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Integration of IoT and AI for Enhanced Efficiency and Control in Smart Energy Management Systems 2

Integration of IoT and AI for Enhanced Efficiency and Contro...

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2nd IEEE International Conference on Emerging Research in Computational Science, ICERCS 2024

作者： Naganandhini, S. Saravanan, S. Sammeta, Naresh Ramkumar, S. PSNA College of Engineering and Technology Department of Computer Science and Engineering Tamilnadu Dindigul India Vel Tech Rangarajan Dr. Sagunthala R&D Institute of Science and Technology Department of Computer Science and Engineering Tamilnadu Chennai India Andhra Pradesh Near Vijayawada India Sri Eshwar College of Engineering Department of Electronics and Communication Engineering Tamilnadu Coimbatore India

ISBN: (纸本)9798331534950

This paper presents a detailed analysis of how the integration of Internet of Things (IoT) and artificial intelligence (AI) to energy management systems (EMS) has a possibility of enhancing energy efficiency and control across various sectors. Therefore, this paper seeks to analyze the new developments in smart EMS through analyzing the possibilities of the combination of IoT and AI. Hence, the research utilises the hybrid approach with regard to the applicable methodology that includes numerical and analytical description, qualitative case and example analysis, and the actual implementation. Sophisticated quantitative analysis was also performed to analyze how the sensors of IoTs and the AI algorithm would affect the real-time energy monitoring, the demand forecasting, and the automated energy control systems. This paper presented case studies which offered more details on implementation including the processes, operations and such gains as experienced from employing IoT and AI. It is evident from the analysis that IoT enables detailed data acquisition and real-time observation and monitoring of devices, whereas AI optimises predictive modelling and decision-making. These result to imperative upgrades in energy efficiency, cuts in operation costs and strengthening in system performance. This paper provides an insight into the subject of smart EMS' application of IoT and AI in the context of energy management and presents recommendations to the practitioners and scholars. The implications also brought out future research directions in terms of improving the AI models and extending the use of IoT across a range of energy management scenarios. © 2024 IEEE.

关键词： demand Forecasting Energy Efficiency Internet of Things Predictive Analytics Smart Energy Management Systems

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Hybrid deep learning for International Trading: Integrating lSTM and Transformer Models 4

Hybrid Deep Learning for International Trading: Integrating ...

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4th International Conference on Sentiment Analysis and deep learning, ICSAdl 2025

作者： Akhila, T.S. Relin Francis Raj, J. Kani Jesintha, d. Santhana Krishnan, R. Vinoth Kumar, V. Sundaravadivel, P. Dhanalakshmi College of Engineering Department of Electronics and Communication Engineering Chennai India Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences Department of Electronics and Communication Engineering Chennai India Scad College of Engineering and Technology Department of Computer Science and Engineering Cheranmahadevi India Scad College of Engineering and Technology Department of Electronics and Communication Engineering Cheranmahadevi India Vel Tech Rangarajan Dr.Sagunthala R&d Institute of Science and Technology Department of Electronics and Communication Engineering Chennai India Saveetha Engineering College Department of Artificial Intelligence and Machine Learning Chennai India

ISBN: (纸本)9798331523923

This research proposes a hybrid deep learning model combining long Short-Term Memory (lSTM) networks and Transformers to enhance decision-making in international trading. The model leverages advanced techniques such as RoBERTa for text tokenization, FinBERT for sentiment analysis, and XlM-Roberta for multilingual language processing. lSTM is employed to analyze sequential numerical data like historical prices and trading volumes, providing accurate price and volatility forecasts. The Transformer component processes textual data to extract sentiment classifications and event-driven market impacts, facilitating a comprehensive understanding of market conditions. A fully connected fusion layer integrates insights from both components, enabling the model to classify market conditions holistically. The optimization process utilizes the Adam optimizer with learning rate scheduling to ensure efficient and effective training. loss functions like Mean Squared Error (MSE) and Cross-Entropy loss are employed to optimize numerical predictions and categorical outcomes, respectively. By combining temporal analysis with sentiment-driven insights, this hybrid model offers improved price forecasting, advanced multilingual sentiment analysis, and event-driven market condition predictions. The proposed approach aims to provide traders with actionable insights, enabling more informed decision-making in the highly dynamic and multilingual environment of international trading. © 2025 IEEE.

关键词： Costs

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Insights into Ocular disease Recognition: A Machine learning Approach

Insights into Ocular Disease Recognition: A Machine Learning...

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Applied Artificial Intelligence and Computing (ICAAIC), International Conference on

作者： Sosmita Paul Geet Bhatt Aditya dey BE in Electronics and Communication Engineering Sarvajanik College of Engineering and Technology Surat Gujarat India BE in Computer Engineering L D College of Engineering Ahmedabad Gujarat India

ISBN: (数字)9798350375190

ISBN: (纸本)9798350375206

This study reviews the potential benefits of machine learning techniques in diagnosing ocular disorders, aiming to address the limitations of traditional diagnostic methods. Various Machine learning (Ml) algorithms, including Support Vector Machine, decision Tree, K-Nearest Neighbor, Random Forest, Naïve Bayes, and Extra Trees, are compared to determine the effectiveness of each algorithm in identifying eye diseases. Through analysis and testing, the study concludes that Extra Trees (ET) has achieved the highest accuracy rate of 97%. This finding suggests that ET could be the most suitable option for enhancing the accuracy and efficiency of eye disease detection systems. Overall, these research discoveries contribute to the ongoing efforts to improve diagnostic tools for ocular health, highlighting the potential of Machine learning (Ml) in transforming healthcare.

关键词： Support vector machines Accuracy Machine learning algorithms Reviews Eye diseases Robustness Bayes methods

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Multi-Modal deep learning for User-Centric Car Recommendations

Multi-Modal Deep Learning for User-Centric Car Recommendatio...

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2025 International Conference on Multi-Agent Systems for Collaborative Intelligence, ICMSCI 2025

作者： Karthikeyan, M. Saravana Blessie, A. Alice Raj, J. Relin Francis Krishnan, R. Santhana Haribabu, K. Balamurugan, S. Vel Tech Rangarajan Dr. Sagunthala R&d Institute of Science and Technology Department of Computer Science and Engineering Avadi Chennai India Government College of Engineering Department of Electronics and Communication Engineering Tirunelveli India Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences Department of Electronics and Communication Engineering Chennai India Scad College of Engineering and Technology Department of Electronics and Communication Engineering Cheranmahadevi India Scad College of Engineering and Technology Department of Mechanical Engineering Cheranmahadevi India Velammal College of Engineering and Technology Department of Mathematics Madurai India

ISBN: (纸本)9798331509828

This paper presents a Multi-Modal deep learning framework for User-Centric Car Recommendations, aimed at transforming the car selection process through advanced artificial intelligence techniques. The proposed system integrates a wide range of data sources, including search history, showroom visits, car performance metrics, and social media engagements, to deliver highly personalized, accurate, and timely car recommendations that align with individual preferences, budgets, and lifestyles. The framework leverages multi-modal data processing to optimize car selection by analyzing both qualitative and quantitative user inputs, ensuring precise and context-aware recommendations. By addressing challenges such as choice overload and decision fatigue, the system enhances user decision-making capabilities. Real-time processing ensures that users receive up-to-date and relevant suggestions, improving engagement and overall satisfaction. The inclusion of a feedback loop further refines the system, enabling continuous improvement and adaptation to evolving user needs and market trends. This innovative framework represents a significant advancement in car recommendation systems by combining data diversity, state-of-the-art deep learning models, and real-time analytics. Its ability to provide dynamic, personalized, and actionable insights empowers users to make confident, well-informed decisions during their car-buying journey. With its robust and scalable design, this framework offers a transformative approach to modern automotive recommendation systems, setting a new standard for user-centric experiences. © 2025 IEEE.

关键词： Budget control

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Machine learning Approach to Predict Trends in Future for Options 7

Machine Learning Approach to Predict Trends in Future for Op...

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2023 7th International Conference On Computing, Communication, Control And Automation, ICCUBEA 2023

作者： Tarle, Sahil Balasaheb Paulkar, Pratik Parmeshwar Phad, Karan Govind Jadhav, Shreyas Vishwanath Chaudhari, Archana Y. Patil, Pramod d. Ahire, Prashant Dr. D. Y. Patil Institute of Technology Pimpri Computer Engineering Department Pune18 India Pimpri Chinchwad College of Engineering Computer Engineering Department Pune India

ISBN: (纸本)9798350304268

It is challenging to make predictions about the stock market since it requires in-depth expertise to weed out news stories, look at past data, and determine how news stories affect stock price movements. There is a drastic increase in challenges because of the fluctuating nature of stock values. The stock market prediction helps investors, policymakers, and businesses make informed decisions based on the expected future performance of the stock market. This paper presents an application of the Prophet algorithm in the stock market, to forecast stock prices. Prophet is a time series forecasting algorithm that takes into account seasonality, trends, and holidays. The data source for this investigation is downloaded from Yahoo Finance includes daily High, low, Open and Close prices of a selected stock from January 2015 to April 2023. The model is trained on the 70 percent of historic data, and the performance is evaluated on the test data which is remaining 30 percent of data. The research results indicate that the Prophet An algorithm is likely to accurately foresee the stock prices with an average MAPE of 20.8 percent. Additionally, the model sheds light into the trends and seasonality of the stock market, which can be useful for investors and traders. Overall, the Findings indicate that the Prophet algorithm can be a valuable tool for stock market prediction. © 2023 IEEE.

关键词： Equities Prediction Facebook Prophet Machine learning Time Series Variance

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Heart disease Prediction Model using various Supervised learning Algorithm 12

Heart Disease Prediction Model using various Supervised Lear...

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12th IEEE International Conference on Communication Systems and Network Technologies, CSNT 2023

作者： Upadhyay, Sachin dwivedi, Anjali Verma, Ashutosh Tiwari, Vatsya Gla University Department of Computer Engineering & Application Mathura India Government Girls Polytechnic Computer Science and Engineering Amethi India G.L. Bajaj Group of Institutions Computer Science & Engineering Mathura India Bsa College of Engineering & Technology Department of Computer Science & Applications Mathura India

ISBN: (纸本)9781665462617

A computer can learn from data and improve its predictive abilities without requiring human interaction through artificial intelligence (AI) derived from machine learning (Ml). Machine learning's core concept is to replicate how the brain functions. Ml approaches have proven to be effective predictors in a variety of application fields, including healthcare and medicine. In this study, supervised machine learning approaches for predicting cardiac disorders were analyzed and compared using medical records from the UCI Machine learning repository. This research examines the effectiveness of various models, including K-Nearest Neighbor (KNN), logistic Regression (lR) models and Support Vector Machines (SVM), and The (Area Under ROC Curve) AUC score was used to evaluate the effectiveness of various algorithms. AUC is a measurement. In order to evaluate the effectiveness of various algorithms, we used the (Area Under ROC Curve) AUC score. The choice to adopt a machine learning algorithm is made if the AUC score is more than 0.5, which is an evaluation metric that aids in validating how effective the algorithm is. AUC scores for logistic regression are the highest of all Ml algorithms in the trial (0.87). © 2023 IEEE.

关键词： Support vector machines

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Trend Analysis of Rainfall and Temperature: A Case Study of Amreli district, Gujarat, India 28th

Trend Analysis of Rainfall and Temperature: A Case Study of ...

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28th International Conference on Hydraulics, Water Resources, River and Coastal engineering, HYdRO 2023

作者： Chovatiya, Zarna J. Rajapara, Gargi Shaikh, Mohdzuned M. Kadi Sarva Vishwavidyalaya Gandhinagar India L.D.R.P. Institute of Technology & Research Kadi Sarva Vishwavidyalaya Gandhinagar India L.D. College of Engineering Ahmedabad India

ISBN: (纸本)9789819791798

The stability of Earth's ecology and temperature is the main consequence of climate change. Climate change is one of the biggest problems around the world. The overall surface temperature has altered as a result of global warming due to an increase in greenhouse gas concentrations. It is directly affecting the environment. The ecology is influenced more to a greater extent when the global temperature rises. As a result, even with great effort, it is hard to turn the process around. The present study is mainly concerned with the changing trend of rainfall of a river basin of Amreli district, Gujarat, India. From the extensive literature review it can be established that the river is facing adverse effects of flood. This is an effort to analyse some of the most important climatic variables, precipitation and temperature for analyzing the trend in the study area. A statistical trend is an important change over time that may be identified using both parametric and non-parametric techniques, and trend analysis of a time series includes the size of the change. The daily rainfall data of 40 years from 1979 to 2019 has been processed in the study to find out the monthly, seasonal and annual variability of precipitation and for this purpose a well established non-parametric test called Mann Kendall test has been used for the determination of trend. From the trend analysis it is found that the trend in precipitation ranges from −0.83 to 3.02 and for temperature it ranges from −3.27 to 3.09. © The Author(s), under exclusive license to Springer Nature Singapore Pte ltd. 2025.

关键词： Kyoto Protocol

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development and Evaluation of Sequential Batch Reactor with Natural Coagulants for dairy Waste Water Treatment 5

Development and Evaluation of Sequential Batch Reactor with ...

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5th International Conference on Mobile Computing and Sustainable Informatics, ICMCSI 2024

作者： daniel, R. Sheela Sundararajan, S. Kumar, C. Antony Vasantha Jothi, J. Nirmal Krishnan, R. Santhana Ram, C. Ramasamy Sankar SCAD College of Engineering and Technology Department of Civil Engineering Cheranmahadevi India SCAD College of Engineering and Technology Department of Mechanical Engineering Cheranmahadevi India School of Electrical and Communication Vel Tech Rangarajan Dr. Sagunthala R&D Institute of Science and Technology Department of Electronics and Communication Engineering Chennai India SCAD College of Engineering and Technology Department of Electronics and Communication Engineering Cheranmahadevi India University College of Engineering Anna University Department of Computer Science and Engineering Tiruchirappalli India

ISBN: (纸本)9798350395235

India is often heralded as the "Cynosure"of the global dairy industry, boasting a staggering valuation of INR 14,899.8 billion in 2022. This industry has exhibited remarkable and sustained growth in milk product availability since the year 1998. However, it's imperative to concede that the dairy sector concurrently bears the dubious distinction of being one of the most ecologically burdensome industries, primarily due to its substantial effluent output. Approximately 50% of the total dairy wastewater emanates from the rinsing of processing units intricately involved in the production of dairy commodities. The sources of this wastewater are multifarious, encompassing spills and seepage from pipelines, suboptimal tanker drainage, foaming incidents, tank spillages, cleaning operations, and liquid losses. This wastewater comprises milk solids, detergents, sanitizers, milk remnants, and cleaning water. It is typified by elevated concentrations of nutrients, both organic and inorganic, and may also encompass acids, alkalis, active agents, disinfectants, in conjunction with a substantial microbiological load, inclusive of pathogenic viruses and bacteria. In recognition of the imperative need to reclaim valuable milk solids to reinvigorate agriculture, particularly in regions grappling with water scarcity, a comprehensive wastewater treatment strategy was proffered. This strategic framework encompassed the meticulous scrutiny of pivotal parameters such as pH, BOd (Biological Oxygen demand), and turbidity during the treatment regimen. Moreover, an ongoing discourse pertains to the utilization of natural coagulants for the efficacious treatment of dairy wastewater. diverse ratios of these coagulants were judiciously introduced to the wastewater under treatment, and the levels of pH, BOd, and turbidity were assiduously assessed upon the culmination of each processing stage. The determination of the treatment process's effectiveness hinged upon the discerning comparison of outc

关键词： Wastewater treatment

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Fine Tuning GANs for Image Colorization: Effects of Training data and Epochs 7

Fine Tuning GANs for Image Colorization: Effects of Training...

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2023 7th International Conference On Computing, Communication, Control And Automation, ICCUBEA 2023

作者： Patil, Rahul Patil, Pramod Joshi, Rohit Joshi, Atharv Hastak, Ninad Joshi, Akshay Pimpri Chinchwad College of Engineering Dept. of Computer Engineering Pune India Dr. D. Y. Patil Institute of Technology Dept. of Computer Engineering Pune India

ISBN: (纸本)9798350304268

Ever since the advent of deep learning, there has been a lot of experimentation and research being carried out in image processing. This gave rise to the conversion of black and white i.e. grayscale images into colorized images. Image colorization is a popular image-to-image translation problem. It finds its application in various domains like image restoration, movie colorization, colorization of old aerial surveillance shots, medical image colorization, etc. Earlier this was done manually by artists and photographers but recent advancements in deep learning have enabled automatic image colorization using techniques like Convolutional Neural Networks and Generative Adversarial Networks. CNN is a neural network that has layers that are essential for extracting features from images and can learn the complex mapping between grayscale images and their corresponding ground truth images. On the other hand, GANs is a relatively new technique that consists of two components: generator and discriminator. This research paper aims to present the results obtained by using both the above-mentioned methodologies for image colorization. Many examples of generated images and their respective accuracies and structure according to the technique used are discussed in the later part. © 2023 IEEE.

关键词： Convolutional Neural Network deep learning Generative Adversarial Network Grayscale Image Colorization

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Smart lawn Mower with Stability Control using AVR Controller 2

Smart Lawn Mower with Stability Control using AVR Controller

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

作者： Selvalakshmi, S. Senthil Kumar, S. Rajeswari, N.P. Yamini, G. Vasudhevan, G. Kamalarajan, P. Department of Electronics and Instrumentation Engineering R.M.K Engineering College Kavaraipettai India Department of Mechanical Engineering R.M.K College of Engineering and Technology Puthuval India Department of Computer Science and Engineering Velammal College of Engineering and Technology Ponneri India Department of English Veltech Rangarajan Dr.Sagunthala Institute of Science and Technology Chennai India Department of Electronics and Communication Engineering Panimalar Engineering College Chennai India Department of Chemistry R.M.D. Engineering College Kavaraipettai India

ISBN: (纸本)9798331541217

This paper describes a robotic lawn mower that precisely and intelligently mow grass on playgrounds and lawns. To carry out the mowing operation, a collection of concurrently operating behaviors is defined. The dynamics of the terrain are obtained by utilizing a variety of accelerometers. AVR microcontroller utilizes the data from the sensors to adjust the mower's mechanisms in order to precisely remove grass. The controller will be relatively easy to use, making it accessible to all regular human users. design will place a high priority on energy efficiency. © 2024 IEEE.

关键词： lawn mowers

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