Category Archives: Uncategorized

Authors: Ar. Yashika Garg, Tasneem Patanwala

Abstract: Energy conservation is now considered an essential consideration in residential architecture owing to urbanization and changes in lifestyle patterns. Contemporary residences require high levels of energy, especially when it comes to air-conditioning, lighting, and other home appliances. Consequently, energy use poses many environmental problems. In addition, the economic aspect of the issue cannot be ignored either. This paper will analyze how residential architecture could become an efficient instrument to decrease the level of energy consumption. Apart from energy-saving mechanical systems, architects should focus on passive energy-saving techniques which include proper orientation, natural ventilation, use of appropriate shading structures, and locally produced materials. All these techniques make it possible to cut down the need for energy consumption, providing residents with thermal comfort at the same time. Qualitative research will be applied in the study with the support of a case study approach. The example under discussion includes the Aranya Low-Cost Housing project designed by Balkrishna Vithaldas Doshi.

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Authors: Ar. Pratik Ahirwar, MD. Shahrukh

Abstract: This paper explores innovative strategies for maximizing small spaces, focusing on urban environments where spatial constraints are a significant challenge. The study evaluates architectural design techniques, multifunctional furniture, modular systems, and minimalist approaches to enhance space utilization. Case studies from global cities are analyzed to understand the practical implications and outcomes of various interventions. The paper also considers cultural and psychological factors that influence space perception and functionality.

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Authors: Assistance Professor Shaik. Sharmila, Oburi Leela Sridevi, Shaik Bajibi, Ganduri Nihitha,, Thokala Madhvi

Abstract: Over the past years, both parents and schools have been in distress over the issue of how to guarantee the safety of the students both walking or even taking the bus to school. This article proposes IoT based Bus Attendance and Notification System, which is built on the facial recognition technology to automate student attendance, security and timely parent and school administration notification. The unit possesses sensor based identification system which is accurate to guarantee ample detecting of students boarding and alighting. It takes the attendance and automatically sends an SMS alarm throughout the IoT based communication. By eradicating errors, the system is aiding in making the student-transportation operations more reliable and safe besides cutting down on delays and making them more easily monitored.

DOI: https://doi.org/10.5281/zenodo.20651120

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Authors: P.Sandhya Krishna, Kondaveeti Vyshnavi Mani, Gutta Bhavyasri, Bachina Lakshmi Sowjanya, Pagidipalli Rupakalpana

Abstract: The Smart Bus Attendance Management System is a face recognition-based system that uses deep learning to automate the school or college bus student attendance tracking. The conventional manual attendance systems are time-consuming, more likely to be erroneous whereas RFID or biometric security demands the implementation of extra equipment and may not provide real-time accuracy. In this system, images of students are captured when they get on the bus and they are identified with the help of deep learning algorithms, which can be Convolutional Neural Networks (CNNs), face embedding models. The identified information is uploaded on a digital record of the attendance and the information such as the name of student, roll number, class, date and time. The system will be able to produce real-time reports on attendance, minimize human intervention, and improve the safety aspect by providing proper monitoring of students on transit. This solution proves to be an efficient combination of computer vision, machine learn and IoT-based transportation management that offers a scalable and smart solution to the present-day learning institutions.

DOI: https://doi.org/10.5281/zenodo.20650876

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Authors: Associate Professor V.Pavani, Kakarla Adi Lakshmi, Velpuri HanuRithikeswari,, Pervali Sravani, Devarasetty Kavya

Abstract: In recent years, Internet of Things (IoT) has been widely applied in greenhouse control to realize intelligent automation and data-driven greenhouses. In IoT based greenhouse, the real time status of soil moisture content, air temperature & humidity and CO2 concentration is monitored and controlled using embedded system technologies (Arduino or Raspberry Pi) and wireless communication modules. Sensors, wireless technology and data analytics can be combined for real-time monitoring and marching orders so that the optimal conditions are met for growth and crop yield. Moreover, the use of artificial intelligence (AI) techniques (fuzzy logic, machine learning and bio-inspired algorithms) increases the flexibility of the platform, the ability of prediction and decision-making performance. These smart systems eliminate manual labour, process costs and resource waste with eco-friendly.

DOI: https://doi.org/10.5281/zenodo.20650660

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Authors: Associates Professor K.Jagadeesh,, K Sravanthi, M Charanya, M Deepika Veera Naga Rajyalakshmi,, G Vineetha Raj

Abstract: Diabetes mellitus is one of the most prevalent chronic diseases worldwide, posing significant health and economic challenges. Early prediction of diabetes can greatly assist in timely diagnosis and effective management of the disease. This study presents a machine learning– based approach for predicting the likelihood of diabetes using clinical and physiological data. The dataset was preprocessed through normalization and feature selection to improve model efficiency. Various supervised learning algorithms, including Logistic Regression, Decision Tree, Random Forest, and Support Vector Machine (SVM), were implemented and evaluated based on accuracy, precision, recall, and F1-score. Among these, the Random Forest classifier demonstrated superior performance with the highest overall accuracy, indicating its robustness in handling complex, non-linear relationships among features. The results suggest that predictive modelling using machine learning can serve as a valuable tool to support healthcare professionals in identifying individuals at high risk of developing diabetes. Future work will focus on incorporating larger and more diverse datasets and exploring deep learning models to further enhance predictive accuracy and reliability.

DOI: https://doi.org/10.5281/zenodo.20650487

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Authors: Assistant Professor Vamsi Krishna, Amara Neelima, Kurapati Naga Venkata Mounika, Nettem Rishitha, Thota Sravana Sruthi

Abstract: The Gas Leak Detection and Prevention System based on IoT is aimed at making homes, offices, and manufacturing premises safer by offering on-time monitoring and prompt detection of dangerous gas escapes. The system comprises gas sensors, microcontrollers, and IoT-enabled modules that would allow constantly measuring the amount of gases in the environment. When abnormal levels are detected, the system provides automated notifications through cloud or mobile applications, and timely act and prevent any possible accidents. Moreover, it has the ability to automatically regulate the ventilation systems or cut off the gas supply with the view of reducing risks. IoT is used to enable remote monitoring, data logging and analysis which is useful to perform predictive maintenance and manage safety better. This system will be a proactive measure to stop gas leaks before they cause harm to human lives, properties, and the environment, particularly in the domestic and industrial environment.

DOI: https://doi.org/10.5281/zenodo.20650075

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Authors: Assistant Professor R Srinivas, Koppula Sneha, Devadasu Aswini, Gattupalli Ekavani Madhur, Pusuluri Surekha

Abstract: Machine Learning-based Gas Leak Detection and Prevention System operates with intelligent and automated methods to detect and prevent gas leakage occurrences in industrial and domestic situations. Existing detection systems have primarily utilized fixed threshold values for such checks, leading to the most effective method for interpreting false alerts and ineffective response times. The proposed system couples sensor components with an ML algorithm method to processes more productive patterns determined for gas releases while using devices to eliminate these differences. Data is acquired from gas sensors, standard MQ-series sensors, to measure LPG, methane, and carbon monoxide. Real-time data is acquired and processed after processing and analysed by machine learning algorithms, like Support Vector Machine SVC, Random Forest to classify conditions as safe or fallacious. An alarm sounds and IoT sends users alerts such as gas shut-off valves and exhaust fans. When gas becomes available, this ML approach impro

DOI: https://doi.org/10.5281/zenodo.20649838

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Authors: Assistant Professor Mrs. B. Aruna Kumari, Immadi Naga Varshitha, Ramadeni Vasavi, Para Prasanthi, Marripudi Jeevana Jyothi

Abstract: One of the essential technologies that allow implementing the human-computer interaction built intuitively and with a certain level of comfort is the recognition of hand gestures, in particular, in the smart home automation systems. This paper presents a new deep learning model, Attention-Enhanced CNN Gesture Recognition (AE-CNN-GR) that can enhance the quality, responsiveness, and resilience of gesture control on live camera streams and improve the accuracy. The model is based on the extension of the traditional CNN architecture, incorporating channel and spatial attention units, to enable the network to concentrate on the most informative parts of the hand, such as fine finger movements and changes of the positions. Channel attention module records finer spectral and intensity differences in parts of the hands and the spatial attention mechanism focuses on important geometric and contextual characteristics of gestures to enhance the accuracy of classification and boundary detection. The methods of transferMediaPipe and OpenCV identifications and preprocessing using hand detection and appliance control with the use of the Arduino simulation.

DOI: https://doi.org/10.5281/zenodo.20649582

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Authors: Assistant Professor G. Lakshmi Durga, Gade Bhagya Sri, Guntaka Vahnitha, Shaik Benazil Bhanu, Devarapalli Thanuja

Abstract: The Internet of Things (IoT) technology allows individuals to have new interfaces to communicate with devices that are smart. We present a Smart IoT Interface with Hand Gesture Recognition and Machine Learning in this work to enhance human- machine interaction (HMI) in smart environments. Being a wearable hand gesture recognition and control device, it relies on sensor networks and embedded systems to obtain real-time hand gesture feedback, which is later interpreted by advanced machine learning algorithms to allow natural and natural interaction with IoT devices. The suggested interface takes advantage of wireless communication and edge processing that allows the practical and low-latency processing of real-time data and cloud integration to provide additional device control and gain analytics. Its applications include IoT automation, home automation and an intelligent IoT control to a flexible and reliable system that enables the user to interact with devices connected to it. Findings suggest that the sugg

DOI: https://doi.org/10.5281/zenodo.20649238

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