Edu Journal of International Affairs and Research, ISSN: 2583-9993

Enhancing Credit Risk Assessment Using Machine Learning: A Case Study on Early Payment Risk Prediction

Saugat Nayak — Fri, 05 Jul 2024 00:00:00 +0000

The financial sector, especially the banking industry, is significantly advancing in evaluating credit risk. While useful, old-style credit risk measurement tools like FICO are somewhat limited and cause problems when quickly evolving markets and changing consumer behavior occur. The advent of machine learning (ML), which is data-driven and adaptive in its approach, will do real-time risk analysis and have better predictions. This paper aims to explore the application of ML in credit risk management with particular reference to early payment risk prediction on consumer credit. Unlike a typical model, the ML models give a more detailed and adaptive evaluation of the consumers by combining behavioral, transactional, and external data. Using models like random forests, SVM, and XGboost, institutions can deal with the nonlinear relationships of each variable. This research reveals the effectiveness of the proposed methods, outperforming traditional methods in identifying early payments, with up to 20-30% enhanced accuracy of the ML models. Other issues identified include data privacy, model interpretability, and implementation issues, which are also described below. Lastly, incorporating ML into tackling credit risk assessment is a critical improvement in financial risk management. It provides a better way of managing risk, decision-making, and dynamics within unstable markets.

IT Operational Automation Solution Strategy

Amit Sengupta — Sat, 06 Jul 2024 00:00:00 +0000

In the rapidly evolving landscape of IT operations, automation has emerged as a pivotal strategy to enhance efficiency, reduce human error, and ensure consistent service delivery. This paper explores the integration of automation technologies within IT operations, focusing on the deployment of Artificial Intelligence for IT Operations (AIOps) and other advanced automation tools. By leveraging machine learning algorithms and big data analytics, AIOps can predict and mitigate potential system failures, optimize resource allocation, and streamline incident management processes. The study highlights key benefits, including improved operational agility, cost savings, and enhanced service reliability. Through a comprehensive review of current trends and case studies, this paper provides actionable insights for IT professionals seeking to harness the full potential of automation in their operational workflows.

Conceptual Overview on Development of Methodologies for Direct Interplanetary

Shreya Mane — Sun, 07 Jul 2024 00:00:00 +0000

Highly effective electric propulsion systems can be employed in interplanetary missions to shorten the journey time or enhance the mass delivered to the destination compared to conventional chemical propulsion systems. Growing concerns about possible habitable environments and the possibility of Earthly species contaminating them are brought about by the rapid speed of scientific discovery of our solar system. Key approaches include gravity assist maneuvers, Hohmann Transfer Orbit, and Patched-Conic Approximation. Multiple gravity-assist trajectory preliminary design is formulated as a global optimization problem. Next, a trajectory model is described, which strikes a balance between the difficulty of the optimization problem and the completeness of the model. Investigating the final resolution space is carried out using a unique global search strategy. The spacecraft travels from the departure planet's parking orbit to the arrival planet's parking orbit during a direct interplanetary transfer. It is necessary to construct the transfer trajectory in a way that ensures the stated arrival parking orbit criteria are met. For a predetermined departure in a direct transfer, four different transfer trajectory design alternatives are depending on the period and flight time. These design choices are not identified by the most popular analytical trajectory design method, the standard patched pop method.

A Review Paper on Sustainable Construction and Recycling of Road Materials in India

Amit Kumar — Mon, 08 Jul 2024 00:00:00 +0000

Sustainable construction practices and the recycling of road materials are gaining significant attention globally, particularly in developing nations like India. With rapid urbanization and increased infrastructure demands, the consumption of raw materials for road construction has resulted in significant environmental challenges, including resource depletion, energy consumption, and waste generation. This review paper critically examines the current practices, advancements, and challenges in sustainable road construction in India, focusing on the utilization of recycled materials such as recycled asphalt pavement (RAP), waste plastic, fly ash, and construction and demolition waste. By reviewing over 60 research studies, theses, and reports, this paper identifies the key materials, methods, and technologies employed in India for road recycling. It also highlights lifecycle assessments, energy savings, and environmental benefits associated with sustainable road construction practices. Furthermore, it discusses policy frameworks, technical constraints, and future opportunities to promote green construction in India. The findings indicate that adopting sustainable materials in road construction can significantly reduce environmental impact and align with India’s sustainable development goals. However, to fully leverage these benefits, enhanced policy support, technological innovation, and public awareness are necessary. This paper serves as a foundational guide for stakeholders in the Indian construction industry to promote sustainable practices in road development.

Identity-Based Integrity Verification and Confidential Data Sharing with Enhanced Privacy for Secure Cloud Storage

Ashish P Modak, Miss. Sneha Tirth — Tue, 09 Jul 2024 00:00:00 +0000

This paper proposes an innovative framework for secure cloud storage that integrates identity-based integrity verification and confidential data sharing mechanisms, aimed at enhancing privacy. As reliance on cloud services for data storage and management increases, so do concerns regarding data integrity and privacy. Our framework addresses these challenges by utilizing identity-based cryptography, ensuring that data integrity can be verified without exposing sensitive information. The proposed system is evaluated through a series of experiments, demonstrating its efficiency, security, and practical applicability in real-world scenarios. The results indicate that our approach significantly improves the security and privacy of cloud storage systems.

शोध विषय-समान नागरिक संहिता मुद्दे और चुनौती

डॉ प्रदीप कुमार तिवारी, डॉ नीरज प्रकाश राय — Wed, 10 Jul 2024 00:00:00 +0000

विश्व में भारत प्रजातांत्रिक व्यवस्था का सिरमौर है, जहॉ विधि का शाषन है, वास्तिविक शक्ति जनता में निहित है, चॅूकि भारतीय सभ्यता संस्किृति ,धर्म, जाति कि विविधिता पालन करने वाला देश है, यहा पर अनैकता में एकता का सिद्वांत लागू है, विभिन्न जाति, धर्म, और सम्ंप्रदाय के लोगो कों अपना धर्म पालन करने की स्वतंत्रता भारतीय संविधान प्रदान करता है, वही पर देश में व्यक्तिगत विधिया भी उपस्थित है,जैसे हिन्दू वि़िध जो हिन्दुओ पर लागू होती है, मुस्लिम विधि जो मुसलमानो पर लागू होती है, क्रिश्चियन विधि क्रिश्चयन समुदाय पर लागू होती है,इसी प्रकार पारसी विधि भी अस्तित्व में है।

Pharmacovigilance: Assessing the Effectiveness of Adverse Drug Reaction Reporting Systems

Thu, 11 Jul 2024 00:00:00 +0000

Pharmacovigilance plays a crucial role in ensuring drug safety by systematically detecting, assessing, and preventing adverse drug reactions (ADRs). Effective ADR reporting systems are essential for identifying potential risks associated with medications, thereby safeguarding public health. This paper examines the effectiveness of ADR reporting systems worldwide, focusing on their structure, data collection, and reporting mechanisms. Through analysis of various regional and national systems, including spontaneous reporting systems (SRS) and electronic health record (EHR)-linked reporting, this study highlights challenges such as underreporting, data quality, and timeliness. Additionally, it assesses the integration of advanced technologies like artificial intelligence (AI) and machine learning (ML) in enhancing signal detection and risk evaluation. The findings suggest that while ADR reporting systems are pivotal for drug safety, improvements in reporting rates, standardization, and technological support are needed to optimize pharmacovigilance practices. Recommendations for policy changes, training, and public awareness campaigns are provided to strengthen ADR reporting and improve the overall effectiveness of pharmacovigilance systems..