Pub Date : 2023-02-22DOI: 10.1109/ICECCT56650.2023.10179635
G. Renganathan, H. Barnamehei, Poongavanam Palani, Y. Kurita
Squat is a closed kinetic chain exercise widely used for strength and conditioning applications. This exercise also supports the preparedness for multiple sports. In Physical rehabilitation, squatting is widely incorporated to strengthen the disorders and activate the muscle extensors. This study collected 3D Motion capture data from 5 healthy, trained individuals. Subjects with a history of injury were excluded. The study aims to examine the difference in kinematic variables between Front and Back squats without load conditions and their postural changes. The Kinematic profile was analyzed using computational musculoskeletal software - OpenSim. Based on the statistical results, it is indicated that there is no significant difference in the lower body joints (p< 0.05), contradicting the upper body joints (p >0.05), especially the spine. The peak joint angle value for the neck, spine, and shoulder in the sagittal plane during the Front squat were -21.53, 16.22, and 22.31 degrees, and during the Back squat were -31.68, 9.19, and 70.90 degrees, respectively. Further statistical test was performed using the Paired sample t-test, indicating a significant difference in knee, neck, spine, and shoulder. Hence, this study differentiates the joint angle variation during the two squat styles. The existence of a dominant component in the squat technique has been identified which potentially adds value and helps in curating the needs of patients requiring rehabilitative techniques. The variables analyzed in this study help identify additional parameters that aid in the qualitative and quantitative analysis of a dedicated posture restoration scheme, including squatting as part of the prescribed exercises.
{"title":"Postural Implications of Back and Front squat using Biomechanical simulation","authors":"G. Renganathan, H. Barnamehei, Poongavanam Palani, Y. Kurita","doi":"10.1109/ICECCT56650.2023.10179635","DOIUrl":"https://doi.org/10.1109/ICECCT56650.2023.10179635","url":null,"abstract":"Squat is a closed kinetic chain exercise widely used for strength and conditioning applications. This exercise also supports the preparedness for multiple sports. In Physical rehabilitation, squatting is widely incorporated to strengthen the disorders and activate the muscle extensors. This study collected 3D Motion capture data from 5 healthy, trained individuals. Subjects with a history of injury were excluded. The study aims to examine the difference in kinematic variables between Front and Back squats without load conditions and their postural changes. The Kinematic profile was analyzed using computational musculoskeletal software - OpenSim. Based on the statistical results, it is indicated that there is no significant difference in the lower body joints (p< 0.05), contradicting the upper body joints (p >0.05), especially the spine. The peak joint angle value for the neck, spine, and shoulder in the sagittal plane during the Front squat were -21.53, 16.22, and 22.31 degrees, and during the Back squat were -31.68, 9.19, and 70.90 degrees, respectively. Further statistical test was performed using the Paired sample t-test, indicating a significant difference in knee, neck, spine, and shoulder. Hence, this study differentiates the joint angle variation during the two squat styles. The existence of a dominant component in the squat technique has been identified which potentially adds value and helps in curating the needs of patients requiring rehabilitative techniques. The variables analyzed in this study help identify additional parameters that aid in the qualitative and quantitative analysis of a dedicated posture restoration scheme, including squatting as part of the prescribed exercises.","PeriodicalId":180790,"journal":{"name":"2023 Fifth International Conference on Electrical, Computer and Communication Technologies (ICECCT)","volume":"42 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-02-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129100985","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 : 2023-02-22DOI: 10.1109/ICECCT56650.2023.10179678
Jaya Dipti Lal, Shahnawaz Ayoub, Dr Prashant D Hakim, Dr. S. Prabagar, Dr. Vijay Kumar Dwivedi, M. Tiwari
The Internet of Things (IoT) is becoming an active research area because of its largescale challenges and implementation. But security is the major concern while seeing the dramatic expansion in its applications and size. It is challenging to independently put security mechanism in all the IoT devices and upgrade it according to newer threats. Furthermore, machine learning (ML) techniques could better apply the massive quantity of data produced by IoT devices. Thus, several Deep Learning (DL) based algorithms were introduced for detecting attacks in IoT. Therefore, this study develops a galactic swarm optimization with Deep Learning based Attack Detection and Classification (GSODL-ADC) Model in IoT Environment. The presented GSODL-ADC technique concentrates on the identification of attacks in the IoT environment. The presented GSODL-ADC technique utilizes deep autoencoder (DAE) as a classifier model which properly recognizes the attacks in the IoT environment. Followed by this, the GSO approach is utilized for the optimum hyperparameter adjustments of the DAE model, which leads to enhanced attack detection efficacy. The experimental evaluation of the GSODL-ADC algorithm is tested against benchmark dataset. The obtained experimental values signify the betterment of the GSODL-ADC technique for attack recognition purposes.
{"title":"Hybrid Deep Learning based Attack Detection and Classification Model on IoT Environment","authors":"Jaya Dipti Lal, Shahnawaz Ayoub, Dr Prashant D Hakim, Dr. S. Prabagar, Dr. Vijay Kumar Dwivedi, M. Tiwari","doi":"10.1109/ICECCT56650.2023.10179678","DOIUrl":"https://doi.org/10.1109/ICECCT56650.2023.10179678","url":null,"abstract":"The Internet of Things (IoT) is becoming an active research area because of its largescale challenges and implementation. But security is the major concern while seeing the dramatic expansion in its applications and size. It is challenging to independently put security mechanism in all the IoT devices and upgrade it according to newer threats. Furthermore, machine learning (ML) techniques could better apply the massive quantity of data produced by IoT devices. Thus, several Deep Learning (DL) based algorithms were introduced for detecting attacks in IoT. Therefore, this study develops a galactic swarm optimization with Deep Learning based Attack Detection and Classification (GSODL-ADC) Model in IoT Environment. The presented GSODL-ADC technique concentrates on the identification of attacks in the IoT environment. The presented GSODL-ADC technique utilizes deep autoencoder (DAE) as a classifier model which properly recognizes the attacks in the IoT environment. Followed by this, the GSO approach is utilized for the optimum hyperparameter adjustments of the DAE model, which leads to enhanced attack detection efficacy. The experimental evaluation of the GSODL-ADC algorithm is tested against benchmark dataset. The obtained experimental values signify the betterment of the GSODL-ADC technique for attack recognition purposes.","PeriodicalId":180790,"journal":{"name":"2023 Fifth International Conference on Electrical, Computer and Communication Technologies (ICECCT)","volume":"96 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-02-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114704115","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 : 2023-02-22DOI: 10.1109/ICECCT56650.2023.10179765
A. Sekhar, L. Suresh
Nowadays, pulmonary vascular disorders, which might result in pulmonary emboli or pulmonary hypertension, affect majority of patients. To diagnose alterations in vascular trees, a manual and automatic study of the ill person's chest CT imaging is performed. The manual analysis of CTPA scans is time-consuming, non-standardized, and exhausting. Therefore, semi-automatic and automatic vascular tree separation in CTPA scans is increasingly used, which enables medical professionals to precisely identify aberrant conditions. Different techniques for pulmonary vascular disease identification and classification using deep learning and machine learning methods have been carried out recently. Here we are using deep learning algorithms like Resnet50,Densenet121 and VGG19 for automatic classification of pulmonary vessels for detecting pulmonary diseases with increased accuracy.
{"title":"Detecting Pulmonary Embolism using Deep Learning Algorithms","authors":"A. Sekhar, L. Suresh","doi":"10.1109/ICECCT56650.2023.10179765","DOIUrl":"https://doi.org/10.1109/ICECCT56650.2023.10179765","url":null,"abstract":"Nowadays, pulmonary vascular disorders, which might result in pulmonary emboli or pulmonary hypertension, affect majority of patients. To diagnose alterations in vascular trees, a manual and automatic study of the ill person's chest CT imaging is performed. The manual analysis of CTPA scans is time-consuming, non-standardized, and exhausting. Therefore, semi-automatic and automatic vascular tree separation in CTPA scans is increasingly used, which enables medical professionals to precisely identify aberrant conditions. Different techniques for pulmonary vascular disease identification and classification using deep learning and machine learning methods have been carried out recently. Here we are using deep learning algorithms like Resnet50,Densenet121 and VGG19 for automatic classification of pulmonary vessels for detecting pulmonary diseases with increased accuracy.","PeriodicalId":180790,"journal":{"name":"2023 Fifth International Conference on Electrical, Computer and Communication Technologies (ICECCT)","volume":"151 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-02-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125960537","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 : 2023-02-22DOI: 10.1109/ICECCT56650.2023.10179784
V. Tharakeswari, M. Kameswari, M. Seenivasan
The linear programming problem is well-known as one of the most promising mathematical methods for efficient resource allocation. Many real-world problems can be expressed as LPPs. Anyways in occurring each day scenarios; it is arduous to obtain appropriate accurate data for the cost parameter, resulting in a fuzzy environment. The subject of fuzzy transportation has a lot of consideration in the present-day. It assists the decision-maker in arriving at the best answer with appropriate data, which is frequently used in engineering and management science situations. The prevalent goal of the transportation problem is to reduce the amount of transporting production from multiple origins to multiple targets. It is necessary to highlight the issue of balanced and unbalanced transportation. We used the defuzzification method for hexagonal fuzzy numbers and offered novel approaches in this research to help determine an Initial Basic Feasible Solution for balanced and unbalanced transportation problems.
{"title":"A New Approach to the Transportation Problem of the Hexagonal Fuzzy Number","authors":"V. Tharakeswari, M. Kameswari, M. Seenivasan","doi":"10.1109/ICECCT56650.2023.10179784","DOIUrl":"https://doi.org/10.1109/ICECCT56650.2023.10179784","url":null,"abstract":"The linear programming problem is well-known as one of the most promising mathematical methods for efficient resource allocation. Many real-world problems can be expressed as LPPs. Anyways in occurring each day scenarios; it is arduous to obtain appropriate accurate data for the cost parameter, resulting in a fuzzy environment. The subject of fuzzy transportation has a lot of consideration in the present-day. It assists the decision-maker in arriving at the best answer with appropriate data, which is frequently used in engineering and management science situations. The prevalent goal of the transportation problem is to reduce the amount of transporting production from multiple origins to multiple targets. It is necessary to highlight the issue of balanced and unbalanced transportation. We used the defuzzification method for hexagonal fuzzy numbers and offered novel approaches in this research to help determine an Initial Basic Feasible Solution for balanced and unbalanced transportation problems.","PeriodicalId":180790,"journal":{"name":"2023 Fifth International Conference on Electrical, Computer and Communication Technologies (ICECCT)","volume":"60 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-02-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126155695","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 : 2023-02-22DOI: 10.1109/ICECCT56650.2023.10179634
S. Salma, H. Khan, B. T. P Madhav, D. S. Reddy, B. Venu, D. Sandeep
This work introduces a novel optimization procedure for converting a MIMO antenna operating in multibands into an Ultra-Wide Band (UWB) operating antenna. The Sequential Non-Linear Programming algorithm (SNLP) of HFSS carried out the antenna's design optimization. First of all, the MIMO antenna design is considered as the optimization task with multiple objectives of operating in UWB ranges with an isolation of 20 dB. This multiple-objective task is the optimizing problem, and the ANSYS HFSS SNLP algorithm was chosen as the proper one to optimize the antenna geometrical variables. A prototype is realized on the low-cost Frame Retardent-4 substrate to validate the optimized parameters. Moreover, the built prototype is authenticated in an anechoic chamber. A good agreement was noted between the measurements and simulation results through achieving UWB, 20 dB isolation, and a gain of around 2dB in the resonating bands. The SNLP algorithm successfully attains the desired optimization.
{"title":"Sequential Non-Linear Programming Optimization: A Novel Design Optimization of a Multiband MIMO Antenna","authors":"S. Salma, H. Khan, B. T. P Madhav, D. S. Reddy, B. Venu, D. Sandeep","doi":"10.1109/ICECCT56650.2023.10179634","DOIUrl":"https://doi.org/10.1109/ICECCT56650.2023.10179634","url":null,"abstract":"This work introduces a novel optimization procedure for converting a MIMO antenna operating in multibands into an Ultra-Wide Band (UWB) operating antenna. The Sequential Non-Linear Programming algorithm (SNLP) of HFSS carried out the antenna's design optimization. First of all, the MIMO antenna design is considered as the optimization task with multiple objectives of operating in UWB ranges with an isolation of 20 dB. This multiple-objective task is the optimizing problem, and the ANSYS HFSS SNLP algorithm was chosen as the proper one to optimize the antenna geometrical variables. A prototype is realized on the low-cost Frame Retardent-4 substrate to validate the optimized parameters. Moreover, the built prototype is authenticated in an anechoic chamber. A good agreement was noted between the measurements and simulation results through achieving UWB, 20 dB isolation, and a gain of around 2dB in the resonating bands. The SNLP algorithm successfully attains the desired optimization.","PeriodicalId":180790,"journal":{"name":"2023 Fifth International Conference on Electrical, Computer and Communication Technologies (ICECCT)","volume":"23 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-02-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125767905","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 : 2023-02-22DOI: 10.1109/ICECCT56650.2023.10179673
Vanshay Gupta, Samit Kapadia, Chetashri Bhadane
This paper aims to analyze the air quality in India and the effects of seasons and COVID-19 on the concentration of pollutants in the air and thereby their effect on the air quality index (AQI). The analysis is performed on a full scale, taking into consideration different levels of granularities such as daily, weekly and monthly data. This study performs extensive preprocessing of the time series data for air quality to make it output the best results. The results evidenced that particulate matter i.e., PM 2.5 and PM 10 have the greatest impact on air quality. Analysis of the effect of change in seasons on the overall air quality has been carried out, along with the impact of the nationwide lockdown due to COVID-19, which led to a substantial improvement in the AQI levels. Furthermore, we also use the state-of-the-art forecasting algorithm Prophet to predict the monthly average air quality index and compare it with the actual recorded values, giving us a highly accurate prediction. We also performed a comparative analysis of AQI for the cities of Delhi and Bengaluru, having different seasons and climates, which results in valuable insights on to what extent the environmental factors affect the air quality measures of that location.
{"title":"Time Series Analysis and Forecasting of Air Quality in India","authors":"Vanshay Gupta, Samit Kapadia, Chetashri Bhadane","doi":"10.1109/ICECCT56650.2023.10179673","DOIUrl":"https://doi.org/10.1109/ICECCT56650.2023.10179673","url":null,"abstract":"This paper aims to analyze the air quality in India and the effects of seasons and COVID-19 on the concentration of pollutants in the air and thereby their effect on the air quality index (AQI). The analysis is performed on a full scale, taking into consideration different levels of granularities such as daily, weekly and monthly data. This study performs extensive preprocessing of the time series data for air quality to make it output the best results. The results evidenced that particulate matter i.e., PM 2.5 and PM 10 have the greatest impact on air quality. Analysis of the effect of change in seasons on the overall air quality has been carried out, along with the impact of the nationwide lockdown due to COVID-19, which led to a substantial improvement in the AQI levels. Furthermore, we also use the state-of-the-art forecasting algorithm Prophet to predict the monthly average air quality index and compare it with the actual recorded values, giving us a highly accurate prediction. We also performed a comparative analysis of AQI for the cities of Delhi and Bengaluru, having different seasons and climates, which results in valuable insights on to what extent the environmental factors affect the air quality measures of that location.","PeriodicalId":180790,"journal":{"name":"2023 Fifth International Conference on Electrical, Computer and Communication Technologies (ICECCT)","volume":"6 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-02-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126574375","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 : 2023-02-22DOI: 10.1109/ICECCT56650.2023.10179735
A. B, T. P
The proposed method is to combine triangular fuzzy number (TFN) with a Neutrosophic set and define Einstein Aggregation operations to aggregate triangular Neutrosophic fuzzy number and their application to Manufacturing engineering sector. The selection of suitable and economic taps plays an important role in the Manufacturing Engineering sectors. A tap is a thread-cutting tool that is cylindrical or conical in shape and has threads of the required shape on the periphery. The tap cuts or forms the internal thread by combining rotary and axial motion. Also, it is used to make thread for nuts. Leading nut manufacturing industries are struggling to select suitable taps to manufacture their nuts. To address such a problem, this proposed aggregation technique, named the “Neutrosophic Triangular Fuzzy Number Einstein Aggregation Operator,” would select the best taps for manufacturing a mass quantity of nuts with adequate cycle time and tool life.
{"title":"Neutrosophic Triangular Fuzzy Number Under Einstein Aggregation Operator with Application for Effective Tap Selection","authors":"A. B, T. P","doi":"10.1109/ICECCT56650.2023.10179735","DOIUrl":"https://doi.org/10.1109/ICECCT56650.2023.10179735","url":null,"abstract":"The proposed method is to combine triangular fuzzy number (TFN) with a Neutrosophic set and define Einstein Aggregation operations to aggregate triangular Neutrosophic fuzzy number and their application to Manufacturing engineering sector. The selection of suitable and economic taps plays an important role in the Manufacturing Engineering sectors. A tap is a thread-cutting tool that is cylindrical or conical in shape and has threads of the required shape on the periphery. The tap cuts or forms the internal thread by combining rotary and axial motion. Also, it is used to make thread for nuts. Leading nut manufacturing industries are struggling to select suitable taps to manufacture their nuts. To address such a problem, this proposed aggregation technique, named the “Neutrosophic Triangular Fuzzy Number Einstein Aggregation Operator,” would select the best taps for manufacturing a mass quantity of nuts with adequate cycle time and tool life.","PeriodicalId":180790,"journal":{"name":"2023 Fifth International Conference on Electrical, Computer and Communication Technologies (ICECCT)","volume":"40 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-02-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125187404","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 : 2023-02-22DOI: 10.1109/ICECCT56650.2023.10179847
M. Jeyakarthic, R. Ramesh
Stock market return forecasting is currently regarded as a prediction issue. The forecasting process is challenging due to the financial markets inherent volatility on a global scale. The risks associated with investment procedures would be significantly reduced by the decrease in prediction error rate. To anticipate stock market return, this research offers a new hybrid WWO-MKELM technique. The three main processes of the described WWO-MKELM model are preprocessing feature extraction, and classification. First, the exponential smoothing approach is used to do preprocessing. The preprocessed dataset will then be used to extract the features. After that, a WWO-MKELM-based model is used to forecast stock prices. The WWO-MKELM model that has been described can foretell whether stock prices will increase or decrease. Utilizing the stocks of APPL and FB simulates the WWO-MKELM method. The obtained experimental findings showed that the WWO-MKELM model performed better than the compared approaches.
{"title":"An Effective Stock Market Direction Using Hybrid WWO-MKELM technique","authors":"M. Jeyakarthic, R. Ramesh","doi":"10.1109/ICECCT56650.2023.10179847","DOIUrl":"https://doi.org/10.1109/ICECCT56650.2023.10179847","url":null,"abstract":"Stock market return forecasting is currently regarded as a prediction issue. The forecasting process is challenging due to the financial markets inherent volatility on a global scale. The risks associated with investment procedures would be significantly reduced by the decrease in prediction error rate. To anticipate stock market return, this research offers a new hybrid WWO-MKELM technique. The three main processes of the described WWO-MKELM model are preprocessing feature extraction, and classification. First, the exponential smoothing approach is used to do preprocessing. The preprocessed dataset will then be used to extract the features. After that, a WWO-MKELM-based model is used to forecast stock prices. The WWO-MKELM model that has been described can foretell whether stock prices will increase or decrease. Utilizing the stocks of APPL and FB simulates the WWO-MKELM method. The obtained experimental findings showed that the WWO-MKELM model performed better than the compared approaches.","PeriodicalId":180790,"journal":{"name":"2023 Fifth International Conference on Electrical, Computer and Communication Technologies (ICECCT)","volume":"3 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-02-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124359152","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 : 2023-02-22DOI: 10.1109/ICECCT56650.2023.10179659
P. Malarvizhi, G. Kavithaa
In recent years, industries have automated processes which mean the amount of human participation has decreased, resulting in the Fourth Industrial Revolution. A highly distributed self-organizing system known as a Wireless Sensor Networks is employed in so many control systems such as monitoring the surroundings, automate the reporting, and detecting the event. High bandwidth needs, high power consumption, security and quality of service delivery are some of the obstacles that wireless sensor networks must overcome. Each sensor node in a wireless sensor networks has a different power consumption rate based on the non-uniformity of event detection and the interspace between the sink node and sensor node. This shortens the lifespan of the network and causes an energy difference between the sensor nodes. Particle Swarm Optimization based Jaya algorithm (PSO-J) has been experimented to lower the power consumption of the sensor node by improving the selection of cluster head. The proposed algorithm provides better results than existing clustering algorithms.
{"title":"Clustering by Improved PSO based Jaya Algorithm for Energy Optimization of Wireless Sensor Networks","authors":"P. Malarvizhi, G. Kavithaa","doi":"10.1109/ICECCT56650.2023.10179659","DOIUrl":"https://doi.org/10.1109/ICECCT56650.2023.10179659","url":null,"abstract":"In recent years, industries have automated processes which mean the amount of human participation has decreased, resulting in the Fourth Industrial Revolution. A highly distributed self-organizing system known as a Wireless Sensor Networks is employed in so many control systems such as monitoring the surroundings, automate the reporting, and detecting the event. High bandwidth needs, high power consumption, security and quality of service delivery are some of the obstacles that wireless sensor networks must overcome. Each sensor node in a wireless sensor networks has a different power consumption rate based on the non-uniformity of event detection and the interspace between the sink node and sensor node. This shortens the lifespan of the network and causes an energy difference between the sensor nodes. Particle Swarm Optimization based Jaya algorithm (PSO-J) has been experimented to lower the power consumption of the sensor node by improving the selection of cluster head. The proposed algorithm provides better results than existing clustering algorithms.","PeriodicalId":180790,"journal":{"name":"2023 Fifth International Conference on Electrical, Computer and Communication Technologies (ICECCT)","volume":"39 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-02-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123470268","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 : 2023-02-22DOI: 10.1109/ICECCT56650.2023.10179676
Saurabh Ganpat Munde, Ajay S Ladkat, R. Patil
Wide area monitoring protection and control system (WAMPAC) screen and control the grid dynamics progressively. Accessibility of PMU information in WAMPAC opened the entryway for information driven displaying. This paper proposes a novel information driven model for power outage chance investigation. Investigation depends on the Kullback-Leibler difference (KLD) along with Machine Learning (ML). The key commitment of this paper is probabilistic investigation of transmission line information to catch the power stream defenselessness in the course disappointment and early forecast of likely power outage dependent on the relative entropy among typical and the bothered power stream information. For power outage expectation the reference KLD limit is ascertained from the past power outage occasions and utilized as an antecedent for power outage early cautioning sign Intentional Islanding.
{"title":"Zero Blackout Avoidance Keeping Emergency Services at Priority using Machine Learning","authors":"Saurabh Ganpat Munde, Ajay S Ladkat, R. Patil","doi":"10.1109/ICECCT56650.2023.10179676","DOIUrl":"https://doi.org/10.1109/ICECCT56650.2023.10179676","url":null,"abstract":"Wide area monitoring protection and control system (WAMPAC) screen and control the grid dynamics progressively. Accessibility of PMU information in WAMPAC opened the entryway for information driven displaying. This paper proposes a novel information driven model for power outage chance investigation. Investigation depends on the Kullback-Leibler difference (KLD) along with Machine Learning (ML). The key commitment of this paper is probabilistic investigation of transmission line information to catch the power stream defenselessness in the course disappointment and early forecast of likely power outage dependent on the relative entropy among typical and the bothered power stream information. For power outage expectation the reference KLD limit is ascertained from the past power outage occasions and utilized as an antecedent for power outage early cautioning sign Intentional Islanding.","PeriodicalId":180790,"journal":{"name":"2023 Fifth International Conference on Electrical, Computer and Communication Technologies (ICECCT)","volume":"52 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-02-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123204991","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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