International Journal of Innovative Research in Engineering and Management (IJIREM), 2025
Pigment extraction from cow dung via animal biotechnology is a sustainable approach that utilizes... more Pigment extraction from cow dung via animal biotechnology is a sustainable approach that utilizes biological principles, tools, and techniques to manage and utilize waste from animal agriculture, minimizing environmental pollution and disease transmission. Cow dung, rich in nutrients like nitrogen, phosphorus, potassium, sulphur, magnesium, and calcium, as well as micro flora including bacteria, protozoa, yeasts, and fungi is used to produce valuable pigments. These pigments have various applications in natural dyes for textiles, leather, and paper, cosmetics like skincare and haircare, food colouring after proper safety testing, paints and coatings, and biomedical applications such as wound healing. This innovative approach integrates animal biotechnology with circular economy principles, offering opportunities for resource recovery, energy self- sufficiency, and sustainable waste management, while also showcasing the potential of animal waste as a valuable resource. UV characterization of cow dung extraction is Analysing pigments from cow dung using ultraviolet (UV) light to understand their properties, identify specific pigments, and explore potential uses in various fields. The process of identifying and quantifying bioactive compounds in cow dung extracts using various biochemical techniques to understand their properties, potential applications, and commercial value.
International Journal of Innovative Research in Engineering and Management (IJIREM), 2025
The initial public offering (IPO) is a critical milestone in a company’s transition from private ... more The initial public offering (IPO) is a critical milestone in a company’s transition from private to public ownership, offering increased capital access and liquidity. However, the long-term performance of IPOs remains debated, with many firms underperforming market benchmarks over extended periods. While initial IPO returns often reflect investor enthusiasm which highlights consistent underperformance over three to five years, attributed to over-optimism, information asymmetry, and risks inherent in young firms. Sector-specific dynamics also play a role, with technology and biotech sectors experiencing higher volatility, while more established industries like financial services and utilities show stable, yet slower growth. Timing and market conditions further influence IPO outcomes, with firms launched in "hot markets" facing long-term corrections. Firm-specific factors, such as governance and insider ownership, also impact performance. This study examines the long-term IPO performance across various sectors, focusing on key industries and identifying factors driving success or failure, offering insights for investors and policymakers navigating IPO challenges and opportunities.
International Journal of Innovative Research in Engineering and Management (IJIREM), 2025
Data augmentation has emerged as a crucial technique for enhancing the robustness, accuracy, and ... more Data augmentation has emerged as a crucial technique for enhancing the robustness, accuracy, and generalization of AI models across various enterprise applications. This research explores the effectiveness of multiple data augmentation strategies, including adversarial training, synthetic data generation, CutMix & Mixup, backtranslation, feature-space augmentation, and noise injection, in improving AI model performance in domains such as computer vision, cybersecurity, healthcare, fraud detection, and natural language processing. The study evaluates the impact of these augmentation methods on model accuracy, precision, recall, F1-score, and resistance to adversarial attacks, demonstrating that advanced techniques like adversarial training and synthetic data generation offer substantial improvements, particularly in security-sensitive and privacy-regulated industries. The findings also emphasize the importance of selecting domain-specific augmentation strategies, balancing computational efficiency with performance gains, and addressing ethical considerations related to synthetic data generation and regulatory compliance. While traditional augmentation methods remain valuable, the study highlights the need for enterprises to adopt more sophisticated techniques to build reliable, scalable, and adaptive AI-driven solutions. Future research should focus on optimizing augmentation frameworks and developing standardized methodologies for evaluating their effectiveness. By leveraging advanced data augmentation techniques, organizations can significantly enhance the robustness of AI models, ensuring their reliability in real-world applications and driving innovation in enterprise AI deployment.
International Journal of Innovative Research in Engineering and Management (IJIREM), 2025
The aim of research was to extract the starch from ginger. It contains gingerol, fat, starch and ... more The aim of research was to extract the starch from ginger. It contains gingerol, fat, starch and volatile oil. It is the rhizome of the plant Zingiber Officinale. Ginger is belonging to the family Zingiberaceae is an ancient Indian medicine used in several disorders. It is a popular spice with important nutraceuticals attributes is cultivated extensively in India. after extraction of starch from ginger. The obtained starch had a yield of 13%. The prepared starch was characterized by FTIR and TGA to investigate the chemical composition, thermal properties. From these findings results suggested that the ginger can be used to extract starch. Due to biodegradable and biocompatible in nature. It will be used for biodegradable film and biomedical applications.
International Journal of Innovative Research in Engineering and Management (IJIREM), 2025
Background Employee satisfaction is a critical factor in organizational success. Satisfied employ... more Background Employee satisfaction is a critical factor in organizational success. Satisfied employees are more productive, engaged, and loyal, which leads to better overall performance. Understanding the factors that influence employee satisfaction can help organizations implement effective strategies to enhance employee well-being. Objective This research aims to model the changes in employee satisfaction levels (Satisfied, Neutral, Not satisfied) based on various dependencies using ordinary differential equations (ODEs). The dependencies considered in this study are work environment, salary, shift hours, leaves, perks, and timely promotions. Methods We defined variables representing satisfaction levels and dependencies, formulated ODEs to model the changes, and performed manual calculations to demonstrate the impact of varying each dependency. The initial proportions of employees in each satisfaction level were assumed, and the changes were calculated over a small-time interval. Results The model shows that improvements in work environment, salary, leaves, perks, and timely promotions positively affect satisfaction levels, while increased shift hours have a negative impact. The manual calculations illustrate how varying each dependency changes the proportions of satisfied, neutral, and not satisfied employees over time. Conclusion The ODE model provides valuable insights into the dynamics of employee satisfaction. Organizations can use this model to inform policies aimed at improving employee well-being. The findings highlight the importance of a positive work environment, competitive salary, adequate leaves, attractive perks, and timely promotions in enhancing employee satisfaction.
International Journal of Innovative Research in Engineering and Management (IJIREM), 2025
Power transformer failures pose a significant challenge to power utilities, leading to service in... more Power transformer failures pose a significant challenge to power utilities, leading to service interruptions, costly repairs, and operational inefficiencies. In Maharashtra, the State Transmission Utility has observed a marked increase in transformer failures over recent years, negatively impacting service reliability and increasing operational costs. This paper investigates the root causes of these failures using Failure Mode and Effects Analysis (FMEA), supported by MATLAB-based modelling by collecting the comprehensive data related to transformer failures. The analysis focuses on identifying critical failure modes, assessing their occurrence rates, and evaluating their severity. Short circuit failures, identified as the most critical failure mode, are further analysed through factory inspections and substation visits to understand the operational and mechanical stresses imposed on transformers during faults. The paper also examines the effectiveness of current repair practices and operating procedures. Based on this comprehensive analysis, recommendations are made to improve transformer reliability by addressing key failure modes, enhancing repair protocols, and implementing operational changes to mitigate future failures. This dual approach provides a robust framework for reducing transformer failures and improving power system reliability to the utility systems.
International Journal of Innovative Research in Engineering and Management (IJIREM), 2025
This study examines how technology implementation and knowledge management enhance product innova... more This study examines how technology implementation and knowledge management enhance product innovation and firm performance in Indonesian SMEs. Drawing from the Technology-Organization-Environment (TOE) framework and the Resource-Based View (RBV) theory, the research explores how digital technologies and knowledge management practices influence innovation and organizational success. Using qualitative methods, data was collected through interviews, observations, and document analysis from senior managers in various Indonesian SMEs. The findings show that adopting digital technologies and systematic knowledge management drives product innovation, which subsequently improves firm performance. Both technology implementation and knowledge management directly impact organizational outcomes, underscoring the importance of leveraging digital capabilities and knowledge-sharing mechanisms. These factors contribute to long-term competitiveness and sustainable growth. This study offers valuable insights for business leaders, policymakers, and researchers interested in digital transformation, innovation, and knowledge management in emerging economies. It highlights how Indonesian SMEs can use these elements to boost growth, enhance market position, and contribute to broader discussions on innovation management and organizational performance in the digital age.
International Journal of Innovative Research in Engineering and Management (IJIREM), 2025
This research involves creating an efficient portfolio construction that aims to guide the retail... more This research involves creating an efficient portfolio construction that aims to guide the retail investors about the significance of the data-driven decision-making using the analytical tool Python, especially for financial securities investments with a focus on mutual funds. A dataset comprising necessary information on nearly 625 mutual fund schemes from the dataset obtained from Kaggle has been utilized for analysis and study. The study focuses on applying the modern portfolio theory for portfolio construction proposed by Markowitz, which is a very popular financial theory, in real-world investment strategy in the case of constructing a mutual fund portfolio with an adjusted risk-return tradeoff. The methodology relies on the construction of a portfolio with modern portfolio theory concepts and predicting the possible outcomes of the portfolio with the Monte Carlo simulation technique by running the codes in Python and constructing two distinct portfolios: one with diversified and lower risk, comprising 15 mutual fund schemes for conservative investors, and another with minimum compromised risk, comprising 5 mutual fund schemes, which achieves a higher return than the previous one. The parameters taken for the choice of selecting the schemes from the data set are based on the renowned ones such as the Sharpe Ratio and Sortino Ratio. The findings reveal that the 15 schemes portfolio returns are in the range of 10% and 12% with risk levels between 1.5% and 2.5%, and the 5 schemes portfolio returns are in the range of 15% and 17% with risk levels between 4% and 4.5%. The optimum weights to be invested in each scheme to achieve maximum return at the lowest possible risk are also mentioned in proportion for both the portfolios. The findings can be interpreted in a way that the construction of a portfolio with rational decisions backed by data is more appropriate in the modern world with the availability of analytical tools such as Python for forecasting and predicting the potential return and constructing the portfolio based on that by minimizing risks with the traditional theories, which can be efficiently and easily used with technology.
International Journal of Innovative Research in Engineering and Management (IJIREM), 2025
This study compares the performance of three machine learning models namely Logistic Regression, ... more This study compares the performance of three machine learning models namely Logistic Regression, Naive Bayes, and K-Nearest Neighbors (KNN) in forecasting customer churn within financial institutions. Predicting customer churn is important for banks and the financial sector because once firms develop targeted interventions to improve customer satisfaction and loyalty, they can be kept for the long run. A set of performance metrics like accuracy, precision, recall, F1 score, and area under the curve were used in the analysis for the better comparison of the prediction capabilities of models. Logistic Regression resulted as the one which performed best. It gained the highest possible accuracy of 89.6% and its recall rate was very good at 96.8%. Also, its AUC score shows high discriminative power at 0.91. Naive Bayes, gaining less accuracy by producing 86.7% yet showed good precision at 92.0%, and having good recall rates, 92.2% and thus forms another competitive selection. Its AUC score of 0.83 establishes its efficiency to differentiate churners from non-churners. KNN's accuracy was good with 89.2% along with excellent recall rate 97.6%. Moreover, AUC score at 0.86 enhances the reliability as well as performance in the role of a good prediction model for churners. Promising results may indicate more research on such advanced techniques. More models that would improve the performance may be neural network models in this regard such as Artificial Neural Network, Feed Forward Neural Network, Multi-layer Perceptron in the sub-field of neural networks and the models applied to Deep Learning that may involve models such as Convolutional Neural Network, Recurrent Neural Networks, Long Short-Term Memory. These techniques are expected to further enhance predictive accuracy by capturing complex patterns in large datasets. This would greatly enhance customer retention for banks and financial institutions in their implementation of machine learning models. The accurate prediction of churn will allow organizations to engage at-risk customers proactively, providing customized interventions to enhance satisfaction and loyalty. The results of this study further highlight the value of machine learning in changing customer relationship management and leading to long-term customer retention and organizational success.
International Journal of Innovative Research in Engineering and Management (IJIREM), 2025
This paper introduces an intuitive approach to clickthrough rate (CTR) prediction, a learning pro... more This paper introduces an intuitive approach to clickthrough rate (CTR) prediction, a learning problem that has been extensively studied over the past several years. As digital marketing continues to grow rapidly into a multibillion-dollar industry, this study aims to find the most effective machine learning model to enhance the CTR of marketing emails by comparing various tree-based models. Key steps in this research include data collection, feature extraction, and CTR prediction through the evaluation of different models. The statistical results prove that the CatBoost model, with optimized feature selection, achieves near-perfect data fitting, indicating its efficiency.
International Journal of Innovative Research in Engineering and Management (IJIREM), 2025
3D printing technology has brought remarkable improvements to manufacturing processes. By convert... more 3D printing technology has brought remarkable improvements to manufacturing processes. By converting digital designs into physical objects, it allows for the quick and accurate production of items with intricate geometries. This technology reduces costs while offering enhanced production flexibility. In fields such as jewelry making, medical prosthetics, the food industry, and construction, 3D printers are continuously expanding their applications and evolving with each passing year. In the construction sector, 3D printing has become a key driver of the automation revolution, significantly changing traditional building methods. It offers notable advantages, such as lower costs, faster construction timelines, and improved sustainability. With this technology, construction materials can be directly printed on-site, making it possible to create even highly complex designs with ease. This approach not only minimizes material waste but also enhances energy efficiency. Ranging from prefabricated building components to cost-effective housing projects, 3D printing provides essential solutions, especially in areas requiring rapid construction. Additionally, it allows for greater creative freedom in architectural design, enabling the realization of more innovative and sustainable structures. In short, 3D printing is playing a transformative role in reshaping the construction industry. In conjunction with other automation technologies, it is making construction practices more efficient, economical, and environmentally friendly. This paper examines the advantages and current uses of 3D printing in construction, drawing on findings from recent studies and real-world applications.
International Journal of Innovative Research in Engineering and Management (IJIREM), 2025
The Environmental Kuznets Curve (EKC) explores the relationship between economic growth and envir... more The Environmental Kuznets Curve (EKC) explores the relationship between economic growth and environmental degradation, proposing an inverted U-shaped curve. It says that the pollution initially increases with the rise in the income levels but declines after a surpassing a certain economic threshold. This study validates that EKC hypothesis in selected developed European countries focusing carbon dioxide (CO2) and nitrogen dioxide (NO2) emissions concerning Gross domestic product per capita (GDPPC). The secondary data were used from World Bank for the year 1990 to 2020. The analysis highlights that the existence of inverted U-shaped curve in some nations Austria, Belgium, and Denmark, where emissions rise during early stages of economic growth but eventually decline. However, the findings for NO2 emissions are mixed, with some countries exhibiting weak or no EKC patterns. This divergence is attributed to differences in industrial structures, energy efficiency, and the adoption of clean technologies. The study highlight's role of regulatory framework, public awareness and investment in achieving sustainable development. Countries with robust environmental policies and higher investments in green technologies, such as Denmark and Sweden, demonstrate more pronounced EKC trends. Conversely, nations reliant on fossil fuels and less stringent environmental policies exhibit weaker results. The outcomes highlight how important it is to balance the economic growth and economic sustainability in industrial countries through environmental policies, investments in cleaner technologies and sustainable conduct.
International Journal of Innovative Research in Engineering and Management , 2025
Soft soil is a material with low bearing capacity that often poses a major challenge in road cons... more Soft soil is a material with low bearing capacity that often poses a major challenge in road construction, particularly in areas with high water content. This study aims to enhance the bearing capacity of soft soil through chemical stabilization using a combination of pumice, aluminium hydroxide (AH), and lime, with lime content variations of 2%, 4%, and 6%. The soft soil samples were collected from Subaim Village, East Halmahera, which naturally has a CBR value of 4,49% (categorized as "Poor to Fair") and is only suitable for use as a subgrade layer. Testing was conducted in accordance with ASTM standards on specimens soaked for 3 to 28 days to assess the impact of lime content and curing duration on the CBR value. The results showed that the addition of lime significantly improved the CBR value compared to the natural soil. At 2% lime content, the CBR value increased from 9,90% to 26,09%, while 4% lime produced the highest CBR value of 33,28%, representing an improvement of over sevenfold compared to the na
International Journal of Innovative Research in Engineering and Management (IJIREM), 2024
Global trade and commerce are inherently fraught with risks ranging from geopolitical tensions to... more Global trade and commerce are inherently fraught with risks ranging from geopolitical tensions to financial market volatility. Effective risk management strategies are crucial for businesses operating in this dynamic environment. This paper explores various risk management techniques, including diversification, hedging, and the use of financial instruments, and evaluates their effectiveness in mitigating risks in global trade. Through case studies and analysis of recent trends, the paper aims to provide a comprehensive understanding of how businesses can safeguard their interests and maintain stability in the face of global uncertainties.
International Journal of Innovative Research in Engineering and Management (IJIREM), 2024
The aspect of food security in all its form has rightly received importance globally. According t... more The aspect of food security in all its form has rightly received importance globally. According to FAO's Food Security and Nutrition in the World report about 193 million people in 53 countries experienced crisis food insecurity in 2021. Food security in individual as well as societal level is crucial for human development. A nation with sustained food secured citizen can help not only to establish the societal stability but also contribute to the future growth of the nation. With respect to the above background, this paper aims to measure annual growth rate and Compound Annual Growth Rate (CAGR) of Per Capita Availability of Food Grains, Rice, Wheat, Other cereals, Cereals, Gram and Pulses for the period 2010-2022 which may represent the food security in India in the last decade. The growth rates and CAGR statistics show that food grain supply is dynamic, affected by climate, agriculture, market demand, and policy. The greater CAGR for other cereals reflects stronger cultivation and availability. Rice and gram have lower CAGRs, indicating more constant but less aggressive growth. Resilience and adaptation in agricultural and food supply systems are needed to maintain availability and manage production and demand changes over years.
International Journal of Innovative Research in Engineering and Management (IJIREM), 2024
The automated method of identifying and deciphering the emotions expressed in written text is cal... more The automated method of identifying and deciphering the emotions expressed in written text is called sentiment analysis (SA). In the last ten years, SA has become incredibly popular in the NLP (Natural Language Processing) domain. Web-based social networking websites have become a powerful tool for influencing user perceptions and how businesses are marketed. People's opinions are very important when analyzing the effects of information propagation on people's lives in a large-scale network such as Twitter. The polarity and predisposition of a large population towards a particular topic, item, or entity can be determined by data analysis of the tweets. These days, it's easy to see how such analysis is applied in a variety of contexts, including public elections, movie marketing, brand endorsements, and many more. We will build a program that examines the content of tweets on a specific subject in this project. The main goal is to present an approach for polarity score analysis in Twitter streams with noise.
We suggest an emotive categorization of a large number of tweets in this paper. Here, we categorize an expression's sentiments into positive and negative emotions using deep learning approaches. Motivation, fun, happiness, affection, neutral, relief, and surprise are other categories for positive feelings, while anger, boredom, emptiness, hatred, sadness, and worry are categories for negative emotions. We demonstrated how to attain high emotion classification accuracy by experimenting with and evaluating the approach using recurrent neural networks and long-term short-term memory on three distinct datasets. Based on a comprehensive evaluation, the system achieves 88.47% accuracy for positive/negative classifications and 89.3% and 93.3% accuracy for both positive and negative subclasses, respectively, for emotion prediction using the LSTM mode
International Journal of Innovative Research in Engineering and Management (IJIREM), 2024
The main constituents of the construction industry usually involve building materials which inclu... more The main constituents of the construction industry usually involve building materials which include cement, bricks, steel, fine and coarse aggregates. The majority of concrete production worldwide incorporates sand as a fine aggregate. Since, all of these materials are obtained from finite natural resources, their ongoing depletion will have a negative impact on the environment. In order to minimize environmental harm and preserve natural resources, this study focuses on the recycling of waste materials through the use of waste marble powder (WMP) as a substitute for fine aggregate (sand) in concrete. The sawing and cutting waste generated by the marble industry is substantial and has a negative impact on the environment. Our research concluded that by replacing sand with waste marble powder, mechanical and durability properties of concrete are enhanced thus, making it more resilient and sustainable. WMP was incorporated to concrete in different amounts. For this investigation sand was replaced in four proportions (10%, 20%, 30%, 40% by weight). The grade of concrete is M30, water-to-cement ratio was maintained at 0.45 and 0.28% superplasticizer was added to cement by weight to increase the workability. The experiments employed both destructive testing (DT) and non-destructive testing (NDT). Following a 28-day curing period, at a 40% replacement ratio, the results of the tests for compressive strength, split tensile strength and flexural strength demonstrated improvements of 17.5%, 27% and 35%, respectively. The maximum value of the rebound hammer test exhibited an increment of 15%; the ultrasonic pulse velocity (UPV) indicated an improvement of 3% and the rate of water absorption declined to 45%, 40% and 36% for cubes, cylinders and beams respectively.
International Journal of Innovative Research in Engineering and Management (IJIREM), 2024
This study looks into the effects of social media involvement, market flexibility, and business m... more This study looks into the effects of social media involvement, market flexibility, and business mentoring on Indonesian entrepreneurship growth. Data were gathered from a sample of 250 entrepreneurs utilizing a quantitative research approach, and Structural Equation Modelling-Partial Least Squares (SEM-PLS) with the PLS-3 software were used for analysis. The findings show a strong correlation between the three variables and the expansion of entrepreneurship. Business mentoring improves business performance and offers crucial tools and direction. Entrepreneurs who possess market adaptability can react to shifting market conditions with effectiveness, and social media participation promotes client contact and brand building. These results highlight how important these elements are to Indonesian entrepreneurship success. To encourage entrepreneurial development, the study highlights the importance of social media engagement tactics, adaptation training, and mentoring programs. These insights are particularly useful for politicians and business support organizations.
International Journal of Innovative Research in Engineering and Management (IJIREM), 2024
The diversity of energy production is vital for India's sustainable development. It enhances ener... more The diversity of energy production is vital for India's sustainable development. It enhances energy security, economic stability, environmental sustainability and resilience to climate change. By investing in a variety of energy sources and technologies, India can ensure a reliable, affordable, and sustainable energy supply for its growing population and economy. India is mostly dependent upon coal for its energy production and consumption. When we consider only primary energy sources, India produced 79.6 % of energy from coal and consumed 52.89 % of energy from coal in 2020. In this paper we have studied about the energy diversity of India based on primary energy sources using data for 53 years (from 1970 to 2022). We have used the four types of primary energy sources i.e., coal, lignite, crude oil and natural gas. We have used different indices [Shannon-Weiner Index (SWI), Herfindahl-Hirschman Index (HHI), Weighted_SWI (D_SWI) and Weighted_HHI (D_HHI)] to find the Energy Diversity Index of India. From the result, we have found that energy diversity of India is very low due to high dependency upon coal and also the index is falling which is of concern in regard to energy security of India.
International Journal of Innovative Research in Engineering and Management (IJIREM), 2024
This study leverages advanced numerical analysis techniques to model the intricate interactions a... more This study leverages advanced numerical analysis techniques to model the intricate interactions among soil, foundation, and structures alongside the nonlinear load-deformation characteristics of soils in three-dimensional environments. This research underscores mat foundation calculations' enhanced realism and cost-efficiency, offering improvements over traditional methodologies. The objective is to explore the effects of different superstructure loading types on mat foundation behaviour using three-dimensional finite element models. Thirty-six distinct models were developed to examine the influence of superstructure loads, which were varied across uniformly distributed loads, column loads, and comprehensive building models. Each mat foundation was analysed using separate models incorporating 3D volume elements, utilizing the Mohr-Coulomb material model to represent soil conditions as either 'normally loaded' or 'over-consolidated.’ The study presents detailed findings on total and differential settlements and internal forces, illustrated through figures and graphs, based on data from an existing ten-story building. Regarding bending moments, the most notable difference among loading types lies in the varied locations of maximum moments, independent of shear wall placement, particularly evident when simulating uniformly distributed loads. It is evident that the type of superstructure loading influences settlement patterns and internal forces, underscoring the necessity of considering this factor in analyses. Our findings reveal a significant disparity in construction costs between buildings erected on different soil classes, particularly between ZA and ZB. Structures situated on ZB-classified soil incur expenses approximately 1.5% higher than those on ZA-classified soil.
International Journal of Innovative Research in Engineering and Management (IJIREM), 2025
Pigment extraction from cow dung via animal biotechnology is a sustainable approach that utilizes... more Pigment extraction from cow dung via animal biotechnology is a sustainable approach that utilizes biological principles, tools, and techniques to manage and utilize waste from animal agriculture, minimizing environmental pollution and disease transmission. Cow dung, rich in nutrients like nitrogen, phosphorus, potassium, sulphur, magnesium, and calcium, as well as micro flora including bacteria, protozoa, yeasts, and fungi is used to produce valuable pigments. These pigments have various applications in natural dyes for textiles, leather, and paper, cosmetics like skincare and haircare, food colouring after proper safety testing, paints and coatings, and biomedical applications such as wound healing. This innovative approach integrates animal biotechnology with circular economy principles, offering opportunities for resource recovery, energy self- sufficiency, and sustainable waste management, while also showcasing the potential of animal waste as a valuable resource. UV characterization of cow dung extraction is Analysing pigments from cow dung using ultraviolet (UV) light to understand their properties, identify specific pigments, and explore potential uses in various fields. The process of identifying and quantifying bioactive compounds in cow dung extracts using various biochemical techniques to understand their properties, potential applications, and commercial value.
International Journal of Innovative Research in Engineering and Management (IJIREM), 2025
The initial public offering (IPO) is a critical milestone in a company’s transition from private ... more The initial public offering (IPO) is a critical milestone in a company’s transition from private to public ownership, offering increased capital access and liquidity. However, the long-term performance of IPOs remains debated, with many firms underperforming market benchmarks over extended periods. While initial IPO returns often reflect investor enthusiasm which highlights consistent underperformance over three to five years, attributed to over-optimism, information asymmetry, and risks inherent in young firms. Sector-specific dynamics also play a role, with technology and biotech sectors experiencing higher volatility, while more established industries like financial services and utilities show stable, yet slower growth. Timing and market conditions further influence IPO outcomes, with firms launched in "hot markets" facing long-term corrections. Firm-specific factors, such as governance and insider ownership, also impact performance. This study examines the long-term IPO performance across various sectors, focusing on key industries and identifying factors driving success or failure, offering insights for investors and policymakers navigating IPO challenges and opportunities.
International Journal of Innovative Research in Engineering and Management (IJIREM), 2025
Data augmentation has emerged as a crucial technique for enhancing the robustness, accuracy, and ... more Data augmentation has emerged as a crucial technique for enhancing the robustness, accuracy, and generalization of AI models across various enterprise applications. This research explores the effectiveness of multiple data augmentation strategies, including adversarial training, synthetic data generation, CutMix & Mixup, backtranslation, feature-space augmentation, and noise injection, in improving AI model performance in domains such as computer vision, cybersecurity, healthcare, fraud detection, and natural language processing. The study evaluates the impact of these augmentation methods on model accuracy, precision, recall, F1-score, and resistance to adversarial attacks, demonstrating that advanced techniques like adversarial training and synthetic data generation offer substantial improvements, particularly in security-sensitive and privacy-regulated industries. The findings also emphasize the importance of selecting domain-specific augmentation strategies, balancing computational efficiency with performance gains, and addressing ethical considerations related to synthetic data generation and regulatory compliance. While traditional augmentation methods remain valuable, the study highlights the need for enterprises to adopt more sophisticated techniques to build reliable, scalable, and adaptive AI-driven solutions. Future research should focus on optimizing augmentation frameworks and developing standardized methodologies for evaluating their effectiveness. By leveraging advanced data augmentation techniques, organizations can significantly enhance the robustness of AI models, ensuring their reliability in real-world applications and driving innovation in enterprise AI deployment.
International Journal of Innovative Research in Engineering and Management (IJIREM), 2025
The aim of research was to extract the starch from ginger. It contains gingerol, fat, starch and ... more The aim of research was to extract the starch from ginger. It contains gingerol, fat, starch and volatile oil. It is the rhizome of the plant Zingiber Officinale. Ginger is belonging to the family Zingiberaceae is an ancient Indian medicine used in several disorders. It is a popular spice with important nutraceuticals attributes is cultivated extensively in India. after extraction of starch from ginger. The obtained starch had a yield of 13%. The prepared starch was characterized by FTIR and TGA to investigate the chemical composition, thermal properties. From these findings results suggested that the ginger can be used to extract starch. Due to biodegradable and biocompatible in nature. It will be used for biodegradable film and biomedical applications.
International Journal of Innovative Research in Engineering and Management (IJIREM), 2025
Background Employee satisfaction is a critical factor in organizational success. Satisfied employ... more Background Employee satisfaction is a critical factor in organizational success. Satisfied employees are more productive, engaged, and loyal, which leads to better overall performance. Understanding the factors that influence employee satisfaction can help organizations implement effective strategies to enhance employee well-being. Objective This research aims to model the changes in employee satisfaction levels (Satisfied, Neutral, Not satisfied) based on various dependencies using ordinary differential equations (ODEs). The dependencies considered in this study are work environment, salary, shift hours, leaves, perks, and timely promotions. Methods We defined variables representing satisfaction levels and dependencies, formulated ODEs to model the changes, and performed manual calculations to demonstrate the impact of varying each dependency. The initial proportions of employees in each satisfaction level were assumed, and the changes were calculated over a small-time interval. Results The model shows that improvements in work environment, salary, leaves, perks, and timely promotions positively affect satisfaction levels, while increased shift hours have a negative impact. The manual calculations illustrate how varying each dependency changes the proportions of satisfied, neutral, and not satisfied employees over time. Conclusion The ODE model provides valuable insights into the dynamics of employee satisfaction. Organizations can use this model to inform policies aimed at improving employee well-being. The findings highlight the importance of a positive work environment, competitive salary, adequate leaves, attractive perks, and timely promotions in enhancing employee satisfaction.
International Journal of Innovative Research in Engineering and Management (IJIREM), 2025
Power transformer failures pose a significant challenge to power utilities, leading to service in... more Power transformer failures pose a significant challenge to power utilities, leading to service interruptions, costly repairs, and operational inefficiencies. In Maharashtra, the State Transmission Utility has observed a marked increase in transformer failures over recent years, negatively impacting service reliability and increasing operational costs. This paper investigates the root causes of these failures using Failure Mode and Effects Analysis (FMEA), supported by MATLAB-based modelling by collecting the comprehensive data related to transformer failures. The analysis focuses on identifying critical failure modes, assessing their occurrence rates, and evaluating their severity. Short circuit failures, identified as the most critical failure mode, are further analysed through factory inspections and substation visits to understand the operational and mechanical stresses imposed on transformers during faults. The paper also examines the effectiveness of current repair practices and operating procedures. Based on this comprehensive analysis, recommendations are made to improve transformer reliability by addressing key failure modes, enhancing repair protocols, and implementing operational changes to mitigate future failures. This dual approach provides a robust framework for reducing transformer failures and improving power system reliability to the utility systems.
International Journal of Innovative Research in Engineering and Management (IJIREM), 2025
This study examines how technology implementation and knowledge management enhance product innova... more This study examines how technology implementation and knowledge management enhance product innovation and firm performance in Indonesian SMEs. Drawing from the Technology-Organization-Environment (TOE) framework and the Resource-Based View (RBV) theory, the research explores how digital technologies and knowledge management practices influence innovation and organizational success. Using qualitative methods, data was collected through interviews, observations, and document analysis from senior managers in various Indonesian SMEs. The findings show that adopting digital technologies and systematic knowledge management drives product innovation, which subsequently improves firm performance. Both technology implementation and knowledge management directly impact organizational outcomes, underscoring the importance of leveraging digital capabilities and knowledge-sharing mechanisms. These factors contribute to long-term competitiveness and sustainable growth. This study offers valuable insights for business leaders, policymakers, and researchers interested in digital transformation, innovation, and knowledge management in emerging economies. It highlights how Indonesian SMEs can use these elements to boost growth, enhance market position, and contribute to broader discussions on innovation management and organizational performance in the digital age.
International Journal of Innovative Research in Engineering and Management (IJIREM), 2025
This research involves creating an efficient portfolio construction that aims to guide the retail... more This research involves creating an efficient portfolio construction that aims to guide the retail investors about the significance of the data-driven decision-making using the analytical tool Python, especially for financial securities investments with a focus on mutual funds. A dataset comprising necessary information on nearly 625 mutual fund schemes from the dataset obtained from Kaggle has been utilized for analysis and study. The study focuses on applying the modern portfolio theory for portfolio construction proposed by Markowitz, which is a very popular financial theory, in real-world investment strategy in the case of constructing a mutual fund portfolio with an adjusted risk-return tradeoff. The methodology relies on the construction of a portfolio with modern portfolio theory concepts and predicting the possible outcomes of the portfolio with the Monte Carlo simulation technique by running the codes in Python and constructing two distinct portfolios: one with diversified and lower risk, comprising 15 mutual fund schemes for conservative investors, and another with minimum compromised risk, comprising 5 mutual fund schemes, which achieves a higher return than the previous one. The parameters taken for the choice of selecting the schemes from the data set are based on the renowned ones such as the Sharpe Ratio and Sortino Ratio. The findings reveal that the 15 schemes portfolio returns are in the range of 10% and 12% with risk levels between 1.5% and 2.5%, and the 5 schemes portfolio returns are in the range of 15% and 17% with risk levels between 4% and 4.5%. The optimum weights to be invested in each scheme to achieve maximum return at the lowest possible risk are also mentioned in proportion for both the portfolios. The findings can be interpreted in a way that the construction of a portfolio with rational decisions backed by data is more appropriate in the modern world with the availability of analytical tools such as Python for forecasting and predicting the potential return and constructing the portfolio based on that by minimizing risks with the traditional theories, which can be efficiently and easily used with technology.
International Journal of Innovative Research in Engineering and Management (IJIREM), 2025
This study compares the performance of three machine learning models namely Logistic Regression, ... more This study compares the performance of three machine learning models namely Logistic Regression, Naive Bayes, and K-Nearest Neighbors (KNN) in forecasting customer churn within financial institutions. Predicting customer churn is important for banks and the financial sector because once firms develop targeted interventions to improve customer satisfaction and loyalty, they can be kept for the long run. A set of performance metrics like accuracy, precision, recall, F1 score, and area under the curve were used in the analysis for the better comparison of the prediction capabilities of models. Logistic Regression resulted as the one which performed best. It gained the highest possible accuracy of 89.6% and its recall rate was very good at 96.8%. Also, its AUC score shows high discriminative power at 0.91. Naive Bayes, gaining less accuracy by producing 86.7% yet showed good precision at 92.0%, and having good recall rates, 92.2% and thus forms another competitive selection. Its AUC score of 0.83 establishes its efficiency to differentiate churners from non-churners. KNN's accuracy was good with 89.2% along with excellent recall rate 97.6%. Moreover, AUC score at 0.86 enhances the reliability as well as performance in the role of a good prediction model for churners. Promising results may indicate more research on such advanced techniques. More models that would improve the performance may be neural network models in this regard such as Artificial Neural Network, Feed Forward Neural Network, Multi-layer Perceptron in the sub-field of neural networks and the models applied to Deep Learning that may involve models such as Convolutional Neural Network, Recurrent Neural Networks, Long Short-Term Memory. These techniques are expected to further enhance predictive accuracy by capturing complex patterns in large datasets. This would greatly enhance customer retention for banks and financial institutions in their implementation of machine learning models. The accurate prediction of churn will allow organizations to engage at-risk customers proactively, providing customized interventions to enhance satisfaction and loyalty. The results of this study further highlight the value of machine learning in changing customer relationship management and leading to long-term customer retention and organizational success.
International Journal of Innovative Research in Engineering and Management (IJIREM), 2025
This paper introduces an intuitive approach to clickthrough rate (CTR) prediction, a learning pro... more This paper introduces an intuitive approach to clickthrough rate (CTR) prediction, a learning problem that has been extensively studied over the past several years. As digital marketing continues to grow rapidly into a multibillion-dollar industry, this study aims to find the most effective machine learning model to enhance the CTR of marketing emails by comparing various tree-based models. Key steps in this research include data collection, feature extraction, and CTR prediction through the evaluation of different models. The statistical results prove that the CatBoost model, with optimized feature selection, achieves near-perfect data fitting, indicating its efficiency.
International Journal of Innovative Research in Engineering and Management (IJIREM), 2025
3D printing technology has brought remarkable improvements to manufacturing processes. By convert... more 3D printing technology has brought remarkable improvements to manufacturing processes. By converting digital designs into physical objects, it allows for the quick and accurate production of items with intricate geometries. This technology reduces costs while offering enhanced production flexibility. In fields such as jewelry making, medical prosthetics, the food industry, and construction, 3D printers are continuously expanding their applications and evolving with each passing year. In the construction sector, 3D printing has become a key driver of the automation revolution, significantly changing traditional building methods. It offers notable advantages, such as lower costs, faster construction timelines, and improved sustainability. With this technology, construction materials can be directly printed on-site, making it possible to create even highly complex designs with ease. This approach not only minimizes material waste but also enhances energy efficiency. Ranging from prefabricated building components to cost-effective housing projects, 3D printing provides essential solutions, especially in areas requiring rapid construction. Additionally, it allows for greater creative freedom in architectural design, enabling the realization of more innovative and sustainable structures. In short, 3D printing is playing a transformative role in reshaping the construction industry. In conjunction with other automation technologies, it is making construction practices more efficient, economical, and environmentally friendly. This paper examines the advantages and current uses of 3D printing in construction, drawing on findings from recent studies and real-world applications.
International Journal of Innovative Research in Engineering and Management (IJIREM), 2025
The Environmental Kuznets Curve (EKC) explores the relationship between economic growth and envir... more The Environmental Kuznets Curve (EKC) explores the relationship between economic growth and environmental degradation, proposing an inverted U-shaped curve. It says that the pollution initially increases with the rise in the income levels but declines after a surpassing a certain economic threshold. This study validates that EKC hypothesis in selected developed European countries focusing carbon dioxide (CO2) and nitrogen dioxide (NO2) emissions concerning Gross domestic product per capita (GDPPC). The secondary data were used from World Bank for the year 1990 to 2020. The analysis highlights that the existence of inverted U-shaped curve in some nations Austria, Belgium, and Denmark, where emissions rise during early stages of economic growth but eventually decline. However, the findings for NO2 emissions are mixed, with some countries exhibiting weak or no EKC patterns. This divergence is attributed to differences in industrial structures, energy efficiency, and the adoption of clean technologies. The study highlight's role of regulatory framework, public awareness and investment in achieving sustainable development. Countries with robust environmental policies and higher investments in green technologies, such as Denmark and Sweden, demonstrate more pronounced EKC trends. Conversely, nations reliant on fossil fuels and less stringent environmental policies exhibit weaker results. The outcomes highlight how important it is to balance the economic growth and economic sustainability in industrial countries through environmental policies, investments in cleaner technologies and sustainable conduct.
International Journal of Innovative Research in Engineering and Management , 2025
Soft soil is a material with low bearing capacity that often poses a major challenge in road cons... more Soft soil is a material with low bearing capacity that often poses a major challenge in road construction, particularly in areas with high water content. This study aims to enhance the bearing capacity of soft soil through chemical stabilization using a combination of pumice, aluminium hydroxide (AH), and lime, with lime content variations of 2%, 4%, and 6%. The soft soil samples were collected from Subaim Village, East Halmahera, which naturally has a CBR value of 4,49% (categorized as "Poor to Fair") and is only suitable for use as a subgrade layer. Testing was conducted in accordance with ASTM standards on specimens soaked for 3 to 28 days to assess the impact of lime content and curing duration on the CBR value. The results showed that the addition of lime significantly improved the CBR value compared to the natural soil. At 2% lime content, the CBR value increased from 9,90% to 26,09%, while 4% lime produced the highest CBR value of 33,28%, representing an improvement of over sevenfold compared to the na
International Journal of Innovative Research in Engineering and Management (IJIREM), 2024
Global trade and commerce are inherently fraught with risks ranging from geopolitical tensions to... more Global trade and commerce are inherently fraught with risks ranging from geopolitical tensions to financial market volatility. Effective risk management strategies are crucial for businesses operating in this dynamic environment. This paper explores various risk management techniques, including diversification, hedging, and the use of financial instruments, and evaluates their effectiveness in mitigating risks in global trade. Through case studies and analysis of recent trends, the paper aims to provide a comprehensive understanding of how businesses can safeguard their interests and maintain stability in the face of global uncertainties.
International Journal of Innovative Research in Engineering and Management (IJIREM), 2024
The aspect of food security in all its form has rightly received importance globally. According t... more The aspect of food security in all its form has rightly received importance globally. According to FAO's Food Security and Nutrition in the World report about 193 million people in 53 countries experienced crisis food insecurity in 2021. Food security in individual as well as societal level is crucial for human development. A nation with sustained food secured citizen can help not only to establish the societal stability but also contribute to the future growth of the nation. With respect to the above background, this paper aims to measure annual growth rate and Compound Annual Growth Rate (CAGR) of Per Capita Availability of Food Grains, Rice, Wheat, Other cereals, Cereals, Gram and Pulses for the period 2010-2022 which may represent the food security in India in the last decade. The growth rates and CAGR statistics show that food grain supply is dynamic, affected by climate, agriculture, market demand, and policy. The greater CAGR for other cereals reflects stronger cultivation and availability. Rice and gram have lower CAGRs, indicating more constant but less aggressive growth. Resilience and adaptation in agricultural and food supply systems are needed to maintain availability and manage production and demand changes over years.
International Journal of Innovative Research in Engineering and Management (IJIREM), 2024
The automated method of identifying and deciphering the emotions expressed in written text is cal... more The automated method of identifying and deciphering the emotions expressed in written text is called sentiment analysis (SA). In the last ten years, SA has become incredibly popular in the NLP (Natural Language Processing) domain. Web-based social networking websites have become a powerful tool for influencing user perceptions and how businesses are marketed. People's opinions are very important when analyzing the effects of information propagation on people's lives in a large-scale network such as Twitter. The polarity and predisposition of a large population towards a particular topic, item, or entity can be determined by data analysis of the tweets. These days, it's easy to see how such analysis is applied in a variety of contexts, including public elections, movie marketing, brand endorsements, and many more. We will build a program that examines the content of tweets on a specific subject in this project. The main goal is to present an approach for polarity score analysis in Twitter streams with noise.
We suggest an emotive categorization of a large number of tweets in this paper. Here, we categorize an expression's sentiments into positive and negative emotions using deep learning approaches. Motivation, fun, happiness, affection, neutral, relief, and surprise are other categories for positive feelings, while anger, boredom, emptiness, hatred, sadness, and worry are categories for negative emotions. We demonstrated how to attain high emotion classification accuracy by experimenting with and evaluating the approach using recurrent neural networks and long-term short-term memory on three distinct datasets. Based on a comprehensive evaluation, the system achieves 88.47% accuracy for positive/negative classifications and 89.3% and 93.3% accuracy for both positive and negative subclasses, respectively, for emotion prediction using the LSTM mode
International Journal of Innovative Research in Engineering and Management (IJIREM), 2024
The main constituents of the construction industry usually involve building materials which inclu... more The main constituents of the construction industry usually involve building materials which include cement, bricks, steel, fine and coarse aggregates. The majority of concrete production worldwide incorporates sand as a fine aggregate. Since, all of these materials are obtained from finite natural resources, their ongoing depletion will have a negative impact on the environment. In order to minimize environmental harm and preserve natural resources, this study focuses on the recycling of waste materials through the use of waste marble powder (WMP) as a substitute for fine aggregate (sand) in concrete. The sawing and cutting waste generated by the marble industry is substantial and has a negative impact on the environment. Our research concluded that by replacing sand with waste marble powder, mechanical and durability properties of concrete are enhanced thus, making it more resilient and sustainable. WMP was incorporated to concrete in different amounts. For this investigation sand was replaced in four proportions (10%, 20%, 30%, 40% by weight). The grade of concrete is M30, water-to-cement ratio was maintained at 0.45 and 0.28% superplasticizer was added to cement by weight to increase the workability. The experiments employed both destructive testing (DT) and non-destructive testing (NDT). Following a 28-day curing period, at a 40% replacement ratio, the results of the tests for compressive strength, split tensile strength and flexural strength demonstrated improvements of 17.5%, 27% and 35%, respectively. The maximum value of the rebound hammer test exhibited an increment of 15%; the ultrasonic pulse velocity (UPV) indicated an improvement of 3% and the rate of water absorption declined to 45%, 40% and 36% for cubes, cylinders and beams respectively.
International Journal of Innovative Research in Engineering and Management (IJIREM), 2024
This study looks into the effects of social media involvement, market flexibility, and business m... more This study looks into the effects of social media involvement, market flexibility, and business mentoring on Indonesian entrepreneurship growth. Data were gathered from a sample of 250 entrepreneurs utilizing a quantitative research approach, and Structural Equation Modelling-Partial Least Squares (SEM-PLS) with the PLS-3 software were used for analysis. The findings show a strong correlation between the three variables and the expansion of entrepreneurship. Business mentoring improves business performance and offers crucial tools and direction. Entrepreneurs who possess market adaptability can react to shifting market conditions with effectiveness, and social media participation promotes client contact and brand building. These results highlight how important these elements are to Indonesian entrepreneurship success. To encourage entrepreneurial development, the study highlights the importance of social media engagement tactics, adaptation training, and mentoring programs. These insights are particularly useful for politicians and business support organizations.
International Journal of Innovative Research in Engineering and Management (IJIREM), 2024
The diversity of energy production is vital for India's sustainable development. It enhances ener... more The diversity of energy production is vital for India's sustainable development. It enhances energy security, economic stability, environmental sustainability and resilience to climate change. By investing in a variety of energy sources and technologies, India can ensure a reliable, affordable, and sustainable energy supply for its growing population and economy. India is mostly dependent upon coal for its energy production and consumption. When we consider only primary energy sources, India produced 79.6 % of energy from coal and consumed 52.89 % of energy from coal in 2020. In this paper we have studied about the energy diversity of India based on primary energy sources using data for 53 years (from 1970 to 2022). We have used the four types of primary energy sources i.e., coal, lignite, crude oil and natural gas. We have used different indices [Shannon-Weiner Index (SWI), Herfindahl-Hirschman Index (HHI), Weighted_SWI (D_SWI) and Weighted_HHI (D_HHI)] to find the Energy Diversity Index of India. From the result, we have found that energy diversity of India is very low due to high dependency upon coal and also the index is falling which is of concern in regard to energy security of India.
International Journal of Innovative Research in Engineering and Management (IJIREM), 2024
This study leverages advanced numerical analysis techniques to model the intricate interactions a... more This study leverages advanced numerical analysis techniques to model the intricate interactions among soil, foundation, and structures alongside the nonlinear load-deformation characteristics of soils in three-dimensional environments. This research underscores mat foundation calculations' enhanced realism and cost-efficiency, offering improvements over traditional methodologies. The objective is to explore the effects of different superstructure loading types on mat foundation behaviour using three-dimensional finite element models. Thirty-six distinct models were developed to examine the influence of superstructure loads, which were varied across uniformly distributed loads, column loads, and comprehensive building models. Each mat foundation was analysed using separate models incorporating 3D volume elements, utilizing the Mohr-Coulomb material model to represent soil conditions as either 'normally loaded' or 'over-consolidated.’ The study presents detailed findings on total and differential settlements and internal forces, illustrated through figures and graphs, based on data from an existing ten-story building. Regarding bending moments, the most notable difference among loading types lies in the varied locations of maximum moments, independent of shear wall placement, particularly evident when simulating uniformly distributed loads. It is evident that the type of superstructure loading influences settlement patterns and internal forces, underscoring the necessity of considering this factor in analyses. Our findings reveal a significant disparity in construction costs between buildings erected on different soil classes, particularly between ZA and ZB. Structures situated on ZB-classified soil incur expenses approximately 1.5% higher than those on ZA-classified soil.
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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We suggest an emotive categorization of a large number of tweets in this paper. Here, we categorize an expression's sentiments into positive and negative emotions using deep learning approaches. Motivation, fun, happiness, affection, neutral, relief, and surprise are other categories for positive feelings, while anger, boredom, emptiness, hatred, sadness, and worry are categories for negative emotions. We demonstrated how to attain high emotion classification accuracy by experimenting with and evaluating the approach using recurrent neural networks and long-term short-term memory on three distinct datasets. Based on a comprehensive evaluation, the system achieves 88.47% accuracy for positive/negative classifications and 89.3% and 93.3% accuracy for both positive and negative subclasses, respectively, for emotion prediction using the LSTM mode
We suggest an emotive categorization of a large number of tweets in this paper. Here, we categorize an expression's sentiments into positive and negative emotions using deep learning approaches. Motivation, fun, happiness, affection, neutral, relief, and surprise are other categories for positive feelings, while anger, boredom, emptiness, hatred, sadness, and worry are categories for negative emotions. We demonstrated how to attain high emotion classification accuracy by experimenting with and evaluating the approach using recurrent neural networks and long-term short-term memory on three distinct datasets. Based on a comprehensive evaluation, the system achieves 88.47% accuracy for positive/negative classifications and 89.3% and 93.3% accuracy for both positive and negative subclasses, respectively, for emotion prediction using the LSTM mode