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
Enhancing organizational Governance: A proposal for COBIT 2019 Maturity Assessment
Background: This paper introduces a structured methodology for assigning maturity levels in accordance with The Control Objectives for Information and Related Technologies (COBIT) 2019 framework. The conventional method of evaluating an organization's COBIT Performance Management (CPM) often concentrates solely on capability levels, given the absence of a well-defined approach for assigning maturity levels in the framework. The proposed methodology introduces an approach aligned with the principles of performance management designed to quantify the maturity of the governance and management objectives from the framework and collectively represent their impact on the overall organization. Discussion: Maturity levels provide management with a comprehensive understanding of the current state of governance and management practices in the organization. Maturity assessment offers a holistic perspective, furnishing management with a thorough overview of the organizational landscape. COBIT 5 released in the year 2012, followed the Process Assessment Model (PAM) model for assigning capability and maturity values. Since there is no defined PAM for COBIT 2019, The Capability Maturity Model Integration (CMMI) levels defined by the CMMI Institute can be used to represent process improvement efforts, in other words, it can measure capability levels along with other factors to give value to the organizations process for measuring maturity. Up to this point, there exists no formalized methodology for assigning or deriving maturity levels for an organization using the COBIT framework. The impetus for this paper stemmed from Luis Gorgona’s, encouragement in the ISACA blog for readers to explore the COBIT 2019 framework as a valuable resource for developing an approach to model, assessing maturity scores, and identifying essential factors for measuring their organization's performance. Research Objectives: This paper aims to achieve the following objectives: 1. The Necessity and Challenges in the Assigning of Maturity Levels. 2. Proposing a Methodology for Assigning Maturity Levels for the COBIT 2019 Framework. Conclusion: In conclusion, this paper encourages readers, auditors, and professionals to go beyond a basic evaluation of capability levels. By which organization leaders can attain an accurate comprehension of their existing Information and Technology(I&T) practices through the adoption of this systematic approach. Consequently, leading to well-informed decision-making, enhancing overall coverage of COBIT 2019 objectives.
Published by: Taha Qureshi
Author: Taha Qureshi
Paper ID: V10I1-1217
Paper Status: published
Published: February 19, 2024
The marketing power of emotion
In today's business landscape, customers are the lifeblood of enterprises, making them invaluable assets that require careful attention to attract and retain. Scholars like Ph. Kotler and D. Jokubauskas underscore the importance of advertising in resonating with customers and eliciting desired responses. This symbiotic relationship between advertising and psychology has deepened over time, with advertisers leveraging psychological concepts to craft more impactful advertisements. This paper offers a comprehensive exploration of this intricate relationship between advertising and psychology, underscoring their pivotal roles in understanding consumer behavior and shaping effective advertising strategies. Through an examination of emotions, music, shock tactics, humor, social proof, liking, and scarcity in advertising, this paper provides valuable insights into how psychological principles inform and enhance advertising practices, ultimately driving customer engagement and loyalty.
Published by: Kanika Rajpal
Author: Kanika Rajpal
Paper ID: V10I1-1215
Paper Status: published
Published: February 17, 2024
