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检索条件"任意字段=2023 International Conference on Software Engineering and Machine Learning, CONF-SEML 2023"

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CovDNet: A Hybrid Multi - Level Densely Connected Neural Network for Improvised Analysis and Classification of COVID-19 Using Chest CT-Scans

CovDNet: A Hybrid Multi - Level Densely Connected Neural Net...

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international conference of Electronics, Communication and Aerospace Technology (ICECA)

作者： Deepa S Praveenkumar G D R Suguna Vivek Duraivelu C Jamunadevi S G Sundhar Department of Computer Technology-PG Kongu Engineering College Perundurai Erode Tamilnadu Department of computer science Vet institute of Arts and Science (Co-Education) College Erode Tamilnadu Department of Computer science and Engineering Bannari Amman Institute of Technology Sathyamangalam Department of Computer Science and Engineering B V Raju Institute of Technology Narsapur Telangana Department of Computer Technology PG Kongu Engineering College Perundurai Erode Tamilnadu Final M.Sc(Software Systems) Kongu Engineering College Perundurai Erode Tamilnadu

COVID-19, caused by the new coronavirus SARS-Co V-2, has turned into a worldwide health emergency, needing speedy and precise diagnostic techniques. This abstract provides a thorough evaluation of research works focusing on COVID-19 identification using DenseNet, a cutting-edge convolutional neural network architecture. DenseNet is notable for its innovative design, which fosters feature reuse while relieving the vanishing gradient problem and enhancing network information flow. Researchers have created novel ways for COVID-19 identification using medical imaging, such as chest X-rays and CT scans, by harnessing the capabilities of DenseNet. The analyzed studies demonstrate DenseNet's efficiency in recognizing COVID-19-specific patterns and distinguishing them from other lung diseases. These investigations have shown remarkable accuracy, sensitivity, and precision, exceeding established machine learning approaches and even professional radiologists. This research study also highlights the difficulties and constraints experienced while using DenseNet to identify COVID-19. Issues such as dataset quantity, class imbalance, and model decision-making process interpretability are addressed. Furthermore, future research paths and prospective enhancements, such as multi-modal data integration and the development of explainable AI systems, are investigated. The experimental results indicate that the proposed approach achieves high accuracy, sensitivity, and precision in the identification of COVID - 19. The proposed methodology achieved an accuracy of 89.74%, sensitivity of 87.25 % , and precision of 79.56%, which outperforms the existing state-of-the-art methods. The proposed approach is robust and can effectively differentiate between COVID-19 positive and negative cases, which is essential for early detection and prompt treatment.

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Application of neural networks in the teacher selection process

Application of neural networks in the teacher selection proc...

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2022 international conference on machine learning and Data engineering, ICMLDE 2022

作者： Ovalle, Christian Auccahuasi, Wilver Meza, Sandra Franklin-Cordova-Buiza Rojas, Karin Cosme, Miryam Inciso-Rojas, Miryam Aiquipa, Gabriel Martínez, Hernando Martin Campos Fuentes, Alfonso Auccahuasi, Aly Universidad Tecnológica Del Perú Lima Peru Universidad Privada Peruano Alemana Lima Peru Universidad ESAN Lima Peru Universidad Continental Huancayo Peru Universidad Científica Del Sur Lima Peru Universidad Privada Del Norte Lima Peru Universidad Tecnológica de Los Andes Apurímac Peru Universidad Autónoma de Ica Ica Peru Universidad César Vallejo Lima Peru Universidad de Ingeniería y Tecnología Lima Peru

The information and communications technologies are revolutionizing the classic ways of carrying out the processes, in particular, for the teacher selection processes we have the classic form of evaluation, according to the criteria of each educational institution, in the present work it is presented a teacher selection model, using neural networks, using 3 criteria and 23 characteristics, which are entered into individual networks for each criterion and additionally a network for the final classification, is presented based on a prototype, an application developed with the computational tool Matlab, which is described in detail for its application and scaling, for purposes of measuring the performance of the network, evaluations were carried out with a group of 30 candidates, grouped into two groups, a group of 15 candidates with positive conditions complying with the policies of the educational institution and a second group with candidates who do not meet the policies of the educational institution, with which sensitivity values of 93% and a specificity level of 86% were obtained, we conclude that the model presented can be replicated and conditioned to the needs and policies of each educational institution. © 2023 The Authors. Published by Elsevier B.V.

关键词： software prototyping

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Vulnerability Name Prediction Based on Enhanced Multi-Source Domain Adaptation

Vulnerability Name Prediction Based on Enhanced Multi-Source...

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IEEE international conference on Trust, Security and Privacy in Computing and Communications (TrustCom)

作者： Ying Xing Mengci Zhao Bin Yang Yuwei Zhang Wenjin Li Jiawei Gu Jun Yuan Lexi Xu School of Artificial Intelligence Beijing University of Posts and Telecommunications Beijing China Key Laboratory for Safety-Critical Software Development and Verification Nanjing University of Aeronautics and Astronautics Ministry of Industry and Information Technology Nanjing China Yunnan Key Laboratory of Software Engineering Yunnan China China Unicom Research Institute Beijing China School of Computer Science Peking University Beijing China NSFOCUS Technologies Group Co. Ltd. Beijing China

ISBN: (数字)9798350381993

ISBN: (纸本)9798350382006

software products have brought convenience to modern society but also pose significant security risks due to various types of vulnerabilities. Identifying vulnerability names is vital for program repair and software maintenance, but the lack of training data presents a challenge. Big data analytics and machine learning can help overcome this challenge by processing large amounts of data and improving the accuracy of vulnerability name prediction. Considering that the data is often from datasets composed of multiple sources, a feature-based or attention-based multi-source domain adaptation (MSDA) approach is required. In this paper, we propose an MSDA method based on both feature and attention to accomplish the task of predicting vulnerability names, called Multi-Source Domain Adaptation for Vulnerability Name Prediction (MSDA-VNP). First, MSDA-VNP reduces domain divergence by adversarial training and then uses domain-invariant features to obtain feature correlations between individual source and target domains. In combination with the obtained domain correlations, Weighted multi-kernel Maximum Mean Discrepancy (WMK-MMD) is proposed as the attention mechanism. Second, a data augmentation strategy is employed to enhance MSDA-VNP to identify privacy-related vulnerabilities. To evaluate our approach, we conducted experiments on eight Java real-world projects in the software Assurance Reference Dataset (SARD). The experimental results show that the proposed method MSDA-VNP performed efficiently and stably for the 44 types of vulnerabilities involved. The data augmentation strategy has also been proved to be effective as an enhancement for the proposed method MSDA-VNP.

关键词： Training software maintenance Java Data privacy Correlation Training data Maintenance engineering

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Proceedings of the 14th international conference on Education Technology and Computers

作者： Veronica Segarra-Faggioni Sylvie Ratté Frank De Jong Dept. Software and IT Engineering École de technologie supérieure Canada and Dept. Computer Science and Electronics Universidad Tecnica Particular de Loja Ecuador Dept. Software and IT Engineering École de technologie supérieure Canada Dept. Learning and Innovation Aeres Wageningen Applied University Netherlands

ISBN: (纸本)9781450397766

Knowledge Building as a pedagogy supports collaborative work between students to improve ideas. The result of knowledge-building discourse in student communities is the development of academic artifacts. Student reports are academic artifacts that can result from a knowledge-building process during their learning. However, the automatic analysis of student reports is quite challenging due to the documents’ length and written language. Given this context, our goal is to analyze the semantic similarity between student reports and curriculum literature using the information coverage measure based on the Skip-gram word2vec model. Word2vec is a well-known set of techniques that provides a way to create a representation of words to predict the nearby words between every word and its context to capture the internal semantic and syntactic information. In this work, through the experimental comparison between the vocabulary list, the best similarity value is 0.90 using word2vec. The results show that each student report was aligned with each document in the curriculum literature using the information coverage measure based on word2vec.

关键词： machine learning natural language processing word2vec

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A LSTM and GRU-Based Hybrid Model in the Cryptocurrency Price Prediction 5th

A LSTM and GRU-Based Hybrid Model in the Cryptocurrency P...

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5th international conference on Blockchain and Trustworthy Systems, BlockSys 2023

作者： Liu, Yue Xiao, Guijiao Chen, Weili Zheng, Zibin School of Computer Science and Engineering Sun Yat-sen University Guangzhou510000 China South China Business College Guangdong University of Foreign Studies Guangzhou510000 China School of Information Science and Technology Guangdong University of Foreign Studies Guangzhou510000 China School of Software Engineering Sun Yat-sen University Guangzhou510000 China

ISBN: (纸本)9789819981038

Cryptocurrency is a new type of digital currency that utilizes blockchain technology and cryptography to achieve transparency, decentralization, and immutability. Bitcoin became the world’s first decentralized cryptocurrency in 2009. With increasing attention given to cryptocurrency, predicting its price has become a popular research topic. Many machine learning and deep learning algorithms, such as Gated Recurrent Unit (GRU), Neural Network (NN), and Long Short-Term Memory (LSTM), have been studied for cryptocurrency price prediction. In this paper, we propose a hybrid cryptocurrency price prediction model based on LSTM and GRU. The model achieves better results than the LSTM model in cryptocurrency price prediction and performs the best among existing hybrid models based on LSTM and GRU. © 2024, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Long short-term memory

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Feature engineering-Based Detection of Buffer Overflow Vulnerability in Source Code Using Neural Networks

Feature Engineering-Based Detection of Buffer Overflow Vulne...

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IEEE Annual international Computer software and Applications conference (COMPSAC)

作者： Mst Shapna Akter Hossain Shahriar Juan Rodriguez Cardenas Sheikh Iqbal Ahamed Alfredo Cuzzocrea Department of Computer Science Kennesaw State University USA Department of Information Technology Kennesaw State University USA Department of Computer Science Marquette University USA iDEA Lab University of Calabria Italy

One of the most significant challenges in the field of software code auditing is the presence of vulnerabilities in software source code. Every year, more and more software flaws are discovered, either internally in proprietary code or publicly disclosed. These flaws are highly likely to be exploited and can lead to system compromise, data leakage, or denial of service. To create a large-scale machine learning system for function-level vulnerability identification, we utilized a sizable dataset of C and C++ open-source code containing millions of functions with potential buffer overflow exploits. We have developed an efficient and scalable vulnerability detection method based on neural network models that learn features extracted from the source codes. The source code is first converted into an intermediate representation to remove unnecessary components and shorten dependencies. We maintain the semantic and syntactic information using state-of-the-art word embedding algorithms such as GloVe and fastText. The embedded vectors are subsequently fed into neural networks such as LSTM, BiLSTM, LSTM-Autoencoder, word2vec, BERT, and GPT-2 to classify the possible vulnerabilities. Furthermore, we have proposed a neural network model that can overcome issues associated with traditional neural networks. We have used evaluation metrics such as F1 score, precision, recall, accuracy, and total execution time to measure the performance. We have conducted a comparative analysis between results derived from features containing a minimal text representation and semantic and syntactic information. We have found that all neural network models provide higher accuracy when we use semantic and syntactic information as features. However, this approach requires more execution time due to the added complexity of the word embedding algorithm. Moreover, our proposed model provides higher accuracy than LSTM, BiLSTM, LSTM-Autoencoder, word2vec and BERT models, and the same accuracy as the GPT-2

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Detailed review on Integrated Healthcare Prediction System Using Artificial Intelligence and machine learning

Detailed review on Integrated Healthcare Prediction System U...

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Advance Computing and Innovative Technologies in engineering (ICACITE), international conference on

作者： Deepa Parasar Arif Ali N. Muthuvairavan Pillai Archana Shahi Badria Sulaiman Alfurhood Kumud Pant CSE Amity University Maharashtra Department of CSE Dev Bhoomi Uttarakhand University Dehradun Uttarakhand India Computer Science and Business Systems R.M.D Engineering College Kavarapettai Chitkara Business School Chitkara University India Department of Computer Sciences College of Computer and Information Sciences Princess Nourahbint Abdulrahman University Saudi Arabia Department of Biotechnology Graphic Era Deemed to be University Dehradun Uttarakhand India

The intricacy and expansion of statistics in increased care sector will lead to an increase in the application of artificial intelligence (AI). There are several different AI techniques in use today by consumers, health professionals, and sciences companies. Guidelines for care and prognosis, patient and commitment, and organizational chores are the key application areas. Challenges will prevent the work of health professionals from being substantially computerized for a considerable length of time, despite the fact that AI can do healthcare tasks in many instances equally as well or better than people. Ethics issues and the implementation of artificial intelligence in healthcare are also discussed. AI technology is essential for producing effective healthcare applications. The definition of several effective software programmers may help in the growth of the health predictive model. Computer vision and cloud services are critical elements for building smart healthcare *** collection and analysis of a wide range of health care data may provide useful insights quickly. Artificial intelligence is also crucial in the process of illness detection and prognosis. This smart and efficient approach may help with early illness diagnosis. Sensors connected through wireless networks make it possible to track the actions of a whole population. Sensors embedded in the human body may aid in the recognition of emotions and facial expressions by using neural networks. There is a plethora of technological implementations that contribute to a more advanced data processing *** primary focus of this research is to determine how effectively various efficient technology’s function when used in the development of healthcare prediction models. AI uses a wide variety of sensor-based methods to boost healthcare's efficacy. Another area of study has substantial implications for the healthcare system. To help identify problems in biomedical goods, modelling tools may be pro

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Research on Graphic Design Image Processing Technology Based on Newton's method in Photoshop

Research on Graphic Design Image Processing Technology Based...

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international conference on Smart Technologies For Smart Nation (SmartTechCon)

作者： Mohammed Hussien Al-Fatlawy M. Sahaya Sheela Santosh Kumar Yadav V Srinivasan S. Gopalakrishnan N V Uma Reddy College of Technical Engineering The Islamic University Najaf Iraq Department of ECE Vel Tech Rangarajan Dr. Sagunthala R&D Institute of Science and Technology (Deemed to be University) Chennai Tamil Nadu India Department of Electronics & Communication Engineering IES College Of Technology Bhopal MP India New Prince Shri Bhavani College Of Engineering And Technology Tamilnadu India Department of Information Technology Hindustan Institute of Technology and Science (Deemed To Be University) Kelambakkam Tamil Nadu India Department of Artificial Intelligence & Machine Learning New Horizon College of Engineering Bengaluru

The term "computer graphics and image processing technology" refers to a technology that is based on computers and makes use of software to process graphics and images in a comprehensive manner. Since its introduction, it has been the subject of extensive research and practical use, and as a result, it has steadily evolved into a fully formed and flawless technical system. Image editing, image composition, color correction, the generation of functional color effects, and other related tasks are among the primary features included in Photoshop. The title bar, the menu bar, the picture editing window, the status bar, the toolbox, the control panel, the drawing mode, the file format, the file size, the color setting, shortcut keys, and the color mode are all components of the operational interface. Studying the image processing technology used in graphic design based on NM (Newton's technique) in Photoshop platform is the focus of this research project. Using NM as a foundation, construct a graphic design image processing model. in order to make the global convergence better and reduce the goal function as much as possible. To verify that the goal function is moving in the desired direction, the modified global criterion of NM is applied. According to the findings, the new and improved algorithm has a running time that is 3.67 percent and 2.25% less than that of the NM and the inexact Newton method respectively. The root-mean-square error of the reconstructed image of the cross-section with a diameter of 19mm is controlled to be approximately 5.03%. This error decreases as the amount of Gaussian noise increases. Even if there is significant interference from Gaussian noise when the algorithm reconstructs sections of varying diameters, the quality of the reconstructed image of the improved NM has only a little amount of impact on the reconstructed image.

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Playing the video games during COVID-19 pandemic and its effects on player's well-being

Playing the video games during COVID-19 pandemic and its eff...

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Business Analytics for Technology and Security (ICBATS), international conference on

作者： Samar Billi Noaman Amer Ibrahim Liaqat Ali M. W. Iqbal Asma Ashraf Usama Haseeb Salman Muneer Rasah Almajed K. Hamid Faculty of Management Canadian University Dubai Dubai UAE College of Computer and Information Technology American University in the Emirates Dubai United Arab Emirates College of Engineering and Technology University of Science and Technology of Fujairah Fujairah UAE Department of Software Engineering Superior University Lahore Pakistan Department of Software Engineering Superior University Lahore Pakistan Department of Computer Science Superior University Lahore Pakistan School of Computer Science NCBA&E Lahore Pakistan Applied Science Research Center Applied Science Private University Amman Jordan College of Computer and Information Technology American University in the Dubai United Arab Emirates

COVID-19 affected our lives intensly. That state of affairs made humans helpless. They had been depressed and experienced loneliness. At that time many human beings were determined to play video games just to loosen up their minds. Many games changed into an additional source of revenue wherein during COVID-19 they were playing while earning money. With these advantages, there were also some poor effects was accrued. A number of players remained playing video games post COVID-19. The carried out survey is based on the social video games results on players’ well-being and additionally on the effects of gamers’ health and their sensible lifestyles. We are going to investigate the behavior of gamers engaged with video games during the COVID-19 lockdown and the video games affects on their well-being, the time they served in playing video games, and the consequential effect on their behavior and social and mental well-being. The results provide a start line for empirically grounded discussions on video games at some stage in the pandemic, their use, and potential outcomes. Different agegroups of players have been investigated. Most players are between 18 and 30 years. A number of the gamers during lock down played a few hours but most of players were males who spent most of their day playing video games. However, now the ratio of playing video games is reduced as examined with past circumstances. Roughly we can say that the condition as a whole is better, the reason why players enforced video games in their post COVID-19 practical life 1 .

关键词： COVID-19 Video games Pandemics Games machine learning Behavioral sciences Security

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Efficient Giant Graph Unlearning via Push-Pull Tuning

Efficient Giant Graph Unlearning via Push-Pull Tuning

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IEEE international conference on Big Data and Cloud Computing (BdCloud)

作者： Fahao Chen Peng Li Shui Yu School of Computer Science and Engineering The University of Aizu Japan School of Computer Science University of Technology Sydney Australia

The advent of deep learning applications for data collection has elicited concerns pertaining to privacy, particularly regarding the potential exposure of data through various vulnerabilities, such as membership inference attacks. In response to these concerns, several machine unlearning techniques have been proposed, which can effectively eliminate specific data from a trained model. However, it is important to note that existing methods primarily concentrate on Euclidean data, leaving non-Euclidean data structures, such as graphs, unexplored. Our preliminary experiments reveal that directly applying these techniques to graph data yields suboptimal results, especially on large graphs. In this work, we shift our focus towards addressing the unlearning challenge within the context of graph data trained by graph neural networks (GNNs). We propose a simple yet effective approach termed push-tuning, which sophistically manipulates loss values associated with the data intended for unlearning. This manipulation redirects the model’s predictions and facilitates the unlearning process. Moreover, we also propose a pull-tuning method to recover accuracy on the remaining data, which is degraded by the unlearning process. To jointly consider data removal and accuracy recovery, we further introduce an alternation method. To assess the effectiveness of our proposed method, we conduct comprehensive experiments using five popular benchmark datasets. The experimental results demonstrate that our approach can effectively unlearn the target nodes while preserving model’s accuracy on the remaining nodes.

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