Rajalakshmi D, Rajesh Kambattan K, Sudharson K, Suresh Kumar A, Vanitha R
This study introduces WirelessGridBoost, an innovative framework designed to revolutionize real-time fault detection in wireless electrical grids by harnessing the power of the LightGBM machine learning algorithm. Traditional fault detection systems in electrical grids often face challenges such as latency and scalability due to the intricate nature of grid operations and limitations in communication infrastructure. To overcome these challenges, WirelessGridBoost integrates LightGBM, a highly efficient gradient boosting decision tree algorithm, with wireless technology to facilitate advanced fault detection capabilities. Trained on historical sensor data, the LightGBM model demonstrates exceptional proficiency in discerning complex fault patterns inherent in electrical grid operations. Deployed across strategically positioned wireless nodes within the grid, WirelessGridBoost enables prompt identification of anomalies in real-time. Extensive simulations and experiments conducted on a real-world grid testbed validate the effectiveness of WirelessGridBoost, achieving a fault detection accuracy of 96.80% and reducing latency by 38% compared to conventional methods. This research presents a promising avenue for enhancing fault detection efficiency in wireless electrical grids through the innovative WirelessGridBoost framework.
{"title":"Advancing Fault Detection Efficiency in Wireless Power Transmission with Light GBM for Real-Time Detection Enhancement","authors":"Rajalakshmi D, Rajesh Kambattan K, Sudharson K, Suresh Kumar A, Vanitha R","doi":"10.54392/irjmt2445","DOIUrl":"https://doi.org/10.54392/irjmt2445","url":null,"abstract":"This study introduces WirelessGridBoost, an innovative framework designed to revolutionize real-time fault detection in wireless electrical grids by harnessing the power of the LightGBM machine learning algorithm. Traditional fault detection systems in electrical grids often face challenges such as latency and scalability due to the intricate nature of grid operations and limitations in communication infrastructure. To overcome these challenges, WirelessGridBoost integrates LightGBM, a highly efficient gradient boosting decision tree algorithm, with wireless technology to facilitate advanced fault detection capabilities. Trained on historical sensor data, the LightGBM model demonstrates exceptional proficiency in discerning complex fault patterns inherent in electrical grid operations. Deployed across strategically positioned wireless nodes within the grid, WirelessGridBoost enables prompt identification of anomalies in real-time. Extensive simulations and experiments conducted on a real-world grid testbed validate the effectiveness of WirelessGridBoost, achieving a fault detection accuracy of 96.80% and reducing latency by 38% compared to conventional methods. This research presents a promising avenue for enhancing fault detection efficiency in wireless electrical grids through the innovative WirelessGridBoost framework.","PeriodicalId":14412,"journal":{"name":"International Research Journal of Multidisciplinary Technovation","volume":" 3","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-07-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141824535","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
In the present investigation, computations based on density functional theory (DFT) were employed to scrutinize the molecular configurations of clascosterone. Optimization was achieved using the DFT/B3LYP method with the 6-31G (d,p) basis set to thoroughly explore its structural and spectroscopic features. Additionally, molecular electrostatic potential (MEP) and Mulliken population analyses were conducted to comprehend the bonding characteristics and reactive sites. The Hirshfeld surface highlighted predominant H•••H interactions (71.5%), followed by O•••H interactions (25.5%). The stability of the compound was confirmed through the determination of hyperconjugative interactions using Natural Bond Orbital (NBO) analysis. Furthermore, molecular docking assessed the potential biological significance of clascosterone as an antitumor agent, targeting SMAD proteins like SMAD3 and SMAD4, resulting in binding energies of -8.22 and -8.57 kcal/mol, respectively.
{"title":"Quantum Chemical Computational Studies on the Structural Aspects, Spectroscopic Properties, Hirshfeld Surfaces, Donor-Acceptor Interactions and Molecular Docking of Clascosterone: A Promising Antitumor Agent","authors":"K. C, Ram Kumar A, Selvaraj S","doi":"10.54392/irjmt2444","DOIUrl":"https://doi.org/10.54392/irjmt2444","url":null,"abstract":"In the present investigation, computations based on density functional theory (DFT) were employed to scrutinize the molecular configurations of clascosterone. Optimization was achieved using the DFT/B3LYP method with the 6-31G (d,p) basis set to thoroughly explore its structural and spectroscopic features. Additionally, molecular electrostatic potential (MEP) and Mulliken population analyses were conducted to comprehend the bonding characteristics and reactive sites. The Hirshfeld surface highlighted predominant H•••H interactions (71.5%), followed by O•••H interactions (25.5%). The stability of the compound was confirmed through the determination of hyperconjugative interactions using Natural Bond Orbital (NBO) analysis. Furthermore, molecular docking assessed the potential biological significance of clascosterone as an antitumor agent, targeting SMAD proteins like SMAD3 and SMAD4, resulting in binding energies of -8.22 and -8.57 kcal/mol, respectively.","PeriodicalId":14412,"journal":{"name":"International Research Journal of Multidisciplinary Technovation","volume":"88 4","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-07-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141834397","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Saravanakumar R, Elango K.S, Gnana Venkatesh S, Saravanaganesh S
This paper mainly dealt with the evaluation of the structural stability of four storied building using non-destructive on-destructive testing methods. During the construction stage, there are many tests available to assess the excellence of concrete. The quality of concrete mainly depends on the quality of materials, concrete grade, and water-cement ratio. In the case of existing structures, to check the quality of concrete destructive tests are not possible, meanwhile, concrete quality will be assessed by using non-destructive testing (NDT) techniques such as rebound hammer, ultrasonic pulse velocity (UPV) etc. In this present study, an attempt has been made to check the quality of concrete in an existing four-storied building using non-destructive testing methods such as rebound hammer test and ultrasonic pulse velocity test. Moreover, the stability of the structure was also assessed. Non-destructive testing method was chosen since existing information of the structure was unavailable. Test results showed that the basement (B1) was susceptible to corrosion, and the compressive strength was not in the recommended range. Ultrasonic pulse velocity (UPV) results also proved that the average quality of the concrete was poor. Hence, significant suggestions were given for necessary retrofitting measures to improve the stability of the structure.
{"title":"Evaluation of Structural Stability of Four-Storied building using Non-Destructive Testing Techniques","authors":"Saravanakumar R, Elango K.S, Gnana Venkatesh S, Saravanaganesh S","doi":"10.54392/irjmt2441","DOIUrl":"https://doi.org/10.54392/irjmt2441","url":null,"abstract":"This paper mainly dealt with the evaluation of the structural stability of four storied building using non-destructive on-destructive testing methods. During the construction stage, there are many tests available to assess the excellence of concrete. The quality of concrete mainly depends on the quality of materials, concrete grade, and water-cement ratio. In the case of existing structures, to check the quality of concrete destructive tests are not possible, meanwhile, concrete quality will be assessed by using non-destructive testing (NDT) techniques such as rebound hammer, ultrasonic pulse velocity (UPV) etc. In this present study, an attempt has been made to check the quality of concrete in an existing four-storied building using non-destructive testing methods such as rebound hammer test and ultrasonic pulse velocity test. Moreover, the stability of the structure was also assessed. Non-destructive testing method was chosen since existing information of the structure was unavailable. Test results showed that the basement (B1) was susceptible to corrosion, and the compressive strength was not in the recommended range. Ultrasonic pulse velocity (UPV) results also proved that the average quality of the concrete was poor. Hence, significant suggestions were given for necessary retrofitting measures to improve the stability of the structure.","PeriodicalId":14412,"journal":{"name":"International Research Journal of Multidisciplinary Technovation","volume":" 12","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-06-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141369552","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
The global COVID-19 pandemic has presented unprecedented challenges, notably the limited availability of test kits, hindering timely and accurate disease diagnosis. Rapid identification of pneumonia, a common COVID-19 consequence, is crucial for effective management. This study focuses on COVID-19 classification from Chest X-ray images, employing an innovative approach: adapting the Xception model into a U-Net architecture via the Segmentation_Models package. Leveraging deep learning and image segmentation, the U-Net architecture, a CNN variant, proves ideal for this task, particularly after tailoring its output layer for classification. By utilizing the Xception model, we aim to enhance COVID-19 classification accuracy and efficiency. The results demonstrate promising autonomous identification of COVID-19 cases, offering valuable support to healthcare professionals. The fusion of medical imaging data with advanced neural network architectures highlights avenues for improving diagnostic accuracy during the pandemic. Notably, precision, recall, and F1 scores for each class are reported: Normal (Precision = 0.98, Recall = 0.9608, F1 Score = 0.9704), Pneumonia (Precision = 0.9579, Recall = 0.9579, F1 Score = 0.9579), and COVID-19 (Precision = 0.96, Recall = 0.9796, F1 Score = 0.9698). These findings underscore the effectiveness of our approach in accurately classifying COVID-19 cases from chest X-ray images, offering promising avenues for enhancing diagnostic capabilities during the pandemic.
{"title":"Diagnosis of COVID-19 in X-ray Images using Deep Neural Networks","authors":"Mohammed Akram Younus Alsaati","doi":"10.54392/irjmt24318","DOIUrl":"https://doi.org/10.54392/irjmt24318","url":null,"abstract":"The global COVID-19 pandemic has presented unprecedented challenges, notably the limited availability of test kits, hindering timely and accurate disease diagnosis. Rapid identification of pneumonia, a common COVID-19 consequence, is crucial for effective management. This study focuses on COVID-19 classification from Chest X-ray images, employing an innovative approach: adapting the Xception model into a U-Net architecture via the Segmentation_Models package. Leveraging deep learning and image segmentation, the U-Net architecture, a CNN variant, proves ideal for this task, particularly after tailoring its output layer for classification. By utilizing the Xception model, we aim to enhance COVID-19 classification accuracy and efficiency. The results demonstrate promising autonomous identification of COVID-19 cases, offering valuable support to healthcare professionals. The fusion of medical imaging data with advanced neural network architectures highlights avenues for improving diagnostic accuracy during the pandemic. Notably, precision, recall, and F1 scores for each class are reported: Normal (Precision = 0.98, Recall = 0.9608, F1 Score = 0.9704), Pneumonia (Precision = 0.9579, Recall = 0.9579, F1 Score = 0.9579), and COVID-19 (Precision = 0.96, Recall = 0.9796, F1 Score = 0.9698). These findings underscore the effectiveness of our approach in accurately classifying COVID-19 cases from chest X-ray images, offering promising avenues for enhancing diagnostic capabilities during the pandemic.","PeriodicalId":14412,"journal":{"name":"International Research Journal of Multidisciplinary Technovation","volume":"5 10","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-05-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141121003","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
This study introduces an ensemble classification model designed to categorize Alzheimer’s disease (AD) into four distinct classes—mild dementia, no dementia, moderate dementia, and very mild dementia—using Magnetic Resonance Imaging (MRI). The proposed model entitled the Ensemble Classification Model to Predict Alzheimer's Incidence as Multiple Classes (PAIMC) that integrates a six-dimensional analysis of MR images, encompassing entropies, Fractal Dimensions, Gray Level Run Length Matrix (GLRLM), Gray Level Co-occurrence Matrix (GLCM), morphological features, and Local Binary Patterns. A four-fold multi-label cross-validation approach was employed on a benchmark dataset to evaluate the model's performance. Quantitative analysis reveals that PAIMC consistently achieves superior Decision Accuracy, F-Score, Specificity, Sensitivity Recall, and Precision metrics compared to existing state-of-the-art models. For instance, PAIMC's Decision Accuracy and Precision outperform the second-best model by a notable margin across all folds. The model also demonstrates a significant improvement in Sensitivity Recall and Specificity, reinforcing its efficacy in the multi-class classification of AD stages. A novel data diversity assessment measure was developed and utilized, further confirming the robustness of the PAIMC model. The results underscore the potential of PAIMC as a highly accurate tool for AD classification in clinical settings.
本研究介绍了一种集合分类模型，旨在利用磁共振成像（MRI）将阿尔茨海默病（AD）分为四个不同的等级--轻度痴呆、无痴呆、中度痴呆和极轻度痴呆。所提出的模型名为 "预测阿尔茨海默氏症多类发病率的集合分类模型（PAIMC）"，它整合了磁共振图像的六维分析，包括熵、分形维数、灰度符长矩阵（GLRLM）、灰度共现矩阵（GLCM）、形态特征和局部二元模式。在基准数据集上采用了四重多标签交叉验证方法来评估模型的性能。定量分析结果表明，与现有的先进模型相比，PAIMC 的判定准确率、F-Score、特异性、灵敏度、召回率和精确度指标都非常出色。例如，在所有折叠中，PAIMC 的决策准确度和精确度都明显优于排名第二的模型。该模型在灵敏度、召回率和特异性方面也有显著提高，加强了其在多类 AD 阶段分类中的功效。研究还开发并使用了一种新的数据多样性评估方法，进一步证实了 PAIMC 模型的稳健性。这些结果凸显了 PAIMC 作为一种高精度的 AD 分类工具在临床环境中的潜力。
{"title":"An Ensemble Classification Model to Predict Alzheimer’s Incidence as Multiple Classes","authors":"Radhika Raju P, Ananda Rao A","doi":"10.54392/irjmt24314","DOIUrl":"https://doi.org/10.54392/irjmt24314","url":null,"abstract":"This study introduces an ensemble classification model designed to categorize Alzheimer’s disease (AD) into four distinct classes—mild dementia, no dementia, moderate dementia, and very mild dementia—using Magnetic Resonance Imaging (MRI). The proposed model entitled the Ensemble Classification Model to Predict Alzheimer's Incidence as Multiple Classes (PAIMC) that integrates a six-dimensional analysis of MR images, encompassing entropies, Fractal Dimensions, Gray Level Run Length Matrix (GLRLM), Gray Level Co-occurrence Matrix (GLCM), morphological features, and Local Binary Patterns. A four-fold multi-label cross-validation approach was employed on a benchmark dataset to evaluate the model's performance. Quantitative analysis reveals that PAIMC consistently achieves superior Decision Accuracy, F-Score, Specificity, Sensitivity Recall, and Precision metrics compared to existing state-of-the-art models. For instance, PAIMC's Decision Accuracy and Precision outperform the second-best model by a notable margin across all folds. The model also demonstrates a significant improvement in Sensitivity Recall and Specificity, reinforcing its efficacy in the multi-class classification of AD stages. A novel data diversity assessment measure was developed and utilized, further confirming the robustness of the PAIMC model. The results underscore the potential of PAIMC as a highly accurate tool for AD classification in clinical settings.","PeriodicalId":14412,"journal":{"name":"International Research Journal of Multidisciplinary Technovation","volume":" 903","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-05-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141127465","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
The rapid global spread of COVID-19 and RT-PCR tests are insensitive in early infection phases, according to hospitals. To find Covid-19, a fast, accurate test is needed. CT scans have shown diagnostic accuracy. CT scan processing using a deep learning architecture may improve illness diagnosis and treatment. A deep learning system for COVID-19 detection was derived using CT scan features. Using and comparing numerous transfer-learning models, fine-tuning, and the embedding process yielded the best infection diagnostic results. All models' diagnostic effectiveness was assessed using 2482 CT scan images. The optimized model demonstrated encouraging outcomes by significantly enhancing the sensitivity metric (86.26±1.72), a critical factor in accurately detecting COVID-19 infection. Additionally, the resulting model demonstrated elevated values for accuracy (81.15±0.17), specificity (77.90±1.33), precision (76.79±0.80), F1_score (81.24±0.37), and AUC (81.88±0.2). Deep learning methodologies have been effectively employed to detect COVID-19 in chest CT scan images. In the future, the suggested approach may be employed by clinical practitioners to study, identify, and effectively mitigate a greater number of pandemics.
{"title":"Early Diagnosis of Lung Infection via Deep Learning Approach","authors":"Marwa A. Shames, Mohammed Y. Kamil","doi":"10.54392/irjmt24316","DOIUrl":"https://doi.org/10.54392/irjmt24316","url":null,"abstract":"The rapid global spread of COVID-19 and RT-PCR tests are insensitive in early infection phases, according to hospitals. To find Covid-19, a fast, accurate test is needed. CT scans have shown diagnostic accuracy. CT scan processing using a deep learning architecture may improve illness diagnosis and treatment. A deep learning system for COVID-19 detection was derived using CT scan features. Using and comparing numerous transfer-learning models, fine-tuning, and the embedding process yielded the best infection diagnostic results. All models' diagnostic effectiveness was assessed using 2482 CT scan images. The optimized model demonstrated encouraging outcomes by significantly enhancing the sensitivity metric (86.26±1.72), a critical factor in accurately detecting COVID-19 infection. Additionally, the resulting model demonstrated elevated values for accuracy (81.15±0.17), specificity (77.90±1.33), precision (76.79±0.80), F1_score (81.24±0.37), and AUC (81.88±0.2). Deep learning methodologies have been effectively employed to detect COVID-19 in chest CT scan images. In the future, the suggested approach may be employed by clinical practitioners to study, identify, and effectively mitigate a greater number of pandemics.","PeriodicalId":14412,"journal":{"name":"International Research Journal of Multidisciplinary Technovation","volume":" 411","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-05-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141127658","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Recently the formation of disasters like earthquakes, Tsunami, etc., are quite common in all parts of the world. Due to the disasters the existence of loss to property as well as human life is quite common and more to avoid/decrease the damage due to disasters, strengthening a structure is one parameter. Retrofitting is the use of revolutionary technology to reinforce the structural elements to resist the upcoming damage due to disaster. In this paper carbon fiber reinforced polymer strengthening is considered for retrofitting technique. Carbon fiber reinforced polymer sheets of 50 mm width are used and wrapped on the beams with four different orientations like 00, 450, 600 and 900. Experimentally ten beams are casted in which two beams are marked as control beams and in remaining eight beam, every two beams are used for each orientation. The beams are subjected to four-point loading, and the greatest deflections and cracks at the beam center are recorded. The beams are tested for flexural loading and studied different parameters like maximum deflection, maximum load, Initial crack load etc are compared. With an emphasis on RC beams specifically, the goal of this work is to close the current research gap by examining the behavior of fiber reinforced polymer orientation in concrete elements. A beam covered with 50 mm strips at a 45-degree angle produced better results than the remaining beams.
{"title":"Identification of Optimum Retrofitting Approach for Strengthening RC Beams using CFRP Sheets","authors":"Sreekanth Gandla Nanabala, Balamurugan S","doi":"10.54392/irjmt24315","DOIUrl":"https://doi.org/10.54392/irjmt24315","url":null,"abstract":"Recently the formation of disasters like earthquakes, Tsunami, etc., are quite common in all parts of the world. Due to the disasters the existence of loss to property as well as human life is quite common and more to avoid/decrease the damage due to disasters, strengthening a structure is one parameter. Retrofitting is the use of revolutionary technology to reinforce the structural elements to resist the upcoming damage due to disaster. In this paper carbon fiber reinforced polymer strengthening is considered for retrofitting technique. Carbon fiber reinforced polymer sheets of 50 mm width are used and wrapped on the beams with four different orientations like 00, 450, 600 and 900. Experimentally ten beams are casted in which two beams are marked as control beams and in remaining eight beam, every two beams are used for each orientation. The beams are subjected to four-point loading, and the greatest deflections and cracks at the beam center are recorded. The beams are tested for flexural loading and studied different parameters like maximum deflection, maximum load, Initial crack load etc are compared. With an emphasis on RC beams specifically, the goal of this work is to close the current research gap by examining the behavior of fiber reinforced polymer orientation in concrete elements. A beam covered with 50 mm strips at a 45-degree angle produced better results than the remaining beams.","PeriodicalId":14412,"journal":{"name":"International Research Journal of Multidisciplinary Technovation","volume":" 407","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-05-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141127661","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Manjesh Bandrehalli Chandrashekaraiah, Beemkumar Nagappan, Y. Devarajan
Global warming and escalating energy consumption have presented pressing issues, catalyzing a pivotal shift towards environmental development worldwide. In recent years, the installed capacity of solar photovoltaic (PV) cells, particularly crystalline silicon cells, has experienced a significant surge. Among the myriad studies aimed at enhancing the efficiency of PV cells' power generation, one prominent avenue involves reducing the internal temperature of these cells. The primary objectives of the present study revolved around augmenting power generation and improving photocell efficiency. This was pursued through the strategic blending of nanoparticles with phase change material (PCM), with variations in insertion percentages to modulate the heat absorption capacity of the PV panel. Additionally, the study sought to evaluate the impact of integrating Thermoelectric Generator (TEG) modules and a water-based nano-fluid cooling system beneath the TEG setup. These measures aimed to effectively monitor the conversion of waste heat into electrical energy. Consequently, the proposed orientation of PV panels – involving PCM adjustment via alteration of insertion percentages, coupled with TEG integration and water-based nano-fluid cooling technology – holds significant promise for enhancing efficiency and mitigating solar cell degradation.
{"title":"Hybrid Power Generation: Experimental Investigation of PCM and TEG Integration with Photovoltaic Systems","authors":"Manjesh Bandrehalli Chandrashekaraiah, Beemkumar Nagappan, Y. Devarajan","doi":"10.54392/irjmt24317","DOIUrl":"https://doi.org/10.54392/irjmt24317","url":null,"abstract":"Global warming and escalating energy consumption have presented pressing issues, catalyzing a pivotal shift towards environmental development worldwide. In recent years, the installed capacity of solar photovoltaic (PV) cells, particularly crystalline silicon cells, has experienced a significant surge. Among the myriad studies aimed at enhancing the efficiency of PV cells' power generation, one prominent avenue involves reducing the internal temperature of these cells. The primary objectives of the present study revolved around augmenting power generation and improving photocell efficiency. This was pursued through the strategic blending of nanoparticles with phase change material (PCM), with variations in insertion percentages to modulate the heat absorption capacity of the PV panel. Additionally, the study sought to evaluate the impact of integrating Thermoelectric Generator (TEG) modules and a water-based nano-fluid cooling system beneath the TEG setup. These measures aimed to effectively monitor the conversion of waste heat into electrical energy. Consequently, the proposed orientation of PV panels – involving PCM adjustment via alteration of insertion percentages, coupled with TEG integration and water-based nano-fluid cooling technology – holds significant promise for enhancing efficiency and mitigating solar cell degradation.","PeriodicalId":14412,"journal":{"name":"International Research Journal of Multidisciplinary Technovation","volume":" July","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-05-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141127801","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Heart-related conditions remain the foremost global cause of mortality. In 2000, heart disease claimed around 14 million lives worldwide, a number that surged to approximately 620 million by 2023. The aging and expanding population significantly contribute to this rising mortality trend. However, this also underscores the potential for significant impact through early intervention, crucial for reducing fatalities from heart failure, where prevention plays a pivotal role. The aim of the present research is to develop a prospective ML framework that can detect important features and predict cardiac conditions as an early stage using a variety of choice of features strategies. The Features subsets that were chosen were designated as FST1, FST2, and FST3, respectively. Three distinct methods, including correlation-based feature selection, chi-square and mutual information, were used for picking features. Next, the most confident theory & the most appropriate feature selection were identified using six alternative machine learning models: Logistical Regression (LR) (AL1), the support vector Machine (SVM ) (AL2), K-nearest neighbor (K-NN) (AL3), Random forest (RF) model (AL4), Naive Bayes (NB) model (AL5), and Decision Tree (DT) (AL6). Ultimately, we discovered that, with 95.25% accuracy, 95.11% sensitivity, 95.23% specificity, 96.96 area below receiver operating characteristic and 0.27 log loss, the random forest model offered the most excellent results for F3 feature sets. No one has investigated coronary artery disease forecasting in depth; however, our study evaluates multiple statistics (specificity, sensitivity, accuracy, AUROC, and log loss) and uses multiple attribute choices to improve algorithms success for important features. The suggested model has considerable promise for medical use to speculate CVD find in Precursor at a minimal cost and in a shorter amount of time as well as will assist limited experience physician to take right decision based on the results of the used model combined with specific criteria.
{"title":"Detect the Cardiovascular Disease's in Initial Phase using a Range of Feature Selection Techniques of ML","authors":"Prashant Maganlal Goad, Pramod J. Deore","doi":"10.54392/irjmt24313","DOIUrl":"https://doi.org/10.54392/irjmt24313","url":null,"abstract":"Heart-related conditions remain the foremost global cause of mortality. In 2000, heart disease claimed around 14 million lives worldwide, a number that surged to approximately 620 million by 2023. The aging and expanding population significantly contribute to this rising mortality trend. However, this also underscores the potential for significant impact through early intervention, crucial for reducing fatalities from heart failure, where prevention plays a pivotal role. The aim of the present research is to develop a prospective ML framework that can detect important features and predict cardiac conditions as an early stage using a variety of choice of features strategies. The Features subsets that were chosen were designated as FST1, FST2, and FST3, respectively. Three distinct methods, including correlation-based feature selection, chi-square and mutual information, were used for picking features. Next, the most confident theory & the most appropriate feature selection were identified using six alternative machine learning models: Logistical Regression (LR) (AL1), the support vector Machine (SVM ) (AL2), K-nearest neighbor (K-NN) (AL3), Random forest (RF) model (AL4), Naive Bayes (NB) model (AL5), and Decision Tree (DT) (AL6). Ultimately, we discovered that, with 95.25% accuracy, 95.11% sensitivity, 95.23% specificity, 96.96 area below receiver operating characteristic and 0.27 log loss, the random forest model offered the most excellent results for F3 feature sets. No one has investigated coronary artery disease forecasting in depth; however, our study evaluates multiple statistics (specificity, sensitivity, accuracy, AUROC, and log loss) and uses multiple attribute choices to improve algorithms success for important features. The suggested model has considerable promise for medical use to speculate CVD find in Precursor at a minimal cost and in a shorter amount of time as well as will assist limited experience physician to take right decision based on the results of the used model combined with specific criteria.","PeriodicalId":14412,"journal":{"name":"International Research Journal of Multidisciplinary Technovation","volume":"3 8","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-05-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140979589","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
In an era driven by sustainable energy solutions, the synergy of photovoltaic (PV) system stands as a beacon of hope for meeting the world's growing energy demands while minimizing environmental impact. This research ventures into the domain of renewable energy integration by seamlessly including a PV system, ingeniously controlled by Chaotic Flower Pollination Optimized Adaptive Neuro Fuzzy Inference System (ANFIS) based MPPT (Maximum Power Point Tracking) controller capable of optimizing the efficiency in the face of ever-changing weather dynamics. The PV system's quest for optimal efficiency receives a substantial boost through the implementation of the High Gain Modified Luo Converter. Designed to achieve an optimal PV output voltage, this converter's prowess finds its true calling in grid applications, where precision and efficiency are paramount. Furthermore, this research extends its purview to incorporate a bidirectional converter linked to an energy storage solution, such as a battery, through a common DC link. The output power is then passed to the Flyback Converter, seamlessly connected to a 31 level Cascaded H Bridge Multi-Level Inverter (31-level CHB MLI) controlled by PI controller. This formidable inverter architecture facilitates the efficient delivery of power to the grid, ensuring a smooth and controlled integration of renewable energy resources. This strategic integration bolsters the system's adaptability, enabling the seamless management of energy flows and grid interactions along with load balancing in MLI. The MATLAB simulation platform is used for confirming the system's overall performance. According to the simulation results, the proposed approach achieves the maximum efficiency with the lowest THD value of 94.5% and 2.5%, respectively.
在以可持续能源解决方案为驱动力的时代，光伏（PV）系统的协同作用是满足全球日益增长的能源需求，同时最大限度减少对环境影响的希望灯塔。这项研究将光伏系统无缝纳入可再生能源集成领域，并通过基于混沌授粉优化自适应神经模糊推理系统（ANFIS）的 MPPT（最大功率点跟踪）控制器进行巧妙控制，该控制器能够在瞬息万变的天气动态中优化效率。通过实施高增益修正罗转换器，光伏系统对最佳效率的追求得到了极大的提升。该转换器旨在实现最佳的光伏输出电压，在对精度和效率要求极高的电网应用中发挥了真正的作用。此外，这项研究还扩展了其范围，将双向转换器与蓄电池等储能解决方案通过共用直流链路连接起来。输出功率随后被输送到反激式转换器，无缝连接到由 PI 控制器控制的 31 级级联 H 桥多级逆变器（31 级 CHB MLI）。这种强大的逆变器结构有助于向电网高效输送电力，确保可再生能源资源的平稳、可控整合。这种战略整合增强了系统的适应性，实现了能量流和电网互动的无缝管理，以及 MLI 中的负载平衡。MATLAB 仿真平台用于确认系统的整体性能。根据仿真结果，建议的方法实现了最高效率和最低总谐波失真（THD）值，分别为 94.5% 和 2.5%。
{"title":"PV based Systems with Advanced Control Strategies for Load Balancing in Multilevel Inverter","authors":"Venkedesh R, Anandha Kumar R, Renukadevi G","doi":"10.54392/irjmt24312","DOIUrl":"https://doi.org/10.54392/irjmt24312","url":null,"abstract":"In an era driven by sustainable energy solutions, the synergy of photovoltaic (PV) system stands as a beacon of hope for meeting the world's growing energy demands while minimizing environmental impact. This research ventures into the domain of renewable energy integration by seamlessly including a PV system, ingeniously controlled by Chaotic Flower Pollination Optimized Adaptive Neuro Fuzzy Inference System (ANFIS) based MPPT (Maximum Power Point Tracking) controller capable of optimizing the efficiency in the face of ever-changing weather dynamics. The PV system's quest for optimal efficiency receives a substantial boost through the implementation of the High Gain Modified Luo Converter. Designed to achieve an optimal PV output voltage, this converter's prowess finds its true calling in grid applications, where precision and efficiency are paramount. Furthermore, this research extends its purview to incorporate a bidirectional converter linked to an energy storage solution, such as a battery, through a common DC link. The output power is then passed to the Flyback Converter, seamlessly connected to a 31 level Cascaded H Bridge Multi-Level Inverter (31-level CHB MLI) controlled by PI controller. This formidable inverter architecture facilitates the efficient delivery of power to the grid, ensuring a smooth and controlled integration of renewable energy resources. This strategic integration bolsters the system's adaptability, enabling the seamless management of energy flows and grid interactions along with load balancing in MLI. The MATLAB simulation platform is used for confirming the system's overall performance. According to the simulation results, the proposed approach achieves the maximum efficiency with the lowest THD value of 94.5% and 2.5%, respectively.","PeriodicalId":14412,"journal":{"name":"International Research Journal of Multidisciplinary Technovation","volume":"16 24","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-05-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140982272","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
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