Pub Date : 2021-09-13DOI: 10.1109/IICAIET51634.2021.9573752
Ding-Wei Lau, Y. Leau, S. Tan, Po-Hung Lai
The amount of data moving across the network at any given time is referred to as network traffic. It is the data units that are encapsulated in packets and sent over a network. Denial-of-Service (DDoS) attacks are various attempts to disrupt typical network, service, or server traffic. DDoS attacks attempt to disrupt legitimate users' work and data transfers by sending large packets or traffic. Various network traffic prediction techniques are investigated in this study, and a nonlinear time series method, Multilayer Perceptron Neural Network (MLPNN), has been chosen to evaluate network traffic prediction. The results with the NSL-KDD dataset show that the approach can improve prediction accuracy by up to 98.87%. With 2.26%, it outperforms other models such as Sequential Minimal Optimization (SMO).
{"title":"Predicting Network Traffic Anomalies in Denial-of-Service Attacks - A Nonlinear Approach","authors":"Ding-Wei Lau, Y. Leau, S. Tan, Po-Hung Lai","doi":"10.1109/IICAIET51634.2021.9573752","DOIUrl":"https://doi.org/10.1109/IICAIET51634.2021.9573752","url":null,"abstract":"The amount of data moving across the network at any given time is referred to as network traffic. It is the data units that are encapsulated in packets and sent over a network. Denial-of-Service (DDoS) attacks are various attempts to disrupt typical network, service, or server traffic. DDoS attacks attempt to disrupt legitimate users' work and data transfers by sending large packets or traffic. Various network traffic prediction techniques are investigated in this study, and a nonlinear time series method, Multilayer Perceptron Neural Network (MLPNN), has been chosen to evaluate network traffic prediction. The results with the NSL-KDD dataset show that the approach can improve prediction accuracy by up to 98.87%. With 2.26%, it outperforms other models such as Sequential Minimal Optimization (SMO).","PeriodicalId":234229,"journal":{"name":"2021 IEEE International Conference on Artificial Intelligence in Engineering and Technology (IICAIET)","volume":"41 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-09-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125456775","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 : 2021-09-13DOI: 10.1109/IICAIET51634.2021.9573543
Kit Guan Lim, Yoong Hean Lee, M. K. Tan, H. Yoong, Tianlei Wang, K. Teo
As technologies are advancing, demand for an intelligent mobile robot also increases. In autonomous robot design, the main problem faced by researchers is the path planning of mobile robot. Various kind of path planning algorithm was introduced in the past, but no algorithm has absolute superior towards the others algorithm. Classical methods like artificial potential field, grid search, and visual method have been easily overtaken by artificial intelligence due to its adaptability and ability to learn from the past mistakes or experience. For example, Ant Colony Optimization (ACO) is an optimization algorithm based on swarm intelligence which is widely used to solve path planning problem. However, the performance of ACO is highly dependent on the selection of its parameters. In this paper, the proposed adaptive ACO introduced two different ants, namely abnormal ant and random ant into the normal ACO to increase its global search ability and reduce the high convergence rate of ACO. Conventional ACO and adaptive ACO are compared in this paper and the results showed that adaptive ACO has better performance than conventional ACO in path planning.
{"title":"Adaptive Route Optimization for Mobile Robot Navigation using Evolutionary Algorithm","authors":"Kit Guan Lim, Yoong Hean Lee, M. K. Tan, H. Yoong, Tianlei Wang, K. Teo","doi":"10.1109/IICAIET51634.2021.9573543","DOIUrl":"https://doi.org/10.1109/IICAIET51634.2021.9573543","url":null,"abstract":"As technologies are advancing, demand for an intelligent mobile robot also increases. In autonomous robot design, the main problem faced by researchers is the path planning of mobile robot. Various kind of path planning algorithm was introduced in the past, but no algorithm has absolute superior towards the others algorithm. Classical methods like artificial potential field, grid search, and visual method have been easily overtaken by artificial intelligence due to its adaptability and ability to learn from the past mistakes or experience. For example, Ant Colony Optimization (ACO) is an optimization algorithm based on swarm intelligence which is widely used to solve path planning problem. However, the performance of ACO is highly dependent on the selection of its parameters. In this paper, the proposed adaptive ACO introduced two different ants, namely abnormal ant and random ant into the normal ACO to increase its global search ability and reduce the high convergence rate of ACO. Conventional ACO and adaptive ACO are compared in this paper and the results showed that adaptive ACO has better performance than conventional ACO in path planning.","PeriodicalId":234229,"journal":{"name":"2021 IEEE International Conference on Artificial Intelligence in Engineering and Technology (IICAIET)","volume":"41 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-09-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124091883","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 : 2021-09-13DOI: 10.1109/IICAIET51634.2021.9573686
Libao Yang, S. Zenian, R. Zakaria
Fuzzy image enhancement is an important method in the process of image processing. In this paper, we present two intensifier operators in fuzzy image enhancement process based on algebraic function and cycloid arc length respectively. The first method directly uses the algebraic function as a membership intensifier operator. The second method also using a intensifier operator which established established by the cycloid arc length as the independent variable. In the last section, the test image is experimentally analyzed, and the results show that the method we proposed can improve enhance the contrast of the image.
{"title":"Fuzzy Image Enhancement Based on Algebraic Function and Cycloid Arc Length","authors":"Libao Yang, S. Zenian, R. Zakaria","doi":"10.1109/IICAIET51634.2021.9573686","DOIUrl":"https://doi.org/10.1109/IICAIET51634.2021.9573686","url":null,"abstract":"Fuzzy image enhancement is an important method in the process of image processing. In this paper, we present two intensifier operators in fuzzy image enhancement process based on algebraic function and cycloid arc length respectively. The first method directly uses the algebraic function as a membership intensifier operator. The second method also using a intensifier operator which established established by the cycloid arc length as the independent variable. In the last section, the test image is experimentally analyzed, and the results show that the method we proposed can improve enhance the contrast of the image.","PeriodicalId":234229,"journal":{"name":"2021 IEEE International Conference on Artificial Intelligence in Engineering and Technology (IICAIET)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-09-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129053485","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 : 2021-09-13DOI: 10.1109/IICAIET51634.2021.9573696
Parabattina Bhagath, Komal Bharti, Abhishek Kotiya, P. Das
Speech analysis is an active research field where different feature extraction techniques are studied for solving various issues. Such studies help to improve the time complexity of solutions by understanding necessary clues to select the features. Choosing essential features by removing irrelevant information is a significant step in feature engineering. Perceptual Linear Predictive (PLP) modeling concentrates on understanding the speech signals by focusing on the features perceived at the listener end. They have been used successfully in many speech processing applications. The selection of the order of PLP coefficients for efficient classification of spoken units plays a crucial role in the recognition task. A conventional speech processing system requires a huge training process to develop an Automatic Speech Recognition system. Such systems are efficient for the languages that have enough resources i.e. data. But, low-resource languages especially Asian languages haven't been developed to provide the data sufficient for such tasks. In this context, alternative methods and techniques are encouraged to enhance or optimize the development process with less amount of data. This paper proposes a pre-clustering technique to improve the classification rate with low resources.
{"title":"Feature Selection using Pre-clustering via Affinity Propagation for Speech Classification in Low-resource Languages","authors":"Parabattina Bhagath, Komal Bharti, Abhishek Kotiya, P. Das","doi":"10.1109/IICAIET51634.2021.9573696","DOIUrl":"https://doi.org/10.1109/IICAIET51634.2021.9573696","url":null,"abstract":"Speech analysis is an active research field where different feature extraction techniques are studied for solving various issues. Such studies help to improve the time complexity of solutions by understanding necessary clues to select the features. Choosing essential features by removing irrelevant information is a significant step in feature engineering. Perceptual Linear Predictive (PLP) modeling concentrates on understanding the speech signals by focusing on the features perceived at the listener end. They have been used successfully in many speech processing applications. The selection of the order of PLP coefficients for efficient classification of spoken units plays a crucial role in the recognition task. A conventional speech processing system requires a huge training process to develop an Automatic Speech Recognition system. Such systems are efficient for the languages that have enough resources i.e. data. But, low-resource languages especially Asian languages haven't been developed to provide the data sufficient for such tasks. In this context, alternative methods and techniques are encouraged to enhance or optimize the development process with less amount of data. This paper proposes a pre-clustering technique to improve the classification rate with low resources.","PeriodicalId":234229,"journal":{"name":"2021 IEEE International Conference on Artificial Intelligence in Engineering and Technology (IICAIET)","volume":"53 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-09-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127960280","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 : 2021-09-13DOI: 10.1109/IICAIET51634.2021.9573974
Suprapto, Taufik, A. Nasuha, E. Riyanto
Synchronization of dual servo motor control has become an important issue due to many applications in engineering fields, such as electric vehicle, robotics, electronics production machines, and others. This paper studies cerebellar model articulation controller (CMAC) neural network (NN) controller to synchronize two servo motors with dynamic LuGre friction model. CMAC is kind of NN method represented by associative memory with more powerful properties. Cross-coupling control structure is employed to synchronize two servo motors in this study. To investigates the performance, MATLAB Simulink is applied to simulate the control design of dual servo motor. The simulation results exhibit that CMAC controller has better output trajectory and works well for two servo motors with different parameters of LuGre friction model.
{"title":"Synchronization of Dual Servo Motor Using CMAC Neural Network-based Lugre Friction Model","authors":"Suprapto, Taufik, A. Nasuha, E. Riyanto","doi":"10.1109/IICAIET51634.2021.9573974","DOIUrl":"https://doi.org/10.1109/IICAIET51634.2021.9573974","url":null,"abstract":"Synchronization of dual servo motor control has become an important issue due to many applications in engineering fields, such as electric vehicle, robotics, electronics production machines, and others. This paper studies cerebellar model articulation controller (CMAC) neural network (NN) controller to synchronize two servo motors with dynamic LuGre friction model. CMAC is kind of NN method represented by associative memory with more powerful properties. Cross-coupling control structure is employed to synchronize two servo motors in this study. To investigates the performance, MATLAB Simulink is applied to simulate the control design of dual servo motor. The simulation results exhibit that CMAC controller has better output trajectory and works well for two servo motors with different parameters of LuGre friction model.","PeriodicalId":234229,"journal":{"name":"2021 IEEE International Conference on Artificial Intelligence in Engineering and Technology (IICAIET)","volume":"102 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-09-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"133030172","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 : 2021-09-13DOI: 10.1109/IICAIET51634.2021.9573711
M. K. Tan, Kar Leong Lee, Kit Guan Lim, A. Haron, P. Ibrahim, K. Teo
As technologies are expanding, the demand for power supply also increases. This causes the demand for power is difficult to be fulfilled as non-renewable sources are reducing. Therefore, the microgrid concept is introduced, where it is constructed with renewable energy sources, energy storage devices and loads. There are two types of microgrid, which are alternating current (AC) microgrid and direct current (DC) microgrid. Various research show that DC microgrid has more advantages over AC microgrid. However, DC microgrid is not widely used due to the lack of studies on it compared to AC microgrid. Besides, DC microgrid has one significant problem not fixed, which is the fault in the DC microgrid. Whenever a fault occurs, the whole DC microgrid will be affected rapidly. Therefore, this project aims to design a fault detector based on artificial intelligence to detect the fault and isolate the fault effectively. A fault detector based artificial intelligence should be implemented into the DC microgrid system to protect it. Two techniques in Artificial Immune System are being compared. The results showed that the improved Negative Selection Algorithm with variable sized detector has better performance than the general Negative Selection Algorithm with constant sized radius in detecting fault in DC microgrid system.
{"title":"Advanced Fault Detection in DC Microgrid System using Reinforcement Learning","authors":"M. K. Tan, Kar Leong Lee, Kit Guan Lim, A. Haron, P. Ibrahim, K. Teo","doi":"10.1109/IICAIET51634.2021.9573711","DOIUrl":"https://doi.org/10.1109/IICAIET51634.2021.9573711","url":null,"abstract":"As technologies are expanding, the demand for power supply also increases. This causes the demand for power is difficult to be fulfilled as non-renewable sources are reducing. Therefore, the microgrid concept is introduced, where it is constructed with renewable energy sources, energy storage devices and loads. There are two types of microgrid, which are alternating current (AC) microgrid and direct current (DC) microgrid. Various research show that DC microgrid has more advantages over AC microgrid. However, DC microgrid is not widely used due to the lack of studies on it compared to AC microgrid. Besides, DC microgrid has one significant problem not fixed, which is the fault in the DC microgrid. Whenever a fault occurs, the whole DC microgrid will be affected rapidly. Therefore, this project aims to design a fault detector based on artificial intelligence to detect the fault and isolate the fault effectively. A fault detector based artificial intelligence should be implemented into the DC microgrid system to protect it. Two techniques in Artificial Immune System are being compared. The results showed that the improved Negative Selection Algorithm with variable sized detector has better performance than the general Negative Selection Algorithm with constant sized radius in detecting fault in DC microgrid system.","PeriodicalId":234229,"journal":{"name":"2021 IEEE International Conference on Artificial Intelligence in Engineering and Technology (IICAIET)","volume":"5 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-09-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127877690","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 : 2021-09-13DOI: 10.1109/IICAIET51634.2021.9573796
Srijan, Samriddhi, Deepak Gupta
Bird populations are declining worldwide, and several species have gone extinct in historical times. Hence for ornithologists and birdwatchers, exploration of rarely found bird species has become a challenging task. We have developed a deep learning based android application to help users recognize 260 Species of birds, making bird classification a lot more user-friendly. In this paper, we use Convolutional Neural Networks (CNN) pre-trained on ImageNet Dataset as freeze layers of the network, and train the last output layer, which consists of 260 different classes. CNN models such as EfficientNet-lite0, Xception, MobilenetV2, ResNet-50, InceptionV3, and InceptionResNetV2 have been compared based on the accuracy, and working of the mobile app is explained. Maximum accuracy of 99.82% on train data and 98.61% on test data is achieved.
{"title":"Mobile Application for Bird Species Identification Using Transfer Learning","authors":"Srijan, Samriddhi, Deepak Gupta","doi":"10.1109/IICAIET51634.2021.9573796","DOIUrl":"https://doi.org/10.1109/IICAIET51634.2021.9573796","url":null,"abstract":"Bird populations are declining worldwide, and several species have gone extinct in historical times. Hence for ornithologists and birdwatchers, exploration of rarely found bird species has become a challenging task. We have developed a deep learning based android application to help users recognize 260 Species of birds, making bird classification a lot more user-friendly. In this paper, we use Convolutional Neural Networks (CNN) pre-trained on ImageNet Dataset as freeze layers of the network, and train the last output layer, which consists of 260 different classes. CNN models such as EfficientNet-lite0, Xception, MobilenetV2, ResNet-50, InceptionV3, and InceptionResNetV2 have been compared based on the accuracy, and working of the mobile app is explained. Maximum accuracy of 99.82% on train data and 98.61% on test data is achieved.","PeriodicalId":234229,"journal":{"name":"2021 IEEE International Conference on Artificial Intelligence in Engineering and Technology (IICAIET)","volume":"562 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-09-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123920589","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 : 2021-09-13DOI: 10.1109/IICAIET51634.2021.9573664
Rovenson V. Sevilla, A. Alon, Mark P. Melegrito, R. Reyes, Bobby M. Bastes, Roselle P. Cimagala
To avoid adversely affecting community health and the global economy, effective ways to limit the COVID-19 pandemic require constant attention. In the absence of efficient antivirals and insufficient medical resources, WHO recommends several methods to minimize infection rates and prevent depletion of scarce healthcare resources. One of the non-pharmaceutical treatments that can be used to decrease the primary source of SARS-CoV2 droplets expelled by an infected individual is to wear a mask. Irrespective of disagreements about medical resources and mask types, all governments enforce the wearing of masks that cover the nose and mouth by the general population. In the next years, the suggested mask detection models might be a valuable tool for ensuring that safety measures are followed correctly. The YOLOv3 model, a deep transfer learning object identification state-of-the-art approach, is used to create a mask detection model in this research article. The suggested model's exceptional performance makes it ideal for video surveillance equipment. The suggested approach focuses on creating an enhanced dataset from a 300-image dataset utilizing data augmentation techniques such as image filtering. The Data augmentation-based mask detection model's mean average precision was found to be 89.8% during training and 100% during overall testing, with detection per frame accuracy ranging from 40.03% to 65.03%.
{"title":"Mask-Vision: A Machine Vision-Based Inference System of Face Mask Detection for Monitoring Health Protocol Safety","authors":"Rovenson V. Sevilla, A. Alon, Mark P. Melegrito, R. Reyes, Bobby M. Bastes, Roselle P. Cimagala","doi":"10.1109/IICAIET51634.2021.9573664","DOIUrl":"https://doi.org/10.1109/IICAIET51634.2021.9573664","url":null,"abstract":"To avoid adversely affecting community health and the global economy, effective ways to limit the COVID-19 pandemic require constant attention. In the absence of efficient antivirals and insufficient medical resources, WHO recommends several methods to minimize infection rates and prevent depletion of scarce healthcare resources. One of the non-pharmaceutical treatments that can be used to decrease the primary source of SARS-CoV2 droplets expelled by an infected individual is to wear a mask. Irrespective of disagreements about medical resources and mask types, all governments enforce the wearing of masks that cover the nose and mouth by the general population. In the next years, the suggested mask detection models might be a valuable tool for ensuring that safety measures are followed correctly. The YOLOv3 model, a deep transfer learning object identification state-of-the-art approach, is used to create a mask detection model in this research article. The suggested model's exceptional performance makes it ideal for video surveillance equipment. The suggested approach focuses on creating an enhanced dataset from a 300-image dataset utilizing data augmentation techniques such as image filtering. The Data augmentation-based mask detection model's mean average precision was found to be 89.8% during training and 100% during overall testing, with detection per frame accuracy ranging from 40.03% to 65.03%.","PeriodicalId":234229,"journal":{"name":"2021 IEEE International Conference on Artificial Intelligence in Engineering and Technology (IICAIET)","volume":"17 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-09-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"133241190","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 : 2021-09-13DOI: 10.1109/IICAIET51634.2021.9574016
Rafeed Rahman, Sifat Tanvir, Md. Tawhid Anwar
Nowadays Cricket has become a much more competitive sport. We can see new bowlers are evolving with their unique bowling styles and variations. A bowler possesses the expertise to bowl multiple categories of bowling in a particular over and baffling the batsman completely. Despite unique bowling styles create confusion in batsmen, the grip of bowlers can reveal greatly what the bowler is trying to bowl. This research concentrates on predicting the type of delivery the bowler is trying to ball with a unique combination of Fuzzy Logic and state-of-the-art machine learning and deep learning models. For the research purpose, a grip dataset is used that contains 5573 images of grips of 13 categories of deliveries. An approach of image contrast enhancement is shown using Fuzzy logic based on the L-channel of the CIE 1976 L*a*b* color space (CIELAB) color space [L*a*b where L=Luminosity and a*b are green, red blue and yellow color] generated from RGB and then the proposed shallow Convolution Neural Network (CNN), VGG 16, KNN, Naïve Bayes, and Decision Tree were trained and the accuracies shown were remarkable.
{"title":"Unique Approach to Detect Bowling Grips Using Fuzzy Logic Contrast Enhancement","authors":"Rafeed Rahman, Sifat Tanvir, Md. Tawhid Anwar","doi":"10.1109/IICAIET51634.2021.9574016","DOIUrl":"https://doi.org/10.1109/IICAIET51634.2021.9574016","url":null,"abstract":"Nowadays Cricket has become a much more competitive sport. We can see new bowlers are evolving with their unique bowling styles and variations. A bowler possesses the expertise to bowl multiple categories of bowling in a particular over and baffling the batsman completely. Despite unique bowling styles create confusion in batsmen, the grip of bowlers can reveal greatly what the bowler is trying to bowl. This research concentrates on predicting the type of delivery the bowler is trying to ball with a unique combination of Fuzzy Logic and state-of-the-art machine learning and deep learning models. For the research purpose, a grip dataset is used that contains 5573 images of grips of 13 categories of deliveries. An approach of image contrast enhancement is shown using Fuzzy logic based on the L-channel of the CIE 1976 L*a*b* color space (CIELAB) color space [L*a*b where L=Luminosity and a*b are green, red blue and yellow color] generated from RGB and then the proposed shallow Convolution Neural Network (CNN), VGG 16, KNN, Naïve Bayes, and Decision Tree were trained and the accuracies shown were remarkable.","PeriodicalId":234229,"journal":{"name":"2021 IEEE International Conference on Artificial Intelligence in Engineering and Technology (IICAIET)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-09-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131272467","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 : 2021-09-13DOI: 10.1109/IICAIET51634.2021.9573699
Pang Tong, Lee-Yeng Ong, M. Leow
In these few years, Wi-Fi technology is widely used for indoor localization to overcome the limitation of global positioning system (GPS) accessibility within the indoor environment. The GPS accuracy is affected with an approximated error rate of 5 to 10 meters because the GPS signal will decrease while penetrating through the building walls or obstacles. This paper describes a low-cost and simple development of a group monitoring system with a single Wi-Fi access point using Raspberry Pi. Distance estimation methods based on the free-space propagation model and received signal strength indicator (RSSI) are implemented. The received signal strength indicator is collected during active scanning for indoor group monitoring in public spaces, such as shopping malls, retail shops, airports, railway stations, hotels, etc. A Group monitoring system with Wi-Fi active scanning opens the possibilities for the utilization of a single Wi-Fi access point to analyze the distance estimation for multiple audiences within the indoor public spaces. The functionalities of the proposed system include device detection, distance estimation, group monitoring, and social distancing alert.
{"title":"Wi-Fi Group Monitoring System Using Free Space Propagation Model with Active Scanning","authors":"Pang Tong, Lee-Yeng Ong, M. Leow","doi":"10.1109/IICAIET51634.2021.9573699","DOIUrl":"https://doi.org/10.1109/IICAIET51634.2021.9573699","url":null,"abstract":"In these few years, Wi-Fi technology is widely used for indoor localization to overcome the limitation of global positioning system (GPS) accessibility within the indoor environment. The GPS accuracy is affected with an approximated error rate of 5 to 10 meters because the GPS signal will decrease while penetrating through the building walls or obstacles. This paper describes a low-cost and simple development of a group monitoring system with a single Wi-Fi access point using Raspberry Pi. Distance estimation methods based on the free-space propagation model and received signal strength indicator (RSSI) are implemented. The received signal strength indicator is collected during active scanning for indoor group monitoring in public spaces, such as shopping malls, retail shops, airports, railway stations, hotels, etc. A Group monitoring system with Wi-Fi active scanning opens the possibilities for the utilization of a single Wi-Fi access point to analyze the distance estimation for multiple audiences within the indoor public spaces. The functionalities of the proposed system include device detection, distance estimation, group monitoring, and social distancing alert.","PeriodicalId":234229,"journal":{"name":"2021 IEEE International Conference on Artificial Intelligence in Engineering and Technology (IICAIET)","volume":"48 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-09-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115933200","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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