ISSN Approved Journal No: E-ISSN 2348-1269, P- ISSN 2349-5138
Journal ESTD Year: 2014
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The INTERNATIONAL JOURNAL OF RESEARCH AND ANALYTICAL REVIEWS (IJRAR) aims to explore advances in research pertaining to applied, theoretical and experimental Technological studies. The goal is to promote scientific information interchange between researchers, developers, engineers, students, and practitioners working in and around the world.
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Subject Category : Multidisciplinary, Monthly scholarly open access journals
Call For Paper (Volume 11 | Issue 4 | Month- December 2024)Paper Title: IMAGE SEGMENTATION USING MACHINE LEARNING
Publisher Journal Name: IJRAR, IJPUBLICATION
DOI Member: 10.6084/m9.doi.one.IJRAR1DUP001 Published Paper URL: http://ijrar.org/viewfull.php?&p_id=IJRAR1DUP001 Published Paper PDF: download.php?file=IJRAR1DUP001 Published Paper PDF: http://www.ijrar.org/papers/IJRAR1DUP001.pdf
Published Paper ID: - IJRAR1DUP001
Register Paper ID - 297774
Title: IMAGE SEGMENTATION USING MACHINE LEARNING
Author Name(s): Mr. Shrinivas Dharma Naik, Mr. Rajesh Naik
Publisher Journal name: IJRAR, IJPUBLICATION
DOI (Digital Object Identifier) :
Pubished in Volume: 11 | Issue: 4 | Year: October 2024
Volume: 11
Issue: 4
Pages: 1-3
Year: October 2024
Subject Area: Science and Technology
Author type: Indian Author
Downloads: 299
In the realm of computer vision, image segmentation plays a crucial role by partitioning complex images into distinct segments or regions. This process enables more profound analysis and understanding of visual data across various applications. Our project focuses on advancing image segmentation through state-of-the-art machine learning techniques. By leveraging deep learning, particularly convolutional neural networks (CNNs) such as U-Net and its variants, our approach aims to achieve highly precise segmentation. Beyond mere pixel classification, our goal is to generate intricate masks that accurately delineate boundaries and structures within each image. This endeavor not only aims for technical excellence but also strives to mimic human-like perception, ensuring our models can handle diverse and nuanced visual information effectively.
Licence: creative commons attribution 4.0
IMAGE SEGMENTATION USING MACHINE LEARNING
Paper Title: APPLICATION OF MACHINE LEARNING ALGORITHMS FOR DETECTION OF INUSTRIAL PRODUCT DEFECTIVE
Publisher Journal Name: IJRAR, IJPUBLICATION
DOI Member: 10.6084/m9.doi.one.IJRAR1DUP002 Published Paper URL: http://ijrar.org/viewfull.php?&p_id=IJRAR1DUP002 Published Paper PDF: download.php?file=IJRAR1DUP002 Published Paper PDF: http://www.ijrar.org/papers/IJRAR1DUP002.pdf
Published Paper ID: - IJRAR1DUP002
Register Paper ID - 297709
Title: APPLICATION OF MACHINE LEARNING ALGORITHMS FOR DETECTION OF INUSTRIAL PRODUCT DEFECTIVE
Author Name(s): Shivashankar, Krishna Prasad K, Manjunath R
Publisher Journal name: IJRAR, IJPUBLICATION
DOI (Digital Object Identifier) :
Pubished in Volume: 11 | Issue: 4 | Year: October 2024
Volume: 11
Issue: 4
Pages: 4-8
Year: October 2024
Subject Area: Science and Technology
Author type: Indian Author
Downloads: 188
This paper presents a high level answer for further developing the quality control process in Printed Circuit Board (PCB) fabricating through the mix of the Consequences be damned (You Just Look Once) object identification calculation. The framework utilizes a transport line, DC engines, and a high-goal camera for continuous distinguishing proof and limitation of imperfections on moving PCBs. The Consequences be damned calculation processes caught pictures, proficiently identifying different imperfections like binding issues and part misalignments. The consistent coordination between the transport line and DC engines empowers exact command over the assessment cycle, improving the speed and precision of imperfection recognition. Following the ID of imperfections, the framework integrates an isolation component to isolate flawed PCBs from the PCBion line. Utilizing the transport line, blemished PCBion are diverted to an assigned region, guaranteeing that main top notch PCBs continue further in the assembling system. This robotized approach limits human mediation, altogether further developing PCBion effectiveness, lessening producing costs, and upgrading the general nature of PCBs. The proposed framework remains as a demonstration of the cooperative energy between state of the art picture handling advances and powerful mechanical parts, offering a far reaching answer for address the difficulties of deformity location and isolation in PCB manufacturing.
Licence: creative commons attribution 4.0
PCB, DC engine, PCBions, Machine Learning, industrial, defect.
Paper Title: The impact of product recommendation on customer purchasing behavior in E-Commerce using Artificial Intelligence
Publisher Journal Name: IJRAR, IJPUBLICATION
DOI Member: 10.6084/m9.doi.one.IJRAR1DUP003 Published Paper URL: http://ijrar.org/viewfull.php?&p_id=IJRAR1DUP003 Published Paper PDF: download.php?file=IJRAR1DUP003 Published Paper PDF: http://www.ijrar.org/papers/IJRAR1DUP003.pdf
Published Paper ID: - IJRAR1DUP003
Register Paper ID - 297708
Title: THE IMPACT OF PRODUCT RECOMMENDATION ON CUSTOMER PURCHASING BEHAVIOR IN E-COMMERCE USING ARTIFICIAL INTELLIGENCE
Author Name(s): Sreedevi V, Dr.Tina Shivnani, Dr.Jampala Maheshchandra Babu, Dr.Jeyakrishnan V
Publisher Journal name: IJRAR, IJPUBLICATION
DOI (Digital Object Identifier) :
Pubished in Volume: 11 | Issue: 4 | Year: October 2024
Volume: 11
Issue: 4
Pages: 9-11
Year: October 2024
Subject Area: Science and Technology
Author type: Indian Author
Downloads: 154
This paper explores that how AI is making significant role in focusing to enhance product recommendations, chat bot and virtual assistance, predictive analysis, sentiment analysis, Automated customer services, voice recognition, visual recognition, customer journey mapping and proactive support by Graph Database. Graph databases, combined with AI, offer powerful capabilities for enhancing e-commerce platforms. Revolutionizing customer experience with AI is a trendsetter for business will judge the engagement, refine processes and personalize shopping experience. The result shows a positive relationship between retailers and customers satisfaction to enhance the business through AI.
Licence: creative commons attribution 4.0
E-Commerce, recommendation system, customer satisfaction.
Paper Title: BRIGHTNESS AND VOLUME CONTROL USING HAND GESTURE WITH OPENCV
Publisher Journal Name: IJRAR, IJPUBLICATION
DOI Member: 10.6084/m9.doi.one.IJRAR1DUP004 Published Paper URL: http://ijrar.org/viewfull.php?&p_id=IJRAR1DUP004 Published Paper PDF: download.php?file=IJRAR1DUP004 Published Paper PDF: http://www.ijrar.org/papers/IJRAR1DUP004.pdf
Published Paper ID: - IJRAR1DUP004
Register Paper ID - 297707
Title: BRIGHTNESS AND VOLUME CONTROL USING HAND GESTURE WITH OPENCV
Author Name(s): Muhammed Shabin Sadique, Sanu Eldose, Shreyas Shashi A, Suryadev TK, Jithendra PR, Sudheesh KP
Publisher Journal name: IJRAR, IJPUBLICATION
DOI (Digital Object Identifier) :
Pubished in Volume: 11 | Issue: 4 | Year: October 2024
Volume: 11
Issue: 4
Pages: 12-18
Year: October 2024
Subject Area: Science and Technology
Author type: Indian Author
Downloads: 121
This paper presents an approach to control brightness and volume using hand gestures, leveraging OpenCV and machine learning techniques. By detecting and interpreting hand gestures through a webcam, the system adjusts the screen brightness or system volume accordingly. This gesture-based control mechanism offers an intuitive and contactless way to interact with devices, enhancing user convenience in various scenarios.
Licence: creative commons attribution 4.0
Hand Gesture Recognition, Brightness Control, Volume Control, OpenCV, Computer Vision.
Paper Title: REVIEW OF VARIOUS OPTIMIZATION TECHNIQUES IN MANET
Publisher Journal Name: IJRAR, IJPUBLICATION
DOI Member: 10.6084/m9.doi.one.IJRAR1DUP005 Published Paper URL: http://ijrar.org/viewfull.php?&p_id=IJRAR1DUP005 Published Paper PDF: download.php?file=IJRAR1DUP005 Published Paper PDF: http://www.ijrar.org/papers/IJRAR1DUP005.pdf
Published Paper ID: - IJRAR1DUP005
Register Paper ID - 297702
Title: REVIEW OF VARIOUS OPTIMIZATION TECHNIQUES IN MANET
Author Name(s): Shyamily P V, Dr.Anoop B K
Publisher Journal name: IJRAR, IJPUBLICATION
DOI (Digital Object Identifier) :
Pubished in Volume: 11 | Issue: 4 | Year: October 2024
Volume: 11
Issue: 4
Pages: 19-22
Year: October 2024
Subject Area: Science and Technology
Author type: Indian Author
Downloads: 91
A Mobile Ad hoc Network (MANET) is characterized as an independent, self-organizing, and infrastructure-free system where multiple mobile nodes are interconnected through wireless links. Within MANETs, several routing protocols exist, including AODV, DSDV, TORA, and DSR, among others. The dynamic topology and lack of infrastructure in MANETs present various challenges and limitations that can impact network performance, such as mobility, overhead, battery depletion, latency, and interference. To address these challenges, various optimization techniques can be employed to identify the most effective solutions. Nature-inspired algorithms, which are metaheuristic approaches that emulate natural processes, have emerged as a promising avenue for solving optimization problems in computational contexts.
Licence: creative commons attribution 4.0
Routing, MANET, Optimization Algorithms, Bio Inspired Algorithms
Paper Title: "Beyond Words": Exploring Sign Language Through Technology
Publisher Journal Name: IJRAR, IJPUBLICATION
DOI Member: 10.6084/m9.doi.one.IJRAR1DUP006 Published Paper URL: http://ijrar.org/viewfull.php?&p_id=IJRAR1DUP006 Published Paper PDF: download.php?file=IJRAR1DUP006 Published Paper PDF: http://www.ijrar.org/papers/IJRAR1DUP006.pdf
Published Paper ID: - IJRAR1DUP006
Register Paper ID - 297476
Title: "BEYOND WORDS": EXPLORING SIGN LANGUAGE THROUGH TECHNOLOGY
Author Name(s): Dhruthi Shetty, Ashwija, Kavanashree, Nagaraja Hebbara N, Lerina Dcruz
Publisher Journal name: IJRAR, IJPUBLICATION
DOI (Digital Object Identifier) :
Pubished in Volume: 11 | Issue: 4 | Year: October 2024
Volume: 11
Issue: 4
Pages: 23-27
Year: October 2024
Subject Area: Science and Technology
Author type: Indian Author
Downloads: 103
In an era where communication transcends spoken and written language, the need for inclusive and accessible means of interaction is paramount. This project, "Beyond Words: Exploring Sign Language Through Technology," leverages the power of Convolutional Neural Networks (CNN) to bridge the communication gap for the deaf and hard-of-hearing community. Our system aims to convert sign language gestures into readable text, thus facilitating seamless communication between sign language users and those unfamiliar with it. The core of our solution lies in the deployment of a CNN model, which is adept at recognizing patterns in images. This process involves several stages: video capture and preprocessing using OpenCV, gesture recognition through the CNN model developed with TensorFlow/Keras, and the display of text output via a user-friendly web interface. This project serves as a testament to the capabilities of modern AI in addressing societal needs. By breaking down the barriers of language, we pave the way for a more inclusive and connected world.
Licence: creative commons attribution 4.0
Deep Learning, CNN, OpenCV, AI
Paper Title: A Deep Learning Approach for Quality Assessment of Betel Nuts
Publisher Journal Name: IJRAR, IJPUBLICATION
DOI Member: 10.6084/m9.doi.one.IJRAR1DUP007 Published Paper URL: http://ijrar.org/viewfull.php?&p_id=IJRAR1DUP007 Published Paper PDF: download.php?file=IJRAR1DUP007 Published Paper PDF: http://www.ijrar.org/papers/IJRAR1DUP007.pdf
Published Paper ID: - IJRAR1DUP007
Register Paper ID - 297475
Title: A DEEP LEARNING APPROACH FOR QUALITY ASSESSMENT OF BETEL NUTS
Author Name(s): Mr. .K N Kanva Patel, Prof. Kiran, Mr.Gagan K Sanil, Mr.Pavan H H, Mr.Shivayogi D N, Mr.Deepak K
Publisher Journal name: IJRAR, IJPUBLICATION
DOI (Digital Object Identifier) :
Pubished in Volume: 11 | Issue: 4 | Year: October 2024
Volume: 11
Issue: 4
Pages: 28-31
Year: October 2024
Subject Area: Science and Technology
Author type: Indian Author
Downloads: 113
This study provides an improved betel nut quality assessment through the use of deep learning techniques. The demand for higher-quality agricultural products is rising, and the traditional methods of assessing quality have proven to be time-consuming and highly subjective. This employed a precise and a vast collection of betel nut photographs, pre-process them, feed them into the VGGNet model as part of our methodology. Size, color, and texture are just a few of the quality factors that the model is trained to identify betel nuts.
Licence: creative commons attribution 4.0
Machine Learning, VGGNet Betel nut.
Paper Title: Deep Learning Approaches for Multiclass Crop Classification in Smart Agriculture
Publisher Journal Name: IJRAR, IJPUBLICATION
DOI Member: 10.6084/m9.doi.one.IJRAR1DUP008 Published Paper URL: http://ijrar.org/viewfull.php?&p_id=IJRAR1DUP008 Published Paper PDF: download.php?file=IJRAR1DUP008 Published Paper PDF: http://www.ijrar.org/papers/IJRAR1DUP008.pdf
Published Paper ID: - IJRAR1DUP008
Register Paper ID - 297474
Title: DEEP LEARNING APPROACHES FOR MULTICLASS CROP CLASSIFICATION IN SMART AGRICULTURE
Author Name(s): Ms. Mokshashree M N, Prof. Nagaraja Hebbar N, Mr. Mohammed Shanshad R, Ms. Bhoomika V G, Mr. Ashwin V
Publisher Journal name: IJRAR, IJPUBLICATION
DOI (Digital Object Identifier) :
Pubished in Volume: 11 | Issue: 4 | Year: October 2024
Volume: 11
Issue: 4
Pages: 32-36
Year: October 2024
Subject Area: Science and Technology
Author type: Indian Author
Downloads: 105
In the context of smart agriculture, enhanced yield prediction and optimal resource management depend heavily on the precise and effective classification of crops. In order to assess and analyze the effectiveness of various deep learning models, such as VGGNet, Sequential, Artificial Neural Network (ANN), and ResNet50. In this work , We a wide range crop picture datasets are process and classify by utilizing these models in order to determine the best method for multiclass crop classification. To guarantee accuracy and resilience, our approach includes exacting preprocessing methods, model training, and validation. The findings show the relative advantages and disadvantages of each model, underscoring the potential of deep learning to transform agricultural practices by precisely identifying and monitoring crops for its ill-health.
Licence: creative commons attribution 4.0
Deep learning, Multi-class classificatios, CNN, smart agriculture
Paper Title: EMOVISION: Real-time facial emotion recognition and analysis using CNN
Publisher Journal Name: IJRAR, IJPUBLICATION
DOI Member: 10.6084/m9.doi.one.IJRAR1DUP009 Published Paper URL: http://ijrar.org/viewfull.php?&p_id=IJRAR1DUP009 Published Paper PDF: download.php?file=IJRAR1DUP009 Published Paper PDF: http://www.ijrar.org/papers/IJRAR1DUP009.pdf
Published Paper ID: - IJRAR1DUP009
Register Paper ID - 297473
Title: EMOVISION: REAL-TIME FACIAL EMOTION RECOGNITION AND ANALYSIS USING CNN
Author Name(s): Ms. Prajna, Prof. Kavitha, Ms. Likitha R Gatty, Ms. Prathiksha J, Ms. Shreya CS Rai, Prof. Nagaraja Hebbara N
Publisher Journal name: IJRAR, IJPUBLICATION
DOI (Digital Object Identifier) :
Pubished in Volume: 11 | Issue: 4 | Year: October 2024
Volume: 11
Issue: 4
Pages: 37-40
Year: October 2024
Subject Area: Science and Technology
Author type: Indian Author
Downloads: 111
The Facial Emotion Detection using CNN project utilizes advanced convolutional neural networks (CNN) to accurately identify and classify human emotions from facial images. Designed for applications in customer service, mental health, and interactive media, the system processes facial expressions to detect emotions such as happiness, sadness, and many more. Through comprehensive testing, the system has demonstrated high accuracy in emotion classification and many efficient performance in processing images both individually and concurrently. By combining sophisticated technology with a user-centered approach, the project delivers a reliable and secure tool for emotion recognition, offering valuable insights and enhancing user interactions across various applications.
Licence: creative commons attribution 4.0
Machine Learning, SVM, PCA
Paper Title: Weed and Crop Image Classification Using Deep Learning Techniques
Publisher Journal Name: IJRAR, IJPUBLICATION
DOI Member: 10.6084/m9.doi.one.IJRAR1DUP010 Published Paper URL: http://ijrar.org/viewfull.php?&p_id=IJRAR1DUP010 Published Paper PDF: download.php?file=IJRAR1DUP010 Published Paper PDF: http://www.ijrar.org/papers/IJRAR1DUP010.pdf
Published Paper ID: - IJRAR1DUP010
Register Paper ID - 297472
Title: WEED AND CROP IMAGE CLASSIFICATION USING DEEP LEARNING TECHNIQUES
Author Name(s): Mr. Ajith Kumar Shetty Artificial Intelligence and, Prof. Bindya, Ms.Smita Pandu Naik, Mr. Deepak S Shettigar, Mr. Shaikh Mohd Arqam Altaf , Prof. Nagaraja Hebbar N
Publisher Journal name: IJRAR, IJPUBLICATION
DOI (Digital Object Identifier) :
Pubished in Volume: 11 | Issue: 4 | Year: October 2024
Volume: 11
Issue: 4
Pages: 41-45
Year: October 2024
Subject Area: Science and Technology
Author type: Indian Author
Downloads: 98
Precision agriculture places great importance on the precise identification of weeds and crops, as it has a direct impact on resource management and yield. Convolutional neural networks (CNNs), in particular, are deep learning approaches that have shown great promise for automating this categorization process. A powerful yet simpler version of the ResNet architecture, ResNet9 strikes a compromise between model complexity and efficiency, which makes it appropriate for agricultural image analysis. We examine the process of using a dataset of tagged photos of weeds and crops to train and assess the ResNet9 model. The model's capacity to distinguish between distinct plant species under a range of situations is demonstrated by the findings, which demonstrate promising accuracy and efficiency. Furthermore, We also talk about how these results might affect practical agricultural methods and where deep learning research for precision farming might go in the future. The goal of this work is to present a thorough understanding of how ResNet9 can be used to improve automated weed and crop detection, leading to more effective and sustainable farming methods.
Licence: creative commons attribution 4.0
Deep learning, classifications, Weeds.
Paper Title: COOKMATE
Publisher Journal Name: IJRAR, IJPUBLICATION
DOI Member: 10.6084/m9.doi.one.IJRAR1DUP011 Published Paper URL: http://ijrar.org/viewfull.php?&p_id=IJRAR1DUP011 Published Paper PDF: download.php?file=IJRAR1DUP011 Published Paper PDF: http://www.ijrar.org/papers/IJRAR1DUP011.pdf
Published Paper ID: - IJRAR1DUP011
Register Paper ID - 297470
Title: COOKMATE
Author Name(s): Aravind Naik, Dr. Suresha D, U Shravith Padvetnaya, Sooraj, Suraj S Roa
Publisher Journal name: IJRAR, IJPUBLICATION
DOI (Digital Object Identifier) :
Pubished in Volume: 11 | Issue: 4 | Year: October 2024
Volume: 11
Issue: 4
Pages: 46-48
Year: October 2024
Subject Area: Science and Technology
Author type: Indian Author
Downloads: 83
Our project intends to create a web application that helps users identify recipes based on the items they have on hand. The programme uses the Spoonacular API to let users enter a list of ingredients, and then it uses the Spoonacular database to find recipes that match. Using Python language, the system uses to manage user requests and interactions. The application sends a POST request to the backend once the ingredient list is submitted. The backend processes the input, retrieves the recipe data from Spoonacular, and returns the results in JSON. The user is presented with the retrieved recipe titles dynamically by the frontend, which is constructed using HTML, CSS, and JavaScript. With this project, we provide consumers an easy-to-use tool to maximise product utilisation, discover a variety of culinary possibilities, and improve their cooking experience. The project also shows how to incorporate external APIs into web applications, highlighting how technology may improve user experiences and quicken routine activities. With this project, we provide consumers an easy-to-use tool to maximise product utilisation, discover a variety of culinary possibilities, and improve their cooking experience. The project also shows how to incorporate external APIs into web applications, highlighting how technology may improve user experiences and quicken routine activities
Licence: creative commons attribution 4.0
Paper Title: Photo Editing Web Application
Publisher Journal Name: IJRAR, IJPUBLICATION
DOI Member: 10.6084/m9.doi.one.IJRAR1DUP012 Published Paper URL: http://ijrar.org/viewfull.php?&p_id=IJRAR1DUP012 Published Paper PDF: download.php?file=IJRAR1DUP012 Published Paper PDF: http://www.ijrar.org/papers/IJRAR1DUP012.pdf
Published Paper ID: - IJRAR1DUP012
Register Paper ID - 297469
Title: PHOTO EDITING WEB APPLICATION
Author Name(s): Deekpthi K, Dr. Suresha D, Sudheesh KP, Vivek G Naik, Sujay S Bhandari, Rishab R Kumar, Sushit R Mapari
Publisher Journal name: IJRAR, IJPUBLICATION
DOI (Digital Object Identifier) :
Pubished in Volume: 11 | Issue: 4 | Year: October 2024
Volume: 11
Issue: 4
Pages: 49-52
Year: October 2024
Subject Area: Science and Technology
Author type: Indian Author
Downloads: 103
The Photo Editing Web Application is a sophisticated tool designed for efficient and versatile photo editing. Developed with HTML5, CSS, and JavaScript, it provides a responsive and intuitive interface for seamless editing. Users can perform tasks like cropping, resizing, filtering, and adjusting brightness and contrast directly from their library. The application supports a wide range of devices, ensuring flexibility and convenience. By leveraging modern web technologies, it delivers a high-quality, user-friendly experience suitable for both novice and experienced editors, setting a new benchmark for web-based photo editing tools. Its integration with advanced features like text addition, drawing tools, and shape overlays further enhances its functionality. The application ensures a streamlined workflow, making it an essential tool for all your photo editing needs.
Licence: creative commons attribution 4.0
Photo Editing Web Application