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Deep Learning based Air Quality Forecasting using Satellite Data 6

Deep Learning based Air Quality Forecasting using Satellite ...

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6th international conference on Mobile computing and Sustainable Informatics, ICMCSI 2025

作者： Sree, K. Navya Reddy, Mancharla Rakesh Saraswathi, S. Department of Computer Science and Engineering Specialization with AI & ML Sathyabama Institute of Science and Technology Chennai India Department Computer science and engineering Sathyabama Institute of Science and Technology Chennai India

ISBN: (纸本)9798331522667

Air pollution is a significant threat to human health and the environment. Accurate air quality forecasting is essential for effective mitigation strategies, including public health advisories, emission control measures, and policy interventions. this research focuses on the development of an advanced air quality forecasting system using machine learning techniques. the system uses historical data on pollutants such as PM2.5, NO2, and O3, along with meteorological parameters, to predict future air quality levels. the model is enhanced with the inclusion of sophisticated machine learning algorithms to better capture the intricate spatiotemporal patterns and enhance the accuracy of forecasting. the system may potentially provide real-time air quality forecasts that enable proactive measures for protecting public health and inform decisions for environmental policy. this research contributes to sustainable and resilient urban environments by providing valuable insights into air quality dynamics and supporting effective air pollution mitigation strategies. © 2025 IEEE.

关键词： air pollution Air quality forecasts cardiovascular diseases data-driven strategies environmental policy global climate international collaboration machine learning NO2 O3 PM2.5 policy-making pollution mitigation proactive alerts public health real-time monitoring respiratory diseases spatial variability temporal variability transboundary pollution

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Boosting Software Quality: A Machine Learning Technique for Predicting Defects 6

Boosting Software Quality: A Machine Learning Technique for ...

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6th international conference on Mobile computing and Sustainable Informatics, ICMCSI 2025

作者： Akilan Daniel, E. Alwin Infant, P. Jainish, G.R. Panimalar Engineering College Chennai City Campus Chennai India Department of Computer Science and Engineering Panimalar Engineering College Chennai India Loyola ICAM College of Engineering & Technology Chennai India

ISBN: (纸本)9798331522667

In order to increase automation, strengthen decision- making, and improve efficiency while reducing costs and maintaining precision, the development of intelligent machinery is becoming more and more dependent on the integration of data, embedded software, and modern technologies. Reliability of embedded software becomes essential for guaranteeing high system performance and an improved user experience as technology and hardware merge to build smart devices. this work explores methods to quantify software reliability, which measures the likelihood of residual failures in a system. the objective is to build a statistical relationship between quality parameters and product or process metrics through the analysis of software metrics produced from development data. these indicators aid in evaluating the software's dependability and performance, enabling developers to spot and fix possible problems early on. Using pattern recognition algorithms to pinpoint software's fault-prone areas is a crucial strategy for enhancing software reliability prediction. these algorithms enable developers to focus attention on software areas that have a higher likelihood of failure by identifying patterns in the product's code or structure that suggest increased risk. In summary, these methods focus on enhancing the reliability of intelligent machines by equipping developers with advanced tools to reduce defects, improve dependability, and ensure that smart systems perform optimally. this approach aligns with modern trends in using machine learning and data-driven models to tackle complex technological challenges. © 2025 IEEE.

关键词： Data reliability

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Sustainable Solutions for Organ Donation and Procurement via Regional Collaboration System 6

Sustainable Solutions for Organ Donation and Procurement via...

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6th international conference on Mobile computing and Sustainable Informatics, ICMCSI 2025

作者： Mily, Farzana Akter Arefin, Md Shamsul Department of Computer Science & Engineering Bangladesh University of Business and Technology Dhaka Bangladesh Department of Electrical & Electronic Engineering Bangladesh University of Business and Technology Dhaka Bangladesh

ISBN: (纸本)9798331522667

the severe shortage of organs requires the efficient management and collaboration of practices. therefore, this paper introduces the Regional Collaboration System (RCS) to deal with this critical issue. the system aims at enhancing organ donation and procurement with the perspective of coordination of regional healthcare organizations. the primary purpose is to assist patients in finding suitable hospitals, donors, and doctors, in their region to carry out a seamless transplantation process. this system has been developed using Visual Studio 2022, SQL Server 2019 Express, and C# for a robust and scalable solution. Key features include an administrative panel, advanced search options, and the user interface to make it easy for administrators, patients, donors, and doctors to use. Moreover, the system also provides a medical history management platform that enables the patients to maintain a record of their history for future use via the administrative panel. this comprehensive approach is designed to improve transplant outcomes, minimize waiting times, and enhance organ availability. By effectively addressing the crisis in organ shortages, RCS holds a great potential to bring revolutionary changes in the existing structure of organ donation and transplantation, thereby saving more lives and hence fostering equality in access to health resources. © 2025 IEEE.

关键词： Records management

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Quantum Generative Adversarial Network for Autonomous Drone Navigation in Urban Wind Zones 6

Quantum Generative Adversarial Network for Autonomous Drone ...

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6th international conference on Mobile computing and Sustainable Informatics, ICMCSI 2025

作者： Swarnalatha, Anumula Archana, Nalluri Mukkapati, Naveen Selvakumar, Subramanian Tamir, Eneyachew Manikandaprabu, P. Department of Aeronautical and Aerospace Engineering KCG College of Technology Tamil Nadu Chennai600097 India Department of Computer Science and Engineering Vignan's Foundation for Science Technology and Research Andhra Pradesh 522213 India Department of Computer Science and Engineering Koneru Lakshmaiah Education Foundation Andhra Pradesh 522502 India Department of Computer Engineering Bahir Dar Institute of Technology Bahir Dar University Bahir Dar Ethiopia Department of Electrical and Computer Engineering Bahir Dar Institute of Technology Bahir Dar University Bahir Dar Ethiopia Department of Mechanical Engineering Sona College of Technology Tamil Nadu Salem636005 India

ISBN: (纸本)9798331522667

Major challenges are observed when autonomous drones fly in the complex, changeable environment such as wind zones regarding accurate path planning, avoiding the object, and completing tasks. Lack of flexibility in different situations reduces the reliability of conventional models in real time. To overcome these difficulties the Savitzky-Golay Denoising CSWin Quantum Generative Adversarial Network with Emperor Penguin Optimizer known as SGDCQGANNet-EPO was developed. the RGB data and GPS-estimated wind forces in this model undergo Savitzky-Golay Denoising;this model does not rely on global data, only RGB camera data and GPS data. Recently a CSWin transformer (CSWin-T) makes feature extraction and obstacles detection as well as wind angle, wind speed and wind position identification easier. Quantum Generative Adversarial Networks (QGANet) are then used for optimized path planning, and the model is enhanced by the Emperor Penguin Optimizer (EPO) to fine tune the generator and discriminator loss functions. More accurate compared to current approaches in accuracy and efficiency, the model achieves an accuracy of 99.28%, a low RMSE of 0.38, a recall of 99.25%, and EVA of 0.95. In Confidence using this new technique, the free roaming and self-organizing drone maneuvers in complex environments achieved remarkable results. © 2025 IEEE.

关键词： Generative adversarial networks

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6G Mobile Network Latency Mobile Edge computing Cloud Architecture 1

6G Mobile Network Latency Mobile Edge Computing Cloud Archit...

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1st international conference on Advances in Computer science, electrical, Electronics, and Communication Technologies, CE2CT 2025

作者： Naval, Preeti Pushparajesh, V. Maharishi School of Engineering & Technology Maharishi University of Information Technology Uttar Pradesh India Faculty of Engineering and Technology Department of Electrical and Electronics Engineering Ramnagar District Karnataka India

ISBN: (纸本)9798331518578

Communication refers to the process by which one individual conveys information to another. Telecommunication and the instruments are made available by technological gadgets, which is now feasible. 5G mobile network standards are being used at present. It is thought that 6th-generation mobile network standards will address several user needs. 6G will be universally available by 2030. In a cloud computing setup, a specific hardware or geographic location is not required. the main aim of this method is to offer computing resources whenever they are needed. By fusing the two, mobile cloud computing enables more flexible and personalized solutions. Machine learning is finding its way into a wide variety of devices. With the use of AI., mobile network services (MNS) may improve the reliability and personalization of user experiences in areas like fraud detection, mobile transaction security, network operation monitoring, and management. A world without traffic accidents, high-tec, specialized healthcare, and criminal activity would be a reality if 6G mobile network services integrated cloud computing with artificial intelligence. the most important requirements for 6G standards are high data rate connection per user and the ability to manage huge record numbers. Research and development are underway for the next-generation mobile network. there are several intriguing aspects of this age. the most pressing problem that requires solving using suitable forensic methods is security. High-performance computers are needed to improve customer service. Two situations are created by combining technological, organizational, and cloud-hosted application-based research approaches in a high-performance computing setting: monitoring performance via remote desktop connection and real-time stream processing. this article seeks to narrow the designated worldwide spectators with a wide variety of empirical chances to allocate resources for trustworthy and defensible on-demand services. ©

关键词： Mobile edge computing

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A Hybrid Approach for Cardiovascular Disease Risk Evaluation 6

A Hybrid Approach for Cardiovascular Disease Risk Evaluation

引用

6th international conference on Mobile computing and Sustainable Informatics, ICMCSI 2025

作者： Sesadri, U. Jumlesha, Shaik Raju, S. Hrushikesava Adinarayna, S. Choudhury, Nabanita Jadala, Vijaya Chandra Department of CSE Vardhaman College of Engineering Telangana Hyderabad India AP Tirupati517501 India Department of Computer Science and Engineering Koneru Lakshmaiah Education Foundation Green Fields Vaddeswaram Guntur522502 India Department of Computer Science and Systems Engineering College of Engineering Andhra University Visakhapatnam531162 India Faculty of Computer Technology Assam down town University Assam Guwahati India School of Computer Science and Artificial Intelligence SR University Ananthasagar Hasanparthy Hanumakonda Telangana Warangal506371 India

ISBN: (纸本)9798331522667

Nowadays, the risk estimators are to be applied based on the population characteristics of their country, which is termed as race attribute. there were specific tools to determine the risk of cardiovascular disease. Among many diseases, the mortality rate of the people is increasing due to a lack of quality lifestyle. In order to provide a guide to the individual based on their specific characteristics and lifestyle, would classify the risk into specific categories like severe (High), Moderate, and normal(Low) with pre-defined threshold constraints. the risk assessment estimators alert the individual to control certain modifiable attributes irrespective of their non-modifiable elements. the integration of random forests and intelligent rule mining would generate the CVD risk prediction. Efficiency as well as interpretability are important in this competitive hybrid model. the ultimate benefits assured are extending the lives of the patients by knowing the importance of daily work, habits, and their control food, presenting a powerful opportunity by practicing better personalization monitoring and improved patient care, and improved informed decisions for immediate action. the early prevention initiation by identifying the risk severity optimizes follow-up of better practices and treatment styles, resulting in improved decision making for better health care of the individual. © 2025 IEEE.

关键词： Accuracy Cardio-vascular disease Efficiency Hybridization Modifiable attributes Prediction Risk estimator

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Smart Home automation with MQTT Using ESP32 and Node-RED 4

Smart Home Automation with MQTT Using ESP32 and Node-RED

引用

4th international conference on Power, control and computing Technologies, ICPC2T 2025

作者： Chouragadey, Pratik Rathore, Bhavna Khare, Gaurav MP Gwalior474005 India Dept. of Electrical Engineering Gati Shakti Vishwavidyalaya Vadodara India

ISBN: (纸本)9798331530402

this paper presents a novel approach to home automation utilizing the MQTT protocol. the system is implemented using an ESP32 node and a Node-RED server running on a Raspberry Pi, allowing users to control the system from the Raspberry Pi as well as client terminals. the proposed system enables users to control and monitor home appliances remotely from anywhere. By using the lightweight MQTT protocol, the system provides real-time communication between the node and the server and ensures responsive control and data monitoring. the implementation of the ESP32 node provides us flexibility and scalability, while Node-RED provides us with a user-friendly interface for system management. Experimental results validate the system's effectiveness in maintaining reliable connectivity between node and server. this work highlights the potential of MQTT-based home automation systems to provide enhanced control and monitoring capabilities in modern smart homes. © 2025 IEEE.

关键词： three term control systems

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Construction of PCOS Prediction Model based on BP Neural Network 6

Construction of PCOS Prediction Model based on BP Neural Net...

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6th international conference on Mobile computing and Sustainable Informatics, ICMCSI 2025

作者： Hu, Xianqing Yadav, Amit Khan, Asif Sah, Abhishek Pratap Azam, Sami School of Computer and Software Chengdu Neusoft University Chengdu China Faculty of Science and Technology Charles Darwin University NT Australia Department of Computer Application Integral University Lucknow India School of Management Science and Engineering University of Electronic Science and Technology of China Chengdu China

ISBN: (纸本)9798331522667

Polycystic ovary syndrome (PCOS), a common endocrine-metabolic disorder affecting about 10-13% of women during reproductive age worldwide, often leads to irregular menstruation, infertility, obesity, and long-term health risks such as diabetes and cardiovascular disease. In this paper, clinical data from PCOS patients and healthy control group were collected from ten hospitals containing 37 key indicators such as age, weight, BMI, menstrual cycle, etc. BP (Back Propagation) Neural Network prediction models were constructed and compared using the collected data and the cross-validation method was used for parameter tuning. It was found that BP Neural Network performed particularly well on the test set, and both demonstrated high prediction accuracy and generalization ability which provided strong evidence for the early identification and early valuable intervention opportunities for PCOS patients. © 2025 IEEE.

关键词： Prediction models

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Identification of Medicinal Plants and Related Fungal Diseases based on Deep Learning Model 6

Identification of Medicinal Plants and Related Fungal Diseas...

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6th international conference on Mobile computing and Sustainable Informatics, ICMCSI 2025

作者： Kani, Mathina M. Mahalakshmi, R. Shunmugavel, V. Kareem, Abdul M. Department of Computer Science and Engineering Sethu Institute of Technology Virudhunagar India Department of Electrical and Electronics Engineering Quantanics TechServ pvt Ltd Madurai India

ISBN: (纸本)9798331522667

In today’s technological landscape, manual methods are increasingly being replaced by automated computer systems, streamlining tasks and enhancing efficiency. Within the realm of agricultural research, two critical areas plant identification and disease classification have shifted toward computerized solutions. With millions of plant species globally, medicinal plants hold particular significance due to their healing properties in traditional medicine. these readily accessible and culturally important remedies not only contribute to affordable healthcare but also sustain livelihoods in indigenous and rural communities. To address these challenges, our study evaluates the performance of two widely used pre-trained deep learning models U-Net and F-Net. By comparing their accuracy levels, we aim to enhance disease detection. Additionally, we present performance metrics such as accuracy and error rates, visualized through graphs. Our user interface (UI) leverages Kivy, an open-source multiplatform GUI development library for Python. Kivy enables cross-device app development, emphasizing code reusability and rapid prototyping, making it a valuable tool for interaction design across iOS, Android, Windows, OS X, and GNU/Linux platforms. © 2025 IEEE.

关键词： Medicinal chemistry

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Unmasking Hypertension through a Fusion of Human Lifestyle Data using Machine Learning Algorithms 6

Unmasking Hypertension Through a Fusion of Human Lifestyle D...

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6th international conference on Mobile computing and Sustainable Informatics, ICMCSI 2025

作者： Vishnu Priya, V. Nisanth, G. Nivethaa, R.P. Vijay, Srinidhi Department of Computer Science and Engineering Kongu Engineering College Erode Perundurai India Department of Computer Applications Kongu Engineering College Erode Perundurai India Department of Computer Technology UG Kongu Engineering College Erode Perundurai India

ISBN: (纸本)9798331522667

Hypertension, also referred to as high blood pressure, is a condition arising from the consistently high blood pressure against artery walls. the volume and output of blood from the heart primarily control blood pressure under stiff or tight arterial conditions. High pulse pressure leads to increased blood pressure, since the pumping of more blood makes the arteries inactive. the World Health Organization has a comprehensive guideline in which an evidence-based pharmacological approach has been recommended for managing adult patients with hypertension, not neglecting to mention lifestyle modification in order to support medicinal treatment. Machine learning may revolutionize the way hypertension treatment is projected for the management of personalized treatment routines for patients based on their characteristics. ML provides great promise in seeking ideal medication and predicting responses thereof against massive sets of data for hypertensive patients. the techniques allow for the determination of the individual risk for hypertension and ensure that treatment protocols are optimal. Compared to methods used to predict hypertension in the past, ML algorithms show a marked improvement in performance. © 2025 IEEE.

关键词： Personalized medicine

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