IoT Assistive Technology for People with Disabilities
Soulease: A Mind-Refreshing Application for Mental Well-Being
AI-Powered Weather System with Disaster Prediction
AI Driven Animal Farming and Livestock Management System
Advances in AI for Automatic Sign Language Recognition: A Comparative Study of Machine Learning Approaches
Design and Evaluation of Parallel Processing Techniques for 3D Liver Segmentation and Volume Rendering
Ensuring Software Quality in Engineering Environments
New 3D Face Matching Technique for an Automatic 3D Model Based Face Recognition System
Algorithmic Cost Modeling: Statistical Software Engineering Approach
Prevention of DDoS and SQL Injection Attack By Prepared Statement and IP Blocking
This system is dedicated to developing a Smart Medication Alert System using Arduino and various hardware components designed to improve medication adherence, particularly for individuals with disabilities. The system combines visual, auditory, and display-based notifications through LEDs, a buzzer, and an LCD screen, making it accessible to users with visual impairments or those managing complex medication schedules. With real-time tracking and user-specific reminders, the system prompts users about scheduled medication times, notifies them of missed doses, and provides dosage instructions if necessary. By streamlining the medication-taking process and reducing the risk of missed doses or errors, the system aims to foster greater independence, enhance the user's quality of life, and ultimately support improved health outcomes for those requiring regular medications. After carrying out all the necessary steps highlighted in this paper and writing the required code, the system produces the following results: it accurately alerts the user at the right time to take medication. Yellow, green, and red LEDs are integrated into the system to indicate the device's status. These colored LEDs make it easier for individuals with disabilities, such as hearing impairments, to know when to take medications. For individuals with poor eyesight or color blindness, a buzzer serves the same purpose. The system provides accurate information both on the device itself and on the user interface.
The major factors affecting mental health and well-being globally include depression, anxiety, and stress. These significant issues pose a serious threat to an individual's mental well-being. However, they can be addressed through modern technological solutions. To address these challenges, Soulease, a mobile platform, was developed to provide support and information to individuals facing mental health concerns. This paper explores Soulease by detailing its development, key features, and the potential impact on users' mental well-being. The study also evaluates how the platform aligns with its objectives and optimizes the user experience to enhance its effectiveness. The study findings indicate that easy to use interface; availability of multiple resources; supportive features and tools enable the users of the platform to better navigate mental health issues and build positive coping mechanisms within the wellness domain. Further, the paper analyzes how psychological underpinnings of Soulease were informed and used with cues from CBT, K10, K7, LSTM, Natural Language Processing (NLP), machine learning approaches and positive psychology. Being evidence-based, Soulease connects clients with mental health management and support solutions such as mood logging, mindfulness exercises, and cognitive re-scripting based on their individual conditions.
This AI-powered weather system enhances disaster prediction and preparedness using advanced machine learning algorithms and real-time meteorological data. Leveraging the OpenWeatherMap API, the system analyzes environmental indicators such as rainfall, temperature, and wind speed to assess risks of floods, droughts, cyclones, hailstorms, and wildfires. Machine learning models, including linear regression and threshold-based heuristics, ensure high predictive accuracy. The threshold-based approach uses historical disaster data to define limits, with exceedances signaling high risk. Linear regression forecasts variables like rainfall and temperature, integrating probabilistic outputs into the heuristic system for improved accuracy. This system demonstrates AI's potential in early warning systems, bolstering community preparedness and enabling timely interventions by authorities.
AI-Driven Animal Farming and Livestock Management System represents a pivotal advancement in agricultural technology, promising to revolutionize traditional farming practices by integrating Artificial Intelligence (AI) into livestock management. This abstract presents an overview of the system's multifaceted capabilities, emphasizing its role in guiding animal farmers towards optimal livestock care, enhancing marketing strategies, and offering a suite of advanced functionalities. At its core, this system employs sophisticated AI algorithms like Natural Language Processing (NLP) and Convolutional Neural Networks (CNNs) to provide personalized guidance to animal farmers, ensuring they adhere to best practices in livestock care. Through real-time monitoring and data analysis, the system offers actionable insights into nutrition, health, and reproduction management, thereby maximizing the well-being and productivity of livestock. Furthermore, the System incorporates innovative features tailored to streamline marketing efforts. By analyzing market trends, consumer preferences, and supply chain dynamics, the system enables farmers to make informed decisions regarding product positioning, pricing strategies, and distribution channels, thereby enhancing market competitiveness and profitability.
Sign language recognition is a vital field within artificial intelligence, aiming to bridge communication gaps between deaf or hard-of-hearing communities and others by translating visual gestures into text or speech. Automatic Sign Language Recognition (ASLR) systems seek to interpret these complex and nuanced gestures with greater accuracy, expanding access to information communicated through sign language. This paper presents a comparative review of machine learning methods used in ASLR, emphasizing their impact on improving communication for hearing-impaired individuals. It also explores the primary challenges in ASLR, such as variability in signs and the complexities of gesture recognition. Advanced feature extraction techniques like SIFT, HOG, and SURF are examined for their role in enhancing ASLR system performance. Additionally, a bibliometric study highlights significant trends and advancements in intelligent systems for sign language recognition over the past two decades. This paper synthesizes recent research on ASLR technologies, supporting the development of more effective communication tools and fostering social inclusivity for deaf and hard-of-hearing communities.
