like hit and run cases is located immediately. The detection model is trained and deployed on edge device integrated to CCTVs and hence overcoming the drawbacks associated with a centralized system. The performance of the proposed vehicle detection model compared to baseline models on the basis of popular classification metrics precision, recall and f1-score proves the superiority of the proposed model compared to the existing ones. Additional experimental analysis of the entire framework further establishes the relevance of the combination of selected input features as well as emphasizes the significance of query-based models over its generic counterparts. © 2023, The Author(s), under exclusive licence to Bharati Vidyapeeth's Institute of computer Applications and Management.

关键词： Edge computing Kalman filter Object detection Tracking Vehicle detection YOLO

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Enhancing Image Segmentation in the Marine Atmosphere for Time-Sensitive Contextualisation using Convolutional Long Short-Term Memory 2

Enhancing Image Segmentation in the Marine Atmosphere for Ti...

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2nd International Conference on Advances in Computation, Communication and Information technology, ICAICCIT 2024

作者： Reethika, A. Rajeshwari, R. Vijayalakshmi, S. Malathy, S. Gnanasaravanan, S. Suresh Kumar, M. Department of Electronics and Communication Engineering Sri Ramakrishna Engineering College Coimbatore India Department of Computer Science and Engineering CMR Institute of Technology Bengaluru India Department of Information Technolog KPR Institute of Engineering and Technology Coimbatore India Department of Biomedical Engineering Karunya Institute of Technology and Sciences Coimbatore India Department of Information Technology Sri Ranganathar Institute of Engineering and Technology Coimbatore India

ISBN: (纸本)9798331541217

The work investigates autonomous surface vehicle obstacle avoidance and detection using computer vision techniques. It recognizes boundaries in marine environments by using the temporal context from video feeds from unmanned surface vehicles. The study shows how successful LSTM cells are in the CNN topologies that are currently in use, emphasizing how they might perform better. Nevertheless, datasets and network training techniques are not perfect. The work also uses a benchmarking approach to compare new network configurations. © 2024 IEEE.

关键词： Image segmentation

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Assessing Methodological Validity and Variable Considerations for Forecasting Renewable Energy Load Requirements: A Review

Assessing Methodological Validity and Variable Consideration...

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2024 International Conference on Science, engineering and Business for Driving Sustainable Development Goals, SEB4SDG 2024

作者： Olagunju, Ademola P. Olusanya, Olamide O. Olaleye, Taiwo Bells University of Technology Department of Electrical/Electronics and Telecommunication Engineering Ogun State Ota Nigeria Bells University of Technology Department of Computer Engineering Ogun State Ota Nigeria Federal University of Agriculture Department of Computer Science Ogun State Abeokuta Nigeria

ISBN: (纸本)9798350358155

This paper presents a systematic literature review conducted to investigate the validity of the adopted methodologies and variable considerations of forecasting renewable energy load requirements. A total of 213 studies from renowned scientific databases, including IEEE, Springer, Elsevier, ACM, and Taylor & Francis, were initially considered, resulting in a final selection of 23 primary studies using the inclusion and exclusion criterion. The review focused on analyzing the internal and external validity threats associated with the methods employed in these studies. Findings revealed several critical gaps in the methodology of the reviewed studies. Studies failed to address the bias-variance tradeoff, which is crucial for ensuring accurate predictions. A significant number of studies ignored parameter optimization in deep learning models, potentially impacting the reliability and precision of the load requirement forecasts. Studies did not conduct tests for multicollinearity of predictive attributes, which may lead to misleading results and inflated model performance. Few studies incorporated environmental and socioeconomic attributes in their datasets, which could have significant implications for energy consumption. Observations from this study highlight the importance of considering these gaps in renewable energy load requirement forecasting to enhance the accuracy and applicability of the resulting models. It emphasizes the significance of properly accounting for bias-variance tradeoffs, optimizing model parameters, and conducting tests for multicollinearity. The inclusion of environmental and socioeconomic variables in training sets is encouraged to capture the intricate relationships between these factors and energy consumption accurately. The study contributes to the improvement of renewable energy load requirement forecasting methodologies by promoting robust practices and considering comprehensive sets of variables. © 2024 IEEE.

关键词： Clean energy

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Deep learning for diabetic retinopathy detection and classification Using Long Short-Term Memory (LSTM) 4

Deep learning for diabetic retinopathy detection and classif...

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4th IEEE International Conference on Data engineering and Communication Systems, ICDECS 2024

作者： Prema Kumar, M. Srinivas Goud, D. Sankar, S. Sharath Kumar, S. Department of Electronics and Communication Engineering Andhra Pradesh Bhimavaram India Vignana Bharathi Institute of Technology Department of Computer Science and Engineering Andhra Pradesh India Saveetha Institute of Medical and Technical Sciences Saveetha School of Engineering Department of Computer Science and Engineering Chennai India Rmd Engineering College Kavaraipettai Department of Computer Science and Engineering Tiruvallur India

ISBN: (纸本)9798350393354

Macular edema, diabetic retinopathy, and other apparent microvascular abnormalities in the human retina are common consequences of diabetes. The primary cause of blindness in the past ten years, diabetic retinopathy (DR), is the subject of this study. Preserving blood glucose control and preventing certain diabetic reactions require immediate or early therapy. Due of its intricacy and depth, DR is difficult to diagnose by laborious manual diagnostic procedures. This paper uses a novel methodology. Instead of manually creating features, we used the Long Short-Term Memory (LSTM) model to extract features for the Region-based Convolutional Neural Network (R-CNN) system. It is an R-CNN-LSTM model with two parts. The proposed R-CNN-LSTM system adds certain ground truth labels to training samples, which enables it to first identify the most informative patches and images before learning basic to complicated retinal properties. Using two independent public datasets, the suggested method investigates the retina vasculature and hard-exudate symptoms of diabetic retinopathy. The recommended illumination normalisation methodology improved the deep learning model and excelled other cutting-edge techniques in terms of accuracy, recall, and precision, according to extensive experimental results. © 2024 IEEE.

关键词： Long short-term memory

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Exploring the Potential of Machine Learning in Automated Real-Time Data Analysis Systems 15

Exploring the Potential of Machine Learning in Automated Rea...

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15th International Conference on Computing Communication and Networking Technologies, ICCCNT 2024

作者： Chandrakala, P. Meshram, Vishal A. Meena, Mansingh Nithya, M.S. Alam, Intekhab Kumar, V. Mathan Prince Shri Venkateshwara Padmavathy Engineering College Department of Electrical and Electronics Engineering Chennai127 India Vishwakarma Institute of Information Technology Department of Computer Engineering Pune India Vivekananda Global University Department of Electrical Engineering Jaipur India Department of Computer Science and Information Technology Karnataka Bangalore India Maharishi University of Information Technology Maharishi School of Engineering and Technology Uttar Pradesh India Karpagam Academy of Higher Education Department of Computer Science Coimbatore641021 India

ISBN: (纸本)9798350370249

This paper explores the current country of gadget learning and its capability to allow real-time records analysis. Automated actual-time statistics analysis structures enable business specialists to reply to situations by extracting complicated patterns faithfully. Gadget learning techniques can assist these units by allowing algorithms to analyze previously amassed information and generalize these consequences to novel scenarios. This paper examines the latest developments in device learning algorithms that will likely be leveraged in automatic actual-time facts evaluation structures. The report also considers challenges associated with data education and the mission's generation selection. ultimately, the paper discusses numerous processes for integrating gadget getting to know into real-time records analytics systems that could permit agencies to take complete advantage of the significant ability of machine mastering. © 2024 IEEE.

关键词： Adversarial machine learning

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