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3rd international conference on Innovative Practices in technology and Management, ICIPTM 2023

作者： Soumya, K.V. Sujitha, S. Mohan Das, R. Kanaujia, Sharmi Agarwalla, Sanskriti Sameer, Shaik Manzoor, Tabasum New Horizon College of Engineering Department of Electrical and Electronics Engineering Bangalore India

ISBN: (纸本)9798350336238

In an environment where terrorist group actions are heavily predominate, the study introduces novel modeling tools that really are adept at controlling, coordinating, manipulating, detecting, and tracing drones. Modern humans now need to simulate their surroundings in order to boost their comfort and productivity at work. The ability to imitate a person's everyday work has undergone tremendous advancement. A simulation is a representation of how a system or process would work in the actual world. © 2023 IEEE.

关键词： Drones

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AI-Based Driver Drowsiness and Distraction detection in Real-Time 3

AI-Based Driver Drowsiness and Distraction Detection in Real...

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3rd IEEE international conference on Computational intelligence and Knowledge Economy, ICCIKE 2023

作者： Kurian, Anna Titu Kumar Soori, Prashant Heriot-Watt University School of Enginnering and Physical Sciences Dubai United Arab Emirates

ISBN: (纸本)9798350338263

This paper proposes a solution to combat risks associated with road accidents namely drowsiness and distractions which have been established to be the prominent causes of accidents worldwide. The proposed methodology integrates camera vision and mathematical computations to accurately detect driver drowsiness and distracted driving. The eye aspect ratio and mouth aspect ratio are utilized to recognize drowsiness characteristics while the eye tracking methodology is adopted to identify distracted behavioral factors. On the detection of the mentioned risk factors, alerts are provided to the driver in visual and audio formats by use of the Raspberry Pi microprocessor, LCD display and buzzer. The developed system was tested under an experimental setup and exposed to various lighting conditions. The results suggested that the approach is capable of recognizing drowsiness and distractions with an accuracy of 94.1% and 89% respectively during both day and night conditions and provide warnings as required. © 2023 IEEE.

关键词： Aspect ratio

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UPerNet with ConvNeXt for Semantic Segmentation 3

UPerNet with ConvNeXt for Semantic Segmentation

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3rd IEEE international conference on Electronic technology, Communication and Information, ICETCI 2023

作者： Wang, Runze Jiang, Haoyu Li, Yufei Xidian University School of Artificial Intelligence Xi'an China Beijing University of Chemical Technology College of Information Science and Technology Beijing China Hefei University of Econimics School of Artificial Intelligence Hefei China

ISBN: (纸本)9798350398410

In this article, we adopt UPerNet as the model and ConvNeXt as the backbone for semantic segmentation of cityscapes. UPerNet is a highly compatible architecture that can handle a variety of visual tasks. At the same time, as a convolutional network without adding attention mechanism, convnext has excellent feature extraction *** this article, we combine UPerNet and ConvNeXt to obtain a semantic segmentation model. Their excellent feature extraction ability and multi-scale object detection capability are utilized to enhance the performance of the network. In addition, we select the appropriate ConvNeXt structure, i.e. ConvNeXt-small, according to the size and characteristics of cityscapes dataset, and optimized the loss function to solve the problem of uneven class distribution. The experimental results show that ConvNeXt has excellent performance in feature extraction. Compared with the classic backbone ResNet50, it has improved by 1.96% on aAcc, 12.28% on mIoU and 11.39% on mAcc. After the loss function is optimized, all metrics also increase, which fully proves that our method is effective. © 2023 IEEE.

关键词： Feature extraction

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Design and Implementation of a LoRa & IoT-Based Fire detection and Alert system 3

Design and Implementation of a LoRa & IoT-Based Fire Detecti...

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3rd international conference on Advancement in Electronics and Communication Engineering, AECE 2023

作者： Malik, Praveen Kumar Roges, Rashmi Kumar, Bura Vijay Shukla, Manoj Gupta, Isha Pandey, Shweta School of Electronics and Electrical Engineering Lovely Professional University Punjab Jalandhar India School of Computer Science and Artificial Intelligence Sr University Warangal India Raj Kumar Goel Institute of Technology Uttar Pradesh Ghaziabad India Law College of Dehradun Uttaranchal University Dehradun India

ISBN: (纸本)9798350330724

Since ancient times, fire detection has been a crucial component of fire protection. Ancient civilizations were the first to use rudimentary fire detection techniques like drums, bells, and water buckets to warn others of a fire. Smoke detectors, heat detectors, and flame detectors are just a few of the several kinds of fire detection systems that are available today thanks to advances in fire detection technology over time. A growing number of people are using LoRa-based fire detection devices because they can send data over vast distances with little battery consumption. The design and execution of a LoRa-based fire detection and alert system are shown in this project. A Fire detection Node and a Gateway Node are the two nodes that make up the system. An STM32 blue pill board, an external power source, a fire sensor, a smoke sensor, and a LoRa RF module are all included in the Fire detection Node. The STM32 Blue pill board, ESP8266 NuttyFi Board, LoRa RF Module, and an external power source are all utilized by the Gateway Node. The Fire detection Node is in charge of spotting fire and smoke, and it uses LoRa wireless communication to convey the information to the Gateway Node. The ESP8266 NuttyFi Board is used by the Gateway Node to communicate alerts to the user after receiving data from the Fire detection Node. The system can be used in huge buildings because the LoRa RF modules allow for long-distance communication between the two nodes. Overall, this LoRa-based fire detection and alert system offers a reliable and affordable option for building fire safety. © 2023 IEEE.

关键词： Fires

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Lightweight pothole detection method based on improved YOLOv5 3

Lightweight pothole detection method based on improved YOLOv...

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3rd IEEE international conference on Information technology, Big Data and Artificial intelligence, ICIBA 2023

作者： Zou, Yang Wan, Jiangcheng Wang, Binjie Chongqing University of Posts and Telecommunications School of Computer Science and Technology Chongqing400065 China

ISBN: (纸本)9781665490788

Aiming at the problems of large model weight volume, low detection accuracy and slow detection speed in traditional road Pothole detection methods, which are not easy to be deployed in embedded mobile devices with limited computing resources, a lightweight YOLOv5s road pothole detection model based on attention mechanism was proposed. The YOLOv5s model is taken as the basic model. Firstly, the C3 module in the backbone network is combined with the SimAM attention mechanism, which is improved to the SiMAM-C3 module, which enhances the capability of pothole feature extraction without introducing additional parameters. Then, CARAFE is used to replace the original nearest neighbor interpolation method as the upsampling method to further improve the detection accuracy of the model. Finally, SIoU loss function is used instead of CIoU loss function as bounding box regression loss function to accelerate the convergence of the model. Training and testing on datasets collected from the Internet and self-made data sets. Experimental results show that: With the same data set and the same experimental equipment, the accuracy and average accuracy of the improved YOLOv5s model reached 90.7% and 89.1%, respectively, which were 20.4% and 2.8% higher than those of the YOLOv5s model, and the parameter size was reduced by 1.1MB, showing obvious improvement effect. It can meet the accuracy and real-time requirements of pothole detection in complex environment. © 2023 IEEE.

关键词： Roads and streets

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An experimental teaching platform for machine vision-based real-time detection taking quality inspection of paper cups as instance 3

An experimental teaching platform for machine vision-based r...

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3rd international conference on Educational technology, ICET 2023

作者： Huang, Yanzhe Luo, Yabo Zhou, Xiangyu Wuhan University of Technology School of Mechanical and Electronic Engineering Wuhan China

ISBN: (纸本)9798350302332

In order to improve the quality of students' practical training, a real-time detection system experimental platform based on machine vision has been designed. The system has functions of real-time sampling, detection, defective product removal, result display and data storage, and its structure covers three parts: hardware platform, detection algorithm, and software function implementation. The hardware platform includes image acquisition module, material transportation module and defective product removal module, and the control circuit is based on the development of Arduino and STM32. The detection algorithm is developed based on OPENCV, and it is designed to detect three types of defects: cup rim concavity, cracking, and out-of-roundness. The software system is jointly developed using Visual Studio 2019 and Qt Creator 4.9.1, and it has good interactivity. With this experimental platform, teaching work such as image acquisition system construction, dynamic sampling problem analysis, image detection algorithm development, and interactive interface implementation can be carried out, so as to improve students' engineering design ability in many aspects. © 2023 IEEE.

关键词： Computer vision

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Salient Object detection Based on Transformer and Multi-scale Feature Fusion 3

Salient Object Detection Based on Transformer and Multi-scal...

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3rd international conference on Neural Networks, Information and Communication Engineering, NNICE 2023

作者： Zhao, Lijun Wang, Han School of Software Engineering Beijing University of Technology Beijing China School of Artificial Intelligence and Automation Beijing University of Technology Beijing China

ISBN: (纸本)9798350335972

It is difficult to learn global remote semantic information based on convolutional neural network, and it is difficult to obtain multi-scale feature information based on Vision Transformer, Swin Transformer and Pyramid Vision Transformer. However, salient objects maybe involve different scales. This paper introduces Shunted Transformer as the backbone network to extract multi-scale features to achieve salient object detection. Aiming at the problem of ignoring the difference between different features and dilution of high-level features when fusing high-level and low-level features, a decoder for progressive fusion of multi-scale features is designed. In addition, to solve the problem that the boundary features obtained may not match the salient object due to the separation of the boundary prediction structure and the salient object prediction branch, this paper refers to the BIG module and optimizes its feature input. Finally, the validity of the proposed model is verified by experiments on four widely used datasets. © 2023 IEEE.

关键词： Semantics

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Intelligent solar panel monitoring system and shading detection using artificial neural networks

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ENERGY REPORTS 2023年 9卷 324-334页

作者： Abdallah, Fahad Saleh M. Abdullah, M. N. Musirin, Ismail Elshamy, Ahmed M. Univ Tun Hussein Onn Malaysia Fac Elect & Elect Engn Green & Sustainable Energy GSEnergy Focus Grp Parit Raja Malaysia Univ Teknol MARA Fac Engn Shah Alam Malaysia Minia Univ Fac Engn Al Minya Egypt

Detecting shading in Photovoltaic panels (PV) is crucial for ensuring optimal energy generation. This paper proposes a novel monitoring system that uses Artificial Neural Network (ANN) technology to detect shading and other faults in PV panels. The system is also supervised using an Internet of Things (IoT) monitoring platform, which provides real-time data analysis and alerts. The proposed system's main contribution is its ability to detect shading, which can significantly impact energy generation. The ANN technology accurately detects shading and other faults, while the IoT platform enables remote monitoring and data analysis. Overall, this paper presents a valuable contribution to the field of PV monitoring systems by proposing a novel system that detects shading using ANN technology and is supervised using an IoT monitoring platform. The system's ability to accurately detect shading and other faults can significantly improve energy generation efficiency and reduce maintenance costs.

关键词： Photovoltaic Artificial neural network Monitoring system Shading detection

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A Novel Deep Belief Network with Butterfly Optimization Algorithm for the Classification of Paddy Leaf Disease detection 3

A Novel Deep Belief Network with Butterfly Optimization Algo...

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3rd international conference on Artificial intelligence and Smart Energy, ICAIS 2023

作者： Lathamaheswari, U. Jebathangam, J. Vels Institute of Science Technology and Advanced Studies Department of Computer Science Chennai India

ISBN: (纸本)9781665462167

The widespread presence of a wide variety of diseases during paddy farming is one of the most significant elements that annually contributes to enormous economic losses. These losses occur as a direct result of the widespread prevalence of these diseases. In this paper, a deep learning algorithm using Deep Belief Network (DBN) and a meta-heuristic optimization using Butterfly optimization algorithm (BOA) is used to classify the images to detect the diseases in a Plant Leaf. The steps of classification involve three different process that includes pre-processing, feature extraction and classification. The simulation is conducted in python to test the efficacy of the classifier. The result of simulation shows that the proposed method has obtained higher classification rate than the existing machine learning classifiers.. © 2023 IEEE.

关键词： Image classification

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Application of Generative Artificial intelligence in English Education: Taking ChatGPT system as An Example 3

Application of Generative Artificial Intelligence in English...

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3rd international conference on Educational technology, ICET 2023

作者： Tang, Zezong Zhang, Yi Northwestern Polytechnical University School of Foreign Studies Xi'an China

ISBN: (纸本)9798350302332

As a crucial strategic support for advancing national education, artificial intelligence possesses immense application potential. Developed by OpenAI, the generative artificial intelligence model, ChatGPT, has attracted considerable attention and sparked extensive debate since its launch. This study, taking ChatGPT as an exemplar, explores the prospective applications of generative artificial intelligence in English education based on its four core competences: text generation, contextual dialogue understanding, sequential task accomplishment, and programming language interpretation capabilities. The inquiry probes into ChatGPT's potential to augment students' English proficiency across all linguistic abilities, encompassing listening, speaking, reading, and writing. Furthermore, this research offers a preliminary validation of the feasibility of implementing generative artificial intelligence within English education through specific instructional tasks such as syllabus creation, self-learning support, and pedagogical feedback from three aspects: educational subject, educational object, and educational process. Additionally, this paper delves into the merits of employing artificial intelligence in the realm of education, hoping to furnish more insights for future inquiries. © 2023 IEEE.

关键词： Education computing

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