Pub Date : 2023-06-12DOI: 10.36079/lamintang.ijai-01001.537
Sholestica Elmie Dansy, A. Yani, A. Manaf, A. Abdulbaqi, Nurul Iksan
The latest issue of the disease called COVID-19 has become famous all over the world. Hence, through this problem, it found that this disease have the same symptom with other diseases such as Influenza and also normal flu. Detecting diseases at early stage can enable to overcome and treat them appropriately. This is because many of peoples does not know and does not aware of the symptom of this various diseases. In an effort to address those problems, an Expert System for Corona Earlier Detection has been proposed to help the doctors to detect those various diseases in human body. Through this research, the researcher will held an interview with the doctors to collect data and information about those diseases’ symptoms and also search for the related articles to make sure this research going successfully. The method that will be used in this research is Certainty Factor. To conclude, this system will be useful to the healthcare department as it will give earlier detection when the patient are positively exposed to the disease that is known by Corona.
{"title":"Expert System for Diagnosis Coronavirus Disease","authors":"Sholestica Elmie Dansy, A. Yani, A. Manaf, A. Abdulbaqi, Nurul Iksan","doi":"10.36079/lamintang.ijai-01001.537","DOIUrl":"https://doi.org/10.36079/lamintang.ijai-01001.537","url":null,"abstract":"The latest issue of the disease called COVID-19 has become famous all over the world. Hence, through this problem, it found that this disease have the same symptom with other diseases such as Influenza and also normal flu. Detecting diseases at early stage can enable to overcome and treat them appropriately. This is because many of peoples does not know and does not aware of the symptom of this various diseases. In an effort to address those problems, an Expert System for Corona Earlier Detection has been proposed to help the doctors to detect those various diseases in human body. Through this research, the researcher will held an interview with the doctors to collect data and information about those diseases’ symptoms and also search for the related articles to make sure this research going successfully. The method that will be used in this research is Certainty Factor. To conclude, this system will be useful to the healthcare department as it will give earlier detection when the patient are positively exposed to the disease that is known by Corona.","PeriodicalId":52221,"journal":{"name":"IAES International Journal of Artificial Intelligence","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2023-06-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"90346487","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 : 2023-06-12DOI: 10.36079/lamintang.ijai-01001.539
Fikri Nur Izzudin Amir Hamzah, A. Saad, ismail Yusuf Panessai
As a current reality, students are frequently questioned about a suitable career path for the future, but they are unaware of the jobs offered by current industries. Moreover, students seeking university admission frequently encounter difficulties selecting courses and educational programs, and they are confronted with a variety of available courses. This research aims to make a mobile application for students to obtain employment career options appropriate to their educational qualifications because student is often asked about a suitable career for their future but have no idea about the available career path that appropriate. The methodology that implements in this research is Mobile Application Development Life Cycle (MADLC) that have four phases which is identification, design, development, and testing. The Visual Studio Code with Flutter plugin is used to develop the mobile application and its function. Firebase is used to get the database to store all the data and works as backend function of the application. The finished system was tested accordingly based on the functionality that listed all available function of the system. The system considers students' educational qualifications and academic achievements to provide personalized recommendations. This system can assist students in making career decisions and pursuing the right career path, saving them time, and reducing the risk of making wrong choices. This research indicates understanding the importance of career decision-making for students before continuing their university studies. In conclusion, this research seeks to enhance the ability of students to make decision of the available career path provided through recommendation system.
目前的现实是，学生们经常被问及未来适合的职业道路，但他们不知道当前行业提供的工作。此外，寻求大学录取的学生经常在选择课程和教育项目方面遇到困难，他们面临着各种各样的可用课程。本研究旨在为学生制作一个适合其教育资格的就业职业选择的移动应用程序，因为学生经常被问及适合他们未来的职业，但却不知道合适的可用职业路径。本研究采用的方法是移动应用程序开发生命周期(MADLC)，它有四个阶段，即识别、设计、开发和测试。使用Visual Studio Code与Flutter插件开发移动应用程序及其功能。Firebase用于获取数据库以存储所有数据，并作为应用程序的后端函数。根据列出系统所有可用功能的功能对完成的系统进行相应的测试。该系统考虑学生的学历和学业成就，提供个性化的推荐。该系统可以帮助学生做出职业决策，追求正确的职业道路，节省时间，减少做出错误选择的风险。这项研究表明，在继续大学学习之前，了解职业决策对学生的重要性。综上所述，本研究旨在提高学生对推荐系统所提供的职业路径的决策能力。
{"title":"Career Finder System using Rule-Based Filtering for University Student Candidates","authors":"Fikri Nur Izzudin Amir Hamzah, A. Saad, ismail Yusuf Panessai","doi":"10.36079/lamintang.ijai-01001.539","DOIUrl":"https://doi.org/10.36079/lamintang.ijai-01001.539","url":null,"abstract":"As a current reality, students are frequently questioned about a suitable career path for the future, but they are unaware of the jobs offered by current industries. Moreover, students seeking university admission frequently encounter difficulties selecting courses and educational programs, and they are confronted with a variety of available courses. This research aims to make a mobile application for students to obtain employment career options appropriate to their educational qualifications because student is often asked about a suitable career for their future but have no idea about the available career path that appropriate. The methodology that implements in this research is Mobile Application Development Life Cycle (MADLC) that have four phases which is identification, design, development, and testing. The Visual Studio Code with Flutter plugin is used to develop the mobile application and its function. Firebase is used to get the database to store all the data and works as backend function of the application. The finished system was tested accordingly based on the functionality that listed all available function of the system. The system considers students' educational qualifications and academic achievements to provide personalized recommendations. This system can assist students in making career decisions and pursuing the right career path, saving them time, and reducing the risk of making wrong choices. This research indicates understanding the importance of career decision-making for students before continuing their university studies. In conclusion, this research seeks to enhance the ability of students to make decision of the available career path provided through recommendation system.","PeriodicalId":52221,"journal":{"name":"IAES International Journal of Artificial Intelligence","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2023-06-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"79004394","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 : 2023-06-01DOI: 10.11591/ijai.v12.i2.pp785-793
S. R. Bharamagoudar, S. Saboji
Mobile opportunistic networks (MON) has been used for provisioning delay-tolerant applications. In MON the device communicates with each other with no assured end-to-end paths from source and destination because of frequent topology changes, node mobility, low density, and intermittent connectivity. In MON the device battery drains very fast for performing activities such as scanning, transceiver, and other computational processes, impacting the overall performance thus, designing energy-efficient routing is a challenging task. The routing employs a store-carry-and-forward mechanism for packet communication, where the packet is composed of time-to-live (TTL) and is kept in buffer till the opportunity arises. In improving delivery ratio message replication has been adopted; however, induces high network congestion. Here we present a location-aware hybrid microscopic routing (LAHMR) scheme for MON. The LAHMR provides an effective packet transmission scheme with location awareness and high reliability by limiting unnecessary packets being circulated in the network. Experiment outcome shows the LAHMR scheme achieves a much better delivery ratio with less delay, and also reduces the number of a forwarder for transmitting a packet, aiding in the reduction of network overhead concerning recent routing method namely the social-aware reliable forwarding (SCARF) technique.
{"title":"Location-aware hybrid microscopic routing scheme for mobile opportunistic network","authors":"S. R. Bharamagoudar, S. Saboji","doi":"10.11591/ijai.v12.i2.pp785-793","DOIUrl":"https://doi.org/10.11591/ijai.v12.i2.pp785-793","url":null,"abstract":"Mobile opportunistic networks (MON) has been used for provisioning delay-tolerant applications. In MON the device communicates with each other with no assured end-to-end paths from source and destination because of frequent topology changes, node mobility, low density, and intermittent connectivity. In MON the device battery drains very fast for performing activities such as scanning, transceiver, and other computational processes, impacting the overall performance thus, designing energy-efficient routing is a challenging task. The routing employs a store-carry-and-forward mechanism for packet communication, where the packet is composed of time-to-live (TTL) and is kept in buffer till the opportunity arises. In improving delivery ratio message replication has been adopted; however, induces high network congestion. Here we present a location-aware hybrid microscopic routing (LAHMR) scheme for MON. The LAHMR provides an effective packet transmission scheme with location awareness and high reliability by limiting unnecessary packets being circulated in the network. Experiment outcome shows the LAHMR scheme achieves a much better delivery ratio with less delay, and also reduces the number of a forwarder for transmitting a packet, aiding in the reduction of network overhead concerning recent routing method namely the social-aware reliable forwarding (SCARF) technique.","PeriodicalId":52221,"journal":{"name":"IAES International Journal of Artificial Intelligence","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2023-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"48766696","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 : 2023-06-01DOI: 10.11591/ijai.v12.i2.pp532-542
S. Kusumadewi, L. Rosita, E. Wahyuni
It is crucial to detect disease complications caused by metabolic syndromes early. High cholesterol, high glucose, and high blood pressure are indicators of metabolic syndrome. The aim of this study is to use adaptive neuro fuzzy inference system (ANFIS) to predict potential complications and compare its performance to other classifiers, namely random forest (RF), C4.5, and naïve Bayesian classification (NBC) algorithms. Fuzzy subtractive clustering is used to construct membership functions and fuzzy rules throughout the clustering process. This study analyzed 148 different data sets. Cholesterol, random glucose, systolic, and diastolic blood pressure are all included in the data collection. This learning process was conducted using a hybrid algorithm. The consequent parameters are adjusted forward using the leastsquare approach, while the premise parameters are adjusted backward using the gradient-descent process. The performance of a system is determined by the following indicators: accuracy, sensitivity, specification, precision, area under the curve (AUC), and root mean squared error (RMSE). The results of the training prove that ANFIS is an "excellent classification" classifier. ANFIS has proven to have very good stability across the six performance parameters. The adaptive properties used in ANFIS training and the implementation of fuzzy subtractive clustering strongly support this stability.
{"title":"Stability of classification performance on an adaptive neuro fuzzy inference system for disease complication prediction","authors":"S. Kusumadewi, L. Rosita, E. Wahyuni","doi":"10.11591/ijai.v12.i2.pp532-542","DOIUrl":"https://doi.org/10.11591/ijai.v12.i2.pp532-542","url":null,"abstract":"It is crucial to detect disease complications caused by metabolic syndromes early. High cholesterol, high glucose, and high blood pressure are indicators of metabolic syndrome. The aim of this study is to use adaptive neuro fuzzy inference system (ANFIS) to predict potential complications and compare its performance to other classifiers, namely random forest (RF), C4.5, and naïve Bayesian classification (NBC) algorithms. Fuzzy subtractive clustering is used to construct membership functions and fuzzy rules throughout the clustering process. This study analyzed 148 different data sets. Cholesterol, random glucose, systolic, and diastolic blood pressure are all included in the data collection. This learning process was conducted using a hybrid algorithm. The consequent parameters are adjusted forward using the leastsquare approach, while the premise parameters are adjusted backward using the gradient-descent process. The performance of a system is determined by the following indicators: accuracy, sensitivity, specification, precision, area under the curve (AUC), and root mean squared error (RMSE). The results of the training prove that ANFIS is an \"excellent classification\" classifier. ANFIS has proven to have very good stability across the six performance parameters. The adaptive properties used in ANFIS training and the implementation of fuzzy subtractive clustering strongly support this stability.","PeriodicalId":52221,"journal":{"name":"IAES International Journal of Artificial Intelligence","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2023-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"42817976","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 : 2023-06-01DOI: 10.11591/ijai.v12.i2.pp648-655
Omar Barki, Z. Guennoun, A. Addaim
In order to improve the selection of multi-point relays (MPRs) by a node node performing the computation (NPC) in the optimized link state routing (OLSR) protocol and therefore to guarantee more security for the routing in the mobile ad hoc network (MANET), we propose new approach that could distinguish between the strong and weak MPRs in the list of MPRs already selected using the standard algorithm described in RFC3626 document. This approach is based on self organizing map (SOM) artificial neural network that processes the collected data and then only selects the strong MPRs using a set of criteria allowing a reliable retransmission and a strong link and therefore better network performances. The obtained results, from the simulations that have been carried out using a customized network simulator 3 (NS3) network simulator, show an improvement in terms of throughput, packets delivery ratio (PDR) and the security of the network compared to the standard approach.
{"title":"New approach for selecting multi-point relays in the optimized link state routing protocol using self-organizing map artificial neural network: OLSR-SOM","authors":"Omar Barki, Z. Guennoun, A. Addaim","doi":"10.11591/ijai.v12.i2.pp648-655","DOIUrl":"https://doi.org/10.11591/ijai.v12.i2.pp648-655","url":null,"abstract":"In order to improve the selection of multi-point relays (MPRs) by a node node performing the computation (NPC) in the optimized link state routing (OLSR) protocol and therefore to guarantee more security for the routing in the mobile ad hoc network (MANET), we propose new approach that could distinguish between the strong and weak MPRs in the list of MPRs already selected using the standard algorithm described in RFC3626 document. This approach is based on self organizing map (SOM) artificial neural network that processes the collected data and then only selects the strong MPRs using a set of criteria allowing a reliable retransmission and a strong link and therefore better network performances. The obtained results, from the simulations that have been carried out using a customized network simulator 3 (NS3) network simulator, show an improvement in terms of throughput, packets delivery ratio (PDR) and the security of the network compared to the standard approach.","PeriodicalId":52221,"journal":{"name":"IAES International Journal of Artificial Intelligence","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2023-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"47827110","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 : 2023-06-01DOI: 10.11591/ijai.v12.i2.pp921-930
Boutaina Hdioud, Mohammed El Haj Tirari
Facial expression recognition (FER) represents one of the most prevalent forms of interpersonal communication, which contains rich emotional information. But it became even more challenging during the times of COVID, where face masks became a mandatory protection measure, leading to the challenge of occluded lower-face during facial expression recognition. In this study, deep convolutional neural network (DCNN) represents the core of both our full-face FER system and our masked face FER model. The focus was on incorporating knowledge distillation in transfer learning between a teacher model, which is the full-face FER DCNN, and the student model, which is the masked face FER DCNN via the combination of both the loss from the teacher soft-labels vs the student soft labels and the loss from the dataset hard-labels vs the student hard-labels. The teacher-student architecture used FER2013 and a masked customized version of FER2013 as datasets to generate an accuracy of 69% and 61% respectively. Therefore, the study proves that the process of knowledge distillation may be used as a way for transfer learning and enhancing accuracy as a regular DCNN model (student only) would result in 46% accuracy compared to our approach (61% accuracy).
{"title":"Facial expression recognition of masked faces using deep learning","authors":"Boutaina Hdioud, Mohammed El Haj Tirari","doi":"10.11591/ijai.v12.i2.pp921-930","DOIUrl":"https://doi.org/10.11591/ijai.v12.i2.pp921-930","url":null,"abstract":"Facial expression recognition (FER) represents one of the most prevalent forms of interpersonal communication, which contains rich emotional information. But it became even more challenging during the times of COVID, where face masks became a mandatory protection measure, leading to the challenge of occluded lower-face during facial expression recognition. In this study, deep convolutional neural network (DCNN) represents the core of both our full-face FER system and our masked face FER model. The focus was on incorporating knowledge distillation in transfer learning between a teacher model, which is the full-face FER DCNN, and the student model, which is the masked face FER DCNN via the combination of both the loss from the teacher soft-labels vs the student soft labels and the loss from the dataset hard-labels vs the student hard-labels. The teacher-student architecture used FER2013 and a masked customized version of FER2013 as datasets to generate an accuracy of 69% and 61% respectively. Therefore, the study proves that the process of knowledge distillation may be used as a way for transfer learning and enhancing accuracy as a regular DCNN model (student only) would result in 46% accuracy compared to our approach (61% accuracy).","PeriodicalId":52221,"journal":{"name":"IAES International Journal of Artificial Intelligence","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2023-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135275272","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 : 2023-06-01DOI: 10.11591/ijai.v12.i2.pp641-647
S. A. Ahmed, H. Desa, A. T. Hussain
The classification of semantic segmentation-based unmanned aerial vehicle (UAV) application based on the datasets used in this work and the necessary data preprocessing steps for the optimization and implementation of the models are also involved. The optimization of the various models was done using the evaluation metrics and loss functions because deep neural networks (DNNs) are just about writing a cost function and its subsequent optimization. convolutional neural network (CNN) is a common type of artificial neural network (ANN) that has found application in numerous tasks, such as image and video recognition, image classification, recommender systems, financial time series, medical image analysis, and natural language processing. CNN is developed to automatically and adaptively learn spatial feature hierarchies via backpropagation using numerous building blocks, such as pooling, convolution, and fully connected layers. The result of identification was excellent. The image segmentation was detected and comprehend the actual components of an image down to the pixel level. The result created an entire image segmentation masks with instances using the new label editor in the label box.
{"title":"Classification of semantic segmentation using fully convolutional networks based unmanned aerial vehicle application","authors":"S. A. Ahmed, H. Desa, A. T. Hussain","doi":"10.11591/ijai.v12.i2.pp641-647","DOIUrl":"https://doi.org/10.11591/ijai.v12.i2.pp641-647","url":null,"abstract":"The classification of semantic segmentation-based unmanned aerial vehicle (UAV) application based on the datasets used in this work and the necessary data preprocessing steps for the optimization and implementation of the models are also involved. The optimization of the various models was done using the evaluation metrics and loss functions because deep neural networks (DNNs) are just about writing a cost function and its subsequent optimization. convolutional neural network (CNN) is a common type of artificial neural network (ANN) that has found application in numerous tasks, such as image and video recognition, image classification, recommender systems, financial time series, medical image analysis, and natural language processing. CNN is developed to automatically and adaptively learn spatial feature hierarchies via backpropagation using numerous building blocks, such as pooling, convolution, and fully connected layers. The result of identification was excellent. The image segmentation was detected and comprehend the actual components of an image down to the pixel level. The result created an entire image segmentation masks with instances using the new label editor in the label box.","PeriodicalId":52221,"journal":{"name":"IAES International Journal of Artificial Intelligence","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2023-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"42215842","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 : 2023-06-01DOI: 10.11591/ijai.v12.i2.pp696-703
P. Soubhagyalakshmi, K. Reddy
The significant enhancement in demand for bring your own device (BYOD) mechanism in several organizations has sought the attention of several researchers in recent years. However, the utilization of BYOD comes with a high risk of losing crucial information due to lesser organizational control on employee-owned devices. The purpose of this article is to review and analyze the various security threats in BYOD; further we review the existing work that was developed in order to reduce the risks present in BYOD. A detailed review is presented to detect BYOD security threats and their respective security policies. A phase-by-phase mitigation strategy is developed based on the components and crucial elements identified using review policy. Managerial-level, social-level and technical level issues are identified such as illegal access, leaking delicate company data, lower flexibility, corporate data breaching, and employee privacy. It is analyzed that collaboration of people, security policy factors and technology in an effective manner can mitigate security threats present in the BYOD mechanism. This article initiates a move towards filling the security gap present the BYOD mechanism. This article can be utilized for providing guidelines in various organizations. Ultimately, successful implementation of BYOD depends upon the balance created between usability and security.
{"title":"An efficient security analysis of bring your own device","authors":"P. Soubhagyalakshmi, K. Reddy","doi":"10.11591/ijai.v12.i2.pp696-703","DOIUrl":"https://doi.org/10.11591/ijai.v12.i2.pp696-703","url":null,"abstract":"The significant enhancement in demand for bring your own device (BYOD) mechanism in several organizations has sought the attention of several researchers in recent years. However, the utilization of BYOD comes with a high risk of losing crucial information due to lesser organizational control on employee-owned devices. The purpose of this article is to review and analyze the various security threats in BYOD; further we review the existing work that was developed in order to reduce the risks present in BYOD. A detailed review is presented to detect BYOD security threats and their respective security policies. A phase-by-phase mitigation strategy is developed based on the components and crucial elements identified using review policy. Managerial-level, social-level and technical level issues are identified such as illegal access, leaking delicate company data, lower flexibility, corporate data breaching, and employee privacy. It is analyzed that collaboration of people, security policy factors and technology in an effective manner can mitigate security threats present in the BYOD mechanism. This article initiates a move towards filling the security gap present the BYOD mechanism. This article can be utilized for providing guidelines in various organizations. Ultimately, successful implementation of BYOD depends upon the balance created between usability and security.","PeriodicalId":52221,"journal":{"name":"IAES International Journal of Artificial Intelligence","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2023-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"45910977","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 : 2023-06-01DOI: 10.11591/ijai.v12.i2.pp522-531
Soly Mathew Biju, Obada Al Khatib, H. Sheikh
Loss of the capability to talk or hear applies psychological and social effects on the affected individuals due to the absence of appropriate interaction. Sign Language is used by such individuals to assist them in communicating with each other. The paper aims to report details of various aspects of wearable healthcare technologies designed in recent years based on the aim of the study, the types of technologies being used, accuracy of the system designed, data collection and storage methods, technology used to accomplish the task, limitations and future research suggested for the study. The aim of the study is to compare the differences between the papers. There is also comparison of technology used to determine which wearable device is better, which is also done with the help of accuracy. The limitations and future research help in determining how the wearable devices can be improved. A systematic review was performed based on a search of the literature. A total of 23 articles were retrieved. The articles are study and design of various wearable devices, mainly the glove-based device, to help you learn the sign language.
{"title":"A review of factors that impact the design of a glove based wearable devices","authors":"Soly Mathew Biju, Obada Al Khatib, H. Sheikh","doi":"10.11591/ijai.v12.i2.pp522-531","DOIUrl":"https://doi.org/10.11591/ijai.v12.i2.pp522-531","url":null,"abstract":"Loss of the capability to talk or hear applies psychological and social effects on the affected individuals due to the absence of appropriate interaction. Sign Language is used by such individuals to assist them in communicating with each other. The paper aims to report details of various aspects of wearable healthcare technologies designed in recent years based on the aim of the study, the types of technologies being used, accuracy of the system designed, data collection and storage methods, technology used to accomplish the task, limitations and future research suggested for the study. The aim of the study is to compare the differences between the papers. There is also comparison of technology used to determine which wearable device is better, which is also done with the help of accuracy. The limitations and future research help in determining how the wearable devices can be improved. A systematic review was performed based on a search of the literature. A total of 23 articles were retrieved. The articles are study and design of various wearable devices, mainly the glove-based device, to help you learn the sign language.","PeriodicalId":52221,"journal":{"name":"IAES International Journal of Artificial Intelligence","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2023-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"48079894","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 : 2023-06-01DOI: 10.11591/ijai.v12.i2.pp840-850
Silvia Joseph, I. Hipiny, Hamimah Ujir
Decorative mats plaited by the Iban communities in Borneo contains motifs that reflect their traditional beliefs. Each motif has its own special meaning and taboos. A typical mat motif contains multiple smaller patterns that surround the main motif hence is likely to cause misclassification. We introduce a classification framework with adaptive sampling to remove smaller features whilst retaining larger (and discriminative) image structures. Canny filter and probabilistic hough transform are gradually applied to a clean greyscale image until a threshold value pertaining to the image’s structural information is reached. Morphological dilation is then applied to improve the appearance of the retained edges. The resulting image is described using binary robust invariant scalable keypoints (BRISK) features with random sample consensus (RANSAC). We reported the classification accuracy against six common image deformations at incremental degrees: scale+rotation, viewpoint, image blur, joint photographic experts group (JPEG) compression, scale and illumination. From our sensitivity analysis, we found the optimal threshold for adaptive smoothing to be 75.0%. The optimal scheme obtained 100.0% accuracy for JPEG compression, illumination, and viewpoint set. Using adaptive smoothing, we achieved an average increase in accuracy of 11.0% compared to the baseline.
{"title":"Iban plaited mat motif classification with adaptive smoothing","authors":"Silvia Joseph, I. Hipiny, Hamimah Ujir","doi":"10.11591/ijai.v12.i2.pp840-850","DOIUrl":"https://doi.org/10.11591/ijai.v12.i2.pp840-850","url":null,"abstract":"Decorative mats plaited by the Iban communities in Borneo contains motifs that reflect their traditional beliefs. Each motif has its own special meaning and taboos. A typical mat motif contains multiple smaller patterns that surround the main motif hence is likely to cause misclassification. We introduce a classification framework with adaptive sampling to remove smaller features whilst retaining larger (and discriminative) image structures. Canny filter and probabilistic hough transform are gradually applied to a clean greyscale image until a threshold value pertaining to the image’s structural information is reached. Morphological dilation is then applied to improve the appearance of the retained edges. The resulting image is described using binary robust invariant scalable keypoints (BRISK) features with random sample consensus (RANSAC). We reported the classification accuracy against six common image deformations at incremental degrees: scale+rotation, viewpoint, image blur, joint photographic experts group (JPEG) compression, scale and illumination. From our sensitivity analysis, we found the optimal threshold for adaptive smoothing to be 75.0%. The optimal scheme obtained 100.0% accuracy for JPEG compression, illumination, and viewpoint set. Using adaptive smoothing, we achieved an average increase in accuracy of 11.0% compared to the baseline.","PeriodicalId":52221,"journal":{"name":"IAES International Journal of Artificial Intelligence","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2023-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"44783008","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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