Agriculture, the backbone of many economies, faces challenges like lack of information, outdated practices, and limited access to technology, hindering farmer productivity. This work proposes a user-friendly, multilingual platform leveraging Generative AI to address farmers' diverse needs. The platform encompasses various features to enhance agricultural practices. An LLM-powered Government Scheme Advisor functions as a multilingual chatbot offering intelligent guidance on government agricultural schemes and subsidies. The Disease Detection module utilizes AI technology for real-time identification and treatment recommendations, minimizing crop diseases and yield losses. The Soil Testing Centre feature locates nearby soil testing centers, providing essential information based on geographical data to assist farmers in optimizing soil quality. A Crop Recommendation feature employs Machine Learning algorithms to offer personalized crop recommendations, considering various factors and aiding informed decision-making. The Crop Planning Tool, with its intuitive user interface, simplifies planning planting schedules and managing resources. Additionally, the platform includes an MSP Center Locator to find nearby Minimum Support Price (MSP) centers based on location. By integrating these innovative solutions, this platform bridges the gap between conventional agricultural techniques and contemporary technology, equipping farmers with the resources and expertise essential for advancing productivity and sustainability. Multilingual support ensures accessibility for a wider audience, breaking down language barriers and promoting inclusivity in the agricultural sector. This work proposes an innovative, multilingual platform powered by Generative AI to address these issues. Key features include an LLM-driven chatbot for government scheme guidance, AI-based real-time disease detection, and location-based tools for soil testing and MSP center identification. Additionally, the platform offers personalized crop recommendations and intuitive crop planning tools, bridging the gap between traditional methods and modern technology. Multilingual support ensures inclusivity, empowering farmers with resources to enhance productivity and sustainability. Future research could focus on improving precision agriculture, climate resilience, market intelligence, and socio-economic assessments to further advance the agricultural sector.

Maintaining good indoor air quality is crucial in buildings dedicated to enhancing the health and well-being of their occupants. This challenge becomes even more complex due to the diverse range of users and spaces within a single institution. Different areas, such as operating rooms and waiting rooms, require specific air quality standards, tailored to the varying health conditions of patients and visitors. Poor air quality can hinder hospital staff in performing their duties effectively and affect patients' comfort during recovery. Hospitals can now achieve indoor air quality standards cost-effectively through Internet of Things (IoT) technology. The IoT enables remote monitoring, offering greater control over indoor conditions like air quality, temperature, humidity, and dust levels. This system monitors air quality, dust concentration, temperature, and humidity within healthcare facilities, sending notifications to staff via an app and push alerts when readings exceed normal levels. It utilizes the MQ135 sensor for air quality, the GP2Y1010AU0F optical dust sensor, and the DHT11 sensor for temperature and humidity, all interfaced with NodeMCU through the Arduino IDE. Data from these sensors is stored on a cloud platform and displayed in a mobile app, with near real-time monitoring from sensors placed throughout the facility. A time-series algorithm, such as Autoregressive Integrated Moving Average (ARIMA), is used to forecast temperature and humidity trends in wards. The system alerts staff when indoor temperature exceeds 27 °C, triggers warnings when air quality surpasses 500 ppm, and issues critical alerts for levels above 650 ppm. Sensor data, sent to the cloud every 120 s, provides staff with insights to better plan actions to improve indoor air quality.

Air pollution has a significant impact on environment resulting in consequences such as global warming and acid rain. Toxic emissions from vehicles are one of the primary sources of pollution. Assessment of air pollution data is critical in order to assist residents in locating the safest areas in the city that are ideal for life. In this work, density‐based spatial clustering of applications with noise (DBSCAN) is used which is among the widely used clustering algorithms in machine learning. It is not only capable of finding clusters of various sizes and shapes but can also detect outliers. DBSCAN takes in two important input parameters—Epsilon (Eps) and Minimum Points (MinPts). Even the slightest of variations in the parameter values fed to DBSCAN makes a big difference in the clustering. There is a need to find Eps value in as minimum time as possible. In this work, the goal is to find the Eps value in less time. For this purpose, a search tree technique is used for finding the Eps input to the DBSCAN algorithm. Predicting air pollution is a complex task due to various challenges associated with the dynamic and multifaceted nature of the atmosphere such as meteorological variability, local emissions and sources, data quality and availability, and emerging pollutants. Extensive experiments prove that the search tree approach to find Eps is quicker and efficient in comparison to the widely used KNN algorithm. The time reduction to find Eps makes a significant impact as the dataset size increases. The input parameters are fed to DBSCAN algorithm to obtain clustering results.

Vision Studio aims to utilize a diverse range of modern deep learning and computer vision principles and techniques to provide a broad array of functionalities in image and video processing. Deep learning is a distinct class of machine learning algorithms that utilize multiple layers to gradually extract more advanced features from raw input. This is beneficial when using a matrix as input for pixels in a photo or frames in a video. Computer vision is a field of artificial intelligence that teaches computers to interpret and comprehend the visual domain. The main functions implemented include deepfake creation, digital ageing (de-ageing), image animation, and deepfake detection. Deepfake creation allows users to utilize deep learning methods, particularly autoencoders, to overlay source images onto a target video. This creates a video of the source person imitating or saying things that the target person does. Digital aging utilizes generative adversarial networks (GANs) to digitally simulate the aging process of an individual. Image animation utilizes first-order motion models to create highly realistic animations from a source image and driving video. Deepfake detection is achieved by using advanced and highly efficient convolutional neural networks (CNNs), primarily employing the EfficientNet family of models.