ORCID Profile
0000-0002-6798-6535
Current Organisation
Charles Sturt University
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Publisher: Springer International Publishing
Date: 2022
Publisher: JMIR Publications Inc.
Date: 02-11-2021
DOI: 10.2196/27114
Abstract: The undergraduate student population has been actively studied in digital mental health research. However, the existing literature primarily focuses on students from high-income nations, and undergraduates from limited-income nations remain understudied. This study aims to identify the broader social determinants of mental health among undergraduate students in Bangladesh, a limited-income nation in South Asia study the manifestation of these determinants in their day-to-day lives and explore the feasibility of self-monitoring tools in helping them identify the specific factors or relationships that affect their mental health. We conducted a 21-day study with 38 undergraduate students from 7 universities in Bangladesh. We conducted 2 semistructured interviews: one prestudy and one poststudy. During the 21-day study, participants used an Android app to self-report and self-monitor their mood after each phone conversation. The app prompted participants to report their mood after each phone conversation and provided graphs and charts so that the participants could independently review their mood and conversation patterns. Our results show that academics, family, job and economic condition, romantic relationship, and religion are the major social determinants of mental health among undergraduate students in Bangladesh. Our app helped the participants pinpoint the specific issues related to these factors, as the participants could review the pattern of their moods and emotions from past conversation history. Although our app does not provide any explicit recommendation, the participants took certain steps on their own to improve their mental health (eg, reduced the frequency of communication with certain persons). Although some of the factors (eg, academics) were reported in previous studies conducted in the Global North, this paper sheds light on some new issues (eg, extended family problems and religion) that are specific to the context of the Global South. Overall, the findings from this study would provide better insights for researchers to design better solutions to help the younger population from this part of the world.
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 2021
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 2020
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 2022
Publisher: MDPI AG
Date: 22-08-2023
DOI: 10.3390/ELECTRONICS12173541
Abstract: The Internet of Medical Things (IoMT) has become an attractive playground to cybercriminals because of its market worth and rapid growth. These devices have limited computational capabilities, which ensure minimum power absorption. Moreover, the manufacturers use simplified architecture to offer a competitive price in the market. As a result, IoMTs cannot employ advanced security algorithms to defend against cyber-attacks. IoMT has become easy prey for cybercriminals due to its access to valuable data and the rapidly expanding market, as well as being comparatively easier to exploit.As a result, the intrusion rate in IoMT is experiencing a surge. This paper proposes a novel Intrusion Detection System (IDS), namely SafetyMed, combining Convolutional Neural Networks (CNN) and Long Short-Term Memory (LSTM) networks to defend against intrusion from sequential and grid data. SafetyMed is the first IDS that protects IoMT devices from malicious image data and sequential network traffic. This innovative IDS ensures an optimized detection rate by trade-off between False Positive Rate (FPR) and Detection Rate (DR). It detects intrusions with an average accuracy of 97.63% with average precision and recall, and has an F1-score of 98.47%, 97%, and 97.73%, respectively. In summary, SafetyMed has the potential to revolutionize many vulnerable sectors (e.g., medical) by ensuring maximum protection against IoMT intrusion.
Publisher: IEEE
Date: 11-2016
Publisher: Springer Singapore
Date: 2018
Publisher: JMIR Publications Inc.
Date: 09-09-2020
Abstract: s the use of smartphones increases globally across various fields of research and technology, significant contributions to the sectors related to health, specifically foot health, can be observed. Numerous smartphone apps are now being used for providing accurate information about various foot-related properties. Corresponding to this abundance of foot scanning and measuring apps available in app stores, there is a need for evaluating these apps, as limited information regarding their evidence-based quality is available. he aim of this review was to assess the measurement techniques and essential software quality characteristics of mobile foot measurement apps, and to determine their potential as commercial tools used by foot care health professionals, to assist in measuring feet for custom shoes, and for in iduals to enhance their awareness of foot health and hygiene to ultimately prevent foot-related problems. n electronic search across Android and iOS app stores was performed between July and August 2020 to identify apps related to foot measurement and general foot health. The selected apps were rated by three independent raters, and all discrepancies were resolved by discussion among raters and other investigators. Based on previous work on app rating tools, a modified rating scale tool was devised to rate the selected apps. The internal consistency of the rating tool was tested with a group of three people who rated the selected apps over 2-3 weeks. This scale was then used to produce evaluation scores for the selected foot measurement apps and to assess the interrater reliability. valuation inferences showed that all apps failed to meet even half of the measurement-specific criteria required for the proper manufacturing of custom-made footwear. Only 23% (6/26) of the apps reportedly used external scanners or advanced algorithms to reconstruct 3D models of a user’s foot that could possibly be used for ordering custom-made footwear (shoes, insoles/orthoses), and medical casts to fit irregular foot sizes and shapes. The apps had varying levels of performance and usability, although the overall measurement functionality was subpar with a mean of 1.93 out of 5. Apps linked to online shops and stores (shoe recommendation) were assessed to be more usable than other apps but lacked some features (eg, custom shoe sizes and shapes). Overall, the current apps available for foot measurement do not follow any specific guidelines for measurement purposes. ost commercial apps currently available in app stores are not viable for use as tools in assisting foot care health professionals or in iduals to measure their feet for custom-made footwear. Current apps lack software quality characteristics and need significant improvements to facilitate proper measurement, enhance awareness of foot health, and induce motivation to prevent and cure foot-related problems. Guidelines similar to the essential criteria items introduced in this study need to be developed for future apps aimed at foot measurement for custom-made or in idually fitted footwear and to create awareness of foot health.
Publisher: IEEE
Date: 05-2017
Publisher: IEEE
Date: 03-2018
Publisher: IEEE
Date: 10-2016
DOI: 10.1109/DSAA.2016.60
Publisher: IEEE
Date: 05-11-2020
Publisher: IEEE
Date: 08-2010
Publisher: Elsevier BV
Date: 10-2014
Publisher: Elsevier BV
Date: 02-2015
Publisher: IEEE
Date: 02-2017
Publisher: IEEE
Date: 04-2011
Publisher: Springer International Publishing
Date: 2016
Publisher: MDPI AG
Date: 02-07-2023
DOI: 10.3390/INFO14070376
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 in iduals 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 overs ling technique (SMOTE) and random overs ling (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 in iduals at risk of diabetes, facilitating timely intervention and care.
Publisher: ACM
Date: 29-04-2022
Publisher: IEEE
Date: 08-2019
Publisher: MDPI AG
Date: 17-08-2021
DOI: 10.3390/S21165540
Abstract: This paper proposes an encryption-based image watermarking scheme using a combination of second-level discrete wavelet transform (2DWT) and discrete cosine transform (DCT) with an auto extraction feature. The 2DWT has been selected based on the analysis of the trade-off between imperceptibility of the watermark and embedding capacity at various levels of decomposition. DCT operation is applied to the selected area to gather the image coefficients into a single vector using a zig-zig operation. We have utilized the same random bit sequence as the watermark and seed for the embedding zone coefficient. The quality of the reconstructed image was measured according to bit correction rate, peak signal-to-noise ratio (PSNR), and similarity index. Experimental results demonstrated that the proposed scheme is highly robust under different types of image-processing attacks. Several image attacks, e.g., JPEG compression, filtering, noise addition, cropping, sharpening, and bit-plane removal, were examined on watermarked images, and the results of our proposed method outstripped existing methods, especially in terms of the bit correction ratio (100%), which is a measure of bit restoration. The results were also highly satisfactory in terms of the quality of the reconstructed image, which demonstrated high imperceptibility in terms of peak signal-to-noise ratio (PSNR ≥ 40 dB) and structural similarity (SSIM ≥ 0.9) under different image attacks.
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 2022
Publisher: Springer New York
Date: 2014
Publisher: Hawaii International Conference on System Sciences
Date: 2018
Publisher: MDPI AG
Date: 27-12-2021
DOI: 10.3390/COMPUTERS11010004
Abstract: Augmented reality (AR) has been widely used in education, particularly for child education. This paper presents the design and implementation of a novel mobile app, Learn2Write, using machine learning techniques and augmented reality to teach alphabet writing. The app has two main features: (i) guided learning to teach users how to write the alphabet and (ii) on-screen and AR-based handwriting testing using machine learning. A learner needs to write on the mobile screen in on-screen testing, whereas AR-based testing allows one to evaluate writing on paper or a board in a real world environment. We implement a novel approach to use machine learning for AR-based testing to detect an alphabet written on a board or paper. It detects the handwritten alphabet using our developed machine learning model. After that, a 3D model of that alphabet appears on the screen with its pronunciation/sound. The key benefit of our approach is that it allows the learner to use a handwritten alphabet. As we have used marker-less augmented reality, it does not require a static image as a marker. The app was built with ARCore SDK for Unity. We further evaluated and quantified the performance of our app on multiple devices.
Publisher: Springer Science and Business Media LLC
Date: 28-04-2022
Publisher: IEEE
Date: 09-2020
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 2021
Publisher: Springer Berlin Heidelberg
Date: 2013
Publisher: IEEE
Date: 07-2017
Publisher: Elsevier BV
Date: 09-2022
Publisher: ACM
Date: 12-09-2016
Publisher: Springer International Publishing
Date: 2017
Publisher: ACM
Date: 28-06-2021
Publisher: IEEE
Date: 07-2019
Publisher: Oxford University Press (OUP)
Date: 07-09-2017
Publisher: Springer International Publishing
Date: 2016
Publisher: Oxford University Press (OUP)
Date: 23-11-2014
Publisher: IEEE
Date: 30-11-2022
Publisher: Elsevier BV
Date: 06-2022
Publisher: Elsevier BV
Date: 04-2021
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 03-2021
Publisher: Public Library of Science (PLoS)
Date: 09-11-2017
Publisher: Elsevier BV
Date: 05-2022
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 02-2021
Publisher: MDPI AG
Date: 10-03-2023
DOI: 10.3390/FI15030108
Abstract: In digital image processing and steganography, images are often described using edges and local binary pattern (LBP) codes. By combining these two properties, a novel hybrid image steganography method of secret embedding is proposed in this paper. This method only employs edge pixels that influence how well the novel approach embeds data. To increase the quantity of computed edge pixels, several edge detectors are applied and hybridized using a logical OR operation. A morphological dilation procedure in the hybridized edge image is employed to this purpose. The least significant bits (LSB) and all LBP codes are calculated for edge pixels. Afterward, these LBP codes, LSBs, and secret bits using an exclusive-OR operation are merged. These resulting implanted bits are delivered to edge pixels’ LSBs. The experimental results show that the suggested approach outperforms current strategies in terms of measuring perceptual transparency, such as peak signal-to-noise ratio (PSNR) and structural similarity index (SSI). The embedding capacity per tempered pixel in the proposed approach is also substantial. Its embedding guidelines protect the privacy of implanted data. The entropy, correlation coefficient, cosine similarity, and pixel difference histogram data show that our proposed method is more resistant to various types of cyber-attacks.
Publisher: ACM Press
Date: 2016
Publisher: JMIR Publications Inc.
Date: 04-03-2021
DOI: 10.2196/24202
Abstract: As the use of smartphones increases globally across various fields of research and technology, significant contributions to the sectors related to health, specifically foot health, can be observed. Numerous smartphone apps are now being used for providing accurate information about various foot-related properties. Corresponding to this abundance of foot scanning and measuring apps available in app stores, there is a need for evaluating these apps, as limited information regarding their evidence-based quality is available. The aim of this review was to assess the measurement techniques and essential software quality characteristics of mobile foot measurement apps, and to determine their potential as commercial tools used by foot care health professionals, to assist in measuring feet for custom shoes, and for in iduals to enhance their awareness of foot health and hygiene to ultimately prevent foot-related problems. An electronic search across Android and iOS app stores was performed between July and August 2020 to identify apps related to foot measurement and general foot health. The selected apps were rated by three independent raters, and all discrepancies were resolved by discussion among raters and other investigators. Based on previous work on app rating tools, a modified rating scale tool was devised to rate the selected apps. The internal consistency of the rating tool was tested with a group of three people who rated the selected apps over 2-3 weeks. This scale was then used to produce evaluation scores for the selected foot measurement apps and to assess the interrater reliability. Evaluation inferences showed that all apps failed to meet even half of the measurement-specific criteria required for the proper manufacturing of custom-made footwear. Only 23% (6/26) of the apps reportedly used external scanners or advanced algorithms to reconstruct 3D models of a user’s foot that could possibly be used for ordering custom-made footwear (shoes, insoles/orthoses), and medical casts to fit irregular foot sizes and shapes. The apps had varying levels of performance and usability, although the overall measurement functionality was subpar with a mean of 1.93 out of 5. Apps linked to online shops and stores (shoe recommendation) were assessed to be more usable than other apps but lacked some features (eg, custom shoe sizes and shapes). Overall, the current apps available for foot measurement do not follow any specific guidelines for measurement purposes. Most commercial apps currently available in app stores are not viable for use as tools in assisting foot care health professionals or in iduals to measure their feet for custom-made footwear. Current apps lack software quality characteristics and need significant improvements to facilitate proper measurement, enhance awareness of foot health, and induce motivation to prevent and cure foot-related problems. Guidelines similar to the essential criteria items introduced in this study need to be developed for future apps aimed at foot measurement for custom-made or in idually fitted footwear and to create awareness of foot health.
Publisher: BMJ
Date: 29-08-2013
Publisher: Springer Science and Business Media LLC
Date: 12-01-2016
Publisher: Springer Science and Business Media LLC
Date: 03-2019
Publisher: Springer Science and Business Media LLC
Date: 29-07-2022
Publisher: Springer Science and Business Media LLC
Date: 21-09-2020
Publisher: Wiley
Date: 2022
Publisher: Springer Science and Business Media LLC
Date: 12-10-2013
Publisher: MDPI AG
Date: 30-04-2023
DOI: 10.3390/COMPUTERS12050092
Abstract: Autism Spectrum Disorder (ASD) is a neurological impairment condition that severely impairs cognitive, linguistic, object recognition, interpersonal, and communication skills. Its main cause is genetic, and early treatment and identification can reduce the patient’s expensive medical costs and lengthy examinations. We developed a machine learning (ML) architecture that is capable of effectively analysing autistic children’s datasets and accurately classifying and identifying ASD traits. We considered the ASD screening dataset of toddlers in this study. We utilised the SMOTE method to balance the dataset, followed by feature transformation and selection methods. Then, we utilised several classification techniques in conjunction with a hyperparameter optimisation approach. The AdaBoost method yielded the best results among the classifiers. We employed ML and statistical approaches to identify the most crucial characteristics for the rapid recognition of ASD patients. We believe our proposed framework could be useful for early diagnosis and helpful for clinicians.
Publisher: Frontiers Media SA
Date: 15-03-2023
DOI: 10.3389/FPUBH.2023.1092755
Abstract: Several research studies have demonstrated the potential of mobile health apps in supporting health management. However, the design and development process of these apps are rarely presented. We present the design and development of a smartphone-based lifestyle app integrating a wearable device for hypertension management. We used an intervention mapping approach for the development of theory- and evidence-based intervention in hypertension management. This consisted of six fundamental steps: needs assessment, matrices, theoretical methods and practical strategies, program design, adoption and implementation plan, and evaluation plan. To design the contents of the intervention, we performed a literature review to determine the preferences of people with hypertension (Step 1) and necessary objectives toward the promotion of self-management behaviors (Step 2). Based on these findings, we implemented theoretical and practical strategies in consultation with stakeholders and researchers (Steps 3), which was used to identify the functionality and develop an mHealth app (Step 4). The adoption (Step 5) and evaluation (Step 6) of the mHealth app will be conducted in a future study. Through the needs analysis, we identified that people with hypertension preferred having education, medication or treatment adherence, lifestyle modification, alcohol and smoking cessation and blood pressure monitoring support. We utilized MoSCoW analysis to consider four key elements, i.e., education, medication or treatment adherence, lifestyle modification and blood pressure support based on past experiences, and its potential benefits in hypertension management. Theoretical models such as (i) the information, motivation, and behavior skills model, and (ii) the patient health engagement model was implemented in the intervention development to ensure positive engagement and health behavior. Our app provides health education to people with hypertension related to their condition, while utilizing wearable devices to promote lifestyle modification and blood pressure management. The app also contains a clinician portal with rules and medication lists titrated by the clinician to ensure treatment adherence, with regular push notifications to prompt behavioral change. In addition, the app data can be reviewed by patients and clinicians as needed. This is the first study describing the design and development of an app that integrates a wearable blood pressure device and provides lifestyle support and hypertension management. Our theory-driven intervention for hypertension management is founded on the critical needs of people with hypertension to ensure treatment adherence and supports medication review and titration by clinicians. The intervention will be clinically evaluated in future studies to determine its effectiveness and usability.
Publisher: Springer International Publishing
Date: 2019
Publisher: Springer International Publishing
Date: 2012
Publisher: JMIR Publications Inc.
Date: 12-01-2021
Abstract: he undergraduate student population has been actively studied in digital mental health research. However, the existing literature primarily focuses on students from high-income nations, and undergraduates from limited-income nations remain understudied. his study aims to identify the broader social determinants of mental health among undergraduate students in Bangladesh, a limited-income nation in South Asia study the manifestation of these determinants in their day-to-day lives and explore the feasibility of self-monitoring tools in helping them identify the specific factors or relationships that affect their mental health. e conducted a 21-day study with 38 undergraduate students from 7 universities in Bangladesh. We conducted 2 semistructured interviews: one prestudy and one poststudy. During the 21-day study, participants used an Android app to self-report and self-monitor their mood after each phone conversation. The app prompted participants to report their mood after each phone conversation and provided graphs and charts so that the participants could independently review their mood and conversation patterns. ur results show that academics, family, job and economic condition, romantic relationship, and religion are the major social determinants of mental health among undergraduate students in Bangladesh. Our app helped the participants pinpoint the specific issues related to these factors, as the participants could review the pattern of their moods and emotions from past conversation history. Although our app does not provide any explicit recommendation, the participants took certain steps on their own to improve their mental health (eg, reduced the frequency of communication with certain persons). lthough some of the factors (eg, academics) were reported in previous studies conducted in the Global North, this paper sheds light on some new issues (eg, extended family problems and religion) that are specific to the context of the Global South. Overall, the findings from this study would provide better insights for researchers to design better solutions to help the younger population from this part of the world.
Publisher: Springer International Publishing
Date: 2017
Publisher: IEEE
Date: 10-2016
DOI: 10.1109/DSAA.2016.86
Publisher: IEEE
Date: 07-2021
Publisher: PeerJ
Date: 26-05-2021
DOI: 10.7717/PEERJ-CS.551
Abstract: The goal of this research is to develop and implement a highly effective deep learning model for detecting COVID-19. To achieve this goal, in this paper, we propose an ensemble of Convolutional Neural Network (CNN) based on EfficientNet, named ECOVNet, to detect COVID-19 from chest X-rays. To make the proposed model more robust, we have used one of the largest open-access chest X-ray data sets named COVIDx containing three classes—COVID-19, normal, and pneumonia. For feature extraction, we have applied an effective CNN structure, namely EfficientNet, with ImageNet pre-training weights. The generated features are transferred into custom fine-tuned top layers followed by a set of model snapshots. The predictions of the model snapshots (which are created during a single training) are consolidated through two ensemble strategies, i.e., hard ensemble and soft ensemble, to enhance classification performance. In addition, a visualization technique is incorporated to highlight areas that distinguish classes, thereby enhancing the understanding of primal components related to COVID-19. The results of our empirical evaluations show that the proposed ECOVNet model outperforms the state-of-the-art approaches and significantly improves detection performance with 100% recall for COVID-19 and overall accuracy of 96.07%. We believe that ECOVNet can enhance the detection of COVID-19 disease, and thus, underpin a fully automated and efficacious COVID-19 detection system.
Publisher: Springer Science and Business Media LLC
Date: 27-08-2018
Publisher: Springer International Publishing
Date: 08-10-2020
Publisher: ACM
Date: 12-09-2016
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 2023
Publisher: Springer International Publishing
Date: 2016
Publisher: Springer International Publishing
Date: 2021
Publisher: IEEE
Date: 08-2018
Publisher: World Scientific Pub Co Pte Lt
Date: 03-2015
DOI: 10.1142/S0218843015400018
Abstract: We present a social context as a service (SCaaS) platform for managing adaptations in collaborative pervasive applications that support interactions among a dynamic group of actors such as users, stakeholders, infrastructure services, businesses and so on. Such interactions are based on predefined agreements and constraints that characterize the relationships between the actors and are modeled with the notion of social context. In complex and changing environments, such interaction relationships, and thus social contexts, are also subject to change. In existing approaches, the relationships among actors are not modeled explicitly, and instead are often hard-coded into the application. Furthermore, these approaches do not provide adequate adaptation support for such relationships as the changes occur in user requirements and environments. In our approach, inter-actor relationships in an application are modeled explicitly using social contexts, and their execution environment is generated and adaptations are managed by the SCaaS platform. The key features of our approach include externalization of the interaction relationships from the applications, representation and modeling of such relationships from the domain and actor perspectives, their implementation using a service oriented paradigm, and support for their runtime adaptation. We quantify the platform's adaptation overhead and demonstrate its feasibility and applicability by developing a telematics application that supports cooperative convoy.
Publisher: IEEE
Date: 08-2018
Publisher: MDPI AG
Date: 10-04-2023
Abstract: (1) Background: Predicting the survival of patients in end-of-life care is crucial, and evaluating their performance status is a key factor in determining their likelihood of survival. However, the current traditional methods for predicting survival are limited due to their subjective nature. Wearable technology that provides continuous patient monitoring is a more favorable approach for predicting survival outcomes among palliative care patients. (2) Aims and objectives: In this study, we aimed to explore the potential of using deep learning (DL) model approaches to predict the survival outcomes of end-stage cancer patients. Furthermore, we also aimed to compare the accuracy of our proposed activity monitoring and survival prediction model with traditional prognostic tools, such as the Karnofsky Performance Scale (KPS) and the Palliative Performance Index (PPI). (3) Method: This study recruited 78 patients from the Taipei Medical University Hospital’s palliative care unit, with 66 (39 male and 27 female) patients eventually being included in our DL model for predicting their survival outcomes. (4) Results: The KPS and PPI demonstrated an overall accuracy of 0.833 and 0.615, respectively. In comparison, the actigraphy data exhibited a higher accuracy at 0.893, while the accuracy of the wearable data combined with clinical information was even better, at 0.924. (5) Conclusion: Our study highlights the significance of incorporating clinical data alongside wearable sensors to predict prognosis. Our findings suggest that 48 h of data is sufficient for accurate predictions. The integration of wearable technology and the prediction model in palliative care has the potential to improve decision making for healthcare providers and can provide better support for patients and their families. The outcomes of this study can possibly contribute to the development of personalized and patient-centered end-of-life care plans in clinical practice.
Publisher: Public Library of Science (PLoS)
Date: 27-07-2015
Publisher: MDPI AG
Date: 08-08-2022
Abstract: In this technologically advanced era, with the proliferation of artificial intelligence, many mobile apps are available for plant disease detection, diagnosis, and treatment, each with a variety of features. These apps need to be categorized and reviewed following a proper framework that ensures their quality. This study aims to present an approach to evaluating plant disease detection mobile apps, which includes providing ratings of distinct features of the apps and insights into the exploitation of artificial intelligence used in plant disease detection. The applicability of these apps for pathogen or disease detection, identification, and treatment will be assessed along with significant insights garnered. For this purpose, plant disease detection apps were searched in three prominent app stores (the Google Play store, Apple App store, and Microsoft store) using a set of keywords. A total of 606 apps were found and from them, 17 relevant apps were identified based on inclusion and exclusion criteria. The selected apps were reviewed by three raters using our devised app rating scale. To validate the rater agreements on the ratings, inter-rater reliability is computed alongside their intra-rater reliability, ensuring their rating consistency. Also, the internal consistency of our rating scale was evaluated against all selected apps. User comments from the app stores are collected and analyzed to understand their expectations and views. Following the rating procedure, most apps earned acceptable ratings in software quality characteristics such as aesthetics, usability, and performance but gained poor ratings in AI-based advanced functionality, which is the key aspect of this study. However, most of the apps cannot be used as a complete solution to plant disease detection, diagnosis, and treatment. Only one app, Plantix–your crop doctor, could successfully identify plants from images, detect diseases, maintain a rich plant database, and suggest potential treatments for the disease presented. It also provides a community where plant lovers can communicate with each other to gain additional benefits. In general, all existing apps need to improve functionalities, user experience, and software quality. Therefore, a set of design considerations has been proposed for future app improvements.
Publisher: Springer Berlin Heidelberg
Date: 2007
Publisher: Association for Computing Machinery (ACM)
Date: 02-2022
DOI: 10.1145/3503159
Abstract: Having precise specifications of service APIs is essential for many Software Engineering activities. Unfortunately, available documentation of services is often inadequate and/or imprecise and, hence, cannot be fully relied upon. Generating service documentation manually is a tedious and error-prone task, especially in light of changes to services. Therefore, there is a need for automated support in generating service documentation. In this work, we present a novel approach to infer the API of a service by analyzing recorded messages sent to and received from this service. Our approach includes a novel, two-level clustering technique to cluster messages, a step that many existing approaches to infer message formats fail to perform precisely in the presence of significant variation of payload information of the available messages. We have evaluated our approach on message traces from four different real-world services. The experimental result shows that our approach is more effective than existing techniques in extracting correct message formats from recorded messages.
Publisher: ACM
Date: 29-01-2018
Publisher: MDPI AG
Date: 27-02-2023
DOI: 10.3390/AGRICULTURE13030574
Abstract: Precise Soil Moisture (SM) assessment is essential in agriculture. By understanding the level of SM, we can improve yield irrigation scheduling which significantly impacts food production and other needs of the global population. The advancements in smartphone technologies and computer vision have demonstrated a non-destructive nature of soil properties, including SM. The study aims to analyze the existing Machine Learning (ML) techniques for estimating SM from soil images and understand the moisture accuracy using different smartphones and various sunlight conditions. Therefore, 629 images of 38 soil s les were taken from seven areas in Sydney, Australia, and split into four datasets based on the image-capturing devices used (iPhone 6s and iPhone 11 Pro) and the lighting circumstances (direct and indirect sunlight). A comparison between Multiple Linear Regression (MLR), Support Vector Regression (SVR), and Convolutional Neural Network (CNN) was presented. MLR was performed with higher accuracy using holdout cross-validation, where the images were captured in indirect sunlight with the Mean Absolute Error (MAE) value of 0.35, Root Mean Square Error (RMSE) value of 0.15, and R2 value of 0.60. Nevertheless, SVR was better with MAE, RMSE, and R2 values of 0.05, 0.06, and 0.96 for 10-fold cross-validation and 0.22, 0.06, and 0.95 for leave-one-out cross-validation when images were captured in indirect sunlight. It demonstrates a smartphone camera’s potential for predicting SM by utilizing ML. In the future, software developers can develop mobile applications based on the research findings for accurate, easy, and rapid SM estimation.
Publisher: MDPI AG
Date: 11-01-2022
Abstract: COVID-19’s unanticipated consequences have resulted in the extended closure of various educational institutions, causing significant hardship to students. Even though many institutions rapidly transitioned to online education programs, various issues have emerged that are impacting many aspects of students’ lives. An online survey was conducted with students of Bangladesh to understand how COVID-19 impacted their study, social and daily activities, plans, and mental health. A total of 409 Bangladeshi students took part in a survey. As a result of the COVID-19 pandemic, 13.7% of all participants are unable to focus on their studies, up from 1.2% previously. More than half of the participants (54%) have spent more time on social media than previously. We found that 45% of the participants have severe to moderate level depression. In addition, 48.6% of the students are experiencing severe to moderate level anxiety. According to our findings, students’ inability to concentrate on their studies, their increased use of social media and electronic communications, changing sleep hours during the pandemic, increased personal care time, and changes in plans are all correlated with their mental health.
Publisher: ACM
Date: 2018
Publisher: ACM
Date: 11-09-2017
Publisher: IEEE
Date: 09-2012
Publisher: Springer Science and Business Media LLC
Date: 06-04-2021
Publisher: MDPI AG
Date: 12-12-2022
Abstract: Background: Smartphones and wearable devices have become a part and parcel of the healthcare industry. The use of wearable technology has already proved its potentials in improving healthcare research, clinical work, and patient care. The real time data allows the care providers to monitor the patients’ symptoms remotely, prioritize the patients’ visits, assist in decision-making, and carry out advanced care planning. Objectives: The primary objective of our study was to investigate the potential use of wearable devices as a prognosis tool among patients in hospice care and palliative care, and the secondary objective was to examine the association between wearable devices and clinical data in the context of patient outcomes, such as discharge and deceased at various time intervals. Methods: We employed a prospective observational research approach to continuously monitor the hand movements of the selected 68 patients between December 2019 and June 2022 via an actigraphy device at hospice or palliative care ward of Taipei Medical University Hospital (TMUH) in Taiwan. Results: The results revealed that the patients with higher scores in the Karnofsky Performance Status (KPS), and Palliative Performance Scale (PPS) tended to live at discharge, while Palliative Prognostic Score (PaP) and Palliative prognostic Index (PPI) also shared the similar trend. In addition, the results also confirmed that all these evaluating tools only suggested rough rather than accurate and definite prediction. The outcomes (May be Discharge (MBD) or expired) were positively correlated with accumulated angle and spin values, i.e., the patients who survived had higher angle and spin values as compared to those who died/expired. Conclusion: The outcomes had higher correlation with angle value compared to spin and ACT. The correlation value increased within the first 48 h and then began to decline. We recommend rigorous prospective observational studies/randomized control trials with many participants for the investigations in the future.
Publisher: ACM
Date: 11-09-2017
Publisher: ACM
Date: 07-11-2017
Publisher: Springer Science and Business Media LLC
Date: 16-09-2022
Publisher: ACM
Date: 13-05-2019
Publisher: IEEE
Date: 03-2013
Publisher: Elsevier BV
Date: 2022
Publisher: MDPI AG
Date: 06-12-2022
DOI: 10.3390/S22239554
Abstract: The foot is a vital organ, as it stabilizes the impact forces between the human skeletal system and the ground. Hence, precise foot dimensions are essential not only for custom footwear design, but also for the clinical treatment of foot health. Most existing research on measuring foot dimensions depends on a heavy setup environment, which is costly and ineffective for daily use. In addition, there are several smartphone applications online, but they are not suitable for measuring the exact foot shape for custom footwear, both in clinical practice and public use. In this study, we designed and implemented computer-vision-based smartphone application OptiFit that provides the functionality to automatically measure the four essential dimensions (length, width, arch height, and instep girth) of a human foot from images and 3D scans. We present an instep girth measurement algorithm, and we used a pixel per metric algorithm for measurement these algorithms were accordingly integrated with the application. Afterwards, we evaluated our application using 19 medical-grade silicon foot models (12 males and 7 females) from different age groups. Our experimental evaluation shows that OptiFit could measure the length, width, arch height, and instep girth with an accuracy of 95.23%, 96.54%, 89.14%, and 99.52%, respectively. A two-tailed paired t-test was conducted, and only the instep girth dimension showed a significant discrepancy between the manual measurement (MM) and the application-based measurement (AM). We developed a linear regression model to adjust the error. Further, we performed comparative analysis demonstrating that there were no significant errors between MM and AM, and the application offers satisfactory performance as a foot-measuring application. Unlike other applications, the iOS application we developed, OptiFit, fulfils the requirements to automatically measure the exact foot dimensions for in idually fitted footwear. Therefore, the application can facilitate proper foot measurement and enhance awareness to prevent foot-related problems caused by inappropriate footwear.
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 2023
Publisher: IGI Global
Date: 04-2021
Abstract: Mobile games can contribute to learning at greater success. In this paper, the authors have developed and evaluated a novel educational game, named FoodCalorie, to learn food calorie intake standard. The game is aimed to learn calorie values of various traditional foods of Bangladesh and the calorie intake standard that varies with age and gender. They are the first in this field to perform an empirical study on women in Bangladesh to see how game-based learning can contribute to learn food calories. They further analyze and report the impact of participants' age, professions, and smartphone proficiency levels on their learning experience and progression. The study also conforms the finding of existing studies that game-based learning can enhance the learning experience.
Publisher: IEEE
Date: 08-2018
Publisher: ACM
Date: 11-09-2017
Publisher: Elsevier BV
Date: 12-2020
Location: Bangladesh
Location: United States of America
No related grants have been discovered for Muhammad Ashad Kabir.