This Amazon Sales Analysis examines key trends, metrics, and insights derived from Amazon's sales data. The study aims to identify sales patterns, top-performing products, customer preferences, and the impact of marketing strategies. Using various data sources, including sales reports, inventory data, and customer reviews, the analysis employs statistical techniques and visualization tools to uncover actionable insights. The findings highlight revenue growth drivers, seasonal fluctuations, and opportunities for optimization. The study also acknowledges challenges such as data limitations and biases. Based on the results, recommendations are provided to enhance sales performance, improve customer satisfaction, and refine marketing strategies.

The evolution of human-computer interaction (HCI) has led to the emergence of more natural, intuitive methods of control, especially in the domain of gaming. Traditional input devices such as keyboards, mice, and joysticks, though reliable, can limit user immersion and interactivity. This project presents a novel hand gesture-based virtual driving simulator that utilizes Open-CV to detect and interpret real-time hand gestures, thereby offering an innovative and engaging alternative to conventional control mechanisms in driving games. The proposed system employs a standard webcam to capture live video input of the user’s hand gestures. Using a combination of computer vision techniques such as contour analysis and convex hull algorithms the system accurately identifies gestures corresponding to steering, acceleration, and braking. These recognized gestures are then mapped to simulated keyboard events, enabling control of a wide range of commercially available driving games. By eliminating the need for additional hardware, the system offers an affordable and accessible solution for enhancing user experience. Experimental results demonstrate the system’s efficiency, achieving over 90% accuracy in gesture recognition and maintaining a response latency under 100 milliseconds, thereby ensuring smooth gameplay. The system was successfully tested with multiple racing simulators, receiving positive feedback from users who appreciated its intuitive and immersive nature. This project not only showcases the potential of vision-based gesture recognition in gaming but also sets a foundation for future enhancements involving complex gesture sets and virtual reality (VR) integration.

Medilink is one of the most innovative health platforms that brings all the healthcare solutions together into one accessible interface. Unlike traditional systems that are different and manual in handling records, reminders, and diagnostic tools, Medilink has been brought together in one approach that makes healthcare easy to manage and more efficient. One of its stand out features is digital prescription storage to ensure patients have their medical history available at a moment's notice, loss of paper prescriptions is thus eliminated. This is really helpful in case of emergency as all health-related information is readily available. Moreover, Medilink offers a global prescription database wherein every user can browse anonymized treatment data from all over the world. This, indeed, makes decisions in a much more informed way based on real-world evidence for appropriate treatments for most conditions. It further assists patients in adhering to their prescribed regimens, thus eliminating the most common problem of missed doses and thus leads to faster recovery. Besides, Medilink's key flagship feature is MED-GPT, which is designed using cutting-edge AI technologies such as machine learning and natural language processing to ensure accurate and dependable diagnostic suggestions. It is particularly helpful in emergencies when a patient needs urgent medical advice and creates a gap in accessibility to professional healthcare. Another feature of Medilink is Tools for Analysts which is the most impressive capabilities when it comes to this learning module is that it provides the possibility to maintain the data’s purity in the field of healthcare. It has been equipped for analysts to remove the improper records so they keep the data free from the mistakes. For instance, the system can identify some variations in the class labels or other key additive features that leads to biased analysis results or finally a flawed treatment. This way, Medilink contributes to sustaining the quality and non-variability of data and contributes to the firm’s correct decision-making processes. Tech stack includes Golang, AngularJS, PostgreSQL, Python FastAPI, and Hugging Face's Transformers, Vectordb , natural language processing, machine learning, LLM, Langchain, sentence-transformers, streamlit, pandasai, google gemini api, google cloud storage, yahoo finance connector tiktoken, Medilink allows the service to be secure, efficient, and scalable. And in turn, Medilink rewrites healthcare's management and delivery to be streamlined, reliable, and accessible to all.

The severity of ischaemic stroke ,the world Intricate connection in the 5110 patient health profiles Leading cause of death and disability ,can be that made up the health care dataset. Three potent Considerably decreased by early detection of stoke . Classifiers light gradient boosting machine(LGBM), The purpose of this research is to use cutting edge extreme gradient boosting(XGBoost),random forest Machine learning techniques to create a predictive (RF)_were integrated to create a stacking ensemble Model for ischaemic strock. This method u.se an model that improved the prediction capacity. Artificial neutral network(ANN).

Every day, many individuals encounter different illnesses. The prognosis of a disease is the most pivotal part of treatment. The enormous increase in healthcare and medical data has enabled accurate medical data analysis, which aids in early sickness discovery and proactive patient care. This study focuses on analyzing extensive medical data by employing supervised classification algorithms, with a primary focus on the XGBoost (Extreme Gradient Boosting) Classifier. The proposed model anticipates the most probable disease based on symptoms and predicts the likelihood of whether a person might be suffering from a particular illness. XGBoost is known for its high performance, scalability, and ability to handle imbalanced and sparse data, making it well-suited for complex healthcare datasets. By combining predictions with XGBoost as the core model, the system achieves improved diagnostic accuracy and reduced false positives compared to traditional individual models. This study enhances the swiftness of clinical decision-making and assists healthcare organizations in providing timely and precise early patient care. It also supports medical professionals in formulating more effective patient treatment strategies.

The availability of educational data in creative ways and formats has given students with special education needs (SEN), whose behaviour and learning are especially sensitive to their physical circumstances and surrounding environments, new possibilities. Multimodal learning analytics (MMLA) gathers and analyses student and learning environment data in a number of ways to clarify the underlying educational ideas. In this work, we used MMLA to forecast the behaviour of special education needs (SEN) children undergoing applied behaviour analysis (ABA) therapy. One kind of special education intervention that aims to address behavioural problems and encourage constructive behaviour changes is ABA treatment. Here, we show that by integrating multimodal educational data, our deep neural network and machine learning models can best predict the behaviour change of SEN children with 98% accuracy and 97% precision. We also demonstrate how adding environmental, psychological, and motion sensor data may statistically improve the performance of prediction models using just traditional educational data. Our efforts have enhanced intensive ABA treatment for over 500 SEN children in Singapore and Hong Kong since 2020 using the Integrated Intelligent Intervention Learning (3I Learning) System.

Metaheuristic algorithms are indispensable for solving complex optimization problems that challenge traditional methods. One of the subclasses among all these, the one that is characterized by the most distinct features, gets its inspiration from human cognitive and social behaviors such as learning, teaching, creativity, and teamwork. The present paper is a thorough review of human-inspired metaheuristic algorithms, and it is going to analyze their basic principles, types, and operational frameworks. The authors will be going into details about the mechanics of well-known algorithms such as Sewing Training-Based Optimization (STBO), Carpet Weaver Optimization (CWO), and the iHow Optimization Algorithm (iHowOA), emphasizing their individual methods for maintaining a balance between global exploration and local exploitation. To support the review, extensive comparative tables will summarize performance on standard benchmark functions and a broad range of real-world applications, including but not limited to, engineering design and feature selection, healthcare, and energy management. The quality of the algorithms deployed in this analysis is confirmed to be very good. They use structured human-like processes to effectively navigate through complex solution spaces. However, in line with the "No Free Lunch" theorem, their superiority is condition-based. The paper ends with a discussion of future research directions, emerging trends, and inherent challenges such as the potential for adaptive and hybrid models to further enhance robustness and versatility in dynamic optimization landscapes.

In today's digital age, mobile apps have transformed our daily lives, but for people with visual impairments, these apps often present accessibility challenges. This research addresses the need for improved solutions by focusing on "voice-guided assistance" for Android smartphones. The existing options for visually impaired users are fragmented, and this study aims to create an integrated mobile application, "Voice Companion," designed to enhance their digital experience. "Voice-Companion" is a specialized Android application designed for visually impaired individuals, developed in Java within the Android Studio environment. It leverages the Android OS to provide comprehensive non-visual access. With features like voice commands, object detection, messaging, a voice-activated calculator, location and time announcements, text-to-speech capabilities, and battery status updates, the user-friendly app bridges accessibility gaps, empowering visually impaired users for equal participation in the digital realm. Its modular, user-centric design emphasizes accessibility through seamless integration of voice commands and gesture recognition, facilitating efficient interactions with mobile devices. This research aims to create "Voice-Companion," enhancing smartphone accessibility through seamless voice command integration and gesture recognition. Anticipated results include improved digital engagement and quality of life for users with visual impairments.

The internet and e-commerce have been among the fastest-growing technologies in recent years, and they have a significant impact on people's daily lives. E-marketing is one of the newest technologies in the IT and e-commerce domains. E-marketing is the process of promoting and selling goods and services online. It is sometimes referred to as online marketing, web-marketing, or Internet marketing (IM). E-marketing helps companies determine which target market to sell their goods and services to. It encompasses all practices and initiatives undertaken with the goal of attracting, locating, acquiring, and retaining customers. Since e-marketing facilitates internet-based, email-based, and wireless media marketing, it is believed to have a broad range of applications. Here, we carried out an investigation to ascertain the benefits and drawbacks of Internet marketing in India.