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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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Double Encryption Technique for Sharing and Storing the Images in the Cloud Environment 4

Double Encryption Technique for Sharing and Storing the Imag...

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4th International Conference on Advances in Electrical, Computing, Communication and Sustainable Technologies, ICAECT 2024

作者： Anusha, P. Maruthi, R. Kumar, D. Maghesh Begum, H. Parveen Periyar Maniammai Institute of Science and Technology Department of Software Engineering Tamil Nadu Thanjavur India Hindustan Institute of Technology and Science Department of Computer Applications Tamil Nadu Chennai India

ISBN: (纸本)9798350343670

Cryptography is used by all organizations to protect the data files and ensures confidentiality mainly at the time of sharing and storing in the cloud data storage. The cloud service providers use a wide range of tools and techniques for encryption and storage technologies. The intruders are finding number of ways to break or attack the cryptographic systems, so that new ways are constantly being explored to safeguard our files from the attackers This paper explores double encryption cryptographic technique based on Elliptic curve cryptography (ECC) and Advanced Encryption Standard (AES) to perform end-to end encryption for sharing and storing files in the cloud environment. © 2024 IEEE.

关键词： Advanced Encryption Standard Cloud Cryptography Elliptic Curve Cryptography File Sharing

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Application of Neural Network Keyword Extraction Methods for Student's CV Compilation from Discipline Work Programs 26

Application of Neural Network Keyword Extraction Methods for...

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26th International Conference on Soft Computing and Measurements, SCM 2023

作者： Kholod, Ivan I. Korytov, Pavel V. Sorochina, Maria V. Saint-Petersburg Electrotechnical University LETI Department of Computer Science and Engineering Saint-Petersburg Russia Saint-Petersburg Electrotechnical University LETI Department of Software Engineering and Computer Applications Saint-Petersburg Russia

ISBN: (纸本)9798350322484

The paper considers application of natural language processing methods (NLP) to solve the task of identifying the core skills acquired by students in the process of education. An ensemble solution, consisting of models WikiNEuRAL, KBIR-Inspec, ruGTP3Large, word2vec, and other NLP methods, is used to extract and process Russian language keywords. The described method was developed to automate the compilation of a student's CV in the information system called 'Partner's Account' of the LETI' university. © 2023 IEEE.

关键词： Students

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A Comparative Analysis and Prediction of Diabetes Using Machine Learning Approaches 5th

A Comparative Analysis and Prediction of Diabetes Using Mach...

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5th Doctoral Symposium on Computational Intelligence, DoSCI 2024

作者： Ansari, Gufran Ahmad Bhat, Salliah Shafi Bhoite, Sachin Department of Computer Science and Engineering and Software Engineering University of Hail Hail Saudi Arabia Computer Applications B.S. Abdur Rahman Crescent Institute of Science and Technology Chennai600048 India Pune411 038 India

ISBN: (纸本)9789819763177

Diabetes is a leading cause of death. People with type 1 diabetes (T1D) can face a number of consequences from their illness. Hyperglycemia is a symptom of T1D disease. Hyperglycemia can be symptoms that can be used for predicting diabetes in patients using the machine learning approach (MLA). Learning algorithms play a role in disease detection and prediction of decision-making. The leading cause of diabetes is really a high blood sugar level or a lack of insulin. As a consequence, it is critical to develop a reliable method to predict diabetes. Once it becomes a serious health concern, it will damage different organs such as the lungs, brain, nerves, blood vessels and heart as a result. Several approaches for detecting diabetes have been used in recent decades. Early diabetic diagnosis is currently very important to help the patients’ health, and this can be done by utilizing a variety of procedures. Tenfold cross-validation technique was used to train dataset for classification. In this paper, authors use different classifications of algorithms such as random forest, decision tree, logistic regression and multilayer perceptron for finding T1D patients. The accuracy of MLP is 98.077% which is the highest among other algorithms. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.

关键词： Contrastive Learning

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The Impact of Transformer Models and Regression Algorithms on Automated Short Answer Grading 5

The Impact of Transformer Models and Regression Algorithms o...

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5th IEEE International Conference on Advances in Electrical, Computing, Communication and Sustainable Technologies, ICAECT 2025

作者： Nampoothiri, Abhijith. N. Thushara, M.G. Pradeesh, N. Anand Krishna, K.R. Santhosh, Abhai Abhijith Kumar, S. Kumar, Suryan Amrita School of Computing Amrita Vishwa Vidyapeetham Department of Computer Science and Applications Amritapuri India Amrita School of Computing Amrita Vishwa Vidyapeetham Department of Computer Science and Engineering Amritapuri India Omnex Software Solutions Pvt. Ltd Thoraipakkam Chennai India

ISBN: (纸本)9798350356236

Grading short answer questions in higher education often poses significant challenges due to its subjective nature and time-consuming process. As educational institutions expand, the need for efficient and objective grading solutions becomes critical. This paper investigates the use of machine learning and natural language processing to automate the evaluation of short answer responses. Our study employs a two-phase methodology: first, several transformer models - including BERT, Sentence-BERT, T5, Genism, and XLNet - are used to transform text into numerical representations. In the second phase, various regression techniques, such as Linear Regression, Support Vector Machine (SVM), Random Forest, and AdaBoost, are applied to these representations to estimate scores. Our findings indicate that the combination of the BERT model with Random Forest performs the best, demonstrating superior accuracy with the lowest error metrics. This optimal model pairing is proposed for integration into the Ample Learning Management System (LMS) to enhance grading efficiency and provide quicker, more reliable feedback. The significant implications of this research provides teachers with a robust tool for automated grading, which can enhance the educational experience by delivering timely and accurate evaluations. © 2025 IEEE.

关键词： Teaching

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Investigation of Local and Global Methods of Explainable Artificial Intelligence. Metrics for Evaluating Explanation Methods 3

Investigation of Local and Global Methods of Explainable Art...

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3rd International Conference on Neural Networks and Neurotechnologies, NeuroNT 2022

作者： Shevskaya, Natalya V. Shishkin, Ivan V. Stepanov, Vladislav D. Oblizanov, Alexander D. Saint Petersburg Electrotechnical University 'LETI' Department of Software Engineering and Computer Applications Saint Petersburg Russia

ISBN: (纸本)9781665467766

Artificial intelligence systems in meaning of machine learning face not only technical (collecting enough data, efficient hardware implementation), but also social difficulties in implementation. The reason for this is distrust due to a lack of understanding by end users how such systems work and why they make certain decisions. Therefore, this paper discusses the methods of explainable artificial intelligence aimed at solving the problem of misunderstanding, also considers existing approaches to the evaluation of these methods and determines the requirements for the evaluation of AI system and the metrics that can be used in it. It will also answer the question that runs through this work - what is the role of hybrid intelligence in the tasks of explaining models? © 2022 IEEE.

关键词： Lime

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Application of Text Analysis Methods to Recommend Student Choices 25

Application of Text Analysis Methods to Recommend Student Ch...

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25th International Conference on Soft Computing and Measurements, SCM 2022

作者： Korytov, Pavel V. Kholod, Ivan I. Saint-Petersburg Electrotechnical University LETI Department of Software Engineering and Computer Applications Saint-Petersburg Russia

ISBN: (纸本)9781665496698

The paper considers the use of natural language processing methods to recommend student choices. Descriptions of subjects taken by the student serve as the basis for compiling the student profile. This profile is used to rate available side projects as most corresponding to that student's educational history. The quality of recommendations is evaluated based on the data from the Individual Educational Trajectories system. © 2022 IEEE.

关键词： Students

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Weapon Detection Identification and Classification for DCNN and YOLO-V5 Techniques 5

Weapon Detection Identification and Classification for DCNN ...

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5th International Conference on IoT Based Control Networks and Intelligent Systems, ICICNIS 2024

作者： Ashok Kumar, J. Dhivya, G. Selvaraj, P. Karthikeyan, V. Ganesh Ramesh, S.P. Lakshmi, S. Jeya Saravanakumar, R. Karpagam Academy of Higher Education Department of Computer Applications Tamil Nadu Coimbatore India School of Computer Science and Engineering Department of Computer Science and Engineering Galgotias University Uttar Pradesh Greater Noida India School of Computing SASTRA Deemed to Be University Thanjavur Tamil Nadu Thirumalaisamudram India Iconix Software Solution Tamil Nadu Tirunelveli India

ISBN: (纸本)9798331518097

Over 850,000 people die every year as a direct result of gun violence, yet civilians hold more than 85% of the world's weapons. Detecting weapons via manual surveillance has not been successful. It is critical to automate a system that detects weapons. A large number of convolution neural network CNN-based automated weapon identification systems have shown promising results. These methods are crucial for weapon identification but expensive. These devices produce a large amount of false negatives since monitoring videos are not high enough quality or visible enough to identify weapons. Finding the sweet spot between fast detection and a few false positives and negatives in weapon detection is the main goal of this research. In addition to defining an Area of Interest, the suggested structure uses YOLO. The models use previously processed images with backgrounds that have been blurred using a median filter. Metrics such as F1 scores, accuracy, recall, False Positive, and False Negative will be used to assess the suggested architecture. This investigation shows that YOLO-v5 can quickly identify and has a high recall rate. The deep convolution neural network DCNN required 0.17 ms for each frame, but this one only required 0.010 ms, which would indicate that security and weapon detection have good promise. Starting with a labeled collection of weapon and non-weapon photos is the initial stage in DCNN and YOLOv5 weapon detection. Use YOLOv5's ability to recognize multiple things within frames to train a real-time object localization and identification model. DCNNs categorize weapons and extract complex data effectively. DCNN classifies objects, whereas YOLOv5 detects, bounds, and tracks them. Optimize both models' hyperparameters and apply data augmentation for better performance. Install the integrated system on cutting-edge equipment for real-time weapon identification and classification. © 2024 IEEE.

关键词： Median filters

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Detection of Different Stages of Alzheimer’s Disease Using CNN Classifier

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computers, Materials & Continua 2023年第9期76卷 3933-3948页

作者： S M Hasan Mahmud Md Mamun Ali Mohammad Fahim Shahriar Fahad Ahmed Al-Zahrani Kawsar Ahmed Dip Nandi Francis M.Bui Department of Computer Science American International University-Bangladesh(AIUB)Dhaka1229Bangladesh Centre for Advanced Machine Learning and Applications(CAMLAs) DhakaBangladesh Department of Software Engineering Daffodil International UniversityDhakaBangladesh Department of Computer Engineering Umm Al-Qura UniversityMecca24381Saudi Arabia Department of ECE University of SaskatchewanSaskatoonSK S7N5A9Canada Group of Biophotomatiχ Department of ICTMBSTUSantoshTangail1902Bangladesh

Alzheimer’s disease(AD)is a neurodevelopmental impairment that results in a person’s behavior,thinking,and memory *** common symptoms ofADare losingmemory and early *** addition to these,there are several serious impacts ***,the impact ofADcanbemitigatedby early-stagedetection though it cannot be cured ***-stage detection is the most challenging task for controlling and mitigating the impact of *** study proposes a predictive model to detect AD in the initial phase based on machine learning and a deep learning approach to address the *** build a predictive model,open-source data was collected where five stages of images of AD were available as Cognitive Normal(CN),Early Mild Cognitive Impairment(EMCI),Mild Cognitive Impairment(MCI),Late Mild Cognitive Impairment(LMCI),and *** stage of AD is considered as a class,and then the dataset was divided into three parts binary class,three class,and five *** this research,we applied different preprocessing steps with augmentation techniques to efficiently *** integrates a random oversampling technique to handle the imbalance problem from target classes,mitigating the model overfitting and *** three machine learning classifiers,such as random forest(RF),K-Nearest neighbor(KNN),and support vector machine(SVM),and two deep learning methods,such as convolutional neuronal network(CNN)and artificial neural network(ANN)were applied on these *** analyzing the performance of the used models and the datasets,it is found that CNN with binary class outperformed 88.20%*** result of the study indicates that the model is highly potential to detect AD in the initial phase.

关键词： Alzheimer’s disease early detection convolutional neural network data augmentation random oversampling machine learning

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Sports Event Detection Using a 3D Convolutional Neural Network with Equiangular Basis Vector in Video Processing 3

Sports Event Detection Using a 3D Convolutional Neural Netwo...

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3rd IEEE International Conference on Distributed Computing and Electrical Circuits and Electronics, ICDCECE 2024

作者： Kumar, M. Ashok Jasim, Laith H. Sudhakar, K. Reddy, R. Archana Saranya, N. Naga Skyline University Nigeria Department Of Computer Science Software Engineering And Cyber Security Kano State Nigeria College Of Technical Engineering The Islamic University Department Of Computers Techniques Engineering Najaf Iraq Nitte Meenakshi Institute Of Technology Dept. Of Ai & Ds Bengaluru India School Of Computer Science And Engineering Sr University Warangal India Saveetha College Of Liberal Arts & Science Simats Department Of Computer Applications Chennai India

ISBN: (纸本)9798350318609

As sports video collections continue to expand at a rapid pace, there is a need for advanced classification methods that can accurately and efficiently categorize and manage the massive amount of content available. These methods must be able to dynamically adapt to changes in the collection, ensuring that the videos are properly organized and easily searchable for users. Traditional classification methods are not able to keep up with the rapid growth and complexity of sports video collections, leading to incomplete classification and impacting search and retrieval processes. This paper addresses this challenge using the UCF sports dataset and proposes a Visual Geometry Group-19 (VGG-19) based CNN classifier and Equiangular Basis Vector (EBV) representation introduced for efficient classification and category representation. Furthermore, a 3D Convolutional Neural Network (3D-CNN) architecture extracts both spatial and temporal features from videos for accurate action classification. The Adam optimizer in the optimization algorithm is employed to optimize the learning process. This paper shows that the proposed 3D-CNN model outperforms existing methods a Hybrid Convolutional Neural Network with a Bidirectional Recurrent Neural Network (HCNN-BiRNN) model, Deformable Convolution Networks (DCN), 2D-Convolutional Neural Network (2D-CNN), Convolutional Neural Network - Long Short-Term Frequency (CNN-LSTM) in terms of accuracy (99.45%), precision (98.00%), sensitivity (96.98%), and F1-score (97.05%). © 2024 IEEE.

关键词： Convolutional neural networks

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