IRJEAS

INTERNATIONAL RESEARCH JOURNAL OF ENGINEERING & APPLIED SCIENCES

ISSN 2322-0821(E), ISSN 2394-9910(P)

An ISO 9001:2015 Certified Publication

Volume 12 Issue 1                        January- March 2024

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Data Warehousing Revolution: AI-driven Solutions

DOI: https://doi.org/10.55083/irjeas.2024.v12i01001

Country- USA

  Shubhodip Sasmal

PAPER ID: IRJEAS04V12I1001

Published: January 2024

Journal: IRJEAS, Volume 12, Issue 1

Pages: 01-06

Keywords: Data Warehousing, Artificial Intelligence, Revolution, Data Management, Machine Learning, Analytics, Business Intelligence, Data Integration, Automation, Scalability, Advanced Analytics, Cognitive Computing.

Abstract:

The contemporary landscape of data warehousing is undergoing a revolutionary transformation propelled by the integration of Artificial Intelligence (AI). This paper explores the intersection of AI and data warehousing, unraveling the dynamics that fuel this revolution and examining the profound implications for businesses and data management practices.

The traditional paradigm of data warehousing has relied on structured data and predefined schemas, limiting its adaptability to the dynamic nature of modern datasets. The advent of AI injects a new dimension, enabling data warehouses to evolve into intelligent, adaptive entities capable of handling diverse data types, volumes, and velocities. This abstract encapsulates the essence of the research, delving into key themes that define the AI-driven revolution in data warehousing.

The paper begins by surveying the historical trajectory of data warehousing, highlighting the challenges posed by the increasing complexity and heterogeneity of contemporary data sources. As businesses grapple with unstructured data, streaming data, and the need for real-time insights, the limitations of traditional data warehousing architectures become apparent.

The introduction of AI-driven solutions revolutionizes data warehousing in several dimensions. Machine learning algorithms are harnessed for automating data integration, cleansing, and transformation processes, mitigating the manual labor associated with traditional ETL (Extract, Transform, Load) methods. Deep learning techniques, such as neural networks, unlock the potential to uncover complex patterns within massive datasets, enhancing predictive analytics and decision support capabilities.

Moreover, the abstract explores the role of AI in enabling self-optimizing data warehouses. Adaptive query optimization, automated indexing, and real-time performance tuning emerge as pivotal components, ensuring that data warehouses evolve in response to changing workloads and user patterns.

Ethical considerations and responsible AI practices within the context of data warehousing are also addressed. The abstract concludes by underlining the transformative impact of AI-driven solutions on the efficiency, agility, and strategic value of data warehousing, offering a glimpse into the future where intelligent data warehouses play a central role in shaping data-driven enterprises.

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Shubhodip Sasmal. Data Warehousing Revolution: AI-driven Solutions. International Research Journal of Engineering & Applied Sciences (IRJEAS). 12(1), pp. 01-06, 2024. 10.55083/irjeas.2024.v12i01001

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Improved Model for Age and Gender Recognition Using deep Learning Techniques

DOI: https://doi.org/10.55083/irjeas.2024.v12i01002

Country- INDIA

Poonam Parihar, Mr.Vaibhav Patel, Mr.Anurag Shrivastava

PAPER ID: IRJEAS04V12I1002

Published: January 2024

Journal: IRJEAS, Volume 12, Issue 1

Pages: 07-12

Keywords: CNN, Gender Classification, Face Detection, Machine Learning, Deep Learning, Convolutional neural network (CNN), Pre-processing, Feature Selection.

Abstract:

Age and gender recognition using deep learning techniques has gained significant attention due to its potential applications in various domains, such as human-computer interaction, surveillance systems, and personalized marketing. With the rapid usage of Artificial Intelligence (AI) based systems in different fields, we expect that such decision making capability of these systems match as much as to the human capability. Researchers continue to examine different methods to enhance its performance. Due to superior performance of Convolutional Neural Networks (CNNs) based approaches have been widely used in recent years for facial analysis. The proposed work employing a comprehensive dataset, the model exhibits superior accuracy in predicting both age and gender attributes. This research work delves into the intricate relationship between facial features and age-gender classification, exploiting convolutional neural networks (CNNs) to capture intricate patterns in facial images. Two-level CNN architecture includes feature extraction and classification itself. The feature extraction process extracts a feature corresponding to age and gender, and the classification process classifies the face images according to age and gender. The Proposed method achieved better result comparison to others model. UTK Face dataset is used for the experimental purpose

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Poonam Parihar, et.al. Improved Model for Age and Gender Recognition Using deep Learning Techniques. International Research Journal of Engineering & Applied Sciences (IRJEAS). 12(1), pp. 16-21, 2024. 10.55083/irjeas.2024.v12i01002

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Predictive Analytics in Data Engineering: An AI Approach

DOI: https://doi.org/10.55083/irjeas.2024.v12i01003

Country- USA

  Shubhodip Sasmal

PAPER ID: IRJEAS04V12I1003

Published: January 2024

Journal: IRJEAS, Volume 12, Issue 1

Pages: 13-18

Keywords: Predictive Analytics, Data Engineering, Artificial Intelligence, Machine Learning, Model Training, Feature Engineering, Data Integration, Automation, Decision Support Systems, Real-time Processing, Advanced Analytics, Scalability, Data Transformation.

Abstract:

Predictive analytics, as a cornerstone of data engineering, has witnessed a paradigm shift with the integration of Artificial Intelligence (AI) methodologies. This abstract provides an overview of the key themes explored in the paper titled “Predictive Analytics in Data Engineering: An AI Approach.”
The paper delves into the transformative impact of AI on predictive analytics within the domain of data engineering. Traditional predictive analytics often relied on statistical models and historical data patterns to forecast future trends. The advent of AI technologies, particularly machine learning and deep learning, has revolutionized the predictive analytics landscape by enabling systems to autonomously learn and adapt from data.
A central focus of the paper is the exploration of advanced AI algorithms in predictive analytics. Machine learning models, such as regression, decision trees, and ensemble methods, are examined for their efficacy in predictive modeling tasks. Additionally, the integration of deep learning architectures, known for their ability to capture intricate patterns in large datasets, is explored for enhancing predictive accuracy. The convergence of predictive analytics and AI introduces a dynamic dimension to data engineering workflows. The paper outlines how AI-driven predictive analytics not only enhances the accuracy of predictions but also automates feature extraction, identifies complex patterns, and adapts to evolving data structures. The synergy between AI and predictive analytics empowers data engineers to navigate the challenges posed by big data and unstructured datasets.
Ethical considerations and interpretability in AI-driven predictive analytics are also scrutinized in the paper. As AI models become increasingly complex, ensuring transparency in decision-making processes and addressing biases are crucial for responsible deployment in real-world scenarios.
The findings presented in this paper contribute to the evolving discourse on the integration of AI in predictive analytics within the realm of data engineering. By examining the practical implications, challenges, and ethical dimensions, the paper provides valuable insights for practitioners, researchers, and organizations aiming to harness the full potential of AI in predictive analytics to drive informed decision-making and innovation in data engineering workflows.

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Shubhodip Sasmal. Predictive Analytics in Data Engineering. International Research Journal of Engineering & Applied Sciences (IRJEAS). 12(1), pp. 13-18, 2024. 10.55083/irjeas.2024.v12i01004.

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A Novel Method for Inducing Clay Turbidity into Larval Walleye Rearing Tanks

DOI: https://doi.org/10.55083/irjeas.2024.v12i01004

Country- USA

Elizabeth A. Kastl, Brian Fletcher, Cody Treft, Jill M. Voorhees, Michael E. Barnes

PAPER ID: IRJEAS04V12I1004

Published: January 2024

Journal: IRJEAS, Volume 12, Issue 1

Pages: 19-24

Keywords: Larviculture; Turbidity; Walleye.

Abstract:

Clay is frequently used to create the constant turbidity required to successfully rear larval walleye Sander vitreus in Recirculating Aquaculture Systems (RAS). This paper describes a novel clay-introduction apparatus. An Ace Roto-Mold tank contained a slurry of Kentucky Style Ball clay and water at a ratio of approximately 87 g clay/L water. A 4.5 L/min diaphragm sprayer pump (recirculating pump) maintained the consistency of the slurry by bringing the clay mixture from the bottom to the top of the tank as needed, and either ran continuously or for 30 seconds (duration) every 180 seconds (interval), depending on the consistency of the slurry. A second 4.5 L/min diaphragm sprayer pump (dosing pump) injected the slurry into the RAS system for three seconds (duration) every 330 seconds (interval). Both pumps used clear polyvinyl chloride tubing (310 kPa; 9.525 mm I.D., 12.7 mm O.D.) for slurry transport. To maintain the desired 50 NTU (nephelometric turbidity units), the clay-to-water ratio was slightly adjusted based on actual turbidity levels in the system. This apparatus greatly improved the consistency of turbidity levels and reduced labor compared to the peristaltic pumps typically used to create and maintain turbidity during intensive larval walleye rearing. In addition, the materials required to fabricate this apparatus can be procured for considerably less than the price of a typical peristaltic pump used at production fish hatcheries..

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Elizabeth A. Kastl et. al. A Novel Method for Inducing Clay Turbidity into Larval Walleye Rearing Tanks. International Research Journal of Engineering & Applied Sciences (IRJEAS). 12(1), pp. 19-24, 2024. https://doi.org/10.55083/irjeas.2024.v12i01004

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Implementation of Green Supply Chain Management Practices

DOI: https://doi.org/10.55083/irjeas.2024.v12i01005

Country- INDIA

Mansi Kulkarni, Sharad Sharma

PAPER ID: IRJEAS04V12I1005

Published: February 2024

Journal: IRJEAS, Volume 12, Issue 1

Pages: 25-33

Keywords: Supply Chain Management, GSCM, Barriers, Sustainable Practices.

Abstract:

This research investigates the barriers to effective Green Supply Chain Management (GSCM) practices, focusing on the critical roles of environmental awareness, financial constraints, and regulatory compliance in organizations. It utilizes a mixed-methods approach, combining quantitative data from structured questionnaires with qualitative insights from expert interviews and case studies. The study begins with a comprehensive literature review to frame the hypotheses, followed by empirical data collection to evaluate these hypotheses. The findings are analyzed using statistical methods, including hypothesis testing and regression analysis, and are further enriched with expert opinions. Cronbach’s Alpha values are used to assess the reliability of measurement instruments for various factors, including Environmental Awareness, Financial Constraints, and Regulatory Compliance. The research culminates in presenting actionable strategies and recommendations for overcoming identified barriers, thereby promoting the adoption of GSCM practices in industries, particularly in the context of organizational culture and regulatory environments. The research aims to provide actionable recommendations based on validated hypotheses and case study findings to enhance the adoption of GSCM practices in underdeveloped nations.

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Mansi Kulkarni et.al. Implementation of Green Supply Chain Management Practices: International Research Journal of Engineering & Applied Sciences (IRJEAS). 12(1), pp. 25-33, 2024. http://doi.org/10.55083/irjeas.2024.v12i01005.

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Review of Artificial Intelligence Techniques for Image Dehazing

DOI: https://doi.org/10.55083/irjeas.2024.v12i01007

Country- INDIA

Aashish Tiwari, Nitesh Gupta

PAPER ID: IRJEAS04V12I1007

Published: February 2024

Journal: IRJEAS, Volume 12, Issue 1

Pages: 34-38

Keywords: Artificial Intelligence, Dehazing, Image, Blur.

Abstract:

This review explores the application of artificial intelligence (AI) techniques for image dehazing, addressing the pervasive challenge of enhancing image quality in hazy or foggy conditions. Traditional dehazing methods and their role as a foundation for AI-based approaches are discussed. Deep learning-based methods, including single-image and multi-image dehazing, are examined, highlighting their strengths and limitations. Data-driven approaches, leveraging large-scale datasets and domain adaptation, are also investigated. Furthermore, the review outlines the challenges in real-time processing, robustness, explain ability, and real-world deployment of AI-based dehazing solutions. As AI technology advances, it is expected that image dehazing will find practical applications across various domains, making it essential to overcome the existing challenges to fully unlock its potential.

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Aashish tiwari, et. al Review of Artificial Intelligence Techniques for Image Dehazing. International Research Journal of Engineering & Applied Sciences (IRJEAS). 12(1), pp. 34-38, 2024. http://doi.org/10.55083/irjeas.2024.v12i01007.

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A Multi Model Classifier for Fake News Detection Using Machine learning Technique

DOI: https://doi.org/10.55083/irjeas.2024.v12i01008

Country- INDIA

Priyanka Ahirwar, Vaibhav Patel, Anurag Shrivastava

PAPER ID: IRJEAS04V12I1008

Published: February 2024

Journal: IRJEAS, Volume 12, Issue 1

Pages: 39-46

Keywords: Multimodal Fake News Detection; Natural Language processing; deep learning; BERT

Abstract:

The proliferation of fake news poses an increasingly serious threat to information integrity in the digital age, undermining trust and distorting public discourse. As a consequence, our society becomes more vulnerable to the harmful effects of misinformation and disinformation spread across various platforms. Consequently, developing robust tools for the automatic detection of fake news is imperative to mitigate its detrimental impact. While many existing methods focus solely on textual information for detection and classification, our study introduces a novel architecture for fake news classification using a multimodal approach, integrating both text and image data. Specifically, we conducted experiments on the Fakeddit dataset, revealing that our multimodal approach, employing a Convolutional Neural Network (CNN) architecture, outperforms single-modal methods. Achieving an impressive accuracy of 90%, this approach demonstrates the efficacy of combining textual and visual cues in fake news detection. In comparison, single-modal approaches relying solely on text, such as Bidirectional Encoder Representations from Transformers (BERT), achieved an accuracy of 81%. These findings underscore the significant improvement in performance gained by leveraging both text and image data, highlighting the potential of multimodal approaches in enhancing fake news detection systems.

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Priyanka Ahirwar, et. al . A Multi Model Classifier for Fake News Detection Using Machine learning Technique. International Research Journal of Engineering & Applied Sciences (IRJEAS). 12(1), pp. 39-46, 2024. http://doi.org/10.55083/irjeas.2024.v12i01008.

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Improved Model for Stock Price Trend Prediction Using Convolution Neural Network

DOI: https://doi.org/10.55083/irjeas.2024.v12i01010

Country- INDIA

Gopal Kumar, Vaibhav Patel, Anurag Shrivastava

PAPER ID: IRJEAS04V12I1010

Published: February 2024

Journal: IRJEAS, Volume 12, Issue 1

Pages: 55-61

Keywords: CNN, Deep learning, Neural Network, Stock Price Prediction.

Abstract:

Investing in the stock market continues to attract widespread attention, offering the potential for substantial returns alongside inherent risks. Given this dynamic landscape, the quest for intelligent stock prediction models remains a paramount concern. This study presents a pioneering approach to forecasting stock price trends by introducing an Enhanced Model leveraging Convolutional Neural Networks (CNNs). Through the utilization of CNNs’ innate ability to capture spatial dependencies within sequential data, the proposed model demonstrates a notable enhancement in both accuracy and efficiency in predicting stock price trends. Central to the model’s efficacy is the integration of feature extraction mechanisms within the CNN architecture, optimizing its capacity to discern intricate patterns within financial time series data. Furthermore, the framework facilitates the identification of the most effective model by considering pertinent inputs while also accommodating the stock’s price sensitivity to diverse market conditions. By forecasting the movement of stock prices for the following day, the proposed framework empowers investors to make well-informed buy-sell decisions, achieving an impressive accuracy rate of approximately 75%.

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Gopal Kumar, et. al . Improved Model for Stock Price Trend Prediction Using Convolution Neural Network International Research Journal of Engineering & Applied Sciences (IRJEAS). 12(1), pp. 55-61, 2024. https://doi.org/10.55083/irjeas.2024.v12i01010.