A Comparative Performance Analysis of Hybrid and Classical Machine Learning Method in Predicting Diabetes

Diabetes mellitus is one of medical science’s most important research topics because of the disease’s severe consequences. High blood glucose levels characterize it. Early detection of diabetes is made possible by machine learning techniques with their intelligent capabilities to accurately predict diabetes and prevent its complications. Therefore, this study aims to find a machine learning approach that can more accurately predict diabetes. This study compares the performance of various classical machine learning models with the hybrid machine learning approach. The hybrid model includes the homogenous model, which comprises Random Forest, AdaBoost, XGBoost, Extra Trees, Gradient Booster, and the heterogeneous model that uses stacking ensemble methods. The stacking ensemble or stacked generalization approach is a meta-classifier in which multiple learners collaborate for prediction. The performance of the homogeneous hybrid models, Stacked Generalization and the classic machine learning methods such as Naive Bayes and Multilayer Perceptron, k-Nearest Neighbour, and support vector machine are compared. The experimental analysis using Pima Indians and the early-stage diabetes dataset demonstrates that the hybrid models achieve higher accuracy in diagnosing diabetes than the classical models. In the comparison of all the hybrid models, the heterogeneous model using the Stacked Generalization approach outperformed other models by achieving 83.9% and 98.5%. Doi: 10.28991/ESJ-2023-07-01-08 Full Text: PD

Smart Farm-Care using a Deep Learning Model on Mobile Phones

Deep learning and its models have provided exciting solutions in various image processing applications like image segmentation, classification, labeling, etc., which paved the way to apply these models in agriculture to identify diseases in agricultural plants. The most visible symptoms of the disease initially appear on the leaves. To identify diseases found in leaf images, an accurate classification system with less size and complexity is developed using smartphones. A labeled dataset consisting of 3171 apple leaf images belonging to 4 different classes of diseases, including the healthy ones, is used for classification. In this work, four variants of MobileNet models - pre-trained on the ImageNet database, are retrained to diagnose diseases. The model’s variants differ based on their depth and resolution multiplier. The results show that the proposed model with 0.5 depth and 224 resolution performs well - achieving an accuracy of 99.6%. Later, the K-means algorithm is used to extract additional features, which helps improve the accuracy to 99.7% and also measures the number of pixels forming diseased spots, which helps in severity prediction. Doi: 10.28991/ESJ-2023-07-02-013 Full Text: PD

Data Driven Models for Contact Tracing Prediction: A Systematic Review of COVID-19

The primary objective of this research is to identify commonly used data-driven decision-making techniques for contact tracing with regards to Covid-19. The virus spread quickly at an alarming level that caused the global health community to rely on multiple methods for tracking the transmission and spread of the disease through systematic contact tracing. Predictive analytics and data-driven decision-making were critical in determining its prevalence and incidence. Articles were accessed from primarily four sources, i.e., Web of Science, Scopus, Emerald, and the Institute of Electrical and Electronics Engineers (IEEE). Retrieved articles were then analyzed in a stepwise manner by applying Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISM) that guided the authors on eligibility for inclusion. PRISM results were then evaluated and summarized for a total of 845 articles, but only 38 of them were selected as eligible. Logistic regression and SIR models ranked first (11.36%) for supervised learning. 90% of the articles indicated supervised learning methods that were useful for prediction. The most common specialty in healthcare specialties was infectious illness (36%). This was followed closely by epidemiology (35%). Tools such as Python and SPSS (Statistical Package for Social Sciences) were also popular, resulting in 25% and 16.67%, respectively. Doi: 10.28991/ESJ-2023-SPER-02 Full Text: PD

The Eye: A Light Weight Mobile Application for Visually Challenged People Using Improved YOLOv5l Algorithm

The eye is an essential sensory organ that allows us to perceive our surroundings at a glance. Losing this sense can result in numerous challenges in daily life. However, society is designed for the majority, which can create even more difficulties for visually impaired individuals. Therefore, empowering them and promoting self-reliance are crucial. To address this need, we propose a new Android application called “The Eye” that utilizes Machine Learning (ML)-based object detection techniques to recognize objects in real-time using a smartphone camera or a camera attached to a stick. The article proposed an improved YOLOv5l algorithm to improve object detection in visual applications. YOLOv5l has a larger model size and captures more complex features and details, leading to enhanced object detection accuracy compared to smaller variants like YOLOv5s and YOLOv5m. The primary enhancement in the improved YOLOv5l algorithm is integrating L1 and L2 regularization techniques. These techniques prevent overfitting and improve generalization by adding a regularization term to the loss function during training. Our approach combines image processing and text-to-speech conversion modules to produce reliable results. The Android text-to-speech module is then used to convert the object recognition results into an audio output. According to the experimental results, the improved YOLOv5l has higher detection accuracy than the original YOLOv5 and can detect small, multiple, and overlapped targets with higher accuracy. This study contributes to the advancement of technology to help visually impaired individuals become more self-sufficient and confident. Doi: 10.28991/ESJ-2023-07-05-011 Full Text: PD

The Necessity of Close Contact Tracing in Combating COVID-19 Infection – A Systemic Study

Many contact tracing solutions developed by countries around the globe in containing the Covid-19 pandemic are in the area of location-based tracing, which does not enable them to identify close contacts accurately. As location-based tracing implementations continuous on, the results have not been as effective as intended. Thus, in providing some closure, this study will dissect the need for close contact tracing solutions for the pandemic by providing a comprehensive contact tracing characteristic framework (CCTCF) for Covid-19, which will help authorities toward better pandemic management. In this study, CCTCF for Covid-19 was constructed by applying several methods. Using Problem, Intervention, Comparison, Outcome (PICO) as the framework, methods conducted were: (1) Case study to analyze the contact tracing systems in 30 countries; (2) Systematic literature review (n=2056) regarding solutions’ elements, (3) Thematic analysis for characteristics framework development. A total of 25 items were obtained for CCTCF, along with valuable insights that necessitate close contact tracing for the pandemic. Results from CCTCF have also shown that the best contact tracing solution for Covid-19 is bi-directional human-to-human close contact tracing, which uses a retrospective approach and is able to identify the source as well as groups of infection using a personal area network (PAN). Doi: 10.28991/esj-2022-SPER-019 Full Text: PD

Framework: Diabetes management system

Diabetes mellitus is a disease with numerous complications. To date, there is no known cure for diabetes, but certain measures can be taken to reduce the complication. One important measure is to keep the blood glucose levels as close to normal as possible. Effective control of diabetes depends on self-monitoring and self-care activities such as blood glucose monitoring, appropriate diet and nutrition, exercise regimen and medication administration strategies. Also, individuals have to keep track of their overall health record — a holistic approach instead of only monitoring their blood glucose reading. Currently there are a lot of diabetes management systems available, but most of these systems are stand-alone and focus only on glucose level or a few factors. Therefore in this research we are developing a framework of diabetes management system which integrates with a personal health record and mobile technology. A personal health record, or PHR, is a health record where health data and information related to all aspects of health of health is maintained by the individual. Therefore the proposed system will not only help individual to manage diabetes but also to monitor all body systems and help prevent the complications that may arise from diabetes

Understanding Sentiment Words and Truthful Opinions from Academic Feedbacks

Online feedbacks have become increasingly popular means of gathering students’ reviews and judging the quality of various services offered by an institution, such as courses, teaching, evaluation, infrastructure and many others. Generally, academic feedbacks include values through numerical ratings and free text comments. In this paper, we employ a natural language-based approach to extract features of feedbacks, capture sentiment words from those feedbacks and build opinion vocabulary from the corpus of academic feedbacks. Also, we focus on studying student behaviour while reporting their feedbacks. Particularly, we investigate the reliability of quantitative features through numerical ratings that students offer, by estimating the linguistic evidence from the free text in the feedback

Comparison between Bag of Words and Word Sense Disambiguation

Bag of Words (BoW) and Word Sense Disambiguation (WSD) are the main approaches utilized in almost every data mining project for classification and data processing. The two approaches are extensively used in constructing various classifiers including supervised, unsupervised and semi-supervised classifiers. In this paper, we introduce new method of defining and comparing between BoW and WSD based on three categories. First, introduce and explain the approaches through the human brain analogy to simplify the overall concept. Secondly, sort their classifiers, methodologies and algorithms in the data mining field. Finally, introduce our developed cognitive miner to illustrate the practical functionality of these two approaches

Optimization Of Patients Turnaround Time in Healthcare Facilities in Malaysia

The world has reached a period when urbanization is taking place rapidly throughout the world, including Malaysia. The number predicted to rise further to even higher 66 per cent by 2050. National Urbanization Policy (NUP), which aims to create a city of vision with a peaceful community and living environment. Urbanization, along with its problems in modern-day cities, combined with the rapid growth of ICT, has allowed researchers to formulate a 'smart city' concept. Smart Healthcare is one of the initiative within the smart city. Although this is the case, a new term known as 'Smart Healthcare' arose in the Smart City context due to substantial developments in information technology. The idea is an expansion to digital healthcare, with the goal to digitalizing all facilities. As in Smart Healthcare, it focuses on a thorough digitization of all facets of healthcare and draws on a mix of innovations such as the Internet of Things ( IoT) to make it smart. Malaysia’s Ministry of Health (MOH) in 2012 stated medical/healthcare solutions in government hospital in Malaysia are still facing challenges in providing quality service to patients especially on Turnaround Time (TAT) of patient receiving treatment. It is true especially the system in many public medical facilities has become a major public concern that would contribute to a decrease in the quality of treatment to patients who end up going home without receiving treatment after waiting for 90 minutes according to Malay Mail 2017. Hospital optimization has the potential to increase the quality of patient care, improve the resource utilization management and increase the staff productivity, which eventually save cost, time and resource of the hospital. As far as this research is concerned, the inspiration comes to shorten the patient Turnaround Time (TAT) with the aid of Real Time Patient Location Monitoring and Tracking Solution for medical professionals at medical facilities in Malaysia to benefit from it. Purpose – The purpose of this research is to conduct a research in developing and testing a Real Time Patient Location Monitoring and Tracking Solution using Bluetooth Low Energy (BLE) IOT digital device for the medical professional in medical facilities in Malaysia, which will improve the quality of service (QoS) of patience and increase the efficiency of the other department in term of cost, time and resource. Patient location data can be stored and analyzed by doctors, analysts and health practitioners to improve the Turnaround Time (TAT) of patient treatment and diagnostic to provide patients satisfaction in Malaysia government medical facilities. In public hospitals, the turnaround time (TAT) for providing services to patients is laborious. It is reported that the healthcare system has been plagued by problems for a long time, as doctors are not able to easily access the patient's location to track and monitor them where they are in medical facilities. There is no Real Time Patient Location Monitoring and Tracking Solution to optimize the TAT for the doctors in Malaysia to track the location of the patients in the medical facilities (main problem to be addressed) which directly leads to the drop of the quality of the service