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Smoke Level Monitoring and Alerting in Automobiles using IoT

Smoke Level Monitoring and Alerting in Automobiles using IoT

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International Conference on Trends in Electronics and Informatics (ICEI)

作者： M. Vani Pujitha Teja Aravind Kumar Rayalavarapu Lal Basha Shaik Sashank Madhabushi Department of Computer Science and Engineering Velagapudi Ramakrishna Siddhartha Engineering College Andhra Pradesh India Dept. of Artificial Intelligence and Data Science Velagapudi Ramakrishna Siddhartha Engineering College Andhra Pradesh India

ISBN: (数字)9798331544607

ISBN: (纸本)9798331544614

Vehicle emissions significantly exacerbate air pollution, releasing harmful gases such as carbon monoxide (CO), nitrogen dioxide (NO 2 ), and hydrocarbons (HC) from poorly maintained engines and inefficient combustion processes. The study presents an Internet of Things (IoT)-based system to monitor the emissions, which mainly includes key pollutants like CO, NO 2 , and HC in real-time. Strategically positioned gas sensors near the vehicle's exhaust capture emission data, which an Arduino microcontroller processes with high accuracy. The system delivers continuous gas level updates via an LCD display and leverages an IoT framework to ensure seamless integration of sensors, microcontrollers, and mobile devices. This connectivity enables real-time data transmission and remote alerting, enhancing emission management. When pollutant levels surpass predefined safety thresholds, the system triggers immediate alerts through a Global System for Mobile Communications (GSM) module (SIM 900) via SMS to the vehicle owner's mobile, complemented by an audible buzzer. This alert notification encourages timely maintenance, reducing emissions and improving vehicle performance and contributing to cleaner air.

关键词： GSM Microcontrollers Real-time systems Sensor systems Mobile handsets Sensors Safety Nitrogen Gas detectors Monitoring

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A Few-Shot Learning Approach for Sound Source Distance Estimation Using Relation Networks

A Few-Shot Learning Approach for Sound Source Distance Estim...

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International Conference on Machine Learning and Applications (ICMLA)

作者： Amirreza Sobhdel Roozbeh Razavi-Far Vasile Palade Dept. of Electrical and Computer Engineering Urmia University Iran Faculty of Computer Science University of New Brunswick Canada Centre for Computational Science Coventry University UK

ISBN: (数字)9798350374889

ISBN: (纸本)9798350374896

In this paper, we study the performance of few-shot learning, specifically meta learning empowered few-shot relation networks, over supervised deep learning and conventional machine learning approaches in the problem of Sound Source Distance Estimation (SSDE). In previous research on deep supervised SSDE, low accuracies have often resulted from the mismatch between the training data (from known environments) and the test data (from unknown environments). By performing comparative experiments on a sufficient amount of data, we show that the few-shot relation network outperforms other competitors including eXtreme Gradient Boosting (XGBoost), Support Vector Machine (SVM), Convolutional Neural Network (CNN), and MultiLayer Perceptron (MLP). Hence it is possible to calibrate a microphone-equipped system, with a few labeled samples of audio recorded in a particular unknown environment to adjust and generalize our classifier to the possible input data and gain higher accuracies.

关键词： Support vector machines Metalearning Adaptation models Accuracy Estimation Training data Multilayer perceptrons Boosting Convolutional neural networks Few shot learning

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Personalized Stress Detection using a Lightweight Machine Learning Framework With Convenient Wrist-Worn Sensors

Personalized Stress Detection using a Lightweight Machine Le...

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IEEE Signal Processing in Medicine and Biology Symposium (SPMB)

作者： M. Hasan S. Mithun M. Mahmud S. Baral K. Poudel Computational and Data Science Middle Tennessee State University Murfreesboro TN USA Dept. of Computer Science University of Memphis TN USA Dept. of Electrical and Computer Engineering University of Memphis TN USA Div. of General Internal Medicine Univ. of Tennessee Health Science Center Memphis TN USA Dept. of Computer Science Middle Tennessee State University Murfreesboro TN USA

ISBN: (数字)9798350388572

ISBN: (纸本)9798350388589

Over 70% Americans experience daily stress, making real-time, accurate stress detection crucial for timely intervention, and promoting physical and mental well-being. In this study, we detect stress using machine learning from publicly available wrist-worn sensor data. We examined both population and individual levels, focusing on simple, computation friendly statistical features for reduced processing to facilitate adoptions in real-life mHealth applications. Our framework demonstrates that stress detection achieves over 96% accuracy at both the population and individual levels, with the Extreme Gradient Boosting classifier achieving the highest predictive accuracy. We found significant variability between individuals and achieved an average accuracy of 99.63% with area under the curve (AUC) 99.23%, F1-score 99.00%, precision, and recall 99.22%. This approach offers a promising solution for real-life stress detection due to its high accuracy and simplicity.

关键词： Analytical models Accuracy Focusing Human factors Signal processing Predictive models Feature extraction Boosting Real-time systems Biology

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Advance Monitoring System for Mountain Climber's Health

Advance Monitoring System for Mountain Climber's Health

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Signal Processing, Computation, Electronics, Power and Telecommunication (IConSCEPT), International Conference on

作者： R Surender P Gowtham Likhith Reddy Sundaresan Sabapathy Dept. of ECE Sathyabama Institute of Science and Technology Chennai India Dept. of EC Sathyabama Institute of Science and Technology Chennai India School of AI Computational Engineering and Networking Amrita Vishwa Vidyapeetham Coimbatore India

ISBN: (数字)9798331540685

ISBN: (纸本)9798331540692

This paper presents an innovative solution for continuously monitoring the health of mountain climbers in real-time. The proposed system collects vital health parameters such as pulse rate, body temperature, live location, and the orientation of the climber's body. In cases where the climber's body temperature drops below a certain threshold, a Peltier device is employed to initiate heating and restore the body temperature to a safe level. The gathered data is transmitted wirelessly using long range (LoRa) technology for efficient long-distance communication. A receiver module captures the transmitted data and displays it on a liquid crystal display (LCD) for immediate observation. Additionally, the receiver module is connected to the Blynk cloud platform via the internet, enabling remote access to the collected data from anywhere in the world. This advanced monitoring system ensures the safety and well-being of mountain climbers by providing real-time health status updates and intervention capabilities, thereby enhancing the overall safety measures in mountainous regions.

关键词： Cloud computing LoRa Receivers Medical services Signal processing Real-time systems Liquid crystal displays

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Real-Time Driver Drowsiness Detection Using Mobile Net for Road Safety Enhancement

Real-Time Driver Drowsiness Detection Using Mobile Net for R...

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Disruptive Technologies (ICDT), International Conference on

作者： Chengamma Chitteti Mallavarapu Sravika Shaik Ayesha Dungavath Sridhar Naik Vangipuram Anil Kumar Muthyamaina Pavan kumar Dept. of Data Science Mohan Babu University (Erst While Sree Vidyaniketan Engineering College) Tirupati A.P India Dept. of CSSE Sree Vidyanikethan Engineering College Tirupati A.P India

ISBN: (数字)9798331519582

ISBN: (纸本)9798331519599

Research shows that fatigue ranks within the three main road accident causes worldwide. This paper develops a driver sleep detection system in real time with MobileNet as the light weight convolutional neural network architecture built specifically for indoor and mobile environments. Real-time system facial scans seek driver indicators of drowsiness which include lengthy eye closures and slow yawn activity leading to sleepiness. MobileNet operates at a low cost which makes it both rapid and functional for vehicle implementation without accuracy compromises. The system helps prevent driver fatigue which leads to better vehicle safety because drivers receive instant feedback through this alert model. This solution presents several advantages such as affordability as well as mobility through mobile devices which makes it ideal for current vehicle safety requirements. Fatigue signs to avoid accidents. When drowsiness accelerates the slowdown of reaction times but increases alertness it causes higher risks of vehicle crashes. System implementations that depend on physical signals need drivers to maintain close positions with the equipment which results in driver distraction. We evaluate the driver eye movement behavior to measure driver behavior through imaging-based dataset measurements in this paper. MobileNet eliminates optical images successfully at a low cost thus speeding up the visual fatigue relief process. MobileNet model demonstrated a performance of 94 % in the evaluation results. The system requires 0.00829 seconds to recognize tiredness. The system delivers exceptional real-time functionality thus making it an extraordinary example. Better performance was achieved through the implementation of hyperparameter optimization in connection with k fold validation. The study matters greatly because its implementation would assist in protecting lives by presenting an effective strategy to stop road fatalities arising from unpredictable seizures.

关键词： Visualization Accuracy Road accidents Costs Sleep Vehicle safety Fatigue Real-time systems Road safety Vehicles

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Measures of Organizational Process Performance in the Capability Maturity Model Integration 24

Measures of Organizational Process Performance in the Capabi...

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24th International Arab Conference on Information Technology, ACIT 2023

作者： Al-Ghuwairi, Abdel-Rahman Al-Fraihat, Dimah Sharrab, Yousef Kittaneh, Ahmed Ali, Ahmed Alsarhan, Ayoub Alkhalidy, Muhsen The Hashemite University Faculty of Prince Al-Hussein Bin Abdallah Ii for Information Technology Dept. of Software Engineering Jordan Isra University Faculty of Information Technology Dept. of Software Engineering Jordan Isra University Faculty of Information Technology Dept. of Artificial Intelligence and Data Science Jordan The Hashemite University Faculty of Prince Al-Hussein Bin Abdallah Ii for Information Technology Dept. of Cyber Security Jordan The Hashemite University Faculty of Prince Al-Hussein Bin Abdallah Ii for Information Technology Dept. of Information Technology Jordan

ISBN: (纸本)9798350384307

The Capability Maturity Model Integration (CMMI) is widely utilized by organizations to facilitate process improvement. Among its 22 process areas, this paper focuses on the Organizational Process Performance (OPP) area, which assesses the effectiveness of a process. Specifically, this study examines one goal (establishing performance baselines and models) and two practices (establishing quality and process performance objectives and selecting processes within OPP. To measure these practices, the Goal Questions Metrics (GQM) approach is employed. data was collected using a questionnaire, and the defined measures are validated quantitatively. 250 responses were used for analysis. The results obtained indicate that implementing the defined measures in companies would be beneficial for achieving high-quality processes in OPP and advancing the maturity level of CMMI. © 2023 IEEE.

关键词： Goal Questions Metrics (GQM) Software Quality, Organizational Process Performance, Capability Maturity Model Integration, CMMI

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AutoMediVision:A Deep Learning System for Accurate Pharmaceutical pill identification

AutoMediVision:A Deep Learning System for Accurate Pharmaceu...

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data science, Agents & Artificial Intelligence (ICDSAAI), International Conference on

作者： G Rajeswarappa B. Venkata Sivaiah M. Navya Y. Lasya Shaik Mohammad Sameer V. Varun Kumar Department of CSE G Pulla Reddy Engineering College Kurnool Dept. of Data Science School of Computing Mohan Babu University (Erstwhile Sree Vidyanikethan Engineering College) Tirupathi India Dept. of Computer Science and Systems Engineering(CSSE) Sree Vidyanikethan Engineering College Tirupati India

ISBN: (数字)9798331537555

ISBN: (纸本)9798331537562

The goal of this project is to build a high performance system that offers prediction of pill information from images, the pill name, medical use and recommended dosage by using MobileNet which is a lightweight and efficient deep learning model. This system serves as an important tool for healthcare professionals and patients as it offers a means of reducing risk because of the large number of medications out there. For this task, MobileNet was chosen for its high accuracy and good balance between computational efficiency and high accuracy, and hence can be deployed on mobile devices. Having trained the model on a wide variety of pills with class wise annotations, it generalized well for diverse pills. The output of the system is the pill name, primary medical indications and common dosage instructions that help in safer medication management by supporting healthcare providers in providing better care and patient safety.

关键词： Deep learning Accuracy computational modeling Medical services Mobile handsets Safety Standards Research and development Biomedical imaging Pharmaceuticals

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Optimizing Production and Inventory Management for Defective Items in Manufacturing Systems 3

Optimizing Production and Inventory Management for Defective...

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3rd International Conference on Intelligent Systems, Advanced Computing, and Communication, ISACC 2025

作者： Ahmed, Khandakar Rabbi Hossain, Arafat Asif, MD Amaddus Bepary Mohammad, Mahathir Rahaman, Mustafizur Dewan, MD Arafat Miyan Research Institute International University of Business Agriculture and Technology Dhaka Bangladesh Central Michigan University Dept. of Business Data Analytics Mt Pleasant United States Southern California State University Dept. of Business Administration and Management Los AngelesCA United States Southern California State University Dept. of Business Administration and Management United States Westcliff University Dept. of Cloud Engineering IrvineCA United States Los Angeles City College Dept. of Computer Science Los Angeles United States

ISBN: (纸本)9798331523893

Manufacturing defective items is one of the challenges for production efficiency and inventory control. Conventional approaches like Economic Order Quantity (EOQ) and Just-in-Time (JIT) often fall short of the flexibility needed to respond to quality variations in real time. It promotes defect detection and control by employing machine learning (ML) for inventory management. Four classifiers were tested using the UCI SECOM dataset (Bagging, AdaBoost, Gradient Boosting, and Random Forest) to identify defective items. We used effective preprocessing techniques to boost model performance, such as SMOTE for class balancing and PCA for reducing dimensionality and standardization. The bagging and random forest classifiers produced 96.6% and 96.9% test accuracies, with good precision and recall scores. Within this context, we propose an Adaptive Inventory Management Framework comprising machine learning algorithms incorporating predictive analytics to reduce waste, optimize production scheduling(s), and improve resource allocation. These results are consistent with defining Industry 4.0 strategies and show that ML has the potential to transform defect management and inventory optimization in manufacturing systems. © 2025 IEEE.

关键词： Industry 4.0

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An Extensive Analysis of Machine Learning and Deep Learning Based Banana Leaf Disease Detection Techniques 10

An Extensive Analysis of Machine Learning and Deep Learning ...

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10th International Conference on Advanced Computing and Communication Systems, ICACCS 2024

作者： Anitha, P. Venkata Ratnam, K. Rayudu, K.M. Naidu, Kamparapu V.V. Satya Trinadh Lalitha, B. Bhaskar, P. Dept.of Cse Andhra Pradesh Ongole India Dept.of Cse Andhra Pradesh India Dept. of Data Science Ongole India Andhra Pradesh Madanapalle517325 India Jntua College of Engineering Head of Cse Chittoor District Andhra Pradesh Kalikiri India Dept.of Cse Andhra Pradesh Ongole India

ISBN: (纸本)9798350384369

Infection of plants is a major limitation and a difficult effort. Diseases affecting plants have the potential to harm fruit and foliage, causing the agriculture sector to suffer large financial losses. Infections of plants are primarily evident on the foliage and stems of the plants. Disease categorization and identification by hand takes a lot of effort and requires the participation of professionals. But quickly, this procedure may be aided by the installation of a system that is computerized. An autonomous maladies identification and categorization system is presented by many existing researchers, which can aid those who cultivate banana crops and make a contribution to the nation's economy thanks to advancements in Machine Learning (ML) and Deep Learning (DL) strategies. The principal objective of this work is to conduct a thorough examination of existing works that have used ML and DL approaches in the agricultural sector, specifically concerning the development of banana plants. As a result, it can be useful for upcoming investigators to determine the caliber and nature of prior studies. The researchers looked at issues about banana plantations. Additionally, the researchers have examined the ML-based models that have been put into use, the data-gathering resources, and the thorough outcomes attained for every study. © 2024 IEEE.

关键词： Fruits

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Simplified Micropayment Mechanism to Eliminate the Risk of Double Payment in E-Commerce

Simplified Micropayment Mechanism to Eliminate the Risk of D...

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Advances in Intelligent Computing and Applications (AICAPS), International Conference on

作者： Narendra Kumar Chahar Krishan Pal Singh Muzzammil Hussain Dept. of Data Science and Analytics Central University of Rajasthan Ajmer India Dept. of Computer Science and Engineering Malaviya National Institute of Technology Jaipur India Dept. of Computer Science and Engineering Central University of Rajasthan Ajmer India

In recent years, the emergence of the Internet and E-commerce has steered significant growth in digital transactions. Businesses today need mobile wallets, credit and debit cards, and e-cash to digitize payments. Digital payment systems are in transition and promise amazing advancements, but they also pose many risks and as the number of online transactions is increasing tremendously, we need a security system that follows all security norms. In this paper, we study digital transaction systems and evaluate various components of E-commerce plat-forms to address the security of these services. We evaluate the attributes that affect the security of digital payment processes and identify several barriers that hinder their performance to propose a simplified payment mechanism for micro-payments that eliminates the double payment problem.

关键词： Micropayments Internet Security Electronic commerce Business

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