Asian Journal of Convergence in Technology
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Studies on Internet of Things Powered E-Health Observing Systems for Safeguarding and Revolutionizing the Healthcare Sector
Get PDFThe Internet of Things (IoT) is revolutionizing healthcare and driving the creation of smart e-health monitoring solutions. Since IoT devices create a lot of data, this evolution depends on providing stakeholders and patients with real-time updates. With new technologies emerging to securely manage health data, technology is essential to modern healthcare monitoring. There are two main types for health data: unstructured data is more diverse and includes things like emails and media content, while structured data follows certain guidelines. Meeting strict security criteria is crucial for utilizing data from these devices in real-time applications. Because the Internet of Things generates a significant amount of data that needs to be analyzed with specialized tools, it is imperative to store data in a secure environment. The creation of an intelligent e-health monitoring system is the main objective. This system gathers health data from several sensors, integrates the data, and filters pertinent information about the patient's current condition. The proposed system also describes an authorized architectural node within the IoT network and a secure platform for exchanging e-health data. For real-time applications, it is essential to ensure the security of data generated by Internet of Things devices. Because so much data needs to be processed, it is imperative that it be stored in a secure environment using specialized technologies. The major goal is to develop an intelligent e-health monitoring system that gathers health data from various sensors, integrates health status, filters pertinent patient data, and enables safe sharing within the Internet of Things
Proactive Fault Localization and Alarm Correlation in DWDM Networks
Get PDF“Proactive Fault Localization and Alarm Correlation in DWDM Networks” approach proposes a new fault localization algorithm for DWDM networks where every entity of DWDM network participates in correlation of alarms and thus reduces the list of suspected components shown to the network operators
Cryptocurrency Price Prediction Using Machine Learning
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The application of machine learning algorithms in predicting cryptocurrency prices has gained significant attention in recent years. Researchers have explored various approaches such as recurrent neural networks, deep learning neural networks, Bayesian regression, k-nearest neighbor, support vector machine, and other algorithms to forecast the prices of cryptocurrencies like Bitcoin, Ethereum, Dogecoin and Litecoin. This paper will draw on established literature on price prediction using machine learning, including studies on NFT sales predictability, NFT sale price fluctuations prediction, gold price prediction, and silver price forecasting. The research paper has focused on utilizing high-dimensional features, time-series analysis, as well as the comparison of different statistical models and machine learning algorithms. Additionally, the prediction models have incorporated factors such as market liquidity, exchange market dynamics. While the literature acknowledges the potential of machine learning in cryptocurrency price prediction, gold, silver and NFT’s there is a recognized gap in the application of these techniques across a broader range of cryptocurrencies. The proposed methodology will integrate various machine learning models and statistical methods to predict the prices of cryptocurrencies, gold, silver, and NFTs, taking into account factors such as market trends, trade networks and visual features. Furthermore, the studies emphasize the importance of feature engineering, sample dimension engineering, and the use of various machine learning techniques to enhance the accuracy and stability of cryptocurrency price predictions. As the cryptocurrency market continues to expand, there is a need for further research to develop robust machine learning models that can effectively forecast the prices of diverse cryptocurrencies, contributing to the advancement of this field
Novel YOLOv5 Model for Automatic Detection of Cowpea Leaves: Smart Agriculture
Get PDFImplementing artificial intelligence, specifically deep learning algorithms, to enhance agricultural productivity is a great initiative, especially in a country like India where agriculture is a crucial sector. Using TensorFlow and Keras for this purpose provides a solid foundation, given their popularity and extensive documentation. Using deep learning to identify and classify cowpea leaves can indeed streamline various agricultural processes, such as monitoring plant health, pest detection, and yield estimation. The utilization of YOLOv5, a CNN-based architecture, for the binary classification of cowpea leaves against other leaves like mangoes is a smart choice. Transfer learning can further optimize this model by leveraging pre-trained weights from similar tasks, which can significantly reduce the computational resources and time required for training. As you proceed with this experiments and model development, ensure robust data collection and preprocessing, as the quality of input data greatly influences the performance of deep learning models. Additionally, consider integrating techniques for data augmentation to further enhance the model's generalization capabilities. Continued research and development in this area can lead to significant advancements in agricultural practices, ultimately benefiting farmers and contributing to food security
Semantic Coherence and NLP: Redesigning post-COVID Mental Health Diagnostics with CNNs and LSTMs
Get PDFThe COVID-19 pandemic has intensified the need for innovative, scalable diagnostic tools for mental health, given the surge in related disorders globally. This study presents a novel neural symbolic approach leveraging natural language processing (NLP) to analyze semantic coherence in text data, aimed at predicting mental health outcomes. Integrating convolutional neural networks (CNNs) with long short-term memory networks (LSTM) and an attention mechanism, this model excels in extracting and emphasizing critical linguistic features from vast datasets of online textual communications. Our evaluations show that the model achieves an accuracy of 92.4%, with precision, recall, and an F1-score significantly superior to traditional LSTM models. The ROC-AUC score of 0.92 highlights its effectiveness at distinguishing various mental health states, while the attention mechanism enhances the model’s interpretability, shedding light on key text features indicative of mental distress. This research underscores the potential of AI in enhancing mental health diagnostics in the context of current events, proposing a powerful tool for early detection and intervention
Development of CNC Plotter Machine for Printing Application
Get PDFThis paper presents the design, development, and application of a CNC (Computer Numerical Control) plotter machine specifically developed for printing applications. The research focuses on the integration of modern CNC technology with printing processes to enhance precision, automation, and versatility in various printing tasks. The paper discusses the hardware design, software control, and operational principles of the CNC plotter, along with the potential applications, challenges, and future trends in the field of printing
Craftify: A Course Creation Application to Help Instructors with Designing New Online Courses
Get PDFCraftify is a course creation platform designed to simplify and accelerate the web-based course design process, addressing the complex and time-intensive challenges often faced by instructors. Through an intuitive graphic frontend, Craftify integrates key instructional design principles, multimedia assets, and adaptive learning techniques to empower users with ready-to-use, customizable course structures. Its features include automated course structuring, quiz creation, multimedia incorporation, and seamless syncing with popular LMS platforms such as Moodle, Blackboard, and Canvas. A pilot study with 50 lecturers—administered through questionnaires—demonstrated Craftify's effectiveness, showing reductions in course development time, higher self-reported satisfaction levels among instructors, and improved student engagement within online environments. By streamlining these essential elements, Craftify effectively supports instructors in creating dynamic, interactive, and high-quality online courses
DEVELOPMENT AND EXPERIMENTAL AND CFD ANALYSIS OF INCLINED PLATE AND TUBE HEAT EXCHANGER
Get PDFThe Heat exchanger is the most important and widely applicable device in this sector so it is also area where these innovations are carried out regularly. Plate & Tube type Heat Exchangers have a number of applications in the pharmaceutical, petrochemical, chemical, power, and dairy, food & beverage industry. Recently, plate heat exchangers are commonly used when compared to other types of heat exchangers such as shell and tube type in heat transfer processes because of their compactness, ease of production, sensitivity, easy care after set-up and efficiency. The temperature approach in plate heat exchangers may be as low as 1 °C whereas shell and tube heat exchangers require an approach of 5 °C or more. However, plate and tube heat exchangers have inherent shortcomings such as the contact resistance between fins and tubes, the existence of a low performance region behind tubes, etc. The plate and tube heat exchangers which are in use are flat type if we incline the plates at some angle to the pipes we can get various data by experimenting the device at various angles. This data can be analyzed and we can have conclusion about its efficiency and effectiveness can be calculated
A MACHINE LEARNING APPROACH TO ASSESS PSYCHOLOGICAL STATE AMONG UNIVERSITY STUDENTS THROUGHOUT THE COVID-19 PANDEMIC: BANGLADESH PERSPECTIVE
Get PDFThe COVID-19 outbreak in Bangladesh had a negative impact on people of all ages. The epidemic's destruction clearly had an effect on people's mental health, especially that of university students. From the beginning of the pandemic in Bangladesh, educational institutes were shut down, complete lockdown condition, unable to get sports and entertainment abilities, which caused the student's psychological health to suffer. Most university-aged students exhibited long-lasting psychological issues corresponding with COVID-19, including significant levels of stress, anxiety, and depression. Predicting the psychological state will indicate a lack of psychological resilience, which will be associated with mental health problems among Bangladeshi university students. Increasing psychological fortitude is essential to ensuring pupils' well-being throughout the epidemic. Through an online survey and several machine learning algorithms, our system predicts the psychological state of Bangladeshi university students. We preprocessed this dataset by cleaning it correctly for the procedure. We utilized hyper-parameter tweaking to extract the features, and then we trained the dataset using a number of classifiers, such as the support vector classifier, random forest, logistic regression, decision tree, naive Bayes, KNN, and gradient boosting. Study suggests that, these algorithms works best in researching on mental health related datasets. Among these several machine learning algorithms, our created dataset of 509 points, comprising support vector classifier (SVC), produced an AUROC score of 0.98, 0.97, and 0.97 for depression, anxiety, and stress states, respectively. Additionally, SVC also delivered respectable outcomes on the open-source dataset we collected for each of the psychological states - depression, anxiety, and stress. Support vector machine (SVM), a supervised machine learning model that employs classification methods, may, in general, produce excellent results when there is a distinct proportion of displacement between classes. By evaluating a dataset we have collected and enhancing the DASS-21 formulation to measure an individual's depression, anxiety, and stress. DAAS-21 is well established screening method for addressing mental health issue. We sincerely considered the ethics and all the data we collected from people are preserved with care and utmost privacy. This study will aid in the growth of research into the area of suicidal thoughts and emotional states
Efficient Sunflower Solar Power Tracking and Monitoring System
Get PDFThe amplified need for renewable energy sources has increased the demand for efficient solar energy systems. This paper brings forth an inspiration of a sunflower-solar power tracking and monitoring system. In this approach, the optimum capturing of energy has been achieved by tracking the movement of a natural sunflower as it follows the movement of the sun. It consists of miniature solar panels, N2O gear motors, Li-Po batteries, MG 996 R servo motors, limit switches, light-dependent resistors (LDRs), and an Arduino Nano 328P microcontroller, integrated along with an L293D motor driver. Integration of proactive sensing and real-time tracking capabilities into the proposed system heavily improves the generation of solar energy, thus significantly reducing energy wastage. Experimental results confirm that this new design is effective and promising in improving the efficiency of solar energy