An essential tool for evaluating energy use trends and spotting potential for efficiency upgrades in buildings is an energy audit. This abstract gives a summary of an energy audit done on a college facility to assess energy use, pinpoint inefficient areas, and suggest long-term energy-saving solutions. The college building's energy systems, including lighting, heating, ventilation, and air conditioning (HVAC), electrical equipment, and insulation, were thoroughly assessed as part of the energy audit. We used a variety of data gathering techniques, including on-site measurements, equipment assessments, power bill analyses, and historical energy use data. The results of the energy audit showed several instances of inefficiency and energy waste. Excessive illumination was shown to be significantly impacted by out-of-date lighting fixtures. It was suggested to use energy-efficient lighting technology like LED bulbs and occupancy sensors when it was determined that outdated lighting fixtures were a key cause of excessive energy use. The investigation of the HVAC system revealed insufficient insulation and air leakage, which resulted in substantial heat loss and increased energy use. Improvements in insulation, air leak repairs, and HVAC control optimization were advised for greater effectiveness. Additionally, the audit found potential for energy reduction through tenant participation and behavioral adjustments. To encourage sustainable practices among students, professors, and staff, awareness campaigns and energy-saving recommendations were offered. The possible return on investment for installing energy-efficient measures was assessed using financial assessments, taking into account elements including energy cost reductions, equipment longevity, and readily available incentives and rebates. The audit study also highlighted the advantages of energy conservation for the environment, such as decreased greenhouse gas emissions and improved sustainability. The energy audit of the college building concluded with a list of various chances for energy savings and suggestions for doable actions to optimize energy use and lessen the institution's environmental impact. The college may gain significant energy savings, support sustainability objectives, and set an example for other educational institutions aiming for energy efficiency by putting the proposed methods into practise.

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Human Body communication is a developing field through which data is transmitted and received using body channel communication. In Internet of Things (IoT), large amount of data are processed and communicated through Human body communication .Health monitoring and prediction is most important now a days since number of patients and diseases are increasing in recent years. Hence efficient smart health care equipment is required. Hence a power management technique for prolong and continuous ECG monitoring based on critical data and energy level of battery is proposed in this paper. In this Health Monitoring System which is based on IoT, wearable devices can be connected to monitor different biomedical signals such as ECG, Breathing level and Temperature. In order to ensure continuous monitoring of health over a long period of time in Wireless Body Area Network (WBAN), power management is a key requirement. To reduce the power consumption, a light-weight power management controller is introduced based on the present status of ECG data and battery. The proposed architecture saves up to 27% power consumption in WBAN compared to the conventional architecture without incurring significant overhead.

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Haulage rope Capple and triangular plate is commonly used as a coupling device, in various applications such as mining etc. Haulage rope Capple are used for connecting winding rope to interlink cage or skip to the mine cage suspension gear (CSG) through links/ropes for transportation of materials, man, and machineries. and This paper deals with the cracks developed and deformation of components under different static load conditions found during quality assessment followed by VON –Misses stress analysis. All the HRC nad triangular plate of different materials are observed under same load and von miss's analysis is done. When different materials under same load analyzed in software, they showed different curves which are very helpful in analyzing and predicting the strength and life of the component. This paper also deals with the health monitoring of the HRC.

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Understanding user preferences, gathering and classifying service provider data, and guaranteeing real-time information are all necessary for developing a digital platform that links users with offline service providers. The system customizes suggestions based on user preferences, historical interactions, and user profiles, surveys, and behavior tracking. The platform keeps an extensive database of service providers, complete with information about their services, locations, costs, ratings, and reviews. The accuracy and applicability of this material are ensured by frequent revisions. In order to achieve seamless integration and improve user experience, cooperation with service providers is essential. The system's capacity to quickly adjust to shifting user requirements is essential to its success since it allows users and nearby service providers to interact in a tailored and effective manner.

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Web Application to Monitor and Alert Social Distancing Violations using Object Detection is an application which uses Machine Learning and Deep Learning concepts integrated with a web application. It is used to identify and monitor social distancing violations in inputs including but not limited to videos and webcam/CCTV footage. It also generates an alarm when the social distancing violations are detected along with statistics of said violations. It also sends an email notification to the administrative team when the violations exceed a threshold value. During pandemic like situations, to prevent the spread of disease in various places and alert people to maintain social distancing, this application could be indispensable in playing a vital role in reducing transmissions.

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The recent work using Deep Learning has shown promising results in various domains. The present scenario of weather detection is laborious and time consuming. Therefore, it's crucial to include a preprocessing step that determines the weather for an incoming picture, followed by the appropriate deweathering procedures. This research considers the most prevalent weather scenarios to offer a deep learning-based environmental image classification system. The proposed system utilized four DCN pretrained networks for weather image classification. The best optimized ResNet attained with accuracy of 97.31%, precision of 86.99%, sensitivity of 86.44% and f1-score of 86.70%. Further, the proposed weather classification will be helpful in deployment of real time environment.

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Language is a really important one when it comes to the multilingual workspace. There are several chat applications are available right now on the play store & App store. These apps come with various features to make the user work very easy. In this project, I have tried an additional feature for translating the user message based on their preference. Also, this application comes with an additional key feature called closed group private chatting. This project introduces a Closed Group Private Chat Application leveraging Firebase as its core infrastructure for real-time communication and data management. Focused on ensuring privacy and security, the application features end-to-end encryption, closed group environments, and Firebase Authentication. One of its standout features is a real-time translation capability powered by machine learning algorithms, facilitating seamless communication among users speaking different languages. The application supports media sharing, utilizing Firebase Cloud Storage for secure file storage and retrieval. With a user-friendly interface and cross-platform compatibility for both iOS and Android, this application aims to provide a secure, versatile, and intuitive platform for private group communication with the added benefit of real-time language translation.

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Problems with crop production are widespread in India and have a significant impact on the country's economy, particularly on rural farmers. Depending on the state of the leaf, farmers may predict the amount and quality of their harvest in advance, making the leaf an essential component of crop management. In this study, we provide a system that uses Tensor Flow technology for preprocessing and feature extraction of plant village dataset leaf pictures, then a convolution neural network for illness classification and pesticide recommendation. Our solution primarily makes use of two processes: deep learning and an Android app that makes use of Java Web Services. We trained our model using a Convolution Neural Network with five, four, and three layers, and we interfaced it with a user-friendly Android app and JWS. We found that a 5-layer model utilizing tensor flow attained the best accuracy at 95.05% after 15 epochs, and the maximum validation accuracy at 89.67% after 20 epochs.

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The existence of landmines in various battle zones and post-war regions poses a remarkable danger to civilian populations and hinders socioeconomic growth. This abstract introduces an original approach to mitigate this danger through the development of a Wireless Landmine Detection Robot (WLDR). This robotic system is equipped with cutting-edge technologies to autonomously detect, locate, and mark landmines, ensuring safety while minimizing human exposure to risk in dangerous terrains. For that, various devices and modules are used such as Bluetooth, sensors, controller, motors and drivers etc. In this research, the robot system is equipped with a metal detector used to detect the metal presence based on coil induction when it's approaching the metal. Wireless data are transmitted through a Bluetooth module. The robot's movement is controlled by DC's current motor programmed using Arduino UNO.

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This paper aims to design and analyze a frequency reconfigurable printed monopole antenna incorporating a parasitic radiator. The antenna is specifically intended for applications in WLAN and Satellite communication. The antenna structure is constructed using a printed strip line, and a parasitic radiator is connected to the patch using a PIN diode. The proposed design is simulated using ANSYS HFSS. The PIN diode is modeled using HFSS as a switch to achieve frequency reconfiguration. The measured -10dB bandwidth for the reflection coefficient spans from 5.4 to 5.6GHz and 7 to 7.8GHz. These bandwidths align with the requirements for WLAN and satellite band applications, demonstrating the antenna's suitability for these communication purposes. This paper introduces a frequency reconfigurable printed monopole antenna with a parasitic radiator, offering versatility for WLAN and satellite applications. The utilization of a PIN diode as a switch for frequency tuning adds a novel aspect to the design, contributing to its adaptability and performance.

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Each element of the roadmap is discussed, emphasizing the potential impact on rural life. For instance, smart entrance gates enhance village security and aid in monitoring activities. Road monitoring ensures well-maintained transportation infrastructure, and street light monitoring improves safety and facilitates maintenance. Garbage collection initiatives contribute to cleanliness and disease prevention, while drainage monitoring addresses blockages. Water purification and healthcare solutions mitigate health risks, and education initiatives, including smart attendance systems and e-libraries, enhance educational opportunities. Transport monitoring addresses transportation challenges within the village. Advocating for small-scale pilot implementations in a few Gram Panchayats to monitor and evaluate the effectiveness of IoT solutions in real-time. This approach, coupled with community feedback, can serve as a model for scaling IoT technologies in rural governance, leading to sustainable development and improved quality of life in rural communities.

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People require some tools to seek and collect useful information because it is difficult in this information era to sift through the large amount of material that is available on internet platforms. One of these tools is referred to as a recommendation system, which is a potent software method that assists in quickly traversing through large volumes of data to determine user's interests and give the necessary information. In this paper the three approaches for a content-based book recommendation system are discussed. In the first strategy, a simple system for recommending books is created, with certain input criteria like author, publisher, language, average rating, and recommendations for the books as the output. In the second method the K nearest neighbor (KNN) algorithm and cosine similarity are used to develop a book recommendation system based on several attributes. In the third method, the user enters a book title, which is then translated into vectors using Term Frequency-Inverse Document Frequency (TF-IDF), which then calculates the cosine similarity between related books before proposing related and similar books to the user. By implementing the above strategies, the user will be able to view book recommendations based on various attributes.

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