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

Authors: Sagar C Saner, Shivam Dubey, Mrs.Supriya kamareddy

Abstract: The rapid growth of digital music streaming platforms has transformed the way individuals access, consume, and discover music. This study examines the impact of digital music streaming platforms on listeners’ music consumption behavior in comparison with traditional music formats such as CDs and cassettes. The research specifically investigates changes in listening behavior, the influence of personalized recommendation systems, the role of streaming platforms in music discovery, and listeners’ perceptions toward AI-generated music. A descriptive cross-sectional research design was adopted, and primary data were collected through a structured questionnaire from 102 respondents using a convenience sampling technique. Data analysis was conducted using descriptive statistics and Chi-square tests with the help of SPSS. The findings indicate that digital music streaming platforms have significantly influenced music consumption behavior by providing greater convenience, wider music accessibility, and increased listening time. Personalized recommendation systems and algorithm-based playlists were found to strongly influence listeners’ music preferences and choices. Streaming platforms also emerged as powerful tools for discovering new songs, artists, and music genres. However, respondents showed only moderate acceptance toward AI-generated music, suggesting cautious openness toward this emerging technology. Statistical analysis further revealed that demographic variables such as education, occupation, and gender showed selective influence on certain aspects of music behavior, whereas age had no significant impact on perceptions of AI-generated music. Overall, the study concludes that digital music streaming platforms have become a dominant force shaping modern music consumption patterns and listener experiences.

Published by: vikaspatanker

Authors: Tejaswini Taware

Abstract: This seminar work presents a reinforcement learn-ing based field-oriented control strategy for Permanent Magnet Synchronous Motor (PMSM) drives. A Twin Delayed Deep Deterministic Policy Gradient (TD3) agent is used to replace the conventional PI current controller in the dq-axis current loop. The controller is validated using a 10 s staircase per-unit speed profile with repeated acceleration and braking transitions. The obtained results show fast tracking, low overshoot, stable dq current regulation, and improved robustness for practical intelligent drive applications.

DOI: https://doi.org/10.5281/zenodo.20180838

Published by: vikaspatanker

Authors: Akashdeep Singh, Ansh Gupta, Anmol Goyal

Abstract: This research paper presents a comprehensive analytical study on strategic campaign restructuring and multi-level segmentation within a revenue intelligence ecosystem. The research was conducted during an industry-integrated business analytics engagement at Reviniti, a revenue intelligence platform developed by 1DigitalStack. The study investigates KPI-driven dashboard optimization, marketing attribution analysis, campaign ROI evaluation, cohort analysis methodologies, data validation procedures, and automated reporting frameworks. The implementation integrated Microsoft Excel, Google Sheets, Metabase, and the Reviniti platform to support analytical processing, visualization, and stakeholder reporting. The project identified major gaps in last-click attribution models and introduced structured multi-touch attribution methodologies for improved revenue allocation. Significant business outcomes included an 18% reduction in cost per acquisition, a 40% increase in dashboard adoption among non-technical stakeholders, a 31% improvement in lead-to-close ratio, and an over 80% reduction in reporting cycle duration. The paper demonstrates the practical significance of structured business intelligence systems, dashboard-centric architectures, and KPI-driven decision-making frameworks in optimizing marketing performance and operational efficiency.

DOI: https://doi.org/10.5281/zenodo.20179426

Published by: vikaspatanker

Authors: Dr. Jermiah Anand Jupalli, Dr. Kiran Koduru

Abstract: The rapid evolution of artificial intelligence (AI) and digital transformation has significantly influenced the domain of human resource management (HRM), enabling the development of intelligent and data-driven systems. This paper proposes an Intelligent Human Resource Management System (IHRMS) framework designed to enhance organizational efficiency and decision-making through AI-driven analytics, automation, and predictive modeling. The study integrates multiple HR functions, including recruitment, performance evaluation, employee engagement, and attrition prediction, into a unified intelligent system. A synthetic dataset is utilized to evaluate the performance of the proposed model, and comparative analysis is conducted with traditional machine learning approaches such as Support Vector Machine and Decision Tree. The results demonstrate that the proposed IHRMS model achieves higher accuracy, improved prediction consistency, and better decision support capabilities. Furthermore, the study addresses ethical considerations such as fairness, transparency, and data privacy in AI-based HR systems. The findings indicate that intelligent HR systems can significantly contribute to organizational excellence by improving workforce management, enhancing employee experience, and enabling strategic decision-making.

DOI: http://doi.org/10.5281/zenodo.20179023

Published by: vikaspatanker

Authors: Aditya Choukiker, Om Prakash Sondhiya

Abstract: Bioethanol remains one of the most important renewable liquid fuels because it can be blended with gasoline, distributed through existing fuel systems, and produced from a broad range of biological feedstocks. While first-generation ethanol relies on sugar- and starch-rich crops, increasing interest has shifted toward agricultural residues such as groundnut shell, sugarcane bagasse, rice straw, and corn stover. These residues are attractive because they are abundant, inexpensive, and do not directly compete with food use. Their conversion is nevertheless technically demanding because lignocellulosic materials contain cellulose and hemicellulose embedded within a lignin-rich matrix that resists hydrolysis. This paper presents a research-style review of residue-based bioethanol production with emphasis on feedstock structure, pretreatment methods, hydrolysis and fermentation pathways, product recovery, and practical engineering challenges. Groundnut shell is examined as a representative residue because it is readily available in many agrarian regions yet comparatively underused as an energy resource. The paper synthesizes published engineering and bioenergy literature into a coherent overview, compares selected residues on the basis of composition and process suitability, and discusses major barriers including pretreatment severity, enzyme cost, inhibitor formation, feedstock variability, and scale-up complexity. The review concludes that residue- derived bioethanol is technically feasible and environmentally relevant, but successful deployment depends on better feedstock logistics, process integration, and biorefinery strategies that improve carbon efficiency and reduce conversion cost.

Published by: vikaspatanker