Pub Date : 2023-01-05DOI: 10.1109/IDCIoT56793.2023.10053418
Shahnawaz Ayoub, N. Behera, Meena Naga Raju, Pankaj Singh, S. Praveena, R. K.
Medical image classifiers roles a crucial play in medical service and teaching tasks. But the classical approach obtained its ceiling on performance. Besides, from their use, much longer and more effort require spent on extracted and selected classifier features. The Deep Neural Network (DNN) is a developing Machine Learning (ML) approach which is verified their potential for distinct classifier tasks. Especially, the Convolutional Neural Network (CNN) leads to optimum outcomes on distinct image classifier tasks. But medical image databases can be hard for collecting as it requires several professional skills to categorize them. This study develops a new Hyperparameter Tuned Deep Learning Model for Healthcare Monitoring Systems (HPTDLM-HMS) in big data environment. The presented HPTDLM-HMS technique concentrates on the examination of medical images in the decision-making process. Initially, the presented HPTDLM-HMS technique derives features using EfficientNet model with Manta Ray Foraging Optimization (MRFO) algorithm as hyperparameter tuner. At last, the classification of medical images takes place by Long Short-Term Memory (LSTM) method. To handle big data, Hadoop MapReduce is utilized. The result analysis of the HPTDLM-HMS technique is tested on medical imaging dataset. The comprehensive study of the HPTDLM-HMS technique highlighted and gives recall value of 87.46% is higher when compared to its promising outcomes over other models.
{"title":"Hyperparameter Tuned Deep Learning Model for Healthcare Monitoring System in Big Data","authors":"Shahnawaz Ayoub, N. Behera, Meena Naga Raju, Pankaj Singh, S. Praveena, R. K.","doi":"10.1109/IDCIoT56793.2023.10053418","DOIUrl":"https://doi.org/10.1109/IDCIoT56793.2023.10053418","url":null,"abstract":"Medical image classifiers roles a crucial play in medical service and teaching tasks. But the classical approach obtained its ceiling on performance. Besides, from their use, much longer and more effort require spent on extracted and selected classifier features. The Deep Neural Network (DNN) is a developing Machine Learning (ML) approach which is verified their potential for distinct classifier tasks. Especially, the Convolutional Neural Network (CNN) leads to optimum outcomes on distinct image classifier tasks. But medical image databases can be hard for collecting as it requires several professional skills to categorize them. This study develops a new Hyperparameter Tuned Deep Learning Model for Healthcare Monitoring Systems (HPTDLM-HMS) in big data environment. The presented HPTDLM-HMS technique concentrates on the examination of medical images in the decision-making process. Initially, the presented HPTDLM-HMS technique derives features using EfficientNet model with Manta Ray Foraging Optimization (MRFO) algorithm as hyperparameter tuner. At last, the classification of medical images takes place by Long Short-Term Memory (LSTM) method. To handle big data, Hadoop MapReduce is utilized. The result analysis of the HPTDLM-HMS technique is tested on medical imaging dataset. The comprehensive study of the HPTDLM-HMS technique highlighted and gives recall value of 87.46% is higher when compared to its promising outcomes over other models.","PeriodicalId":60583,"journal":{"name":"物联网技术","volume":"13 1","pages":"281-287"},"PeriodicalIF":0.0,"publicationDate":"2023-01-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"86761580","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 : 2023-01-05DOI: 10.1109/IDCIoT56793.2023.10053524
W. Rajan Babu, M. Sundaram, A. Kavithamani, S. Sam Karthik, N. Abinaya, V. Bharath Choudry
Most of the machines are driven by Induction motor nowadays. Induction motor gets failure due to various reasons. This fault mostly occurs in the stator. By measuring the current of a motor and comparing it to a fixed value, any fault can be detected. Different kind of faults exhibits different types of electrical current profile. The nature of this current is measured by a Power Quality Analyzer and converted into waveforms and spectrums. By looking closely at these three-phase current readings, one can predict when a machine is about to fail. Motor Stator Current Profile (MSCP) based method is proposed to identify the different stator faults.
{"title":"MSCP Based Stator Fault Identification in Induction Motor Using Power Quality Analyzer","authors":"W. Rajan Babu, M. Sundaram, A. Kavithamani, S. Sam Karthik, N. Abinaya, V. Bharath Choudry","doi":"10.1109/IDCIoT56793.2023.10053524","DOIUrl":"https://doi.org/10.1109/IDCIoT56793.2023.10053524","url":null,"abstract":"Most of the machines are driven by Induction motor nowadays. Induction motor gets failure due to various reasons. This fault mostly occurs in the stator. By measuring the current of a motor and comparing it to a fixed value, any fault can be detected. Different kind of faults exhibits different types of electrical current profile. The nature of this current is measured by a Power Quality Analyzer and converted into waveforms and spectrums. By looking closely at these three-phase current readings, one can predict when a machine is about to fail. Motor Stator Current Profile (MSCP) based method is proposed to identify the different stator faults.","PeriodicalId":60583,"journal":{"name":"物联网技术","volume":"33 1","pages":"1001-1005"},"PeriodicalIF":0.0,"publicationDate":"2023-01-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"85655892","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 : 2023-01-05DOI: 10.1109/IDCIoT56793.2023.10053459
Surekha Chalnewad, Arati Manjaramkar
A license plate is alphanumeric rectangular plate. It is fixed on the vehicle and used for identification of the vehicle. Generally, huge numbers of vehicles move-on the road which is the major issue of concern in identifying the vehicle(s) owner, registration place of vehicle, address, etc. The automatic license plate detection is one of the solutions for such kind of problems. There are numerous methodologies available for license plate detection, but certain factors like speed of vehicles, language used on license plate, non-uniform letter effects on license plate, etc. makes the task of recognition difficult. The license plate detection system has many applications like payment of parking fees; toll fee on the highway; traffic monitoring system; border security system; signal system, etc. This research work proposes a novel license plate detection technique with the extension of Sobel mask. In proposed system, first step is acquisition of image. Second step is to detect the vehicle from the acquired image. In third step, segmentation of license plate from vehicle image is done. Finally, neural network classifier is used to classify the vehicle(s) license plate. The proposed system gives promising, robust, and efficient results for license plate detection. Proposed system achieves accuracy of 98% is achieved in detecting the license plate.
{"title":"Detection and Classification of License Plate by Neural Network Classifier","authors":"Surekha Chalnewad, Arati Manjaramkar","doi":"10.1109/IDCIoT56793.2023.10053459","DOIUrl":"https://doi.org/10.1109/IDCIoT56793.2023.10053459","url":null,"abstract":"A license plate is alphanumeric rectangular plate. It is fixed on the vehicle and used for identification of the vehicle. Generally, huge numbers of vehicles move-on the road which is the major issue of concern in identifying the vehicle(s) owner, registration place of vehicle, address, etc. The automatic license plate detection is one of the solutions for such kind of problems. There are numerous methodologies available for license plate detection, but certain factors like speed of vehicles, language used on license plate, non-uniform letter effects on license plate, etc. makes the task of recognition difficult. The license plate detection system has many applications like payment of parking fees; toll fee on the highway; traffic monitoring system; border security system; signal system, etc. This research work proposes a novel license plate detection technique with the extension of Sobel mask. In proposed system, first step is acquisition of image. Second step is to detect the vehicle from the acquired image. In third step, segmentation of license plate from vehicle image is done. Finally, neural network classifier is used to classify the vehicle(s) license plate. The proposed system gives promising, robust, and efficient results for license plate detection. Proposed system achieves accuracy of 98% is achieved in detecting the license plate.","PeriodicalId":60583,"journal":{"name":"物联网技术","volume":"41 1","pages":"531-535"},"PeriodicalIF":0.0,"publicationDate":"2023-01-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"79844832","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 : 2023-01-05DOI: 10.1109/IDCIoT56793.2023.10053471
Jayashree M, Rachana P, Ashin Kunjumon, Meena Thamban, Athul Roy
Nowadays when a accident occurs people are afraid or create a major chaos while informing the emergency services, or a accident gets unnoticed and eventually when the emergency services arrive its too late. Using the already in-place and functioning CCTV infrastructure, a complete system has been developed to actively detect all kinds of accidents on the road and alert the necessary personal, for a accident the nearest police station, hospitals, general ambulances and the registrant of the vehicle in accident and their emergency contacts, for a hit and run case the vehicle number of the accused vehicle can be provided to the police.
{"title":"Convolutional Neural Networks (CNN)-based Vehicle Crash Detection and Alert System","authors":"Jayashree M, Rachana P, Ashin Kunjumon, Meena Thamban, Athul Roy","doi":"10.1109/IDCIoT56793.2023.10053471","DOIUrl":"https://doi.org/10.1109/IDCIoT56793.2023.10053471","url":null,"abstract":"Nowadays when a accident occurs people are afraid or create a major chaos while informing the emergency services, or a accident gets unnoticed and eventually when the emergency services arrive its too late. Using the already in-place and functioning CCTV infrastructure, a complete system has been developed to actively detect all kinds of accidents on the road and alert the necessary personal, for a accident the nearest police station, hospitals, general ambulances and the registrant of the vehicle in accident and their emergency contacts, for a hit and run case the vehicle number of the accused vehicle can be provided to the police.","PeriodicalId":60583,"journal":{"name":"物联网技术","volume":"48 1","pages":"161-164"},"PeriodicalIF":0.0,"publicationDate":"2023-01-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"80743537","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 : 2023-01-05DOI: 10.1109/IDCIoT56793.2023.10053391
Mahima R, M. M, Manjari K, Rovenal S, K. S, Sruthi M. P
Traffic-related injuries and deaths are a serious problem that all industrialized nations are dealing with today. Object recognition techniques are employed in this study to develop a low cost and simple solution for automated detection and tracking on highways in order to avoid animal-vehicle collisions. In real-world units, a technique for measuring the animal distances from the camera mounted vehicle is also developed. Wild animal monitoring in their natural settings must be efficient and trustworthy in order to update manage decisions. Because of their effectiveness and accuracy in capturing wildlife data in an inconspicuous, continuous, and massive volume, automatic covert camera traps or cameras are becoming extremely popular as a tool for monitoring wildlife. Hand-taking a massive number of photos and films from camera setups is very costly and tedious. It is a significant barrier for researchers and environmental scientists who want to observe wildlife in a natural setting. This research presents a structure for developing automated animal detection in the wild, with the goal of creating an automated wildlife monitoring system, based on current breakthroughs in deep learning methods. In aspects of recognition, the suggested method attains a total precision of about 85.51 percent.
{"title":"Highway Collision Avoidance by Detection of Animal’s Images","authors":"Mahima R, M. M, Manjari K, Rovenal S, K. S, Sruthi M. P","doi":"10.1109/IDCIoT56793.2023.10053391","DOIUrl":"https://doi.org/10.1109/IDCIoT56793.2023.10053391","url":null,"abstract":"Traffic-related injuries and deaths are a serious problem that all industrialized nations are dealing with today. Object recognition techniques are employed in this study to develop a low cost and simple solution for automated detection and tracking on highways in order to avoid animal-vehicle collisions. In real-world units, a technique for measuring the animal distances from the camera mounted vehicle is also developed. Wild animal monitoring in their natural settings must be efficient and trustworthy in order to update manage decisions. Because of their effectiveness and accuracy in capturing wildlife data in an inconspicuous, continuous, and massive volume, automatic covert camera traps or cameras are becoming extremely popular as a tool for monitoring wildlife. Hand-taking a massive number of photos and films from camera setups is very costly and tedious. It is a significant barrier for researchers and environmental scientists who want to observe wildlife in a natural setting. This research presents a structure for developing automated animal detection in the wild, with the goal of creating an automated wildlife monitoring system, based on current breakthroughs in deep learning methods. In aspects of recognition, the suggested method attains a total precision of about 85.51 percent.","PeriodicalId":60583,"journal":{"name":"物联网技术","volume":"11 1","pages":"307-311"},"PeriodicalIF":0.0,"publicationDate":"2023-01-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"82821596","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 : 2023-01-05DOI: 10.1109/IDCIoT56793.2023.10053429
C. M. Raj, W. R. Babu, Karparthi Uday, R. Senthilkumar, V. Sudha, V. Anandhakumar
In logistics services, measuring of volumetric weight is done manually. In order to reduce manpower, automation is required. Here we use Programmable Logic Controller (PLC) and RV-2FB-Q Series robotic arm for automation. PLC system helps in operating pick & place robotic arm, processing the data acquired through ultrasonic sensors, Light Detecting Resistor, load cell and also controlling (ON/OFF) PMDC motor attached with conveyor belt based on inductive proximity sensor signal. The charges for customers are calculated based on different volumetric weights. Using HMI (Human Machine Interface) a bill is projected to customers. In the end, the right amount for proper volumetric weight can be calculated and collected from the customers. Furthermore, with the use of PLC, the logistics management can be monitored and being connected to other hardware application which improve the operation of parcelling. Hence, customer reliability can be improved and also computation of volumetric weight can be done precisely. Finally, our project leads to achieve industry 4.0
{"title":"Smart Dimensional Measurement and Material Transportation (SDMMT) System using Artificial Intelligence","authors":"C. M. Raj, W. R. Babu, Karparthi Uday, R. Senthilkumar, V. Sudha, V. Anandhakumar","doi":"10.1109/IDCIoT56793.2023.10053429","DOIUrl":"https://doi.org/10.1109/IDCIoT56793.2023.10053429","url":null,"abstract":"In logistics services, measuring of volumetric weight is done manually. In order to reduce manpower, automation is required. Here we use Programmable Logic Controller (PLC) and RV-2FB-Q Series robotic arm for automation. PLC system helps in operating pick & place robotic arm, processing the data acquired through ultrasonic sensors, Light Detecting Resistor, load cell and also controlling (ON/OFF) PMDC motor attached with conveyor belt based on inductive proximity sensor signal. The charges for customers are calculated based on different volumetric weights. Using HMI (Human Machine Interface) a bill is projected to customers. In the end, the right amount for proper volumetric weight can be calculated and collected from the customers. Furthermore, with the use of PLC, the logistics management can be monitored and being connected to other hardware application which improve the operation of parcelling. Hence, customer reliability can be improved and also computation of volumetric weight can be done precisely. Finally, our project leads to achieve industry 4.0","PeriodicalId":60583,"journal":{"name":"物联网技术","volume":"29 1","pages":"493-497"},"PeriodicalIF":0.0,"publicationDate":"2023-01-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"81059712","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 : 2023-01-05DOI: 10.1109/IDCIoT56793.2023.10053442
Chereddy Spandana, Ippatapu Venkata Srisurya, S. Aasha Nandhini, R. P. Kumar, G. Bharathi Mohan, Parathasarathy Srinivasan
In recent years, AutoML is booming as the time-consuming and iterative tasks involved in developing a machine learning model can be automated using AutoML. It aims to lessen the requirement for skilled individuals to create the ML model. Additionally, it helps to increase productivity and advance machine learning research. Hence, this paper focusses on developing an AutoML model using genetic algorithm to automatically fulfill the function of network architecture search. The proposed methodology has been evaluated in different scenarios such as binary classification and regression. From the results it is observed that the accuracy achieved for binary classification and regression is 98%.
{"title":"An Efficient Genetic Algorithm based Auto ML Approach for Classification and Regression","authors":"Chereddy Spandana, Ippatapu Venkata Srisurya, S. Aasha Nandhini, R. P. Kumar, G. Bharathi Mohan, Parathasarathy Srinivasan","doi":"10.1109/IDCIoT56793.2023.10053442","DOIUrl":"https://doi.org/10.1109/IDCIoT56793.2023.10053442","url":null,"abstract":"In recent years, AutoML is booming as the time-consuming and iterative tasks involved in developing a machine learning model can be automated using AutoML. It aims to lessen the requirement for skilled individuals to create the ML model. Additionally, it helps to increase productivity and advance machine learning research. Hence, this paper focusses on developing an AutoML model using genetic algorithm to automatically fulfill the function of network architecture search. The proposed methodology has been evaluated in different scenarios such as binary classification and regression. From the results it is observed that the accuracy achieved for binary classification and regression is 98%.","PeriodicalId":60583,"journal":{"name":"物联网技术","volume":"77 1","pages":"371-376"},"PeriodicalIF":0.0,"publicationDate":"2023-01-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"89618350","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 : 2023-01-05DOI: 10.1109/IDCIoT56793.2023.10053425
U. Sakthi, Thomas M. Chen, Mithileysh Sathiyanarayanan
Suicide is a very critical and important issue in modern society. Suicide is the third-leading cause of death for college and high school students. Social media allows students in the digital environment to share their suicidal ideas and thoughts with others. Accurate and early detection and prevention of suicidal ideation in students can save the students' lives. To identify the risk factor for suicidal attempts, a suitable method of analysing the suicidal behaviour of students using their sentiment text posted on social media can be used. This paper presents an optimized Dragonfly algorithm (DFA) using a Deep Belief Network (DBN) for the automatic detection of suicidal ideation in students. In our CyberHelp Solution, the proposed DFA-based DBN model analyses student social media data, predicts suicidal behavior, and treats students appropriately. The sentiment analysis performs automated categorization of online messages and makes accurate predictions of the student’s suicidal behaviors. The dragonfly heuristic optimization algorithm is used for tuning the hyperparameter in the deep belief network. The proposed DFA-DBN technique has been implemented to predict suicidal ideation in students with a higher accuracy of 95.5% compared with other classification models.
{"title":"CyberHelp: Sentiment Analysis on Social Media Data Using Deep Belief Network to Predict Suicidal Ideation of Students","authors":"U. Sakthi, Thomas M. Chen, Mithileysh Sathiyanarayanan","doi":"10.1109/IDCIoT56793.2023.10053425","DOIUrl":"https://doi.org/10.1109/IDCIoT56793.2023.10053425","url":null,"abstract":"Suicide is a very critical and important issue in modern society. Suicide is the third-leading cause of death for college and high school students. Social media allows students in the digital environment to share their suicidal ideas and thoughts with others. Accurate and early detection and prevention of suicidal ideation in students can save the students' lives. To identify the risk factor for suicidal attempts, a suitable method of analysing the suicidal behaviour of students using their sentiment text posted on social media can be used. This paper presents an optimized Dragonfly algorithm (DFA) using a Deep Belief Network (DBN) for the automatic detection of suicidal ideation in students. In our CyberHelp Solution, the proposed DFA-based DBN model analyses student social media data, predicts suicidal behavior, and treats students appropriately. The sentiment analysis performs automated categorization of online messages and makes accurate predictions of the student’s suicidal behaviors. The dragonfly heuristic optimization algorithm is used for tuning the hyperparameter in the deep belief network. The proposed DFA-DBN technique has been implemented to predict suicidal ideation in students with a higher accuracy of 95.5% compared with other classification models.","PeriodicalId":60583,"journal":{"name":"物联网技术","volume":"9 1","pages":"206-211"},"PeriodicalIF":0.0,"publicationDate":"2023-01-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"78908706","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 : 2023-01-05DOI: 10.1109/IDCIoT56793.2023.10053505
Anjanee Kumar, T. Das
Any organisation’s network infrastructure is insecure as different cyber-attacks have constantly mounted and destabilised these systems. There is a rapid upsurge in the usage of the internet in the modern era. This extensive use of the internet has given a chance to attackers to do malicious activities on the network field. To combat these attacks, we need an Intrusion Detection System (IDS). IDS is a robust technological system that protects the system by detecting any intrusions in it. In this study, different machine learning algorithms, which include Support Vector Machine (SVM), Naive Bayes, Random Forest (RF), and Decision Tree (DT), are compared with the method of Logical Analysis of Data (LAD) on NSL-KDD dataset. NSL-KDD is the benchmark dataset used in the network field. The results have been compared on the basis of accuracy, recall, F1-score, G-mean, detection time and ROC-AUC curve. Based on the result obtained, it is evident that the LAD method has outperformed in comparison with other ML-based methods and also detects intrusions in real time.
{"title":"Rule-based Intrusion Detection System using Logical Analysis of Data","authors":"Anjanee Kumar, T. Das","doi":"10.1109/IDCIoT56793.2023.10053505","DOIUrl":"https://doi.org/10.1109/IDCIoT56793.2023.10053505","url":null,"abstract":"Any organisation’s network infrastructure is insecure as different cyber-attacks have constantly mounted and destabilised these systems. There is a rapid upsurge in the usage of the internet in the modern era. This extensive use of the internet has given a chance to attackers to do malicious activities on the network field. To combat these attacks, we need an Intrusion Detection System (IDS). IDS is a robust technological system that protects the system by detecting any intrusions in it. In this study, different machine learning algorithms, which include Support Vector Machine (SVM), Naive Bayes, Random Forest (RF), and Decision Tree (DT), are compared with the method of Logical Analysis of Data (LAD) on NSL-KDD dataset. NSL-KDD is the benchmark dataset used in the network field. The results have been compared on the basis of accuracy, recall, F1-score, G-mean, detection time and ROC-AUC curve. Based on the result obtained, it is evident that the LAD method has outperformed in comparison with other ML-based methods and also detects intrusions in real time.","PeriodicalId":60583,"journal":{"name":"物联网技术","volume":"1 1","pages":"129-135"},"PeriodicalIF":0.0,"publicationDate":"2023-01-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"77555435","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 : 2023-01-05DOI: 10.1109/IDCIoT56793.2023.10053513
Surya Pandey, A. K, M. R. Shaikh, Dhanush Y P, Yajat Vishwakarma
The process and functioning of data integration is termed as combining information from several sources to provide users with a coherent perspective. The fundamental idea behind data integration is to open up data and make it simpler for individuals and systems to access, utilize, and process. The process of converting data from one format to another, typically from that of a source system into that required by a destination system, is known as data transformation. Data transformation is a component of the majority of data integration and management processes, including data manipulation and data warehousing. Many organizations carry out data transformation and integration because they have requirements with respect to data usage that is important in every situation. This paper proposes an architecture that reduces manual work and abstracts the decisions to be made in the integration and transformation process. This approach can lower the risk of human error and result in significant financial savings for various organizations. A modular approach is followed to ease these complex tasks and get desired results.
{"title":"Data Integration and Transformation using Artificial Intelligence","authors":"Surya Pandey, A. K, M. R. Shaikh, Dhanush Y P, Yajat Vishwakarma","doi":"10.1109/IDCIoT56793.2023.10053513","DOIUrl":"https://doi.org/10.1109/IDCIoT56793.2023.10053513","url":null,"abstract":"The process and functioning of data integration is termed as combining information from several sources to provide users with a coherent perspective. The fundamental idea behind data integration is to open up data and make it simpler for individuals and systems to access, utilize, and process. The process of converting data from one format to another, typically from that of a source system into that required by a destination system, is known as data transformation. Data transformation is a component of the majority of data integration and management processes, including data manipulation and data warehousing. Many organizations carry out data transformation and integration because they have requirements with respect to data usage that is important in every situation. This paper proposes an architecture that reduces manual work and abstracts the decisions to be made in the integration and transformation process. This approach can lower the risk of human error and result in significant financial savings for various organizations. A modular approach is followed to ease these complex tasks and get desired results.","PeriodicalId":60583,"journal":{"name":"物联网技术","volume":"7 1","pages":"844-849"},"PeriodicalIF":0.0,"publicationDate":"2023-01-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"79203934","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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