In today's world, stress has become an undoubtedly severe problem that affects people's health. Stress can modify a person's behavior, ideas, and feelings in addition to having an impact on mental health. Unchecked stress can contribute to chronic illnesses including high blood pressure, diabetes, and obesity. Early stress detection promotes a healthy lifestyle in society. This work demonstrates a deep learning-based method for identifying stress from facial expressions and speech signals.An image dataset formed by collecting images from the web is used to construct and train the model Convolution Neural Network (CNN), which then divides the images into two categories: stressed and normal. Recurrent Neural Network (RNN), which is used to categorize speech signals into stressed and normal categories based on the features extracted by the MFCC (Mel Frequency Cepstral Coefficient), is thought to perform better on sequential data since it maintains the past results to determine the final output.

In today's rapidly evolving technological landscape, the successful execution of software projects relies on effective requirement management, accurate estimations, and persuasive project proposals. This research paper introduces an innovative robotic framework designed to enhance and optimize important aspects of project development and planning. The Robotic Framework enhances project management by reducing human error, increasing efficiency, and ensuring that all project elements align with industry standards and best practices. It learns from past projects and adapt to changing requirements makes it an invaluable asset for software development teams, fostering collaboration and improving project success rates.

Recognizing counterfeit goods can be difficult in some situations. If a person does not thoroughly inspect the product's details, it becomes simpler to create and sell counterfeit goods. For less tech-savvy clients who can scan the product with the use of a smartphone application to check the authenticity of the product received, this paper offers a superior alternative employing machine learning. The detection of logos (which includes both visual and textual representations) is the main focus. The model also includes the sentiment analysis of the product’s reviews. This technique is useful for predicting the validity of a product. The paper describes the Fake Product Identification Model developed using Convolution Neural Network (CNN) and Optical Character Recognition (OCR). This model determines whether a product is real or fake, and the user can make a wise decision before buying the product.

The research focuses on applying artificial intelligence and robotics in precision agriculture to improve farming practices, productivity, and resource usage. The research employs a combination of quantitative and qualitative methods, structured into three phases: The first is analysis and planning, the second one is design and build and the last one is testing and review. AI techniques, machine learning frameworks, and robotic systems were developed and implemented in different agricultural applications including soil identification, plant disease diagnosis, and crop production estimation. Sensor technologies enabled real-time monitoring of the parameters such as moisture content, temperature and nutrient content of the soil which are vital for the efficient use of water and fertilizers. The outcomes reveal a much higher efficiency of applying AI in comparison to traditional methods in terms of accuracy, resource consumption, and crop productivity. The integration strategy allowed to have the effective cooperation between AI systems and robotic parts, and improved decision-making in farming. It focuses on the opportunities of AI & robotics in contemporary agriculture and the necessity of the ethic approach to the technology implementation. The research evidence indicates that the use of the above technologies enhances sustainable use of the resources in farming thus enhancing food security and conserving the environment.