Journal of Soft Computing and Computational Intelligence (e-ISSN: 3048-6610)

A Novel Placement Prediction System

Chinmai Adhav — 2024-07-04

Predicting the placement outcomes of students is a critical task for educational institutions and students themselves. This project will conduct a comprehensive performance analysis of various placement prediction models, aiming to enhance the accuracy and reliability of such placement predictions. In this project, we will analyze a dataset of student profiles, academic records, internships, Average marks of 10^th, 12^th/diploma and placement results, employing machine learning and data mining techniques. The model's predictive accuracy will be used across multiple algorithms, including logistic regression, decision trees, K-Nearest Neighbor, Support Vector Machine and random forest, to evaluate the study's performance and to help the final-year students for the placement. The results of this analysis offer valuable insights into the strengths and weaknesses of different prediction models. They can guide the development of more effective placement prediction systems, ultimately benefiting final-year students and educational institutions for student placement and professional growth.

Big Data and Cloud Computing Synergies: Changing IT Landscapes and Promoting Innovation

Ashish Riwal — 2024-08-07

Cloud computing and prominent data paradigms are related and are changing the information technology sector. Cloud computing allows for the efficient use and scalability of computing infrastructure by providing on-demand access to a shared pool of programmable computer resources. Conversely, big data describes extensive and intricate datasets that are difficult to handle and evaluate using conventional data processing methods. The present abstract delves into the convergence of big data and cloud computing, emphasizing their mutually beneficial association and revolutionary influence on diverse sectors. Organizations may effectively handle, store, and analyze enormous volumes of data by utilizing the elasticity and agility of cloud resources. This leads to the discovery of essential insights and spurs innovation.

Additionally, cloud-based big data solutions offer cost-effectiveness, flexibility, and accessibility, democratizing data analytics and empowering businesses of all sizes to harness the power of data-driven decision-making. However, data privacy, security, and regulatory compliance remain significant concerns in this evolving landscape. Addressing these challenges is crucial to fully leverage the transformative power of cloud computing and big data in shaping the future of technology and business. This abstract underscores the importance of understanding and addressing these challenges to fully realize the potential of cloud computing and big data in shaping the future of technology and business.

Enhancing Workflow Efficiency through Machine Learning- Based Email Sorting

Harshini B.V — 2024-08-07

The present scenario of email management poses significant challenges due to the overwhelming volume of emails received daily across various domains. Traditional manual email sorting and classification methods are time-consuming, inefficient, and prone to errors. One of the critical limitations of the present scenario is the reliance on rule-based or keyword-based approaches for email classification. These methods often need more flexibility and help to adapt to the dynamic nature of email content, leading to inaccurate classification results. Additionally, the sheer volume of emails and the presence of spam, phishing attempts, and irrelevant messages exacerbate the difficulty of effectively categorizing emails. The problem addressed in this project is needing a more efficient and accurate email classification system to automate organizing and prioritizing incoming emails. The goal is to develop a solution that overcomes the limitations of existing approaches and enhances productivity by streamlining email management workflows. The proposed solution involves applying machine learning techniques, specifically natural language processing (NLP) and supervised learning algorithms, to classify emails into predefined categories automatically. By training a machine learning model on a labelled dataset of emails, the system can learn to recognize patterns and relationships within the email content, allowing for more accurate classification. Furthermore, the proposed solution aims to leverage advanced features such as sentiment analysis, entity recognition, and context-aware classification to improve the granularity and effectiveness of email categorization.

Machine Learning-Based Automatic Timetable Generation

S. Meera — 2024-08-22

Creating an automatic timetable generator using machine learning represents a cutting-edge advancement in scheduling technology designed to address the complexities inherent in educational institutions like schools, colleges, and universities. This system utilizes sophisticated machine learning algorithms to streamline and enhance the scheduling process, providing a robust solution for managing class timetables, teacher assignments, and room allocations. By analyzing historical data and identifying patterns, the system can generate optimized schedules that minimize conflicts and maximize the efficient use of resources. The integration of machine learning allows the generator to adapt to varying constraints and preferences, such as room capacities, teacher availability, and student course requirements. This dynamic approach not only reduces the manual effort involved in timetable creation but also ensures that the generated schedules are balanced and practical, accommodating the diverse needs of educational institutions. Ultimately, the Automatic Time Table Generator aims to improve operational efficiency, reduce scheduling conflicts, and support a more organized educational environment, fostering an optimal learning experience for students and a more manageable workload for faculty and administrative staff.

Challenges and Solutions for Security and Privacy in Soft Computing Systems

Vinay Kumar Singh — 2024-08-28

Systems that maintain security and privacy (S&P) are essential for decision support in delivering services via the Internet. Systems and users must be able to express, manage, and update their security settings efficiently and privately while interacting with peers in open networks, particularly when sharing files and gaining access to essential services. This requirement increases even further in situations involving computers and the natural world when safe and dependable interactions are crucial. Concerns about security and privacy must be addressed, and the international research community which includes experts from many fields and specialities must provide firm answers. This paper aims to provide a comprehensive and perceptive analysis of various platforms' security and privacy challenges, strategies, and roadblocks. It highlights particular difficulties and developments to deepen comprehension and raise the calibre of data in processing environments. The emphasis is mainly on privacy and security concerns relevant to different computer platforms; however, associated issues like complexity, scalability, trust, and efficiency are also considered. By effectively exposing the primary obstacles and prerequisites for attaining private and safe interactions in real-time settings, the review technique used in this study has provided insightful information for further research and development in this crucial field.