Volume-11 , Issue-3 , Mar - 2023 Go Back

Healthcare data [1] is becoming more complex and voluminous, which makes it difficult to extract valuable insights and improve healthcare services. Data analysis can help solve this challenge by providing a powerful solution. In this paper, the authors introduce the concept of data analysis in healthcare and explain its significance in enhancing patient outcomes, reducing healthcare costs, and improving the quality of care. The authors also discuss the different types of healthcare data, including electronic health records, claims data, medical imaging data, and patient-generated data, and explain the techniques used in data preprocessing, including data cleaning, transformation, and integration. Moreover, the authors describe the techniques used in exploratory data analysis (EDA), such as data visualization, summary statistics, and correlation analysis, which can help identify patterns and trends in healthcare data. They also explain the various predictive modeling techniques used in healthcare data analysis, including regression analysis, decision trees, and neural networks, which can be used for predicting patient outcomes and identifying risk factors. Additionally, the authors discuss the development of clinical decision support systems using data analysis, which can assist healthcare professionals in making informed decisions about patient care. The paper provides real-world examples of how data analysis has been used in healthcare, such as predicting hospital readmissions, identifying high-risk patients, and improving medication adherence. Finally, the authors discuss emerging trends in data analysis in healthcare, such as the use of artificial intelligence and machine learning, and their potential impact on healthcare. Overall, this paper highlights the importance of data analysis in healthcare and its potential to revolutionize the industry.

Data Analysis, Healthcare Data, EHR, Claims Data, Medical Imaging Data, Data Preprocessing, Data Cleaning, Data transformation, Data Integration, EDA, Data Visualization, Clinical Decision Support, AI, ML.

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Sameer Shukla, "Unlocking the Power of Data: An Introduction to Data Analysis in Healthcare," International Journal of Computer Sciences and Engineering, Vol.11, Issue.3, pp.1-9, 2023.

The Internet of Medical Things (IoMT) is a group of internet-connected medical devices used in e-healthcare for remote patient diagnosis, medical equipment regulation, and tracking of quarantined patients. However, the IoMT faces security and privacy concerns due to the vast amounts of data handled by these devices, which can lead to sensitive personal data being exposed to various attacks. This paper proposes a secure route management strategy using the AODV protocol and a modified lightweight AES encryption approach to protect healthcare data. The approach is demonstrated to be secure against black hole attacks and requires relatively low computation and communication resources. These findings indicate that the proposed paradigm is suitable for deployment in IoMT devices with limited.

IoMT Intelligent routing, lightweight cryptography, IoMT, E-healthcare, IoMT-Security.

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Mohammad Sirajuddin, B. Sateesh Kumar, "Energy-Efficient and Secure Framework for IoMT-Based E-Healthcare Systems Using Intelligent Routing," International Journal of Computer Sciences and Engineering, Vol.11, Issue.3, pp.10-16, 2023.

This paper discusses the use of machine learning techniques for predicting and identifying suicide risk on social networking websites and suggests an approach that involves analyzing social media posts using different machine learning algorithms, including deep learning models, to detect suicidal ideation. The effectiveness of the model is evaluated using various metrics such as precision, recall, accuracy, and F1-score. The results show that machine learning techniques can successfully identify individuals at risk of suicide. The findings are significant for mental health professionals, social media companies, and individuals at risk of suicide, and contribute to the ongoing efforts to use technology for suicide prevention and improved mental health outcomes.

Machine Learning, Predictive Modeling, Social Media, Suicide Prevention, Text

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Ed Gowhar Hafiz Wani, Virendra K. Sharma, "Predictive Analytics for identifying Suicide Risk on Social Media Forums using Machine Learning Algorithms," International Journal of Computer Sciences and Engineering, Vol.11, Issue.3, pp.17-23, 2023.

The present work investigates the application of e-learning in the education system in the situations of social distancing caused by the pandemic COVID-19. The likelihoods and challenges in the execution of e-learning have been discussed. The recent widely used platforms for e-learning are presented in the present work. An investigation of these platforms, along with their advantages and disadvantages have been addressed. Based upon the analysis conducted, the challenges for the future enhancement and successful deployment of e-learning are addressed.

Web Conferencing, E-learning, LMS, Schoology, Google Classroom.

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A. Thakur, "Applications and Challenges of E-Learning Technologies," International Journal of Computer Sciences and Engineering, Vol.11, Issue.3, pp.24-27, 2023.

In 2020, the events that transpired revealed the fragility of society and its vulnerability to abrupt shifts in governing paradigms. The outbreak of COVID-19 pandemic globally altered the manner in which people engage in activities such as communication, work, study, and interaction. This resulted in a significant change in the way society operates, including education. To accommodate the new reality, education embraced the use of technology, specifically information and communication technologies. One such example is the increased reliance on learning management systems as a platform for resource management and educational activities. This proposal seeks to enhance the learning experience by incorporating artificial intelligence and data analysis into learning management systems. The aim is to establish robust educational models in the new normal, where students have access to virtual assistants for guidance during online learning.

analysis of data; artificial intelligence; machine learning; online education

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Parveen Singh, Meenakshi Handa, Anwal Ul Haq, "Revolutionizing Online Education: Integrating Machine Learning and Data Analysis into LMS," International Journal of Computer Sciences and Engineering, Vol.11, Issue.3, pp.28-33, 2023.

Breast Cancer has turned into the normal reason for death among ladies. There are several machine learning algorithms that can be used for breast cancer predictive analysis, such as logistic regression, decision trees, random forests, support vector machines, and neural networks. These algorithms can be trained on large datasets of patient information, including demographic data, medical history, and genetic markers, to identify patterns and make accurate predictions. One of the key benefits of machine learning in breast cancer predictive analysis is the ability to personalize treatment plans based on individual patient characteristics. By analyzing a patient`s unique combination of risk factors, doctors can develop tailored treatment plans that are more effective and less invasive. Our point is to group whether the breast cancer is harmless or dangerous and foresee the repeat and non-repeat of threatening cases after a specific period. To accomplish this we have utilized AI strategies, for example, “Support Vector Machine”, “Logistic Regression”, “KNN and Naive Bayes”. We additionally have investigated the precision of expectation of by applying different calculation on the new arrangement of information that has been joined. This paper explores the use of predictive analytics in healthcare decision-making through machine learning. The application of machine learning algorithms in healthcare can assist in identifying patterns and trends in patient data, which can then be used to predict potential health risks, recommend treatment options, and optimize healthcare delivery. The paper discusses various predictive analytics techniques such as decision trees, random forests, logistic regression, and neural networks, and their applications in healthcare. Additionally, the paper also highlights the challenges associated with the implementation of predictive analytics in healthcare, such as data quality, privacy concerns, and ethical issues. Finally, the paper concludes by emphasizing the need for healthcare organizations to leverage predictive analytics to improve patient outcomes, reduce costs, and enhance the quality of care.

Breast Cancer, Support Vector Machine, K-Nearest Neighbor, Naive Bayes, Logistic Regression, Classification

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Walli Prasadu, Ravishankar Kumar, Ravi Kiran Katta, Pinki Sagar, "Predictive Analysis for Decision Making Using Machine Learning in Health Care," International Journal of Computer Sciences and Engineering, Vol.11, Issue.3, pp.34-38, 2023.

As the prevalence of cloud computing continues to surge, cloud computing entities face the formidable challenge of meeting coordinated Service Level Agreement (SLA) understandings, particularly in terms of stability and operational efficiency, all while achieving cost and energy efficiency. This paper introduces Shadow Replication, a novel adaptation to internal failure mechanisms for cloud computing that seamlessly addresses faults at scale, concurrently limiting energy consumption and reducing its impact on costs. Energy conservation is realized by establishing dynamic cores as opposed to static cores, achieved through the deployment of cloudlets. Essentially, equivalent cores are created, with core failure metrics considering memory capacity, energy, and power consumption. If any of these parameters exceed the threshold value, the core is flagged, and progress is maintained within a shadow, assigned one for each host. The workload of a failed core is transferred to the next core within another virtual machine (VM). In the event of all cores within a VM failing, VM migration is executed. Results obtained through the proposed system exhibit improvements in indexed energy consumption, latency, cost, and fault rate.

Shadow Replication; fault tolerance; Energy Conservation

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Vikas Mongia, "Dynamic Core Allocation: Enhancing Fault Tolerance and Energy Efficiency in Cloud Computing," International Journal of Computer Sciences and Engineering, Vol.11, Issue.3, pp.39-43, 2023.