Pub Date : 2024-03-01DOI: 10.11591/ijres.v13.i1.pp69-75
Gangadharaiah Soralamavu Lakshmaiah, C. Narayanappa, Lakshmi Shrinivasan, Divya Muddenahalli Narasimhaiah
The effectiveness of adaptive filters are mainly dependent on the design techniques and the algorithm of adaptation. The most common adaptation technique used is least mean square (LMS) due its computational simplicity. The application depends on the adaptive filter configuration used and are well known for system identification and real time applications. In this work, a modified delayed μ-law proportionate normalized least mean square (DMPNLMS) algorithm has been proposed. It is the improvised version of the µ-law proportionate normalized least mean square (MPNLMS) algorithm. The algorithm is realized using Ladner-Fischer type of parallel prefix logarithmic adder to reduce the silicon area. The simulation and implementation of very large-scale integration (VLSI) architecture are done using MATLAB, Vivado suite and complementary metal–oxide– semiconductor (CMOS) 90 nm technology node using Cadence register transfer level (RTL) Genus Compiler respectively. The DMPNLMS method exhibits a reduction in mean square error, a higher rate of convergence, and more stability. The synthesis results demonstrate that it is area and delay effective, making it practical for applications where a faster operating speed is required.
{"title":"Efficient very large-scale integration architecture design of proportionate-type least mean square adaptive filters","authors":"Gangadharaiah Soralamavu Lakshmaiah, C. Narayanappa, Lakshmi Shrinivasan, Divya Muddenahalli Narasimhaiah","doi":"10.11591/ijres.v13.i1.pp69-75","DOIUrl":"https://doi.org/10.11591/ijres.v13.i1.pp69-75","url":null,"abstract":"The effectiveness of adaptive filters are mainly dependent on the design techniques and the algorithm of adaptation. The most common adaptation technique used is least mean square (LMS) due its computational simplicity. The application depends on the adaptive filter configuration used and are well known for system identification and real time applications. In this work, a modified delayed μ-law proportionate normalized least mean square (DMPNLMS) algorithm has been proposed. It is the improvised version of the µ-law proportionate normalized least mean square (MPNLMS) algorithm. The algorithm is realized using Ladner-Fischer type of parallel prefix logarithmic adder to reduce the silicon area. The simulation and implementation of very large-scale integration (VLSI) architecture are done using MATLAB, Vivado suite and complementary metal–oxide– semiconductor (CMOS) 90 nm technology node using Cadence register transfer level (RTL) Genus Compiler respectively. The DMPNLMS method exhibits a reduction in mean square error, a higher rate of convergence, and more stability. The synthesis results demonstrate that it is area and delay effective, making it practical for applications where a faster operating speed is required.","PeriodicalId":158991,"journal":{"name":"International Journal of Reconfigurable and Embedded Systems (IJRES)","volume":"76 22","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140087128","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 : 2024-03-01DOI: 10.11591/ijres.v13.i1.pp143-150
Sushma Priyadarshini, Asma Parveen
Internet of things (IoT) has become one of the eminent phenomena in human life along with its collaboration with wireless sensor networks (WSNs), due to enormous growth in the domain; there has been a demand to address the various issues regarding it such as energy consumption, redundancy, and overhead. Data aggregation (DA) is considered as the basic mechanism to minimize the energy efficiency and communication overhead; however, security plays an important role where node security is essential due to the volatile nature of WSN. Thus, we design and develop proximate node aware secure data aggregation (PNA-SDA). In the PNA-SDA mechanism, additional data is used to secure the original data, and further information is shared with the proximate node; moreover, further security is achieved by updating the state each time. Moreover, the node that does not have updated information is considered as the compromised node and discarded. PNA-SDA is evaluated considering the different parameters like average energy consumption, and average deceased node; also, comparative analysis is carried out with the existing model in terms of throughput and correct packet identification.
{"title":"Proximate node aware optimal and secure data aggregation in wireless sensor network based IoT environment","authors":"Sushma Priyadarshini, Asma Parveen","doi":"10.11591/ijres.v13.i1.pp143-150","DOIUrl":"https://doi.org/10.11591/ijres.v13.i1.pp143-150","url":null,"abstract":"Internet of things (IoT) has become one of the eminent phenomena in human life along with its collaboration with wireless sensor networks (WSNs), due to enormous growth in the domain; there has been a demand to address the various issues regarding it such as energy consumption, redundancy, and overhead. Data aggregation (DA) is considered as the basic mechanism to minimize the energy efficiency and communication overhead; however, security plays an important role where node security is essential due to the volatile nature of WSN. Thus, we design and develop proximate node aware secure data aggregation (PNA-SDA). In the PNA-SDA mechanism, additional data is used to secure the original data, and further information is shared with the proximate node; moreover, further security is achieved by updating the state each time. Moreover, the node that does not have updated information is considered as the compromised node and discarded. PNA-SDA is evaluated considering the different parameters like average energy consumption, and average deceased node; also, comparative analysis is carried out with the existing model in terms of throughput and correct packet identification.","PeriodicalId":158991,"journal":{"name":"International Journal of Reconfigurable and Embedded Systems (IJRES)","volume":"106 47","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140090230","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 : 2024-03-01DOI: 10.11591/ijres.v13.i1.pp179-191
M. Sharma, M. Kaiser, K. Ray
The biomedical profession has gained importance due to the rapid and accurate diagnosis of clinical patients using computer-aided diagnosis (CAD) tools. The diagnosis and treatment of Alzheimer’s disease (AD) using complementary multimodalities can improve the quality of life and mental state of patients. In this study, we integrated a lightweight custom convolutional neural network (CNN) model and nature-inspired optimization techniques to enhance the performance, robustness, and stability of progress detection in AD. A multi-modal fusion database approach was implemented, including positron emission tomography (PET) and magnetic resonance imaging (MRI) datasets, to create a fused database. We compared the performance of custom and pre-trained deep learning models with and without optimization and found that employing natureinspired algorithms like the particle swarm optimization algorithm (PSO) algorithm significantly improved system performance. The proposed methodology, which includes a fused multimodality database and optimization strategy, improved performance metrics such as training, validation, test accuracy, precision, and recall. Furthermore, PSO was found to improve the performance of pre-trained models by 3-5% and custom models by up to 22%. Combining different medical imaging modalities improved the overall model performance by 2-5%. In conclusion, a customized lightweight CNN model and nature-inspired optimization techniques can significantly enhance progress detection, leading to better biomedical research and patient care.
{"title":"Deep convolutional neural network framework with multi-modal fusion for Alzheimer’s detection","authors":"M. Sharma, M. Kaiser, K. Ray","doi":"10.11591/ijres.v13.i1.pp179-191","DOIUrl":"https://doi.org/10.11591/ijres.v13.i1.pp179-191","url":null,"abstract":"The biomedical profession has gained importance due to the rapid and accurate diagnosis of clinical patients using computer-aided diagnosis (CAD) tools. The diagnosis and treatment of Alzheimer’s disease (AD) using complementary multimodalities can improve the quality of life and mental state of patients. In this study, we integrated a lightweight custom convolutional neural network (CNN) model and nature-inspired optimization techniques to enhance the performance, robustness, and stability of progress detection in AD. A multi-modal fusion database approach was implemented, including positron emission tomography (PET) and magnetic resonance imaging (MRI) datasets, to create a fused database. We compared the performance of custom and pre-trained deep learning models with and without optimization and found that employing natureinspired algorithms like the particle swarm optimization algorithm (PSO) algorithm significantly improved system performance. The proposed methodology, which includes a fused multimodality database and optimization strategy, improved performance metrics such as training, validation, test accuracy, precision, and recall. Furthermore, PSO was found to improve the performance of pre-trained models by 3-5% and custom models by up to 22%. Combining different medical imaging modalities improved the overall model performance by 2-5%. In conclusion, a customized lightweight CNN model and nature-inspired optimization techniques can significantly enhance progress detection, leading to better biomedical research and patient care.","PeriodicalId":158991,"journal":{"name":"International Journal of Reconfigurable and Embedded Systems (IJRES)","volume":"117 24","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140089341","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 : 2024-03-01DOI: 10.11591/ijres.v13.i1.pp76-84
I. V. S. Reddy, P. Lavanya, V. Selvakumar
Today, health monitoring relies heavily on technological advancements. This study proposes a low-power wide-area network (LPWAN) based, multinodal health monitoring system to monitor vital physiological data. The suggested system consists of two nodes, an indoor node, and an outdoor node, and the nodes communicate via long range (LoRa) transceivers. Outdoor nodes use an MPU6050 module, heart rate, oxygen pulse, temperature, and skin resistance sensors and transmit sensed values to the indoor node. We transferred the data received by the master node to the cloud using the Adafruit cloud service. The system can operate with a coverage of 4.5 km, where the optimal distance between outdoor sensor nodes and the indoor master node is 4 km. To further predict fall detection, various machine learning classification techniques have been applied. Upon comparing various classifier techniques, the decision tree method achieved an accuracy of 0.99864 with a training and testing ratio of 70:30. By developing accurate prediction models, we can identify high-risk individuals and implement preventative measures to reduce the likelihood of a fall occurring. Remote monitoring of the health and physical status of elderly people has proven to be the most beneficial application of this technology.
{"title":"Machine learning classifiers for fall detection leveraging LoRa communication network","authors":"I. V. S. Reddy, P. Lavanya, V. Selvakumar","doi":"10.11591/ijres.v13.i1.pp76-84","DOIUrl":"https://doi.org/10.11591/ijres.v13.i1.pp76-84","url":null,"abstract":"Today, health monitoring relies heavily on technological advancements. This study proposes a low-power wide-area network (LPWAN) based, multinodal health monitoring system to monitor vital physiological data. The suggested system consists of two nodes, an indoor node, and an outdoor node, and the nodes communicate via long range (LoRa) transceivers. Outdoor nodes use an MPU6050 module, heart rate, oxygen pulse, temperature, and skin resistance sensors and transmit sensed values to the indoor node. We transferred the data received by the master node to the cloud using the Adafruit cloud service. The system can operate with a coverage of 4.5 km, where the optimal distance between outdoor sensor nodes and the indoor master node is 4 km. To further predict fall detection, various machine learning classification techniques have been applied. Upon comparing various classifier techniques, the decision tree method achieved an accuracy of 0.99864 with a training and testing ratio of 70:30. By developing accurate prediction models, we can identify high-risk individuals and implement preventative measures to reduce the likelihood of a fall occurring. Remote monitoring of the health and physical status of elderly people has proven to be the most beneficial application of this technology.","PeriodicalId":158991,"journal":{"name":"International Journal of Reconfigurable and Embedded Systems (IJRES)","volume":"4 7","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140087780","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 : 2024-03-01DOI: 10.11591/ijres.v13.i1.pp210-216
Jagadevi Bakka, Sanjeev C. Lingareddy
Scientific workload execution on distributed computing platform such as cloud environment is time intense and expensive. The scientific workload has task dependencies with different service level agreement (SLA) prerequisite at different levels. Existing workload scheduling (WS) design are not efficient in assuring SLA at task level. Alongside, induce higher cost as majority of scheduling mechanisms reduce either time or energy. In reducing, cost both energy and makespan must be optimized together for allocating resource. No prior work has considered optimizing energy and processing time together in meeting task level SLA requirement. This paper present task level energy and performance assurance (TLEPA)-WS algorithm for distributed computing environment. The TLEPA-WS guarantees energy minimization with performance requirement of parallel application under distributed computational environment. Experiment results shows significant reduction in using energy and makespan; thereby reduces cost of workload execution in comparison with various standard workload execution models.
{"title":"Task level energy and performance assurance workload scheduling model in distributed computing environment","authors":"Jagadevi Bakka, Sanjeev C. Lingareddy","doi":"10.11591/ijres.v13.i1.pp210-216","DOIUrl":"https://doi.org/10.11591/ijres.v13.i1.pp210-216","url":null,"abstract":"Scientific workload execution on distributed computing platform such as cloud environment is time intense and expensive. The scientific workload has task dependencies with different service level agreement (SLA) prerequisite at different levels. Existing workload scheduling (WS) design are not efficient in assuring SLA at task level. Alongside, induce higher cost as majority of scheduling mechanisms reduce either time or energy. In reducing, cost both energy and makespan must be optimized together for allocating resource. No prior work has considered optimizing energy and processing time together in meeting task level SLA requirement. This paper present task level energy and performance assurance (TLEPA)-WS algorithm for distributed computing environment. The TLEPA-WS guarantees energy minimization with performance requirement of parallel application under distributed computational environment. Experiment results shows significant reduction in using energy and makespan; thereby reduces cost of workload execution in comparison with various standard workload execution models.","PeriodicalId":158991,"journal":{"name":"International Journal of Reconfigurable and Embedded Systems (IJRES)","volume":"115 20","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140090427","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 : 2024-03-01DOI: 10.11591/ijres.v13.i1.pp111-116
S. S. Suprapto, Vicky Andria Kusuma, Aji Akbar Firdaus, Wahyu Haryanto Putra, Risty Jayanti Yuniar
The use of technology has a significant impact to reduce the consequences of accidents. Sensors, small components that detect interactions experienced by various components, play a crucial role in this regard. This study focuses on how the MPU6050 sensor module can be used to detect the movement of people who are falling, defined as the inability of the lower body, including the hips and feet, to support the body effectively. An airbag system is proposed to reduce the impact of a fall. The data processing method in this study involves the use of a threshold value to identify falling motion. The results of the study have identified a threshold value for falling motion, including an acceleration relative (AR) value of less than or equal to 0.38 g, an angle slope of more than or equal to 40 degrees, and an angular velocity of more than or equal to 30 °/s. The airbag system is designed to inflate faster than the time of impact, with a gas flow rate of 0.04876 m3 /s and an inflating time of 0.05 s. The overall system has a specificity value of 100%, a sensitivity of 85%, and an accuracy of 94%.
{"title":"Design and build an airbag system for elderly fall protection using the MPU6050 sensor module","authors":"S. S. Suprapto, Vicky Andria Kusuma, Aji Akbar Firdaus, Wahyu Haryanto Putra, Risty Jayanti Yuniar","doi":"10.11591/ijres.v13.i1.pp111-116","DOIUrl":"https://doi.org/10.11591/ijres.v13.i1.pp111-116","url":null,"abstract":"The use of technology has a significant impact to reduce the consequences of accidents. Sensors, small components that detect interactions experienced by various components, play a crucial role in this regard. This study focuses on how the MPU6050 sensor module can be used to detect the movement of people who are falling, defined as the inability of the lower body, including the hips and feet, to support the body effectively. An airbag system is proposed to reduce the impact of a fall. The data processing method in this study involves the use of a threshold value to identify falling motion. The results of the study have identified a threshold value for falling motion, including an acceleration relative (AR) value of less than or equal to 0.38 g, an angle slope of more than or equal to 40 degrees, and an angular velocity of more than or equal to 30 °/s. The airbag system is designed to inflate faster than the time of impact, with a gas flow rate of 0.04876 m3 /s and an inflating time of 0.05 s. The overall system has a specificity value of 100%, a sensitivity of 85%, and an accuracy of 94%.","PeriodicalId":158991,"journal":{"name":"International Journal of Reconfigurable and Embedded Systems (IJRES)","volume":" 369","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140092419","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 : 2024-03-01DOI: 10.11591/ijres.v13.i1.pp52-58
Kharis Sugiarto, Vicky Andria Kusuma, Aji Akbar Firdaus, S. S. Suprapto, Dimas Fajar Uman Putra
The ball-wheeled robot relies on a camera for receiving information on the object to be followed. Object tracing is one of the methods that can be used for detecting object movement. In recognizing objects around it, the robot requires an image analysis process that involves visual perception. Image processing is the process of processing and analyzing images that involves visual perception, and is characterized by input data and output information in the form of images. This is how the robot can see objects around it and then be assisted by computer vision to make a decision. The object tracking method with hue-saturation-value (HSV) colour filtering and shape recognition with circle hough transform (CHT) is applied to the ball-wheeled robot. The front vision of the robot uses HSV colour filtering with various test values to determine the thresholding value, and it was found that the ball could be identified up to a distance of 1,000 cm. To further improve the performance of recognizing the ball object, CHT was applied. It was found that the ball could be identified up to a distance of 700 cm. Furthermore, the ball can be identified in obstructed conditions up to 75%.
{"title":"Implementing hue-saturation-value filter and circle hough transform for object tracking on ball-wheeled robot","authors":"Kharis Sugiarto, Vicky Andria Kusuma, Aji Akbar Firdaus, S. S. Suprapto, Dimas Fajar Uman Putra","doi":"10.11591/ijres.v13.i1.pp52-58","DOIUrl":"https://doi.org/10.11591/ijres.v13.i1.pp52-58","url":null,"abstract":"The ball-wheeled robot relies on a camera for receiving information on the object to be followed. Object tracing is one of the methods that can be used for detecting object movement. In recognizing objects around it, the robot requires an image analysis process that involves visual perception. Image processing is the process of processing and analyzing images that involves visual perception, and is characterized by input data and output information in the form of images. This is how the robot can see objects around it and then be assisted by computer vision to make a decision. The object tracking method with hue-saturation-value (HSV) colour filtering and shape recognition with circle hough transform (CHT) is applied to the ball-wheeled robot. The front vision of the robot uses HSV colour filtering with various test values to determine the thresholding value, and it was found that the ball could be identified up to a distance of 1,000 cm. To further improve the performance of recognizing the ball object, CHT was applied. It was found that the ball could be identified up to a distance of 700 cm. Furthermore, the ball can be identified in obstructed conditions up to 75%.","PeriodicalId":158991,"journal":{"name":"International Journal of Reconfigurable and Embedded Systems (IJRES)","volume":"94 16","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140086539","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
The fundamental principle of the paper is that the soil moisture sensor obtains the moisture content level of the soil sample. The water pump is automatically activated if the moisture content is insufficient, which causes water to flow into the soil. The water pump is immediately turned off when the moisture content is high enough. Smart home, smart city, smart transportation, and smart farming are just a few of the new intelligent ideas that internet of things (IoT) includes. The goal of this method is to increase productivity and decrease manual labour among farmers. In this paper, we present a system for monitoring and regulating water flow that employs a soil moisture sensor to keep track of soil moisture content as well as the land’s water level to keep track of and regulate the amount of water supplied to the plant. The device also includes an automated led lighting system.
{"title":"Design of Arduino UNO based smart irrigation system for real time applications","authors":"P. Ramasamy, Nagarajan Pandian, Krishnamurthy Mayathevar, Ramkumar Ravindran, Srinivasa Rao Kandula, Selvabharathi Devadoss, Selvakumar Kuppusamy","doi":"10.11591/ijres.v13.i1.pp105-110","DOIUrl":"https://doi.org/10.11591/ijres.v13.i1.pp105-110","url":null,"abstract":"The fundamental principle of the paper is that the soil moisture sensor obtains the moisture content level of the soil sample. The water pump is automatically activated if the moisture content is insufficient, which causes water to flow into the soil. The water pump is immediately turned off when the moisture content is high enough. Smart home, smart city, smart transportation, and smart farming are just a few of the new intelligent ideas that internet of things (IoT) includes. The goal of this method is to increase productivity and decrease manual labour among farmers. In this paper, we present a system for monitoring and regulating water flow that employs a soil moisture sensor to keep track of soil moisture content as well as the land’s water level to keep track of and regulate the amount of water supplied to the plant. The device also includes an automated led lighting system.","PeriodicalId":158991,"journal":{"name":"International Journal of Reconfigurable and Embedded Systems (IJRES)","volume":"107 38","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140088474","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
The construction industry is an industry that is always surrounded by uncertainties and risks. The industry is always associated with a threatindustry which has a complex, tedious layout and techniques characterized by unpredictable circumstances. It comprises a variety of human talents and the coordination of different areas and activities associated with it. In this competitive era of the construction industry, delays and cost overruns of the project are often common in every project and the causes of that are also common. One of the problems which we are trying to cater to is the improper handling of materials at the construction site. In this paper, we propose developing a system that is capable of tracking construction material on site that would benefit the contractor and client for better control over inventory on-site and to minimize loss of material that occurs due to theft and misplacing of materials.
{"title":"Radio frequency identification based materials tracking system for construction industry","authors":"Sameer Jain, Gustavo Sanchez, Taruna Sunil, Dinesh Kumar Sharma","doi":"10.11591/ijres.v13.i1.pp85-95","DOIUrl":"https://doi.org/10.11591/ijres.v13.i1.pp85-95","url":null,"abstract":"The construction industry is an industry that is always surrounded by uncertainties and risks. The industry is always associated with a threatindustry which has a complex, tedious layout and techniques characterized by unpredictable circumstances. It comprises a variety of human talents and the coordination of different areas and activities associated with it. In this competitive era of the construction industry, delays and cost overruns of the project are often common in every project and the causes of that are also common. One of the problems which we are trying to cater to is the improper handling of materials at the construction site. In this paper, we propose developing a system that is capable of tracking construction material on site that would benefit the contractor and client for better control over inventory on-site and to minimize loss of material that occurs due to theft and misplacing of materials.","PeriodicalId":158991,"journal":{"name":"International Journal of Reconfigurable and Embedded Systems (IJRES)","volume":" 738","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140092334","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}
Investigating human subjects is the goal of predicting human emotions in the stock market. A significant number of psychological effects require (feelings) to be produced, directly releasing human emotions. The development of effect theory leads one to believe that one must be aware of one's sentiments and emotions to forecast one's behavior. The proposed line of inquiry focuses on developing a reliable model incorporating neurophysiological data into actual feelings. Any change in emotional affect will directly elicit a response in the body's physiological systems. This approach is named after the notion of Gaussian mixture models (GMM). The statistical reaction following data processing, quantitative findings on emotion labels, and coincidental responses with training samples all directly impact the outcomes that are accomplished. In terms of statistical parameters such as population mean and standard deviation, the suggested method is evaluated compared to a technique considered to be state-of-the-art. The proposed system determines an individual's emotional state after a minimum of 6 iterative learning using the Gaussian expectation-maximization (GEM) statistical model, in which the iterations tend to continue to zero error. Perhaps each of these improves predictions while simultaneously increasing the amount of value extracted.
{"title":"Affective analysis in machine learning using AMIGOS with Gaussian expectation-maximization model","authors":"Balamurugan Kaliappan, Bakkialakshmi Vaithialingam Sudalaiyadumperumal, Sudalaimuthu Thalavaipillai","doi":"10.11591/ijres.v13.i1.pp201-209","DOIUrl":"https://doi.org/10.11591/ijres.v13.i1.pp201-209","url":null,"abstract":"Investigating human subjects is the goal of predicting human emotions in the stock market. A significant number of psychological effects require (feelings) to be produced, directly releasing human emotions. The development of effect theory leads one to believe that one must be aware of one's sentiments and emotions to forecast one's behavior. The proposed line of inquiry focuses on developing a reliable model incorporating neurophysiological data into actual feelings. Any change in emotional affect will directly elicit a response in the body's physiological systems. This approach is named after the notion of Gaussian mixture models (GMM). The statistical reaction following data processing, quantitative findings on emotion labels, and coincidental responses with training samples all directly impact the outcomes that are accomplished. In terms of statistical parameters such as population mean and standard deviation, the suggested method is evaluated compared to a technique considered to be state-of-the-art. The proposed system determines an individual's emotional state after a minimum of 6 iterative learning using the Gaussian expectation-maximization (GEM) statistical model, in which the iterations tend to continue to zero error. Perhaps each of these improves predictions while simultaneously increasing the amount of value extracted.","PeriodicalId":158991,"journal":{"name":"International Journal of Reconfigurable and Embedded Systems (IJRES)","volume":"21 21","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140084769","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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