Pub Date : 2022-11-19DOI: 10.1109/ASSIC55218.2022.10088323
Madhumini Mohapatra, Ami Kumar Parida, P. Mallick, Neelamadhab Padhy
This study introduces a new method of disease prediction for mango leaves by breaking it down into four main steps: preprocessing, image segmentation, feature extraction, and disease prediction. Firstly, noise and other undesired artifacts are removed from the acquired raw image by median filtering & histogram equalization to improve the image's quality. The Otsu Threshold Method is then used to segment the preprocessed images. Then, from the segmented images, the most pertinent Texture Features Extraction are made, such as the Upgraded local binary pattern (ULBP) and grey level co-occurrence matrix (GLCM), colour features and pixel features. The framework for detecting mango leaf disease uses these features as input, and it is represented by an improved recurrent neural network (RNN). Additionally, the weight function of the improved RNN will be fine-tuned by employing Arithmetic Operators Customized with Dingoes Optimization (AOCDO) to improve the accuracy of illness identification. The traditional Arithmetic Optimization Algorithm (AOA) and the dingo optimizer are combined to create the new hybrid optimization model (DOX). A comparative assessment is also conducted to confirm the effectiveness of the proposed AOCDO+RNN model.
{"title":"Mango Leaf Disease Detection Based on Deep Learning Approach","authors":"Madhumini Mohapatra, Ami Kumar Parida, P. Mallick, Neelamadhab Padhy","doi":"10.1109/ASSIC55218.2022.10088323","DOIUrl":"https://doi.org/10.1109/ASSIC55218.2022.10088323","url":null,"abstract":"This study introduces a new method of disease prediction for mango leaves by breaking it down into four main steps: preprocessing, image segmentation, feature extraction, and disease prediction. Firstly, noise and other undesired artifacts are removed from the acquired raw image by median filtering & histogram equalization to improve the image's quality. The Otsu Threshold Method is then used to segment the preprocessed images. Then, from the segmented images, the most pertinent Texture Features Extraction are made, such as the Upgraded local binary pattern (ULBP) and grey level co-occurrence matrix (GLCM), colour features and pixel features. The framework for detecting mango leaf disease uses these features as input, and it is represented by an improved recurrent neural network (RNN). Additionally, the weight function of the improved RNN will be fine-tuned by employing Arithmetic Operators Customized with Dingoes Optimization (AOCDO) to improve the accuracy of illness identification. The traditional Arithmetic Optimization Algorithm (AOA) and the dingo optimizer are combined to create the new hybrid optimization model (DOX). A comparative assessment is also conducted to confirm the effectiveness of the proposed AOCDO+RNN model.","PeriodicalId":441406,"journal":{"name":"2022 International Conference on Advancements in Smart, Secure and Intelligent Computing (ASSIC)","volume":"28 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-11-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116602437","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}
With the big data revolution, medical organizations are turning to machine learning and predictive analytics to make data-driven decisions and improve patient outcomes. Early predictions can help prevent the progression of diseases. It allows healthcare businesses to take quick actions in time and avoid the long-term effects of epidemics. A tool can be set up to predict and create a risk score based on different datasets. In the proposed model how various ensembling techniques affects the results over machine learning algorithms is observed. The suggested model uses various models like Support vector classifier, Hyper parameter tuned Support vector classifier, Naive Bayes and Decision tree are used to perform the predictive analysis. Later these models are compared with models using the ensemble techniques. By doing so the process of decision making got much easier. This helped the overall process of predictive analysis by giving better predictions of diseases by outperforming the accuracy of single classifier models which gave the maximum accuracy of 95%. The proposed models using ensemble learning gave accuracy of 99%.
{"title":"Predictive analysis of multiple diseases using ensemble learning","authors":"P. Ghadekar, Khushi Jhanwar, Ameya Karpe, Tanishka Shetty, Akash Sivanandan, Prannay Khushalani","doi":"10.1109/ASSIC55218.2022.10088335","DOIUrl":"https://doi.org/10.1109/ASSIC55218.2022.10088335","url":null,"abstract":"With the big data revolution, medical organizations are turning to machine learning and predictive analytics to make data-driven decisions and improve patient outcomes. Early predictions can help prevent the progression of diseases. It allows healthcare businesses to take quick actions in time and avoid the long-term effects of epidemics. A tool can be set up to predict and create a risk score based on different datasets. In the proposed model how various ensembling techniques affects the results over machine learning algorithms is observed. The suggested model uses various models like Support vector classifier, Hyper parameter tuned Support vector classifier, Naive Bayes and Decision tree are used to perform the predictive analysis. Later these models are compared with models using the ensemble techniques. By doing so the process of decision making got much easier. This helped the overall process of predictive analysis by giving better predictions of diseases by outperforming the accuracy of single classifier models which gave the maximum accuracy of 95%. The proposed models using ensemble learning gave accuracy of 99%.","PeriodicalId":441406,"journal":{"name":"2022 International Conference on Advancements in Smart, Secure and Intelligent Computing (ASSIC)","volume":"92 6","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-11-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"120854666","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 : 2022-11-19DOI: 10.1109/ASSIC55218.2022.10088327
Lewlisa Saha, H. K. Tripathy, K. Shaalan
The ultimate goal in designing and recommending an appropriate tariff plan is to be able to predict customers' behavioral patterns in light of the current situation in the telecommunications market. The clients' behavioral patterns and their background in terms of demographics are quite important. The study model put forth in this paper uses a variety of machine learning techniques to anticipate customers' behavioral patterns based on their demographic information. The model was developed after researching a number of classification-based machine learning techniques, including some ensemble techniques like random forest, adaboost, gradient boosting machine, extreme gradient boosting, bagging, and stacking, as well as more conventional ones like decision tree, k-nearest neighbor, logistic regression, and artificial neural networks. Understanding consumer needs is important, but the telecommunications business also needs to be able to anticipate customer attrition. The goal is to use the same research methodology to anticipate customer turnover rates more accurately while maintaining profit. With the suggested model's ability to function on many dataset types, the main goal has been accomplished.
{"title":"Adaptable model based on ensemble learning for different telecommunication data","authors":"Lewlisa Saha, H. K. Tripathy, K. Shaalan","doi":"10.1109/ASSIC55218.2022.10088327","DOIUrl":"https://doi.org/10.1109/ASSIC55218.2022.10088327","url":null,"abstract":"The ultimate goal in designing and recommending an appropriate tariff plan is to be able to predict customers' behavioral patterns in light of the current situation in the telecommunications market. The clients' behavioral patterns and their background in terms of demographics are quite important. The study model put forth in this paper uses a variety of machine learning techniques to anticipate customers' behavioral patterns based on their demographic information. The model was developed after researching a number of classification-based machine learning techniques, including some ensemble techniques like random forest, adaboost, gradient boosting machine, extreme gradient boosting, bagging, and stacking, as well as more conventional ones like decision tree, k-nearest neighbor, logistic regression, and artificial neural networks. Understanding consumer needs is important, but the telecommunications business also needs to be able to anticipate customer attrition. The goal is to use the same research methodology to anticipate customer turnover rates more accurately while maintaining profit. With the suggested model's ability to function on many dataset types, the main goal has been accomplished.","PeriodicalId":441406,"journal":{"name":"2022 International Conference on Advancements in Smart, Secure and Intelligent Computing (ASSIC)","volume":"27 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-11-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115491143","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 : 2022-11-19DOI: 10.1109/ASSIC55218.2022.10088413
Madhavi Tota, S. Karmore
The area of large collection of Information is developing at an upsetting rate. The extreme usage of Person-to-person communication Locales, combination of Information from Sensors for assessment and assumption for future events, improvement in Consumer loyalty on Web based Shopping entrances by observing their previous way of behaving and giving them data, things and offers of their advantage momentarily, and so on had prompted this ascent in the field of Big Data. Security of Information and Protection of Client is of particular interest and high significance for people, industry and the scholarly world. Everybody guarantee that their Sensitive data should be avoided unapproved access and their resources should be remained careful from security breaks. Protection and Security are likewise similarly significant for Huge Information and here, ensuring the Protection and Security is common and complex, as how much information is colossal. One potential choice to actually and proficiently handle, process and dissected the Large Information is to use AI techniques. Simulated intelligence strategies are clear; applying them on Large Information requires objective of various issues and is a troublesome endeavor, as the size of Information is as well enormous. The proposed work is connected with further develop protection and security issues and hazard at various phases of Big Data.
{"title":"Security and Privacy Preserving in Big Data","authors":"Madhavi Tota, S. Karmore","doi":"10.1109/ASSIC55218.2022.10088413","DOIUrl":"https://doi.org/10.1109/ASSIC55218.2022.10088413","url":null,"abstract":"The area of large collection of Information is developing at an upsetting rate. The extreme usage of Person-to-person communication Locales, combination of Information from Sensors for assessment and assumption for future events, improvement in Consumer loyalty on Web based Shopping entrances by observing their previous way of behaving and giving them data, things and offers of their advantage momentarily, and so on had prompted this ascent in the field of Big Data. Security of Information and Protection of Client is of particular interest and high significance for people, industry and the scholarly world. Everybody guarantee that their Sensitive data should be avoided unapproved access and their resources should be remained careful from security breaks. Protection and Security are likewise similarly significant for Huge Information and here, ensuring the Protection and Security is common and complex, as how much information is colossal. One potential choice to actually and proficiently handle, process and dissected the Large Information is to use AI techniques. Simulated intelligence strategies are clear; applying them on Large Information requires objective of various issues and is a troublesome endeavor, as the size of Information is as well enormous. The proposed work is connected with further develop protection and security issues and hazard at various phases of Big Data.","PeriodicalId":441406,"journal":{"name":"2022 International Conference on Advancements in Smart, Secure and Intelligent Computing (ASSIC)","volume":"99 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-11-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122411918","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 : 2022-11-19DOI: 10.1109/ASSIC55218.2022.10088292
C. Vaishnavi, Suja Palaniswamy
Emotion recognition is one of the most important application of computer vision and artificial intelligence. Academic and online teaching institutes must be able to recognize emotion of students from classroom video. This helps to determine the attitude of the students and also devise techniques to engage students that makes learning an interesting activity. This paper presents work on emotion recognition from online classroom videos using layer based Convolutional Neural Networks (CNN) and Siamese Neural Network. The proposed method for emotion recognition is named as SNSER (Siamese Network for Student Emotion Recognition Model). For training the model CAFE dataset is used and an accuracy of 80% is obtained. Neutral, Anger, Happy, Surprise, Sad, Fear, and Disgust are the emotions considered for training the model. In addition to these 7 basic emotions used during training, boring and confused are also included for testing.
情感识别是计算机视觉和人工智能的重要应用之一。学术和在线教学机构必须能够从课堂视频中识别学生的情绪。这有助于确定学生的态度，并设计出吸引学生的技术，使学习成为一种有趣的活动。本文介绍了使用基于层的卷积神经网络(CNN)和暹罗神经网络对在线课堂视频进行情感识别的工作。提出的情感识别方法被命名为SNSER (Siamese Network for Student emotion recognition Model)。对于模型的训练，使用CAFE数据集，获得了80%的准确率。中性、愤怒、快乐、惊讶、悲伤、恐惧和厌恶是训练模型所考虑的情绪。除了训练中使用的这7种基本情绪外，无聊和困惑也包括在测试中。
{"title":"Emotion Recognition From Online Classroom Videos Using Meta Learning","authors":"C. Vaishnavi, Suja Palaniswamy","doi":"10.1109/ASSIC55218.2022.10088292","DOIUrl":"https://doi.org/10.1109/ASSIC55218.2022.10088292","url":null,"abstract":"Emotion recognition is one of the most important application of computer vision and artificial intelligence. Academic and online teaching institutes must be able to recognize emotion of students from classroom video. This helps to determine the attitude of the students and also devise techniques to engage students that makes learning an interesting activity. This paper presents work on emotion recognition from online classroom videos using layer based Convolutional Neural Networks (CNN) and Siamese Neural Network. The proposed method for emotion recognition is named as SNSER (Siamese Network for Student Emotion Recognition Model). For training the model CAFE dataset is used and an accuracy of 80% is obtained. Neutral, Anger, Happy, Surprise, Sad, Fear, and Disgust are the emotions considered for training the model. In addition to these 7 basic emotions used during training, boring and confused are also included for testing.","PeriodicalId":441406,"journal":{"name":"2022 International Conference on Advancements in Smart, Secure and Intelligent Computing (ASSIC)","volume":"59 5","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-11-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114039989","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 : 2022-11-19DOI: 10.1109/ASSIC55218.2022.10088334
S. K. Mohapatra, Sushruta Mishra, H. K. Tripathy
As with economic growth and urbanization, there is a significant impact on energy consumption in residential and commercial buildings. Analyzing the energy consumption of buildings is not a simple task to perform so it's much necessary to design an effective building energy management system, which can be helpful to evaluate the energy efficiency of different building structures. Recently, artificial intelligence, machine learning, and deep learning models have become most useful in the field of prediction and forecasting. This research presents a unique deep learning model using LSTM and GRU recurrent neural network (RNN) to predict the exact pattern of time series data for predicting building appliances energy consumption. The model is trained for the required features and evaluated by comparing the actual and predicted values. We have performed the analysis using a benchmark appliance energy data set and have taken metrics such as error rate, loss value, mean squared error (MSE), mean absolute error (MAE), mean absolute percentage error (MAPE) value, prediction accuracy, and model accuracy to evaluate the performance of the model. The outcome of this work shows that GRU exhibits better performance and achieved the minimum value of root mean square error and model loss.
{"title":"Energy Consumption Prediction in Electrical Appliances of Commercial Buildings Using LSTM-GRU Model","authors":"S. K. Mohapatra, Sushruta Mishra, H. K. Tripathy","doi":"10.1109/ASSIC55218.2022.10088334","DOIUrl":"https://doi.org/10.1109/ASSIC55218.2022.10088334","url":null,"abstract":"As with economic growth and urbanization, there is a significant impact on energy consumption in residential and commercial buildings. Analyzing the energy consumption of buildings is not a simple task to perform so it's much necessary to design an effective building energy management system, which can be helpful to evaluate the energy efficiency of different building structures. Recently, artificial intelligence, machine learning, and deep learning models have become most useful in the field of prediction and forecasting. This research presents a unique deep learning model using LSTM and GRU recurrent neural network (RNN) to predict the exact pattern of time series data for predicting building appliances energy consumption. The model is trained for the required features and evaluated by comparing the actual and predicted values. We have performed the analysis using a benchmark appliance energy data set and have taken metrics such as error rate, loss value, mean squared error (MSE), mean absolute error (MAE), mean absolute percentage error (MAPE) value, prediction accuracy, and model accuracy to evaluate the performance of the model. The outcome of this work shows that GRU exhibits better performance and achieved the minimum value of root mean square error and model loss.","PeriodicalId":441406,"journal":{"name":"2022 International Conference on Advancements in Smart, Secure and Intelligent Computing (ASSIC)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-11-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129833903","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 : 2022-11-19DOI: 10.1109/ASSIC55218.2022.10088375
Farzan Mohammed, Nor Azlina Abd Rahman, Yusnita Yusof, Julia Juremi
Information gathering is one of the most important methodologies within Cybersecurity allowing pen-testers and security researchers to find information about a host or a network. Nmap is one of the most popular information gathering tools for finding information about a network or host and it is a highly versatile tool which can be fine grained using the command line. Now for new students, beginners or script kiddies that come into cybersecurity fail to use the full functionality of Nmap or fail to continue forward due the vast versatility of Nmap. This paper documents how a toolkit based on Nmap is automated to help in achieving the same results but made so much easier for the user.
{"title":"Automated Nmap Toolkit","authors":"Farzan Mohammed, Nor Azlina Abd Rahman, Yusnita Yusof, Julia Juremi","doi":"10.1109/ASSIC55218.2022.10088375","DOIUrl":"https://doi.org/10.1109/ASSIC55218.2022.10088375","url":null,"abstract":"Information gathering is one of the most important methodologies within Cybersecurity allowing pen-testers and security researchers to find information about a host or a network. Nmap is one of the most popular information gathering tools for finding information about a network or host and it is a highly versatile tool which can be fine grained using the command line. Now for new students, beginners or script kiddies that come into cybersecurity fail to use the full functionality of Nmap or fail to continue forward due the vast versatility of Nmap. This paper documents how a toolkit based on Nmap is automated to help in achieving the same results but made so much easier for the user.","PeriodicalId":441406,"journal":{"name":"2022 International Conference on Advancements in Smart, Secure and Intelligent Computing (ASSIC)","volume":"42 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-11-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128652411","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}
Chatbots improve a customer relationship by re-sponding to requests faster and meeting their expectations at the same time. In our paper, we investigate the increasing psychological problems, the pressure, and the lack of time for some people due to the rapid development of the IT era. We propose a knowledge-based chatbot that interacts with end users using natural language input. It is trained in advance to detect the user's psychological situation (fear, anger,…) and suggests solutions to overcome this.
{"title":"Psychological Advisor Chatbot","authors":"Arwa AlOtaibi, Khouloud AlFif, Emtinan AlHuthaili, Fatma Masmoudi, Elham Kariri","doi":"10.1109/ASSIC55218.2022.10088307","DOIUrl":"https://doi.org/10.1109/ASSIC55218.2022.10088307","url":null,"abstract":"Chatbots improve a customer relationship by re-sponding to requests faster and meeting their expectations at the same time. In our paper, we investigate the increasing psychological problems, the pressure, and the lack of time for some people due to the rapid development of the IT era. We propose a knowledge-based chatbot that interacts with end users using natural language input. It is trained in advance to detect the user's psychological situation (fear, anger,…) and suggests solutions to overcome this.","PeriodicalId":441406,"journal":{"name":"2022 International Conference on Advancements in Smart, Secure and Intelligent Computing (ASSIC)","volume":"10 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-11-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129150691","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 : 2022-11-19DOI: 10.1109/ASSIC55218.2022.10088363
Utkrisht Singh, A. Jena, Mohammed Taha Haque
A stroke is an illness that results in traumatic brain injury by tearing blood vessels. A brain stroke can also occur if blood flow and other nutrients to the brain are interrupted abruptly. It is one of the major global causes of disability and death, as per the report given by the World Health Organization (WHO). With increased convergence amongst technology and medical diagnosis, practitioners create possibilities for improved management of patients by comprehensively quarrying as well as archiving patient's records containing their medical background. As a result, it becomes critical to investigate the interdependence of these factors (risk) in patient's medical records and comprehend the relative impact of these factors for the prediction of brain stroke. This research establishes an early estimation of stroke diseases by combining the existence of hypertension, heart disease, body mass index, smoking status, prior stroke, age, and some other feature attributes. For forecasting the stroke, various statistical methods and five different ML models including some ensemble learning techniques like Support Vector Machine (SVM), Random Forest (RF), Ada-Boost Classifier (ABC), Decision Tree Classifier (DTC), and XG-Boost Classifier (XGB) were used to train the feature attributes. Furthermore, the proposed research work has accomplished an accuracy of 95.08 percent, with the XG-Boost Classifier outperforming the Machine Learning (ML) Models. As a result, XG-Boost is nearly the most preferable classifier for predicting strokes, which can be used as a reference model by physicians and also used by patients considering aid in the early detection of a potential stroke.
{"title":"An Ensemble Learning Approach and Analysis for Stroke Prediction Dataset","authors":"Utkrisht Singh, A. Jena, Mohammed Taha Haque","doi":"10.1109/ASSIC55218.2022.10088363","DOIUrl":"https://doi.org/10.1109/ASSIC55218.2022.10088363","url":null,"abstract":"A stroke is an illness that results in traumatic brain injury by tearing blood vessels. A brain stroke can also occur if blood flow and other nutrients to the brain are interrupted abruptly. It is one of the major global causes of disability and death, as per the report given by the World Health Organization (WHO). With increased convergence amongst technology and medical diagnosis, practitioners create possibilities for improved management of patients by comprehensively quarrying as well as archiving patient's records containing their medical background. As a result, it becomes critical to investigate the interdependence of these factors (risk) in patient's medical records and comprehend the relative impact of these factors for the prediction of brain stroke. This research establishes an early estimation of stroke diseases by combining the existence of hypertension, heart disease, body mass index, smoking status, prior stroke, age, and some other feature attributes. For forecasting the stroke, various statistical methods and five different ML models including some ensemble learning techniques like Support Vector Machine (SVM), Random Forest (RF), Ada-Boost Classifier (ABC), Decision Tree Classifier (DTC), and XG-Boost Classifier (XGB) were used to train the feature attributes. Furthermore, the proposed research work has accomplished an accuracy of 95.08 percent, with the XG-Boost Classifier outperforming the Machine Learning (ML) Models. As a result, XG-Boost is nearly the most preferable classifier for predicting strokes, which can be used as a reference model by physicians and also used by patients considering aid in the early detection of a potential stroke.","PeriodicalId":441406,"journal":{"name":"2022 International Conference on Advancements in Smart, Secure and Intelligent Computing (ASSIC)","volume":"26 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-11-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124563700","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 : 2022-11-19DOI: 10.1109/ASSIC55218.2022.10088407
Arpita Nayak, I. Satpathy, Bhabani S. Mishra, B. Patnaik, B. Das
This review article's objective is to describe how one of the major advantages of biometric technology, which involves identifying and verifying people by examining their bodily characteristics, in employee attendance helps in better functioning of HR practices. The majority of biometric technology users struggle with the challenge of choosing an precise and cost-effective biometric system in addressing specific issues in a given environment, despite the many benefits of the biometric system and its influence on many job sectors throughout the world. To improve the conventional staff attendance system, which currently has an impact on the organization's efficiency, this article investigates the biometric attendance identifier that may be utilized. Implementing a qualitative (exploratory) technique, the study was conducted. It. is purely exploratory research that provides comprehensive details on biometrics, biometric attendance systems, and their application to Talent Management. Nevertheless, the study demonstrates that a biometric identifier is efficient and cost-effective for the organization's human resources attendance management system as an element of HR procedures, which implies that consideration is given before suggesting the use of biometric technology to improve the effectiveness of business operations in a firm.
{"title":"Biometric A Helping Hand in Talent Management: A Modern Time Tracking Tool","authors":"Arpita Nayak, I. Satpathy, Bhabani S. Mishra, B. Patnaik, B. Das","doi":"10.1109/ASSIC55218.2022.10088407","DOIUrl":"https://doi.org/10.1109/ASSIC55218.2022.10088407","url":null,"abstract":"This review article's objective is to describe how one of the major advantages of biometric technology, which involves identifying and verifying people by examining their bodily characteristics, in employee attendance helps in better functioning of HR practices. The majority of biometric technology users struggle with the challenge of choosing an precise and cost-effective biometric system in addressing specific issues in a given environment, despite the many benefits of the biometric system and its influence on many job sectors throughout the world. To improve the conventional staff attendance system, which currently has an impact on the organization's efficiency, this article investigates the biometric attendance identifier that may be utilized. Implementing a qualitative (exploratory) technique, the study was conducted. It. is purely exploratory research that provides comprehensive details on biometrics, biometric attendance systems, and their application to Talent Management. Nevertheless, the study demonstrates that a biometric identifier is efficient and cost-effective for the organization's human resources attendance management system as an element of HR procedures, which implies that consideration is given before suggesting the use of biometric technology to improve the effectiveness of business operations in a firm.","PeriodicalId":441406,"journal":{"name":"2022 International Conference on Advancements in Smart, Secure and Intelligent Computing (ASSIC)","volume":"64 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-11-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124611820","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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