Pub Date : 2023-05-01DOI: 10.1109/REEDCON57544.2023.10150867
Faiyaz Ahmad, Zeenat Waseem, Musheer Ahmad, M. Z. Ansari
Forest fires are the most destructive and devastating natural disasters. Forest fire prediction is done to lessen the impact of forest fires in the future. There are several fire detection systems available each with its own strategy. The fire-affected area is forecasted with the help of satellite images. This paper utilizes barometrical factors such as temp, rain, speed, wind, and relative humidity to anticipate the occurrence of a woodland conflagration for fire prediction. The machine learning techniques such as Decision tree, Random forest, Bagging, and Extra tree to solve the fire prediction problem. To prevent overfitting, separate data sets for training and evaluating the model along with cross-validation is performed. Using the Grid Search CV approach, the Decision tree on a range of sub-samples of the dataset is trained and used aggregating to boost projected accuracy to prevent over-fitting. With the proposed model a testing accuracy of 98.36% is achieved for the presented Decision tree based forest fires forecast model. The performance of our hyperparameter tuned model using Grid Search CV performs better compared to existing ML-based model.
{"title":"Forest Fire Prediction Using Machine Learning Techniques","authors":"Faiyaz Ahmad, Zeenat Waseem, Musheer Ahmad, M. Z. Ansari","doi":"10.1109/REEDCON57544.2023.10150867","DOIUrl":"https://doi.org/10.1109/REEDCON57544.2023.10150867","url":null,"abstract":"Forest fires are the most destructive and devastating natural disasters. Forest fire prediction is done to lessen the impact of forest fires in the future. There are several fire detection systems available each with its own strategy. The fire-affected area is forecasted with the help of satellite images. This paper utilizes barometrical factors such as temp, rain, speed, wind, and relative humidity to anticipate the occurrence of a woodland conflagration for fire prediction. The machine learning techniques such as Decision tree, Random forest, Bagging, and Extra tree to solve the fire prediction problem. To prevent overfitting, separate data sets for training and evaluating the model along with cross-validation is performed. Using the Grid Search CV approach, the Decision tree on a range of sub-samples of the dataset is trained and used aggregating to boost projected accuracy to prevent over-fitting. With the proposed model a testing accuracy of 98.36% is achieved for the presented Decision tree based forest fires forecast model. The performance of our hyperparameter tuned model using Grid Search CV performs better compared to existing ML-based model.","PeriodicalId":429116,"journal":{"name":"2023 International Conference on Recent Advances in Electrical, Electronics & Digital Healthcare Technologies (REEDCON)","volume":"61 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"120962250","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}
Pub Date : 2023-05-01DOI: 10.1109/REEDCON57544.2023.10151449
S. Saxena, Prasadini Mahapatra, A. Rizvi
Electrocardiogram (ECG) signals are used to diagnose heart diseases. The position of the R-peak has the greatest influence on diagnosing cardiovascular conditions. Existing methods use continuous wavelet transform (CWT) to detect the R-peaks. But it is critical to select the best wavelet basis for detecting it. This article focuses on solving this issue by constructing the wavelet. It proposes a novel method for the construction of the wavelet using M-Estimation. The aim of this method is to improve accuracy and reduce false prediction errors. The algorithm extracts the pattern from the signal and constructs the wavelet MEOW. After that, CWT is used to detect R-peaks. To demonstrate the validity and effectiveness of the proposed method, the results are compared with the existing methods. The results are tested on other pre-defined wavelets. The results show that the proposed method outperforms another wavelet with better resolution. The proposed method achieves better accuracy in comparison to other existing methods. Thus, this method has the potential to be a valuable tool n detecting the R-peaks in the ECG signals.
{"title":"Construction of wavelet using M-estimation and its Application in R-peak detection*","authors":"S. Saxena, Prasadini Mahapatra, A. Rizvi","doi":"10.1109/REEDCON57544.2023.10151449","DOIUrl":"https://doi.org/10.1109/REEDCON57544.2023.10151449","url":null,"abstract":"Electrocardiogram (ECG) signals are used to diagnose heart diseases. The position of the R-peak has the greatest influence on diagnosing cardiovascular conditions. Existing methods use continuous wavelet transform (CWT) to detect the R-peaks. But it is critical to select the best wavelet basis for detecting it. This article focuses on solving this issue by constructing the wavelet. It proposes a novel method for the construction of the wavelet using M-Estimation. The aim of this method is to improve accuracy and reduce false prediction errors. The algorithm extracts the pattern from the signal and constructs the wavelet MEOW. After that, CWT is used to detect R-peaks. To demonstrate the validity and effectiveness of the proposed method, the results are compared with the existing methods. The results are tested on other pre-defined wavelets. The results show that the proposed method outperforms another wavelet with better resolution. The proposed method achieves better accuracy in comparison to other existing methods. Thus, this method has the potential to be a valuable tool n detecting the R-peaks in the ECG signals.","PeriodicalId":429116,"journal":{"name":"2023 International Conference on Recent Advances in Electrical, Electronics & Digital Healthcare Technologies (REEDCON)","volume":"78 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126167147","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}
Pub Date : 2023-05-01DOI: 10.1109/REEDCON57544.2023.10151060
Arif Ali, Fiza Fatima, Syed Adnan Mazhari, A. A. Khan
The purpose of this article is to investigate the enhancement of AC breakdown voltage (AC-BDV) of pure synthetic ester oil and mineral oil using Fe-Cu bimetallic nanoparticles (NPs) based nanofluids. Bi-metallic or hybrid nanoparticles has not been explored much and they have potential to improve the electrical properties of insulating oils. There is consensus among studies that the production process used for nanofluids and the ideal concentration of nanoparticles are the most important factors affecting how well they perform, particularly in regard to their electrical properties. An investigation of the effects of different concentrations of nanoparticle and the configuration of the electrodes (Sphere-Sphere) and (Mushroom-Mushroom) have been performed, and their respective enhancements has been documented with the help of graphs. The possible cause of change in AC BDV has also been discussed. The maximum enhancement found in this experiment are 44.14% for synthetic ester oil and 54.32% for mineral oil in sphere electrode system.
{"title":"Experimental Investigation on AC Breakdown Strength of Insulating Oils Using Fe-Cu Bimetallic Nanoparticles","authors":"Arif Ali, Fiza Fatima, Syed Adnan Mazhari, A. A. Khan","doi":"10.1109/REEDCON57544.2023.10151060","DOIUrl":"https://doi.org/10.1109/REEDCON57544.2023.10151060","url":null,"abstract":"The purpose of this article is to investigate the enhancement of AC breakdown voltage (AC-BDV) of pure synthetic ester oil and mineral oil using Fe-Cu bimetallic nanoparticles (NPs) based nanofluids. Bi-metallic or hybrid nanoparticles has not been explored much and they have potential to improve the electrical properties of insulating oils. There is consensus among studies that the production process used for nanofluids and the ideal concentration of nanoparticles are the most important factors affecting how well they perform, particularly in regard to their electrical properties. An investigation of the effects of different concentrations of nanoparticle and the configuration of the electrodes (Sphere-Sphere) and (Mushroom-Mushroom) have been performed, and their respective enhancements has been documented with the help of graphs. The possible cause of change in AC BDV has also been discussed. The maximum enhancement found in this experiment are 44.14% for synthetic ester oil and 54.32% for mineral oil in sphere electrode system.","PeriodicalId":429116,"journal":{"name":"2023 International Conference on Recent Advances in Electrical, Electronics & Digital Healthcare Technologies (REEDCON)","volume":"37 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125494277","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}
Pub Date : 2023-05-01DOI: 10.1109/REEDCON57544.2023.10150766
Syed Rameem Zahra, Tausifa Jan Saleem, Sonia
Just like the game for Information Technology (IT) security was turned topsy-turvy by the arrival of mobile devices a few decades ago, the Internet of Things (IoT) today is changing it again a big time. Data breaches, loss of productivity and profitability were quite a commonplace when mobile security became an issue. Today as IoT tries to connect everything to the internet, it intensifies the problem (more privacy violations, phishing/skimming attacks, security incidents) by creating a vast attack surface for the rogue players implying that important national (ports, airports, bridges, power-pants etc.) and cyber infrastructure could now be attacked. Moreover, if the network of your company has a weak point, it can easily be compromised to set off an attack. These Cyber terrorism attacks instigate identical responses from individuals as those for conventional terrorism attacks e.g. they heighten anxiety and stress, worsen feelings of vulnerability and harden the political attitudes of people. But this dimension is often overlooked by the policy makers. However, the increase in cyber terrorism cases has increased the research interest in the domain. Researchers are focusing on improving the security of internet and IoT devices. The field is still in its infancy. A lot needs to be done to mitigate these attacks. Motivated to demystify the repercussions of the cyber terrorism, this paper analyzes the distinctive characteristics of the most significant cyberattacks, investigates the expenditures incurred by the industries as a result of these cyber events, and then examines the impact of these attacks on the psychological health of the populace and the trust in public institutions.
{"title":"Demystifying Cyber terrorism: Causes, Costs & its impact on individuals’ psyche and public confidence","authors":"Syed Rameem Zahra, Tausifa Jan Saleem, Sonia","doi":"10.1109/REEDCON57544.2023.10150766","DOIUrl":"https://doi.org/10.1109/REEDCON57544.2023.10150766","url":null,"abstract":"Just like the game for Information Technology (IT) security was turned topsy-turvy by the arrival of mobile devices a few decades ago, the Internet of Things (IoT) today is changing it again a big time. Data breaches, loss of productivity and profitability were quite a commonplace when mobile security became an issue. Today as IoT tries to connect everything to the internet, it intensifies the problem (more privacy violations, phishing/skimming attacks, security incidents) by creating a vast attack surface for the rogue players implying that important national (ports, airports, bridges, power-pants etc.) and cyber infrastructure could now be attacked. Moreover, if the network of your company has a weak point, it can easily be compromised to set off an attack. These Cyber terrorism attacks instigate identical responses from individuals as those for conventional terrorism attacks e.g. they heighten anxiety and stress, worsen feelings of vulnerability and harden the political attitudes of people. But this dimension is often overlooked by the policy makers. However, the increase in cyber terrorism cases has increased the research interest in the domain. Researchers are focusing on improving the security of internet and IoT devices. The field is still in its infancy. A lot needs to be done to mitigate these attacks. Motivated to demystify the repercussions of the cyber terrorism, this paper analyzes the distinctive characteristics of the most significant cyberattacks, investigates the expenditures incurred by the industries as a result of these cyber events, and then examines the impact of these attacks on the psychological health of the populace and the trust in public institutions.","PeriodicalId":429116,"journal":{"name":"2023 International Conference on Recent Advances in Electrical, Electronics & Digital Healthcare Technologies (REEDCON)","volume":"59 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116130670","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}
Pub Date : 2023-05-01DOI: 10.1109/REEDCON57544.2023.10151205
M. M. Sankar, K. Chatterjee
Rapid adoption of plug-in electric vehicles (PEVs) can create a sizable burden on distribution networks. For proactive planning of the distribution network, it is vital to consider PEV loads while optimally allocating distributed generators (DGs). In this study, renewable wind turbines and solar photovoltaic based DG units are optimally accommodated in the distribution system while addressing the uncertainties in the wind and solar power generation. A realistic time-varying mixed load model is adopted, and the PEV loads are integrated considering different charging profiles. Gorilla troops optimizer (GTO) algorithm is employed for determining the best locations and ratings of renewable DGs with minimization of real power loss, bus voltage deviation and augmentation of voltage stability index as objectives. The methodology is tested on a 33-bus benchmark distribution network. The outcomes are objectively evaluated in terms of the optimization objectives, and a comparative analysis is presented to substantiate the potency of GTO algorithm.
{"title":"Optimal Accommodation of Renewable DGs in Distribution System Considering Plug-in Electric Vehicles Using Gorilla Troops Optimizer","authors":"M. M. Sankar, K. Chatterjee","doi":"10.1109/REEDCON57544.2023.10151205","DOIUrl":"https://doi.org/10.1109/REEDCON57544.2023.10151205","url":null,"abstract":"Rapid adoption of plug-in electric vehicles (PEVs) can create a sizable burden on distribution networks. For proactive planning of the distribution network, it is vital to consider PEV loads while optimally allocating distributed generators (DGs). In this study, renewable wind turbines and solar photovoltaic based DG units are optimally accommodated in the distribution system while addressing the uncertainties in the wind and solar power generation. A realistic time-varying mixed load model is adopted, and the PEV loads are integrated considering different charging profiles. Gorilla troops optimizer (GTO) algorithm is employed for determining the best locations and ratings of renewable DGs with minimization of real power loss, bus voltage deviation and augmentation of voltage stability index as objectives. The methodology is tested on a 33-bus benchmark distribution network. The outcomes are objectively evaluated in terms of the optimization objectives, and a comparative analysis is presented to substantiate the potency of GTO algorithm.","PeriodicalId":429116,"journal":{"name":"2023 International Conference on Recent Advances in Electrical, Electronics & Digital Healthcare Technologies (REEDCON)","volume":"15 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116196684","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}
Pub Date : 2023-05-01DOI: 10.1109/REEDCON57544.2023.10150762
Aman Pandey, H. R. S. S. N. Chatla, Margi Pandya, Aneesa Farhan M A, Ankur Singh Rana
Edge detection finds a greater significance in image processing and computer vision, as many machine learning models require images as input data. Edge detection can be used to extract important features to simplify the visual data. With the increased use of AI, latency can be reduced by processing the data locally which enhances the performance capabilities of the model. This paper reviews the effectiveness of the Fuzzy Inference System over traditional gradient-based approaches such as the Canny edge detection technique and presents a fuzzy logic-based approach for image edge detection. The fuzzy-based approach uses an open-loop fuzzy logic controller which comprises a series of steps instead of a simple thresholding techniques whose values are emperically determined. The performance is analysed for implementation in Python and MATLAB Platforms, with some variations in logic for algorithms in each software. The proposed model is applied to MRI images inorder to detect abnormalities such as tumours.
{"title":"Image Edge Detection Using Fuzzy Logic Controller","authors":"Aman Pandey, H. R. S. S. N. Chatla, Margi Pandya, Aneesa Farhan M A, Ankur Singh Rana","doi":"10.1109/REEDCON57544.2023.10150762","DOIUrl":"https://doi.org/10.1109/REEDCON57544.2023.10150762","url":null,"abstract":"Edge detection finds a greater significance in image processing and computer vision, as many machine learning models require images as input data. Edge detection can be used to extract important features to simplify the visual data. With the increased use of AI, latency can be reduced by processing the data locally which enhances the performance capabilities of the model. This paper reviews the effectiveness of the Fuzzy Inference System over traditional gradient-based approaches such as the Canny edge detection technique and presents a fuzzy logic-based approach for image edge detection. The fuzzy-based approach uses an open-loop fuzzy logic controller which comprises a series of steps instead of a simple thresholding techniques whose values are emperically determined. The performance is analysed for implementation in Python and MATLAB Platforms, with some variations in logic for algorithms in each software. The proposed model is applied to MRI images inorder to detect abnormalities such as tumours.","PeriodicalId":429116,"journal":{"name":"2023 International Conference on Recent Advances in Electrical, Electronics & Digital Healthcare Technologies (REEDCON)","volume":"149 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122459366","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}
Pub Date : 2023-05-01DOI: 10.1109/REEDCON57544.2023.10150800
Komal Singh, M. Rizwan
Energy demand and concerns over greenhouse gases have made the integration of solar PV into the grid imperative. Solar power forecasting models must have a high prediction accuracy to address the intermittent nature of solar irradiation. Solar PV power is significantly affected by the dust deposited on the PV panel surface. The amount of dust deposited on PV panel as one of the input parameters to predict solar PV power and solar irradiation is performed in this paper. Multivariate analysis of three deep learning techniques that is LSTM (Long short-term memory), 1D CNN (Convolution Neural Network) and BilSTM (Bidirectional Long short-term memory) to predict the solar PV power and solar irradiation with varying dust accumulated levels for the 335 watt PV module installed on the rooftop of the lab at Delhi Technological University is presented. An artificial dust scenario is created by continually incrementing the dust level by 1.258 mg/cm2.
{"title":"AI based Approach for Solar PV Power Prediction with Varying Dust Accumulation Levels","authors":"Komal Singh, M. Rizwan","doi":"10.1109/REEDCON57544.2023.10150800","DOIUrl":"https://doi.org/10.1109/REEDCON57544.2023.10150800","url":null,"abstract":"Energy demand and concerns over greenhouse gases have made the integration of solar PV into the grid imperative. Solar power forecasting models must have a high prediction accuracy to address the intermittent nature of solar irradiation. Solar PV power is significantly affected by the dust deposited on the PV panel surface. The amount of dust deposited on PV panel as one of the input parameters to predict solar PV power and solar irradiation is performed in this paper. Multivariate analysis of three deep learning techniques that is LSTM (Long short-term memory), 1D CNN (Convolution Neural Network) and BilSTM (Bidirectional Long short-term memory) to predict the solar PV power and solar irradiation with varying dust accumulated levels for the 335 watt PV module installed on the rooftop of the lab at Delhi Technological University is presented. An artificial dust scenario is created by continually incrementing the dust level by 1.258 mg/cm2.","PeriodicalId":429116,"journal":{"name":"2023 International Conference on Recent Advances in Electrical, Electronics & Digital Healthcare Technologies (REEDCON)","volume":"141 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122837274","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}
Pub Date : 2023-05-01DOI: 10.1109/REEDCON57544.2023.10151342
Ritesh Maurya, Nageshwar Nath Pandey, V. Singh, T. Gopalakrishnan
Agriculture is the backbone of the Indian economy and it caters to the basic necessity of food for billions. Hence, increasing the production yield is a serious challenge, however, it sometimes gets affected with the microorganism-caused infections that severely affects the per acre produce. Therefore, the objective of this study is to develop an automated system for an early detection of plant disease. In the proposed work, pre-trained Vision Transformer architecture has been fine-tuned for plant disease classification. The classification decision made by the proposed model has also been interpreted using the GradCAM algorithm with the help of visualisation. The performance of the proposed method has also been compared with the state-of-the-art pre-trained convolution neural networks fine-tuned for the same purpose. The proposed method has been tested with the ‘PlantVillage' public dataset which consisting of 39 classes of plant images. The experimental results show that the proposed method classifies the 39 classes (38 diseased/healthy, 1 leaf image without background) of plant images with 98.22% accuracy.
{"title":"Plant Disease Classification using Interpretable Vision Transformer Network","authors":"Ritesh Maurya, Nageshwar Nath Pandey, V. Singh, T. Gopalakrishnan","doi":"10.1109/REEDCON57544.2023.10151342","DOIUrl":"https://doi.org/10.1109/REEDCON57544.2023.10151342","url":null,"abstract":"Agriculture is the backbone of the Indian economy and it caters to the basic necessity of food for billions. Hence, increasing the production yield is a serious challenge, however, it sometimes gets affected with the microorganism-caused infections that severely affects the per acre produce. Therefore, the objective of this study is to develop an automated system for an early detection of plant disease. In the proposed work, pre-trained Vision Transformer architecture has been fine-tuned for plant disease classification. The classification decision made by the proposed model has also been interpreted using the GradCAM algorithm with the help of visualisation. The performance of the proposed method has also been compared with the state-of-the-art pre-trained convolution neural networks fine-tuned for the same purpose. The proposed method has been tested with the ‘PlantVillage' public dataset which consisting of 39 classes of plant images. The experimental results show that the proposed method classifies the 39 classes (38 diseased/healthy, 1 leaf image without background) of plant images with 98.22% accuracy.","PeriodicalId":429116,"journal":{"name":"2023 International Conference on Recent Advances in Electrical, Electronics & Digital Healthcare Technologies (REEDCON)","volume":"33 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122993966","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}
Pub Date : 2023-05-01DOI: 10.1109/REEDCON57544.2023.10151272
P. Pallavi, Shashi Ranjan, Niravkumar A. Patel, Manasi Kanetkar, U. Lahiri
Freezing of Gait (FoG) is a debilitating motor symptom of individuals with Parkinson’s Disease (PD). Literature reports that often the FoG can be linked with an increased variability in gait-related (e.g., Step Time, Double Limb Support Time, etc.) and postural (e.g., knee flexion) indices. The FoG can be triggered by various environmental factors, changes in motor task requirements and can become prevalent with disease progression. Research shows that there exists a time window (during gait) before freezing episode when there occurs a progressive decrease in the strides being made. This window might offer an opportunity to gauge one’s possibility of freezing so that measures to avoid freezing can be adopted. Given the importance of (i) quantifying the variability in gait-related and postural indices to characterize one’s FoG and (ii) measuring these indices within specific time window (during gait) before freezing happens, we have come up with a wearable system (SmartWear). This can help quantify variability in one’s gait and posture (namely knee flexion) during one’s overground walk (under free living conditions) for every consecutive steps (to enable measuring these indices within specific time window before actual freezing happens). Results of a study conducted with 14 individuals with PD who walked overground on pathways with and without turn under single and dual task conditions are impressive in terms of understanding the relative importance of the indices in the perspective of identification of one’s proneness to freeze before the actual freezing episode along with the clinical relevance of the index of interest.
{"title":"Investigating the Potential of Gait and Postural Indices to Identify the Possibility of Freezing in Individuals with Parkinson’s Disease using Wearable System","authors":"P. Pallavi, Shashi Ranjan, Niravkumar A. Patel, Manasi Kanetkar, U. Lahiri","doi":"10.1109/REEDCON57544.2023.10151272","DOIUrl":"https://doi.org/10.1109/REEDCON57544.2023.10151272","url":null,"abstract":"Freezing of Gait (FoG) is a debilitating motor symptom of individuals with Parkinson’s Disease (PD). Literature reports that often the FoG can be linked with an increased variability in gait-related (e.g., Step Time, Double Limb Support Time, etc.) and postural (e.g., knee flexion) indices. The FoG can be triggered by various environmental factors, changes in motor task requirements and can become prevalent with disease progression. Research shows that there exists a time window (during gait) before freezing episode when there occurs a progressive decrease in the strides being made. This window might offer an opportunity to gauge one’s possibility of freezing so that measures to avoid freezing can be adopted. Given the importance of (i) quantifying the variability in gait-related and postural indices to characterize one’s FoG and (ii) measuring these indices within specific time window (during gait) before freezing happens, we have come up with a wearable system (SmartWear). This can help quantify variability in one’s gait and posture (namely knee flexion) during one’s overground walk (under free living conditions) for every consecutive steps (to enable measuring these indices within specific time window before actual freezing happens). Results of a study conducted with 14 individuals with PD who walked overground on pathways with and without turn under single and dual task conditions are impressive in terms of understanding the relative importance of the indices in the perspective of identification of one’s proneness to freeze before the actual freezing episode along with the clinical relevance of the index of interest.","PeriodicalId":429116,"journal":{"name":"2023 International Conference on Recent Advances in Electrical, Electronics & Digital Healthcare Technologies (REEDCON)","volume":"163 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116590625","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}
Pub Date : 2023-05-01DOI: 10.1109/REEDCON57544.2023.10150748
Md Faizan Shamsi, F. Danish, M. Sarwar, F. I. Bakhsh, A. Siddiqui
To address the increasing electricity demand in developing countries, the use of microgrids equipped with Renewable Energy Sources (RES) has been proposed. While these microgrids can meet local energy demands, the surplus energy can be injected into the utility grid for distribution. As a case study, the paper focuses on the BR Ambedkar hostel at Jamia Millia Islamia to explore the optimization and implementation of sustainable microgrids in institutional settings. The analysis includes evaluating the "Cost of Energy (COE)", "Net Present Cost (NPC)", "Operating Cost (OC)", value of "Carbon emissions (CO2)", and the Renewable Fraction (RF). This study utilized the HOMER® simulation tool, wind speed data from Synergy Environmental Engineers, and irradiance data from NASA's solar satellite data to analyze the optimal solution for a 200kW PV solar and a 150kW inverter connected to the grid. The results showed a significant reduction in various factors compared to the current grid system, including a 46.50% reduction in the (COE), a 26.73% decrease in (NPC), a 62.42% reduction in (OC), and a 37% decrease in CO2 emissions, along with a contribution of 54% renewable fraction.
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