International Journal of Engineering and Management Research
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1311 research outputs found
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CV Summary and Professional Recommendations Using RAG and NLP
Full text linkJob searching can be a very tedious affair as one has to tailor-make resumes to fit every job posting. This article provides an AI-driven approach that will cut down the fuss of making resumes, choosing keywords, and matching them precisely with job postings through RAG and NLP. The system merges a transformer-based LLM with semantic search and vector embeddings to quickly identify the roles, qualifications, experience, and skills that users highlight in their extracts. Keyword extraction also aligns with job market trends to increase application success rates. The job matching module uses FAISS-based semantic search, ranking opportunities by relevance and skill match. Mass-scale experimentation with different sets of resume and job posting data confirms the effectiveness of the system with an astonishing 92% accuracy in job matching and skill extraction. By bridging the gap between recruiters and job candidates, the process streamlines candidate profiling, making the hiring process more accurate, precise, and data-driven
Optimization Forward- Fly back converter using MATLAB
Full text linkThis study offers an examination of forward and flyback converters by emphasizing important performance metrics, including power factor, efficiency, offset current, and core loss. Each topology has benefits and disadvantages of its own. A comparative assessment of performance characteristics is carried out through conversation and observation to overcome these constraints. The results suggest that combining both topologies with suitable switching devices, such as MOSFETs with quick switching capabilities, may improve total performance. This paper also looks at a suggested merged forward-flyback converter architecture and shows how it could be used to make a single-stage system more efficient and improve its power factor
Integrating Deep Residual Learning and Thematic Analysis in a Hybrid Framework for Precision Oncology: Advancing Cancer Diagnosis and Personalized Treatment
Full text linkThis study presents a novel hybrid framework that integrates deep residual learning with thematic analysis to enhance diagnostic accuracy and treatment personalization in oncology. By combining quantitative imaging features extracted via ResNet-50 with qualitative thematic embeddings derived from unstructured electronic health record (EHR) narratives, the system models both morphological tumor characteristics and patient-centered contextual factors. The framework was evaluated in a controlled simulation environment using synthetic multimodal datasets for breast and lung cancer. Results demonstrated that the hybrid approach significantly outperformed conventional image-only models. The late fusion model achieved an accuracy of 93.1%, F1-score of 91.3%, and an AUC of 0.96, compared to 87.4%, 84.9%, and 0.91, respectively, for the image-only baseline. Error rates were reduced by 45.2%, and thematic embeddings influenced classification decisions in 21% of cases—78% of which led to improved diagnostic correctness. Furthermore, the model exhibited strong calibration, with predicted probabilities aligning within ±3% of actual outcomes across all confidence bins. Attention-based mechanisms enabled dynamic prioritization of modalities, emphasizing thematic content in over 60% of clinically ambiguous scenarios. These findings provide compelling evidence for the integration of deep learning and thematic analysis in precision oncology. The hybrid framework not only improves predictive performance but also brings artificial intelligence systems closer to the interpretive and patient-centered standards of real-world clinical practice
Impact of Diversification Strategies on Multinational Firms’ Performance in Zambia’s Food Sector
No full textThis study investigates the impact of diversification strategies on the performance of multinational firms within Zambia’s food sector. Drawing from four types of diversification strategies—vertical, horizontal, concentric, and conglomerate—the study employs a descriptive survey approach and multiple regression analysis. Results demonstrate that horizontal and concentric diversification strategies significantly enhance firm performance, explaining 45.6% of its variation. The study concludes that carefully aligned diversification can provide competitive advantage and improve financial performance, especially in developing markets with volatile operating environments
Biogenesis and Characterization of Pilea-Microphylla Ferric Oxide Nanoparticles
No full textNanotechnology involves the manipulation of materials at the nanoscale, enabling their application across fields such as environmental remediation, medicine, and engineering. However, conventional nanoparticle synthesis methods often rely on toxic reducing agents like sodium borohydride, posing environmental and health risks. This research focuses on the eco-friendly synthesis of ferric oxide nanoparticles (Fe2O3NPs) using the plant extract Pilea microphylla, a herb rich in bioactive phytochemicals such as quercetin, luteolin, and apigenin derivatives. These phytochemicals act as natural reducing and capping agents, eliminating the need for hazardous chemicals. In this study, Pilea microphylla was processed into an aqueous extract and used to synthesize Fe2O3NPs from ferric nitrate. The reaction mixture was stirred with NaOH and centrifuged to collect the nanoparticles, which were subsequently calcined at 300–400°C. The synthesized Fe2O3NPs were characterized using X-ray Diffraction (XRD) and Field Emission Scanning Electron Microscopy (FESEM) to confirm particle size and morphology. The results demonstrate that plant-based synthesis offers a scalable, cost-effective, and sustainable approach to nanoparticle production, with potential applications in wastewater treatment and environmental remediation
The Role of Internal Controls in Optimizing Hospital Capacity and Staffing Efficiency
No full textHospitals today encounter persistent challenges in maintaining optimal capacity and achieving staffing efficiency, all while striving to deliver high-quality healthcare services. Fluctuating patient volumes, resource constraints, and staffing shortages can significantly impact operational performance and patient outcomes. Internal control systems are crucial in addressing these challenges by promoting efficiency, ensuring regulatory compliance, and enhancing overall hospital operations. These systems provide a structured framework for monitoring processes, identifying inefficiencies, mitigating risks, and improving resource allocation.
This study conducts a comprehensive analysis of hospital capacity and staffing data to uncover critical operational gaps and inefficiencies that hinder effective service delivery. Through detailed evaluation, key factors contributing to capacity bottlenecks and staffing imbalances are identified. Based on the findings, the study proposes a set of targeted internal control mechanisms aimed at strengthening hospital management practices. These recommendations include the implementation of real-time capacity monitoring systems, predictive analytics for staffing optimization, automated scheduling tools, and enhanced workforce management protocols.
By integrating these internal control strategies, hospitals can improve operational efficiency, reduce staff burnout, optimize resource utilization, and ultimately enhance the quality of patient care. This research highlights the indispensable role of internal controls in fostering organizational resilience, improving healthcare outcomes, and positioning hospitals for sustainable success in an increasingly complex and dynamic healthcare environment
Gene Editing: A New Era in Medicine, Agriculture, and Ethics
No full textGene editing projects to a set of technologies that allow worldwide scientists to make corrections and changes in the DNA of a living organism. This ability correctly modifies genes in advanced fields like medicine and agriculture. In medicine, it finds out possibilities for curing genetic disorders, while on the other hand in farming practices, it holds credit for creating genetically modified crops that are more resistant to various diseases and environmental stresses. However, the ethical challenges on the society of gene editing remain a sign of concern, raising concerns about its potential misuse. This paper finds out the applications, capacity and logical considerations surrounding gene editing, with a focus on its impact in India
A Study on Factors Influencing Women Customers towards Choice of Insurance Company
Full text linkThe insurance sector in India is a fast-growing industry that plays a essential role in the country’s economy. The objective of the research paper is to study the demographic profile of the Women customer and to identify various factors influencing women customer’s choice for insurance company. For this study, a random sampling technique has been adopted. The present study has been conducted in the State of Karnataka. The sample size 386 individuals who had an experience of offline and online insurance systems were taken. A statistical tool used is Percentage analysis and Mean. To achieve the above objectives the researcher identified 25 factors for choosing the insurance company. From the above study, researchers conclude that the insurance company management should analyze all these factors and give due weightage to these factors and make appropriate marketing strategies to retain and attract the potential customers in order to increase their market share and profits
The Evolution of Fast Fashion Business: Key Drivers and Historical Perspectives
Full text linkThis paper explores the historical trajectory of fast fashion and its underlying drivers using Whetten\u27s framework. By bridging existing theories across disciplines, we aim to provide multi-level insights into the industry\u27s evolution and propose actionable directions for future inquiry. To provide a robust analytical structure, we employ David Whetten\u27s framework for theory building. This framework, emphasizing the crucial elements of "what," "how," "why," "who," "when," and "where," will serve as a lens through which we examine the multifaceted nature of fast fashion\u27s development. By systematically addressing these fundamental questions, we aim to move beyond descriptive accounts and offer a more theoretically grounded understanding of the industry\u27s dynamics
Global Trend Analysis and Forecasting Model Construction of Mineral Resource Markets
Full text linkGlobal economic expansion and technological innovation have driven sustained growth in demand for mineral resources, making accurate analysis and forecasting of their market trends a core need for policymakers, investors, and industry practitioners. This study focuses exclusively on strategic minerals (lithium, cobalt, rare earths)—core raw materials for green technologies such as electric vehicle batteries and wind power equipment—and proposes a specialized forecasting model integrating econometrics and machine learning to provide targeted decision support for stakeholders.
First, based on 1990–2023 specialized data from authoritative institutions (World Bank, IMF, USGS, IEA), including production, consumption, trade, prices of strategic minerals, and green technology indicators (e.g., electric vehicle sales, wind power installed capacity), this study uses econometric methods to systematically analyze consumption patterns and trade characteristics of the three minerals. Second, key empirical findings are embedded into a machine learning framework, integrating three core factors—green technology penetration, resource-country geopolitical policies, and macroeconomic indicators (U.S. dollar index, global GDP)—to optimize short-term (1–3 years) and long-term (5–10 years) forecasting accuracy.
The model clarifies quantitative impacts of green technologies on demand (e.g., a 10% increase in electric vehicle penetration drives a 15%±2% growth in lithium demand). Two scenarios—"EU carbon tariff adjustment" and "Congo (Kinshasa) cobalt supply disruption"—are designed, combined with historical cases (2022 cobalt mine ban in Congo, 2021 China rare earth export quota adjustment) to quantify market resilience. Finally, a risk assessment tool for strategic minerals is developed, providing scientific and practical references for global mineral resource management and investment decisions