Pub Date : 2024-03-29DOI: 10.13052/jmm1550-4646.20210
Kamal Upreti, Anmol Kapoor, Sheela N. Hundekari, Shitiz Upreti, Kajal Kaul, Shreya Kapoor, Akhilesh Tiwari
In the landscape of diabetes-related ocular complications, diabetic retinopathy stands as a formidable challenge, reigning as the leading cause of vision impairment worldwide. Despite extensive research, the quest for effective treatments remains an ongoing pursuit. This study explores the burgeoning domain of AI-driven approaches in ocular research, particularly focusing on diabetic retinopathy detection. It delves into various diagnostic methodologies, encompassing the detection of microaneurysms, identification of hemorrhages, and segmentation of blood vessels, primarily utilizing retinal fundus photographs. Our findings juxtapose conventional machine learning techniques against deep neural networks, showcasing the remarkable efficacy of Convolutional neural network (CNN) and Random Forest (RF) in segmenting blood vessels and the robustness of deep learning in lesion identification. As we navigate the quest for clearer vision, artificial intelligence takes center stage, promising a transformative leap forward in the realm of vision care.
{"title":"Deep Dive Into Diabetic Retinopathy Identification: A Deep Learning Approach with Blood Vessel Segmentation and Lesion Detection","authors":"Kamal Upreti, Anmol Kapoor, Sheela N. Hundekari, Shitiz Upreti, Kajal Kaul, Shreya Kapoor, Akhilesh Tiwari","doi":"10.13052/jmm1550-4646.20210","DOIUrl":"https://doi.org/10.13052/jmm1550-4646.20210","url":null,"abstract":"In the landscape of diabetes-related ocular complications, diabetic retinopathy stands as a formidable challenge, reigning as the leading cause of vision impairment worldwide. Despite extensive research, the quest for effective treatments remains an ongoing pursuit. This study explores the burgeoning domain of AI-driven approaches in ocular research, particularly focusing on diabetic retinopathy detection. It delves into various diagnostic methodologies, encompassing the detection of microaneurysms, identification of hemorrhages, and segmentation of blood vessels, primarily utilizing retinal fundus photographs. Our findings juxtapose conventional machine learning techniques against deep neural networks, showcasing the remarkable efficacy of Convolutional neural network (CNN) and Random Forest (RF) in segmenting blood vessels and the robustness of deep learning in lesion identification. As we navigate the quest for clearer vision, artificial intelligence takes center stage, promising a transformative leap forward in the realm of vision care.","PeriodicalId":38898,"journal":{"name":"Journal of Mobile Multimedia","volume":"64 5","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-03-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140366298","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 : 2024-03-29DOI: 10.13052/jmm1550-4646.2024
S. P. A. Kumar, H. Thakur, K. D. Gupta, Deepak Kumar Sharma
Transferring data between nodes in the Opportunistic Internet of Things (OppIoT) network may lead to the transmission of multiple copies of each message, which can increase communication costs and jeopardise network security. This necessitates a routing method that is effective and can address both problems. To protect transmitted data and reduce communication overhead, this study suggests a Secured and Mobility Aware Routing Method (SMART) routing algorithm for OppIoT networks in smart cities. With a buffer size of 30 MB and an overhead ratio of 27.9, the delivery probability can be increased by more than 50%. The simulation’s findings demonstrate that, in terms of delivery probability, overhead ratio, and reports, the proposed SMART protocol outperforms more traditional routing methods.
{"title":"SMART: Secured and Mobility Aware Routing Technique for Opportunistic IoT Network in Smart Cities","authors":"S. P. A. Kumar, H. Thakur, K. D. Gupta, Deepak Kumar Sharma","doi":"10.13052/jmm1550-4646.2024","DOIUrl":"https://doi.org/10.13052/jmm1550-4646.2024","url":null,"abstract":"Transferring data between nodes in the Opportunistic Internet of Things (OppIoT) network may lead to the transmission of multiple copies of each message, which can increase communication costs and jeopardise network security. This necessitates a routing method that is effective and can address both problems. To protect transmitted data and reduce communication overhead, this study suggests a Secured and Mobility Aware Routing Method (SMART) routing algorithm for OppIoT networks in smart cities. With a buffer size of 30 MB and an overhead ratio of 27.9, the delivery probability can be increased by more than 50%. The simulation’s findings demonstrate that, in terms of delivery probability, overhead ratio, and reports, the proposed SMART protocol outperforms more traditional routing methods.","PeriodicalId":38898,"journal":{"name":"Journal of Mobile Multimedia","volume":"65 20","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-03-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140365074","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 : 2024-03-29DOI: 10.13052/jmm1550-4646.2028
S. Wongsila, S. Chernbumroong, Kritsana Boonprasit, P. Sureephong
In this research study, the primary objective was to develop a comprehensive exercise program specifically designed for elderly individuals. The focus was on evaluating various exercise postures and validating their impact on muscle groups. By integrating knowledge management systems with knowledge engineering methodologies, the aim was to optimize the design of exercise postures and promote optimal health outcomes for the elderly. Divided into three distinct experiments, the study employed a systematic approach to acquire, represent, and validate knowledge related to exercise postures for the elderly population. The use of knowledge management systems and engineering methodologies facilitated the design of effective exercise postures tailored to meet the unique needs and capabilities of elderly individuals. Experiment I focused on knowledge acquisition through structured interviews with physical therapists. The acquired knowledge was used to screen and prioritize exercise postures suitable for elderly individuals. Expert recommendations and analysis were employed to select a set of exercise postures. Using a matrix combination approach, 189 possible exercise postures were generated by combining aerobic and dance postures. Through a screening process, 52 postures were selected as suitable for elderly individuals. Experiment II utilized kinesthetic representation techniques to visually represent the 52 selected exercise postures for the elderly. Additionally, frame representation was employed to capture muscle specifications associated with each posture. The representation design was validated by physical exercise experts. In the first step of Experiment III, a total of 52 exercise postures were implemented and evaluated with elderly participants. The implementation and validation process aimed to identify the best and most appropriate postures for the elderly, considering factors such as satisfaction levels, difficulty levels, and safety considerations. Through this rigorous evaluation, the initial selection of 52 postures was narrowed down to a final set of 21 suitable postures. The validation results provided valuable insights into the effectiveness of the exercise postures and their impact on the elderly participants. It ensured that the chosen postures were not only effective in promoting optimal health outcomes but also minimized the risk of injury. The iterative assessment and refinement process contributed to the development of an evidence-based exercise program specifically tailored to the unique needs and capabilities of elderly individuals. For the second step of Experiment III, the effects of the selected exercise postures on different muscle groups were validated. Three physical exercise experts evaluated the impact on upper limb, trunk, and lower limb muscles. Specific muscle groups, such as brachioradialis, deltoid, quadriceps, and hamstring, were found to be strongly focused on during the exercises, while trunk muscles were rated a
在这项研究中，首要目标是制定一项专为老年人设计的综合锻炼计划。重点是评估各种运动姿势，并验证其对肌肉群的影响。通过将知识管理系统与知识工程方法相结合，目的是优化运动姿势的设计，促进老年人获得最佳的健康结果。这项研究分为三个不同的实验，采用了系统的方法来获取、呈现和验证与老年人运动姿势相关的知识。知识管理系统和工程方法的使用促进了有效运动姿势的设计，以满足老年人的独特需求和能力。实验 I 的重点是通过对物理治疗师进行结构化访谈来获取知识。所获得的知识被用于筛选适合老年人的运动姿势并确定其优先次序。专家建议和分析被用来选择一组运动姿势。通过矩阵组合方法，结合有氧运动和舞蹈姿势，产生了 189 种可能的运动姿势。通过筛选，选出了 52 个适合老年人的运动姿势。实验二利用动觉表征技术，直观地表现出 52 种选定的老年人运动姿势。此外，还采用了框架表示法来捕捉与每个姿势相关的肌肉规格。表征设计通过了体育锻炼专家的验证。在实验 III 的第一步，共实施了 52 个运动姿势，并对老年参与者进行了评估。实施和验证过程旨在确定最适合老年人的最佳姿势，同时考虑到满意度、难度和安全性等因素。通过这种严格的评估，最初选定的 52 个姿势被缩小到 21 个合适的姿势。验证结果为了解运动姿势的有效性及其对老年参与者的影响提供了宝贵的见解。它确保了所选姿势不仅能有效促进最佳健康结果，还能最大限度地降低受伤风险。反复评估和改进的过程有助于开发出一套以证据为基础、专门针对老年人独特需求和能力的锻炼计划。实验 III 的第二步是验证所选运动姿势对不同肌肉群的影响。三位体育锻炼专家评估了对上肢、躯干和下肢肌肉的影响。结果发现，肱骨肌、三角肌、股四头肌和腿筋等特定肌肉群在练习过程中受到了强烈关注，而躯干肌肉的整体评价较差。评估包括对老年参与者实施和评估总共 52 个锻炼姿势。根据评估结果选择了最终的 21 个姿势，并深入了解了每个锻炼姿势的满意度、难度和安全考虑因素。验证结果表明，专家们的意见高度一致，从 79% 到 91%不等。总之，这项研究旨在通过开发更有效的运动姿势设计方法来加强老年人运动项目。通过考虑老年人群的特殊要求并利用知识管理系统，该研究成功地设计出了能最大限度地提高健康效益和整体健康水平的运动姿势。通过实施知识工程方法和利用知识管理系统，该研究优化了老年人运动姿势的设计。通过将研究分为三个实验，可以对所获得的知识进行全面分析，从而开发出专门针对老年人群需求的运动姿势。
{"title":"Leveraging Knowledge Management Techniques for Developing Multimedia Exercise Guides for Elderly Fall Prevention","authors":"S. Wongsila, S. Chernbumroong, Kritsana Boonprasit, P. Sureephong","doi":"10.13052/jmm1550-4646.2028","DOIUrl":"https://doi.org/10.13052/jmm1550-4646.2028","url":null,"abstract":"In this research study, the primary objective was to develop a comprehensive exercise program specifically designed for elderly individuals. The focus was on evaluating various exercise postures and validating their impact on muscle groups. By integrating knowledge management systems with knowledge engineering methodologies, the aim was to optimize the design of exercise postures and promote optimal health outcomes for the elderly.\u0000Divided into three distinct experiments, the study employed a systematic approach to acquire, represent, and validate knowledge related to exercise postures for the elderly population. The use of knowledge management systems and engineering methodologies facilitated the design of effective exercise postures tailored to meet the unique needs and capabilities of elderly individuals. Experiment I focused on knowledge acquisition through structured interviews with physical therapists. The acquired knowledge was used to screen and prioritize exercise postures suitable for elderly individuals. Expert recommendations and analysis were employed to select a set of exercise postures. Using a matrix combination approach, 189 possible exercise postures were generated by combining aerobic and dance postures. Through a screening process, 52 postures were selected as suitable for elderly individuals. Experiment II utilized kinesthetic representation techniques to visually represent the 52 selected exercise postures for the elderly. Additionally, frame representation was employed to capture muscle specifications associated with each posture. The representation design was validated by physical exercise experts. In the first step of Experiment III, a total of 52 exercise postures were implemented and evaluated with elderly participants. The implementation and validation process aimed to identify the best and most appropriate postures for the elderly, considering factors such as satisfaction levels, difficulty levels, and safety considerations. Through this rigorous evaluation, the initial selection of 52 postures was narrowed down to a final set of 21 suitable postures. The validation results provided valuable insights into the effectiveness of the exercise postures and their impact on the elderly participants. It ensured that the chosen postures were not only effective in promoting optimal health outcomes but also minimized the risk of injury. The iterative assessment and refinement process contributed to the development of an evidence-based exercise program specifically tailored to the unique needs and capabilities of elderly individuals. For the second step of Experiment III, the effects of the selected exercise postures on different muscle groups were validated. Three physical exercise experts evaluated the impact on upper limb, trunk, and lower limb muscles. Specific muscle groups, such as brachioradialis, deltoid, quadriceps, and hamstring, were found to be strongly focused on during the exercises, while trunk muscles were rated a","PeriodicalId":38898,"journal":{"name":"Journal of Mobile Multimedia","volume":"76 2","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-03-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140368721","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 : 2024-03-29DOI: 10.13052/jmm1550-4646.2025
Bhaskar Kapoor, Bharti Nagpal
Background and aim: In recent years, research in the fields of brain-computer interfacing techniques and related areas are developing at a very rapid rate with the help of exploding of Artificial Intelligence, Machine Learning and Deep Learning. A new concept of Gradient Boosting has become popular research area among the researchers related to the field of automatic classification of Electroencephalograph (EEG) signals for predication of mental health issues like seizures. Methods: However effective feature extraction from EEG and accurately classify them with efficient classifiers is still an important task and attracted wide attention in this area. Therefore in this paper, we presented the detailed mathematical analysis of these methods and ensemble learnings based EEG signals classification method for seizures classification in EEG using Extreme Gradient Boosting Model such as Light Gradient Boosting Machine Learning (LGBM) and XGBoost. Results: Time-frequency domain based non-linear features are selected from preprocessed EEG Dataset, and PCA (Principal Component Analysis) is used for dimensionality reduction for features engineering, then optimized feature based training and testing is done for two class classification in ensemble learning method i.e. LGBM and XGBoost. Finally, both models are tested with dataset of University of Bonn, Germany to classify the signals. Conclusions: In addition this paper highlights the Correlation Analysis Methodology to Identify Strong Predictor and Attributes Correlation-based Attribute Ranking for the Feature Engineering which has proved to be more efficient in EEG signals Classification and provide comparative analysis with other existing models for performance evaluation in terms of accuracy which is 87.34 and 92.31 for LBGM and XGBoost, sensitivity of 85.21 and 90.18 and specificity of 83.0 and 90.04 for LBGM and XGBoost.
{"title":"Gradient Boosting for Predicting the Relation Between Bio-medical Signals and Seizures Using LGBM and XGBoost","authors":"Bhaskar Kapoor, Bharti Nagpal","doi":"10.13052/jmm1550-4646.2025","DOIUrl":"https://doi.org/10.13052/jmm1550-4646.2025","url":null,"abstract":"Background and aim: In recent years, research in the fields of brain-computer interfacing techniques and related areas are developing at a very rapid rate with the help of exploding of Artificial Intelligence, Machine Learning and Deep Learning. A new concept of Gradient Boosting has become popular research area among the researchers related to the field of automatic classification of Electroencephalograph (EEG) signals for predication of mental health issues like seizures.\u0000Methods: However effective feature extraction from EEG and accurately classify them with efficient classifiers is still an important task and attracted wide attention in this area. Therefore in this paper, we presented the detailed mathematical analysis of these methods and ensemble learnings based EEG signals classification method for seizures classification in EEG using Extreme Gradient Boosting Model such as Light Gradient Boosting Machine Learning (LGBM) and XGBoost.\u0000Results: Time-frequency domain based non-linear features are selected from preprocessed EEG Dataset, and PCA (Principal Component Analysis) is used for dimensionality reduction for features engineering, then optimized feature based training and testing is done for two class classification in ensemble learning method i.e. LGBM and XGBoost. Finally, both models are tested with dataset of University of Bonn, Germany to classify the signals.\u0000Conclusions: In addition this paper highlights the Correlation Analysis Methodology to Identify Strong Predictor and Attributes Correlation-based Attribute Ranking for the Feature Engineering which has proved to be more efficient in EEG signals Classification and provide comparative analysis with other existing models for performance evaluation in terms of accuracy which is 87.34 and 92.31 for LBGM and XGBoost, sensitivity of 85.21 and 90.18 and specificity of 83.0 and 90.04 for LBGM and XGBoost.","PeriodicalId":38898,"journal":{"name":"Journal of Mobile Multimedia","volume":"47 5","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-03-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140365774","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 : 2024-03-29DOI: 10.13052/jmm1550-4646.2029
Jayasri Kotti, B. Padmaja, D. Deepa
Artificial Intelligence has become an essential part of modern technology. Although computer technology is advanced, it can still be improved to make it more user-friendly. One way to do this is to replace touchscreen desktops with a virtual mouse and keyboard. This can reduce the need for gadgets and enhance human-computer interaction. During the COVID-19 pandemic, reducing human intervention and dependency on devices has been critical in controlling the spread of the virus. This is where a battery-powered or Bluetooth mouse, powered by virtual reality technology, can be helpful. The virtual mouse is created using OpenCV and virtual reality technology, with the proposed system utilizing advanced tools such as MediaPipe and Python. The MediaPipe library is particularly useful in artificial intelligence projects, as it enhances the efficiency of the model. The system is an AI-based mouse and keyboard that can be controlled using hand gestures. The user interacts with the system through the camera output displayed on the screen, while the webcam serves as an input device. Python and OpenCV tools are used for implementation, making it applicable in pandemic situations and smart teaching systems. The proposed system works on Enhancing gesture Controlled Virtual Mouse and Virtual Keyboard through Virtual Assistant using AI Techniques.
{"title":"Enhancing Gesture-Controlled Virtual Mouse and Virtual Keyboard Using AI Techniques","authors":"Jayasri Kotti, B. Padmaja, D. Deepa","doi":"10.13052/jmm1550-4646.2029","DOIUrl":"https://doi.org/10.13052/jmm1550-4646.2029","url":null,"abstract":"Artificial Intelligence has become an essential part of modern technology. Although computer technology is advanced, it can still be improved to make it more user-friendly. One way to do this is to replace touchscreen desktops with a virtual mouse and keyboard. This can reduce the need for gadgets and enhance human-computer interaction. During the COVID-19 pandemic, reducing human intervention and dependency on devices has been critical in controlling the spread of the virus. This is where a battery-powered or Bluetooth mouse, powered by virtual reality technology, can be helpful. The virtual mouse is created using OpenCV and virtual reality technology, with the proposed system utilizing advanced tools such as MediaPipe and Python. The MediaPipe library is particularly useful in artificial intelligence projects, as it enhances the efficiency of the model. The system is an AI-based mouse and keyboard that can be controlled using hand gestures. The user interacts with the system through the camera output displayed on the screen, while the webcam serves as an input device. Python and OpenCV tools are used for implementation, making it applicable in pandemic situations and smart teaching systems. The proposed system works on Enhancing gesture Controlled Virtual Mouse and Virtual Keyboard through Virtual Assistant using AI Techniques.","PeriodicalId":38898,"journal":{"name":"Journal of Mobile Multimedia","volume":"47 11","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-03-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140367991","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 : 2024-03-29DOI: 10.13052/jmm1550-4646.2021
Chunduru Anilkumar, Meesala Shobha Rani, Venkatesh B, G. S. Rao
One of the leading causes of serious injuries in motorcycle accidents is the failure to wear a helmet, pressing the need for effective measures to encourage riders to use helmets. To stop these frequent violations of business regulations, regular observation is necessary. The proposed system is for detecting traffic violations in India related to riding motorcycles without helmets. The system utilizes deep learning-based object detection using YOLO and OCR techniques to automatically detect non-helmet riders and extract license plate numbers. The proposed system aims to improve efficiency and accuracy in detecting violations by automating the process and reducing the need for manpower. The system involves three levels of object detection: person and motorcycle/moped, helmet, and license plate. OCR is used to extract the license plate number, and all techniques are subject to predefined conditions and constraints. The system is designed to operate on video input to ensure high-speed execution, and it intends to offer a complete solution for both detection of helmet and extracting the license plate number.
{"title":"Automated License Plate Recognition for Non-Helmeted Motor Riders Using YOLO and OCR","authors":"Chunduru Anilkumar, Meesala Shobha Rani, Venkatesh B, G. S. Rao","doi":"10.13052/jmm1550-4646.2021","DOIUrl":"https://doi.org/10.13052/jmm1550-4646.2021","url":null,"abstract":"One of the leading causes of serious injuries in motorcycle accidents is the failure to wear a helmet, pressing the need for effective measures to encourage riders to use helmets. To stop these frequent violations of business regulations, regular observation is necessary. The proposed system is for detecting traffic violations in India related to riding motorcycles without helmets. The system utilizes deep learning-based object detection using YOLO and OCR techniques to automatically detect non-helmet riders and extract license plate numbers. The proposed system aims to improve efficiency and accuracy in detecting violations by automating the process and reducing the need for manpower. The system involves three levels of object detection: person and motorcycle/moped, helmet, and license plate. OCR is used to extract the license plate number, and all techniques are subject to predefined conditions and constraints. The system is designed to operate on video input to ensure high-speed execution, and it intends to offer a complete solution for both detection of helmet and extracting the license plate number.","PeriodicalId":38898,"journal":{"name":"Journal of Mobile Multimedia","volume":"50 5","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-03-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140365926","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}
This research aimed to achieve two main objectives: (1) to assess the learning achievements in online task-based language learning, and (2) to evaluate satisfaction with the utilization of the online task-based language learning model. The study’s population comprised Buddhist monk students from four provinces in Thailand: Chiang Rai, Phrae, Phayao, and Nan. A total of 80 participants took part in a 30-hour English language training program that focused on task-based language teaching (TBLT) implemented through online technology to enhance Thai monks’ speaking performance. Zoom and free online tools such as Pronunciation Checker App were integrated into the TBL learning cycle to deliver this online training. The research process can be summarized four stages consisting of orientation stage, pre-practicing stage, practicing stage, and post-practicing stage. The comparison of learning outcomes before and after the implementation of the instructional model for monks revealed a significant difference in the average scores. The overall score before training was 38.78 (S.D. = 5.85), while the post-training learning outcomes had a higher average score of 47.34 (S.D. = 4.99). The assessment of satisfaction was divided into four dimensions: (1) content and language usage; (2) English instructional activities for monks; (3) teaching and learning process; and (4) development of English-speaking skills. Overall, participants expressed high levels of satisfaction across all four dimensions of the instructional design approach, with a mean rating of 4.49 and a standard deviation of 0.56.
{"title":"Online Task-Based Language Learning to Enhance Thai Monks’ Speaking Performance","authors":"Sirikanya Dawilai, Natthaphon Santhi, Bhudthree Wetpichetkosol","doi":"10.13052/jmm1550-4646.2023","DOIUrl":"https://doi.org/10.13052/jmm1550-4646.2023","url":null,"abstract":"This research aimed to achieve two main objectives: (1) to assess the learning achievements in online task-based language learning, and (2) to evaluate satisfaction with the utilization of the online task-based language learning model. The study’s population comprised Buddhist monk students from four provinces in Thailand: Chiang Rai, Phrae, Phayao, and Nan. A total of 80 participants took part in a 30-hour English language training program that focused on task-based language teaching (TBLT) implemented through online technology to enhance Thai monks’ speaking performance. Zoom and free online tools such as Pronunciation Checker App were integrated into the TBL learning cycle to deliver this online training. The research process can be summarized four stages consisting of orientation stage, pre-practicing stage, practicing stage, and post-practicing stage.\u0000The comparison of learning outcomes before and after the implementation of the instructional model for monks revealed a significant difference in the average scores. The overall score before training was 38.78 (S.D. = 5.85), while the post-training learning outcomes had a higher average score of 47.34 (S.D. = 4.99). The assessment of satisfaction was divided into four dimensions: (1) content and language usage; (2) English instructional activities for monks; (3) teaching and learning process; and (4) development of English-speaking skills. Overall, participants expressed high levels of satisfaction across all four dimensions of the instructional design approach, with a mean rating of 4.49 and a standard deviation of 0.56.","PeriodicalId":38898,"journal":{"name":"Journal of Mobile Multimedia","volume":"17 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-03-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140367276","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 : 2024-03-29DOI: 10.13052/jmm1550-4646.2026
Deepti Aggarwal, Sonu Mittal, Kamal Upreti, Pinki Nayak
Most of the time in our surroundings we come across the overfilled garbage bins near the lakes. When the bins are full, people just throw the waste here and there, which eventually goes into the lakes and pollutes the water bodies. This is because of improper dumping of garbage that is practiced in our society. With the increase in population, this problem is taking really bad shape. The prime need is to maintain a clean and healthy environment with proper disposal of waste. This paper presents a small effort to reduce this garbage problem. An Android app has been created which keeps on checking whether the dustbin is full. Also, the people will be rewarded for throwing waste into the dustbins. A QR code has been attached to the dustbin which will be scanned for rewarding the people. The dustbins use an IR sensor that detects the receiver of waste in bins. Major part of this proposed system includes the proper working of mobile application and proximity sensors. Arduino is used to maintain the proper connection with sensors and application and that is done by Bluetooth sensor. The main objective of this proposed system is to lure people to put waste into the dustbin along with the contribution towards smart city vision. This paper also gives a brief overview of the technologies and work done so far in this field.
{"title":"Reward Based Garbage Monitoring and Collection System Using Sensors","authors":"Deepti Aggarwal, Sonu Mittal, Kamal Upreti, Pinki Nayak","doi":"10.13052/jmm1550-4646.2026","DOIUrl":"https://doi.org/10.13052/jmm1550-4646.2026","url":null,"abstract":"Most of the time in our surroundings we come across the overfilled garbage bins near the lakes. When the bins are full, people just throw the waste here and there, which eventually goes into the lakes and pollutes the water bodies. This is because of improper dumping of garbage that is practiced in our society. With the increase in population, this problem is taking really bad shape. The prime need is to maintain a clean and healthy environment with proper disposal of waste. This paper presents a small effort to reduce this garbage problem. An Android app has been created which keeps on checking whether the dustbin is full. Also, the people will be rewarded for throwing waste into the dustbins. A QR code has been attached to the dustbin which will be scanned for rewarding the people. The dustbins use an IR sensor that detects the receiver of waste in bins. Major part of this proposed system includes the proper working of mobile application and proximity sensors. Arduino is used to maintain the proper connection with sensors and application and that is done by Bluetooth sensor. The main objective of this proposed system is to lure people to put waste into the dustbin along with the contribution towards smart city vision. This paper also gives a brief overview of the technologies and work done so far in this field.","PeriodicalId":38898,"journal":{"name":"Journal of Mobile Multimedia","volume":"13 7","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-03-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140367458","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 : 2024-03-29DOI: 10.13052/jmm1550-4646.2027
S. Arunprasath, A. Suresh
Information and communication technology based inter-organizational systems enable companies to integrate information and conduct business electronically across different parts of the organization. For organizations embracing blockchain, smart contracts provide automation and operational efficiency for inter-organizational systems. Initially utilised for financial transactions, smart contract are extended beyond banking and deployed in wide number of organizations. Smart contracts are regarded as self-executing type of contract consisting of agreement’s terms embedded directly into the code which plays a vital role in operability for inter-organizational systems, however, smart contract vulnerabilities can arise due to programming errors, leading to security issues. The effects of smart contract vulnerabilities can be significant, including loss of funds, unauthorized access to sensitive information, manipulation of data, and loss of trust in the application leading to catastrophic financial losses followed by legal implications for an organization based on blockchain technology. The goal of smart contracts exploiting vulnerabilities is to discover and eliminate potential security vulnerabilities in smart contract code prior to it being deployed. Detecting vulnerabilities in a timely manner helps to prevent financial losses, unauthorized access, and data manipulation. In order to provide a robust solution to detect vulnerabilities in smart contracts, the proposed methodology presents a novel approach for rapid detection of vulnerabilities by integrating genetic algorithm with isolation forest. Furthermore, enhancing smart contract vulnerability identification with higher accuracy and false-positive rate provides a reliable gateway for organizations to adopt blockchain.
{"title":"A Reliable Framework for Detection of Smart Contract Vulnerabilities for Enhancing Operability in Inter-Organizational Systems","authors":"S. Arunprasath, A. Suresh","doi":"10.13052/jmm1550-4646.2027","DOIUrl":"https://doi.org/10.13052/jmm1550-4646.2027","url":null,"abstract":"Information and communication technology based inter-organizational systems enable companies to integrate information and conduct business electronically across different parts of the organization. For organizations embracing blockchain, smart contracts provide automation and operational efficiency for inter-organizational systems. Initially utilised for financial transactions, smart contract are extended beyond banking and deployed in wide number of organizations. Smart contracts are regarded as self-executing type of contract consisting of agreement’s terms embedded directly into the code which plays a vital role in operability for inter-organizational systems, however, smart contract vulnerabilities can arise due to programming errors, leading to security issues. The effects of smart contract vulnerabilities can be significant, including loss of funds, unauthorized access to sensitive information, manipulation of data, and loss of trust in the application leading to catastrophic financial losses followed by legal implications for an organization based on blockchain technology. The goal of smart contracts exploiting vulnerabilities is to discover and eliminate potential security vulnerabilities in smart contract code prior to it being deployed. Detecting vulnerabilities in a timely manner helps to prevent financial losses, unauthorized access, and data manipulation. In order to provide a robust solution to detect vulnerabilities in smart contracts, the proposed methodology presents a novel approach for rapid detection of vulnerabilities by integrating genetic algorithm with isolation forest. Furthermore, enhancing smart contract vulnerability identification with higher accuracy and false-positive rate provides a reliable gateway for organizations to adopt blockchain.","PeriodicalId":38898,"journal":{"name":"Journal of Mobile Multimedia","volume":"56 11","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-03-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140367357","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 : 2024-03-29DOI: 10.13052/jmm1550-4646.2022
Sai Woon Sheng, S. Wicha
The Southern regions of India, Myanmar, Thailand, Laos, and Indonesia are where teak originated. Teak is a high-value wood that used to be an export good for Thailand, bringing in a lot of money. Thailand produced approximately 71,954.53 m3 of teak plantation timber and exported wood products worth 1.1 billion baht overseas in 2018, according to the Forest Industry Organization (FIO). Due to its high demand, there is also a chance that smuggled wood from within the country or wood that has been illegally obtained abroad will enter the supply chain. Encroachment and illegal logging are still major problems in Thailand. Blockchain technology has become extremely popular due to its distinctive immutability and traceability properties, which have the opportunity to overcome a variety of issues. In order to get rid of illegal teak timber and achieve traceable, transparent, and reliable teak data that is moved through the teak supply chain, we present a decentralized application (DApp) based on the Ethereum blockchain that implements a traceability system for teak identity. According to the findings of the experiment, our DApp achieves a good trade-off between the system’s gas cost of 116K (2.53 USD) to store data in the Ethereum blockchain and provide high security, transparency, privacy, resilience, and robustness. We observed that the newly proposed blockchain-based system can reduce illegal logging, the usage of paper-based documentation, and the time needed to validate the documentation in teak supply chain controls when we compared it to the traditional process used in the supply chain.
{"title":"Blockchain-based Traceability for Teak Identity: A Transformational Approach","authors":"Sai Woon Sheng, S. Wicha","doi":"10.13052/jmm1550-4646.2022","DOIUrl":"https://doi.org/10.13052/jmm1550-4646.2022","url":null,"abstract":"The Southern regions of India, Myanmar, Thailand, Laos, and Indonesia are where teak originated. Teak is a high-value wood that used to be an export good for Thailand, bringing in a lot of money. Thailand produced approximately 71,954.53 m3 of teak plantation timber and exported wood products worth 1.1 billion baht overseas in 2018, according to the Forest Industry Organization (FIO). Due to its high demand, there is also a chance that smuggled wood from within the country or wood that has been illegally obtained abroad will enter the supply chain. Encroachment and illegal logging are still major problems in Thailand. Blockchain technology has become extremely popular due to its distinctive immutability and traceability properties, which have the opportunity to overcome a variety of issues. In order to get rid of illegal teak timber and achieve traceable, transparent, and reliable teak data that is moved through the teak supply chain, we present a decentralized application (DApp) based on the Ethereum blockchain that implements a traceability system for teak identity. According to the findings of the experiment, our DApp achieves a good trade-off between the system’s gas cost of 116K (2.53 USD) to store data in the Ethereum blockchain and provide high security, transparency, privacy, resilience, and robustness. We observed that the newly proposed blockchain-based system can reduce illegal logging, the usage of paper-based documentation, and the time needed to validate the documentation in teak supply chain controls when we compared it to the traditional process used in the supply chain.","PeriodicalId":38898,"journal":{"name":"Journal of Mobile Multimedia","volume":"3 9","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-03-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140367330","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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