IRJEAS

INTERNATIONAL RESEARCH JOURNAL OF ENGINEERING & APPLIED SCIENCES

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

An ISO 9001:2015 Certified Publication

Volume 11 Issue 4                        October-December 2023

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Fake News Detection Using Machine learning Technique : A Review

DOI: https://doi.org/10.55083/irjeas.2023.v11i04001

Country- INDIA

Priya Ahirwar, Vaibhav Patel, Anurag Shrivastava

PAPER ID: IRJEAS04V11I4001

Published: October 2023

Journal: IRJEAS, Volume 11, Issue 4

Pages: 01-07

Keywords: CNN, Deep Learning, Face Recognition, Face Detection, Gender Classification, ML/ Machine Learning, Recurrent neural network (RNN), 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. This paper presents a comprehensive review of the existing literature on age and gender recognition using deep learning techniques. The review encompasses different approaches, including Convolutional neural networks (CNNs), recurrent neural networks (RNNs), and hybrid models. Various datasets used for training and evaluation are discussed, along with their strengths and limitations. Furthermore, the paper analyzes the performance metrics commonly employed for evaluating age and gender recognition systems, such as accuracy, precision, recall, and F1 score. Challenges and open research directions in this field are also identified, including improving robustness to variations in lighting conditions, facial expressions, and occlusions. The insights gained from this review contribute to the understanding of state-of-the-art techniques and pave the way for future advancements in age and gender recognition using deep learning.

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Priya Ahirwar et.al. “Fake News Detection Using Machine learning Technique : A Review.” International Research Journal of Engineering & Applied Sciences (IRJEAS). 11(4), pp.  01-07, 2023. https://doi.org/10.55083/irjeas.2023.v11i04001

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A Review on Age and Gender Recognition using Deep learning Techniques

DOI: https://doi.org/10.55083/irjeas.2023.v11i04002

Country- INDIA

Poonam Parihar, Vaibhav Patel, Anurag Shrivastava

PAPER ID: IRJEAS04V11I4002

Published: October 2023

Journal: IRJEAS, Volume 11, Issue 4

Pages: 08-12

Keywords: Machine learning, NLP, fake news classification, social networks

Abstract:

The proliferation of fake news has become a significant challenge in the digital era, threatening the credibility of information shared online. To combat this menace, researchers have turned to machine learning techniques for automated detection. This paper presents a comprehensive review of various machine learning approaches employed for fake news detection. We analyze a wide range of methodologies, including supervised, unsupervised, and deep learning algorithms, discussing their strengths and limitations. Moreover, we examine the datasets and evaluation metrics commonly used in this domain. By synthesizing existing research, we identify key trends and promising directions for future investigations. The review aims to provide a comprehensive understanding of the state-of-the-art in fake news detection using machine learning, fostering advancements in this critical field of research.

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Poonam Parihar et.al. “A Review on Age and Gender Recognition using Deep learning Techniques.” International Research Journal of Engineering & Applied Sciences (IRJEAS). 11(4), pp.  08-12, 2023. https://doi.org/10.55083/irjeas.2023.v11i04002

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Smart Data Lakes: AI Innovations in Data Engineering

DOI: https://doi.org/10.55083/irjeas.2023.v11i04003

Country- USA

Shubhodip Sasmal

PAPER ID: IRJEAS04V11I4003

Published: October 2023

Journal: IRJEAS, Volume 11, Issue 4

Pages: 13-19

Keywords: Smart Data Lakes, Artificial Intelligence, Data Engineering, Data Lakes, Machine Learning, Advanced Analytics, Cognitive Computing, Automation, Data Integration, Innovations, Scalability, Data Management, Data Processing, Predictive Analytics, Analytics Integration.

Abstract:

The evolution of data engineering is undergoing a paradigm shift with the advent of Smart Data Lakes, where Artificial Intelligence (AI) plays a pivotal role in reshaping traditional data management approaches. This research explores the integration of AI innovations in the context of Smart Data Lakes, focusing on their transformative impact on data engineering practices. The abstract provides a concise overview of key themes, methodologies, and findings within this burgeoning field.
Smart Data Lakes represent an intelligent and adaptive approach to managing vast and diverse datasets. Unlike conventional data lakes, Smart Data Lakes leverage advanced AI techniques to automate and optimize various aspects of data engineering processes. The abstract delves into the fundamental principles guiding the integration of AI into Smart Data Lakes and its implications for data engineering workflows.
The research methodology encompasses an in-depth literature review, conceptual framework development, and practical implementations to validate the effectiveness of AI innovations in enhancing data engineering capabilities. Key AI technologies, including Machine Learning algorithms, Natural Language Processing, and predictive analytics, are harnessed to enable Smart Data Lakes to autonomously organize, analyze, and derive actionable insights from complex datasets.
The findings highlight the substantial advantages of Smart Data Lakes infused with AI. These include improved data discovery, enhanced data quality through automated cleansing, and the ability to uncover latent patterns and correlations within the data. The abstract outlines specific use cases and real-world applications where AI-driven innovations in Smart Data Lakes are proving instrumental in addressing the challenges posed by the exponential growth of data.
Moreover, ethical considerations in the implementation of AI in data engineering are explored, emphasizing the need for responsible and transparent practices. The abstract concludes by outlining the broader implications of Smart Data Lakes with AI innovations, positioning them as a cornerstone in the evolution of data engineering paradigms.
In summary, this research sheds light on the synergies between Smart Data Lakes and AI, providing insights into the transformative potential of these technologies in revolutionizing data engineering. The abstract serves as a preview to the comprehensive exploration of methodologies, findings, and implications detailed in the full research paper, offering valuable contributions to the rapidly advancing field of Smart Data Lakes and AI-driven data engineering.

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Shubhodip Sasmal. Smart Data Lakes: International Research Journal of Engineering & Applied Sciences (IRJEAS). 11(3), pp. 13-19, 2023. 10.55083/irjeas.2023.v11i04003

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Machine Learning Based Customer Churn Prediction Using Improved Feature Selection Techniques

DOI: https://doi.org/10.55083/irjeas.2023.v11i04005

Country- INDIA

  Dr. Manish Kumar Sharma, Vaibhav Patel, Anurag Shrivastava

PAPER ID: IRJEAS04V11I4005

Published: October 2023

Journal: IRJEAS, Volume 11, Issue 4

Pages: 26-36

Keywords: Machine Learning, Customer churn prediction, Churn in telecom, Feature selection, Classification.

Abstract:

In the contemporary, fiercely competitive business environment, the retention of customers holds utmost significance for the continued growth and profitability of companies. Customer churn, which entails the defection of customers to rival services, stands out as a critical and prevalent challenge in a variety of sectors, spanning from telecommunications to e-commerce. This study delves into the realm of customer churn prediction, presenting an innovative approach that leverages machine learning and advanced feature selection techniques. The research utilizes a comprehensive dataset encompassing customer attributes, historical behavior, and churn labels. Employing state-of-the-art machine learning algorithms, including decision trees, random forests, and support vector machines, a predictive model is developed to identify potential churners accurately. The distinctive aspect of this study lies in its feature selection methodology, which integrates recursive feature elimination (RFE) with feature importance ranking. This hybrid approach refines the feature set, eliminating irrelevant or redundant variables, thereby enhancing model efficiency and interpretability. Empirical results showcase the effectiveness of the enhanced feature selection approach in improving predictive accuracy and model performance.

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Manish Kumar Sharma et.al. Machine Learning Based Customer Churn Prediction Using Improved Feature Selection Techniques. International Research Journal of Engineering & Applied Sciences (IRJEAS). 11(4), pp.  26-36, 2023. https://doi.org/10.55083/irjeas.2023.v11i04005

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A Systematic Review on Recent Advancement in Electric Vehicle Technologies

DOI: https://doi.org/10.55083/irjeas.2023.v11i04006

Country- INDIA

Shailendra Kumar Mittal, Pragati Korde, Shenbagalakshmi Palaniraja,
Nikita Omase, Pabitra Guchhait, Prateek Mundra

PAPER ID: IRJEAS04V11I4006

Published: November 2023

Journal: IRJEAS, Volume 11, Issue 4

Pages: 37-44

Keywords: Electric Vehicles, Emerging Technologies, Sustainability, Battery Technology, Environmental Impact

Abstract:

The automotive industry is undergoing a transformative shift towards electric vehicles (EVs) in response to environmental concerns and sustainability imperatives. This paper provides brief information about emerging technologies that are propelling this transition, shaping the future of sustainable transportation. Charging infrastructure developments have made EVs more practical and accessible to consumers. Artificial intelligence is playing a pivotal role in optimizing electric vehicle performance. The adoption of these emerging technologies not only extends the driving range of EVs but also brings about significant environmental benefits. This paper highlights the incredible potential of electric vehicles to revolutionize the automotive industry and address pressing environmental challenges, offering a promising vision of a more sustainable and eco-friendly transportation sector.

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Shailendra Kumar Mittal. A Systematic Review on Recent Advancement in Electric Vehicle Technologies. International Research Journal of Engineering & Applied Sciences (IRJEAS). 11(4), pp.  37-44, 2023.10.55083/irjeas.2023.v11i04006

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Hybrid Energy Generation System with Brushless Generators

DOI: https://doi.org/10.55083/irjeas.2023.v11i04007

Country- INDIA

Aaradhna Soni

PAPER ID: IRJEAS04V11I4007

Published: November 2023

Journal: IRJEAS, Volume 11, Issue 4

Pages: 45-56

Keywords: Micro grid, PMBLDC, Power quality, Voltage regulation, Hybrid Renewable Power Generation Introduction

Abstract:

With the capacity extension of the grid to remote areas. The discovery and usage of interconnected power production using alternative power supply sources has been promoted. This article proposes a fulfillment with the use of Voltage Source Converter (VSC) and brushless generators with a standalone micro grid topology. With numerous Renewable Energy Sources (RES), including solar PV and wind the micro grid device is energized. However, to ensure the system’s stability, a Diesel Generator (DG) collection and a Battery Energy Storage System (BESS) are also used. The topology suggested has the benefit of less few switches and easy power. The structure applied has been fixed as an AC supply by DG. The wind and PV sources are DC sources related to the VSC DC connection. At the DC connection, the BESS is often used to promote the instantaneous equilibrium of power under complex conditions. The VSC further has the potential to alleviate power quality challenges such as harmonics, voltage control and load balancing, in addition to system integration. To illustrate all the capabilities of the proposed method, a good range of Mat lab/Simulink yield outcomes are conferred.

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Aaradhna Soni. Hybrid Energy Generation System with Brushless Generators. International Research Journal of Engineering & Applied Sciences (IRJEAS). 11(4), pp. 45-56, 2023.10.55083/irjeas.2023.v11i04007

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The Role of Ionic Liquids in Green Solvent Chemistry: Properties and Applications

DOI: https://doi.org/10.55083/irjeas.2023.v11i04009

Country- INDIA

Ishank Jhanji

PAPER ID: IRJEAS04V11I4009

Published: November 2023

Journal: IRJEAS, Volume 11, Issue 4

Pages: 57-67

Keywords: Ionic Liquids, Green Solvent Chemistry, Sustainability, Catalysis, and Environmental Advantages

Abstract:

Ionic liquids have gained significant attention in recent years as environmentally friendly and versatile solvents. This research paper delves into the properties and applications of ionic liquids in the context of green solvent chemistry. We explore the unique characteristics of these solvents, including their low vapor pressure, wide liquids range, and tunable properties, which make them an attractive choice for various applications. The paper also highlights the environmental advantages of ionic liquids, such as their negligible volatility, non-toxic nature, and recyclability, all contributing to their role in sustainable chemistry practices. Furthermore, we discuss their diverse applications, ranging from catalysis and extraction to energy storage and materials synthesis. Through an in-depth analysis of recent developments and case studies, this paper aims to provide a comprehensive overview of how ionic liquids are shaping the landscape of green solvent chemistry and contributing to a more sustainable future.

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Ishank Jhanji. Hybrid Energy Generation System with Brushless Generators. International Research Journal of Engineering & Applied Sciences (IRJEAS). 11(4), pp. 57-67, 2023.11.55083/irjeas.2023.v11i04009

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Improved Facial Expression Recognition SystemUsing Convolutional Neural Networks

DOI: https://doi.org/10.55083/irjeas.2023.v11i04010

Country- INDIA

Abhimanyu Kumar, Poornima Dwivedi, Vinod Kumar Yadav

PAPER ID: IRJEAS04V11I4010

Published: December 2023

Journal: IRJEAS, Volume 11, Issue 4

Pages: 68-79

Keywords: Facial Expression Recognition (FER), Pre-processing, Feature Selection

Abstract:

The task of identifying human emotions based on facial expressions is known as facial expression recognition (FER). There is no clear connection between emotions and facial expressions and there is significant variability making facial recognition a challenging research area. In recent years, Machine Learning (ML) and Neural Networks (NNs) have been used for emotion recognition. Facial Expression Detection using Convolutional Neural Networks (CNNs) is a cutting-edge research area that leverages advanced deep learning techniques to discern and analyze facial expressions accurately. In this work, a Convolutional Neural Network (CNN) is used to extract features from images to detect emotions. This work identifies and extracts 64 important landmarks on a face. A CNN model is trained with grayscale images from the FER 2013 dataset to classify expressions into seven emotions, namely happy, sad, neutral, fear, disgust, surprise and angry. To improve the accuracy and avoid overfitting of the model, batch normalization and dropout are used. The best model parameters are determined considering the training results. The test results obtained show that Proposed CNN Model is 84% accurate for three emotions (happy, sad and angry) and 76% accurate for five emotions (happy, sad, angry, neutral, fear).

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Thermal Effects on Photovoltaic Panel Efficiency and Enhanced Cooling Strategies for Optimal Performance

DOI: https://doi.org/10.55083/irjeas.2023.v11i04011

Country- INDIA

Neetu Singh, Dr. V. N. Bartaria

PAPER ID:IRJEAS04V11I4011

Published: December 2023

Journal: IRJEAS, Volume 11, Issue 4

Pages: 80-90

Keywords: Photovoltaic Panel Efficiency, Thermal Effects, Enhanced Cooling Strategies, Optimal Performance

Abstract:

The influence of temperature on the performance of photovoltaic (PV) panels is a critical consideration in harnessing the potential of solar energy technology. This compilation of research papers explores the multifaceted impact of operating temperature on PV systems and the utilization of cooling technologies to enhance their efficiency. The studies consistently emphasize the detrimental effects of elevated temperatures on PV cells and modules and investigate various aspects, including temperature-dependent parameters, real-world environmental conditions, geographical distribution, and mitigation strategies. Accurate modeling and location-specific considerations are underscored as vital for optimizing PV system efficiency. As the world embraces renewable energy and sustainability, the insights presented in these papers offer valuable contributions to advancing solar power technology for a more sustainable future. Furthermore, this collection emphasizes the significance of addressing temperature-related efficiency losses in PV panels and the potential for cooling solutions to improve performance. Various cooling methods, such as air cooling, water cooling, evaporative cooling, and phase change materials, are explored to mitigate the impact of high operating temperatures on PV systems. Integrated systems that combine cleaning and cooling mechanisms to address both soiling and heating issues are proposed. Environmental and economic considerations are pivotal in assessing the feasibility of implementing cooling solutions in different settings. These studies collectively stress the need for effective cooling technologies, especially in regions with extreme temperatures and intense solar radiation, to unlock the full potential of PV systems. Researchers, engineers, and policymakers working in the field of renewable energy will find these findings valuable for advancing the efficient utilization of solar power.

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Real-time Data Processing with Machine Learning Algorithms

DOI: https://doi.org/10.55083/irjeas.2023.v11i04012

Country- USA

  Shubhodip Sasmal

PAPER ID:IRJEAS04V11I4012

Published: December 2023

Journal: IRJEAS, Volume 11, Issue 4

Pages: 91-96

Keywords: Real-time Data Processing, Machine Learning Algorithms, Data Streaming, Predictive Analytics, Stream Processing, Real-time Analytics, Data Engineering, Automation, Model Deployment, Data Integration, Scalability, Advanced Analytics, Feature Engineering.

Abstract:

In the era of information abundance, organizations are faced with the challenge of harnessing real-time data streams to extract valuable insights swiftly. This research paper explores the intersection of real-time data processing and machine learning algorithms, aiming to develop a comprehensive understanding of their integration for efficient decision-making in dynamic environments.
The paper begins by delineating the landscape of real-time data processing, emphasizing the significance of timely and accurate information in contemporary business scenarios. It delves into the challenges posed by the velocity and volume of data generated continuously, necessitating advanced processing mechanisms capable of handling data streams in real-time.
As the focus shifts to machine learning algorithms, the research outlines the diverse range of algorithms suitable for real-time applications. From online learning methods to streaming algorithms, the exploration encompasses techniques tailored to adapt and evolve with incoming data. This section also addresses the trade-offs between accuracy and computational efficiency, crucial considerations in real-time processing environments. The core of the paper lies in the synthesis of real-time data processing and machine learning algorithms. It investigates how machine learning models can be seamlessly integrated into data processing pipelines to analyze and respond to streaming data instantaneously. Case studies and practical implementations exemplify instances where predictive analytics and anomaly detection algorithms contribute to real-time decision support.
Ethical considerations and challenges related to the deployment of machine learning in real-time settings are also examined. The paper advocates for responsible and transparent use of algorithms, emphasizing the importance of mitigating biases and ensuring accountability in decision-making processes driven by machine learning insights. this research paper provides a roadmap for organizations seeking to harness the synergy between real-time data processing and machine learning. The insights gained from this exploration pave the way for advancements in adaptive decision-making systems, offering a competitive edge in industries where rapid response to evolving data is paramount.

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Shubhodip Sasmal. Real-time Data Processing with Machine Learning Algorithms. International Research Journal of Engineering & Applied Sciences (IRJEAS). 11(4), pp. 91-96, 2023. 10.55083/irjeas.2023.v11i04012