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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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Towards Developing an Intelligent Robotic System for Assisting Children in Basic Drawing

Towards Developing an Intelligent Robotic System for Assisti...

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2023 international conference on Next-Generation Computing, IoT and Machine learning, NCIM 2023

作者： Baki, Ramisha Fariha Rahman, Mouneeta Khandakar, Nawreen Anan Zannat, Riyadil Faisal, Faiz Al Islam, Muhammad Nazrul Green University of Bangladesh Dept. of Computer Science and Engineering Dhaka Bangladesh Military Institute of Science and Technology Mirpur Cantonment Department of Computer Science and Engineering Dhaka1216 Bangladesh

ISBN: (纸本)9798350316001

Children need assistance to learn basic drawing in school. However, it may not always be possible for a teacher to assist every individual separately, especially in a school with a large number of students in each class. This research aims to design and develop a robotic system to help children learn how to draw basic shapes. While the use of robotic arms has been explored in various fields such as industry and biomedicine, little attention has been given to assisting children in drawing basic shapes. To achieve this objective, a system was developed that consists of an android-based mobile application and a robotic arm. The application allows users to upload a picture of a shape or alphabet, convert it into line art, and then send the necessary instructions to the robotic arm. Based on these instructions, the robotic arm demonstrates the drawing steps to help children learn. The application also has the ability to evaluate the accuracy of the user's drawing. Finally, the effectiveness of the system's features was tested with a group of children in a laboratory environment, and it was found that the proposed system effectively supports children in basic sketching. © 2023 IEEE.

关键词： Robotic arms

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Advancements in Breast Cancer Detection: A Comprehensive Review of Deep learning Techniques 1

Advancements in Breast Cancer Detection: A Comprehensive Rev...

引用

1st international conference on Artificial Intelligence for Innovations in Healthcare Industries, ICAIIHI 2023

作者： Arora, Drishti Gupta, Shruti Chawla, Jashekam Singh Garg, Rakesh Amity University Dept. of Computer Science and Engineering Uttar Pradesh India

ISBN: (纸本)9798350330915

This paper addresses the pressing issue of breast cancer-related mortality by comprehensively analyzing six distinct deep learning models utilized in the past decade. With a focus on timely and precise diagnosis, the study evaluates the performance metrics of each model across diverse datasets, image modalities, and methodologies. The primary goal is to identify the most efficient and precise deep learning architecture tailored to classify breast cancer tumors, with potential implications for optimizing early detection strategies and advancing clinical decision-making processes. The findings hold significant implications for refining clinical decision-making processes, potentially leading to more efficacy in timely detection strategies. This research emphasizes on the broader field of image analysis and its role in combating breast cancer. © 2023 IEEE.

关键词： Breast Tumor Detection Deep learning techniques Performance Evaluation

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Improved Convolutional Neural Network and Transfer learning with VGG16 Approach for Image Classification

Improved Convolutional Neural Network and Transfer Learning ...

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2023 international conference on Innovation and Intelligence for Informatics, Computing, and Technologies, 3ICT 2023

作者： Habibullah Ullah, Md. Oli Kamal, Minhaz Sohag, Md. Sabbir Hasan Rahman, Md. Saifur Bangladesh University of Business and Technology Department of Computer Science and Engineering Dhaka Bangladesh Bangladesh University of Business and Technology Department of Electrical and Electronic Engineering Dhaka Bangladesh

ISBN: (纸本)9798350307771

Transfer learning may boost modeling speed. This research unifies the improved VGG16 model. Skipping VGG16's entirely connected layer and tying it to the layer following it improves CNN's architecture and reduces its processing burden. CNNs are classifying cat and dog photos. With a small dataset, CNN training may take time and resources. Transfer learning uses ImageNet models to handle similar challenges in new contexts. Transfer learning enhances CNN cat-dog classification. The proposed technique selects a suitable pre-trained model, freezes its layers to retain acquired properties, adds layers to learn task-specific features, and tunes it using the cat and dog dataset. Data augmentation improves model performance and prevents overfitting. Hyperparameter optimization and validation enhance model accuracy and speed. This method enhances cat and dog photo classification even without adequate data to train CNNs with an accuracy of 97%. © 2023 IEEE.

关键词： Convolutional neural networks

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Utilizing Machine learning Algorithms to Optimize Real-Time Data Analysis 3

Utilizing Machine Learning Algorithms to Optimize Real-Time ...

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

作者： Singh, Vaishali Gupta, Eesha Jana, D. Little Femilin Deshmukh, Gopal B. Singla, Abhishek Krishna, K. Gopala Maharishi School of Engineering and Technology Maharishi University of Information Technology Uttar Pradesh India Vivekananda Global University Department of Computer Science & Engineering Jaipur India Prince Shri Venkateshwara Padmavathy Engineering College Department of Science and Humanities Chennai127 India Vishwakarma Institute of Information Technology Department of Computer Engineering Pune India Chitkara University Institute of Engineering and Technology Chitkara University Centre of Interdisciplinary Research in Business and Technology Punjab India Faculty of Engineering and Technology Department of Electronics and Communication Engineering Karnataka Bangalore India

ISBN: (纸本)9798350319125

This paper offers a way for optimizing actual-time records analysis the usage of a variety of machine gaining knowledge of algorithms. The algorithms paintings by creating predictive fashions which extract useful information from datasets, follow appropriate preprocessing techniques to make sure the high-quality of the records, and model the information to deduce meaningful capabilities from the data. The predictive models are then used to discover applicable developments, find patterns, and summarize the data for clean analysis. specifically, this paper outlines the technique of using supervised and unsupervised learning algorithms, consisting of Random wooded area, Naïve Bayes, and k-approach clustering, to extract meaningful styles and relationships from facts points in real-time. Additionally, the paper discusses wonderful capacity packages of the proposed technique, including fraud detection and prediction of stock costs. Such capability programs demonstrate the fee of actual-time records analysis for organizations and organizations. © 2023 IEEE.

关键词： Clustering algorithms

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Securing Files on Cloud Storage with Group Key Management Protocol 5

Securing Files on Cloud Storage with Group Key Management Pr...

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5th international conference on Inventive Research in Computing Applications, ICIRCA 2023

作者： Gowri, S. Geetha Rani, K. Appathurai, K. Gomathi, R.M. Surendran, R. School of Computing Sathyabama Institute of Science and Technology Chennai India Department of Computer Science and Engineering Bangalore India Kg College of Arts and Science Department of Computer Technology Coimbatore India Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences Department of Computer Science and Engineering Chennai India

ISBN: (纸本)9798350321425

Due to an exponential rise in the incidence of cyberattacks, there is a greater than ever need for enhanced Intrusion Detection Systems (IDS). In this aspect, the early classification of the attacks is greatly aided by the machine learning (ML) technique. Since there are numerous algorithms available, selecting the most suitable method was difficult. This study looked at some of the most cutting-edge intrusion detection methods and assessed their benefits and drawbacks in an effort to address this problem. Also, the best ML approach for classifying attacks is chosen from a selection of four ML methods. The effectiveness of the Collision detection system is assessed using a variety of methods that have been selected and tested. These Collision detection systems are divided into binary and multiclass attacks to verify whether the traffic has been classified as benign or an attack. The results show that the classification technique produces more accurate and consistent results. The experimental findings demonstrate that multi-classification improves performance in terms of intrusion detection, specifically allowing for a more targeted attack response. © 2023 IEEE.

关键词： Cloud storage

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Leveraging Deep learning Techniques for Profiling and Categorizing Lung and Pancreatic Tumors

Leveraging Deep Learning Techniques for Profiling and Catego...

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2023 international conference on Recent Advances in Science and engineering Technology, ICRASET 2023

作者： Jeelani, Azra Vaishnawi, Chittamuru Yadav, Rakesh Kumar Presidency University Department of Electronics and Communication Engineering Karnataka Bangalore India Department of Computer Science and Information Technology Bangalore India Maharishi University of Information Technology Maharishi School of Engineering & Technology Uttar Pradesh India

ISBN: (纸本)9798350306927

The study provides a unique strategy for "Leveraging Deep learning Techniques for Profiling and Categorizing Lung and Pancreatic Tumors."The suggested technique incorporates Convolutional Neural Networks (CNNs) for image analysis, Recurrent Neural Networks (RNNs) for temporal modeling, and Transfer learning to fine-tune pre-trained models. It aims to improve tumor classification precision by tackling the particular challenges given by pancreatic and lung cancers. The outcomes of our investigation show that the proposed strategy outperforms existing methods in numerous key areas. F1 was 0.85, specificity was 0.87, recall was 0.82, accuracy was 0.85, precision was 0.88, and area under the curve was 0.92, according to the results. These values routinely exceed six original conventional approaches, including CNNs, RNNs, Transfer learning, Autoencoders, Support Vector Machines (SVMs), and Reinforcement learning. The confusion matrix measurements further underscore the superiority of the suggested strategy with more true positives and true negatives, and fewer false positives and false negatives. In conclusion, the suggested strategy improves the accuracy and objectivity of lung and pancreatic tumor profiling by harnessing the power of deep learning and a multi-faceted approach. It offers a more tailored, efficient, and data-driven approach to the arduous issue of tumor classification. The data clearly illustrate its superiority over traditional approaches, making it a potential innovation in the field of cancer. © 2023 IEEE.

关键词： Convolutional neural networks

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Development of Machine learning Model to Detect Fault Data Injection Attack on Vehicle Following Systems

Development of Machine Learning Model to Detect Fault Data I...

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2023 international conference on Recent Advances in Electrical, Electronics, Ubiquitous Communication, and Computational Intelligence, RAEEUCCI 2023

作者： Dharsini, S. Prabhakar, G. Rajaram, S. Shanthi, J. Thiagarajar College of Engineering Dept of Ece Madurai India

ISBN: (纸本)9798350337426

A Vehicle-following system is a system that controls a following vehicle to maintain a fixed distance and acceleration behind a preceding vehicle. Cyber attack is an issue on Vehicle following systems due to its wireless connectivity. Cyber dangers to physical systems result from this. As a result, controlling the action of the vehicle is necessary to stop the cyber attack, which causes the development of cyber physical system(CPS). CPS can be accessed and controlled remotely. Their features make them more easily vulnerable to cyber attacks. Fault Data Injection (FDI) attack is an attack that is mathematically fed into the altered Vehicle Following System and modeled to study the attack's effects. The Scope of this work is to develop a machine learning model by generating the cyberattack dataset by analyzing the Physical Model of Vehicle following system. Decision tree algorithm is used to detect the attack. It considerably raises FDI detection accuracy. © 2023 IEEE.

关键词： Cyber Physical System

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A Novel Method for Pneumothorax Diagnosis and Segmentation Using Deep Convolution Neural Network

A Novel Method for Pneumothorax Diagnosis and Segmentation U...

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2023 international conference on computer Communication and Informatics, ICCCI 2023

作者： Shanthi, S. Nirmaladevi, K. Mathiarasi, N. Tharani, As. Krithika Monisha, A. Kuppuchamy, R. Krishnamoorthy, N. Kongu Engineering College Department of Computer Science & Engineering Perundurai India PSNA College of Engineering and Technology Department of Computer Applications Tamilnadu Dindigul India Vellore Institute of Technology Department of Software Systems and Engineering Vellore Campus India

ISBN: (纸本)9798350348217

A pneumothorax is the accumulation of air in the pleural space between the chest and lungs. Air that is in the lungs during breathing can leak out of the lungs and become trapped between the chest and lungs in the thoracic cavity, causing chest pain, difficulty breathing, and ultimately the complete collapse of the taut lungs. pneumothorax. Therefore, early diagnosis and treatment should be started. Neural networks have proven effective in image classification tasks, and many researchers believe that training neural network models can detect radiologist-diagnosed chest abnormalities such as pneumonia and pneumothorax. is proving. Moreover, we utilize various techniques, including Stochastic Weight Averaging (SWA), aggressive data augmentation, and learning rate schedule. We use the chest x-ray dataset provided by the 2019 SIIM-ACR In this study, a ResNet-34 neural network is trained on a data set of chest x-rays containing over 10,675 images to create a model that predicts the presence of pneumothorax from chest *** suggested system receives a Dice Similarity Coefficient (DSC) of 0.8410 © 2023 IEEE.

关键词： Image classification

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Hand Gesture Recognition: An Approach of Multiple Object Tracking 14

Hand Gesture Recognition: An Approach of Multiple Object Tra...

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

作者： Chaturvedi, Shubha Rawat, Manoj Kumar Sage University Computer Science & Engineering Department Indore India

In the recent year, Object Detection and Tracking technology is one of the challenging and most emerging topic in computer Vision. Multiple Object Tracking & Detection (MOTD) has gained a lot of importance in various fields such as Autonomous driving, Monitoring security, Surveillance, Health-care monitoring and Gesture recognition etc. Gesture recognition is widely used in various fields of intelligent driving, Virtual reality, and Human-computer interaction. With the development in various technologies such as Deep learning, Artificial Intelligence and Human-computer interaction a new revolution arise in field of computer vision. We endeavor to provide a thorough review on the development of this gesture recognition approach in recent decades. Here our major discussions are about the benefits and limitations of existing approach where focus is on the method of feature extraction in spatiotemporal structure .Various research difficulties that could be pursued will be the research directions. © Grenze Scientific Society, 2023.

关键词： Object detection

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IoT Security Based on Machine learning 2

IoT Security Based on Machine Learning

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2nd international conference on Smart Technologies for Smart Nation, SmartTechCon 2023

作者： Vanakamamidi, Rama Krishna Abirami, N. Sasi Kumar, C. Ramalingam, L. Priyanka, S. Murugan, S. HCL Technology LTD Andhra Pradesh Vijayawada India Department of Information Technology R.M.D. Engineering College Tamil Nadu Chennai India Department of Computer Science and Engineering Shree Sathyam College of Engineering and Technology Tamil Nadu Salem India Tamil Nadu Chennai India Tamil Nadu Namakkal India Department of Biomedical Engineering Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences Saveetha University Tamil Nadu Chennai India

ISBN: (纸本)9798350305418

The protection of user privacy and mitigation of threats like spoofing, DoS, jamming, and eavesdropping are essential for the Internets of Things (IoT) to fulfill its promise of bringing improved and intelligent services to users via the integration of diverse devices into networks. Machine learning (ML) approaches, including supervised learning, unsupervised learning, and Reinforcement learning (RL), are explored as potential solutions for securing the IoT. This article focuses on the use of ML in IoT authentications, access controls, secured offloading, and malware detection strategies to safeguard sensitive information. The challenges of incorporating these ML-based security strategies into real-world IoT systems are also discussed. A complete analysis of the threats facing IoT networks is provided, as the methods already in use to protect against them and the need for securing such networks. Then, highlight the areas where these security solutions are lacking and might benefit from ML and DL methods. Finally, go through in-depth the current ML and DL methods for dealing with various IoT network security issues. Several potential research possibilities for IoT securities based on ML and DL are also discussed. © 2023 IEEE.

关键词： Deep learning IoT ML Privacy Reinforcement learning Security

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