A Machine Learning Approach to Real Time Vehicle Safety System
Autistic Continuum and Pervasive Developmental Disorder Employing Machine Learning and Image Processing
A Comprehensive Study on Signature Recognition using Python, AI and ML
A Web-Based Placement Portal with Web Data Mining for Recruiter Insights
A Review of Predicting Agriculture Yields Based on Machine Learning using Regression and Deep Learning for Diversified Atmosphere and Crops in India
A Review of Predicting Agriculture Yields Based on Machine Learning using Regression and Deep Learning for Diversified Atmosphere and Crops in India
A Machine Learning Approach to Real Time Vehicle Safety System
Autistic Continuum and Pervasive Developmental Disorder Employing Machine Learning and Image Processing
A Web-Based Placement Portal with Web Data Mining for Recruiter Insights
A Comprehensive Study on Signature Recognition using Python, AI and ML
Autistic Continuum and Pervasive Developmental Disorder Employing Machine Learning and Image Processing
A Machine Learning Approach to Real Time Vehicle Safety System
A Review of Predicting Agriculture Yields Based on Machine Learning using Regression and Deep Learning for Diversified Atmosphere and Crops in India
A Comprehensive Study on Signature Recognition using Python, AI and ML
A Web-Based Placement Portal with Web Data Mining for Recruiter Insights
Vehicle security could be a developing concern in today's world, with increasing cases of robbery and unauthorized access. This paper presents an AI-powered vehicle security framework that guarantees access to only authorized and enlisted users to start the vehicle. The framework utilizes fake insights procedures for client confirmation, avoiding unauthorized get-in and activating prompt e-mail alarms to the proprietor in case of suspicious movement. All authorized client information, counting get to times and movement logs, is put away safely in a backend framework, open through a web-based dashboard for real-time observation. It uses Raspberry Pi as hardware and machine learning for computing and computation to improve security and decision-making. The solution is to predict unauthorized access and generate alarms in real time, and implement a reliable solution for modern security and challenges, and data of authorized vehicle users are stored for real-time monitoring.
Autism spectrum disorder (ASD) is becoming more and more prevalent in the modern era. Using image processing methods, it takes a lot of effort and money to identify autism characteristics through screening testing. The development of machine learning (ML) and artificial intelligence (AI) has made it possible to predict autism at a very young age. Numerous research studies have been conducted using various methodologies, but they have not produced any conclusive findings on the prediction of autism features in relation to age groups. Therefore, the purpose of this study is to design a user interface for predicting ASD in individuals of any age and to offer an effective prediction model based on machine learning techniques. As a result of this study, Random Forest-CART (Classification and Regression Trees) and Random Forest-ID3 (Iterative Dichotomiser 3) and the CNN algorithm were combined to create a prediction model for autism. A user interface was also created based on the suggested prediction model. The AQ-10 (Autism Spectrum Quotient - 10 item version) is a brief screening tool used to identify whether an individual may exhibit traits associated with Autism Spectrum Disorder (ASD). Autism can be diagnosed at any age, although symptoms typically start to show up in the first two years of life and progress over time. Autism sufferers deal with a variety of issues, including learning deficiencies, concentration issues, mental health issues like anxiety and depression, movement difficulties, sensory issues, and many more. ASD-related data, including brain imaging, facial expression recognition, and a dataset of 250 actual datasets gathered from individuals with and without autistic features, were used to assess the suggested model, false positive rate (FPR), sensitivity, and precision. According to the evaluation findings, the suggested prediction model performs better in terms of accuracy and data specificity.
Signature verification and recognition is a critical biometric technique used for identity authentication in various domains, including banking, legal documents, and access control. With the advancements in Artificial Intelligence (AI) and Machine Learning (ML), automated signature recognition systems have become increasingly sophisticated and accurate. This paper provides a comprehensive overview of developing a signature recognition system using Python, AI, and ML techniques. It covers the fundamental concepts and methodologies involved, including data acquisition, preprocessing, feature extraction, model selection, and evaluation. Furthermore, it discusses the role of popular Python libraries and frameworks in implementing such systems and explores common challenges and future directions in the field. The aim is to offer a foundational understanding for studies and practitioners interested in automated signature analysis.
This paper proposes Talent Track, a centralized web-based placement portal designed to streamline and digitize campus recruitment for colleges and universities. The platform allows students to create profiles, upload academic records, resumes, and certificates, which are securely stored and accessible only to verified recruiters. By integrating web data mining techniques, the portal enhances the recruitment process by enabling recruiters to search, filter, and match student profiles based on specific skills, qualifications, and academic performance. This data-driven approach saves time and helps companies identify the most suitable candidates efficiently. College staff and administrators can monitor placement activities through a dashboard, generate reports, and track student performance, reducing paperwork and improving coordination. Overall, the system automates key operations, ensures secure data handling, and bridges the gap between education and employment through smart, data-centric recruitment.
Artificial Intelligence (AI) has emerged as a transformative force in agriculture, particularly in crop yield prediction. Accurate forecasting aids farmers and policymakers in making informed decisions regarding resource allocation, risk management, and food security. This paper critically reviews AI-driven crop yield prediction methodologies, focusing on machine learning (ML) and deep learning (DL) approaches. Various techniques including Decision Trees, Random Forest, XG Boost, Artificial Neural Networks (ANNs), and Convolutional Neural Networks (CNNs); are analyzed for their effectiveness in predicting yield outcomes based on meteorological, soil and environmental parameters. Additionally, challenges such as data limitations, model interpretability, and environmental variability are discussed. The review concludes with recommendations for future improvements, including hybrid models, explainable AI, and integration with IoT and remote sensing technologies.
