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检索条件"任意字段=2023 International Conference on Computer Engineering and Distance Learning, CEDL 2023"

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Restricted Area Sign Detector Using YOLO v5 5

Restricted Area Sign Detector Using YOLO v5

引用

2023 5th international conference on Artificial Intelligence and computer Applications, ICAICA 2023

作者： Lucero, Harold Moya, Viviana Pilco, Andrea Tirira, Alexander Lei, Qin Mechatronics School International University of Ecuador Quito Ecuador School of Computer Science and Engineering Tianjin University of Technology Tianjin China

ISBN: (纸本)9798350323313

This paper presents the implementation of a YOLO v5 model in a mobile robot for the detection of restricted area signs and the delivery of medicines. The objective is to enhance privacy by preventing the robot from entering restricted areas and also improve the time that a delivery round takes by avoiding unneeded paths. To achieve this, artificial vision and transfer learning techniques are used to train the YOLO v5 model, allowing the robot to detect and recognize a type of standard visual signal that indicates restricted areas. The implementation is performed on a Raspberry Pi 4, chosen for its processing power, 8GB of RAM, and easy installation on a mobile robot. Experimental results show that the implementation of this architecture in the mobile robot achieves a detection with maximum reliability of 93% in distances of 0.6 meters between the robot camera and the signal to be detected, thus allowing safe navigation of the robot and reducing the time to complete the delivery round. © 2023 IEEE.

关键词： Mobile robots

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Spatial Attention-guided Deep learning for Accurate Kidney Disease Classification in CT Scans 26

Spatial Attention-guided Deep Learning for Accurate Kidney D...

引用

26th international conference on computer and Information Technology, ICCIT 2023

作者： Islam, Md Nazmul Al Mamun, Md. Faruk, Md. Farukuzzaman Srizon, Azmain Yakin Hasan, S. M. Mahedy Roy, Barsha Rajshahi University of Engineering & Technology Department of Computer Science & Engineering Rajshahi6204 Bangladesh

ISBN: (纸本)9798350359015

Kidney diseases are globally prevalent and often lead individuals to seek urgent medical attention due to severe discomfort. The timely identification of these conditions necessitates a reliable automated diagnostic approach. Imaging techniques are integral in the diagnosis of kidney diseases, frequently requiring specialized expertise for comprehensive interpretation. Leveraging deep learning methodologies for kidney disease detection can offer valuable support to medical practitioners during diagnosis. This investigation delved into the categorization of kidney conditions based on CT scan images encompassing four distinct classes: cysts, normal, stone, and tumor. To facilitate this, a deep learning framework with spatial attention was introduced. Spatial attention modules were seamlessly integrated within Convolutional Neural Network (CNN) architecture to bolster interpretability, offering insights into influential regions guiding the model's decisions and enhancing the model's capacity to effectively process visual data, leading to improved classification performance. The proposed spatial attention-guided CNN model exhibited remarkable accuracy at 99.52%, accompanied by the precision, recall, and f1-score metrics of 99.54%, 99.52%, and 99.52% respectively. Impressively, the proposed framework surpassed the majority of recent comparable studies in terms of both accuracy and computational efficiency. © 2023 IEEE.

关键词： Attention maps CNN CT images Kidney diseases Spatial attention

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TL-GAN: Transfer learning with Generative Adversarial Network Model for Satellite Image Resolution Enhancement 26

TL-GAN: Transfer Learning with Generative Adversarial Networ...

引用

26th international conference on computer and Information Technology, ICCIT 2023

作者： Islam, Rafiqul Khatun, Mousumi Popy, Sumaiya Haider Dhaka University of Engineering & Technology Gazipur Department of Computer Science and Engineering Gazipur Bangladesh

ISBN: (纸本)9798350359015

Satellite imagery is essential in many sectors, including remote sensing, environmental monitoring, and urban planning. However, the low-resolution of satellite images makes retrieving fine-grained features difficult and limits their usefulness. Several resolution enhancement methods, including iterative algorithms and deep network-based algorithms, were widely employed to overcome this problem. In this paper, a hybrid model based on a deep neural network is designed to overcome the problem of low-resolution satellite images. To improve resolution, the proposed hybrid network combines a Generative Adversarial Network (GAN) and a pre-trained DenseNet model. The leading benefit of the technique is that GAN learns to generate high-resolution images with realistic and visually appealing details through adversarial training, and the incorporated DenseNet model improves the accuracy of the generated GAN images. To validate the effectiveness of the suggested approach, experimental assessments are designed utilizing a variety of satellite image datasets. The proposed hybrid network produced significant improvements in visual quality and detail preservation in super-resolved satellite images. Finally, the algorithm was compared to the state-of-the-arts method for better evaluation, and the experimental findings demonstrated quantitative and qualitative improvements. Experimental result showed that in terms of peak signal to noise ratio and structure similarity index measurement, the proposed technique achieved 36.28 dB and 0.8304 accuracy, respectively. © 2023 IEEE.

关键词： Deep learning Generative Adversarial Network Satellite Imaging Single Image Super Resolution Transfer learning

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Big Data Framework to Detect and Mitigate Distributed Denial of Service(DDoS) Attacks in Software-Defined Networks (SDN)

Big Data Framework to Detect and Mitigate Distributed Denial...

引用

2023 international conference on Electrical, computer, Communications and Mechatronics engineering, ICECCME 2023

作者： Hamedani, Amirreza Fazely Aziz, Muzzamil Wieder, Philipp Yahyapour, Ramin Göettingen University Department of Computer Science Göttingen Germany EScience Group Göttingen Germany

ISBN: (纸本)9798350322972

The software-defined network is in recent years come into the sight of so many network designers as a successor to the traditional network. This type of network is vulnerable to DDoS attacks, targeting a different part of the SDN network architecture by continuously injecting fake flows. It imposes substantial processing on the controller, and the result ultimately leads to the inaccessibility of the controller and the lack of network service to legitimate *** paper proposes a scalable and reliable real-time DDoS attack detection and mitigation framework using machine learning incorporated with a big data pipeline infrastructure for the SDN environment. The framework supports the detection of DDoS attacks with the ability of processing and analyzing the huge amount of network traffic in near real-time. The framework employs the power of big data solutions, such as Apache Kafka, and Apache Spark in order to realize a scalable pipeline for big data *** main objective of this research is to offer a vital supporter which can assist network security systems in addressing the DDoS threats in the SDN environment. For this purpose, machine learning algorithms are also utilized to provide a viable opportunity for classifying network data and detecting DDoS attacks in this regard. © 2023 IEEE.

关键词： Machine learning

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Statistical Exploration and Sentiment Analysis of WhatsApp Chats using Supervised learning 5

Statistical Exploration and Sentiment Analysis of WhatsApp C...

引用

5th international conference on Information Management and Machine Intelligence, ICIMMI 2023

作者： Sharma, Aakarsh Sinha, Shweta Department of Computer Science and Engineering Amity University Haryana Gurugram India

ISBN: (纸本)9798400709418

This paper presents the sentiment analysis of WhatsApp chat data while also evaluating user activity, employing VADER and supervised learning. The study encompasses both sentiment classification utilizing VADER's lexicon-based approach and supervised sentiment analysis using the Naive Bayes and SVM model. The research extends to statistical exploration like identifying the most active user within the group chat dataset and other statistical information. By contrasting the efficiency and accuracy of these techniques, the paper aids in method selection based on specific analysis goals. Results offer insights into sentiment trends and user engagement for informed decision-making. © 2023 ACM.

关键词： Sentiment analysis

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A Statistical Method for API Usage learning and API Misuse Violation Finding 21

A Statistical Method for API Usage Learning and API Misuse V...

引用

21st IEEE/ACIS international conference on Software engineering Research, Management and Applications, SERA 2023

作者： Panda, Deepak Basia, Piyush Nallavolu, Kushal Zhong, Xin Siy, Harvey Song, Myoungkyu University of Nebraska Department of Computer Science Omaha United States

ISBN: (纸本)9798350345889

A large corpus of software repositories enables an opportunity for using machine learning (ML) approaches to create new software engineering tools. In this paper, we propose a novel technique which leverages ML approaches for automating software engineering tasks and thus improves software quality. Our concrete goal is to (1) explore the abundance of predictable repetitive regularities of such a massive codebase, (2) develop an ML approach for training a statistical model to identify common patterns in software corpora, and then (3) use these patterns to statistically detect anomalous, likely buggy, program behavior that significantly deviates from these typical patterns. These internal regularities and repetitive properties of software can be captured as patterns to detect violations of these common patterns. Such violations have a critical impact on program behavior such as bugs, security vulnerabilities, or even program crashes. Our approach focuses on usage patterns of application programming interfaces (APIs). API usage patterns are commonly recurring, representative examples of how real-world applications use APIs in software corpora. These desirable patterns of API usage are learnable to validate or improve developers' implementations. This paper shows preliminary results that we use standard cross-entropy and perplexity to measure how surprising a test subject application is to a statistical model estimated from a software corpus. We continue to develop our approach and evaluate the effectiveness to focus on the following research questions. Are our ML models effectively trainable on large code corpora to learn desirable API usage patterns? How does the performance of our ML-based approach compare to state-of-The-Art language models for software when learning API usage for detecting API misuse violations? © 2023 IEEE.

关键词： Software testing

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An automatic annotation method based on federated learning

An automatic annotation method based on federated learning

引用

2022 international conference on Signal Processing, computer Networks, and Communications, SPCNC 2022

作者： Bochao, A.O. Bingbing, F.A.N. School of Computer Science South China Normal University Guangdong Guangzhou510631 China

ISBN: (纸本)9781510664616

With the explosive growth of neoconiosis worldwide, medical institutions worldwide generate a large number of chest radiograph images to be annotated for neoconiosis diagnosis every day. Moreover, due to the privacy of patient information, it is not possible to pool medical data from multiple medical institutions to jointly build a fast and accurate medical image annotation system for the task of new coronary pneumonia annotation. To this end, we propose a federal learning-based automatic annotation method that enables multiple medical institutions to design and develop an accurate and robust annotation system for neocoronary pneumonia without sharing data. Experimental results show that the proposed federated learning-based automatic annotation method (93.37% accuracy) is able to protect the privacy of medical image data across hospitals and accomplish a higher accuracy rate of medical image annotation compared to the automatic annotation method constructed by aggregating data (95.29% accuracy). © 2023 SPIE.

关键词： learning systems

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Language Model-based Deep learning for Automated Disease Prediction from Symptoms 26

Language Model-based Deep Learning for Automated Disease Pre...

引用

26th international conference on computer and Information Technology, ICCIT 2023

作者： Sarkar, Ripa Hossain, Ahbab Ifti, Akib Zabed BRAC University Department of Computer Science and Engineering Dhaka Bangladesh

ISBN: (纸本)9798350359015

Effective medical care depends on accurate and prompt disease prognosis based on patient symptoms. In this study, we suggest creating deep linguistics models that given a user's brief description of symptoms, can correctly predict the condition. We want to increase the effectiveness of disease prediction by utilizing deep learning technologies, leading to better patient outcomes. We demonstrate the efficacy and validity of our suggested techniques of disease prediction based on symptom reports through extensive trials and evaluation. A full grasp of the disease state and its corresponding symptoms is necessary in order to accurately predict disease from symptoms. Traditional methods frequently rely on manual diagnosis by medical experts which can be laborious and arbitrary. With the help of recent developments in deep learning, it is now possible to automate and enhance disease prediction based on symptom descriptions. In this paper, we suggest creating deep learning language models that, given a user's brief description of their symptoms can reliably predict illnesses. By learning from a lot of medical data, our model tries to identify complicated correlations between symptoms and diseases. By adopting deep learning architectures, we can leverage the ability of models to extract meaningful representations from text data and make accurate predictions. In our research paper, DistilBERT achieves the highest accuracy surpassing Ensemble classification with an accuracy rate of 93.3%. © 2023 IEEE.

关键词： DistilBERT DL Ensemble F1-Score Gradient Boosting Precision Random Forest Recall

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Lung Cancer Detection Techniques using Deep learning Methods 14

Lung Cancer Detection Techniques using Deep Learning Methods

引用

14th international conference on Advances in Computing, Control, and Telecommunication Technologies, ACT 2023

作者： Shraddha, C. Badre, Ranjana R. School of Computer Engineering and Technology MIT Academy of Engineering Pune India

Cancer disorders are caused when certain body cells enlarge uncontrollably and spread throughout the body. Lung cancerous lesion tumours can be detected by radiologists with the help of medical imaging techniques like Computed Tomography (CT) scan images, by using chest X-rays, or by using MRI. We examine various medical image-based datasets, their availability, and the number of cases they contain. Due to the abundance of pictures, a CT scan-based strategy is employed. The vastness of the collection made it difficult for radiologists to diagnose quickly and correctly. Numerous computer Tomography (CT) scan images can be found in the provided dataset. In 90% of the cases, errors in the diagnosis of lung cancer occur on chest radiographs. On chest radiographs, it may be challenging for radiologists to distinguish a lung lesion from bones, pulmonary vessels, mediastinal structures, and other complex anatomical structures. In order to tackle this issue, distinct predictive analytics and intelligent retrieval techniques are employed, like traditional computer Aided Diagnosis (CAD), Convolutional Neural Network (CNN), DensNet, Agile CNN, and LeNet. We compared the different approaches and compared the results to the best approach. These deep structured learning-based methods mostly used complex neural networks, which caused vanishing gradient problems. To solve such a problem, we suggest using the skip connection based method approach to get better accuracy in the results. © Grenze Scientific Society, 2023.

关键词： Deep learning

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SES: Smart Education System 5

SES: Smart Education System

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5th international conference on computer and Applications, ICCA 2023

作者： Alyammahi, Wadha Alrabaee, Saed United Arab Emirates University College of Information Technology Dept. of Computer and Network Engineering Dept. of Information Systems and Security AlAin United Arab Emirates

ISBN: (纸本)9798350303254

Responding to inefficiencies in traditional classroom management methods, this paper proposes an innovative, technology-driven approach to enhance educational processes. Central to our proposal is the development of a facial recognition system designed for efficient student attendance tracking, thus eliminating time-consuming roll calls. Additionally, we plan to introduce interactive whiteboard features to enrich classroom dynamics between students and faculty. However, our approach extends beyond mere attendance tracking and interactive learning. We aim to launch a Program learning Outcomes (PLO) and Course learning Outcomes (CLO) mapper. Leveraging Natural Language Processing (NLP) techniques, this tool will auto-align CLOs with PLOs, facilitating a more efficient curriculum development process. We also suggest implementing a feature powered by YOLOv5 to monitor and assess student attention in the classroom. Our comprehensive suite of tools is designed to equip educators with resources to refine their teaching strategies and boost student learning outcomes. By integrating facial recognition for attendance, interactive whiteboard features, NLP-based CLO/PLO mapping, and attention monitoring, we aspire to provide a robust solution enabling educators to adapt their teaching methods to students' unique needs. © 2023 IEEE.

关键词： Students

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