Pub Date : 2023-04-11DOI: 10.5614/itbj.ict.res.appl.2023.17.1.7
M. Rahman, Mohammad Rabiul Islam, Md. Anzir Hossain Rafath, Simron Mhejabin
Covid-19 is a respirational condition that looks much like pneumonia. It is highly contagious and has many variants with different symptoms. Covid-19 poses the challenge of discovering new testing and detection methods in biomedical science. X-ray images and CT scans provide high-quality and information-rich images. These images can be processed with a convolutional neural network (CNN) to detect diseases such as Covid-19 in the pulmonary system with high accuracy. Deep learning applied to X-ray images can help to develop methods to identify Covid-19 infection. Based on the research problem, this study defined the outcome as reducing the energy costs and expenses of detecting Covid-19 in X-ray images. Analysis of the results was done by comparing a CNN model with a DenseNet model, where the first achieved more accurate performance than the second.
{"title":"CNN Based Covid-19 Detection from Image Processing","authors":"M. Rahman, Mohammad Rabiul Islam, Md. Anzir Hossain Rafath, Simron Mhejabin","doi":"10.5614/itbj.ict.res.appl.2023.17.1.7","DOIUrl":"https://doi.org/10.5614/itbj.ict.res.appl.2023.17.1.7","url":null,"abstract":"Covid-19 is a respirational condition that looks much like pneumonia. It is highly contagious and has many variants with different symptoms. Covid-19 poses the challenge of discovering new testing and detection methods in biomedical science. X-ray images and CT scans provide high-quality and information-rich images. These images can be processed with a convolutional neural network (CNN) to detect diseases such as Covid-19 in the pulmonary system with high accuracy. Deep learning applied to X-ray images can help to develop methods to identify Covid-19 infection. Based on the research problem, this study defined the outcome as reducing the energy costs and expenses of detecting Covid-19 in X-ray images. Analysis of the results was done by comparing a CNN model with a DenseNet model, where the first achieved more accurate performance than the second.","PeriodicalId":42785,"journal":{"name":"Journal of ICT Research and Applications","volume":" ","pages":""},"PeriodicalIF":0.6,"publicationDate":"2023-04-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"45346379","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 : 2023-04-11DOI: 10.5614/itbj.ict.res.appl.2023.17.1.3
K. Lakshmi, M. Soranamageswari
One of the most critical tasks in Vehicular Ad-hoc Networks (VANETs) is broadcasting Emergency Messages (EMs) at considerable data delivery rates (DDRs). The enhanced spider-web-like Transmission Mechanism for Emergency Data (TMED) is based on request spiders and authenticated spiders to create the shortest route path between the source vehicle and target vehicles. However, the adjacent allocation is based on the DDR only and it is not clear whether each adjacent vehicle is honest or not. Hence, in this article, the Improved Restricted Greedy Forwarding (IRGF) scheme is proposed for adjacent allocation with the help of trust computation in TMED. The trust and reputation score value of each adjacent vehicle is estimated based on successfully broadcast emergency data. The vehicles’ position, velocity, direction, density, and the reputation score, are fed to a fuzzy logic (FL) scheme, which selects the most trusted adjacent node as the forwarding node for broadcasting the EM to the destination vehicles. Finally, the simulation results illustrate the TMED-IRGF model’s efficiency compared to state-of-the-art models in terms of different network metrics.
{"title":"Emergency Data Transmission Mechanism in VANETs using Improved Restricted Greedy Forwarding (IRGF) Scheme","authors":"K. Lakshmi, M. Soranamageswari","doi":"10.5614/itbj.ict.res.appl.2023.17.1.3","DOIUrl":"https://doi.org/10.5614/itbj.ict.res.appl.2023.17.1.3","url":null,"abstract":"One of the most critical tasks in Vehicular Ad-hoc Networks (VANETs) is broadcasting Emergency Messages (EMs) at considerable data delivery rates (DDRs). The enhanced spider-web-like Transmission Mechanism for Emergency Data (TMED) is based on request spiders and authenticated spiders to create the shortest route path between the source vehicle and target vehicles. However, the adjacent allocation is based on the DDR only and it is not clear whether each adjacent vehicle is honest or not. Hence, in this article, the Improved Restricted Greedy Forwarding (IRGF) scheme is proposed for adjacent allocation with the help of trust computation in TMED. The trust and reputation score value of each adjacent vehicle is estimated based on successfully broadcast emergency data. The vehicles’ position, velocity, direction, density, and the reputation score, are fed to a fuzzy logic (FL) scheme, which selects the most trusted adjacent node as the forwarding node for broadcasting the EM to the destination vehicles. Finally, the simulation results illustrate the TMED-IRGF model’s efficiency compared to state-of-the-art models in terms of different network metrics.","PeriodicalId":42785,"journal":{"name":"Journal of ICT Research and Applications","volume":" ","pages":""},"PeriodicalIF":0.6,"publicationDate":"2023-04-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"46932243","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 : 2023-04-11DOI: 10.5614/itbj.ict.res.appl.2023.17.1.4
Rawan ELhadad, Yi-Fei Tan
In most countries, the old-age people population continues to rise. Because young adults are busy with their work engagements, they have to let the elderly stay at home alone. This is quite dangerous, as accidents at home may happen anytime without anyone knowing. Although sending elderly relatives to an elderly care center or hiring a caregiver are good solutions, they may not be feasible since it may be too expensive over a long-term period. The behavior patterns of elderly people during daily activities can give hints about their health condition. If an abnormal behavior pattern can be detected in advance, then precautions can be taken at an early stage. Previous studies have suggested machine learning techniques for such anomaly detection but most of the techniques are complicated. In this paper, a simple model for detecting anomaly patterns in human activity sequences using Random forest (RF) and K-nearest neighbor (KNN) classifiers is presented. The model was implemented on a public dataset and it showed that the RF classifier performed better, with an accuracy of 85%, compared to the KNN classifier, which achieved 73%.
{"title":"Anomaly Detection for Human Home Activities Using Pattern Based Sequence Classification","authors":"Rawan ELhadad, Yi-Fei Tan","doi":"10.5614/itbj.ict.res.appl.2023.17.1.4","DOIUrl":"https://doi.org/10.5614/itbj.ict.res.appl.2023.17.1.4","url":null,"abstract":"In most countries, the old-age people population continues to rise. Because young adults are busy with their work engagements, they have to let the elderly stay at home alone. This is quite dangerous, as accidents at home may happen anytime without anyone knowing. Although sending elderly relatives to an elderly care center or hiring a caregiver are good solutions, they may not be feasible since it may be too expensive over a long-term period. The behavior patterns of elderly people during daily activities can give hints about their health condition. If an abnormal behavior pattern can be detected in advance, then precautions can be taken at an early stage. Previous studies have suggested machine learning techniques for such anomaly detection but most of the techniques are complicated. In this paper, a simple model for detecting anomaly patterns in human activity sequences using Random forest (RF) and K-nearest neighbor (KNN) classifiers is presented. The model was implemented on a public dataset and it showed that the RF classifier performed better, with an accuracy of 85%, compared to the KNN classifier, which achieved 73%.","PeriodicalId":42785,"journal":{"name":"Journal of ICT Research and Applications","volume":" ","pages":""},"PeriodicalIF":0.6,"publicationDate":"2023-04-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"45692456","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 : 2023-04-11DOI: 10.5614/itbj.ict.res.appl.2023.17.1.2
Asmar Hasan, Widodo Widodo, K. Mutaqin, Muhammad Taufik, Sri Hendrastuti Hidayat
One effect of viral infection on plant physiology is increased stomata closure so that the transpiration rate is low, which in turn causes an increase in leaf temperature. Changes in plant leaf temperature can be measured by thermography using high-resolution thermal cameras. The results can be used as an indicator of virus infection, even before the appearance of visible symptoms. However, the higher the sensor resolution of the thermal camera, the more expensive it is, which is an obstacle in developing the method more widely. This article describes the potential of thermography in detecting Tobacco mosaic virus infection in chili-pepper plants using a low-cost camera. A FLIR C2 camera was used to record images of plants in two treatment groups, non-inoculated (V0) and virus-inoculated plants (V1). Significantly, V1 had a lower temperature at 8 and 12 days after inoculation (dai) than those of V0, but their temperature was higher than V0 before symptoms were visible, i.e., at 17 dai. Thermography using low-cost thermal cameras has potency to detect early viral infection at 8 dai with accuracy levels (AUC) of 80.0% and 86.5% based on k-Nearest Neighbors and Naïve Bayes classifiers, respectively.
{"title":"The Potential of a Low-Cost Thermal Camera for Early Detection of Temperature Changes in Virus-Infected Chili Plants","authors":"Asmar Hasan, Widodo Widodo, K. Mutaqin, Muhammad Taufik, Sri Hendrastuti Hidayat","doi":"10.5614/itbj.ict.res.appl.2023.17.1.2","DOIUrl":"https://doi.org/10.5614/itbj.ict.res.appl.2023.17.1.2","url":null,"abstract":"One effect of viral infection on plant physiology is increased stomata closure so that the transpiration rate is low, which in turn causes an increase in leaf temperature. Changes in plant leaf temperature can be measured by thermography using high-resolution thermal cameras. The results can be used as an indicator of virus infection, even before the appearance of visible symptoms. However, the higher the sensor resolution of the thermal camera, the more expensive it is, which is an obstacle in developing the method more widely. This article describes the potential of thermography in detecting Tobacco mosaic virus infection in chili-pepper plants using a low-cost camera. A FLIR C2 camera was used to record images of plants in two treatment groups, non-inoculated (V0) and virus-inoculated plants (V1). Significantly, V1 had a lower temperature at 8 and 12 days after inoculation (dai) than those of V0, but their temperature was higher than V0 before symptoms were visible, i.e., at 17 dai. Thermography using low-cost thermal cameras has potency to detect early viral infection at 8 dai with accuracy levels (AUC) of 80.0% and 86.5% based on k-Nearest Neighbors and Naïve Bayes classifiers, respectively.","PeriodicalId":42785,"journal":{"name":"Journal of ICT Research and Applications","volume":"1 1","pages":""},"PeriodicalIF":0.6,"publicationDate":"2023-04-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"70736809","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 : 2023-04-11DOI: 10.5614/itbj.ict.res.appl.2023.17.1.1
In this paper, two techniques for sentiment classification are proposed: Gujarati Lexicon Sentiment Analysis (GLSA) and Gujarati Machine Learning Sentiment Analysis (GMLSA) for sentiment classification of Gujarati text film reviews. Five different datasets were produced to validate the machine learning-based and lexicon-based methods’ accuracy. The lexicon-based approach employs a sentiment lexicon known as GujSentiWordNet, which identifies sentiments with a sentiment score for feature generation, while in the machine learning-based approach, five classifiers are used: logistic regression (LR), random forest (RF), k-nearest neighbors (KNN), support vector machine (SVM), naive Bayes (NB) with TF-IDF, and count vectorizer for feature selection. Experiments were carried out and the results obtained were compared using accuracy, precision, recall, and F-score as performance evaluation criteria. According to the test results, the machine learning-based technique improved accuracy by 3 to 10% on average when compared to the lexicon-based approach.
{"title":"Sentiment Classification for Film Reviews in Gujarati Text Using Machine Learning and Sentiment Lexicons","authors":"","doi":"10.5614/itbj.ict.res.appl.2023.17.1.1","DOIUrl":"https://doi.org/10.5614/itbj.ict.res.appl.2023.17.1.1","url":null,"abstract":"In this paper, two techniques for sentiment classification are proposed: Gujarati Lexicon Sentiment Analysis (GLSA) and Gujarati Machine Learning Sentiment Analysis (GMLSA) for sentiment classification of Gujarati text film reviews. Five different datasets were produced to validate the machine learning-based and lexicon-based methods’ accuracy. The lexicon-based approach employs a sentiment lexicon known as GujSentiWordNet, which identifies sentiments with a sentiment score for feature generation, while in the machine learning-based approach, five classifiers are used: logistic regression (LR), random forest (RF), k-nearest neighbors (KNN), support vector machine (SVM), naive Bayes (NB) with TF-IDF, and count vectorizer for feature selection. Experiments were carried out and the results obtained were compared using accuracy, precision, recall, and F-score as performance evaluation criteria. According to the test results, the machine learning-based technique improved accuracy by 3 to 10% on average when compared to the lexicon-based approach.","PeriodicalId":42785,"journal":{"name":"Journal of ICT Research and Applications","volume":"1 1","pages":""},"PeriodicalIF":0.6,"publicationDate":"2023-04-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"42133228","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 : 2023-01-10DOI: 10.5614/itbj.ict.res.appl.2022.16.3.8
Isaac Kofi Nti, O. Nyarko-Boateng, J. Aning, G. Fosu, Henrietta Adjei Pokuaa, F. Kyeremeh
Stroke is believed to be among the leading causes of adult disability worldwide. It is wreaking havoc on African people, families, and governments, with ramifications for the continent’s socio-economic development. On the other hand, stroke research output is insufficient, resulting in a dearth of evidence-based and context-driven guidelines and strategies to combat the region’s expanding stroke burden. Indeed, for African and other developing economies to meet the UN Sustainable Development Goals (SDGs), particularly SDG 3, which aims to guarantee healthy lifestyles and promote well-being for people of all ages, the issue of stroke must be addressed to reduce early death from non-communicable illnesses. This study sought to create a robust predictive model for early stroke diagnosis using an understandable machine learning (ML) technique. We implemented a categorical gradient boosting machine model for early stroke prediction to protect patients’ health and well-being. We compared the effectiveness of our proposed model to existing state-of-the-art machine learning models and previous studies by empirically testing it on a real-world public stroke dataset. The proposed model outperformed the others when compared to the other methods using the research data, achieving the maximum accuracy (96.56%), the area under the curve (AUC) (99.73%), F1-measure (96.68%), recall (99.24%), and precision (93.57%). Functional outcome prediction models based on machine learning for stroke were verified and shown to be adaptable and helpful.
{"title":"Early Detection of Stroke for Ensuring Health and Well-Being Based on Categorical Gradient Boosting Machine","authors":"Isaac Kofi Nti, O. Nyarko-Boateng, J. Aning, G. Fosu, Henrietta Adjei Pokuaa, F. Kyeremeh","doi":"10.5614/itbj.ict.res.appl.2022.16.3.8","DOIUrl":"https://doi.org/10.5614/itbj.ict.res.appl.2022.16.3.8","url":null,"abstract":"Stroke is believed to be among the leading causes of adult disability worldwide. It is wreaking havoc on African people, families, and governments, with ramifications for the continent’s socio-economic development. On the other hand, stroke research output is insufficient, resulting in a dearth of evidence-based and context-driven guidelines and strategies to combat the region’s expanding stroke burden. Indeed, for African and other developing economies to meet the UN Sustainable Development Goals (SDGs), particularly SDG 3, which aims to guarantee healthy lifestyles and promote well-being for people of all ages, the issue of stroke must be addressed to reduce early death from non-communicable illnesses. This study sought to create a robust predictive model for early stroke diagnosis using an understandable machine learning (ML) technique. We implemented a categorical gradient boosting machine model for early stroke prediction to protect patients’ health and well-being. We compared the effectiveness of our proposed model to existing state-of-the-art machine learning models and previous studies by empirically testing it on a real-world public stroke dataset. The proposed model outperformed the others when compared to the other methods using the research data, achieving the maximum accuracy (96.56%), the area under the curve (AUC) (99.73%), F1-measure (96.68%), recall (99.24%), and precision (93.57%). Functional outcome prediction models based on machine learning for stroke were verified and shown to be adaptable and helpful.","PeriodicalId":42785,"journal":{"name":"Journal of ICT Research and Applications","volume":" ","pages":""},"PeriodicalIF":0.6,"publicationDate":"2023-01-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"44203231","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 : 2023-01-10DOI: 10.5614/itbj.ict.res.appl.2022.16.3.7
Qusai Q. Abuein, M. Shatnawi, Lujain Ghazalat
Social network websites have become a rich place for detecting and analyzing people’s attitudes, perceptions, and feelings towards news, products, and other real-world issues. Facebook is a popular platform among different age groups and countries and is generally used to convey ideas about certain topics based on likes, comments and sharing. In recent years, one of the most controversial topics were the idea behind Islamophobia and other ideas built in people’s minds about Islam around the world. This research studied the public opinion of American citizens about Islam during the presidency of Donald Trump, as that period was rich in diversity of opinion between his supporters and detractors. In this paper, sentiment analysis was used to analyze American citizens’ behavior towards posts about Islam during Trump’s presidency in various states across the United States. Sentiment analysis was performed on Facebook posts and comments extracted from American news channels from the year 2017. Several machine learning methods were used to detect the polarity in the dataset. The highest classification accuracy among the classifiers used in this research was achieved using a logistic regression classifier, reaching 84%.
{"title":"Detection of Americans’ Behavior toward Islam on Facebook","authors":"Qusai Q. Abuein, M. Shatnawi, Lujain Ghazalat","doi":"10.5614/itbj.ict.res.appl.2022.16.3.7","DOIUrl":"https://doi.org/10.5614/itbj.ict.res.appl.2022.16.3.7","url":null,"abstract":"Social network websites have become a rich place for detecting and analyzing people’s attitudes, perceptions, and feelings towards news, products, and other real-world issues. Facebook is a popular platform among different age groups and countries and is generally used to convey ideas about certain topics based on likes, comments and sharing. In recent years, one of the most controversial topics were the idea behind Islamophobia and other ideas built in people’s minds about Islam around the world. This research studied the public opinion of American citizens about Islam during the presidency of Donald Trump, as that period was rich in diversity of opinion between his supporters and detractors. In this paper, sentiment analysis was used to analyze American citizens’ behavior towards posts about Islam during Trump’s presidency in various states across the United States. Sentiment analysis was performed on Facebook posts and comments extracted from American news channels from the year 2017. Several machine learning methods were used to detect the polarity in the dataset. The highest classification accuracy among the classifiers used in this research was achieved using a logistic regression classifier, reaching 84%.","PeriodicalId":42785,"journal":{"name":"Journal of ICT Research and Applications","volume":" ","pages":""},"PeriodicalIF":0.6,"publicationDate":"2023-01-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"45409967","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2022-12-31DOI: 10.5614/itbj.ict.res.appl.2022.16.3.6
D. Wijendra, K. Hewagamage
The cognitive complexity of a software application determines the amount of human effort required to comprehend its internal logic, which results in a subjective measurement. The quantification process of the cognitive complexity as a metric is problematic since the factors representing the computation do not represent the exact human cognition. Therefore, the determination of cognitive complexity requires expansion beyond its quantification. The human comprehension effort related with a software application is associated with each phase of its development process. Correct requirements identification and accurate logical diagram generation prior to code implementation can lead to proper logical identification of software applications. Moreover, human comprehension is essential for software maintenance. Defect identification, correction and handling of code quality issues cannot be maintained without good comprehension. Therefore, cognitive complexity can be effectively applied to demonstrate human understandability inside the respective phases of requirements analysis, design, defect tracking, and code quality optimization. This study involved automation of the above-mentioned phases to reduce the manual human cognitive load and reduce cognitive complexity. It was found that the proposed system could enhance the average accuracy of requirements analysis and class diagram generation by 14.44% and 9.89% average accuracy incrementation through defect tracking and code quality issues compared to manual procedures.
{"title":"Cognitive Complexity Applied to Software Development: An Automated Procedure to Reduce the Comprehension Effort","authors":"D. Wijendra, K. Hewagamage","doi":"10.5614/itbj.ict.res.appl.2022.16.3.6","DOIUrl":"https://doi.org/10.5614/itbj.ict.res.appl.2022.16.3.6","url":null,"abstract":"The cognitive complexity of a software application determines the amount of human effort required to comprehend its internal logic, which results in a subjective measurement. The quantification process of the cognitive complexity as a metric is problematic since the factors representing the computation do not represent the exact human cognition. Therefore, the determination of cognitive complexity requires expansion beyond its quantification. The human comprehension effort related with a software application is associated with each phase of its development process. Correct requirements identification and accurate logical diagram generation prior to code implementation can lead to proper logical identification of software applications. Moreover, human comprehension is essential for software maintenance. Defect identification, correction and handling of code quality issues cannot be maintained without good comprehension. Therefore, cognitive complexity can be effectively applied to demonstrate human understandability inside the respective phases of requirements analysis, design, defect tracking, and code quality optimization. This study involved automation of the above-mentioned phases to reduce the manual human cognitive load and reduce cognitive complexity. It was found that the proposed system could enhance the average accuracy of requirements analysis and class diagram generation by 14.44% and 9.89% average accuracy incrementation through defect tracking and code quality issues compared to manual procedures.","PeriodicalId":42785,"journal":{"name":"Journal of ICT Research and Applications","volume":" ","pages":""},"PeriodicalIF":0.6,"publicationDate":"2022-12-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"44315296","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2022-12-31DOI: 10.5614/itbj.ict.res.appl.2022.16.3.4
Faiq Gmira, W. Sabbar, Said Hraoui
In this work, three sub-processes are serially integrated into just one process in order to construct a robust new image encryption scheme for all types of images, especially color images. This integration architecture aims to create a robust avalanche effect property while respecting the constraints of confusion and diffusion that have been identified by Claude Shannon as properties required of a secure encryption scheme. The performance of the proposed encryption scheme is measured and discussed with several analyses, including computational cost analysis, key space analysis, randomness metrics analysis, histogram analysis, adjacent pixel correlation, and entropy analysis. The experimental results demonstrated and validated the performance and robustness of the proposed scheme.
{"title":"A Low Computational Cost RGB Color Image Encryption Scheme Process based on PWLCM Confusion, Z/nZ Diffusion and ECBC Avalanche Effect","authors":"Faiq Gmira, W. Sabbar, Said Hraoui","doi":"10.5614/itbj.ict.res.appl.2022.16.3.4","DOIUrl":"https://doi.org/10.5614/itbj.ict.res.appl.2022.16.3.4","url":null,"abstract":"In this work, three sub-processes are serially integrated into just one process in order to construct a robust new image encryption scheme for all types of images, especially color images. This integration architecture aims to create a robust avalanche effect property while respecting the constraints of confusion and diffusion that have been identified by Claude Shannon as properties required of a secure encryption scheme. The performance of the proposed encryption scheme is measured and discussed with several analyses, including computational cost analysis, key space analysis, randomness metrics analysis, histogram analysis, adjacent pixel correlation, and entropy analysis. The experimental results demonstrated and validated the performance and robustness of the proposed scheme.","PeriodicalId":42785,"journal":{"name":"Journal of ICT Research and Applications","volume":" ","pages":""},"PeriodicalIF":0.6,"publicationDate":"2022-12-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"49599629","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2022-12-31DOI: 10.5614/itbj.ict.res.appl.2022.16.3.5
M. Jonathan, Erdefi Rakun
The SIBI gesture translation framework by Rakun was built using a series of machine learning technologies: MobileNetV2 for feature extraction, Conditional Random Field for finding the epenthesis movement frame, and Long Short-Term Memory for word classification. This high computational translation system was previously implemented on a personal computer system, which lacks portability and accessibility. This study implemented the system on a smartphone using an on-device inference method: the translation process is embedded into the smartphone to provide lower latency and zero data usage. The system was then improved using a parallel multi-inference method, which reduced the average translation time by 25%. The final mobile SIBI gesture-to-text translation system achieved a word accuracy of 90.560%, a sentence accuracy of 64%, and an average translation time of 20 seconds.
{"title":"Translating SIBI (Sign System for Indonesian Gesture) Gesture-to-Text in Real-Time using a Mobile Device","authors":"M. Jonathan, Erdefi Rakun","doi":"10.5614/itbj.ict.res.appl.2022.16.3.5","DOIUrl":"https://doi.org/10.5614/itbj.ict.res.appl.2022.16.3.5","url":null,"abstract":"The SIBI gesture translation framework by Rakun was built using a series of machine learning technologies: MobileNetV2 for feature extraction, Conditional Random Field for finding the epenthesis movement frame, and Long Short-Term Memory for word classification. This high computational translation system was previously implemented on a personal computer system, which lacks portability and accessibility. This study implemented the system on a smartphone using an on-device inference method: the translation process is embedded into the smartphone to provide lower latency and zero data usage. The system was then improved using a parallel multi-inference method, which reduced the average translation time by 25%. The final mobile SIBI gesture-to-text translation system achieved a word accuracy of 90.560%, a sentence accuracy of 64%, and an average translation time of 20 seconds.","PeriodicalId":42785,"journal":{"name":"Journal of ICT Research and Applications","volume":" ","pages":""},"PeriodicalIF":0.6,"publicationDate":"2022-12-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"44216193","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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