International Journal of Innovative Research in Engineering and Management (IJIREM), 2024
This study presents a novel framework for personalized causal inference in media effectiveness us... more This study presents a novel framework for personalized causal inference in media effectiveness using Hierarchical Bayesian Market Mix Models (ABM). The proposed approach integrates individual-level data with aggregate market information to estimate personalized media effects while addressing the challenges of data sparsity and high dimensionality. By combining the identity layer and the optimization process in a Bayesian hierarchical model, the model captures heterogeneity across consumers and provides robust predictions of individual causality. Affect different media. The framework is used for e-commerce business data, which includes 500,000 customers across 50 markets in 24 months. The model shows better prediction performance than the integrated business model, with a 30.4% reduction in RMSE. Empirical results reveal significant heterogeneity in media effectiveness across channels and consumer segments. Email marketing emerges as the most effective channel on average, followed by TV advertising, digital display ads, and social media engagements. Sensitivity analyses and robustness checks, including alternative prior specifications and placebo tests, support the validity of the estimated causal effects. The findings provide valuable insights for media planning and marketing strategy, highlighting the importance of tailored budget allocation and campaign design approaches. This research contributes to the growing body of literature on personalized marketing analytics and offers a powerful tool for estimating individualized media effects in complex marketing environments.
International Journal of Innovative Research in Engineering and Management (IJIREM), 2024
This paper aims at exploring the effects of performance tuning on concerns such as reliability an... more This paper aims at exploring the effects of performance tuning on concerns such as reliability and uptime with respect to the three current architectures: cloud, on-premise, and hybrid models. This paper shows that performance tuning does enhance system stability and availability based on comparative data on CPU load, memory consumption, disk I/O operations, network delays, application response time, and system failure rates. Employing both quantitative and qualitative data, the study compares features taken from real-world system logs and information obtained from expert interviews indicating that tuning brings about balanced resource distribution as well as ensures faster data processing free from system constraints. The results presented prove that performance tuning positively affects the availability of the system and lowers the failure rates significantly after tuning. This work underscores the need for pre-emptive approach to performance calibration as a means of guaranteeing the systems availabilities and reliability where systems integration is complex and distributed across several nodes. The conclusions help those organizations striving for improvements of the systems in their concern areas for better performances and less failures in a world where competition and operations demands are growing.
International Journal of Innovative Research in Engineering and Management (IJIREM), 2024
This research focuses on the role and efficiency of Principal Component Analysis (PCA) and autoen... more This research focuses on the role and efficiency of Principal Component Analysis (PCA) and autoencoders, when working separately and concurrently for dimensionality reduction in large scale data. This is because the data obtained from sources such as the IoT sensors and the social media platforms is a lot more complicated than before and therefore proper dimensionality reduction is critical for real time analysis. The performance of these methods is analyzed on synthetic and real datasets based on which explained variance, reconstruction error and processing time of these methods are compared to define the optimal configuration. The results show that solely PCA is fast in linear data and autoencoders capture nonlinear dependence with slightly higher time complexity. This preserves considerable variance alongside a reasonable reconstruction error and thus makes the PCA-autoencoder model well suited to dynamic environments while incurring less computational expense than alternative PCA models. This work shows that it is possible to utilize relevant combinations of methods for dimensionality reduction to boost real-time data stream analysis especially in applications that demand for high accuracy at the same time as low delays.
International Journal of Innovative Research in Engineering and Management (IJIREM), 2024
This study presents a new deep-learning model for predicting default risk in peer-to-peer (P2P) m... more This study presents a new deep-learning model for predicting default risk in peer-to-peer (P2P) microlending platforms. The model integrates convolutional neural networks (CNNs) and short-term (LSTM) networks to capture both spatial and temporal patterns in lending data. An extensive database including 150,000 loan records from a major P2P platform was used, including 78 characteristics related to borrowers, loan characteristics, and platform-specific metrics. The model uses a hybrid selection method that combines filtering and wrapping methods to identify the most relevant parameters. An ensemble learning strategy is implemented, combining deep learning models with gradient boosting and random forest classifiers. The experimental results show the best model performance, achieving an accuracy of 92.34% and an AUC-ROC of 0.9687, outperforming the scoring model and the machine learning model. Factor analysis shows that annual income, debt-to-income ratio, and credit score are the most important factors in predicting bad credit. This study provides insight into the interpretation of the SHAP and LIME criteria, improving transparency in credit risk assessment. The findings have important implications for P2P lending platforms and investors, providing better risk management strategies and more informed decision-making capabilities in microloan evaluation.
International Journal of Innovative Research in Engineering and Management (IJIREM), 2024
High-intensity knowledge workers, such as lecturers, are expected to maintain a fully charged psy... more High-intensity knowledge workers, such as lecturers, are expected to maintain a fully charged psychological battery to sustain effort in challenging circumstances. Various concepts explain how individuals sustain effort under such conditions, often focusing on grit, hardiness, self-control, and resilience to understand performance during adversity. Previous research has identified key points that can generate empirically sound propositions, but overlapping attributes among these concepts make them difficult to differentiate. This often leads to confusion and debate over how these factors collectively contribute to success, especially when practitioners try to apply these ideas in real-life settings. This study employs a peer-to-peer research method to resolve existing confusion and debate. It proposes a model of psychological endurance, a unified theory that explores how multiple concepts contribute to sustained goal-directed behaviours and individual success. Central to this model is the metaphor of a psychological battery, which powers and sustains optimal performance despite adversity. We found that grit and hardiness are linked to the maximum charge of the psychological battery, indicating how long an individual can sustain effort. Self-control regulates energy management, increasing the effort required to maintain endurance, while resilience reflects the ability to recharge. These factors are influenced by both psychological and physiological stressors in the environment that deplete the psychological battery. These concepts create an innovative framework for exploring related psychological theories and, ideally, for improving interventions aimed at enhancing psychological endurance.
International Journal of Innovative Research in Engineering and Management (IJIREM), 2024
Classification of organic food is crucial to ensuring authenticity and consumer trust and disting... more Classification of organic food is crucial to ensuring authenticity and consumer trust and distinguishing it from conventionally grown produce. Accurate labelling and certification help prevent fraud and guarantee adherence to organic farming standards. Machine learning methods are utilized to categorize organic food exclusively based on nutritional data by analysing macro and micronutrient profiles. This approach improves classification precision and offers significant insights into the dietary advantages of organic food. Moreover, the results might enhance consumer knowledge and facilitate informed dietary decisions by emphasizing the improved nutritional quality of organic products relative to conventional alternatives. In this research, we have used five different machine learning algorithms to classy organic food. Experimental results show that decision trees perform better than other machine learning-based models.
International Journal of Innovative Research in Engineering and Management (IJIREM), 2024
This study investigates the complex dynamics of female labor force participation (FLF) in India, ... more This study investigates the complex dynamics of female labor force participation (FLF) in India, with a focus on trends and relevant factors from 2000 to2022 collected from World Development Indicator provided by World Bank. Annual changes and compound annual growth rates of FLF has assessed using the year-on-year growth rate and compound annual growth rate (CAGR). The study uses Ordinary Least Squares regression analysis to evaluate the link between FLF and its major drivers like fertility rate and marriage score. The result shows, India's female labour force participation rose in 2001 to 2005 before declining in 2006. This decrease has anticipated owing to the COVID-19 pandemic's economic impact in 2020. CAGR (-0.960) shows a decline in female labour force participation in India for the entire period, which may be due to cultural norms, access to education and employment, and socioeconomic inequalities. FLF is positively affected by Fertility rate, and marriage score.
International Journal of Innovative Research in Engineering and Management (IJIREM), 2024
In India, agricultural sector output plays a significant role in the beginning phase of the growt... more In India, agricultural sector output plays a significant role in the beginning phase of the growth of the economy because it absorbs the majority of employment. The agriculture sector includes crop production, livestock rearing, forestry, and fishing. However, the agricultural sector has experienced several fluctuations due to various factors, such as monsoon variability, natural disasters, government policies, and market conditions. These fluctuations have had a substantial influence on the overall economic growth of the nation. In this present paper, we try to find the linear growth and fluctuations around the linear of India's GDP and the agricultural sector output for 50 years long period from 1970-71 to 2019-20. The outcome of this study reveals that the annual average growth rate is 5.52% for GDP and 2.89% for the agricultural sector. The Coppock based year-to-year fluctuation around the trend is 0.00089 for gross domestic product and 0.00191 for the agricultural sector output. The overall fluctuation using the RSS-based method is 0.00520 for GDP and 0.00309 for GDPA.
International Journal of Innovative Research in Engineering and Management (IJIREM), 2024
Concrete is a crucial material in civil engineering construction, but its traditional components,... more Concrete is a crucial material in civil engineering construction, but its traditional components, coarse and fine aggregate, are becoming increasingly expensive and scarce. This study aimed to investigate the potential of replacing cement with silica fumes and fine aggregate with carbon fibers to improve concrete's hardened properties. Different concrete mixes were created with varying percentages of these replacements. The resulting concrete specimens were tested for compression, split, and flexural strength after curing periods of 7 and 28 days.
International Journal of Innovative Research in Engineering and Management (IJIREM), 2024
This paper explores the application of machine learning models, specifically XGBoost, Stacking Re... more This paper explores the application of machine learning models, specifically XGBoost, Stacking Regressor, and Long Short-Term Memory (LSTM), for predicting earthquake magnitudes in Düzce, Turkey. The models were trained and tested on seismic data to predict moment magnitude (Mw). The performance of each model was evaluated using Mean Absolute Error (MAE), Root Mean Square Error (RMSE), and the coefficient of determination (R²). The results indicate that the XGBoost model outperforms the other models with a higher R² value and lower error metrics, providing a more accurate prediction of seismic events.
International Journal of Innovative Research in Engineering and Management (IJIREM), 2024
The goal of this research is to find out the effects of multithreading on the consumption of syst... more The goal of this research is to find out the effects of multithreading on the consumption of system resources and efficiency by examining CPU utilization, memory use, Input/Output operation, and power consumption in the multithreaded systems. In order to measure static, adaptive, and dynamic multithreading performance under different workloads, the study compares the three models both through theory and by applying it to various experiments. That is why the results show that, when multithreading is implemented, the general CPU load and I/O performance improve, especially for computational and data-consuming operations. However, some problems include memory contention, context-switching overhead and, higher energy consumption are noted especially when threading is over-provisioned for. Real-time threading control strategies were the most effective as they periodically reconfigured the number of threads in a way that optimizes performance while optimizing resources. In addition, while the asynchronous I/O models provided the best performance improvements, energy use went up when multithreading was incorporated, thus the need for implementing trade-offs between performance and power usage. It offers significant findings related to the tuning of multithreading strategies so as to maximize system performance but with an acceptable level of resource utilization, should be of significance for practitioners, who are working with multicore processors and dealing with high-performing systems.
International Journal of Innovative Research in Engineering and Management (IJIREM, 2024
An important aspect of trimaran ship research is to obtain an optimal model through the analysis ... more An important aspect of trimaran ship research is to obtain an optimal model through the analysis of resistance and power requirements. Trimaran ships have the advantage of a wide deck surface and crew members can move freely. In this study, the monohull and trimaran ships were calculated using computational fluid dynamics (CFD) approach. This study explores the optimal model for a fishing boat trimaran with a smaller power requirement than that of monohulls. The computational fluid dynamics (CFD) approach was used to calculate the resistance and power requirements of the ship. The resistance of the trimaran hull affects the performance of the ship, and the selection of the right hull minimises its interaction with the fluid. The study found that trimarans with S/L = 0.2 and 0.3 reduced the resistance by 33.42% and 68.94%, respectively. This study aims to improve the performance and catch efficiency of the traditional Indonesian fishing industry by selecting an appropriate trimaran hull. The selection of an appropriate trimaran hull can optimally affect the performance of a ship and result in abundant catches.
International Journal of Innovative Research in Engineering and Management (IJIREM), 2024
Throughout the past couple of decades, this is an increasing concern regarding the disposal of te... more Throughout the past couple of decades, this is an increasing concern regarding the disposal of textile scraps. The textile market is influenced by both population growth, capital, and style. The fast fashion cycle in the apparel industry has resulted in top levels of utilization while also generating waste. The textile industry's fast fashion cycle promotes excessive consumption and waste. The textile and garment industry is notoriously polluting, so this could hurt the environment. Textile manufacturing requires a significant amount of water due to its chemical nature. Waste from textile production primarily consists of wastewater and fiber waste. Unwanted clothing in the textile production chain led to the majority of fiber waste. This paper investigates how adding textile and electronic waste to concrete affects its properties. Electronic waste and textile fibers were cut into 20mm x 8mm pieces and used at a 1.5% volume ratio. The concrete grades used were M30, M35, and M40. The concrete combined plan is based on IRC 44:2008 standards. Current evaluations involving the durability properties of fiber-reinforced concrete show significant improvements over 28 days: compressive strength increased by 18%, while flexural and vibration strengths boosted by 39% and 32%, respectively. Experimental findings indicate a 17% reduction in deflection during 4-point bending tests and a 33% decrease in double shear test deflection. Deflection calculations were performed using energy-based approaches, with computed and empirical results aligning within acceptable margins. Notably, electronic and textile waste demonstrate promising potential as effective reinforcements for cement-based concrete.
International Journal of Innovative Research in Engineering and Management (IJIREM), 2024
This academic research paper explores the critical challenge of humanizing Artificial Intelligenc... more This academic research paper explores the critical challenge of humanizing Artificial Intelligence (AI) for personalized and adaptive learning, moving beyond algorithmic approaches to create more inclusive and effective educational experiences. It examines key AI technologies such as intelligent tutoring systems, adaptive learning platforms, and predictive analytics tools, evaluating their potential to revolutionize education while addressing their limitations. The research highlights the importance of a human-centered framework for AI in education (AIEd), emphasizing participatory design, teacher empowerment, and the promotion of socio-emotional learning alongside cognitive development. Major findings include the need for AI-enabled pedagogies that transcend conventional personalization, such as collaborative knowledge building and place-based learning, to foster deeper engagement and understanding. The academic research paper underscores the significance of these approaches in creating more equitable and inclusive learning environments, particularly in addressing educational disparities in diverse global contexts. Ethical considerations, including data privacy, algorithmic bias, and the potential erosion of teacher autonomy, are critically examined. The research proposes a multi-stakeholder roadmap for implementing ethical and equitable AIEd, combining policy recommendations with practical strategies like risk sandboxing and open-source AIEd development. By advocating for a balance between technological innovation and human values in education, this academic research paper contributes to the advancement of human-centered AI in personalized and adaptive learning, paving the way for more nuanced, ethical, and effective integration of AI in educational settings worldwide.
International Journal of Innovative Research in Engineering and Management (IJIREM), 2024
This research paper aims at analyzing the factors that can help improve scalability of cloud by i... more This research paper aims at analyzing the factors that can help improve scalability of cloud by incorporating different machine learning algorithms in management of resources. Since controlling and managing cloud resources is becoming more challenging with compounded base requirements, the majority of conventional resource management solutions may not prove adequate. This research assesses the performance of five state-of-art machine learning techniques namely Reinforcement Learning, Long Short-Term Memory, Gradient Boosting Machines, Autoencoders and Neural Architecture Search in minimizing operational cost and enhancing resource utilization and overall system efficiency for improving business outcomes. The findings reveal that the use of RL-based approaches to optimize operational cost reduction and minimizing provisioning delay by 20% and 30% respectively and LSTM network to increase the accuracy of demand forecasting by 12% and overall efficiency of resource utilization by 22%. The use of GBM models in forecasts results in 30% error reduction in costs that drop by 20% while service improves by 25%. Using autoencoders, the models achieve 97% accuracy in detecting anomalies and infinityai1411@gmail.com in turn increasing the efficiency of allocation by 15 percent. The NAS-optimized models yield increased accuracy by a percentage point of 18 % as well as a 25% faster computational speed. Altogether, these theoretical developments demonstrate the ability of AI-based methodologies to enhance the cloud scalability promising and provide practical recommendations for improving resource management approaches in the cloud environment.
International Journal of Innovative Research in Engineering and Management (IJIREM), 2024
Recent research focuses on mental health and brain informatics. Emerging technologies like AI, de... more Recent research focuses on mental health and brain informatics. Emerging technologies like AI, deep learning, and machine learning drove the advancements. Customizing, diagnosing, and treating depression with data-driven approaches could improve mental health care. A growing field, precision psychiatry uses cutting-edge computer tools to provide tailored mental health care. AI in precision psychiatry is examined in this paper. Complex formulations aid therapy. These tools can identify and treat mental health patients. They can customize therapies for most patients. Unsupervised learning algorithms have shown considerable sadness-related sickness disparities. These methods separate diagnostic categories. Artificial intelligence could help us suggest drugs based on facts, not group averages. Our findings show that data-driven paradigms in healthcare face several challenges. Surprisingly, none of the survey studies reveal how current procedures improve patient outcomes. Standardizing field terminology, forming diverse research teams, evaluating models, identifying flaws, and making datasets accessible are crucial. Randomized controlled trials must show that computer algorithms improve patient outcomes to make models more feasible.
International Journal of Innovative Research in Engineering and Management (IJIREM), 2024
This paper estimated the growth, endogenous break, and fluctuation in India’s gross domestic prod... more This paper estimated the growth, endogenous break, and fluctuation in India’s gross domestic product from 1970–71 to 2019–20. The linear growth estimation method is well established in the existing literature. The estimation of endogenous structural break and fluctuation measuring methods needs some modification and adjustment. The resulting estimates reveal that India's GDP consists of three breaks and four distinct phases in different policy regimes in the considered time interval, of which all four are full-length regimes, two upward spikes, one downward spike were created during the period. The highest growth rate was achieved at 6.54% from 2005-06 to 2019-20.
International Journal of Innovative Research in Engineering and Management (IJIREM), 2024
This study explores the incorporation of Artificial Intelligence (AI) into traditional Six Sigma'... more This study explores the incorporation of Artificial Intelligence (AI) into traditional Six Sigma's DMAIC (Define, Measure, Analyze, Improve, Control) methodology to enhance continuous process improvement and achieve significant economic growth across industries. AI’s data analysis, machine learning algorithms coupled with real-time insights can expedite problem identification in manufacturing processes before they become substantial issues – eliminating the need for human oversight by proactively identifying potential errors or bottlenecks - this reduces wastage and optimizes resource utilization. Coupling AI’s predictive capabilities with Six Sigma's systematic approach not only boosts productivity but also ensures robust quality control standards are met – leading to continuous nonstop improvement in various sectors globally, particularly supply chain management where operational efficiency is critical for success and sustainability. By enhancing resource allocation effectiveness through AI automation while reducing waste generation via predictive analytics - this integration holds the key towards achieving both economic growth objectives alongside environmental stewardship as complementary facets of successful business strategies in today's global marketplace, fostering a future where operational excellence and sustainability go hand-in-hand.
International Journal of Innovative Research in Engineering and Management (IJIREM), 2024
The Minimum Support Price (MSP) is a key element of India's agricultural policy, aimed at protect... more The Minimum Support Price (MSP) is a key element of India's agricultural policy, aimed at protecting farmers from market price volatility and ensuring they receive a fair return for their crops. The paper critically examines the role of the Minimum Support Price (MSP) in India's agricultural sector, addressing the ongoing debate over the need for a legal guarantee of MSP. Through a balanced analysis, it presents arguments both for and against the legal guarantee of MSP, acknowledging concerns over market distortions and fiscal burdens. Suggestions for improving farmers’ incomes, such as investment in infrastructure, price stabilization mechanisms, and income support schemes, are discussed. The paper concludes by emphasizing the need for comprehensive reforms in the MSP system, advocating for a balanced approach that incorporates global best practices and modernizes market mechanisms, rather than solely relying on legal guarantees to sustain the agricultural economy.
International Journal of Innovative Research in Engineering and Management (IJIREM), 2024
Micro, Small, and Medium Enterprises contribute significantly to the Gross Domestic Product of th... more Micro, Small, and Medium Enterprises contribute significantly to the Gross Domestic Product of the country and also happen to be the largest generator of employment in Indonesia. Nevertheless, most of them find it difficult to adopt digitalization because of their limitations in infrastructure and relevant information systems and also due to poor digital literacy. These challenges, therefore, extend to fundamental business operations such as financial management, inventory monitoring, and sales, which are usually done manually. In line with this, the development of an efficient and user-friendly Point of Sale application, available on both Android and web platforms, is a strategy in the right direction. The application is designed to bring efficiency in financial management, recording of inventories, and sales operations for MSMEs, since it offers them the instruments needed for migrating to digital workflows. In addressing these constraints, the POS application hopes to empower MSMEs so that they can participate in the thriving digital economy and contribute more firmly to Indonesia's development. The focus of the study is on designing and developing a POS system that will respond to the needs of MSMEs in the digital era.
International Journal of Innovative Research in Engineering and Management (IJIREM), 2024
This study presents a novel framework for personalized causal inference in media effectiveness us... more This study presents a novel framework for personalized causal inference in media effectiveness using Hierarchical Bayesian Market Mix Models (ABM). The proposed approach integrates individual-level data with aggregate market information to estimate personalized media effects while addressing the challenges of data sparsity and high dimensionality. By combining the identity layer and the optimization process in a Bayesian hierarchical model, the model captures heterogeneity across consumers and provides robust predictions of individual causality. Affect different media. The framework is used for e-commerce business data, which includes 500,000 customers across 50 markets in 24 months. The model shows better prediction performance than the integrated business model, with a 30.4% reduction in RMSE. Empirical results reveal significant heterogeneity in media effectiveness across channels and consumer segments. Email marketing emerges as the most effective channel on average, followed by TV advertising, digital display ads, and social media engagements. Sensitivity analyses and robustness checks, including alternative prior specifications and placebo tests, support the validity of the estimated causal effects. The findings provide valuable insights for media planning and marketing strategy, highlighting the importance of tailored budget allocation and campaign design approaches. This research contributes to the growing body of literature on personalized marketing analytics and offers a powerful tool for estimating individualized media effects in complex marketing environments.
International Journal of Innovative Research in Engineering and Management (IJIREM), 2024
This paper aims at exploring the effects of performance tuning on concerns such as reliability an... more This paper aims at exploring the effects of performance tuning on concerns such as reliability and uptime with respect to the three current architectures: cloud, on-premise, and hybrid models. This paper shows that performance tuning does enhance system stability and availability based on comparative data on CPU load, memory consumption, disk I/O operations, network delays, application response time, and system failure rates. Employing both quantitative and qualitative data, the study compares features taken from real-world system logs and information obtained from expert interviews indicating that tuning brings about balanced resource distribution as well as ensures faster data processing free from system constraints. The results presented prove that performance tuning positively affects the availability of the system and lowers the failure rates significantly after tuning. This work underscores the need for pre-emptive approach to performance calibration as a means of guaranteeing the systems availabilities and reliability where systems integration is complex and distributed across several nodes. The conclusions help those organizations striving for improvements of the systems in their concern areas for better performances and less failures in a world where competition and operations demands are growing.
International Journal of Innovative Research in Engineering and Management (IJIREM), 2024
This research focuses on the role and efficiency of Principal Component Analysis (PCA) and autoen... more This research focuses on the role and efficiency of Principal Component Analysis (PCA) and autoencoders, when working separately and concurrently for dimensionality reduction in large scale data. This is because the data obtained from sources such as the IoT sensors and the social media platforms is a lot more complicated than before and therefore proper dimensionality reduction is critical for real time analysis. The performance of these methods is analyzed on synthetic and real datasets based on which explained variance, reconstruction error and processing time of these methods are compared to define the optimal configuration. The results show that solely PCA is fast in linear data and autoencoders capture nonlinear dependence with slightly higher time complexity. This preserves considerable variance alongside a reasonable reconstruction error and thus makes the PCA-autoencoder model well suited to dynamic environments while incurring less computational expense than alternative PCA models. This work shows that it is possible to utilize relevant combinations of methods for dimensionality reduction to boost real-time data stream analysis especially in applications that demand for high accuracy at the same time as low delays.
International Journal of Innovative Research in Engineering and Management (IJIREM), 2024
This study presents a new deep-learning model for predicting default risk in peer-to-peer (P2P) m... more This study presents a new deep-learning model for predicting default risk in peer-to-peer (P2P) microlending platforms. The model integrates convolutional neural networks (CNNs) and short-term (LSTM) networks to capture both spatial and temporal patterns in lending data. An extensive database including 150,000 loan records from a major P2P platform was used, including 78 characteristics related to borrowers, loan characteristics, and platform-specific metrics. The model uses a hybrid selection method that combines filtering and wrapping methods to identify the most relevant parameters. An ensemble learning strategy is implemented, combining deep learning models with gradient boosting and random forest classifiers. The experimental results show the best model performance, achieving an accuracy of 92.34% and an AUC-ROC of 0.9687, outperforming the scoring model and the machine learning model. Factor analysis shows that annual income, debt-to-income ratio, and credit score are the most important factors in predicting bad credit. This study provides insight into the interpretation of the SHAP and LIME criteria, improving transparency in credit risk assessment. The findings have important implications for P2P lending platforms and investors, providing better risk management strategies and more informed decision-making capabilities in microloan evaluation.
International Journal of Innovative Research in Engineering and Management (IJIREM), 2024
High-intensity knowledge workers, such as lecturers, are expected to maintain a fully charged psy... more High-intensity knowledge workers, such as lecturers, are expected to maintain a fully charged psychological battery to sustain effort in challenging circumstances. Various concepts explain how individuals sustain effort under such conditions, often focusing on grit, hardiness, self-control, and resilience to understand performance during adversity. Previous research has identified key points that can generate empirically sound propositions, but overlapping attributes among these concepts make them difficult to differentiate. This often leads to confusion and debate over how these factors collectively contribute to success, especially when practitioners try to apply these ideas in real-life settings. This study employs a peer-to-peer research method to resolve existing confusion and debate. It proposes a model of psychological endurance, a unified theory that explores how multiple concepts contribute to sustained goal-directed behaviours and individual success. Central to this model is the metaphor of a psychological battery, which powers and sustains optimal performance despite adversity. We found that grit and hardiness are linked to the maximum charge of the psychological battery, indicating how long an individual can sustain effort. Self-control regulates energy management, increasing the effort required to maintain endurance, while resilience reflects the ability to recharge. These factors are influenced by both psychological and physiological stressors in the environment that deplete the psychological battery. These concepts create an innovative framework for exploring related psychological theories and, ideally, for improving interventions aimed at enhancing psychological endurance.
International Journal of Innovative Research in Engineering and Management (IJIREM), 2024
Classification of organic food is crucial to ensuring authenticity and consumer trust and disting... more Classification of organic food is crucial to ensuring authenticity and consumer trust and distinguishing it from conventionally grown produce. Accurate labelling and certification help prevent fraud and guarantee adherence to organic farming standards. Machine learning methods are utilized to categorize organic food exclusively based on nutritional data by analysing macro and micronutrient profiles. This approach improves classification precision and offers significant insights into the dietary advantages of organic food. Moreover, the results might enhance consumer knowledge and facilitate informed dietary decisions by emphasizing the improved nutritional quality of organic products relative to conventional alternatives. In this research, we have used five different machine learning algorithms to classy organic food. Experimental results show that decision trees perform better than other machine learning-based models.
International Journal of Innovative Research in Engineering and Management (IJIREM), 2024
This study investigates the complex dynamics of female labor force participation (FLF) in India, ... more This study investigates the complex dynamics of female labor force participation (FLF) in India, with a focus on trends and relevant factors from 2000 to2022 collected from World Development Indicator provided by World Bank. Annual changes and compound annual growth rates of FLF has assessed using the year-on-year growth rate and compound annual growth rate (CAGR). The study uses Ordinary Least Squares regression analysis to evaluate the link between FLF and its major drivers like fertility rate and marriage score. The result shows, India's female labour force participation rose in 2001 to 2005 before declining in 2006. This decrease has anticipated owing to the COVID-19 pandemic's economic impact in 2020. CAGR (-0.960) shows a decline in female labour force participation in India for the entire period, which may be due to cultural norms, access to education and employment, and socioeconomic inequalities. FLF is positively affected by Fertility rate, and marriage score.
International Journal of Innovative Research in Engineering and Management (IJIREM), 2024
In India, agricultural sector output plays a significant role in the beginning phase of the growt... more In India, agricultural sector output plays a significant role in the beginning phase of the growth of the economy because it absorbs the majority of employment. The agriculture sector includes crop production, livestock rearing, forestry, and fishing. However, the agricultural sector has experienced several fluctuations due to various factors, such as monsoon variability, natural disasters, government policies, and market conditions. These fluctuations have had a substantial influence on the overall economic growth of the nation. In this present paper, we try to find the linear growth and fluctuations around the linear of India's GDP and the agricultural sector output for 50 years long period from 1970-71 to 2019-20. The outcome of this study reveals that the annual average growth rate is 5.52% for GDP and 2.89% for the agricultural sector. The Coppock based year-to-year fluctuation around the trend is 0.00089 for gross domestic product and 0.00191 for the agricultural sector output. The overall fluctuation using the RSS-based method is 0.00520 for GDP and 0.00309 for GDPA.
International Journal of Innovative Research in Engineering and Management (IJIREM), 2024
Concrete is a crucial material in civil engineering construction, but its traditional components,... more Concrete is a crucial material in civil engineering construction, but its traditional components, coarse and fine aggregate, are becoming increasingly expensive and scarce. This study aimed to investigate the potential of replacing cement with silica fumes and fine aggregate with carbon fibers to improve concrete's hardened properties. Different concrete mixes were created with varying percentages of these replacements. The resulting concrete specimens were tested for compression, split, and flexural strength after curing periods of 7 and 28 days.
International Journal of Innovative Research in Engineering and Management (IJIREM), 2024
This paper explores the application of machine learning models, specifically XGBoost, Stacking Re... more This paper explores the application of machine learning models, specifically XGBoost, Stacking Regressor, and Long Short-Term Memory (LSTM), for predicting earthquake magnitudes in Düzce, Turkey. The models were trained and tested on seismic data to predict moment magnitude (Mw). The performance of each model was evaluated using Mean Absolute Error (MAE), Root Mean Square Error (RMSE), and the coefficient of determination (R²). The results indicate that the XGBoost model outperforms the other models with a higher R² value and lower error metrics, providing a more accurate prediction of seismic events.
International Journal of Innovative Research in Engineering and Management (IJIREM), 2024
The goal of this research is to find out the effects of multithreading on the consumption of syst... more The goal of this research is to find out the effects of multithreading on the consumption of system resources and efficiency by examining CPU utilization, memory use, Input/Output operation, and power consumption in the multithreaded systems. In order to measure static, adaptive, and dynamic multithreading performance under different workloads, the study compares the three models both through theory and by applying it to various experiments. That is why the results show that, when multithreading is implemented, the general CPU load and I/O performance improve, especially for computational and data-consuming operations. However, some problems include memory contention, context-switching overhead and, higher energy consumption are noted especially when threading is over-provisioned for. Real-time threading control strategies were the most effective as they periodically reconfigured the number of threads in a way that optimizes performance while optimizing resources. In addition, while the asynchronous I/O models provided the best performance improvements, energy use went up when multithreading was incorporated, thus the need for implementing trade-offs between performance and power usage. It offers significant findings related to the tuning of multithreading strategies so as to maximize system performance but with an acceptable level of resource utilization, should be of significance for practitioners, who are working with multicore processors and dealing with high-performing systems.
International Journal of Innovative Research in Engineering and Management (IJIREM, 2024
An important aspect of trimaran ship research is to obtain an optimal model through the analysis ... more An important aspect of trimaran ship research is to obtain an optimal model through the analysis of resistance and power requirements. Trimaran ships have the advantage of a wide deck surface and crew members can move freely. In this study, the monohull and trimaran ships were calculated using computational fluid dynamics (CFD) approach. This study explores the optimal model for a fishing boat trimaran with a smaller power requirement than that of monohulls. The computational fluid dynamics (CFD) approach was used to calculate the resistance and power requirements of the ship. The resistance of the trimaran hull affects the performance of the ship, and the selection of the right hull minimises its interaction with the fluid. The study found that trimarans with S/L = 0.2 and 0.3 reduced the resistance by 33.42% and 68.94%, respectively. This study aims to improve the performance and catch efficiency of the traditional Indonesian fishing industry by selecting an appropriate trimaran hull. The selection of an appropriate trimaran hull can optimally affect the performance of a ship and result in abundant catches.
International Journal of Innovative Research in Engineering and Management (IJIREM), 2024
Throughout the past couple of decades, this is an increasing concern regarding the disposal of te... more Throughout the past couple of decades, this is an increasing concern regarding the disposal of textile scraps. The textile market is influenced by both population growth, capital, and style. The fast fashion cycle in the apparel industry has resulted in top levels of utilization while also generating waste. The textile industry's fast fashion cycle promotes excessive consumption and waste. The textile and garment industry is notoriously polluting, so this could hurt the environment. Textile manufacturing requires a significant amount of water due to its chemical nature. Waste from textile production primarily consists of wastewater and fiber waste. Unwanted clothing in the textile production chain led to the majority of fiber waste. This paper investigates how adding textile and electronic waste to concrete affects its properties. Electronic waste and textile fibers were cut into 20mm x 8mm pieces and used at a 1.5% volume ratio. The concrete grades used were M30, M35, and M40. The concrete combined plan is based on IRC 44:2008 standards. Current evaluations involving the durability properties of fiber-reinforced concrete show significant improvements over 28 days: compressive strength increased by 18%, while flexural and vibration strengths boosted by 39% and 32%, respectively. Experimental findings indicate a 17% reduction in deflection during 4-point bending tests and a 33% decrease in double shear test deflection. Deflection calculations were performed using energy-based approaches, with computed and empirical results aligning within acceptable margins. Notably, electronic and textile waste demonstrate promising potential as effective reinforcements for cement-based concrete.
International Journal of Innovative Research in Engineering and Management (IJIREM), 2024
This academic research paper explores the critical challenge of humanizing Artificial Intelligenc... more This academic research paper explores the critical challenge of humanizing Artificial Intelligence (AI) for personalized and adaptive learning, moving beyond algorithmic approaches to create more inclusive and effective educational experiences. It examines key AI technologies such as intelligent tutoring systems, adaptive learning platforms, and predictive analytics tools, evaluating their potential to revolutionize education while addressing their limitations. The research highlights the importance of a human-centered framework for AI in education (AIEd), emphasizing participatory design, teacher empowerment, and the promotion of socio-emotional learning alongside cognitive development. Major findings include the need for AI-enabled pedagogies that transcend conventional personalization, such as collaborative knowledge building and place-based learning, to foster deeper engagement and understanding. The academic research paper underscores the significance of these approaches in creating more equitable and inclusive learning environments, particularly in addressing educational disparities in diverse global contexts. Ethical considerations, including data privacy, algorithmic bias, and the potential erosion of teacher autonomy, are critically examined. The research proposes a multi-stakeholder roadmap for implementing ethical and equitable AIEd, combining policy recommendations with practical strategies like risk sandboxing and open-source AIEd development. By advocating for a balance between technological innovation and human values in education, this academic research paper contributes to the advancement of human-centered AI in personalized and adaptive learning, paving the way for more nuanced, ethical, and effective integration of AI in educational settings worldwide.
International Journal of Innovative Research in Engineering and Management (IJIREM), 2024
This research paper aims at analyzing the factors that can help improve scalability of cloud by i... more This research paper aims at analyzing the factors that can help improve scalability of cloud by incorporating different machine learning algorithms in management of resources. Since controlling and managing cloud resources is becoming more challenging with compounded base requirements, the majority of conventional resource management solutions may not prove adequate. This research assesses the performance of five state-of-art machine learning techniques namely Reinforcement Learning, Long Short-Term Memory, Gradient Boosting Machines, Autoencoders and Neural Architecture Search in minimizing operational cost and enhancing resource utilization and overall system efficiency for improving business outcomes. The findings reveal that the use of RL-based approaches to optimize operational cost reduction and minimizing provisioning delay by 20% and 30% respectively and LSTM network to increase the accuracy of demand forecasting by 12% and overall efficiency of resource utilization by 22%. The use of GBM models in forecasts results in 30% error reduction in costs that drop by 20% while service improves by 25%. Using autoencoders, the models achieve 97% accuracy in detecting anomalies and infinityai1411@gmail.com in turn increasing the efficiency of allocation by 15 percent. The NAS-optimized models yield increased accuracy by a percentage point of 18 % as well as a 25% faster computational speed. Altogether, these theoretical developments demonstrate the ability of AI-based methodologies to enhance the cloud scalability promising and provide practical recommendations for improving resource management approaches in the cloud environment.
International Journal of Innovative Research in Engineering and Management (IJIREM), 2024
Recent research focuses on mental health and brain informatics. Emerging technologies like AI, de... more Recent research focuses on mental health and brain informatics. Emerging technologies like AI, deep learning, and machine learning drove the advancements. Customizing, diagnosing, and treating depression with data-driven approaches could improve mental health care. A growing field, precision psychiatry uses cutting-edge computer tools to provide tailored mental health care. AI in precision psychiatry is examined in this paper. Complex formulations aid therapy. These tools can identify and treat mental health patients. They can customize therapies for most patients. Unsupervised learning algorithms have shown considerable sadness-related sickness disparities. These methods separate diagnostic categories. Artificial intelligence could help us suggest drugs based on facts, not group averages. Our findings show that data-driven paradigms in healthcare face several challenges. Surprisingly, none of the survey studies reveal how current procedures improve patient outcomes. Standardizing field terminology, forming diverse research teams, evaluating models, identifying flaws, and making datasets accessible are crucial. Randomized controlled trials must show that computer algorithms improve patient outcomes to make models more feasible.
International Journal of Innovative Research in Engineering and Management (IJIREM), 2024
This paper estimated the growth, endogenous break, and fluctuation in India’s gross domestic prod... more This paper estimated the growth, endogenous break, and fluctuation in India’s gross domestic product from 1970–71 to 2019–20. The linear growth estimation method is well established in the existing literature. The estimation of endogenous structural break and fluctuation measuring methods needs some modification and adjustment. The resulting estimates reveal that India's GDP consists of three breaks and four distinct phases in different policy regimes in the considered time interval, of which all four are full-length regimes, two upward spikes, one downward spike were created during the period. The highest growth rate was achieved at 6.54% from 2005-06 to 2019-20.
International Journal of Innovative Research in Engineering and Management (IJIREM), 2024
This study explores the incorporation of Artificial Intelligence (AI) into traditional Six Sigma'... more This study explores the incorporation of Artificial Intelligence (AI) into traditional Six Sigma's DMAIC (Define, Measure, Analyze, Improve, Control) methodology to enhance continuous process improvement and achieve significant economic growth across industries. AI’s data analysis, machine learning algorithms coupled with real-time insights can expedite problem identification in manufacturing processes before they become substantial issues – eliminating the need for human oversight by proactively identifying potential errors or bottlenecks - this reduces wastage and optimizes resource utilization. Coupling AI’s predictive capabilities with Six Sigma's systematic approach not only boosts productivity but also ensures robust quality control standards are met – leading to continuous nonstop improvement in various sectors globally, particularly supply chain management where operational efficiency is critical for success and sustainability. By enhancing resource allocation effectiveness through AI automation while reducing waste generation via predictive analytics - this integration holds the key towards achieving both economic growth objectives alongside environmental stewardship as complementary facets of successful business strategies in today's global marketplace, fostering a future where operational excellence and sustainability go hand-in-hand.
International Journal of Innovative Research in Engineering and Management (IJIREM), 2024
The Minimum Support Price (MSP) is a key element of India's agricultural policy, aimed at protect... more The Minimum Support Price (MSP) is a key element of India's agricultural policy, aimed at protecting farmers from market price volatility and ensuring they receive a fair return for their crops. The paper critically examines the role of the Minimum Support Price (MSP) in India's agricultural sector, addressing the ongoing debate over the need for a legal guarantee of MSP. Through a balanced analysis, it presents arguments both for and against the legal guarantee of MSP, acknowledging concerns over market distortions and fiscal burdens. Suggestions for improving farmers’ incomes, such as investment in infrastructure, price stabilization mechanisms, and income support schemes, are discussed. The paper concludes by emphasizing the need for comprehensive reforms in the MSP system, advocating for a balanced approach that incorporates global best practices and modernizes market mechanisms, rather than solely relying on legal guarantees to sustain the agricultural economy.
International Journal of Innovative Research in Engineering and Management (IJIREM), 2024
Micro, Small, and Medium Enterprises contribute significantly to the Gross Domestic Product of th... more Micro, Small, and Medium Enterprises contribute significantly to the Gross Domestic Product of the country and also happen to be the largest generator of employment in Indonesia. Nevertheless, most of them find it difficult to adopt digitalization because of their limitations in infrastructure and relevant information systems and also due to poor digital literacy. These challenges, therefore, extend to fundamental business operations such as financial management, inventory monitoring, and sales, which are usually done manually. In line with this, the development of an efficient and user-friendly Point of Sale application, available on both Android and web platforms, is a strategy in the right direction. The application is designed to bring efficiency in financial management, recording of inventories, and sales operations for MSMEs, since it offers them the instruments needed for migrating to digital workflows. In addressing these constraints, the POS application hopes to empower MSMEs so that they can participate in the thriving digital economy and contribute more firmly to Indonesia's development. The focus of the study is on designing and developing a POS system that will respond to the needs of MSMEs in the digital era.
Energy conservation is an essential step towards overcoming the mounting problems of global energ... more Energy conservation is an essential step towards overcoming the mounting problems of global energy crisis and related environmental issues. Energy Management is considered the key to effective energy conservation. Present study is dealing with developing an " Energy Management System " , and implementing it in a real situation. A program was established as a continues improvement cycle. It started with formation of an energy management team, data collection, monitoring reports and audits. Hence the performance of a process plant can be improved by monitoring, analysis and adjustment of the appropriate plant operating parameters and in some cases, plant modifications. This involves studies of plant energy distribution and the corresponding losses associated with the various uses of energy, the scope and duration of a study my vary depending on the complexity of a plant and various operational requirements and constraints. Energy auditing has been established as useful tool for identifying energy wastage and opportunities for energy saving in a process industry. Heat recovery system is recommended for installation by retrofitting into existing plant in addition to other good housekeeping measures. Pyrethrum processing can be made profitable if energy losses are minimized. From the energy audit, it is possible for plant managers to differentiate the immediate energy saving potentials from those requiring requiring either some investments or further studies. This paper outlines the methodology and the scope for carrying out an energy audit with the main objective of improving the overall plant energy efficiency. The procedure included plant familiarization, data collection and parameter measurement, analysis of energy consumption and losses in the various sections of a steam plant and recommendation for remedial measures
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