Search

This paper explores the role of international partnerships to facilitate low-energy building design, construction, and operations. We present the strategic approach, joint research and development outcomes, and implementation activities of a unique U.S.-India program on buildings energy efficiency, the Center for Building Energy Research and Development. We discuss the collaboration successes in both countries despite their dissimilar building contexts, implementation challenges and opportunities. We highlight a range of R&D outcomes, such as novel tools and technologies developed and tested by the joint teams, with their technical energy savings potential, as well as results of capacity building and technology demonstrations. A deep-dive into key new scientific methods around building energy monitoring and benchmarking that could have a significant impact on high-performanceof buildings in both countries is also provided. Finally, in addition to joint R&D successes, pathways to deployment, and lessons learned are discussed as key takeaways.

This paper explores the role of international partnerships to facilitate low-energy building design, construction, and operations. We present the strategic approach, joint research and development outcomes, and implementation activities of a unique U.S.-India program on buildings energy efficiency, the Center for Building Energy Research and Development. We discuss the collaboration successes in both countries despite their dissimilar building contexts, implementation challenges and opportunities. We highlight a range of R&D outcomes, such as novel tools and technologies developed and tested by the joint teams, with their technical energy savings potential, as well as results of capacity building and technology demonstrations. A deep-dive into key new scientific methods around building energy monitoring and benchmarking that could have a significant impact on high-performanceof buildings in both countries is also provided. Finally, in addition to joint R&D successes, pathways to deployment, and lessons learned are discussed as key takeaways.

Air pollution is a major concern in the mining industry, and it's vital that workers have access to information about the air quality in their workplace. To address this issue, we're developing an Air Quality Monitoring and Prediction system that utilizes sensors like MQ135, DHT11, buzzer, and LED to detect various pollutants in the air, including carbon monoxide, Sulphur dioxide, and nitrogen oxide. The collected data will be transmitted wirelessly to a central server for analysis and visualization. The proposed system will provide real-time data on air pollution levels to operators, enabling them to take proactive measures to mitigate potential risks to human health and the environment. The forecast was done on the collected data with the help of excel. As per the forecast the carbon dioxide in air was about 500 to 600 ppm which is around the real value. Our project aims to enhance transparency and promote better decision-making by providing accurate and up-to-date information about air quality in the mining industry. [ABSTRACT FROM AUTHOR]

The rise of social media and global communication requires multilingual image captioning for diverse participants, enabling cross-cultural understanding and engagement. By automatically generating captions in multiple languages, this technology enables seamless communication and enriches the user experience in an increasingly interconnected world. Existing image captioning models leverage alternative methodologies, such as transformerbased models, attention mechanisms, and pre-trained language models, to generate descriptive captions for images without relying on the CNN-LSTM architecture. These approaches provide effective solutions for image captioning, showcasing advancements in natural language processing and machine learning. However, the integration of CNN-LSTM architecture with the Flickr8k dataset and translation enables more accurate and contextually relevant multilingual image captions, enhancing versatility and adaptability for real-world applications. This approach combines deep learning techniques, diverse training data, and machine translation capabilities for superior multilingual image caption generation. The model's accuracy is assessed using accepted evaluation metrics and the model generates accurate captions in multiple languages for the images provided. [ABSTRACT FROM AUTHOR]

Global Warming has become an emerging threat to mankind. Sudden changes caused in the environment are making survival, a difficult task. The flora and fauna, not only of the country India but also the whole world is disturbed; hence, the ecosystems are not easily adapting to the changes. Many hands-on methods adopted by the people do not contribute to the solution needed. To resolve this issue, prediction of the extent of Global Warming to name the solutions is needed and the proposed system suggests the same. The system proposed uses the technique of Machine Learning and based upon the hypothesis proposed, the prediction of global warming for an upcoming decade is made. Also, the formula to calculate the extent of global warming is derived. [ABSTRACT FROM AUTHOR]

This article presents a Smart Water Metering and Billing system that provides accurate and automated water usage monitoring and billing. The suggested system tracks water consumption in real-time and sends the information to a central server using IoT technology. The server uses an algorithm to calculate the water usage based on the received data and generates a bill accordingly. The system also provides consumers with a user-friendly API server to monitor their water consumption. The suggested method does away with the necessity for manual meter reading and enables prompt and precise invoicing, improving water conservation and facilitating effective management. The results of the proposed system's implementation demonstrate its effectiveness in accurately measuring water consumption and generating bills, thereby addressing the challenges of traditional water metering syste [ABSTRACT FROM AUTHOR]

Predicting climatic conditions plays a very vital role in the different prominent industries including farming, transportation in affiliation with supply chain, construction, travel and tourism industries, etc. Capturing the changes in the weather is necessarily important and highly efficient for predicting the upcoming changes in the atmosphere. Hence the developed framework approach monitors the weather using the IOT technology along with an approach to forecast and predict it with efficient accuracy. The system is distinctly useful for smaller areas. Similarly, the system emphasizes weather prediction using different ML algorithms using Multiple Linear Regression, Random Forest Classifier, Decision Tree, and Gradient Boosting in a phased manner. Additionally, it searches for the best algorithm with the maximum prediction accuracy. The system compares the performances of the ML algorithms. The system works on the patchwork of IOT technology and ML algorithms to create a platform to view the weather conditions and predict the weather conditions. The combination of both technologies gives insights to users about weather conditions and predictions on the same platform. [ABSTRACT FROM AUTHOR]

Being independent is crucial to surviving in today's environment. For persons with physical disabilities, this is regrettably not feasible. They require help to move about freely and are reliant on it continuously. These individuals benefit from the suggested system since it enables them to handle their everyday tasks on their own. This system makes use of an embedded Wi-Fi module in the ESP32 microcontroller. This eliminates the need for any help by allowing the user to maneuver the wheelchair using voice instructions through an app. Also, while the wheelchair is in use, the user's gadget, such as a smartphone, tablet, etc., may be charged. [ABSTRACT FROM AUTHOR]

An innovative smart wheelchair system designed to enhance the mobility and safety of individuals with disabilities. The proposed system utilizes a combination of accelerometer, gyroscopes, and MPU6050 sensors to detect hand gestures, allowing users to control the wheelchair intuitively. Instead of the traditional joystick, a Node MCU sensor is employed for wireless data transmission, enabling seamless communication between the wheelchair and the user. Additionally, the system incorporates ultrasonic sensors for obstacle detection, triggering an automated braking system to ensure user safety. By integrating advanced sensor technology and automated braking, the proposed system offers a more efficient and secure means of wheelchair control, empowering individuals with limited mobility to navigate their surroundings with increased ease and confidence. The user-friendly and intuitive nature of the system makes it a valuable solution for improving the overall quality of life for those with disabilities. [ABSTRACT FROM AUTHOR]

Efficient inventory management is essential for businesses to optimize their supply chain, reduce costs, and enhance operational efficiency. At the same time due to manual scanning at the checkout processes, customers have to wait for a long time in the queue. As a solution to these different problems, we have proposed a system using IoT. This research paper presents a novel approach to inventory management by integrating two cutting-edge technologies: Load Cell Sensor HX711 and Smart Cart System using Radio Frequency Identification (RFID) technology. By combining the capabilities of these technologies, the paper aims to automate inventory tracking, monitoring, and replenishment, providing real-time data for accurate inventory control and decision-making. The Smart Cart System enhances the customer satisfaction while the Smart Shelves increase efficiency of inventory management by automating the data capture process. It provides convenience by eliminating the need for manual scanning or checkout processes, and generating bill on webpage allowing customers to simply place items in the cart and proceed with their shopping. Also we present the machine learning aspect of our project aimed at revolutionizing the shopping experience using RFID technology and load cells. We explore the implementation of three prominent algorithms - Linear Regression, Random Forest, and XG Boost - to predict future sales. [ABSTRACT FROM AUTHOR]

Every time job recruiters (HR) have to go through a huge pile of resumes submitted by applicants. which becomes a very burdensome task, with Python, NumPy, and Pandas, a resume parser is to be created as part of the project. A structured format will be used to store the data that the parser extracts from resumes, including name, contact information, education, work experience, and abilities. Pandas will be used to store the data in a data frame, and NumPy will be used to manipulate the data. The parser will recognise and extract pertinent information from resumes using methods of natural language processing, improving the accuracy and efficiency of the procedure. By swiftly extracting pertinent information from resumes, the initiative will assist in automating the hiring process and save time for recruiters and HR personnel. [ABSTRACT FROM AUTHOR]