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检索条件"任意字段=2023 3rd International Conference on Detection Technology and Intelligence System, DTIS 2023"

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Human Posture detection on Lightweight DCNN and SVM in a Digitalized Healthcare system 3

Human Posture Detection on Lightweight DCNN and SVM in a Dig...

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3rd international conference on Applied Artificial intelligence (ICAPAI) - Human-Centric AI for the Digital Society

作者： Ogundokun, Roseline Oluwaseun Maskeliunas, Rytis Damasevicius, Robertas Kaunas Univ Technol Dept Multimedia Engn Kaunas Lithuania Landmark Univ Omu Aran Dept Comp Sci Omu Aran Nigeria Landmark Univ Omu Aran SDG 11 Sustainable Cities & Commun Omu Aran Nigeria Kaunas Univ Technol Fac Informat Kaunas Lithuania

ISBN: (纸本)9798350328929

The domains of Human Posture (HP) and artificial intelligence (AI) have placed significant emphasis on Human Posture Classification (HPC). By recognizing the standing, sitting, and walking positions of aged people, HPC could effectively monitor their health condition and ensure their well-being. detection of various postures is difficult owing to the need for enough datasets and a classification framework. MobileNet is a lightweight Deep Convolution Neural Network (DCNN) with a better recognition rate and fewer parameters. To further increase the generalization ability while reducing the number of models hyperparameters, techniques such as regularization, Transfer Learning (TL) and neural architecture search can be employed. To address the issue of a shortage of annotated data and improve approach effectiveness, TL and Data Augmentation (DA) are utilized for MobileNet and Xception in this study. The Support Vector Machine (SVM) model boosts performance instead of the final Fully Connected Layer (FCL). To facilitate the effectiveness of our approach, TL and DA are both used for SVM. The investigation associated the efficacy of the suggested approach with that of other cutting-edge image classification techniques, including ResNet50V2, InceptionV3, and DenseNet201, and found that the presented methodology is better. Our recommended method captures temporal and depth characteristics from the image separately and incorporates them into classification computations. The suggested approach founded on MobileNet hybridized with SVM attains the top performance with 92.12% test accuracy (Acc), 95 % area under the curve (AUC), 92% recall (Rec), 93% precision (Prec), 92% F1 score and a computational period of 3974secs.

关键词： Human Posture detection Deep Learning Transfer learning Image classification Human Posture Artificial intelligence

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detection of COVID-19 Using Convolutional Neural Networks 3

Detection of COVID-19 Using Convolutional Neural Networks

引用

3rd international conference on Innovative Sustainable Computational Technologies, CISCT 2023

作者： Srivastava, Arshita Mishra, Shubham Rameshwaram Institute of Management and Technology Dr. APJ Abdul Kalam Technical University Department of Computer Science and Engineering Lucknow India

ISBN: (纸本)9798350303360

A highly contagious illness caused by the COVID-19 pandemic is proven to havoc with people's health and well-being all over the globe. Chest radiography is one of the most crucial databases for applying detection techniques. The respiratory system is contaminated by COVID-19, which also affects the alveoli and replicates itself. Conventional approaches such as RT - PCR tests, rapid antigen tests, serological tests, etc. are generally used for the detection of COVID came out to be costly and time-consuming. There have been several suggested artificial intelligence (AI)-based models for the detection of COVID-19 in contaminated individuals' using lung ultrasound images, voice patterns, chest sounds, etc. The proposed model shows how disease cases could be identified using features and variations in chest radiography images. A deep convolutional neural network (CNN) with ResNet50 modified configuration has been used for the identification of COVID-19 cases from the chest radiography image dataset. The suggested model also depicts the comparison of the proposed technique with the Resnet 101 model. Chest radiography dataset containing Covid infected, normal, and pneumonia-infected images. Additionally, the suggested model can identify covid-19 patients' current conditions in real-time by identifying coronavirus diseases from CT scan pictures. The database has the ability to monitor detected patients and keep their databases in order to improve the training and model's accuracy. The proposed model provides approximately 96.73% of accuracy and shows explicit competency with ResNet 101 and other existing models. © 2023 IEEE.

关键词： COVID-19

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Deep Learning with Histogram of Oriented Gradients- based Computer-Aided Diagnosis for Breast Cancer detection and Classification 3

Deep Learning with Histogram of Oriented Gradients- based Co...

引用

3rd international conference on Smart Data intelligence, ICSMDI 2023

作者： Ponraj, Anitha Canessane, R.Aroul Sathyabama Institute of Science and Technology Sathyabama University Chennai India

ISBN: (纸本)9781665464871

In the modern era, cancer is a major public health concern. Breast cancer is one of the leading causes of death among women. Breast cancer is becoming the top cause of death in women worldwide. Early identification of breast cancer allows patients to receive proper treatment, improving their chances of survival. The proposed Generative Adversarial Networks (GAN) approach is designed to aid in the detection and diagnosis of breast cancer. GANs are deep learning algorithms that generate new data instances that mimic the training data. GAN is made up of two parts: a generator that learns to generate false data and a discriminator that learns from this false data. Furthermore, the histogram of oriented gradients (HOG) is utilized as a feature descriptor in image processing and other computer vision techniques. Gradient orientation in the detection window and region of interest is determined by the histogram of oriented gradients descriptor approach. Using an image dataset and deep learning techniques, the proposed research (GAN-HOG) aims to improve the efficiency and performance of breast cancer diagnosis. The deep learning method is used here to analyze image data by segmenting and classifying the input photographs from the dataset. Unlike many existing nonlinear classification models, the proposed method employs a conditional distribution for the outputs. The proposed model GAN-HOG had an accuracy of 98.435%, a ResNet50 accuracy of 87.826%, a DCNN accuracy of 92.547%, a VGG16 accuracy of 89.453%, and an SVM accuracy of 95.546%. © 2023 IEEE.

关键词： Classification (of information)

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Research on steering detection and correction method based on integrated GNSS/INS/Magnetometer 3

Research on steering detection and correction method based o...

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3rd IEEE international conference on Digital Twins and Parallel intelligence, DTPI 2023

作者： Yang, Jiahao Xu, Ming Ma, Longhua Chang, Fangle Wu, Wenxiang Zhejiang Sci-Tech University Ningbo China Ningbo Tech University Ningbo China Zhejiang University Ningbo Innovation Center Ningbo China Ningbo Special Equipment Inspection and Research Institute Ningbo China

ISBN: (纸本)9798350318470

In order to promote the construction of smart ports and the high-quality development of ports, a method for detecting and compensating the yaw angle turn of the GNSS/INS integrated navigation system based on the Extended Kalman Filter (EKF) algorithm is proposed to address the insufficient accuracy of navigation positioning in the daily inspection tasks of unmanned robots in ports. The new observation state model is created by adding the heading angle data received from the steering detection and compensation to the nonlinear filtering model that was previously used to simulate the combined navigation system. Experimental verification is done to confirm the combined GNSS/INS/ magnetometer navigation system. The findings demonstrate that the algorithm presented in this research, when compared to the traditional EKF algorithm, may greatly enhance the final heading angle output and, consequently, the accuracy of the heading angle of the combined navigation system. © 2023 IEEE.

关键词： Extended Kalman filters

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Social media fake news detection algorithm based on multiple feature groups 3

Social media fake news detection algorithm based on multiple...

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

作者： Luo, Yangyang Shi, Yuliang Li, ShaoShuai Beijing University of Technology College of Software Beijing China

ISBN: (纸本)9781665490788

The spread of fake news on social media seriously jeopardizes the authenticity of online content. Such misinformation has the potential to affect public opinion, government credibility, and more. Therefore, automatic detection of fake news is an important and challenging problem that has not been well solved today. Fake news has three universally recognized characteristics: the text of an article, the user response it receives, and the source users promoting it. Existing work mainly focuses on formulating a solution for a certain feature, which cannot detect fake news more accurately. In this work, we propose a model that combines all three features to achieve more accurate and automated predictions. We propose a model called TSRI, which consists of four modules: Text, Source, Response, and Integrate. The first module is based on news text content, using the BERT+LSTM model, while considering the semantic representation of sentences and the long-distance dependencies between sentences. The second module uses GAT to model the network of replying and retweeting users based on social network features. The third module is based on the user's response to the news, using LDA to find conflicting opinions in the news tweets, and based on the identified tweets' opinions, constructs the credibility of the tweets and spreads the network, and finally obtains the module's value through iterative deduction. result. The last module combines the above three and outputs whether the news is true or false. © 2023 IEEE.

关键词： Semantics

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Streamlining Machine Learning Model Execution Time with Hyperparameter Optimization 3

Streamlining Machine Learning Model Execution Time with Hype...

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3rd international conference on Innovative Mechanisms for Industry Applications, ICIMIA 2023

作者： Dhivya, P. Jegavardhini, M. Chandru, M. Vaigunth, Krishnan Harish Kumar, S. Rahul, S. Computer Science and Engineering Bannari Amman Institute of Technology Erode India

ISBN: (纸本)9798350343632

Breast cancer is a common disease that mostly affects women. It can be fatal if not detected early. Early detection and accurate diagnosis can lead to a longer survival rate for patients. Various classification models have been used to identify breast cancer, including logistic regression, decision trees, random forests, k-nearest neighbors, and gradient-boosting algorithms. A Parameterized Random Forest (PRF) classifier has been proposed that chooses the best parameter for classification based on the highest probability of a particular value in the parameters. The proposed model achieves an accuracy of 98.25% with a false negative rate of 1%. It is also more accurate (95.31%) than other classification algorithms when applied to the Hepatitis C dataset. The PRF classifier is a promising new tool for the early detection and accurate diagnosis of breast cancer. It is more accurate than other classification algorithms and can be used to reduce the time required to choose the best parameter value. © 2023 IEEE.

关键词： Artificial intelligence Benign Malignant Random Forest Receiver Operating Characteristics Tuning

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Impedance Severity Index Based Hybrid Islanding detection Approach for Zonal DC Microgrids 3

Impedance Severity Index Based Hybrid Islanding Detection Ap...

引用

3rd IEEE international conference on Sustainable Energy and Future Electric Transportation, SeFet 2023

作者： Choudhury, Bhabani Kumari Jena, Premalata Indian Institute of Technology Roorkee Department of Electrical Engineering Roorkee247667 India

ISBN: (纸本)9798350319972

A significant obstacle to the greater integration of renewable energy sources is the absence of an efficient protection system. Unintentional islanding can cause system instability and pose a threat to the safety of workers. However, DC microgrid islanding detection schemes have not been rigorously addressed in the literature yet. In order to overcome the above-mentioned issues, this research proposes a two stage hybrid islanding detection approach (IDA) for DC microgrids (DC MGs) to address the significant non detection zone (NDZ) and power quality degradation of prior IDAs. Using the proposed impedance emergency severity index (ESIz) based hybrid IDA, a disturbance is first recognized by calculating the magnitude of ESIz. In order to distinguish between islanding and other disruptions, an under over voltage (UOV) based current perturbation is then placed on top of the current. The proposed approach makes use of local measurement parameters of the distributed generation (DG) units. It does not impact the power quality while operating normally and has a nearly zero NDZ. On a 750 V zonal DC MG test system, numerous case studies are performed to demonstrate the nerformance of the proposed hybrid IDA. © 2023 IEEE.

关键词： Power quality

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Research on Power Stability Control technology of Light Source for Steel Ball Surface Defect detection 3

Research on Power Stability Control Technology of Light Sour...

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3rd IEEE international conference on Power, Electronics and Computer Applications, ICPECA 2023

作者： Gao, Dongdong Li, Guoping Li, Meng University of Jinan Jinan China

ISBN: (纸本)9781665472784

When the semiconductor laser is used as the light source device to detect the surface defects of steel balls, because its output light power and light wavelength are greatly affected by injection current, ambient temperature and other factors, the output power of the light source is unstable, which will seriously affect the accuracy of steel ball surface defect detection. To solve this problem, this paper designs a power stability control system of small power semiconductor laser based on single-chip microcomputer, which can feedback control the output power and working temperature of the laser, and add an optical isolator in the laser to achieve automatic control of laser luminous power stability. After the simulation of the system, the 1310nm and 1550nm lasers are tested experimentally. The test results show that the circuit works reliably and the optical power control effect is good. The instability of the output power of the two lasers is 1.42% and 1.7% respectively. This design provides a relatively stable light source for the detection of steel ball surface defects. © 2023 IEEE.

关键词： Semiconductor lasers

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An Exploration of the Use Cases for Artificial intelligence in Data Access Optimization 3

An Exploration of the Use Cases for Artificial Intelligence ...

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3rd international conference on Smart Generation Computing, Communication and Networking, SMART GENCON 2023

作者： Sharmila, P. Rathi, Snehal R. Singh, Manpreet Naval, Preeti Batri, Krishnan Agnihotri, Bhavesh Prince Shri Venkateshwara Padmavathy Engineering College Department of Science and Humanities Chennai127 India Vishwakarma Institute of Information Technology Department of Computer Engineering Pune India Centre of Interdisciplinary Research in Business and Technology Chitkara University Institute of Engineering and Technology Chitkara University Punjab India Maharishi School of Engineering and Technology Maharishi University of Information Technology Uttar Pradesh India Karnataka Bangalore India Vivekananda Global University Department of Computer Science & Engineering Jaipur India

ISBN: (纸本)9798350319125

This paper provides an exploration of the use cases for artificial intelligence (AI) in information access optimization. AI-based tactics to statistics optimization have the ability to enhance performance and reduce cost drastically. The paper discusses the characteristics of AI-pushed optimization techniques, together with fusion of heterogeneous facts assets, predictive modeling, forecasting, anomaly detection, and clustering. It also explores the numerous statistics assets that can be optimized using AI, which includes conventional database systems, disbursed information lakes, and streaming event statistics. Moreover, the paper considers the demanding situations related to AI-enabled facts get admission to optimization, such as loss of availability of labeled data and issue in imposing commercial enterprise good judgment. Finally, excellent practices and considerations for implementation are mentioned. Through exploring the use instances for AI in records get entry to optimization, this paper provides important insights for builders and businesses considering the usage of AI for data get entry to optimization. © 2023 IEEE.

关键词： businesses commercial data exploring optimization

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Automated Motorcycle Helmet detection Using The Combination of YOLO AND CNN 3

Automated Motorcycle Helmet Detection Using The Combination ...

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3rd international conference on Advance Computing and Innovative Technologies in Engineering, ICACITE 2023

作者： Anne Sanchana, Mary Eliyas, Sherin Hindustan Institute of Technology and Science Department of Computer Applications Padur603 103 India

ISBN: (纸本)9798350399264

Motorcycle accidents are a significant public concern worldwide. Accidents have been increasing rapidly which leads to the loss of several lives. One of the main contributing factors to the severity of injuries in these accidents is the lack of helmet use. Though the usage of helmets is necessary, it is also considered a piece of mandatory safety equipment among drivers, but they do not make use of it which causes unfortunate incidents to take place. Traffic police do try to bring awareness among the people but most of the population refuses to follow them. To avoid such incidents and to detect people from breaking the rules, this research was introduced. Here, we present a study based on an automated helmet detection system for motorcyclists using the combined techniques of YOLO and CNN. The combination of these image processing techniques such as You Only Look Once (YOLO) and Convolutional Neural Network (CNN) helps in the identification of helmets from image datasets. Because of this combination, the model can accurately identify helmets with a high degree of reliability due to the use of image datasets. And due to this combination, our system can accurately detect helmets with an accuracy of 94.29%. © 2023 IEEE.

关键词： Object detection

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