International Journal of Engineering and Management Research
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The Role of Machine Learning in Predicting Patient Outcomes and Hospital Readmissions
Full text linkWith an aging population, ascendent prevalence of chronic disease and rising therapy costs, the demands on global health care systems have reached new levels, calling for new solutions to improve patients’ care and health care delivery efficiency. Thus, in a clinical context, Machine Learning (ML) is a rapidly evolving subbranch of Artificial Intelligence (AI) which can provide a transformational potential to automate the data-intensive decision making. Vast and complicated datasets spawned from electronic health records (EHRs), laboratory results, diagnostic imaging, patient histories and other sources can be analysed by ML algorithms to find patterns that humans cannot. Moreover, these predictive capabilities come into play when it comes to predicting patient outcome or patients at high risk of readmission so that suitable interventions can be taken place and healthcare costs can be claimed. This paper systematically studies the application of ML in predicting clinical outcomes and readmissions through a comparative analysis of different ML model: such as logistic regression, decision trees, ensemble, and different deep learning architectures. We evaluate the performance, accuracy, and practical utility of these models in hospital settings by leveraging real world datasets. We also discuss broader ML adoption related to healthcare, including model interpretability and integration issues and ethics. We show that ML has the unique potential to drive precision medicine and improve the entire healthcare delivery
Railroad Track Defect Detections using Deep Learning
Full text linkHowever, railroad transportation continues to be one of the most important components to global terms of freight and passenger transport, and is integral to the success of economic development and logistics efficiency. With bigger demand for rail service comes ever more need to not only maintain functionality of the rail system, but also to keep it safe and reliable. With railroad tracks being the case, one of the most pressing concerns at hand is to be able to detect defects in the tracks early and accurate. Due to labor intensive, time consuming and human error prone nature of traditional inspection methods, there is an immediate requirement of approaches to automated, intelligent inspection. Using high resolution inspection images of railroad tracks, this study investigates the use of deep learning techniques for automation in detecting defects in railroad tracks. Then based on the leakage of convolutional neural networks (CNNs), transfer learning strategies and real time object detection frameworks (e.g., YOLOv5), provide the good enough track anomaly classification and locating capabilities such as cracks, broken rail, loose fasteners and vegetation interference. Experimental results show that the classification accuracy was 94% for InceptionV3 model, and the real time detection using YOLOv5 has the mAP of 0.89 and the inference time of just 23ms per image. Deep learning appears to be a robust scalable and practical method of improving railway safety and maintenance operation
A Study on Consumer’s Intention to Adopt Technology for UPI Payments
No full textOur research was conducted to examine the consumer’s intention towards adopting technology for UPI payments and to identify the factors affecting the consumer’s intention. UPI is a digital payment system that facilitates instant fund transfers between bank accounts through a mobile application that is available 24*7.
The research methodology involves a survey of a representative sample of consumers who are using and have used UPI or have the potential to use it in the future. The survey was conducted with data collected from 124 respondents.
This study helps to understand the importance of perceived ease of use, perceived usefulness and perceived security in driving consumer’s intention to adopt technology for UPI payments. The results suggests that it is important for the UPI technology providers to focus on enhancing the perceived usefulness, perceived ease of use and perceived security of their technology to encourage adoption and usage among customers
Unmasking the Evolution of Social Engineering in Cybersecurity: Techniques, Vulnerabilities, and Countermeasures
No full textThis research explores the historical evolution, tactics, and classifications of social engineering in the realm of cyber security. Tracing its roots back to the 1990s when attackers would exploit human vulnerabilities through phone calls, the paper highlights the shift towards sophisticated techniques targeting individuals to transfer substantial sums or disclose sensitive information. The term "social engineering" was coined in 1894, gaining prominence in cybersecurity in the 1990s and evolving with the proliferation of the internet. The attackers meticulously research their targets, utilizing human-based and computer-based social engineering tactics.The classification section delineates human-based social engineering techniques, including impersonation, posing as an important user, using a third person, calling technical support, shoulder surfing, and dumpster diving. Computer-based social engineering involves fake emails, email attachments, pop-up windows, and other deceptive practices. The paper delves into various types of social engineering attacks, such as manipulating conversations, piggybacking, tracking, baiting, phishing, smishing, Trojan horse attacks, water hole attacks, and reverse social engineering.The document emphasizes the need for self-protection measures, providing guidelines to recognize and thwart social engineering attacks. It also discusses real-time examples like email phishing scams and suggests multi-factor authentication as a potential solution. In conclusion, the research underscores the significance of understanding and combating social engineering, offering insights into its dynamics and countermeasures to fortify cybersecurity in an ever-evolving digital landscape
The Effects of Working Capital Management on Profitability of Microfinance Institutions in Zambia: A Case Study of Bayport Zambia Limited
Full text linkThis study investigated the effects of working capital management on the profitability of Bayport Zambia Limited, a microfinance institution in Zambia. The specific objectives were to find out the effect of cash management profitability of Bayport, to assess the effect of non-performing loans (debtors) on Bayport Profitability, to examine the effect of operational expenses on the Profitability of Bayport and to determine the effect of debt recovery effort on the profitability of Bayport. Data were collected through questionnaires and face-to-face interviews with employees and senior management of Bayport Zambia Limited. Descriptive statistics were used to analyse the data, including frequency distribution and percentages. The study found that effective management of liquid cash is crucial for sustaining and maximizing profits. Weak cash management negatively impacts profitability. Managing and mitigating non-performing loans significantly affects financial performance. Weak management of operational expenses can impact profitability. Timely and efficient debt recovery efforts positively influence profitability. Based on these findings, the study makes several recommendations. Bayport should reassess and strengthen debt recovery strategies, reduce high operational expenses, and utilize technology solutions to effectively monitor customer status. Collaboration with regulatory authorities is necessary to develop and enforce a regulatory framework for responsible lending and efficient working capital management. Financial literacy and education programs should be implemented to promote awareness and responsible borrowing. These recommendations aim to improve working capital management and overall profitability in Bayport Zambia Limited and the microfinance sector in Zambia
A Study of the Effects of Working Capital Management on SME’s Financial Performance: A Case of Zam Manufacturing Limited
No full textIn Zambia, Small Medium Enterprises represent 97% of all businesses, contributes 70% of the country’s Gross Domestic Product and employs 88% of the country’s workforce (FSDZ, 2021). In-spite of this, most small medium enterprises are victims of acute business failure attributed to inadequate managerial acumen, restricted technical skills, limited access to capital and poor internal financial management. The aim of the study was to investigate the relationship between working capital management and small medium enterprises’ financial performance. Cash conversion cycle was used as a comprehensive measure of working capital, which was further broken down to accounts collection period, inventory conversion period and accounts payable period was adopted as independent variables while gross operating profit was used as the dependent variable. Multiple regression analysis was applied to elucidate the relationship between working capital management and firm financial performance. The results revealed that accounts collection period and accounts payable period had a negative relationship with gross operating profit. While inventory conversion period showed a positive relationship with gross operating profit. Based on the regression analysis results, it was recommended that Zam Manufacturing Limited adopts 85% cash sales and 15% credit sales of its total sales target. Furthermore, departmental managers must undergo regular training in aspect of working capital under their responsibility to improve capacity. Additionally, the company should regulate the risk of overtrading by gradually monitoring and controlling growth
Banks Plays a Pivotal Role in Entrepreneurship Development
No full textBanks contribute significantly and positively in advising and providing loans for the development of entrepreneur in India. Banks are essential for the survival and their unique role as the engine of growth in the country. The central government organization regulates banking and non-banking financial institutions. Entrepreneurship development is a concept that has to do with the formation, financing, growth and expansion of business or enterprises in an economy. This paper focus on the role of banks in the development of entrepreneurship in Mysore. It is aimed at to find out problems faced by entrepreneurs in obtaining the loans from the banks for their startups/expansion and the major difficulties faced by the banks towards sanctioning and recovery of the loans. This paper also focused on factors considered by banks before lending loans to entrepreneurs
A Study of the Effect of Firm Size on the Financial Performance of LuSE Listed Companies
No full textThe Zambian Economy has gone through various financial changes in the recent past. The economy is a growing economy with great potential to expand to great heights. The Lusaka Stock Exchange (LuSE) plays a crucial role in how the economy is faring. Although it has been recording poor liquidity rates, it has since listed a total number of 24 firms since inception. These firms, their size and performance play a crucial role in understanding how both internal and external functions are aligning. The size of a firm has proven to be an important characteristic especially in these modern times where competition is at its all-time high. The research aimed to investigate this topic and determine what models, policies or procedures different sized firms should undertake in-order to survive. The main objective of this study was to evaluate if there a relationship between firm size and financial performance. The target population of this study was all the firms listed on the financial sector of the Lusaka Stock Exchange. The data collected spanned a period of 10 years- from 2012 to 2022. The three independent variables; total debt, total assets and total number of employees measured firm size while the dependent variable- Return on Assets (ROA), measured financial performance. The relationship between the independent variables and the dependent variable were found to be all statistically significant in the long run. However, total assets were found to be statistically insignificant in the short run. The study therefore concludes that firms with more assets, more employees and less debt are more likely to enjoy greater financial profitability in their long run periods. This implies that a positive relationship is expected between the size of the firm and the profitability levels. The association between the two is positive when ROA is employed as the proxy for firm performance
Enhancing E-Commerce Recommendation Systems with Deep Learning-based Sentiment Analysis of User Reviews
Full text linkThis study introduces a novel approach to enhancing e-commerce recommendation systems by integrating deep learning-based sentiment analysis of user reviews. We propose a sentiment-aware neural collaborative filtering model that leverages the emotional content of reviews to enrich user and item representations. Our method employs a hierarchical attention network for fine-grained sentiment analysis, capturing nuanced user opinions at both word and sentence levels. The sentiment information is then incorporated into a neural collaborative filtering framework, allowing for more personalized and context-aware recommendations. We evaluate our model on a large-scale e-commerce dataset, demonstrating significant improvements in recommendation accuracy, diversity, and user satisfaction compared to state-of-the-art baselines. Our experiments show that the proposed model achieves a 7.66% improvement in NDCG@10 over the strongest baseline, while also enhancing beyond-accuracy metrics such as diversity and novelty. The integration of sentiment analysis proves particularly effective in capturing evolving user preferences and item perceptions, addressing key challenges in traditional recommendation systems. This research contributes to the field by showcasing the potential of leveraging deep learning-based sentiment analysis to create more nuanced, responsive, and user-centric e-commerce recommendation systems
Challenges and Opportunities of Women Entrepreneurs - With Special Reference to MSMES in the State of Karnataka
Full text linkThe present article aims at discussing the challenges and opportunities of women entrepreneurs of MSMEs sector in Karnataka state. Primary data was collected with the help of structured questionnaire and collected the information from the selected women entrepreneurs of MSMEs from the Karnataka state. In the present study, the researcher used simple percentage analysis. The researcher discussed and analyzed the financial, personal, social, labour, marketing, infrastructural and technological problems faced by the women entrepreneurs. In this article the researcher also discuss about the opportunities of women entrepreneurs at national and state level