Most current non-intrusive load monitoring methods focus on traditional load characteristic analysis and algorithm optimization, lack knowledge of users’ electricity consumption behavior habits, and have poor accuracy. We propose a novel attention-guided bidirectional dynamic graph IndRNN approach. The method first extends sequence or multidimensional data to a topological graph structure. It effectively utilizes the global context by following an adaptive graph topology derived from each set of data content. Then, the bidirectional Graph IndRNN network (Graph IndRNN) encodes the aggregated signals into different graph nodes, which use node information transfer and aggregation based on the entropy measure, power attribute characteristics, and the time-related structural characteristics of the corresponding device signals. The function dynamically incorporates local and global contextual interactions from positive and negative directions to learn the neighboring node information for non-intrusive load decomposition. In addition, using the sequential attention mechanism as a guide while eliminating redundant information facilitates flexible reasoning and establishes good vertex relationships. Finally, we conducted experimental evaluations on multiple open source data, proving that the method has good robustness and accuracy.
{"title":"AttG-BDGNets: Attention-Guided Bidirectional Dynamic Graph IndRNN for Non-Intrusive Load Monitoring","authors":"Zuoxin Wang, Xiaohu Zhao","doi":"10.3390/info14070383","DOIUrl":"https://doi.org/10.3390/info14070383","url":null,"abstract":"Most current non-intrusive load monitoring methods focus on traditional load characteristic analysis and algorithm optimization, lack knowledge of users’ electricity consumption behavior habits, and have poor accuracy. We propose a novel attention-guided bidirectional dynamic graph IndRNN approach. The method first extends sequence or multidimensional data to a topological graph structure. It effectively utilizes the global context by following an adaptive graph topology derived from each set of data content. Then, the bidirectional Graph IndRNN network (Graph IndRNN) encodes the aggregated signals into different graph nodes, which use node information transfer and aggregation based on the entropy measure, power attribute characteristics, and the time-related structural characteristics of the corresponding device signals. The function dynamically incorporates local and global contextual interactions from positive and negative directions to learn the neighboring node information for non-intrusive load decomposition. In addition, using the sequential attention mechanism as a guide while eliminating redundant information facilitates flexible reasoning and establishes good vertex relationships. Finally, we conducted experimental evaluations on multiple open source data, proving that the method has good robustness and accuracy.","PeriodicalId":13622,"journal":{"name":"Inf. Comput.","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2023-07-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"72910602","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}
Semen Mukhamadiev, S. Nesteruk, S. Illarionova, A. Somov
Plant segmentation is a challenging computer vision task due to plant images complexity. For many practical problems, we have to solve even more difficult tasks. We need to distinguish plant parts rather than the whole plant. The major complication of multi-part segmentation is the absence of well-annotated datasets. It is very time-consuming and expensive to annotate datasets manually on the object parts level. In this article, we propose to use weakly supervised learning for pseudo-annotation. The goal is to train a plant part segmentation model using only bounding boxes instead of fine-grained masks. We review the existing weakly supervised learning approaches and propose an efficient pipeline for agricultural domains. It is designed to resolve tight object overlappings. Our pipeline beats the baseline solution by 23% for the plant part case and by 40% for the whole plant case. Furthermore, we apply instance-level augmentation to boost model performance. The idea of this approach is to obtain a weak segmentation mask and use it for cropping objects from original images and pasting them to new backgrounds during model training. This method provides us a 55% increase in mAP compared with the baseline on object part and a 72% increase on the whole plant segmentation tasks.
{"title":"Enabling Multi-Part Plant Segmentation with Instance-Level Augmentation Using Weak Annotations","authors":"Semen Mukhamadiev, S. Nesteruk, S. Illarionova, A. Somov","doi":"10.3390/info14070380","DOIUrl":"https://doi.org/10.3390/info14070380","url":null,"abstract":"Plant segmentation is a challenging computer vision task due to plant images complexity. For many practical problems, we have to solve even more difficult tasks. We need to distinguish plant parts rather than the whole plant. The major complication of multi-part segmentation is the absence of well-annotated datasets. It is very time-consuming and expensive to annotate datasets manually on the object parts level. In this article, we propose to use weakly supervised learning for pseudo-annotation. The goal is to train a plant part segmentation model using only bounding boxes instead of fine-grained masks. We review the existing weakly supervised learning approaches and propose an efficient pipeline for agricultural domains. It is designed to resolve tight object overlappings. Our pipeline beats the baseline solution by 23% for the plant part case and by 40% for the whole plant case. Furthermore, we apply instance-level augmentation to boost model performance. The idea of this approach is to obtain a weak segmentation mask and use it for cropping objects from original images and pasting them to new backgrounds during model training. This method provides us a 55% increase in mAP compared with the baseline on object part and a 72% increase on the whole plant segmentation tasks.","PeriodicalId":13622,"journal":{"name":"Inf. Comput.","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2023-07-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"76334897","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}
In the context of an increasingly digitized global marketplace, this study seeks to shed light on its adoption in developing countries, focusing on Morocco. Applying logit, probit, and conditional mixed-process probit models to a sample of 807 Moroccan firms, we identify key factors that influence e-commerce adoption. The results show that younger, innovation-driven firms and those with a highly educated workforce tend to adopt e-commerce more readily. However, digital skills required in hiring do not significantly affect adoption, suggesting a complex relationship between digital skills and e-commerce use. The results also show that firms that are active on digital platforms and engage in innovative practices are more likely to adopt e-commerce. Therefore, this study argues for the need to improve digital skills training and for firms to establish a presence on digital platforms and promote innovation. On the policy front, the study suggests the promotion of supportive policies such as financial assistance, improved Internet infrastructure, and robust regulatory frameworks. As an important starting point for future research, these findings underscore the complexities of e-commerce adoption in Morocco and can guide further research, particularly in the context of similar emerging economies.
{"title":"Examining the Drivers of E-Commerce Adoption by Moroccan Firms: A Multi-Model Analysis","authors":"A. B. Youssef, Mounir Dahmani","doi":"10.3390/info14070378","DOIUrl":"https://doi.org/10.3390/info14070378","url":null,"abstract":"In the context of an increasingly digitized global marketplace, this study seeks to shed light on its adoption in developing countries, focusing on Morocco. Applying logit, probit, and conditional mixed-process probit models to a sample of 807 Moroccan firms, we identify key factors that influence e-commerce adoption. The results show that younger, innovation-driven firms and those with a highly educated workforce tend to adopt e-commerce more readily. However, digital skills required in hiring do not significantly affect adoption, suggesting a complex relationship between digital skills and e-commerce use. The results also show that firms that are active on digital platforms and engage in innovative practices are more likely to adopt e-commerce. Therefore, this study argues for the need to improve digital skills training and for firms to establish a presence on digital platforms and promote innovation. On the policy front, the study suggests the promotion of supportive policies such as financial assistance, improved Internet infrastructure, and robust regulatory frameworks. As an important starting point for future research, these findings underscore the complexities of e-commerce adoption in Morocco and can guide further research, particularly in the context of similar emerging economies.","PeriodicalId":13622,"journal":{"name":"Inf. Comput.","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2023-07-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"73113961","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}
Yumeng Zhang, Chia-Yuan Cheng, Chih-Lung Lin, Chun-Chieh Lee, Kuo-Chin Fan
Biometrics has become an important research issue in recent years, and the use of deep learning neural networks has made it possible to develop more reliable and efficient recognition systems. Palms have been identified as one of the most promising candidates among various biometrics due to their unique features and easy accessibility. However, traditional palm recognition methods involve 3D point clouds, which can be complex and difficult to work with. To mitigate this challenge, this paper proposes two methods which are Multi-View Projection (MVP) and Light Inverted Residual Block (LIRB).The MVP simulates different angles that observers use to observe palms in reality. It transforms 3D point clouds into multiple 2D images and effectively reduces the loss of mapping 3D data to 2D data. Therefore, the MVP can greatly reduce the complexity of the system. In experiments, MVP demonstrated remarkable performance on various famous models, such as VGG or MobileNetv2, with a particular improvement in the performance of smaller models. To further improve the performance of small models, this paper applies LIRB to build a lightweight 2D CNN called Tiny-MobileNet (TMBNet).The TMBNet has only a few convolutional layers but outperforms the 3D baselines PointNet and PointNet++ in FLOPs and accuracy. The experimental results show that the proposed method can effectively mitigate the challenges of recognizing palms through 3D point clouds of palms. The proposed method not only reduces the complexity of the system but also extends the use of lightweight CNN. These findings have significant implications for developing biometrics and could lead to improvements in various fields, such as access control and security control.
{"title":"Develop a Lightweight Convolutional Neural Network to Recognize Palms Using 3D Point Clouds","authors":"Yumeng Zhang, Chia-Yuan Cheng, Chih-Lung Lin, Chun-Chieh Lee, Kuo-Chin Fan","doi":"10.3390/info14070381","DOIUrl":"https://doi.org/10.3390/info14070381","url":null,"abstract":"Biometrics has become an important research issue in recent years, and the use of deep learning neural networks has made it possible to develop more reliable and efficient recognition systems. Palms have been identified as one of the most promising candidates among various biometrics due to their unique features and easy accessibility. However, traditional palm recognition methods involve 3D point clouds, which can be complex and difficult to work with. To mitigate this challenge, this paper proposes two methods which are Multi-View Projection (MVP) and Light Inverted Residual Block (LIRB).The MVP simulates different angles that observers use to observe palms in reality. It transforms 3D point clouds into multiple 2D images and effectively reduces the loss of mapping 3D data to 2D data. Therefore, the MVP can greatly reduce the complexity of the system. In experiments, MVP demonstrated remarkable performance on various famous models, such as VGG or MobileNetv2, with a particular improvement in the performance of smaller models. To further improve the performance of small models, this paper applies LIRB to build a lightweight 2D CNN called Tiny-MobileNet (TMBNet).The TMBNet has only a few convolutional layers but outperforms the 3D baselines PointNet and PointNet++ in FLOPs and accuracy. The experimental results show that the proposed method can effectively mitigate the challenges of recognizing palms through 3D point clouds of palms. The proposed method not only reduces the complexity of the system but also extends the use of lightweight CNN. These findings have significant implications for developing biometrics and could lead to improvements in various fields, such as access control and security control.","PeriodicalId":13622,"journal":{"name":"Inf. Comput.","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2023-07-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"87434222","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}
S. Mystakidis, Athanasios Christopoulos, M. Fragkaki, Konstantinos Dimitropoulos
Higher education teaching staff members need to build a scientifically accurate and comprehensive understanding of the function of the brain in learning as neuroscience evidence can constitute a way to optimize teaching and achieve learning excellence. An international consortium developed a professional development six-module course on educational neuroscience and online community of practice by applying design thinking. A mixed methods research design was employed to investigate the attitudes of thirty-two (N = 32) participating academics using a survey comprising eleven closed and open questions. Data analysis methods included descriptive statistics, correlation, generalized additive model and grounded theory. The overall evaluation demonstrated a notable satisfaction level with regard to the quality of the course. Given the power of habits, mentoring and peer interactions are recommended to ensure the effective integration of theoretical neuroscientific evidence into teaching practice.
{"title":"Online Professional Development on Educational Neuroscience in Higher Education Based on Design Thinking","authors":"S. Mystakidis, Athanasios Christopoulos, M. Fragkaki, Konstantinos Dimitropoulos","doi":"10.3390/info14070382","DOIUrl":"https://doi.org/10.3390/info14070382","url":null,"abstract":"Higher education teaching staff members need to build a scientifically accurate and comprehensive understanding of the function of the brain in learning as neuroscience evidence can constitute a way to optimize teaching and achieve learning excellence. An international consortium developed a professional development six-module course on educational neuroscience and online community of practice by applying design thinking. A mixed methods research design was employed to investigate the attitudes of thirty-two (N = 32) participating academics using a survey comprising eleven closed and open questions. Data analysis methods included descriptive statistics, correlation, generalized additive model and grounded theory. The overall evaluation demonstrated a notable satisfaction level with regard to the quality of the course. Given the power of habits, mentoring and peer interactions are recommended to ensure the effective integration of theoretical neuroscientific evidence into teaching practice.","PeriodicalId":13622,"journal":{"name":"Inf. Comput.","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2023-07-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"86691380","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}
K. M. Abdelgaber, Mostafa Salah, O. Omer, Ahmed E. A. Farghal, Ahmed S. A. Mubarak
In this paper, a beat-based autoencoder is proposed for mapping photoplethysmography (PPG) to a single-lead electrocardiogram (single-lead ECG) signal. The main limiting factors represented in uncleaned data, subject dependency, and erroneous beat segmentation are regarded. The dataset is cleaned by a two-stage clustering approach. Rather than complete single–lead ECG signal reconstruction, a beat-based PPG-to-single-lead-ECG (PPG2ECG) conversion is introduced for providing a simple lightweight model that meets the computational capabilities of wearable devices. In addition, peak-to-peak segmentation is employed for alleviating errors in PPG onset detection. Furthermore, subject-dependent training is highlighted as a critical factor in training procedures because most existing work includes different beats/signals from the same subject’s record in both training and testing sets. So, we provide a completely subject-independent model where the testing subjects’ records are hidden in the training stage entirely, i.e., a subject record appears once either in the training or testing set, but testing beats/signals belong to records that never appear in the training set. The proposed deep learning model is designed for providing efficient feature extraction that attains high reconstruction quality over subject-independent scenarios. The achieved performance is about 0.92 for the correlation coefficient and 0.0086 for the mean square error for the dataset extracted/cleaned from the MIMIC II dataset.
{"title":"Subject-Independent per Beat PPG to Single-Lead ECG Mapping","authors":"K. M. Abdelgaber, Mostafa Salah, O. Omer, Ahmed E. A. Farghal, Ahmed S. A. Mubarak","doi":"10.3390/info14070377","DOIUrl":"https://doi.org/10.3390/info14070377","url":null,"abstract":"In this paper, a beat-based autoencoder is proposed for mapping photoplethysmography (PPG) to a single-lead electrocardiogram (single-lead ECG) signal. The main limiting factors represented in uncleaned data, subject dependency, and erroneous beat segmentation are regarded. The dataset is cleaned by a two-stage clustering approach. Rather than complete single–lead ECG signal reconstruction, a beat-based PPG-to-single-lead-ECG (PPG2ECG) conversion is introduced for providing a simple lightweight model that meets the computational capabilities of wearable devices. In addition, peak-to-peak segmentation is employed for alleviating errors in PPG onset detection. Furthermore, subject-dependent training is highlighted as a critical factor in training procedures because most existing work includes different beats/signals from the same subject’s record in both training and testing sets. So, we provide a completely subject-independent model where the testing subjects’ records are hidden in the training stage entirely, i.e., a subject record appears once either in the training or testing set, but testing beats/signals belong to records that never appear in the training set. The proposed deep learning model is designed for providing efficient feature extraction that attains high reconstruction quality over subject-independent scenarios. The achieved performance is about 0.92 for the correlation coefficient and 0.0086 for the mean square error for the dataset extracted/cleaned from the MIMIC II dataset.","PeriodicalId":13622,"journal":{"name":"Inf. Comput.","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2023-07-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"73050536","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}
Viet Q. Vu, Minh-Quang Tran, Mohammed Amer, Mahesh Khatiwada, S. Ghoneim, M. Elsisi
Facial mask detection technology has become increasingly important even beyond the context of the COVID-19 pandemic. Along with the advancement in facial recognition technology, face mask detection has become a crucial feature for various applications. This paper introduces an Internet of Things (IoT) architecture based on a developed deep learning algorithm named You Only Look Once (YOLO) to keep society healthy, and secured, and collect data for future research. The proposed paradigm is built on the basis of economic consideration and is easy to implement. Yet, the used YOLOv4-tiny is one of the fastest object detection models to exist. A mask detection camera (MaskCam) that leverages the computing power of NVIDIA’s Jetson Nano edge nanodevices was built side by side with a smart camera application to detect a mask on the face of an individual. MaskCam distinguishes between mask wearers, those who are not wearing masks, and those who are not wearing masks properly according to MQTT protocol. Furthermore, a self-developed web browsing application comes with the MaskCam system to collect and visualize statistics for qualitative and quantitative analysis. The practical results demonstrate the superiority and effectiveness of the proposed smart mask detection system. On the one hand, YOLOv4-full obtained the best results even at smaller resolutions, although the frame rate is too small for real-time use. On the other hand, it is twice as fast as the other detection models, regardless of the quality of detection. Consequently, inferences may be run more frequently over the entire video sequence, resulting in more accurate output.
即使在COVID-19大流行的背景下，口罩检测技术也变得越来越重要。随着人脸识别技术的进步，人脸检测已成为各种应用的关键功能。本文介绍了一种基于深度学习算法You Only Look Once (YOLO)的物联网(IoT)架构，以保持社会的健康和安全，并为未来的研究收集数据。所提出的范式是建立在经济考虑的基础上的，并且易于实现。然而，使用的YOLOv4-tiny是现有最快的目标检测模型之一。利用NVIDIA Jetson Nano边缘纳米设备计算能力的面具检测摄像头(MaskCam)与智能相机应用程序并排构建，用于检测个人脸上的面具。MaskCam区分口罩佩戴者，未佩戴口罩的人，以及根据MQTT协议未正确佩戴口罩的人。此外，MaskCam系统自带一个自行开发的网页浏览应用程序，用于收集和可视化统计数据，用于定性和定量分析。实际结果证明了所提出的智能掩模检测系统的优越性和有效性。一方面，即使在较小的分辨率下，YOLOv4-full也获得了最好的结果，尽管帧率太小，无法实时使用。另一方面，无论检测质量如何，它的速度都是其他检测模型的两倍。因此，推理可以在整个视频序列上更频繁地运行，从而产生更准确的输出。
{"title":"A Practical Hybrid IoT Architecture with Deep Learning Technique for Healthcare and Security Applications","authors":"Viet Q. Vu, Minh-Quang Tran, Mohammed Amer, Mahesh Khatiwada, S. Ghoneim, M. Elsisi","doi":"10.3390/info14070379","DOIUrl":"https://doi.org/10.3390/info14070379","url":null,"abstract":"Facial mask detection technology has become increasingly important even beyond the context of the COVID-19 pandemic. Along with the advancement in facial recognition technology, face mask detection has become a crucial feature for various applications. This paper introduces an Internet of Things (IoT) architecture based on a developed deep learning algorithm named You Only Look Once (YOLO) to keep society healthy, and secured, and collect data for future research. The proposed paradigm is built on the basis of economic consideration and is easy to implement. Yet, the used YOLOv4-tiny is one of the fastest object detection models to exist. A mask detection camera (MaskCam) that leverages the computing power of NVIDIA’s Jetson Nano edge nanodevices was built side by side with a smart camera application to detect a mask on the face of an individual. MaskCam distinguishes between mask wearers, those who are not wearing masks, and those who are not wearing masks properly according to MQTT protocol. Furthermore, a self-developed web browsing application comes with the MaskCam system to collect and visualize statistics for qualitative and quantitative analysis. The practical results demonstrate the superiority and effectiveness of the proposed smart mask detection system. On the one hand, YOLOv4-full obtained the best results even at smaller resolutions, although the frame rate is too small for real-time use. On the other hand, it is twice as fast as the other detection models, regardless of the quality of detection. Consequently, inferences may be run more frequently over the entire video sequence, resulting in more accurate output.","PeriodicalId":13622,"journal":{"name":"Inf. Comput.","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2023-07-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"83729781","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}
Md. Jamal Uddin, Md. Martuza Ahamad, Md. Nesarul Hoque, Md. Abul Ala Walid, Sakifa Aktar, Naif Alotaibi, S. Alyami, M. A. Kabir, M. Moni
Diabetes is a chronic disease caused by a persistently high blood sugar level, causing other chronic diseases, including cardiovascular, kidney, eye, and nerve damage. Prompt detection plays a vital role in reducing the risk and severity associated with diabetes, and identifying key risk factors can help individuals become more mindful of their lifestyles. In this study, we conducted a questionnaire-based survey utilizing standard diabetes risk variables to examine the prevalence of diabetes in Bangladesh. To enable prompt detection of diabetes, we compared different machine learning techniques and proposed an ensemble-based machine learning framework that incorporated algorithms such as decision tree, random forest, and extreme gradient boost algorithms. In order to address class imbalance within the dataset, we initially applied the synthetic minority oversampling technique (SMOTE) and random oversampling (ROS) techniques. We evaluated the performance of various classifiers, including decision tree (DT), logistic regression (LR), support vector machine (SVM), gradient boost (GB), extreme gradient boost (XGBoost), random forest (RF), and ensemble technique (ET), on our diabetes datasets. Our experimental results showed that the ET outperformed other classifiers; to further enhance its effectiveness, we fine-tuned and evaluated the hyperparameters of the ET. Using statistical and machine learning techniques, we also ranked features and identified that age, extreme thirst, and diabetes in the family are significant features that prove instrumental in the detection of diabetes patients. This method has great potential for clinicians to effectively identify individuals at risk of diabetes, facilitating timely intervention and care.
{"title":"A Comparison of Machine Learning Techniques for the Detection of Type-2 Diabetes Mellitus: Experiences from Bangladesh","authors":"Md. Jamal Uddin, Md. Martuza Ahamad, Md. Nesarul Hoque, Md. Abul Ala Walid, Sakifa Aktar, Naif Alotaibi, S. Alyami, M. A. Kabir, M. Moni","doi":"10.3390/info14070376","DOIUrl":"https://doi.org/10.3390/info14070376","url":null,"abstract":"Diabetes is a chronic disease caused by a persistently high blood sugar level, causing other chronic diseases, including cardiovascular, kidney, eye, and nerve damage. Prompt detection plays a vital role in reducing the risk and severity associated with diabetes, and identifying key risk factors can help individuals become more mindful of their lifestyles. In this study, we conducted a questionnaire-based survey utilizing standard diabetes risk variables to examine the prevalence of diabetes in Bangladesh. To enable prompt detection of diabetes, we compared different machine learning techniques and proposed an ensemble-based machine learning framework that incorporated algorithms such as decision tree, random forest, and extreme gradient boost algorithms. In order to address class imbalance within the dataset, we initially applied the synthetic minority oversampling technique (SMOTE) and random oversampling (ROS) techniques. We evaluated the performance of various classifiers, including decision tree (DT), logistic regression (LR), support vector machine (SVM), gradient boost (GB), extreme gradient boost (XGBoost), random forest (RF), and ensemble technique (ET), on our diabetes datasets. Our experimental results showed that the ET outperformed other classifiers; to further enhance its effectiveness, we fine-tuned and evaluated the hyperparameters of the ET. Using statistical and machine learning techniques, we also ranked features and identified that age, extreme thirst, and diabetes in the family are significant features that prove instrumental in the detection of diabetes patients. This method has great potential for clinicians to effectively identify individuals at risk of diabetes, facilitating timely intervention and care.","PeriodicalId":13622,"journal":{"name":"Inf. Comput.","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2023-07-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"91552026","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}
Jonas Grande-Barreto, Eduardo Polanco-Castro, H. Peregrina-Barreto, Eduardo Rosas-Mialma, Carmina Puig-Mar
Creating synthetic images of trabecular tissue provides an alternative for researchers to validate algorithms designed to study trabecular bone. Developing synthetic images requires baseline data, such as datasets of digital biological samples or templates, often unavailable due to privacy restrictions. Even when this baseline is available, the standard procedure combines the information to generate a single template as a starting point, reducing the variability in the generated synthetic images. This work proposes a methodology for building synthetic images of trabecular bone structure, creating a 3D network that simulates it. Next, the technical characteristics of the micro-CT scanner, the biomechanical properties of trabecular bones, and the physics of the imaging process to produce a synthetic image are simulated. The proposed methodology does not require biological samples, datasets, or templates to generate synthetic images. Since each synthetic image built is unique, the methodology is enabled to generate a vast number of synthetic images, useful in the performance comparison of algorithms under different imaging conditions. The created synthetic images were assessed using microarchitecture parameters of reference, and experimental results provided evidence that the obtained values match approaches requiring initial data. The scope of this methodology covers research aspects related to using synthetic images in further biomedical research or the development of educational training tools to understand the medical image.
{"title":"Generation of Synthetic Images of Trabecular Bone Based on Micro-CT Scans","authors":"Jonas Grande-Barreto, Eduardo Polanco-Castro, H. Peregrina-Barreto, Eduardo Rosas-Mialma, Carmina Puig-Mar","doi":"10.3390/info14070375","DOIUrl":"https://doi.org/10.3390/info14070375","url":null,"abstract":"Creating synthetic images of trabecular tissue provides an alternative for researchers to validate algorithms designed to study trabecular bone. Developing synthetic images requires baseline data, such as datasets of digital biological samples or templates, often unavailable due to privacy restrictions. Even when this baseline is available, the standard procedure combines the information to generate a single template as a starting point, reducing the variability in the generated synthetic images. This work proposes a methodology for building synthetic images of trabecular bone structure, creating a 3D network that simulates it. Next, the technical characteristics of the micro-CT scanner, the biomechanical properties of trabecular bones, and the physics of the imaging process to produce a synthetic image are simulated. The proposed methodology does not require biological samples, datasets, or templates to generate synthetic images. Since each synthetic image built is unique, the methodology is enabled to generate a vast number of synthetic images, useful in the performance comparison of algorithms under different imaging conditions. The created synthetic images were assessed using microarchitecture parameters of reference, and experimental results provided evidence that the obtained values match approaches requiring initial data. The scope of this methodology covers research aspects related to using synthetic images in further biomedical research or the development of educational training tools to understand the medical image.","PeriodicalId":13622,"journal":{"name":"Inf. Comput.","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2023-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"76131637","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}
Parvez Faruki, R. Bhan, V. Jain, Sajal Bhatia, Nour El Madhoun, Raj Pamula
Android platform security is an active area of research where malware detection techniques continuously evolve to identify novel malware and improve the timely and accurate detection of existing malware. Adversaries are constantly in charge of employing innovative techniques to avoid or prolong malware detection effectively. Past studies have shown that malware detection systems are susceptible to evasion attacks where adversaries can successfully bypass the existing security defenses and deliver the malware to the target system without being detected. The evolution of escape-resistant systems is an open research problem. This paper presents a detailed taxonomy and evaluation of Android-based malware evasion techniques deployed to circumvent malware detection. The study characterizes such evasion techniques into two broad categories, polymorphism and metamorphism, and analyses techniques used for stealth malware detection based on the malware’s unique characteristics. Furthermore, the article also presents a qualitative and systematic comparison of evasion detection frameworks and their detection methodologies for Android-based malware. Finally, the survey discusses open-ended questions and potential future directions for continued research in mobile malware detection.
{"title":"A Survey and Evaluation of Android-Based Malware Evasion Techniques and Detection Frameworks","authors":"Parvez Faruki, R. Bhan, V. Jain, Sajal Bhatia, Nour El Madhoun, Raj Pamula","doi":"10.3390/info14070374","DOIUrl":"https://doi.org/10.3390/info14070374","url":null,"abstract":"Android platform security is an active area of research where malware detection techniques continuously evolve to identify novel malware and improve the timely and accurate detection of existing malware. Adversaries are constantly in charge of employing innovative techniques to avoid or prolong malware detection effectively. Past studies have shown that malware detection systems are susceptible to evasion attacks where adversaries can successfully bypass the existing security defenses and deliver the malware to the target system without being detected. The evolution of escape-resistant systems is an open research problem. This paper presents a detailed taxonomy and evaluation of Android-based malware evasion techniques deployed to circumvent malware detection. The study characterizes such evasion techniques into two broad categories, polymorphism and metamorphism, and analyses techniques used for stealth malware detection based on the malware’s unique characteristics. Furthermore, the article also presents a qualitative and systematic comparison of evasion detection frameworks and their detection methodologies for Android-based malware. Finally, the survey discusses open-ended questions and potential future directions for continued research in mobile malware detection.","PeriodicalId":13622,"journal":{"name":"Inf. Comput.","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2023-06-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"74889052","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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