Daily Archives: May 15, 2026

Authors: Geofrey Nyabuto, Charles Kibet Ng'etich, Edwin Seno, George Kihara Mburu, Marion Jeptoo, Joyadams Munene, Muriithi Alex Karani, John kimani Muragu, Nyairo Charles Magati

Abstract: Association rule mining (ARM) is a data mining approach used to discover frequent co-occurrence patterns and conditional relationships in large datasets. In healthcare, ARM has been applied to electronic health records, claims databases, laboratory data, prescription data, disease registries, and public health datasets to reveal clinically meaningful patterns that may support diagnosis, medication safety, risk stratification, and service planning. Objective: This review synthesizes how ARM has been applied in healthcare, focusing on methods, clinical application areas, implementation challenges, and future research directions. A systematic review design guided by PRISMA 2020 was used to structure the manuscript. Literature was organized around peer-reviewed ARM studies in healthcare, including clinical decision support, diagnostic test ordering, disease-medication association mining, adverse drug reaction signal detection, risk factor discovery, hospital readmission analysis, privacy-preserving mining, and emerging causal or hybrid ARM approaches. The literature shows that Apriori remains the most frequently used ARM algorithm, although FP-Growth, weighted Apriori, class association rules, negative association mining, privacy-preserving ARM, and causal irredundant ARM are increasingly used to address computational, interpretability, privacy, and clinical validity limitations. ARM is valuable because it produces transparent IF-THEN rules that clinicians can inspect, but uncontrolled rule generation, weak validation, data quality limitations, and spurious associations remain major barriers. ARM has clear potential in healthcare knowledge discovery and decision support, particularly where interpretability is required. Future research should prioritize external validation, clinician-centered rule evaluation, integration with electronic medical records, explainable hybrid models, privacy-preserving analytics, and evidence from low- and middle-income healthcare settings.

DOI: https://doi.org/10.5281/zenodo.20199703

Published by: vikaspatanker

Published by: vikaspatanker

Authors: Gunjan and Rishita Vohra, Ms. Preeti Kumari

Abstract: The AI-Based Food Expiry Tracker and Smart Recipe Suggestion System is a web-based platform developed tointelligentlymonitorfooditems,predictexpirydates,andsuggestsuitablerecipesbasedonavailableingredients.Thesystemoperates through three major components — user, business, and admin — each offering specializedfunctionalities for foodtracking,recipegeneration,andsystemmanagement.Fooditemscanbeaddedmanuallyorthroughsmartlogging,andusersreceive timely notifications before items expire. The integrated AI module enhances usability by analyzing ingredientcombinations and recommending region-specific Indianrecipes to prevent wastage. For efficient and reliable performance, thesystemarchitectureincorporatestechnologiessuchasPHP, Laravel, HTML, CSS, JavaScript, MySQL, andPythonforAIandmachinelearningintegration.Thisprojectpresentsascalableandsustainablemodelthataddressesthegrowingissueoffoodwastage by promoting intelligent kitchen management and mindful consumption.

Published by: vikaspatanker

Authors: Madhusudan Kumar, Abhinav Kumar Singh, Prof. Vishal Mehtre

Abstract: Optimal Power Flow (OPF) is a crucial problem in power systems that involves generating power at minimum cost while ensuring safe and feasible operation. This problem is normally solved by using mathematical approaches but they are not always effective due to the problem's complexity. In this context, researchers have started to apply smart, nature-inspired algorithms such as Genetic Algorithm (GA), Particle Swarm Optimization (PSO) and Ant Colony Optimization (ACO). These techniques help find better solutions, even for complex problems. In this paper, we analyse these algorithms in terms of speed, accuracy, computational effort and robustness. After analysing results of various research papers, we find what algorithm works best under various power system conditions.

DOI: https://doi.org/10.5281/zenodo.20198493

Published by: vikaspatanker

Authors: Priyanka Thakur, Dr. Sweta Dixit

Abstract: This paper looks into the effect of social media on the recruitment of employees in the Information Technology (IT) and Information Technology Enabled Services (ITES) sectors, with a focus on hiring efficiency, employer branding, and candidate engagement. The study examines how platforms such as LinkedIn, Facebook, and Instagram are increasingly being used by organizations to attract, screen, and select potential candidates. A mixed-method approach has been adopted, combining primary data collected through questionnaires with secondary data from existing research and literature. The findings indicate that social media has significantly improved recruitment processes by reducing hiring time and cost, expanding access to a wider pool of candidates, and enhancing employer branding. It also enables better communication and interaction between recruiters and job seekers. However, the study also highlights certain challenges, including privacy concerns, the authenticity of online information, and the possibility of bias in candidate evaluation. These issues may affect the fairness and reliability of recruitment decisions. Overall, the study concludes that social media is a valuable and effective recruitment tool, but it should be used carefully and in combination with traditional recruitment methods to ensure balanced, ethical, and efficient hiring practices.

DOI: https://doi.org/10.5281/zenodo.20196517

Published by: vikaspatanker