Volume 12 | Issue 5 |

 Abstract

The challenge of content-based image retrieval (CBIR) lies in its reliance on low-level visual features from user query images, making query formulation difficult and often yielding unsatisfactory retrieval results [1] . Previously, image annotation emerged as a promising solution for CBIR, employing automatic assignment of keywords to images for improved retrieval based on user queries. picture annotation essentially mirrors picture class, where low-level features are mapped to excessive-level principles (elegance labels) through supervised mastering algorithms. However, achieving effective feature representations and similarity measures remains critical for CBIR performance. The semantic gap, characterized by the disparity between machine- captured low-level image pixels and human-perceived high-level semantics, poses a significant challenge in this context. Recent advancements in deep learning, particularly Convolutional Neural Networks (CNNs), have demonstrated remarkable success in various computer vision tasks, motivating my pursuit to address the CBIR problem using a dataset of annotated images [6].

Licence: creative commons attribution 4.0

  License

Creative Commons Attribution 4.0 and The Open Definition

 Keywords

Deep learning, Convolutional Neural Networks (CNNs), Similarity measures, Semantic gap, Computer vision

  License

Creative Commons Attribution 4.0 and The Open Definition

 Abstract

The Contest Lister project aims to revolutionize the way coding enthusiasts engage with coding contests, hackathons, and hiring challenges across various online platforms. With the exponential growth of coding competitions, enthusiasts face the daunting task of keeping track of events, often leading to missed opportunities and disorganized participation[8]. In response to this challenge, the Contest Lister project presents a comprehensive solution that aggregates contest information from prominent platforms like CodeChef, HackerRank, and LeetCode into a centralized platform. By leveraging modern web technologies and APIs, the system provides users with real-time updates on upcoming contests, intuitive filtering options, and a user-friendly interface for seamless navigation. This paper outlines the architecture, methodology, results, and future prospects of the Contest Lister project, offering insights into its potential to streamline contest discovery and enhance user engagement in the coding community.

Licence: creative commons attribution 4.0

  License

Creative Commons Attribution 4.0 and The Open Definition

 Keywords

Coding contests, Competitive programming, Contest Lister, System architecture, API integration, User interface

  License

Creative Commons Attribution 4.0 and The Open Definition

 Abstract

The Contest Lister project aims to revolutionize the way coding enthusiasts engage with coding contests, hackathons, and hiring challenges across various online platforms. With the exponential growth of coding competitions, enthusiasts face the daunting task of keeping track of events, often leading to missed opportunities and disorganized participation[8]. In response to this challenge, the Contest Lister project presents a comprehensive solution that aggregates contest information from prominent platforms like CodeChef, HackerRank, and LeetCode into a centralized platform. By leveraging modern web technologies and APIs, the system provides users with real-time updates on upcoming contests, intuitive filtering options, and a user-friendly interface for seamless navigation. This paper outlines the architecture, methodology, results, and future prospects of the Contest Lister project, offering insights into its potential to streamline contest discovery and enhance user engagement in the coding community.

Licence: creative commons attribution 4.0

  License

Creative Commons Attribution 4.0 and The Open Definition

 Keywords

Coding contests, Competitive programming, Contest Lister, System architecture, API integration, User interface

  License

Creative Commons Attribution 4.0 and The Open Definition

 Abstract

The aim of this study is to provide a platform where SPPU university students can easily convert their cumulative grade point average (CGPA) to percentage. Usually, students are more comfortable in the percentage system as CGPA is a new concept to most of them. Percentage is the basic requirement criteria to be addressed/written in many official forms like job applications, scholarships forms, etc. As university students get their result in CGPA format, they need to convert them first in percentage, but the procedure of converting CGPA to percentage is not known to many students. There are many websites and formulae available on the internet, but the accuracy of each is different. In addition, the manual way of calculation can increase error rate, and may be hectic. To resolve this issue, a system is proposed that converts CGPA to percentage in an accurate format. A student needs to enter his/her CGPA, and in just one click, user gets a converted percentage. The system is crafted utilizing HTML, CSS, and JavaScript. The system can be executed in any operating system and is user- friendly.

Licence: creative commons attribution 4.0

  License

Creative Commons Attribution 4.0 and The Open Definition

 Keywords

CGPA, Percentage, Grade, Class, User interface.

  License

Creative Commons Attribution 4.0 and The Open Definition

 Abstract

The process of image colorization has gained popularity, especially for converting old black and white photos into colorful ones, which provides a more immersive view of historical material. This process holds significant relevance for historical preservation and storytelling, providing a means to visualize the past with enhanced accuracy and realism. There are two primary approaches to image colorization: manual and automatic. Manual colorization relies on skilled individuals using software like Adobe Photoshop to meticulously add colors to grayscale images. This method demands expertise to ensure the chosen colors align with the historical context and period depicted in the image. In automatic colorization represents a newer advancement driven by deep learning technologies, such as convolutional neural networks. This technique determines the appropriate colors for a given image by analyzing its grayscale values. It frequently achieves these results with little assistance from a human. Moreover, preserving the original integrity of the image poses another challenge. While colorization can enhance visual appeal, it must be done sensitively to avoid altering the original meaning behind the image. Striking a balance between enhancing visual aesthetics and preserving historical authenticity is crucial to ensure that colorization efforts remain respectful to the original image's significance.

Licence: creative commons attribution 4.0

  License

Creative Commons Attribution 4.0 and The Open Definition

 Keywords

Convolutional Neural Network(CNN), Mean Square Error(MSE) ,peak signal noise ratio(PSNR),Structural Similarity(SSIM),Gray Scale , RGB Scale

  License

Creative Commons Attribution 4.0 and The Open Definition

 Abstract

If not caught and diagnosed early, brain tumors can turn into cancer. Today, the detection and classification of brain tumors is done by doing the biopsy, which can be a timeconsuming process. With less time and effort radiologists can now construct tumors thanks to advancement in technology and machine learning algorithms . First, are going to propose a model that will determine whether there are tumors in the brain by segmenting MRI images and, if they are detected, we use an architecture based on SVM to classify tumors in MRI images as tumors and no tumors function well. The foundation allows staff to decide on the repair process. The development of the model will be divided into a training and testing phases and will be tested using more data and more methods. The proposed SVM/KMEANS architecture achieves high accuracy, reliability and execution speed and will become a powerful diagnostic decision for radiologists.

Licence: creative commons attribution 4.0

  License

Creative Commons Attribution 4.0 and The Open Definition

 Keywords

Magnetic Resonance Imaging(MRI), Support Vector Machine (SVM), K- Nearest Neighbor(KNN).

  License

Creative Commons Attribution 4.0 and The Open Definition

 Abstract

Tumors present in the brain can develop into cancer if they are not discovered and treated quickly. These days, brain tumors are identified and categorized by the timeconsuming biopsy procedure. With less time and effort, radiologists can now construct tumors thanks to technological advancements and machine learning algorithms. Firstly, we provide a brain tumor detection model based on MRI image segmentation. If brain tumors are found, a deep learning- based SVM/KNN architecture is used to identify the tumors as well as their functional characteristics. The foundation gives personnel discretion over the repair procedure. The model will go through phases of training and testing, with additional methods and data being used for testing. With its excellent accuracy dependability, and speed of execution, the suggested SVM/KNN architecture will be a valuable diagnostic tool for radiologists.

Licence: creative commons attribution 4.0

  License

Creative Commons Attribution 4.0 and The Open Definition

 Keywords

MRI ( Magnetic Resonance Imaging) , SVM( Support Vector Machine) , KNN( K Nearest Neighbour)

  License

Creative Commons Attribution 4.0 and The Open Definition

 Abstract

This study presents an innovative approach for accurately recognizing diverse Yoga poses using deep learning algorithms. Our proposed system introduces a method for Yoga pose assessment leveraging pose detection to facilitate self-learning of Yoga. Utilizing multi-parts detection solely with a PC camera, the system effectively identifies Yoga poses. Furthermore, we introduce an enhanced algorithm for scoring applicable to all poses. Evaluation of our application encompasses various Yoga poses across different scenarios, demonstrating its robustness. We propose a hybrid deep learning model incorporating linear regression for real-time Yoga recognition in videos. This model utilizes linear regression to extract features from key-points in each frame, obtained through Open-Pos.

Licence: creative commons attribution 4.0

  License

Creative Commons Attribution 4.0 and The Open Definition

 Keywords

Yoga Pose Recognition, Deep Learning Algorithms, Linear Regression, Real-time Video Analysis, Pose Detection System

  License

Creative Commons Attribution 4.0 and The Open Definition

 Abstract

In this study, there is exploration of how people's BMI changed pre and post the onset of covid pandemic, considering factors like food habits (the nutritious value of what they eat) and physical activities. They not just eyeballing the data--system using super-smart computer techniques called Reinforcement Learning, specifically Deep Q Network and Random Forest Regression and Gradient Boost Regression. Before COVID-19, know people had certain eating food habits and lifestyle habits. Now, with the pandemic, those might have changed. Using Deep Q Network, our computer system learns from this data and figures out how these changes are linked to BMI. It's like teaching a computer to understand the consequences of different habits on weight. Gradient Boost Regression is another technique being used. It helps the computer learn not just from the data have but also by exploring possibilities like, what if someone changed their eating habits or exercise routines? This way, system not just looking at what happened but also predicting what could happen. By combining these techniques, study aim to unravel how food choices and physical activities during and after Covid-19 might have influenced BMI. It's like having a smart assistant to help us understand the connection between lifestyle changes and weight, shedding light on how they can stay healthy in this challenging time.

Licence: creative commons attribution 4.0

  License

Creative Commons Attribution 4.0 and The Open Definition

 Keywords

Body Mass Index (BMI), Random Forest Regression, Gradient Boosting Regression, Deep Q-Network (DQN).

  License

Creative Commons Attribution 4.0 and The Open Definition

 Abstract

In this study, there is exploration of how people's BMI changed pre and post the onset of covid pandemic, considering factors like food habits (the nutritious value of what they eat) and physical activities. They not just eyeballing the data--system using super-smart computer techniques called Reinforcement Learning, specifically Deep Q Network and Random Forest Regression and Gradient Boost Regression. Before COVID-19, know people had certain eating habits and physical activities. Now, with the pandemic, those might have changed. Using Deep Q Network, our computer system learns from this data and figures out how these changes are linked to BMI. It's like teaching a computer to understand the consequences of different habits on weight. Gradient Boost Regression is another technique being used. It helps the computer learn not just from the data have but also by exploring possibilities like, what if someone changed their eating habits or exercise routines? This way, system not just looking at what happened but also predicting what could happen. By combining these techniques, study aim to unravel how food choices and physical activities during and after Covid-19 might have influenced BMI. It's like having a smart assistant to help us understand the connection between lifestyle changes and weight, shedding light on how they can stay healthy in this challenging time.

Licence: creative commons attribution 4.0

  License

Creative Commons Attribution 4.0 and The Open Definition

 Keywords

Body Mass Index (BMI), Random Forest Regression, Gradient Boosting Regression, Deep Q-Network (DQN).

  License

Creative Commons Attribution 4.0 and The Open Definition