Paper Title

MACHINE LEARNING-BASED DISEASE PREDICTION FRAMEWORK FOR THE DIABETES HEALTHCARE INDUSTRY

Abstract

Diabetes is a chronic disease that continues to be a significant and global concern since it affects the entire population’s health. It is a metabolic disorder that leads to high blood sugar levels and many other problems such as stroke, kidney failure, and heart and nerve problems. Several researchers have attempted to construct an accurate diabetes prediction model over the years. However, this subject still faces significant open research issues due to a lack of appropriate data sets and prediction approaches, which pushes researchers to use big data analytics and machine learning (ML)-based methods. Applying four different machine learning methods, the research tries to overcome the problems and investigate healthcare predictive analytics. The study’s primary goal was to see how big data analytics and machine learning-based techniques may be used in diabetes. The examination of the results shows that the suggested ML-based framework may achieve a score of 86. Health experts and other stakeholders are working to develop categorization models that will aid in the prediction of diabetes and the formulation of preventative initiatives. The authors perform a review of the literature on machine models and suggest an intelligent framework for diabetes prediction based on their findings. Machine learning models are critically examined, and an intelligent machine learning-based architecture for diabetes prediction is proposed and evaluated by the authors. In this study, the authors utilize our framework to develop and assess decision tree (DT)-based random forest (RF) and support vector machine (SVM) learning models for diabetes prediction, which are the most widely used techniques in the literature at the time of writing. It is proposed in this study that a unique intelligent diabetes mellitus prediction framework (IDMPF) is developed using machine learning. According to the framework, it was developed after conducting a rigorous review of existing prediction models in the literature and examining their applicability to diabetes. Using the framework, the authors describe the training procedures, model assessment strategies, and issues associated with diabetes prediction, as well as solutions they provide. The findings of this study may be utilized by health professionals, stakeholders, students, and researchers who are involved in diabetes prediction research and development. The proposed work gives 83% accuracy with the minimum error rate.
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Paper Title

REVIEWS ON VARIOUS TECHNOLOGIES USED FOR VISUALLY DISABLE PEOPLE

Abstract

In an effort to overcome a variety of challenges encountered by individuals with disabilities including visual impairment, motor disability, and communication difficulties computer vision has demonstrated considerable promise. This report provides an examination of the current state of computer vision-based assistive technology, as well as significant challenges and areas for future research. This study specifically investigates the applications of computer vision in the domains of gesture-based control interfaces, object recognition, navigation, facial recognition, and sign language interpretation. Additionally, the article examines the advantages and disadvantages of different methodologies and technologies, and provides illustrations of how computer vision can be integrated into existing assistive technologies to enhance their effectiveness. This investigation addresses the privacy and ethical concerns associated with the application of computer vision to assistive technologies. Additionally, the research emphasizes the necessity for protocol standardization, enhanced user-centered design, and practical efficacy evaluation as prospective areas of investigation to refine the application of computer vision in assistive technology. In general, this article illuminates the potential transformative impact that computer vision could have on assistive technologies designed for individuals with disabilities.
KEYWORDS : Computer Vision, Assistive Technologies, Disabilities, Visual Impairment, Accessibility, Human-Computer Interaction (HCI)

Paper Title

UNDERSTANDING THE SECURITY CONCERNS SURROUNDING CLOUD COMPUTING: A REVIEW

Abstract

A proved, adaptable, and economical delivery model, cloud computing enables the provision of enterprise or consumer IT services via the Internet. Nevertheless, cloud computing introduces an additional degree of vulnerability due to the frequent delegation of critical services to external entities. This practice complicates the task of ensuring data security and privacy, ensuring the availability of data and services, and substantiating compliance. Cloud Computing utilizes numerous technologies (SOA, virtualization, Web 2.0); consequently, it inherits their security concerns, which are examined in this article. Our objective is to identify and correlate potential solutions to the most significant threats and vulnerabilities discovered in the literature pertaining to Cloud Computing and its environment.
KEYWORDS : Cloud Computing, Security Issues, Cyber security, Data Privacy.