Online-Journals.org (International Association of Online Engineering)
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The Effectiveness of Using Mobile Learning Application on Undergraduates’ Intrinsic Motivation and Their General Nutrition Knowledge
Get PDFThis research aims to investigate the effectiveness of using a mobile learning application on undergraduates’ intrinsic motivation and their general nutrition knowledge. This quasi-experimental study involved the participation of 125 students who were divided into two groups: a control group consisting of 62 students and an experimental group comprising 63 students. Pre- and post-intrinsic motivation questionnaires and a nutritional knowledge test were administered. The results showed significant differences between the control group and the experimental group. The experimental group exhibited a higher level of knowledge, and there were no statistically significant differences in intrinsic motivation between the control group and the experimental group. The application was found to be effective in improving nutrition knowledge but did not enhance intrinsic motivation. Further research may be needed to understand the factors that influence intrinsic motivation and how to effectively increase it
Optimizing Clustering Approaches in Cloud Environments
Get PDFThis study focuses on the challenge of developing abstract models to differentiate various cloud resources. It explores the advancements in cloud products that offer specialized services to meet specific external needs. The study proposes a new approach to request processing in clusters, improving downtime, load distribution, and overall performance. A comparison of three clustering approaches is conducted: local single cluster, local multiple clusters, and multiple cloud clusters. Performance, scalability, fault tolerance, resource allocation, availability, and cost-effectiveness are evaluated through experiments with 50 requests. All three approaches achieve a 100% success rate, but processing times vary. The local single cluster has the longest duration, while the local multiple clusters and multiple cloud clusters perform better and offer faster processing, scalability, fault tolerance, and availability. From a cost perspective, the local single cluster and local multiple clusters incur capital and operational expenses, while the multiple cloud clusters follow a pay-as-you-go model. Overall, the local multiple clusters and multiple cloud clusters outperform the local single cluster in terms of performance, scalability, fault tolerance, resource allocation, availability, and cost-effectiveness. These findings provide valuable insights for selecting appropriate clustering strategies in cloud environments
Signature Verification Based on Dex CRC and Blake2 Algorithm to Prevent Reverse Engineering Attack in Android Application
Get PDFThe rapid growth of Android applications has led to more cybercrime cases, specifically Reverse Engineering attacks, on Android apps. One of the most common cases of reverse engineering is application repackaging, where the application is downloaded via the Play Store or the official website and then repackaged with various additions or changes. One of the ways to avoid Application Repackaging attacks is to check the signature of an application. However, hackers can manipulate the application by adding a hook, i.e., replacing the original function for getting signatures with a new modified function in the application. In this research, the development of a verification method for Android applications is carried out by utilizing Dex CRC and the Blake2 algorithm, which will be written in C using the Java Native Interface (JNI). The results of this study indicate that the verification method using Dex CRC and the Blake2 algorithm can effectively protect Android applications from Application Repackaging attacks without burdening application performance
Recruitment Algorithm in Edge-Cloud Servers based on Mobile Crowd-Sensing in Smart Cities
Get PDFAs more and more mobile devices rely on cloud services since the introduction of cloud computing, data privacy has emerged as one of the most pressing security concerns. Users typically encrypt their important data before uploading it to cloud servers to safeguard data privacy, which makes data usage challenging. On the other side, this also increases the possibility of brand-new issues in cities. A clever, effective and efficient urban monitoring system is required to address possible challenges that may arise in urban settings. In the smart city concept, which makes use of sensors, one strategy that might be used in IoT and cloud computing is to monitor and gather data on problems that develop in cities in real-time. However, it will take a while and be rather expensive to install IoT and sensors throughout the city. The Mobile Crowd-Sensing (MCS) method is proposed to be used in this study to retrieve and gather data on issues that arise in metropolitan areas from citizen reports made using mobile devices. And we suggest a budget-constrained, reputation-based collaborative user recruitment (RCUR) procedure for a MCS system. To construct an edge-assisted MCS system in urban situations, we first integrate edge computing into MCS. We also examine how user reputation affects user recruitment. Finally, we create a collaborative sensing approach using the edge nodes’ sensing capabilities
Patient Monitoring for Personalized Mobile Health (PMH) based on Medical Virtual Instruments
Get PDFOne of the newest technologies, mobile health, has the potential to support the provision of care for older adults and offer them individualised treatment. This study’s goal is to evaluate the benefits and challenges of personalised mobile health (PMH) for elderly residential care. Virtual worlds are quickly integrating into the landscape of instructional technologies. One of the most well-known of these settings is Second Life (SL). Despite the potential of SL for health professions education, there aren’t many official SL applications for this purpose, and the effectiveness of these applications hasn’t been evaluated to the fullest extent possible. Similarly, it appears that nothing is known about the use of virtual worlds for continuing medical education. In order to better grasp the fundamentals of the aid of MVIs for personal health monitoring (PHM), we were able to pinpoint the key disease regions, sensors, channels, calculations and communication protocols. The main obstacles limiting MVIs’ degree of integration into the international health care system were also identified. The analysis demonstrates that MVIs offer an excellent possibility for the creation of affordable, personalised health systems that meet the unique equipment requirements of a certain field of medicine
Heterogeneous Convolutional Neural Networks for Emotion Recognition Combined with Multimodal Factorised Bilinear Pooling and Mobile Application Recommendation
Get PDFThe field of emotion recognition has garnered considerable interest due to its diverse applications in mental health, personalised advertising and enhancing user experiences. This research paper introduces a unique and innovative method for emotion recognition by integrating heterogeneous convolutional neural networks (CNNs) with multimodal factorised bilinear pooling. Furthermore, the paper also incorporates the integration of mobile application recommendations as part of the overall approach. The proposed method leverages the power of CNNs to extract high-level features from different modalities, including facial expressions, speech signals and physiological signals. By using heterogeneous CNNs, each modality is processed independently to capture modality-specific emotional cues effectively. To fuse the extracted features, multimodal factorised bilinear pooling is employed, which captures the complex interactions between different modalities while reducing the computational complexity. This pooling technique efficiently combines the modality-specific features, resulting in a compact and discriminative representation of the emotional state. In addition to emotion recognition, this paper also introduces the integration of mobile app recommendations. By leveraging the recognised emotion, the system recommends relevant mobile applications that are tailored to the user’s emotional state. This integration enhances user experience and facilitates emotion regulation through the utilisation of appropriate mobile apps. Experimental evaluations are conducted on benchmark emotion recognition datasets, including the DEAP and MAHNOB_HCI datasets. The findings of the study highlight the effectiveness of the proposed methodology in terms of accuracy and robustness, surpassing existing approaches in the field. Additionally, the integration of the mobile app recommendation system showcases encouraging outcomes by offering personalised recommendations tailored to the user’s emotional state
Current Knowledge and Future Possibilities of Medical Digital Technologies based on Mobile Health
Get PDFMedical digital technologies have the potential to improve public access to healthcare by enhancing its flexibility. The availability of complementary details about health, ailments, complications, and the most recent advancements in biomedical research are all included. Diagnostic and medical services are now becoming more available and accessible, especially in low-income countries. Regarding digital health technology, there are still a lot of challenges that need to be overcome, such as dependability, safety, testing, and ethical concerns. We propose that mobile technology should enhance rather than replace the psychiatrist-patient connection in the existing environment of inadequate regulatory oversight and scientific research to lessen possible clinical and moral harm to patients at risk. We identify potential areas of moral conflict between consumer-driven mobile businesses and healthcare practice and create a decision tree model for putting ethical safeguards in place. Informed consent, confidentiality, and shared treatment expectations are the main areas of attention for this paradigm when it comes to risk management in the therapeutic partnership. To comprehend the utility, capacity, and limitations, this study covers recent breakthroughs in mobile technologies, their applications, and a comparative analysis of their performance metrics. Understanding the weaknesses of current technologies can facilitate the creation of new frameworks with increased performance capabilities and superior service quality
Multiple Disease Detection using Machine Learning Techniques
Get PDFThe COVID-19 disease outbreak resulted in a worldwide pandemic. Currently, the reverse transcription-polymerase chain reaction (RT-PCR), which relies on nasopharyngeal swabs to examine the existence of the ribonucleic acid (RNA) of SARS-CoV-27, is still a popular approach to testing for the disease. Despite the high level of specificity of testing with RT-PCR, the sensitivity of the method could be relatively low, and there is significant variability in efficacy depending on different sampling methods and the time of occurrence of symptoms. It is therefore essential for us to develop a machine-learning algorithm that can analyze computerized tomography images to detect the presence of COVID-19. Besides COVID-19, lung computerized tomography (CT) scan images can detect many other diseases, such as lung cancer, pneumonia, etc. This paper deals with the implementation of an algorithm that takes lung CT scans and lung X-ray images as input and predicts a list of probable diseases and possible diagnoses that infect the lungs. Machine learning algorithms will be able to predict disease by scanning the tiniest of regions easily missed by the human eye. This paper presents a survey of various machine learning algorithms that aid in detecting multiple diseases in lung CT scan images. Apart from the study of standard algorithms best suited for COVID-19 detection, this paper also includes recent trends. One of the major recent trends that can be incorporated into COVID-19 detection is TinyML. Tiny ML is an emerging area in machine learning algorithms that can be used to detect multiple diseases in lung CT scan images with better accuracy and in less time. This tool can aid doctors in their diagnosis and treatment of patients and help increase the efficiency of the treatment process. While understanding the features and mapping them using a hidden layer, there is a probability of compressing the dataset, as well as the model to process and classify the low-bit images in real-time using TinyML
Enhancing Health Monitoring and Active Aging in the Elderly Population: A Study on Wearable Technology and Technology-Assisted Care
Get PDFThe constant monitoring of human physical activity, behaviour, and physiological signs has undergone a revolution thanks to wearable technology, which has provided invaluable insights into many facets of everyday life. Wearable technology has become a potent instrument for improving healthcare efficiency and lowering costs as the prevalence of an ageing population and the need for technology-assisted care increase. By analysing gait patterns and promoting a healthy lifestyle in age-friendly settings, this study aims to investigate how valid and trustworthy wearable devices, in conjunction with technology-assisted care, can facilitate the monitoring and improvement of the health of the elderly population. The Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) 2020 standards for data inclusion and exclusion are used in the study, which collects data from the Scopus database. The results show that wearable technology, which offers two main device categories—those intended for healthcare professionals and those aimed at consumers—plays a critical role in gait-pattern analysis. These gadgets offer robust data-collecting capabilities, allowing for precise evaluation and tracking of walking habits. Furthermore, the incorporation of wearable technology with eHealth solutions enhances the quality of life for the aged population by enabling them to live in settings that are age-friendly and successfully manage their health. The study also emphasises how critical it is to use reliable wearable technology to address the diversity of health issues and the occurrence of chronic diseases among the senior population. According to the study’s findings, wearable technologies have great promise for promoting the health and well-being of senior citizens. To meet the unique requirements and problems of the ageing population and to guarantee the successful integration of wearable devices into healthcare practices, more research and development are needed. In the end, wearable technology has enormous potential to revolutionise aged care and enhance health outcomes in ageing-friendly settings
Students’ Alternative Conceptions and Teachers’ Views on the Implementation of Pedagogical Strategies to Improve the Teaching of Chemical Bonding Concepts
Get PDFThe concept of chemical bonding is a crucial one in chemistry that occurs throughout the school curriculum and forms the basis of many topics in chemistry. Furthermore, learning about chemical bonding allows the learner to make predictions and provide explanations regarding the physical and chemical properties of substances. However, chemical bonding has been cited as one of the most difficult chemistry concepts for many secondary and higher education students to understand, and therefore, teachers can find it difficult to teach this concept due to the complexity of the underlying theory as well as the need to use abstract models to represent chemical bonds. The teaching methods used in the implementation of the concept can also be challenging. The aim of this study is to reveal the difficulties and alternative conceptions encountered by Moroccan secondary school students when learning concepts related to chemical bonding, the main causes of these difficulties, and the strategies used by teachers to help students overcome these obstacles. In this study, we conducted a survey of 57 Moroccan secondary school physical science teachers by means of a questionnaire. The questionnaire, consisting of three parts, was used to collect the data. Each part contains closed questions, open questions, and multiple-choice questions. The analysis of the results highlights the difficulties and alternative conceptions most frequently made by the students, namely: the octet rule, the geometry of molecules, and the polarity of molecules. Factors contributing to students’ misconceptions include the nature of abstract concepts, the use of models, and the difficulty for teachers to explain certain concepts related to chemical bonding. The study also presents some suggestions for improving the teaching of chemical bonding, such as integrating information and communication technologies (ICT), diversifying the teaching tools used, and taking into account students’ pre-existing conceptions. This can help teachers, curriculum developers, and textbook authors make the subject easier for students and address their misconceptions