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A Review of Smart Grid Management Systems Using Machine Learning Algorithms for Efficient Energy Distribution
The smart grid is an intelligent electricity network that uses digital technology to improve the efficiency, reliability, and sustainability of power delivery. Machine learning is a type of artificial intelligence that can be used to analyze data and learn from it. This makes it a valuable tool for the smart grid, as it can be used to solve a variety of problems, such asorecasting energy demand, detecting, and preventing outages, optimizing power flows, managing distributed energy resources, ensuring grid security.In this article, we will review the use of machine learning in the smart grid. We will discuss the different machine learning algorithms that are being used, the challenges that need to be addressed, and the future of machine learning in the smart grid.. The Authors, published by EDP Sciences, 2024. -
Blockchain-service quality-service value model to tourism experience
The goal of this study is to determine impact of Blockchain Technology in the Tourism Industry enhancing the Service Value and Service Quality among the players in the industry through BSS (Blockchain Technology-Service Quality-Service Value) model. Design/Methodology/Approach: A structured self-administered 380 questionnaires were designed and circulated to collect the preliminary information from the Tourists, Tour Operators, Travel Agents, and Hoteliers across the metropolitan cities using the Multi-Stage Cluster Sampling method to obtain a sample size of 284. Service Quality, technology, Service Value are the observed constructs to validate the hypothesis through SEM using Smart PLS 4. Findings: It can be observed that the technology addresses the pain points of the various participants operating in the tourism industry through the conceptualized BSS Model. The technology enhances the Service Quality by 80.2 per cent and Service Value by 81.3 per cent in the tourism industry. There is positive and strong relationship between Blockchain technology & Service Quality (0.893), Service value & Service quality (0.897), service value & blockchain technology (0.901). Original Value: The technology promises an instant, safe & advanced engine to the customers for managing bookings, payments, and hotel & property management, leading the clients to enjoy the maximum benefits eliminating the intermediaries and commission fees. It also ensures addressing the key players' pain points more effectively, adding customized service value offering quality service focused on customer satisfaction. 2024 Author(s). -
AI-Controlled Wind Turbine Systems: Integrating IoT and Machine Learning for Smart Grids
Advances in renewable energy technologies are pivotal in addressing the challenges posed by the depletion of traditional energy sources and their associated environmental impacts. Among these, wind energy stands out as a promising avenue, with wind turbine farms proliferating globally. However, the unpredictable nature of wind and intricate interplay between turbines necessitate innovative solutions for efficient operation and maintenance. This paper reviews advancements in intelligent control systems, notably those proposed by Smart Wind technologies. These systems leverage a network of sensors and IoT devices to gather real-time data, such as wind speed, temperature, and humidity, to optimize turbine performance. A significant focus is on turbines employing doubly-fed induction generators, which offer benefits like adjustable speed and consistent frequency operation. Their integration into smart grids introduces challenges concerning power system dynamics'security and reliability. This review delves into the dynamics, characteristics, and potential instabilities of such integrations, emphasizing the uncertainties in wind and nonlinear load predictions. A noteworthy finding is the rising prominence of artificial intelligence, particularly machine and deep learning, in predictive diagnostics. These methodologies offer costeffective, accurate, and efficient solutions, holding potential for enhancing power system stability and accuracy in the smart grid context. The Authors, published by EDP Sciences, 2024. -
Deposition and characterization of ZnO thin films on corning glass substrate using Magnetron sputtering
The Zinc Oxide (ZnO) thin films were deposited on corning glass substrates using RF Magnetron sputtering at a substrate temperature of 400 C and thicknesses of 1000 nm and 2000 nm. SEM, EDX, XRD, and UV-Vis spectrometers were used to analyse the thin films' morphological, structural, and optical characteristics. SEMwas used to analyse the surface morphology of the thin films. The composition of the created thin films was evaluated using EDX. XRD was used to examine the crystalline structure of the deposited ZnO films. Using the Debye-Scherrer equation, the average sample crystal size was determined. Uv-Vis was used to analyse the optical characteristics of the thin films. The findings showing how well-piezoelectric the produced thin films are may be useful in developing Surface Acoustic Wave Devices. 2024 Author(s). -
IoT-Powered Innovations in Renewable Energy Generation and Electric Drive
This review explores the growing impact of the Internet of Things (IoT) on the energy sector, particularly in the context of renewable energy generation and electric drive systems. IoT technology has rapidly expanded into various sectors, including energy, smart cities, and industrial automation, revolutionizing monitoring, control, and management processes. In this paper, we examine the existing literature on IoT applications in energy systems, with a focus on smart grids. We also delve into the core IoT technologies, such as cloud computing and data analysis platforms, that underpin these innovations. Additionally, we address challenges associated with IoT implementation in the energy sector, notably privacy and security concerns, and suggest potential solutions, such as blockchain technology. Our findings provide valuable insights for energy policy-makers, economists, and managers, offering a comprehensive overview of how IoT can optimize energy systems. Furthermore, we highlight IoT's expanding role in renewable energy and electric drive applications, enhancing performance monitoring, management, and energy savings while also advancing research and education in engineering. The Authors, published by EDP Sciences, 2024. -
A study on impact of Green human resource management practice on its sector - Chennai
The implementation of important plans and choices is a crucial part of the function that Human Resource Management plays in the operation of an organisation throughout its life. This makes it possible for the organisation to continue to cultivate and sustain its culture of supportability. In order to bring about sustainable success via Human Resource Management, the fundamental thinking process behind Green Human Resource Management is to motivate it. Green HRM (Green Human Resource Management) initiatives are currently in progress and are gaining traction among workers and representatives. These initiatives are embracing new work methodologies such as energy conservation, the implementation of E-HRM, telecommuting, and, most importantly, avoiding potential risks to protect the mother climate. Over the course of the last several years, the rate of worry on a worldwide scale has begun to quicken. 2024 Author(s). -
A Review of biophilic design at Kuttikattoor school for the children
The objective of this paper is the creation of a school to help the children of Kutikatoor live a more accessible and simpler life: students in the concentration range from 4 to 16 (Play School - - Grade 10). The proposed location, which is around 18.3 acres in size, lies in Kuttikattoor, Kozhikode, and Kerala. The land is surrounded by greenery and situated in a mountainous area. The study will concentrate on how biophilic architecture and design may enhance students' lives. This paper will discuss how biophilic design may benefit schools by creating circulation and spatial connections between the built and natural environments. The biophilic design can have quantifiable beneficial effects on student performance and well-being by including natural components. It is necessary to thoroughly analyze the biophilic design in relation to the learning environment for students, using ideas of ecological, visual, and spatial integration. By fostering a soothing atmosphere, lowering anxiety, and boosting physical fitness, biophilic design, which incorporates natural light, greenery, and nature vistas, can increase attention, decrease stress, stimulate creativity, and improve academic accomplishment. The school's design will be implemented by incorporating the architectural design into the contoured regions and using the idea of biophilic design patterns. Depending on the climate and the site's orientation, the design will be implemented such that locally accessible materials are employed in a hilly area. This detailed analysis of the case study and literature review for the school design will help us to design and conceptualize as an architect. Further, the study will also emphasize biophilic design which is aligned with the built environment in school design. The Authors, published by EDP Sciences. -
A study on effect of branding on customer buying behaviour with reference to Vellore
We set out to discover how consumers really feel about different branded items by conducting this study. This research aims to examine what variables impact consumers when they buy a product. There are a lot of aspects that affect a product's brand value these days, but consumers are especially sensitive to the product's reputation when making purchases. Another important factor in how people perceive a brand is advertisements. The study was conducted with a sample size of 50 and was confined to the Chennai area. All of the tests performed here made use of SPSS statistical software, and the data used is primary data. 2024 AIP Publishing LLC. -
Farm field security system using CNN and GSM module
Loss of crops and the destruction of livestock have been a major problem for many people in rural areas due to grass-fed animals whose food is derived from plants. According to research 32% are herbivores [1]. Reduced emissions from deforestation as well as deforestation are the main reason for wildlife moving towards urban areas. It results in wildlife infestation and human and animal conflicts. Therefore, compensating for the rapid loss of crops and the slaughter of livestock requires animal shelter and isolation in order to restrict the entry of animals into farm fields. The paper describes an effective and reliable way to identify and repel wildlife from farmland and to send real-time data to the farmer indicating animal attack on fields. An image of an animal will be obtained by convolution neural networks using intensive reading algorithms that provide a message to the farmer using the GSM module. It uses a user alert system and the animal scaring method. The test results show that the proposed algorithm has high visual accuracy. The detection level of the test set is achievable and the detection result is reliable. 2024 Author(s). -
Efficient multipath model based cross layer routing techniques for Gauss Markov movable node management in MANET
This research unveils an innovative cross-layer routing methodology tailored for managing Gauss Markov mobile nodes within MANETs. The primary focus deceits cutting-edge inspiring network performance through the efficient utilization of resources and the steadfast maintenance of mobile node connectivity. Central to this model is the implementation of joint optimization, which takes into account both node mobility patterns and resource allocation dynamics to pinpoint the most favorable data transmission pathway. Incorporating multipath routing, the methodology enables the simultaneous exploration of multiple transmission routes, thereby fortifying the network against potential link failures and disruptions. By embracing a cross-layer approach, it seamlessly integrates functionalities across network, and steering layers, thereby amplifying the complete system efficacy. Comprehensive simulations conducted reveal the superior performance of this approach compared to existing techniques, particularly in terms of network throughput, latency reduction, and augmentation of packet delivery ratios. Such findings underscore the immense potential of this methodology across a spectrum of MANET applications that demand streamlined and dependable data transmission mechanisms. 2024 Author(s). -
AI-enabled risk identification and traffic prediction in vehicular Ad hoc Networks
The proposed research presents a two-fold approach for advancing Vehicular Ad-Hoc Networks (VANETs). Firstly, it introduces a Residual Convolutional Neural Network (RCNN) architecture to extract real-time traffic data features, enabling accurate traffic flow prediction and hazard identification. The RCNN model, trained and tested on real- world data, outperforms existing models in both accuracy and efficiency, promising improved road safety and traffic management within VANETs. Secondly, the study introduces a Genetic Algorithm-enhanced Convolutional Neural Network (GACNN) routing algorithm, challenging traditional VANET routing methods with metaheuristic techniques. Experiments in various VANET network scenarios confirm GACNN's superior performance over existing routing protocols, marking a significant step toward more efficient and adaptive VANET traffic management. 2024 Author(s). -
Detection of colorectal cancer using dilated convolutional network via Raman spectra
Colorectal cancer (CRC) is a leading cause of cancer-related deaths worldwide. Early detection plays a crucial role in improving patient outcomes and reducing mortality rates. In recent years, Raman spectroscopy has emerged as a promising tool for non-invasive cancer detection. This research introduces a new method for identifying colorectal cancer (CRC). It combines Raman spectroscopy, a technique that analyzes the molecular fingerprint of tissues, with a powerful deep learning algorithm called a dilated convolutional network (DCN). By combining these two tools, the researchers aim to improve the accuracy and reliability of diagnosing CRC. Intraoperative diagnostics and pathology need to distinguish tumors from normal tissues. This proposal explores Raman spectroscopy as a new surgical tool for identifying colorectal cancer during surgery. Raman spectroscopy offers a way to directly analyze the makeup of tissue, potentially revealing the presence of cancer. However, surrounding tissue can create background interference, making it difficult to detect the key signal. The authors suggest that high-quality data from Raman spectroscopy combined with advanced deep learning algorithms could be a solution to overcome this challenge. We collect a large Raman spectroscopy dataset from 26 colorectal cancer patients with Raman shifts from 385 to 1545 cm-1. Second, dilated convolutional networks classify colorectal cancer tumour tissues. Following the deep learning model's output, we proceed by visualizing and analyzing the identified fingerprint peaks. Our deep learning algorithm exceeds previous colorectal cancer detection methods with 99.1% accuracy. Colorectal cancer detection using Raman spectra is unique. Our ensemble DCN could classify colorectal tumour and normal tissue Raman spectra. 2024 Author(s). -
Mitigating post-harvest losses through IoT-based machine learning algorithms in smart farming
This research paper explores the transformative potential of Internet of Things (IoT) technology in mitigating the longstanding issue of post-harvest losses within the agriculture sector. These losses, which encompass both quantitative and qualitative deterioration of food commodities from harvest to consumption, have posed persistent challenges, resulting in economic losses and food wastage. By delving into the current landscape of post-harvest losses and the application of IoT technology, the paper offers valuable insights into how IoT can be harnessed to reduce these losses effectively. It not only highlights the benefits and existing IoT solutions but also addresses the inherent challenges, providing recommendations for their resolution. Moreover, the research introduces a machine learning-based model, specifically Random Forest ML, to identify and prevent losses in tandem with IoT devices, empowering farmers with timely alert messages for informed decision-making, thus fostering a more sustainable and efficient agricultural ecosystem. 2024 Author(s). -
Improved diabetes disease prediction IWFO model using machine learning algorithms
Diabetic disease is the mostly affected and massive disease on a global level. Diagnosing the diabetic earlier will help the medicalist to give the improved and latest clinical treatment. The healthcare specialist unit uses many machine learning techniques, methodologies and tools for decision making in diabetic field. The machine learning techniques are utilized for the prediction of the diabetic diseases in the initial level. To eliminate such issues, optimized detection techniques are proposed. First of all, the training samples are increased using the sliding window protocol. Further, class imbalanced training data classes are balanced and resolved using the adaptive and gradient booster technique. Further, the diabetic feature selection process is improved by the Intensity Weighted Firefly Optimization firefly techniques (IWFO), in which irrelevant features are reduced based on the correlation between the features that deducts the unwanted features involved in the diabetic disease process. Then the feature transformation problem is faced by the PCA technique, which manages the several types of features. Finally, the improved and optimal hybrid random forest is applied into the normal and diabetes classes respectively. The proposed system predicts the diabetic disease efficiently and maximizes its precision of the prediction system. The present paper is compared with different classifiers to determine the efficiency of the work. Overall, the initiated system improved the present studies accuracy level. 2024 Author(s). -
Classification of fibroid using novel fully connected CNN with back propagation classifier (NFCCNNBP)
In this phase, we utilize features extracted from a prior stage to classify uterine fibroids. We employ a predefined dataset with feature values as our training set for a novel classifier called the "Novel Fully Connected CNN with Back Propagation Classifier."This classifier learns from the training set. We then put this method to the test with new images not included in the training dataset. Its primary objective is to assess the extent of infection across the entire uterine surface. Through the adoption of a Convolutional Neural Network (CNN) combined with Back Propagation (BP), we have achieved an impressive accuracy rate of 98.3% for predictions. When we compare this accuracy to existing classifiers like Fuzzy Logic, Naive Bayes, and SVM, our proposed model, NFCCNNBP, outperforms them significantly. 2024 Author(s). -
Prediction of heart disease using XGB classifier
Predicting heart disease in advance could be a significant medical breakthrough because it is widespread. A reliable strategy that can be utilized to do this is machine learning. Decision tree classifiers, random forests, and multilayer perceptron have all been used in studies to predict heart disease. However, several of these techniques could be improved, like poor precision. In our research, we have taken the South African heart Disease dataset and implemented a few models, which include Support Vector Machine (SVM), K Neighbors (KNN), Artificial neural network and XG Boost Classifier. We have used different methods for measuring performance. SVM with 69.0 accuracy, KNN with 86.0 accuracy, and ANN with 80.0 accuracy. However, the XGB classifier has shown some promising results in predicting heart disease with an accuracy of 90%. Further, when the hyperparameters were tuned using the random search method, the accuracy increased to 92.8%. The benefit of this work is that it uses machine-learning approaches to enhance the performance of coronary heart disease prediction. 2024 Author(s). -
Multilayer classification based Alzheimer's disease detection
Hippocampus, a small brain region plays a role in the initiation of the neurodegenerative pathways that leadto Alzheimer's. Humans with MCI are probable to develop Alzheimer's disorder. Hippocampal volume has been proven to indicate which patients with MCI will later develop Alzheimer's. Brain degeneration in MCI progresses over time and varies from person - to - person, making early detection difficult. Magnetic resonance imaging is a tool in diagnosing clinically suspected Alzheimer's disease. Information about the historical development of structural changes as the disease progresses from preclinical to overt stages is shaping understanding of the disease, and also guides diagnosis and treatment decisions in the future. In this study, we developed a new multilayer classification method to identify Alzheimer's disease from brain MRI using contour model and multilayer classifier. This method is evaluated on 436 samples of OASIS dataset and achieved accuracy of method is 93.75 %. 2024 Author(s). -
Life Cycle Assessment of Battery Energy Storage Technologies for Vehicular Applications
The necessity of sustainable energy sources and storage technologies is emerging due to growing energy demands. Thus, it encourages the need to perform sustainability analysis in terms of energy efficiency. For battery technologies, energy production and recycling holds a significance. In this study, the direct and indirect requirements of various battery technologies including production to transportation. The five battery technologies taken into account for the analysis are Lithium ion, Nickel Metal Hydride, Lead acid, Valve Regulated lead Acid, and Nickel Cadmium. The characteristics analyzed here are cycle life, energy density and energy efficiency. The study also covers the life cycle assessment in an structured way from raw to evaluation of materials, energy flow, installation, usage to end of life. The Authors, published by EDP Sciences, 2024. -
Modernizing Electrical Grids with TCR-Based Flexible AC Transmission Systems
Modernizing electrical grids is imperative to meet the growing demand for reliable, efficient, and sustainable energy. Thyristor-Controlled Reactors (TCRs) are integral components of modern Flexible AC Transmission Systems (FACTS). These systems offer a robust solution for enhancing grid stability, improving power quality, and optimizing transmission efficiency, ensuring that electric grids can support future energy needs. TCR-based FACTS are a collection of technologies designed to enhance the controllability, stability, and power transfer capability of AC electrical grid systems. In this paper, we will discuss the role of TCRs in modernizing AC transmission systems and their role in addressing grid challenges and improving performance, highlighting their critical role in future grid infrastructure. To discuss the future prospects and developments in TCR technology, with ongoing advancements and research efforts paving the way for more efficient, reliable, and flexible grid management solutions. The Authors, published by EDP Sciences, 2024. -
A PV-Powered Single Phase Seven-Level Invertera's Photocurrent and Injected Power
The PV inverter in this study is linked to the grid and its performance analysis is evaluated using a PI controller. It is a single phase multi-level PV inverter. The major objective of this research is to increase efficiency and eliminate harmonics caused by DC link voltage fluctuations created by Maximum Power Point Tracking (MPPT) during foggy situations. PV inverters generate and inject actual power into the main grid. This study uses a transformer-less photovoltaic inverter to cut down on losses, cost, and size. A transformer-less multilayer inverter is described in this paper. There is no high-frequency leakage current since that inverter can distribute both actual and reactive electricity. MATLAB/Simulink software was used to analyze and assess the effects of various PV-based seven-level techniques on the devicea's Maximum Power Point Tracking (MPPT) performance. The Authors, published by EDP Sciences, 2024.