Cyber Analytics: Modelling the Factors Behind Healthcare Data Breaches for Smarter Security Solutions
This study employs a comprehensive methodology to analyze healthcare data breaches in the United States, utilizing information extracted from the U.S. Department of Health and Human Services Portal. The unbalanced nature of the data across different years is addressed through meticulous examination of breach occurrences, encompassing diverse factors such as state, covered entity type, affected individuals, breach type, and entity classification. The results section unveils key insights into the prevalence and impact of healthcare data breaches. Hacking and IT incidents emerge as the predominant breach type, significantly affecting individuals, followed closely by unauthorized access/disclosure and theft. The study further dissects the data by business type, revealing that business associates and healthcare providers bear the brunt of breaches, with health plans and healthcare clearing houses also facing substantial challenges. The study conducted cyber analytics on the factor behind healthcare data breaches for smarter security solutions. This is based on the backdrop of increasing cybercrime in the United States. The study utilized secondary data, which includes indicators such type of breach, location of breach, the number of individuals affected, business type, and the time of cyberattack. The findings revealed that hacking and information technology incidents are the most prevailing cyberattack on healthcare data, with healthcare providers and business associate being the most affected entity. The findings also revealed that network server and email are the major location of healthcare data breached. Furthermore, the data indicated that there is more breach in 2023 than other years, indicating a significant rise in cyberattacks in the healthcare. It was suggested that healthcare entities need to develop and regularly update incident response plans to ensure a swift and effective response in the event of a cybersecurity breach, which should include clear communication strategies to prevent losing data to cybercriminals. The concentration of breaches in specific entities, states, and quarters underscores the diverse and pervasive nature of cybersecurity challenges in the healthcare sector. Continuous efforts to enhance cybersecurity frameworks are deemed critical to safeguard sensitive healthcare data and protect individuals' privacy.
Published by: Tosin Clement, Callistus Obunadike, Darlington C. Ekweli, Oluomachi E. Ejiofor, Oluwadamilola Ogunleye, Simo Sevidzem Yufenyuy, Chukwu I. Nnaji, Chinenye J. Obunadike
Author: Tosin Clement
Paper ID: V10I1-1197
Paper Status: published
Published: February 23, 2024
Risk Management in Renewable Energy Finance; Analyzing the Implication of Quantitative Risk Management Techniques Applied in Financing Renewable Energy Projects on Fostering Renewable Energy Growth and Its Integration into the US Energy Sector
Risk management plays a critical role in the development of renewable energy projects in the United States. This paper analyzes the implications of quantitative risk management techniques applied in financing renewable energy projects on fostering renewable energy growth and its integration into the US energy sector. By examining the financial risks associated with renewable energy investments and the strategies to mitigate them, this study sheds light on how efficient risk management can enhance the attractiveness of renewable energy initiatives to investors and financial institutions. The paper also explores the role of government policies, market dynamics, and project selection criteria in shaping the risk landscape of renewable energy investments. Through a systematic review of the literature and case studies, the paper demonstrates how quantitative risk management methodologies, such as probabilistic modeling, scenario analysis, sensitivity analysis, and Monte Carlo simulation, provide valuable insights for decision-making, resource allocation, and project resilience in the dynamic energy market. Overall, this research contributes to a deeper understanding of the importance of risk management in promoting the growth and sustainability of renewable energy in the United States.
Published by: Samson Edozie, Ibukunoluwa Okunnuga, Damilare Olutimehin
Author: Samson Edozie
Paper ID: V10I1-1222
Paper Status: published
Published: February 22, 2024
Urban Development Sustainability: Public Policy Perspectives in South Asia and Europe
In South Asia, the existence of approximately 250 million individuals in informal settlements indicates a pressing challenge posed by urbanization. While urban growth presents opportunities for economic revitalization and improved living standards, the region confronts formidable barriers to prosperity and enhanced quality of life. Achieving sustainable urban development necessitates forward-thinking public policies that prioritize environmental preservation, social equity, and economic advancement. A comparative analysis of implementation strategies underscores the significance of tailored approaches responsive to the distinct challenges and potentials of cities. By exchanging best practices, lessons learned, and innovative solutions, urban centers can advance sustainable development agendas and foster resilient, thriving communities for generations to come. This research investigates the hurdles faced by urban areas in achieving sustainability amidst rapid population expansion, environmental decline, and socio-economic disparities. Through comparative analysis of policy frameworks and implementation tactics across varied urban landscapes, the study assesses the efficacy of diverse interventions, identifies pivotal factors driving success, and offers insights into optimal practices for promoting urban sustainability.
Published by: Debashis Chakrabarti
Author: Debashis Chakrabarti
Paper ID: V10I1-1221
Paper Status: published
Published: February 22, 2024
Analysis of Brain Tumor Detection and Segmentation Using Enhanced Deep Learning Algorithm Kernel CNN with M-SVM
The prevalence of brain tumors necessitates the development of accurate and efficient diagnostic tools. This study presents an innovative approach to brain tumor detection and segmentation by leveraging an enhanced deep learning algorithm, specifically a Kernel Convolutional Neural Network (CNN) coupled with a Modified Support Vector Machine (M-SVM). The proposed method aims to improve both the sensitivity and specificity of brain tumor detection while enhancing the precision of tumor boundary delineation. The study begins with the preprocessing of magnetic resonance imaging (MRI) data, including normalization and noise reduction, to optimize the input for the subsequent deep learning model. The Kernel CNN is designed to extract hierarchical features from the MRI images, capturing intricate patterns indicative of tumor presence. The integration of a kernelized approach enhances the model's ability to discern complex relationships within the data, thereby improving overall detection accuracy. In addition to tumor detection, the study introduces a novel segmentation strategy based on a Modified Support Vector Machine (M-SVM). The M-SVM algorithm refines the results obtained from the CNN, facilitating precise delineation of tumor boundaries. This two-step approach not only enhances the accuracy of tumor localization but also provides valuable information for subsequent medical interventions. To evaluate the proposed methodology, extensive experiments are conducted using benchmark datasets, and the results are compared with existing state-of-the-art techniques. Quantitative metrics such as sensitivity, specificity, precision, and Dice coefficient are employed to assess the performance of the model. The findings demonstrate that the proposed Kernel CNN with M-SVM outperforms conventional methods, showcasing its efficacy in both tumor detection and segmentation tasks. In conclusion, this research presents a robust and advanced framework for brain tumor analysis, offering a promising avenue for accurate diagnosis and treatment planning. The synergy between deep learning and support vector machines, coupled with the innovative use of kernelization, underscores the potential of this approach in contributing to the ongoing efforts to improve brain tumor diagnostics and patient outcomes
Published by: Nishant Kumar Singh, Dr. pushpneel verma
Author: Nishant Kumar Singh
Paper ID: V10I1-1187
Paper Status: published
Published: February 22, 2024
Emotional wellness in education: A dive into the academic consequences of mental health
This research explores the relationship between mental health and academic performance among university students. A qualitative survey of 100 second-year students at Indira Gandhi Delhi Technical University for Women uncovers challenges and coping mechanisms related to mental health. It was found that a considerable percentage remains unaware of available mental health resources on campus. Preliminary findings include the critical need for mental health support, suggesting the importance of integrating such facilities within universities. The relationship can further be explored by leveraging the right computer science tools to collect and analyze data.
Published by: Pallika Dhingra
Author: Pallika Dhingra
Paper ID: V10I1-1194
Paper Status: published
Published: February 22, 2024
Prediction and detection of malicious URL using machine learning
The efficient identification of malicious URLs has become crucial due to their growing hazard to individuals, companies, and digital infrastructure. This study evaluated multiple machine learning algorithms for their ability to predict and identify dangerous URLs. The research focused on the Random Forest Classifier since it outperformed rival models in binary and multi-class classification tasks. With 98.9% accuracy in binary classification, the Random Forest Classifier performed well. This shows the classifier can identify safe and hazardous URLs. The system's precision of 98.8%, F1 score of 99.3%, true positive rate of 99.7%, and true negative rate of 95.6 demonstrate its dependability. Multi-class classification accuracy was 97.0%, and precision, recall, and F1 scores were good again for the Random Forest Classifier. This research provides practical tips for enhancing web security and shows how transparent AI models and interdisciplinary teamwork may solve complicated cybersecurity problems. This research has made a significant contribution to the body of known information, and its significance lies in the fact that it provides both benefits.
Published by: Ibukunoluwa D. Okunnuga
Author: Ibukunoluwa D. Okunnuga
Paper ID: V10I1-1211
Paper Status: published
Published: February 21, 2024
