Scrum and Kanban are some of the most common methodologies that are used in software development because of flexibility and focus on the team. Nevertheless, applying Agile at a project level and within large IT projects including workforce distributed across different areas, manages some remarkable difficulties, for example, coordination, communication, or resources. This paper examines ideas on how to improve the implementation of Agile system to increase the performance of the team and results of the projects. The study focuses on four key goals: proving Agile improvements in practice via pilot surveys, applying best-practice structures, such as defence-grade SAFe or LeSS at the scale, encouraging organizations-wide Agile mindset, and using collaboration and automation technologies when working in remote environments. These issues serve as the focus of this research with the intention of preserving Agile’s principles of flexibility and practicability across various size and scale projects. It presents suggestions for further research and informs practitioners and organizations wishing to obtain the most out of Agile methodologies in real environments.

This study provides insight into how machine learning methods, in particular k-means clustering algorithm could contribute to greater degree of employee engagement in the businesses. Using Work-Life Balance, Environment Satisfaction and Job Satisfaction found in employee survey data as an illustrative lens of the engagement phenomenon, patterns are identified that differ from traditional perspectives with implications for organizational actions. The study categorizes workers in clusters and identifies the significant gaps of satisfaction among them, using k-means clustering. Logistic regression analysis is used for the prediction of attrition risk, which also helps in determining factors responsible behind employee retention. The findings reveal the importance of understanding such facilitators to generate targeted interventions and strategies that foster a positive work environment and improve organisational performance. This approach ensures less attrition risks, and better job satisfaction leading to greater overall organisation productivity / wellbeing.

Over the years, learning has shifted from a conventional classroom environment to a digital space due to an increased interest in e-learning and swift innovations in information technology. This has brought the attention of many individuals and institutions to delve into building various approaches for adaptive e-learning technologies. Most existing e-learning systems are teacher-based, time-wasting and do not monitor learners’ progress levels. This paper presents a type-1 rule-based fuzzy logic model to implement an adaptive e-learning system by identifying students’ prior knowledge, learning style, and learning pace. The system was designed with Object-Oriented Analysis and Design Methodology and implemented using PHP, JavaScript, and MySQL technologies. A total of 31 first-year students of the University of Nigeria, Nsukka, participated in the evaluation of the software. The pre-test measured the students' prior knowledge, and the performance of each student was mapped. The system monitors students’ engagement levels and performance to improve learning outcomes. It also has an ‘Ask Teacher’ feature, which allows a student to ask the teacher questions outside the forum and the student’s feedback form. Each chapter has a pre-test to test the student’s existing knowledge, well-explained chapter content in text and audio-visual format, and a post-test to test their performance at the end of each chapter. After participating in the experiment, a questionnaire was used to collect the general students’ views on online-adaptive learning. The study implies that it assists students, teachers, and universities to have seamless learning and offers a quick feedback mechanism for the university’s decision-making.  

The rising frequency of data breaches and unauthorized access in healthcare systems has heightened concerns over privacy protection in Electronic Health Records (EHRs), especially when third parties, such as insurance companies, are involved. Traditional EHR solutions are often inadequate in safeguarding data confidentiality during multi-user access. This paper presents the Blockchain-based Smart Medical Privacy-preserving Contract System (BSMPCS), a novel framework designed to address these privacy challenges. By integrating Bitcoin smart contracts and homomorphic encryption, BSMPCS ensures secure management of EHRs while restricting access to only authorized entities. The decentralized and immutable characteristics of blockchain enhance data integrity, while smart contracts prevent unauthorized disclosure of sensitive health information to insurance companies. Additionally, homomorphic encryption enables insurance claim verifications without exposing the actual health records, preserving both patient identity and medical privacy. Unlike conventional systems, BSMPCS eliminates the need for third-party intermediaries, significantly reducing the risk of data leaks. Combining blockchain and advanced cryptographic methods, this framework provides a robust, privacy-preserving solution suitable for modern healthcare systems.

East Kalimantan Province holds significant potential in developing cocoa commodities, a key sector in regional plantation development. However, there is a lack of an effective, web-based information system to visualize and manage data related to cocoa land distribution. This limitation hampers access for both the public and policymakers. To address this issue, a web-based Geographic Information System (GIS) application was developed using Leaflet JS and GeoJSON. This application aims to provide an interactive map that can display the location of cocoa plantations in the distribution of East Kalimantan Province, especially in Kutai Kartanegara, East Kutai, West Kutai, Mahakam Ulu, Penajam Paser Utara, Paser, Berau, Samarinda, Balikpapan, and Bontang, as well as providing information about the land area and cocoa production. Spatial data and cocoa production data are integrated into this application so that users, including farmers, entrepreneurs, and governments, can obtain comprehensive information about cocoa commodities in the East Kalimantan Province. By visualizing cocoa land distribution, the GIS tool is expected to support informed policymaking aimed at improving productivity and land use efficiency. It also serves to raise public awareness of cocoa potential in local communities.