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Precision Pistachios: Enhancing Agricultural Quality control with Deep Learning 2

Precision Pistachios: Enhancing Agricultural Quality Control...

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2nd international conference on intelligent Cyber Physical systems and Internet of Things (ICoICI)

作者： Kaur, Arpanpreet Chitkara Univ Inst Engn & Technol Rajpura Punjab India

ISBN: (纸本)9798331540661;9798331540678

Originating from the pistachio tree (Pistacia vera) and prized for their nutritional content and adaptability in the kitchen, pistachios have great financial worth in the agricultural field. Ensuring the quality and safety of pistachio nuts is absolutely vital and calls for effective industrial post-harvest methods. Modern technology, especially image processing and computer vision algorithms, are augmenting conventional approaches of pistachio categorization and separation. This work intends to build an enhanced classification model for pistachio identification using deep learning, most especially the VGG16 Convolutional Neural Network (CNN). After gathering a dataset of 2,148 high-resolution photos of pistachios, the approach uses resizing and data augmentation methods including rotation, flipping, and zooming to improve the generalization and prevent overfitting of the model. Pretrained on ImageNet, the VGG16 model is fine-tuned to fit the particular job of pistachio recognition by means of feature extraction capacity. For feature extraction, the model architecture comprises convolutional layers;for classification, it features fully connected layers. With performance criteria including accuracy, precision, recall, and F1-score watched to guarantee efficient learning, optimization methods including gradient descent and backpropagation are utilized during training. On a test set, the model shows an extraordinary accuracy of 0.98, so highlighting the potential of deep learning models-especially VGG16-in automating and improving quality control systems in agricultural uses. With possible uses in related agricultural research, this highperformance classification model not only meets the demand for effective separation of pistachio species but also considerably increases their economic value.

关键词： Pistachio detection Convolutional Neural Networks (CNNs) VGG16 Image processing Computer vision Deep learning Classification model Data augmentation Quality control Agricultural Feature extraction post-harvest processes Economic species

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intelligent Edge-Based System for Driver Drowsiness Monitoring in Mobile Crowd-sourcing Environments 2

Intelligent Edge-Based System for Driver Drowsiness Monitori...

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2nd international conference on Recent Trends in Microelectronics, Automation, Computing, and Communications systems, ICMACC 2024

作者： Neels Ponkumar, D. David Ramesh, S. Ahamed, A. Niyas Baskar, S. Ashok, Chitrada Charan, M. Vel Tech Rangarajan Dr. Sagunthala R&D Institute of Science and Technology Department of Electronics and Communication Engineering Tamil Nadu Chennai India School of Computing College of Engineering and Technology SRMIST Department of Computing Technologies Kattankulathur Campus Tamil Nadu Chengalpattu India Thamirabharani Engineering College Department of Electronics and Communication Engineering Tamil Nadu Tirunelveli India Vel Tech Rangarajan Dr. Sagunthala R&D Institute of Science and Technology Department of Civil Engineering Tamil Nadu Chennai India

ISBN: (纸本)9798350366570

Using CNN and GAN improves road safety, says study. Modern machine learning techniques for mobile crowdsourcing may identify driver fatigue early. Our convolutional neural network (CNN) model detects drivers' fatigue using facial expressions, head movements, and other visual clues. Mobile devices and high-resolution cameras make the system straightforward to install in automobiles. GAN (Generative Advisal Network) handles unequal and limited training data. Creating realistic, synthetic weary driver photos improves the dataset and helps CNN learn from different driving scenarios and face features. Our edge-based computing distinguishes it. In mobile crowdsourcing with unpredictable internet connections, processing data locally on users' smartphones minimizes latency and bandwidth. The edge-based solution processes sensitive data locally on the device rather than sending it to servers, increasing user privacy. Extensive testing showed our system diagnosed driver weariness better than others. GAN-generated data increased model performance in under-represented training datasets. The study shows how to merge CNN and GAN in edge computing with 98.4% accuracy and driver fatigue monitoring. We found important implications for mobile crowdsourcing and intelligent transportation system development. © 2024 IEEE.

关键词： Convolutional neural networks

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Millimeter-Wave Radar for Non-Contact Detection of Respiratory and Cardiac signals: A Comparative Study of signal Decomposition Algorithms 2

Millimeter-Wave Radar for Non-Contact Detection of Respirato...

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2nd IEEE international conference on signal, Information and Data processing, ICSIDP 2024

作者： Tao, Pengyan Yeh, Chien-Hung Shi, Wenbin Beijing Institute of Technology School of Information and Electronics Beijing China Key Laboratory of Brain Health Intelligent Evaluation and Intervention Beijing Institute of Technology Beijing China

ISBN: (纸本)9798331515669

The advancement of non-contact physiological signal monitoring technology is becoming increasingly important. This study combines millimeter-wave radar and advanced signal processing methods to accurately detect heart and respiratory signals. Variational Mode Decomposition (VMD), Dynamic Bandwidth Variational Mode Decomposition (DB-VMD), Fast Ensemble Empirical Mode Decomposition (fEEMD), and band-pass filters were employed to decompose radar signals into respiratory and cardiac components. The findings demonstrate that VMD and its variants outperform other methods, maintaining high accuracy across different distances and monitoring durations. Experimental results show that DB-VMD can effectively isolate vital signals even in noisy environments, making it well-suited for non-contact health monitoring applications. This study contributes to the development of reliable and user-friendly monitoring systems, enhancing patient comfort. © 2024 IEEE.

关键词： Variational mode decomposition

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Real-Time Capable Transient H2 Partial Pressure Model for Proton Exchange Membrane Fuel Cell systems 11

Real-Time Capable Transient H2 Partial Pressure Model for Pr...

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11th Vienna international conference on Mathematical Modelling (MATHMOD)

作者： Osterhammer, Martin Du, Fengmin Formanski, Volker Heldwein, Marcelo Lobo Tech Univ Munich Arcisstr 21 D-80333 Munich Germany BMW Grp Petuelring 130 D-80809 Munich Germany

Fuel cell electric vehicles hold a significant promise for reducing the carbon footprint in the automotive sector by leveraging H-2 as a clean fuel source. Maintaining a consistent H-2 supply to the fuel cell system is vital. N-2 crossover can lead to an inert gas built-up on the H-2 supply side, adversely affecting fuel cell performance and durability. Through purging, gases are released, and a N-2 built-up in the H-2 supply system can be prevented, yet this also leads to fuel loss. This fuel loss can be minimized by keeping an optimal N-2 molar fraction. We developed a dynamic model for effectively designing, controlling, and diagnosing fuel cell systems by predicting the N-2 molar fraction in the H-2 supply. This model considers factors such as N-2 distribution throughout the fuel cell stack, N-2 crossover, and the purge process. The model is simplified to a differential equation of first order and solved using the explicit Euler method at a typical automotive time step of 0.01 s. The proposed model is validated by a H-2 measurement in a fuel cell system with passive recirculation. Copyright (c) 2025 The Authors. This is an open access article under the CC BY-NC-nd license (https://***/licenses/by-nc-nd/4.0/)

关键词： Automotive control Automotive system identification and modeling Modeling supervision control and diagnosis of automotive systems Fuel cells for Automotive Applications Model validation

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Traffic Light control Using Reinforcement Learning 2

Traffic Light Control Using Reinforcement Learning

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2nd IEEE international conference on Integrated Circuits and Communication systems, ICICACS 2024

作者： Masfequier Rahman Swapno, S.M. Nuruzzaman Nobel, S.M. Ramachandra, A.C. Islam, Md Babul Haque, Rezaul Rahman, Mohammad Mominur Bangladesh University of Business and Technology Dept of CSE Dhaka Bangladesh Nitte Meenakshi Institute of Technology Department of Electronics and Communication Engineering Bengaluru India UNICAL Dept. of Computer Modeling Electronic and System Engineering Rende Italy East West University Dept of CSE Dhaka1212 Bangladesh Hamad Bin Khalifa University College of Science and Engineering Qatar

ISBN: (纸本)9798350317558

Nowadays, one of the biggest issues in urban areas is traffic. This problem wastes important time and contributes to air and sound pollution. This affects people's general quality of life in addition to posing health dangers. Our study attempts to mitigate these problems, effectively cutting down on wait times and delays. Our Reinforcement Learning method creates intelligent agents that can adjust traffic lights at crossings instantly. Our objective is to minimize delays, reduce congestion, reduce travel times, improve safety, and improve traffic flow. We implemented the Deep Q Learning algorithm which activities yield the greatest benefits under various traffic scenarios. Our model can the sequence time since the Green signal (GS) lasts 10 seconds and the Red signal (RS) lasts 5 seconds. The waiting period is shortened by 50% as a result. This study suggests reinforcement learning may improve traffic signal controller synchronization and urban traffic congestion. This novel method may improve transport efficiency and sustainability. © 2024 IEEE.

关键词： Action Deep Q Learning GS Model Q Value Rewards RS State Traffic signal vehicles Waiting Time

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intelligent Framework to Detection of Fake Review Using Machine Learning Algorithms 2

Intelligent Framework to Detection of Fake Review Using Mach...

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2nd international conference on Recent Trends in Microelectronics, Automation, Computing, and Communications systems, ICMACC 2024

作者： Janu, Neha Anjali Manipal University Jaipur Department of Computer Science & Engineering Rajasthan Jaipur India Poornima College of Engineering Department of Computer Science & Engineering Rajasthan Jaipur India

ISBN: (纸本)9798350366570

Online reviews are becoming a more important resource for consumers as they make purchasing decisions. Recent years have seen a rise in the importance of the study fields of social media, hotel reviews, and e-commerce websites. People's decisions are influenced by online reviews. The bogus review has an impact on both consumers and commercial organizations in day-to-day existence. Inflated ratings and fake reviews paint a false picture of a product or service and are intended to mislead consumers away from or towards that product or service. In this research paper, machine learning algorithms are used for detecting fake reviews. The prevalence of fake reviews on online platforms has become a major concern, making it difficult for customers to trust the reviews they read. In this study, we used a dataset of Yelp Chicago restaurants' online reviews and applied various techniques of machine learning to identify phony reviews. The algorithms used included Naive Bayes, Random Forest, Linear SVM, and NN. The performance was evaluated for the models based on F1-score, recall, precision, and accuracy. Our results show that fake reviews can be detected effectively with high accuracy with the help of machine learning algorithms. This study provides a useful framework for identifying fake reviews on online platforms and can help improve the credibility and trustworthiness of online reviews. © 2024 IEEE.

关键词： Random forests

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Conceptualization of a Cardiac Monitoring System via Bluetooth Communication Protocol to Mobile Devices 2

Conceptualization of a Cardiac Monitoring System via Bluetoo...

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2nd international conference on intelligent Data Communication Technologies and Internet of Things, IDCIoT 2024

作者： Karuppiah, Santhi Anuradha, T. Sivachandar, K. Thyla, B. Dhivya, D. Jose Anand, A. Vel Tech High Tech Dr. Rangarajan Department of Computer Science and Engineering Dr Sakunthala Engineering College AvadiTamil Nadu Chennai India KCG College of Technology Department of Electrical and Electronics Engineering Karapakkam Tamil Nadu Chennai600097 India RMK College of Engineering and Technology Department of Electronics and Communication Engineering Puduvoyal Tamilnadu Thiruvallur601206 India KCG College of Technology Department of Electronics and Communication Engineering Karapakkam Chennai600097 India Veltechmultech Dr Rangarajan Dr Sakunthala Engineering College Department of AIDS Avadi Tamil Nadu Chennai600062 India

ISBN: (纸本)9798350327533

The widespread use of mobile devices around the world ends up making the subject interesting for the development of new research in the most diverse areas. One of them is medicine, in which eHealth can be mentioned, which consists of the use of information technologies aimed at health, using electronic means to improve services and access to information. Within this concept is mHealth, which represents the presence of mobile devices (mobility) in relation to existing commercial eHealth telemedicine systems. The present work presents the use of mobile devices as a platform for monitoring physiological signals. Its objective is to acquire a patient's cardiac signals through a pair of electrodes (a lead), signal processing, transmission via Bluetooth communication protocol to mobile devices. © 2024 IEEE.

关键词： Bluetooth communication protocol 3 Cardiac monitoring system 2 Heart rate monitoring Mobile devices 4 Wearable sensor 5

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Analysis of Speaker Power control System Based on Automobile Mechanical Structure Characteristics 2

Analysis of Speaker Power Control System Based on Automobile...

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2nd IEEE international conference on control, Electronics and Computer Technology, ICCECT 2024

作者： Jiang, Wei Feng, Pengfei Intelligent Transportation Modern Industry College Sanlian University Hefei China

ISBN: (纸本)9798350380958

Cars gradually became the main means of transportation. However, every advantage has its disadvantages, and urban congestion caused by driving is becoming more and more serious. The main reason is that the driving level of drivers in our country is generally low, and the growth rate of vehicles is much higher than the road construction speed, which will inevitably cause congestion. Many drivers will inevitably become irritable and honk their horns too much, and the volume of vehicle horns is currently too high. Speakers that are too loud are bound to scare pedestrians and worsen traffic congestion. Therefore, we must conduct necessary research on noise. In order to ensure that the sound of car horns cannot only alert pedestrians and attract the attention of other vehicles, but also ensure the effect of bringing noise to the urban environment, many domestic experts and scholars have conducted in-depth research on the types of car horns. There are some characteristics of sound. Research has found that the sound of the whistle is closely related to the power of the speaker. Therefore, according to the mechanical structural characteristics of the car, this paper designs a power control system including a power module, ranging module, main control system and display alarm system. The main control system is the core of the entire power system, mainly completing the collection, output, display and alarm system control of signals. The power module mainly completes the driving work of the speaker. © 2024 IEEE.

关键词： Alarm systems

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Doppler Measurement-Based Positioning and Tracking Methods with External Radiation Radar 2

Doppler Measurement-Based Positioning and Tracking Methods w...

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2nd IEEE international conference on signal, Information and Data processing, ICSIDP 2024

作者： Liu, Weifeng He, Linqing Tian, Zhengwang Shaanxi University of Science and Technology School of Electrical and Control Engineering Xi'an China Hangzhou Dianzi University School of Automation Hangzhou China

ISBN: (纸本)9798331515669

This paper introduces a new approach for addressing the complex problem of Multi-target tracking in external radiation radar systems equipped with Multi-static Doppler measurements. Drawing from research on sensor arrays and mobile sensors, the method utilizes the Generalized Labelled Multi-Bernoulli (GLMB) filter to navigate the intricacies of Multi-target tracking across sensor arrays. The study validates the effectiveness of the GLMB and the computationally efficient Gibbs-GLMB filters in a setup involving three transmitters and one receiver. Experimental results, assessed with the Optimal Sub-Pattern Assignment (OSPA) metric, suggest that the Gibbs-GLMB filter accelerates computation while potentially compromising on measurement precision, which could lead to reduced accuracy in specific cases. The investigation also examines the influence of sensor geometry on tracking results, indicating that there is an association between tracking performance and sensor arrangement. © 2024 IEEE.

关键词： Radar target recognition

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Technologies and systems for Monitoring the Onset of Accidents at Strategic Construction 2nd

Technologies and Systems for Monitoring the Onset of Acciden...

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2nd international conference on Information Technologies and Their Applications

作者： Aliev, Telman Musaeva, Naila Rzayeva, Narmin Mammadova, Ana Azerbaijan Univ Architecture & Construct Ayna Sultanova 11 Baku Azerbaijan Inst Control Syst Minist Sci & Educ 68 B Vahabzade AZ-1141 Baku Azerbaijan Azerbaijan Tech Univ G Javid Ave 25 AZ-1148 Baku Azerbaijan

ISBN: (纸本)9783031734199;9783031734205

Currently, the beginning of transition to emergency states is not reflected in the readings of measurement equipment of control systems of many essential technical facilities. The signaling of the beginning of the latent period of an emergency state is not implemented either. There are no tools for monitoring malfunctions of the railroad bed, or for controlling the beginning of the emergence of various typical malfunctions in the equipment of urban water supply systems, which are operated in continuous rotary motion under high pressure. control systems of said facilities detect malfunctions only when they already have a clearly pronounced form. Nevertheless, specialists and operating personnel use this information to take action and prevent various accidents. However, in some cases, it is too late and an accident becomes unavoidable. At the same time, the emergence of a malfunction at these facilities is accompanied with the emergence of noise in the vibration signals at the outputs of the vibration sensors in their control systems. This noise contains diagnostic information. In this study, we propose technologies for generating informative attributes that can be used to control the beginning of the latent period of accidents. We also show the possible applications of the proposed technologies in developing intelligent systems for controlling the onset of accidents at facilities under investigation and other strategic construction.

关键词： control signaling noise informative attribute construction facilities pumping stations malfunction seismic stability railroad track vibration signal

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