Volume 12 | Issue 5 |

 Abstract

Agriculture is one of the main factor that decides the economic growth of any country. This is the one of the reasons that disease detection in plants plays an important role in agriculture field, as having disease in plants are quite natural. Plant disease identification is a significant process to prevent the losses in the quality and quantity of the agricultural product. It is essential to detect any disease in time to ensure healthy and proper growth of the plants prior to applying required treatment to the affected plants. Since manual detection of diseases costs a large amount of time and labour, it is inevitably prudent to have an automated system.With the worldwide increase in digital cameras and continuous improvement in computer vision domain, the automated techniques for detection of disease are highly possible. After necessary pre processing, the dataset was trained on using different deep learning algorithms. This approach of ours is to increase the productivity of crops in agriculture.We aim to raise awareness about the disease and also provide solutions to the disease using generative AI.

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 Keywords

Agriculture, Plant disease, Deep learning algorithms, Generative AI

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Creative Commons Attribution 4.0 and The Open Definition

 Abstract

The project's primary aim is to develop an efficient system specialized in recognizing handwritten text, facilitating the smooth conversion of handwritten text images into digital text format. Utilizing cutting-edge machine learning techniques and neural network architectures, the overarching goal is to construct a robust model capable of accurately identifying handwritten words and characters across a diverse spectrum of handwriting styles and languages. By achieving this objective, the project endeavors to simplify and enhance the digitization process of handwritten documents. This advancement will not only improve archival practices but also enhance the searchability and accessibility of significant handwritten content across historical and contemporary domains. Through innovative approaches and rigorous methodology, the project seeks to contribute to the broader field of Handwritten Text Recognition, driving forward advancements in technology and paving the way for more efficient and accurate solutions in the future..

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HAND WRITTEN TEXT RECOGNITION

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 Abstract

This paper proposes a hybrid machine learning approach for URL phishing detection, combining supervised and unsupervised techniques. By leveraging features like domain information and employing models such as random forest and clustering algorithms, the system achieves high accuracy in identifying phishing URLs while minimizing false positives. This hybrid system offers robust protection against evolving phishing tactics, enhancing online security for users. Complementing the supervised approach, our system incorporates unsupervised learning techniques to uncover hidden structures within the data. Clustering algorithms, are utilized to group URLs based on similarity metrics derived from their feature representations. This unsupervised clustering aids in identifying anomalous patterns indicative of phishing behavior, thereby enhancing the system's ability to detect novel threats.

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 Keywords

Hybrid machine learning, URL phishing detection, supervised learning, unsupervised learning, domain information, random forest, clustering algorithms, high accuracy, false positives, evolving tactics, online security

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Creative Commons Attribution 4.0 and The Open Definition

 Abstract

Machine learning-powered facial age and gender estimation utilizes advanced algorithms such as Support Vector Machine (SVM) or K Nearest Neighbor (KNN) to estimate age of a person and gender from features of face in human face. It relies on extensive datasets of labeled facial images, which undergo preprocessing activites such as face detection, alignment, and feature extraction. These tasks ensure the extraction of relevant facial features like landmarks and textures. The SVM/KNN algorithms are used on curated dataset to learn decision boundaries separating different age groups and genders. This training process enables the models to make accurate predictions based on facial characteristics. The technology benefits from its ability to automate age and gender estimation tasks with high accuracy, facilitating applications in various domains such as security, marketing, and healthcare. However, challenges such as bias in training data and variations in facial expressions can affect the reliability of predictions. Additionally, privacy concerns related to facial recognition technologies are important considerations in its deployment.

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Creative Commons Attribution 4.0 and The Open Definition

 Keywords

Facial age and gender estimation, Machine learning, KNN Algorithm, Labeled dataset.

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Creative Commons Attribution 4.0 and The Open Definition

 Abstract

The "Animeflix" project outlines the design, development, and implementation of an Anime Streaming Website, leveraging modern web technologies such as TypeScript, HTML, CSS, and React for the frontend. The backend functionality is seamlessly integrated by directly importing data from an external API, ensuring a robust and dynamic streaming experience for anime enthusiasts.The frontend development is conducted using TypeScript, a statically-typed superset of JavaScript, to enhance code quality and maintainability. HTML and CSS are employed for creating a user-friendly and visually appealing interface, ensuring an immersive anime-watching experience for users. The backend architecture relies on importing data directly from an external API, eliminating the need for an independent backend server. This approach not only simplifies development but also ensures real-time updates and a vast library of anime content for users.

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Creative Commons Attribution 4.0 and The Open Definition

 Keywords

Animeflix, TypeScript, HTML, CSS, React, maintainability, user-friendly, visually appealing interface

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Creative Commons Attribution 4.0 and The Open Definition

 Abstract

SecureFaces stands out as a resolute guardian of online security in a time of digital interactions by fusing cutting-edge face authentication with a powerful Deepfake detecting tool. In this research, two powerful models--MesoNet for quick authentication and ResNetLSTM for increased accuracy--are shown. MesoNet and ResNetLSTM each address different user priorities. To ensure efficiency and data integrity, the system starts with an easy-to-use registration module that collects and safely stores facial data in MangoDB. Users can select between MesoNet and ResNetLSTM during the authentication process, depending on their unique needs, creating a customized identity verification strategy. SecureFaces is essentially an innovative cybersecurity solution that embodies trust, transparency, and adaptability in the digital sphere, going beyond simple facial authentication. This initiative acts as a beacon, guiding users through a trustworthy and safe authentication process as internet threats change

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Creative Commons Attribution 4.0 and The Open Definition

 Keywords

Cybersecurity, Facial Authentication, Deepfake, Machine Learning, Deep Learning, Artificial Intelligence.

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Creative Commons Attribution 4.0 and The Open Definition

 Abstract

The internet's The "Face Mask Detection Using Convolutional Neural Network" project presents an innovative solution leveraging computer vision and deep learning techniques to automate the detection and categorization of individuals based on their adherence to face mask mandates. The system utilizes convolutional neural networks (CNNs) for robust feature extraction and classification, allowing real-time analysis of images or video frames to determine whether individuals are wearing masks and, if so, the specific type of mask. The project addresses the crucial need for efficient monitoring and enforcement of mask-wearing protocols in various settings, including public spaces, healthcare facilities, retail environments, and educational institutions. The application of this technology contributes to public health and safety by providing an automated, reliable, and scalable solution to ensure compliance with face mask guidelines, mitigating the spread of infectious diseases and enhancing overall safety measures in diverse sectors.

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 Keywords

Face Mask Detection, Architecture, CNN

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Creative Commons Attribution 4.0 and The Open Definition

 Abstract

Extractive text summarization using Latent Semantic Analysis (LSA) involves analyzing the underlying structure of a document by creating a matrix of term-document relationships. The TFIDF (Term Frequency-Inverse Document Frequency) vectorizer is employed to highlight important words in the document, assigning weights based on their frequency and uniqueness. Machine learning algorithms leverage these vectorized representations to identify and extract key sentences or phrases, forming the basis of the summary. Additionally, Deep Neural Networks (DNN) come into play, employing intricate layers of interconnected nodes to learn and understand complex patterns within the text. The DNN further refines the summarization process, enhancing the model's ability to capture nuanced relationships and context. This fusion of traditional ML and DNN approaches results in a powerful summarization system capable of distilling large volumes of information into concise, informative abstracts.Keywords--CNN, maximum pooling layers, dropout layers, softmax activation

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 Keywords

LSA, DNN, TF-IDF Vectorizer

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Creative Commons Attribution 4.0 and The Open Definition

 Abstract

The Sugar Cane is particularly crucial agricultural commodities in the world with a number of cuisines scattered across the globe, which are incomplete without it. In developing countries like India, Sugar Cane has spurred agriculture driven growth in the past century, when export of agricultural produce was the major source of foreign exchange. At times, the prices face a blow from the demand side, while at times facing drastic conditions on the supply side, owing to which, the prices of the commodity have seen a drastic fall. In such years, farmers often cannot afford the services of agricultural consultants for tasks such as of sicknesses of the leaves and addressing them at the earliest. The prescribed remedy is an inexpensive strategy which is easy use of image processing to detect leaf diseases in the leaves of Sugar Cane plants a way to streamline life for landowners in addition to consumers, since this would balance the prices at a median price. In this project, the affected leaves are captures as images using a camera. upon then, these photographs are adjusted. further using various methods and the key characteristics originate via them using various methods.

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Creative Commons Attribution 4.0 and The Open Definition

 Keywords

Sugar cane, Image Processing

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Creative Commons Attribution 4.0 and The Open Definition

 Abstract

Approximately 39 percent of adult people on the planet are overweight. The fact presented above makes it clear how important and necessary exercise is. Exercise helps us maintain a healthy weight, a fit body, and a calm mind in addition to helping us lose weight. Regular exercise also keeps us active and improves blood circulation to keep our bodies in the same condition as when we used to visit the gym. It is expensive or not within everyone's reach to train under a trainer, visit the gym, take yoga courses, or both. Self-training is an additional option that provides pre-recorded yoga practice steps without any feedback. Without proper feedback about our postures, injuries can happen and it will do more harm than good and that's exactly where our project comes into play. These days, human position estimation is a widely pursued project in computer vision. The study of strategies and systems that retrieve an articulated body's stance is known as articulated pose estimation in computer vision. The course of determining the human body's location parts and joints in a given image is known as "articulated body pose estimation" in the context of this study. We study the many applications that we may put into practice with the data, which we received using a pre-trained posture estimation model called MediaPipe. Motion capture, gait analysis, anomaly detection, sign language recognition, and other uses are among them.

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Creative Commons Attribution 4.0 and The Open Definition

 Keywords

Human pose estimation, pose detection, pose estimation survey.

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Creative Commons Attribution 4.0 and The Open Definition