Pub Date : 2022-12-10DOI: 10.1109/STCR55312.2022.10009610
R. Ramesh, A. Bazila Banu
Nowadays Digital Display Boards (DDB) are used to post information in a variety of locations, including public spaces, hospitals, general stores, institutions, and colleges. Earlier, for displaying large data, the message needs to be changed for every instance. As of now digital displays are more preferred to static and attract the attention of viewers. The microcontroller present in the DDB write the information to the showing device. DDBs are connecting to the controller to continually scroll the message on the screen. The research article proposes the DDB system in office environments. The proposed system is fully designed and analyzed by IoT and Machine learning Techniques. The device will identify the DDB usage rate and reduce the wastage of DDB energy in unoccupied places and also forecasting future energy usage requirement. The proposed work applies a prediction system to detect and analyze the one Month energy consumption of DDB in the office environment and evaluates the existing model with the ARIMA algorithm for generating time-series based prediction models. To find out the precision of the proposed system, DDB along with sensor devices were installed in the office environment, which consist of current sensors, microcontrollers with cloud database connectivity. The set of data has been obtained from the database being utilized to evaluate and test the proposed models. according to the results of the prediction and analysis proposed DDB outperformed the ARIMA Model, with good accuracy. Based on the proposed method, the predicted accuracy value is 97.8% and R-squared for the model is 0.89. The Proposed DDB Energy Consumption system helps to monitor and detect the energy usage in office environments.
{"title":"Detection and Analysis of Digital Display Board Energy Consumption using IoT and Machine Learning Techniques","authors":"R. Ramesh, A. Bazila Banu","doi":"10.1109/STCR55312.2022.10009610","DOIUrl":"https://doi.org/10.1109/STCR55312.2022.10009610","url":null,"abstract":"Nowadays Digital Display Boards (DDB) are used to post information in a variety of locations, including public spaces, hospitals, general stores, institutions, and colleges. Earlier, for displaying large data, the message needs to be changed for every instance. As of now digital displays are more preferred to static and attract the attention of viewers. The microcontroller present in the DDB write the information to the showing device. DDBs are connecting to the controller to continually scroll the message on the screen. The research article proposes the DDB system in office environments. The proposed system is fully designed and analyzed by IoT and Machine learning Techniques. The device will identify the DDB usage rate and reduce the wastage of DDB energy in unoccupied places and also forecasting future energy usage requirement. The proposed work applies a prediction system to detect and analyze the one Month energy consumption of DDB in the office environment and evaluates the existing model with the ARIMA algorithm for generating time-series based prediction models. To find out the precision of the proposed system, DDB along with sensor devices were installed in the office environment, which consist of current sensors, microcontrollers with cloud database connectivity. The set of data has been obtained from the database being utilized to evaluate and test the proposed models. according to the results of the prediction and analysis proposed DDB outperformed the ARIMA Model, with good accuracy. Based on the proposed method, the predicted accuracy value is 97.8% and R-squared for the model is 0.89. The Proposed DDB Energy Consumption system helps to monitor and detect the energy usage in office environments.","PeriodicalId":338691,"journal":{"name":"2022 Smart Technologies, Communication and Robotics (STCR)","volume":"146 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-12-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"133422360","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 : 2022-12-10DOI: 10.1109/STCR55312.2022.10009238
P. R, Vairavel K S, K. S, S. S, S. S
The goal of the solar powered drip irrigation technique is to lessen the quantity of water used in farming. The suggested module makes use of a microprocessor and other sensors, including those for monitoring soil moisture and temperature in root system belts and ultrasonic waves in the tank’s ceiling. The on-off cycling of the pump keeps the water level in the tank constant. The solenoid valve is then used to control the humidity and temperature. The user may monitor the farm in real time from any location thanks to a Wi-Fi module connected to the control unit that interprets sensor data, activates actuators, and broadcasts data over the internet. The volume of water provided to the region for irrigation and the pump to fill the tank are both governed by code programmed into the central controller, with the code being designed based on soil moisture, humidity, and temperature readings. The entire structure is run on clean, solar energy. Connected through wires are the sensor unit, actuator unit, and central control unit; the control unit then talks to the app via a wireless connection. This study’s principal objective is to lessen the need for the farmer to use his or her own two hands, hence reducing labour costs and maximising efficiency.
{"title":"Intelligent Irrigation System with Monitoring and Control of Natural Parameter using IOT","authors":"P. R, Vairavel K S, K. S, S. S, S. S","doi":"10.1109/STCR55312.2022.10009238","DOIUrl":"https://doi.org/10.1109/STCR55312.2022.10009238","url":null,"abstract":"The goal of the solar powered drip irrigation technique is to lessen the quantity of water used in farming. The suggested module makes use of a microprocessor and other sensors, including those for monitoring soil moisture and temperature in root system belts and ultrasonic waves in the tank’s ceiling. The on-off cycling of the pump keeps the water level in the tank constant. The solenoid valve is then used to control the humidity and temperature. The user may monitor the farm in real time from any location thanks to a Wi-Fi module connected to the control unit that interprets sensor data, activates actuators, and broadcasts data over the internet. The volume of water provided to the region for irrigation and the pump to fill the tank are both governed by code programmed into the central controller, with the code being designed based on soil moisture, humidity, and temperature readings. The entire structure is run on clean, solar energy. Connected through wires are the sensor unit, actuator unit, and central control unit; the control unit then talks to the app via a wireless connection. This study’s principal objective is to lessen the need for the farmer to use his or her own two hands, hence reducing labour costs and maximising efficiency.","PeriodicalId":338691,"journal":{"name":"2022 Smart Technologies, Communication and Robotics (STCR)","volume":"33 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-12-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128437058","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 : 2022-12-10DOI: 10.1109/STCR55312.2022.10009393
V. Kiruthika, N. Sathish Kumar, SP Vimal
During disaster or emergency condition wireless communication serves a main role in saving the victims. The rescuers can safeguard the life of victims through the data provided by wireless communication. The main backdrops in the current communication are low bandwidth, limited battery life, lack of security and high latency. The rescuers and the victims cannot effectively communicate with each other during these crisis situations. The responsiveness during emergencies should be improved and the patients should immediately be taken care. The cognitive radio network integrated with 5G technology paves effective communication between the rescuers and the victims. The proposed Interference Cancellation with Traffic Scheduling Algorithm(ICTSA) cancels the interference and provides efficient traffic scheduling during emergencies. The performance factors investigates are Duration,Delay, Energy Efficiency, Speed and Throughput. From the experimental investigation it is inferred that the proposed algorithm improves throughput and decreases delay resulting in efficient communication between the rescuers and the victims. Thus the model is much suitable for emergency networks or preferred during disasters.
{"title":"Performance Analysis of Interference Cancellation and Traffic Scheduling Algorithm (ICTSA) with Mobility Management in 5G Emergency Networks","authors":"V. Kiruthika, N. Sathish Kumar, SP Vimal","doi":"10.1109/STCR55312.2022.10009393","DOIUrl":"https://doi.org/10.1109/STCR55312.2022.10009393","url":null,"abstract":"During disaster or emergency condition wireless communication serves a main role in saving the victims. The rescuers can safeguard the life of victims through the data provided by wireless communication. The main backdrops in the current communication are low bandwidth, limited battery life, lack of security and high latency. The rescuers and the victims cannot effectively communicate with each other during these crisis situations. The responsiveness during emergencies should be improved and the patients should immediately be taken care. The cognitive radio network integrated with 5G technology paves effective communication between the rescuers and the victims. The proposed Interference Cancellation with Traffic Scheduling Algorithm(ICTSA) cancels the interference and provides efficient traffic scheduling during emergencies. The performance factors investigates are Duration,Delay, Energy Efficiency, Speed and Throughput. From the experimental investigation it is inferred that the proposed algorithm improves throughput and decreases delay resulting in efficient communication between the rescuers and the victims. Thus the model is much suitable for emergency networks or preferred during disasters.","PeriodicalId":338691,"journal":{"name":"2022 Smart Technologies, Communication and Robotics (STCR)","volume":"15 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-12-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128518073","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 : 2022-12-10DOI: 10.1109/STCR55312.2022.10009627
H. Dg, T. .V., S. M., S. R, E. S.
A In today's technological development and the advancement in IC technology, a huge number of intellectual property (IP)cores can be consolidated onto a single chip. Due to this, communication between the IP cores becomes more difficult. To overcome the restriction of this communication, we introduce a technology called NETWORK ON CHIP(NoC). This is an on-chip packet-switched network with IP cores connected to the network via interfaces, and the packets are sent to their respective destination to a multi-chip routing path. A router is an essential component for NoC architecture. The design had to be done effectively to build a competitive NoC architecture. In this proposed work router can be designed using Verilog. It has stored a forward type of flow control round robin arbitration and deterministic XY routing. The essential parts for a router are FIFO, arbiter, and crossbar. The plan behind the five-port router is intended to be used with the FPGA design platform to test the functionality of the NoC on hardware. The outline of the router is designed through Verilog and simulated using zynq board 7000 series and verified using system Verilog, and its feasible model is also verified.
{"title":"Performance Enhancement of SoC with Five Port Router by Replacing APB Protocol","authors":"H. Dg, T. .V., S. M., S. R, E. S.","doi":"10.1109/STCR55312.2022.10009627","DOIUrl":"https://doi.org/10.1109/STCR55312.2022.10009627","url":null,"abstract":"A In today's technological development and the advancement in IC technology, a huge number of intellectual property (IP)cores can be consolidated onto a single chip. Due to this, communication between the IP cores becomes more difficult. To overcome the restriction of this communication, we introduce a technology called NETWORK ON CHIP(NoC). This is an on-chip packet-switched network with IP cores connected to the network via interfaces, and the packets are sent to their respective destination to a multi-chip routing path. A router is an essential component for NoC architecture. The design had to be done effectively to build a competitive NoC architecture. In this proposed work router can be designed using Verilog. It has stored a forward type of flow control round robin arbitration and deterministic XY routing. The essential parts for a router are FIFO, arbiter, and crossbar. The plan behind the five-port router is intended to be used with the FPGA design platform to test the functionality of the NoC on hardware. The outline of the router is designed through Verilog and simulated using zynq board 7000 series and verified using system Verilog, and its feasible model is also verified.","PeriodicalId":338691,"journal":{"name":"2022 Smart Technologies, Communication and Robotics (STCR)","volume":"23 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-12-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116774778","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 : 2022-12-10DOI: 10.1109/STCR55312.2022.10009354
G. C, G. M., G. P., Priyanka G S, V. B
In this study, the PPG signal was taken from a capnography data source and used along with statistical characteristics and machine learning techniques to diagnose respiratory disorders. After the statistical properties of the data have been extracted using a method called least square linear regression, the signal is then processed by a number of different classification tasks, and the outcomes of the classification algorithm are examined. Results show that the linear regression and nonlinear classifiers gives the best accuracy of 88.11% and 85.73% for both normal and respiratory disease cases.
{"title":"Performance Analysis of Least Square Linear Regression with Various Classifiers for Cardiovascular Respiratory Detection from Capnography","authors":"G. C, G. M., G. P., Priyanka G S, V. B","doi":"10.1109/STCR55312.2022.10009354","DOIUrl":"https://doi.org/10.1109/STCR55312.2022.10009354","url":null,"abstract":"In this study, the PPG signal was taken from a capnography data source and used along with statistical characteristics and machine learning techniques to diagnose respiratory disorders. After the statistical properties of the data have been extracted using a method called least square linear regression, the signal is then processed by a number of different classification tasks, and the outcomes of the classification algorithm are examined. Results show that the linear regression and nonlinear classifiers gives the best accuracy of 88.11% and 85.73% for both normal and respiratory disease cases.","PeriodicalId":338691,"journal":{"name":"2022 Smart Technologies, Communication and Robotics (STCR)","volume":"386 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-12-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123364699","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 : 2022-12-10DOI: 10.1109/STCR55312.2022.10009549
S. Jayanthi, V. Vijayakumar, P. Prasanth
The grid connected hybrid system power flow management control is introduced in this system and it is based on the power conversion is used to the bidirectional converters is based on field programmable gate array (FPGA) controller. The proposed FPGA based Predictive Energy Optimization Algorithm (PEOA) technique is used to give the proper switching pulse to the converter is minimize the fault and increase the stability of this grid connected hybrid system. Therefore, the reliability and effectiveness of the system can be enhanced by these control strategies; Power fluctuations are essentially constant. The proposed grid connected hybrid system is analysis with help of the IOT is a process that is utilized to verify data using the Stochastic Linear Data Monitoring (RLDM TM) method, which enables data to be taken from the bus system to the decision-making process required. Through this integrated control strategy, proper power interactions can be achieved in different sub-phases, supporting the power fluctuations and further stability of other sub-phases. The development of a hybrid system proposed by MPPT with a stand-alone DC-DC converter and a voltage-regulated inverter was developed and simulated in the MATLAB environment.
{"title":"Enhancing the Performance of Hybrid Grid Connected System with Bi-Directional Energy Management using FPGA Based Predictive Energy Optimization Algorithm with Data Monitoring","authors":"S. Jayanthi, V. Vijayakumar, P. Prasanth","doi":"10.1109/STCR55312.2022.10009549","DOIUrl":"https://doi.org/10.1109/STCR55312.2022.10009549","url":null,"abstract":"The grid connected hybrid system power flow management control is introduced in this system and it is based on the power conversion is used to the bidirectional converters is based on field programmable gate array (FPGA) controller. The proposed FPGA based Predictive Energy Optimization Algorithm (PEOA) technique is used to give the proper switching pulse to the converter is minimize the fault and increase the stability of this grid connected hybrid system. Therefore, the reliability and effectiveness of the system can be enhanced by these control strategies; Power fluctuations are essentially constant. The proposed grid connected hybrid system is analysis with help of the IOT is a process that is utilized to verify data using the Stochastic Linear Data Monitoring (RLDM TM) method, which enables data to be taken from the bus system to the decision-making process required. Through this integrated control strategy, proper power interactions can be achieved in different sub-phases, supporting the power fluctuations and further stability of other sub-phases. The development of a hybrid system proposed by MPPT with a stand-alone DC-DC converter and a voltage-regulated inverter was developed and simulated in the MATLAB environment.","PeriodicalId":338691,"journal":{"name":"2022 Smart Technologies, Communication and Robotics (STCR)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-12-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131549914","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 : 2022-12-10DOI: 10.1109/STCR55312.2022.10009385
M. Abdullah, S. Prakash, Kedir Beshir, Abrha Ftsum Berhe, Yibeltal Petros Abo
With the rising demands of public safety systems, face recognition has gained extra notice for the researchers in recent times. Real-world face recognition systems require cautious balancing of two important concerns: execution Time, recognition rate. The most important methods for feature extraction and classification are Dimension Reduction-Discrete Cosine Transform (DR-DCT), Dimension Reduction-Discrete Fourier Transform (DR-DFT) and Dimension Reduction-Difference of Gaussian (DoG) along with the Extreme Learning Machine (ELM) classifiers. The feature vector of the proposed algorithm is reduced by means of subspace method Principal Component Analysis (PCA). The execution time of the proposed DR-DFT, DR-DoG and DR-DCT algorithms along with ELM classifier is less when compared to the traditional methods such as Hough transform, Radon Transform, Discrete Fourier Transform (DFT) and Discrete Cosine Transform (DCT) for ORL face database. Similarly, Hybrid methods are proposed by combining DR-DCT &DR-DFT (Hybrid Method 1), DR-DCT & DR-DoG (Hybrid Method 2) and DR-DFT & DR-DoG (Hybrid Method3) along with ELM classifier. Hybrid Method 1 attains 98.50% recognition rate with the feaure size of 60×1 for ORL dataset. It achieves optimal execution time of 0.020 sec.
{"title":"Hybrid Frequency Domain based Feature Extraction methods for Human Face Recognition","authors":"M. Abdullah, S. Prakash, Kedir Beshir, Abrha Ftsum Berhe, Yibeltal Petros Abo","doi":"10.1109/STCR55312.2022.10009385","DOIUrl":"https://doi.org/10.1109/STCR55312.2022.10009385","url":null,"abstract":"With the rising demands of public safety systems, face recognition has gained extra notice for the researchers in recent times. Real-world face recognition systems require cautious balancing of two important concerns: execution Time, recognition rate. The most important methods for feature extraction and classification are Dimension Reduction-Discrete Cosine Transform (DR-DCT), Dimension Reduction-Discrete Fourier Transform (DR-DFT) and Dimension Reduction-Difference of Gaussian (DoG) along with the Extreme Learning Machine (ELM) classifiers. The feature vector of the proposed algorithm is reduced by means of subspace method Principal Component Analysis (PCA). The execution time of the proposed DR-DFT, DR-DoG and DR-DCT algorithms along with ELM classifier is less when compared to the traditional methods such as Hough transform, Radon Transform, Discrete Fourier Transform (DFT) and Discrete Cosine Transform (DCT) for ORL face database. Similarly, Hybrid methods are proposed by combining DR-DCT &DR-DFT (Hybrid Method 1), DR-DCT & DR-DoG (Hybrid Method 2) and DR-DFT & DR-DoG (Hybrid Method3) along with ELM classifier. Hybrid Method 1 attains 98.50% recognition rate with the feaure size of 60×1 for ORL dataset. It achieves optimal execution time of 0.020 sec.","PeriodicalId":338691,"journal":{"name":"2022 Smart Technologies, Communication and Robotics (STCR)","volume":"86 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-12-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126345628","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 : 2022-12-10DOI: 10.1109/STCR55312.2022.10009395
H. Rajaguru, G. M., Santhosh B, Senthamil Selvi A
Epilepsy is a fatal disease of the nervous system that affects a big part of the world's population. Electroencephalography (EEG) is the tool that is most often used in clinical settings to measure how the brain works. EEG is often used to diagnose and treat a wide range of health problems, such as mental exhaustion, epileptic seizures, coma, schizophrenia, and sleep problems. The large dimensionality of data is one of the problems with epilepsy detection that is discussed in this work. When attempting to analyze the data, a high dimensionality might be problematic. The primary purpose of lowering the dimensionality of the EEG data is to augment the classification results. In this paper, the dimensionality of the EEG data can be reduced by the use of procedures such as dimensionality reduction and feature selection. For dimensionality reduction, the autoencoder and Hessian LLE method are employed. As a technique of feature selection, the PAC Bayesian algorithm is employed. Logistic Regression classifier is used to detect epilepsy. Results show that the when Hessian LLE with PAC-Bayesian is identified with logistic regression gives the best accuracy of 96.87%.
{"title":"Logistic Regression Classifier with Hybrid Dimensionality Reduction Techniques for Epilepsy Detection from EEG Signals","authors":"H. Rajaguru, G. M., Santhosh B, Senthamil Selvi A","doi":"10.1109/STCR55312.2022.10009395","DOIUrl":"https://doi.org/10.1109/STCR55312.2022.10009395","url":null,"abstract":"Epilepsy is a fatal disease of the nervous system that affects a big part of the world's population. Electroencephalography (EEG) is the tool that is most often used in clinical settings to measure how the brain works. EEG is often used to diagnose and treat a wide range of health problems, such as mental exhaustion, epileptic seizures, coma, schizophrenia, and sleep problems. The large dimensionality of data is one of the problems with epilepsy detection that is discussed in this work. When attempting to analyze the data, a high dimensionality might be problematic. The primary purpose of lowering the dimensionality of the EEG data is to augment the classification results. In this paper, the dimensionality of the EEG data can be reduced by the use of procedures such as dimensionality reduction and feature selection. For dimensionality reduction, the autoencoder and Hessian LLE method are employed. As a technique of feature selection, the PAC Bayesian algorithm is employed. Logistic Regression classifier is used to detect epilepsy. Results show that the when Hessian LLE with PAC-Bayesian is identified with logistic regression gives the best accuracy of 96.87%.","PeriodicalId":338691,"journal":{"name":"2022 Smart Technologies, Communication and Robotics (STCR)","volume":"50 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-12-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122325272","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 : 2022-12-10DOI: 10.1109/STCR55312.2022.10009355
Pon Bharathi A, Rajeesh Kumar N V, A. S, Vedha Vinodha D, A. J. Wilson
Providing better performance and energy optimization in the Internet of Things (IoT) in a network environment typically presents numerous challenges. So, here clustering is perhaps the most popular strategy for increasing the lifetime of an IoT, which immediately leads to a more robust routing procedure. This procedure entails grouping sensor nodes into clusters and assigning appropriate cluster heads to each cluster. The purpose of this research is to develop a novel clustering strategy that uses a new hybrid algorithm called Overtaker Assisted Political Update to select cluster heads (OA-PU). This CH selection takes four factors into account: energy, distance, cluster radius, and time. The hybrid algorithm is composed of the Fusion Rider Optimization Algorithm (F-ROA) and a political optimization algorithm (PO) inspired by nature. Fusion-ROA is based on groups of riders attempting to reach a goal, whereas PO is a cutting-edge meta-heuristic optimization approach for global as well as structural analysis performance prediction in the IoT environment. In terms of the evaluation of alive nodes, cost function analysis, as well as energy analysis, the proposed OA-PU outperforms conventional approaches, finally, the optimal system is developed and lifetime of network is increased.
{"title":"Cluster Head Selection using the OA-PU Algorithm in the IoT","authors":"Pon Bharathi A, Rajeesh Kumar N V, A. S, Vedha Vinodha D, A. J. Wilson","doi":"10.1109/STCR55312.2022.10009355","DOIUrl":"https://doi.org/10.1109/STCR55312.2022.10009355","url":null,"abstract":"Providing better performance and energy optimization in the Internet of Things (IoT) in a network environment typically presents numerous challenges. So, here clustering is perhaps the most popular strategy for increasing the lifetime of an IoT, which immediately leads to a more robust routing procedure. This procedure entails grouping sensor nodes into clusters and assigning appropriate cluster heads to each cluster. The purpose of this research is to develop a novel clustering strategy that uses a new hybrid algorithm called Overtaker Assisted Political Update to select cluster heads (OA-PU). This CH selection takes four factors into account: energy, distance, cluster radius, and time. The hybrid algorithm is composed of the Fusion Rider Optimization Algorithm (F-ROA) and a political optimization algorithm (PO) inspired by nature. Fusion-ROA is based on groups of riders attempting to reach a goal, whereas PO is a cutting-edge meta-heuristic optimization approach for global as well as structural analysis performance prediction in the IoT environment. In terms of the evaluation of alive nodes, cost function analysis, as well as energy analysis, the proposed OA-PU outperforms conventional approaches, finally, the optimal system is developed and lifetime of network is increased.","PeriodicalId":338691,"journal":{"name":"2022 Smart Technologies, Communication and Robotics (STCR)","volume":"36 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-12-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"133611843","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 : 2022-12-10DOI: 10.1109/STCR55312.2022.10009494
G. C, G. M., H. Rajaguru
The anatomical elements and the actions are amazing for the nervous system, but the human brain is susceptible to more neurological conditions, and epilepsy is one of such abnormalities. In medical parlance, a person is said to have the disease known as epilepsy if they have recurring seizures. In this study, Lion Optimization Algorithm (LOA) is employed to reduce the features dimensionality from EEG outputs. Following this, the reduced records are evaluated with the use of a hybrid learning approach that combines a Gaussian Mixture Model (GMM) with an Expectation Maximization (EM) technique. Results indicate that an average accuracy of 91% is achieved when the LOA features is identified using GMM with EM.
{"title":"Performance Analysis of Lion Optimization Algorithm with Hybrid Classifier for Epilepsy Detection","authors":"G. C, G. M., H. Rajaguru","doi":"10.1109/STCR55312.2022.10009494","DOIUrl":"https://doi.org/10.1109/STCR55312.2022.10009494","url":null,"abstract":"The anatomical elements and the actions are amazing for the nervous system, but the human brain is susceptible to more neurological conditions, and epilepsy is one of such abnormalities. In medical parlance, a person is said to have the disease known as epilepsy if they have recurring seizures. In this study, Lion Optimization Algorithm (LOA) is employed to reduce the features dimensionality from EEG outputs. Following this, the reduced records are evaluated with the use of a hybrid learning approach that combines a Gaussian Mixture Model (GMM) with an Expectation Maximization (EM) technique. Results indicate that an average accuracy of 91% is achieved when the LOA features is identified using GMM with EM.","PeriodicalId":338691,"journal":{"name":"2022 Smart Technologies, Communication and Robotics (STCR)","volume":"7 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-12-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114471271","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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