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15th International Conference on Computing Communication and Networking Technologies, ICCCNT 2024

作者： Tun, Nay Kyi Godwin Premi, M.S. Myanmar Institute of Information Technology Mandalay Myanmar Department of Electronic Computer and Engineering Satayabama Institute of Science and Technology Chennai India

ISBN: (纸本)9798350370249

The covid epidemic started in 2019 and has become the most terrifying disease that has destroyed many people's family lives, and it is frightening as an event that cannot be forgotten for the whole country to this day. To prevent the disease, the WHO has issued warnings to keep hands clean and to wear a mask whenever people go out to prevent the spread of the disease. For those involved in policy implementation;it is very difficult to identify people without a mask or wearing it in an improper manner. Crowded shopping centers, markets, hospitals, we all know that it has become an important matter to monitor whether the mask is mandatory in the departmental areas. However, monitoring in real time has become a waste of manpower and money. Therefore, even today, we still see the development of deep learning-based technologies that can monitor whether a facemask Detection based on CNN is implemented as one of the Deep Learning methods. In this system, transfer learning and ResNet-50 is used for Face Mask Detection. The dataset is collected from CCTV in the university and also standard dataset (Kaggel) for the training and testing. The main contribution of the proposed work is DL based real-time system development using the proposed CNN for determining whether the people on CCTV is wearing mask, not wearing mask and improperly mask. This system will provide better result in many position. © 2024 IEEE.

关键词： Classification CNN Detection ResNet50 YOLO

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Automated Semantic Interpretations in Web Applications via Cloud Computing Aspects

Automated Semantic Interpretations in Web Applications via C...

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2024 International Conference on Communication, computer sciences and Engineering, IC3SE 2024

作者： Preethi, D. Mehta, Poornima Sood, Devendra Department Of Computer Science And Information Technology Bangalore India Noida Institute Of Engineering And Technology Department Of It And M. Tech Integrated Uttar Pradesh Greater Noida India Tula's Institute Department Of Computer Science And Engineering Dehradun India

ISBN: (纸本)9798350366846

The creation and execution of applications in several disciplines have been greatly aided by the integration of cloud computing and semantic web technologies. Although they are independent entities, they may also be combined in many ways to provide answers - a subject that has been thoroughly researched lately. The number of 'new cloud providers, applications, facilities, management systems, and data' has significantly increased in recent years. The complexity that has resulted from this expansion emphasises the need for new technology in order to efficiently manage and support these varied and many services and resources. As a result, difficulties with 'cloud services and resources' transferability, compatibility, security, choice, negotiation, identity, and specification might arise. Semantic Technologies are a vital tool for reassessing these issues because of their enormous 'cloud computing' capacity. This study uses a variety of sources to examine and evaluate the impact of 'Semantic-Web technology' in the cloud. Moreover, a cloud-based engagement strategy shows how we may leverage the features and advantages of cloud computing to build the semantic web and provide automatic semantic annotations to online applications on a large scale. © 2024 IEEE.

关键词： Semantic Web

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The Performance Analysis of Health Care Automation using Artificial Intelligence Model

The Performance Analysis of Health Care Automation using Art...

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2024 International Conference on Communication, computer sciences and Engineering, IC3SE 2024

作者： Sawanni, Ritu Mathur, Mansi Gupta, Richa Kumar, Anuj Richa Garg, Ashish Department Of Information Technology New Delhi India Graphic Era Hill University Department Of Computer Science And Engineering Uttarakhand Dehradun India Rajkiya Engineering College Kannauj Affiliated To Dr. A.P.J. Aktu Department Of Computer Science And Engineering Lucknow India Graphic Era Deemed To Be University Department Of Computer Science & Engineering Uttarakhand Dehradun India

ISBN: (纸本)9798350366846

Artificial intelligence (AI) has shown great promise in healthcare, especially in healthcare automation. Without human input, AI systems are able to analyze vast volumes of data and make predictions and judgments based on that data. As a result, patient outcomes may be improved, and several healthcare systems may operate more accurately and efficiently. Processes in administration can be streamlined and optimized using AI. As a result, medical staff members are less burdened and can concentrate on giving patients high-quality treatment. AI's application in healthcare presents difficulties. A few issues to be concerned about are AI algorithms' lack of explainability and transparency, possible bias in the training data, and moral problems with patient privacy and data security. Medical diagnosis is a crucial area in which artificial intelligence has been used to automate healthcare. AI algorithms can produce diagnoses and treatment regimens by examining a patient's symptoms, medical history, and other pertinent information. This has demonstrated encouraging outcomes in raising the accuracy of diagnoses and lowering mistake rates. © 2024 IEEE.

关键词： Diagnosis

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Intelligent system for Classification ECG Based on Convolutional Neural Networks (CNN) 8

Intelligent system for Classification ECG Based on Convoluti...

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8th International Conference on Contemporary information technology and Mathematics, ICCITM 2022

作者： Hammad, Anfal Hamid Abdulbaqi, Azmi Shawkat University of Anbar College of Computer Sciences and Information Technology Department of Computer Science Anbar Iraq

ISBN: (纸本)9798350334869

The electrocardiogram (ECG) is a non-invasive procedure used to diagnose cardiovascular disorders. The 2D ECG system gives important information on the electrophysiology of heart illnesses and possible brain changes, as well as important information about the cardiovascular system's functional characteristics. The classification proposed system detected four different classes from the ECG cases: myocardial infarction (MI), Arrhythmia, History MI, and Normal case. The suggested system used three steps for classification ECG, include: Image processing, feature extraction, and classification. The first step involves utilizing Image preprocessing methods to prepare and enhance the input images, which includes two steps: converting RGB images to a binary image and applying filters to reduce noise from a binary image. Utilizing local binary pattern algorithm, features are retrieved from the images in the second step (LBP). The collected characteristics are then supplied into a 1D 'convolutional neural network' (1D-CNN) model for object detection and classification in the third step. The findings demonstrate that the suggested model of the CNN has a high accuracy rate, with a classification accuracy rate of 97 percent. The suggested method works extremely well in the categorization of the 2D ECG, as demonstrated by the low rate of false alarms and high rate of accuracy. © 2022 IEEE.

关键词： Electrocardiograms

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Hyper-Tuned Convolutional Neural Networks for Authorship Verification in Digital Forensic Investigations

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computers, Materials & Continua 2023年第8期76卷 1947-1976页

作者： Asif Rahim Yanru Zhong Tariq Ahmad Sadique Ahmad Mohammed A.ElAffendi School of Computer and Information Security Guilin University of Electronic TechnologyGuilin541004China Guangxi Key Laboratory of Intelligent Processing of Computer Images and Graphics Guilin University of Electronic TechnologyGuilin541004China School of Information and Communication Guilin University of Electronic TechnologyGuilin541004China EIAS:Data Science and Blockchain Laboratory College of Computer and Information SciencesPrince Sultan UniversityRiyadh11586Saudi Arabia

Authorship verification is a crucial task in digital forensic investigations,where it is often necessary to determine whether a specific individual wrote a particular piece of *** Neural Networks(CNNs)have shown promise in solving this problem,but their performance highly depends on the choice of *** this paper,we explore the effectiveness of hyperparameter tuning in improving the performance of CNNs for authorship *** conduct experiments using a Hyper Tuned CNN model with three popular optimization algorithms:Adaptive Moment Estimation(ADAM),StochasticGradientDescent(SGD),andRoot Mean Squared Propagation(RMSPROP).The model is trained and tested on a dataset of text samples collected from various authors,and the performance is evaluated using accuracy,precision,recall,and F1 *** compare the performance of the three optimization algorithms and demonstrate the effectiveness of hyperparameter tuning in improving the accuracy of the CNN *** results show that the Hyper Tuned CNN model with ADAM Optimizer achieves the highest accuracy of up to 90%.Furthermore,we demonstrate that hyperparameter tuning can help achieve significant performance improvements,even using a relatively simple model architecture like *** findings suggest that the choice of the optimization algorithm is a crucial factor in the performance of CNNs for authorship verification and that hyperparameter tuning can be an effective way to optimize this ***,this paper demonstrates the effectiveness of hyperparameter tuning in improving the performance of CNNs for authorship verification in digital forensic *** findings have important implications for developing accurate and reliable authorship verification systems,which are crucial for various applications in digital forensics,such as identifying the author of anonymous threatening messages or detecting cases of plagiarism.

关键词： Convolutional Neural Network(CNN) hyper-tuning authorship verification digital forensics

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Symbiotic Organisms Search with Deep Learning Driven Biomedical Osteosarcoma Detection and Classification

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computers, Materials & Continua 2023年第4期75卷 133-148页

作者： Abdullah M.Basahel Mohammad Yamin Sulafah M.Basahel Mona M.Abusurrah K.Vijaya Kumar E.Laxmi Lydia Faculty of Economics and Administration King Abdulaziz UniversityJeddahSaudi Arabia E-Commerce Department College of Administrative and Financial SciencesSaudi Electronic UniversityJeddahSaudi Arabia Department of Management Information Systems College of Business AdministrationTaibah UniversityAl-MadinahSaudi Arabia Department of Computer Science and Engineering GITAM School of TechnologyVisakhapatnam CampusGITAM(Deemed to be a University)India Department of Computer Science and Engineering GMR Institute of TechnologyAndhra PradeshRajamIndia

Osteosarcoma is one of the rare bone cancers that affect the individualsaged between 10 and 30 and it incurs high death rate. Early diagnosisof osteosarcoma is essential to improve the survivability rate and treatmentprotocols. Traditional physical examination procedure is not only a timeconsumingprocess, but it also primarily relies upon the expert’s *** this background, the recently developed Deep Learning (DL) models canbe applied to perform decision making. At the same time, hyperparameteroptimization of DL models also plays an important role in influencing overallclassification performance. The current study introduces a novel SymbioticOrganisms Search with Deep Learning-driven Osteosarcoma Detection andClassification (SOSDL-ODC) model. The presented SOSDL-ODC techniqueprimarily focuses on recognition and classification of osteosarcoma usinghistopathological images. In order to achieve this, the presented SOSDL-ODCtechnique initially applies image pre-processing approach to enhance the qualityof image. Also, MobileNetv2 model is applied to generate a suitable groupof feature vectors whereas hyperparameter tuning of MobileNetv2 modelis performed using SOS algorithm. At last, Gated Recurrent Unit (GRU)technique is applied as a classification model to determine proper class *** order to validate the enhanced osteosarcoma classification performance ofthe proposed SOSDL-ODC technique, a comprehensive comparative analysiswas conducted. The obtained outcomes confirmed the betterment of SOSDLODCapproach than the existing approaches as the former achieved a maximumaccuracy of 97.73%.

关键词： Osteosarcoma medical imaging deep learning feature vectors computer aided diagnosis image classification

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Anomalous Sound Detection Using Time-Frequency Feature and Mixbatch

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Journal of Shanghai Jiaotong University (science) 2025年 1-8页

作者： Huang, Shun Zhang, Yunxiang Fang, Zhihua Tang, Minrui Xu, Ruifeng He, Liang School of Computer Science and Technology Xinjiang University Urumqi830017 China Xinjiang Key Laboratory of Signal Detection and Processing Urumqi830017 China School of Intelligence Science and Technology Xinjiang University Urumqi830017 China Department of Electronic Engineering Beijing National Research Center for Information Science and Technology Tsinghua University Beijing100084 China

The sound emitted by machines under abnormal working conditions exhibits various frequency patterns. Currently, the most advanced anomalous sound detection (ASD) approach is to apply a multi-head self-attention mechanism to the Log-Mel spectrogram for automatic frequency pattern analysis. However, the Log-Mel spectrogram may filter out high-frequency components of abnormal sounds;thus the use of self-attention mechanisms on Log-Mel spectrogram seems to have certain limitations. In this paper, we construct a simple convolutional neural network to extract comprehensive frequency features from raw audio to complement spectral-temporal information fusion. The parameters of this neural network are continuously updated during the training process to extract better frequency features for downstream classification neural networks in ASD. Additionally, a method for data augmentation in the batch dimension was developed to help the classification model learn both types of comprehensive features simultaneously. The proposed method achieved an AUC of 94.13% and a pAUC of 89.09% on the DCASE 2020 Challenge Task 2 dataset. Even when using only the features proposed in this paper, an AUC of 84.62% was achieved. (Figure presented.). © Shanghai Jiao Tong University 2025.

关键词： Spectrographs

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A novel deep learning approach for investigating liquid fuel injection in combustion system

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Discover Artificial Intelligence 2025年第1期5卷 1-14页

作者： Inam, Syed Azeem Khan, Abdullah Ayub Ahmed, Noor Mazhar, Tehseen Shahzad, Tariq Khan, Sunawar Saeed, Mamoon M. Hamam, Habib Department of Artificial Intelligence and Mathematical Science Sindh Madressatul Islam University Sindh Karachi Pakistan Department of Computer Science and Information Technology Shaheed Benazir Bhutto University Lyari Karachi Pakistan Department of Computer Science Shaheed Zulfiqar Ali Bhutto Institute of Science and Technology Karachi Pakistan Department of Computer Science School Education Department Government of Punjab Layyah31200 Pakistan School of Computer Science National College of Business Administration and Economics Lahore54000 Pakistan Department of Computer Engineering COMSATS University Islamabad Sahiwal Campus Sahiwal57000 Pakistan Department of Software Engineering Islamia University of Bahawalpur Bahawalnagar62300 Pakistan Sana’a00967 Yemen Faculty of Engineering Uni de Moncton MonctonNBE1A3E9 Canada Department of Electrical and Electronic Engineering Sciences University of Johannesburg Johannesburg South Africa Av. Grandes Ecoles BP 1989 Libreville Gabon Bridges for Academic Excellence Spectrum Center-ville Tunis Tunisia

The intricacies and instability of introducing cryogenic propellants into the combustion system have piqued the curiosity of scientists studying the procedure. The latest innovation is utilizing data-driven machine learning and deep learning approaches to gain deeper insights into the related difficulties. However, the current work serves as a baseline for future research because relatively few studies have used data-driven methodologies to assess the temperature of liquid fuel injections in combustion systems. The performance of Linear Regression (LR), Random Forest (RF), Extra Trees Regressor (ETR), Polynomial Regression (PR), Support Vector Regressor (SVR), Decision Tree Regressor (DTR), Gradient Boost Regressor (GBR), XGB Regressor (XGBoost), AdaBoost Regressor (ABR), K-Neighbors Regressor (KNR), Long-Short Term Memory (LSTM), Bi-LSTM (Bi-directional Long-Short Term Memory) has all been investigated in this study. The study also suggested a Fully Connected Neural Network (FCNN) to examine its performance and paired it with an Extra Tree Regressor (ETR). The coupled FCNN and Extra Tree Regressor outperform the other algorithms with a Mean Square Error (MSE) of 0.0000005062, Root Mean Square Error (RMSE) of 0.00071148, Mean Absolute Error (MAE) of 0.00020672, and R-squared (R2) value of 0.99998689. Linear Regression, Polynomial Regression, and Support Vector Regressor are found to be the least-performing algorithms. The current work uses machine learning and deep learning methods to make data-driven decisions for liquid fuel injection in the combustion system. © The Author(s) 2025.

关键词： Deep learning

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An Efficient ODR for computer Night Vision Using Multi-Scale Retinex Network-Aided Enhanced Images 4

An Efficient ODR for Computer Night Vision Using Multi-Scale...

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4th International Conference on Power, Energy, Control and Transmission Systems, ICPECTS 2024

作者： Charles Prabu, V. Pandiaraja, P. Sathiyamoorthi, V. Durgadevi, P. Vel Tech Rangarajan Dr. Sagunthala R&d Institute of Science and Technology Department of Computer Science and Engineering Tamilnadu Chennai India Government Polytechnic College Department of Computer Engineering Baisuhalli Tamilnadu Dharmapuri India Srm Institute of Science and Technology Department of Computer Science and Engineering Vadapalani Campus Tamilnadu Chennai India

ISBN: (纸本)9798331508845

In the current scenario, recognizing various objects and tracking their movements in the real-time surveillance footage is the most difficult task. To detect objects, a combination of image processing and computer vision algorithms is utilized. computer-vision based automatic human activity recognition from surveillance video can be utilized for applications such as the identification of violent acts and the study of human behavior. Consequently, this work implements a new "Object Detection and Recognition (ODR)"model for computer night vision utilizing a deep learning technique. In order to improve the supplied input image, the combined images are first transmitted to the "Multi-scale Retinex (MSR)"model. The You Only Look Once Version 7 (YoloV7) model uses the improved image from MSR as input to identify and recognize things. After conducting experiments, the implemented ODR model on the ExDark Dataset obtained an efficiency of 94.8%. © 2024 IEEE.

关键词： Deep learning

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YOLOv5 UAV Feature Detection with Swin-Transformer 8

YOLOv5 UAV Feature Detection with Swin-Transformer

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8th International Conference on Intelligent Informatics and Biomedical sciences, ICIIBMS 2023

作者： Xiao, Lizhong Li, Shuai Shanghai Institute of Technology School of Computer Science and Information Engineering Shanghai China Shanghai Institute of Technology College of Science Shanghai China

ISBN: (纸本)9798350369564

Unmanned Aerial Vehicle (UAV) has the characteristics of "low, slow and small"and complex flight environment. Using existing target recognition algorithms to identify UAV faces high model complexity, large parameter amount, and is prone to missed detection and false detection. Therefore, this paper proposes a special object detection method of YOLOv5 UAV based on Swin-Transformer, namely ST-YOLO. ST-YOLO uses Swin Transformer to integrate into the skeleton network of this improved model and uses other improvements to improve the performance of the model on visual tasks such as object detection. Compared with the original network, ST-YOLO has the parameter amount reduced by 10.70%, the calculation amount reduced by 10.13%, the FPS is basically the same, and the mAP is improved by 3.83%. Therefore, the proposed ST-YOLO has better results in terms of object detection accuracy, calculation amount and model scale. © 2023 IEEE.

关键词： Object detection

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