Pub Date : 2023-05-25DOI: 10.1109/ACCAI58221.2023.10201114
R. Ranjan, S. Pasari, Sonu Devi, H. Verma
On April 25, 2015, a devastating earthquake of magnitude Mw 7.8 hit Nepal, killing around 9000 people and injuring 22000 more. Following the disaster, extensive field research and inspections were conducted in Nepal to determine the extent of damage to the earthquake-affected structures. The post-earthquake investigation procedure becomes extremely difficult due to the vast number of structures and types of buildings in the area. However, knowing a building’s description beforehand can assist in determining the extent of possible damages due to a large event. In light of this, the present study aims to provide an effective formulation for building vulnerability assessment using several parameters, such as number of floors, construction materials, house type (public or private), and age of building. A huge dataset comprising building information of around 3,50,000 buildings on 39 variables is used for this purpose. Six machine learning methods, namely logistic regression, decision-tree classifier, k-nearest neighbor, linear discriminant analysis, random forest, and extreme gradient boosting algorithms are implemented. Based on the score, the grading boosting algorithm is found to be the most suitable algorithm. The findings are helpful for better urban planning, social policymaking, suitable material identification for building construction, and moreover, to set up a national level disaster risk reduction (DRR) strategy to minimize earthquake losses in Nepal..
{"title":"A Novel Framework for Building Vulnerability Assessment for the 2015 Nepal Earthquake","authors":"R. Ranjan, S. Pasari, Sonu Devi, H. Verma","doi":"10.1109/ACCAI58221.2023.10201114","DOIUrl":"https://doi.org/10.1109/ACCAI58221.2023.10201114","url":null,"abstract":"On April 25, 2015, a devastating earthquake of magnitude Mw 7.8 hit Nepal, killing around 9000 people and injuring 22000 more. Following the disaster, extensive field research and inspections were conducted in Nepal to determine the extent of damage to the earthquake-affected structures. The post-earthquake investigation procedure becomes extremely difficult due to the vast number of structures and types of buildings in the area. However, knowing a building’s description beforehand can assist in determining the extent of possible damages due to a large event. In light of this, the present study aims to provide an effective formulation for building vulnerability assessment using several parameters, such as number of floors, construction materials, house type (public or private), and age of building. A huge dataset comprising building information of around 3,50,000 buildings on 39 variables is used for this purpose. Six machine learning methods, namely logistic regression, decision-tree classifier, k-nearest neighbor, linear discriminant analysis, random forest, and extreme gradient boosting algorithms are implemented. Based on the score, the grading boosting algorithm is found to be the most suitable algorithm. The findings are helpful for better urban planning, social policymaking, suitable material identification for building construction, and moreover, to set up a national level disaster risk reduction (DRR) strategy to minimize earthquake losses in Nepal..","PeriodicalId":382104,"journal":{"name":"2023 International Conference on Advances in Computing, Communication and Applied Informatics (ACCAI)","volume":"57 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-05-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127093356","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2023-05-25DOI: 10.1109/ACCAI58221.2023.10201066
L. Maguluri, S. Farook, S. R., Ashutosh Dixit, K.P. Aishwarya, J. Dhanraj
Partial shadowing of a solar array is known to decrease the array's output power. Unfortunately, it is not always possible to calculate the exact degree of decrease in energy output from the darkened region alone. In this study, the process of partial PV shadowing on multiple PV cells by using IoT wired in series and/or parallel, with and without bypass diodes, is elucidated. A layperson interested in learning how a certain shading geometry affects a PV system may benefit from this study, which is provided in clear language. Commercial 100 W panel and 10 kW PV array data are used to show the study.
{"title":"An Appraisal in to the Effects of Partial Shading on an Urban Photovoltaic System Using the Internet of Things","authors":"L. Maguluri, S. Farook, S. R., Ashutosh Dixit, K.P. Aishwarya, J. Dhanraj","doi":"10.1109/ACCAI58221.2023.10201066","DOIUrl":"https://doi.org/10.1109/ACCAI58221.2023.10201066","url":null,"abstract":"Partial shadowing of a solar array is known to decrease the array's output power. Unfortunately, it is not always possible to calculate the exact degree of decrease in energy output from the darkened region alone. In this study, the process of partial PV shadowing on multiple PV cells by using IoT wired in series and/or parallel, with and without bypass diodes, is elucidated. A layperson interested in learning how a certain shading geometry affects a PV system may benefit from this study, which is provided in clear language. Commercial 100 W panel and 10 kW PV array data are used to show the study.","PeriodicalId":382104,"journal":{"name":"2023 International Conference on Advances in Computing, Communication and Applied Informatics (ACCAI)","volume":"85 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-05-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127539814","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2023-05-25DOI: 10.1109/ACCAI58221.2023.10201136
Anitha Patil, S. Govindaraj
These days, it seems that medical image processing is a good fit for deep learning-based models. There is a potential basis for exploiting healthcare services thanks to advancements in deep learning and pre-trained models in computer vision applications. According to the World Health Organization (WHO), a clinical decision support system (CDSS) based on deep learning has the potential to advance the state of the art in medical image analysis. Timely management is essential due to the high death and disability rates associated with stroke. Timely study of brain imaging allows for rapid medical action. Existing research on MRI-based brain stroke analysis requires a refined and enhanced strategy to reap the potential advantages of deep learning, and MRI is discovered to provide additional possibilities for medical picture analysis. As may be seen in the material, a lot of focus has been placed here. In this study, we present the Deep Automated Brain Stroke Detection Framework (DABSDF), a deep learning-based system for detecting strokes in brain MRI. The Deep Convolutional Neural Network-based Pipeline for Brain Stroke Detection is a method we proposed (DCNNP-BSD). To test the effectiveness of the proposed framework and its algorithms, a prototype application has been developed in the Python data science environment. We evaluate our model against current deep learning models. The effectiveness of the various models on the MRI dataset varies widely. In terms of performance, VGG16 fares the worst while the suggested model, DCNNP-BSD, fares the best. With a dice similarity coefficient of 0.8822979, 0.8554022 sensitivity, 0.99595785 specificity, and 0.97774774 accuracy, the suggested CNN-based deep learning model beat the state-of-the-art.
{"title":"An AI Enabled Framework for MRI-based Data Analytics for Efficient Brain Stroke Detection","authors":"Anitha Patil, S. Govindaraj","doi":"10.1109/ACCAI58221.2023.10201136","DOIUrl":"https://doi.org/10.1109/ACCAI58221.2023.10201136","url":null,"abstract":"These days, it seems that medical image processing is a good fit for deep learning-based models. There is a potential basis for exploiting healthcare services thanks to advancements in deep learning and pre-trained models in computer vision applications. According to the World Health Organization (WHO), a clinical decision support system (CDSS) based on deep learning has the potential to advance the state of the art in medical image analysis. Timely management is essential due to the high death and disability rates associated with stroke. Timely study of brain imaging allows for rapid medical action. Existing research on MRI-based brain stroke analysis requires a refined and enhanced strategy to reap the potential advantages of deep learning, and MRI is discovered to provide additional possibilities for medical picture analysis. As may be seen in the material, a lot of focus has been placed here. In this study, we present the Deep Automated Brain Stroke Detection Framework (DABSDF), a deep learning-based system for detecting strokes in brain MRI. The Deep Convolutional Neural Network-based Pipeline for Brain Stroke Detection is a method we proposed (DCNNP-BSD). To test the effectiveness of the proposed framework and its algorithms, a prototype application has been developed in the Python data science environment. We evaluate our model against current deep learning models. The effectiveness of the various models on the MRI dataset varies widely. In terms of performance, VGG16 fares the worst while the suggested model, DCNNP-BSD, fares the best. With a dice similarity coefficient of 0.8822979, 0.8554022 sensitivity, 0.99595785 specificity, and 0.97774774 accuracy, the suggested CNN-based deep learning model beat the state-of-the-art.","PeriodicalId":382104,"journal":{"name":"2023 International Conference on Advances in Computing, Communication and Applied Informatics (ACCAI)","volume":"4 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-05-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124988924","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2023-05-25DOI: 10.1109/ACCAI58221.2023.10201122
C. Ebenesh, R. S. Kumar, Ezhil Grace. A
The methodology that is recommended makes an attempt to anticipate and forecast changes in the price indices of the stock market for three specific equities that are traded on the stock market. The stock market is comprised of all of the many types of equities that are now being discussed. This paradigm makes an attempt to classify two unique types of classification algorithms, namely Long-Term Memory (LSTM) and Logistics Regression (LR). Long-Term Memory is an acronym for "Long-Term Memory," while Logistics Regression is an acronym for "LR." (LSTM). One of the criteria that is used to assess the performance of the models is the ability of both models to accurately anticipate the movement of an index that is traded on the Bombay Stock Exchange. (BSE). For the purposes of performing a study of the suggested structure for the projection of three stocks, an estimated total of thirty different participants were used. (AAPL, MSFT, and AMZN). When comparing the two models' levels of performance, it was found that the LR model (99.8%) performed substantially better than the LTSM model (72.3%) on average. This was noticed while conducting the comparison. (p0.05). When it comes to predicting stock indices by making use of the various parameters, the LR model performed noticeably better than the LTSM model.
{"title":"A Novel Technique to Minimising Mean Square Error in Stock Price Index Prediction Utilising Logistics Regression and LSTM Model","authors":"C. Ebenesh, R. S. Kumar, Ezhil Grace. A","doi":"10.1109/ACCAI58221.2023.10201122","DOIUrl":"https://doi.org/10.1109/ACCAI58221.2023.10201122","url":null,"abstract":"The methodology that is recommended makes an attempt to anticipate and forecast changes in the price indices of the stock market for three specific equities that are traded on the stock market. The stock market is comprised of all of the many types of equities that are now being discussed. This paradigm makes an attempt to classify two unique types of classification algorithms, namely Long-Term Memory (LSTM) and Logistics Regression (LR). Long-Term Memory is an acronym for \"Long-Term Memory,\" while Logistics Regression is an acronym for \"LR.\" (LSTM). One of the criteria that is used to assess the performance of the models is the ability of both models to accurately anticipate the movement of an index that is traded on the Bombay Stock Exchange. (BSE). For the purposes of performing a study of the suggested structure for the projection of three stocks, an estimated total of thirty different participants were used. (AAPL, MSFT, and AMZN). When comparing the two models' levels of performance, it was found that the LR model (99.8%) performed substantially better than the LTSM model (72.3%) on average. This was noticed while conducting the comparison. (p0.05). When it comes to predicting stock indices by making use of the various parameters, the LR model performed noticeably better than the LTSM model.","PeriodicalId":382104,"journal":{"name":"2023 International Conference on Advances in Computing, Communication and Applied Informatics (ACCAI)","volume":"41 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-05-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125049643","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2023-05-25DOI: 10.1109/ACCAI58221.2023.10201034
M. Shyam, M. Amalasweena, S. S., K. Balasaranya, R. Renugadevi, K. P. Chandran
Expanding human wants coincide with the development of ever more sophisticated technologies. The majority of this study focuses on robotic arms, which are used daily in the job done to satisfy these objectives and make life simpler. Mechanical limbs can interact with humans or be programmed to follow specific instructions. Law enforcement officers face a high-stakes, high-precision challenge while neutralizing explosives, since any misstep might result in serious injury or death. An idea derived from wireless bomb disposal robots is offered as a solution to the issue. Through using wireless control module, the robot will dispose of the explosive. A transmitter makes up the control module, while a receiver makes up the Robot itself. An ARDUINO UNO R3 board connects to the receiver module. Input from the transmitter controls the Robot's actions. An LCD screen is connected to a wireless video receiver in the control module, so you can watch what the camera captures on the screen. The robot may be operated with a wave of the hand thanks to a sensor module linked to an Arduino Nano microcontroller. The hand gesture based robot is better option to pick up the explosive items and diffusing
人类需求的扩大与越来越复杂的技术的发展相一致。这项研究主要集中在机械臂上，它每天都在工作中使用，以满足这些目标，使生活更简单。机械肢体可以与人类互动，也可以按照特定指令进行编程。执法人员在拆除爆炸物时面临高风险、高精度的挑战，因为任何失误都可能导致严重伤害或死亡。一种来自无线拆弹机器人的想法被提出作为解决这个问题的办法。机器人通过无线控制模块对爆炸物进行处理。发射器组成控制模块，接收器组成机器人本身。ARDUINO UNO R3板连接到接收器模块。发射器的输入控制着机器人的动作。液晶显示屏与控制模块中的无线视频接收器相连，因此您可以在屏幕上观看摄像机拍摄的内容。借助与Arduino纳米微控制器相连的传感器模块，机器人可以通过挥手来操作。基于手势的机器人是捡起爆炸性物品和扩散的更好选择
{"title":"Intellectual Design of Bomb Identification and Defusing Robot based on Logical Gesturing Mechanism","authors":"M. Shyam, M. Amalasweena, S. S., K. Balasaranya, R. Renugadevi, K. P. Chandran","doi":"10.1109/ACCAI58221.2023.10201034","DOIUrl":"https://doi.org/10.1109/ACCAI58221.2023.10201034","url":null,"abstract":"Expanding human wants coincide with the development of ever more sophisticated technologies. The majority of this study focuses on robotic arms, which are used daily in the job done to satisfy these objectives and make life simpler. Mechanical limbs can interact with humans or be programmed to follow specific instructions. Law enforcement officers face a high-stakes, high-precision challenge while neutralizing explosives, since any misstep might result in serious injury or death. An idea derived from wireless bomb disposal robots is offered as a solution to the issue. Through using wireless control module, the robot will dispose of the explosive. A transmitter makes up the control module, while a receiver makes up the Robot itself. An ARDUINO UNO R3 board connects to the receiver module. Input from the transmitter controls the Robot's actions. An LCD screen is connected to a wireless video receiver in the control module, so you can watch what the camera captures on the screen. The robot may be operated with a wave of the hand thanks to a sensor module linked to an Arduino Nano microcontroller. The hand gesture based robot is better option to pick up the explosive items and diffusing","PeriodicalId":382104,"journal":{"name":"2023 International Conference on Advances in Computing, Communication and Applied Informatics (ACCAI)","volume":"22 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-05-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125921307","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}
Osteoarthritis (OA) disease most caused in elderly people which causes muscle and skeleton system damage. [1] Early prediction of this disease helps to reduce its severity. This paper presents a decent literature review of different prediction models related to OA. Due to the availability of different technical algorithms, the image-based prediction to detect the presence of osteoarthritis is carried out from a dataset available on Kaggle. This work was carried out with different deep learning models like Efficient-V2L, MobileNet, VGG16, and GoogleNet. The findings justify that the Efficient-V2L model has obtained a good accuracy with 93.96% and performs well to predict OA when compared with other existing models.
{"title":"Osteoarthritis Disease Detection using Efficient Hyper-Tuning Parameters","authors":"Nagendra Panini Challa, Beebi Naseeba, Gudigntla Vyshnavi, Thanneeru Priyanka, Nagaraju Jajam, K. Prasanna","doi":"10.1109/ACCAI58221.2023.10200102","DOIUrl":"https://doi.org/10.1109/ACCAI58221.2023.10200102","url":null,"abstract":"Osteoarthritis (OA) disease most caused in elderly people which causes muscle and skeleton system damage. [1] Early prediction of this disease helps to reduce its severity. This paper presents a decent literature review of different prediction models related to OA. Due to the availability of different technical algorithms, the image-based prediction to detect the presence of osteoarthritis is carried out from a dataset available on Kaggle. This work was carried out with different deep learning models like Efficient-V2L, MobileNet, VGG16, and GoogleNet. The findings justify that the Efficient-V2L model has obtained a good accuracy with 93.96% and performs well to predict OA when compared with other existing models.","PeriodicalId":382104,"journal":{"name":"2023 International Conference on Advances in Computing, Communication and Applied Informatics (ACCAI)","volume":"2021 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-05-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115508565","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2023-05-25DOI: 10.1109/ACCAI58221.2023.10200194
S. Manikandan, A. G, Josiah Samuel Raj. J
The major aim of this study was to use MRI scans as a diagnostic tool for identifying strokes in the brain. K-Nearest Neighbors, an innovative alternative to the Support Vector Machine, was used to improve accuracy and specificity beyond what had been achieved before. K-Nearest Neighbors (with a total of 20 participants) and Support Vector Machines (with a total of 10 participants) are compared and contrasted here. (which had a total of 10 participants). Alpha = 0.05, the enrollment ratio = 0.1, 95% confidence interval, and pre-test power = 98% were used in conjunction with the G Power software to arrive at the final sample size. In comparison to the Support Vector Machine algorithm's 89% accuracy and 76% specificity, the unique K-Nearest Neighbors algorithm achieves 97% accuracy and 89% specificity using the proposed method. According to the data, the level of statistical significance attained for accuracy is p = 0.005, while the level of significance attained for specificity is p = 0.045. These results are provided in light of the research's conclusions. When comparing K-Nearest Neighbors to Support Vector Machine classifiers, the state-of-the-art K-Nearest Neighbors method outperformed its predecessors.
{"title":"Improved Accuracy in Early Identification of Ischaemic Stroke using K- Nearest Neighbors with Support Vector Machine","authors":"S. Manikandan, A. G, Josiah Samuel Raj. J","doi":"10.1109/ACCAI58221.2023.10200194","DOIUrl":"https://doi.org/10.1109/ACCAI58221.2023.10200194","url":null,"abstract":"The major aim of this study was to use MRI scans as a diagnostic tool for identifying strokes in the brain. K-Nearest Neighbors, an innovative alternative to the Support Vector Machine, was used to improve accuracy and specificity beyond what had been achieved before. K-Nearest Neighbors (with a total of 20 participants) and Support Vector Machines (with a total of 10 participants) are compared and contrasted here. (which had a total of 10 participants). Alpha = 0.05, the enrollment ratio = 0.1, 95% confidence interval, and pre-test power = 98% were used in conjunction with the G Power software to arrive at the final sample size. In comparison to the Support Vector Machine algorithm's 89% accuracy and 76% specificity, the unique K-Nearest Neighbors algorithm achieves 97% accuracy and 89% specificity using the proposed method. According to the data, the level of statistical significance attained for accuracy is p = 0.005, while the level of significance attained for specificity is p = 0.045. These results are provided in light of the research's conclusions. When comparing K-Nearest Neighbors to Support Vector Machine classifiers, the state-of-the-art K-Nearest Neighbors method outperformed its predecessors.","PeriodicalId":382104,"journal":{"name":"2023 International Conference on Advances in Computing, Communication and Applied Informatics (ACCAI)","volume":"39 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-05-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122398842","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2023-05-25DOI: 10.1109/ACCAI58221.2023.10199826
Dr. A. V. Sriharsha
The cracks, patches, and holes in Indian roads are a major problem. The issue slows down transportation and causes ineffective communication. In this study, we suggest a methodology for the creation and rollout of a model enabled by information and communication technologies for the rapid detection and repair of road damage in India. "Noise reduction,""parameter extraction from road photos," and a "classification framework" for identifying and maintaining road conditions may accomplish the complete task.
{"title":"Detection of Holes on Indian Roads Using Information and Communication Technologies","authors":"Dr. A. V. Sriharsha","doi":"10.1109/ACCAI58221.2023.10199826","DOIUrl":"https://doi.org/10.1109/ACCAI58221.2023.10199826","url":null,"abstract":"The cracks, patches, and holes in Indian roads are a major problem. The issue slows down transportation and causes ineffective communication. In this study, we suggest a methodology for the creation and rollout of a model enabled by information and communication technologies for the rapid detection and repair of road damage in India. \"Noise reduction,\"\"parameter extraction from road photos,\" and a \"classification framework\" for identifying and maintaining road conditions may accomplish the complete task.","PeriodicalId":382104,"journal":{"name":"2023 International Conference on Advances in Computing, Communication and Applied Informatics (ACCAI)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-05-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122494607","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2023-05-25DOI: 10.1109/ACCAI58221.2023.10199812
K. Shriraam, N. Deepa, Ezhil Grace. A
A manually tracking thus every employee's attendance usually produces issues including precision and employee productivity. The current study focuses on the research to develop an IP and geo tracking-based attendance system software. This suggested effort seeks to lessen Time complexity of an employee attendance when compared to an existing system like Biometrics. Materials and Methods: The study setup is in our University. Since it is an Attendance Application Since there are no human samples involved, there is no ethical approval. The current study focuses on the research to design and develop a software of IP and Geo Tracking based attendance system. Accuracy of the Employee attendance application is performed with two groups: IP and Geo tracking, and Biometrics of sample size (N=10), and G power is 80% threshold 0.05% , CI 95%. Results:- Its objective of the suggested article includes several characteristics and features of tracking employees, data management and monitoring and maintaining their records and providing information services. Independent sample T-Test was carried out using IP and Geo Tracking and Biometrics. IP and Geo Tracking (81.25%) performs better than Biometrics (79%). A statistically significant disparity exists between Geo Tracking and (p <0.01) 2- tailed. Conclusion: This type of attendance system has several components such that an employee’s mobile IP is monitored and GPS is tracked which reads the employee’s information and marks their attendance automatically.
{"title":"An Innovative Application for Employee Attendance using Near Field Communication to Reduce the Time Complexity using IP and Geo Tracking Comparing with Biometrics","authors":"K. Shriraam, N. Deepa, Ezhil Grace. A","doi":"10.1109/ACCAI58221.2023.10199812","DOIUrl":"https://doi.org/10.1109/ACCAI58221.2023.10199812","url":null,"abstract":"A manually tracking thus every employee's attendance usually produces issues including precision and employee productivity. The current study focuses on the research to develop an IP and geo tracking-based attendance system software. This suggested effort seeks to lessen Time complexity of an employee attendance when compared to an existing system like Biometrics. Materials and Methods: The study setup is in our University. Since it is an Attendance Application Since there are no human samples involved, there is no ethical approval. The current study focuses on the research to design and develop a software of IP and Geo Tracking based attendance system. Accuracy of the Employee attendance application is performed with two groups: IP and Geo tracking, and Biometrics of sample size (N=10), and G power is 80% threshold 0.05% , CI 95%. Results:- Its objective of the suggested article includes several characteristics and features of tracking employees, data management and monitoring and maintaining their records and providing information services. Independent sample T-Test was carried out using IP and Geo Tracking and Biometrics. IP and Geo Tracking (81.25%) performs better than Biometrics (79%). A statistically significant disparity exists between Geo Tracking and (p <0.01) 2- tailed. Conclusion: This type of attendance system has several components such that an employee’s mobile IP is monitored and GPS is tracked which reads the employee’s information and marks their attendance automatically.","PeriodicalId":382104,"journal":{"name":"2023 International Conference on Advances in Computing, Communication and Applied Informatics (ACCAI)","volume":"64 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-05-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122522069","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2023-05-25DOI: 10.1109/ACCAI58221.2023.10200647
Nikhil Mahesh, P. Pati, K. Deepa, Suresh Yanan
Predicting body fat percentage is essential for addressing the obesity problem. This paper compares the performance of several machine learning models based on Regression, to predict the body fat percentage. Using a dataset of 252 participants with information on age, weight, height, and fat percentage, the models were assessed based on multiple performance criteria, including Root Mean Squared Error (RMSE), Mean Absolute Error (MAE) and Mean Squared Error(MSE). The results demonstrates that Random Forest Regressor surpass other models with a lower RMSE of 0.276. These findings suggest that machine learning models can be a valuable tool for precise BFP, the use of machine learning provides a faster and more precise method for predicting body fat percentage. Overall, the study’s results suggest that machine learning models can be valuable tool for accurate body fat percentage prediction.
{"title":"Body Fat Prediction using Various Regression Techniques","authors":"Nikhil Mahesh, P. Pati, K. Deepa, Suresh Yanan","doi":"10.1109/ACCAI58221.2023.10200647","DOIUrl":"https://doi.org/10.1109/ACCAI58221.2023.10200647","url":null,"abstract":"Predicting body fat percentage is essential for addressing the obesity problem. This paper compares the performance of several machine learning models based on Regression, to predict the body fat percentage. Using a dataset of 252 participants with information on age, weight, height, and fat percentage, the models were assessed based on multiple performance criteria, including Root Mean Squared Error (RMSE), Mean Absolute Error (MAE) and Mean Squared Error(MSE). The results demonstrates that Random Forest Regressor surpass other models with a lower RMSE of 0.276. These findings suggest that machine learning models can be a valuable tool for precise BFP, the use of machine learning provides a faster and more precise method for predicting body fat percentage. Overall, the study’s results suggest that machine learning models can be valuable tool for accurate body fat percentage prediction.","PeriodicalId":382104,"journal":{"name":"2023 International Conference on Advances in Computing, Communication and Applied Informatics (ACCAI)","volume":"72 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-05-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128312496","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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