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computer Technology and Information Science (ISCTIS), International Symposium on

作者： Sen Sun Yizheng Tao China Academy of Engineering Physics Institute of Computer Application Mianyang China

ISBN: (数字)9798350354560

ISBN: (纸本)9798350354577

In software engineering practice, accurate and efficient system scale evaluation is crucial for ensuring the accuracy of project management and fund allocation. Traditional software system scale assessment methods, such as expert decision-making, COCOMO models, functional point analysis, and analogy methods, are widely used, but are limited by high manual dependencies, low efficiency, and subjectivity. Therefore, this study proposes an innovative collaborative evaluation method - DALSE (Domain Adaptation Learning Size Evaluation) evaluation method, which combines automation models with expert experience. This method is based on the IFPUG function point standard, constructs the BiLSTM-CRF model software for automatic recognition of function point types, and introduces domain adaptive learning technology to effectively alleviate the problem of data scarcity in specific fields, achieving flexibility and accuracy in cross domain applications. Although the DALSE automation model has shown excellent performance in experiments, considering the complexity and importance of software scale evaluation, this study advocates an "Intelligent+Expert" collaborative evaluation model, which combines expert experience to verify the results and multi expert collaborative decision-making to determine the complexity factor of the project. The initial evaluation results are adjusted to ensure the comprehensiveness and reliability of the evaluation. This collaborative work mode effectively integrates automation technology while retaining manual verification to increase evaluation credibility, greatly improving evaluation efficiency and quality. By complementing intelligence and manual labor, DALSE provides a more reliable and efficient solution for software scale assessment, especially suitable for capital intensive projects, ensuring the scientific and rational cost estimation, and strongly supporting modern decision-making in project management.

关键词： Adaptation models Accuracy Automation Decision making Collaboration Project management Manuals Software Complexity theory Standards

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A Collaborative Framework for Intrusion Detection in Cloud Computing Based on HLA-CNN-BiLSTM-SVM Model

A Collaborative Framework for Intrusion Detection in Cloud C...

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Intelligent Technologies (ACOIT), Asian Conference on

作者： Amit K. Mogal Vaibhav P. Sonaje Department of Computer Science & Application School of Computer Science and Engineering Sandip University Nashik India

ISBN: (数字)9798350374957

ISBN: (纸本)9798350374964

In addition to describing a way to make better use of preexisting computer infrastructures, the phrase “cloud computing” describes a technology that facilitates the sale of access to shared computer resources and services. The flip side is that these dispersed and convoluted infrastructures are attractive targets for attackers. Because cyberattacks can reduce the quality of service offered to clients, the system constitute a substantial risk. This proposed method consists of three steps like preprocessing, feature selection, and model training. Data preprocessing, which includes both feature discretization and feature normalization, is a critical step in any knowledge discovery process. In this proposed to evaluate the impact of feature selection on improving classifier algorithm accuracy by comparing two feature selection methods: one that takes linear correlation into account, and another that takes mutual information into account. When training the model, an HLA-CNN-BiLSTM-SVM was utilized. With a 92.35 percent average accuracy, the suggested method surpasses CNN-SVM and BiLSTM.

关键词： Training Cloud computing Accuracy Correlation Computational modeling Intrusion detection Bidirectional long short term memory Feature extraction Classification algorithms Mutual information

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News-Driven Stock Price Forecasting in Indian Markets: A Comparative Study of Advanced Deep Learning Models 3

News-Driven Stock Price Forecasting in Indian Markets: A Com...

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3rd International Conference for Advancement in Technology, ICONAT 2024

作者： Attaluri, Kaushal Tripathi, Mukesh Kumar Reddy, Srinithi Shivendra Software Engineer-Machine Learning and AI Pegasystems India Worldwide Pvt Ltd Hyderabad India Vardhaman College of Engineering Department of Computer Science and Engineering Hyderabad India Vasavi College of Engineering Department of Information Technology Hyderabad India D. K. College Department of Computer Application Bihar Dumraon India

ISBN: (纸本)9798350354171

Forecasting stock market prices is a challenging task for traders, analysts, and engineers due to the myriad of variables influencing stock prices. However, the advent of artificial intelligence (AI) and natural language processing (NLP) has significantly advanced stock market forecasting. AI's ability to analyze complex data sets allows for more informed predictions. Despite these advancements, stock price forecasting remains an area where AI has not yet achieved optimal results. In this paper, we forecast stock prices using 30 years of historical data from various national banks in India sourced from the National Stock Exchange. We employ advanced deep learning models, including multivariate multi-step Long Short-Term Memory (LSTM), Facebook Prophet with LightGBM and Optuna, and Seasonal Auto-Regressive Integrated Moving Average (SARIMA). Additionally, we analyze news data from tweets and reliable sources like Business Standard and Reuters, recognizing their significant impact on stock price movements. © 2024 IEEE.

关键词： Contrastive Learning

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Herbaceous Plant Leaf Disease Identification

Herbaceous Plant Leaf Disease Identification

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International Conference for Convergence of Technology (I2CT)

作者： Arpana Prasad V Asha S P Sreeja Dheeraj P Ajay G. Saber Hussain Master of Computer Application New Horizon College of Engineering Bengaluru India

ISBN: (数字)9798350394474

ISBN: (纸本)9798350394481

The precise identification and control of foliar diseases in herbaceous species play a crucial part in maintaining agricultural efficiency and environmental equilibrium. This research tackles the difficulties in pinpointing foliar diseases by merging sophisticated image analysis methods with artificial intelligence algorithms. In this study, a comprehensive collection of over 10,000 high-definition images was amassed, showcasing a wide variety of herbaceous plants afflicted by prevalent diseases such as leaf rust, blight, and powdery mildew. These images underwent a series of feature extraction processes to underscore the disease signatures. We employed and assessed several machine learning frameworks, particularly Convolutional Neural Networks and Support Vector Machines With regard to their precision and operational efficacy. The outcomes suggested that the CNN model excelled, achieving an impressive 95% accuracy rate in pinpointing diseases, thereby surpassing conventional diagnostic approaches. It not only provides a powerful solution for swift and precise disease detection in herbaceous plants but also paves the way for future advancements in automated plant health surveillance systems. The findings help farming methodologies, suggesting a pathway towards enhanced disease control and minimization of crop loss.

关键词： Support vector machines Image analysis Surveillance Merging Machine learning Minimization Feature extraction

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A Few-Shot Semantic Segmentation Network with Mixed Prototypes and Base Learner

A Few-Shot Semantic Segmentation Network with Mixed Prototyp...

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IEEE Advanced Information Technology, Electronic and Automation Control Conference (IAEAC)

作者： Han Wang Ziyang Meng Yakun Zhang Institute of Computer Application China Academy of Engineering Physics Sichuan China

Few-shot semantic segmentation methods aim to segment new object categories with limited labeled samples. Existing approaches struggle to extract comprehensive semantic matching information from target regions and mitigate noise from complex backgrounds. To address background noise, this paper proposes a novel few-shot semantic segmentation approach using prototype generation and base class learning. A pyramid pooling convolutional layer extracts rich prototype vectors by discerning differences between the core mask and label mask. Additionally, a base class learning module captures semantic information from base class objects during training, effectively suppressing interference from background regions and aiding in background semantic matching. Comparative and ablation experiments demonstrate the superior performance of the proposed model. Evaluation on benchmark datasets shows its effectiveness, with results on Pascal-5i dataset surpassing the baseline model, achieving an average intersection over union of 67.15% in one-shot scenarios.

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Networked Vehicle Practices Based on Networked Firing Ranges

Networked Vehicle Practices Based on Networked Firing Ranges

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IEEE International Conference on Data Science in Cyberspace (DSC)

作者： Wenqi Zheng Hao Ren Siqi Yang Zhi Chen Jiajun Xie Wenqiang Pan Institute of Computer Application China Academy of Engineering Physics Mianyang China

ISBN: (数字)9798350391367

ISBN: (纸本)9798350391374

In recent years, with the rapid development of network and computer technologies, the Governments of the United States, Japan, the United Kingdom and the European Union, as well as influential organizations, have successively carried out the construction of cyberspace ranges targeting the verification of new network technologies. New technology validation based on cyberspace ranges can solve the problems of high cost, difficult to manage iterative versions, and difficult to measure the accuracy of results. Based on the above advantages, this paper, in order to solve the problem of autonomous driving of cars in the park, which is difficult to drive safely under the influence of environmental uncertainties, completes the hardware design of intelligent cars in the cyberspace target range and proposes a Bayesian network-based design method of steering redundancy for hardware circuits, which divides the steering data of the two cars under different environmental factors into training set and test set. The Bayesian network parameters are predicted by the training set, and the prediction effect of the Bayesian network is tested by the test set. The prediction results show that the steering accuracy prediction of the smart car under different environmental factors is within the 85% confidence interval of the Bayesian network prediction, which provides a feasible way to design a safe and efficient underlying controller for smart cars.

关键词： Training Accuracy Uncertainty Redundancy Cyberspace Hardware Environmental factors Bayes methods Automobiles Wire

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Chinese Offensive Language Detection Algorithm based on Pre-trained Language model and Pointer Network Augmentation

Chinese Offensive Language Detection Algorithm based on Pre-...

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Artificial Intelligence, Networking and Information Technology (AINIT), International Seminar on

作者： Boyuan Hou Xin Xie Dongcheng Zhang Liyuan Zheng Guojun Yan China Academy of Engineering Physics Institute of Computer Application Mianyang China

ISBN: (数字)9798350385557

ISBN: (纸本)9798350385564

The traditional offensive language detection methods suffer from issues such as inadequate understanding of semantic information and sensitivity to noise in text. To address these problems, this paper proposes a Chinese offensive language detection algorithm based on a pre-trained language model and augmented with a pointer network. Firstly, the algorithm incorporates task prompt information into the text using the prompt layer. Then, it feeds the integrated text with task prompt information into the pre-trained language model RoBERTa to extract deep semantic information and obtain a semantic vector. Subsequently, the generated semantic vector is inputted into the focus layer of the pointer network to obtain a focus vector that strongly relates to offensive language. Finally, by concatenating the semantic vector and focus vector, they are passed through a classification layer for text classification and determining whether it contains offensive language. Experimental results on the Chinese offensive language dataset COLD demonstrate that our proposed method outperforms mainstream methods significantly in terms of detection effectiveness.

关键词： Seminars Sensitivity Splicing Semantics Text categorization Noise Prediction algorithms

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A human-in-the-loop method for pulmonary nodule detection in CT scans

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Visual Intelligence 2024年第1期2卷 1-13页

作者： Zeng, Qingjie Xie, Yutong Lu, Zilin Xia, Yong National Engineering Laboratory for Integrated Aero-Space-Ground-Ocean Big Data Application Technology School of Computer Science and Engineering Northwestern Polytechnical University Xi’an 710072 China Australian Institute for Machine Learning The University of Adelaide Adelaide 5000 SA Australia

Automated pulmonary nodule detection using computed tomography scans is vital in the early diagnosis of lung cancer. Although extensive well-performed methods have been proposed for this task, they suffer from the domain shift issue between training and test images. Unsupervised domain adaptation (UDA) methods provide a promising means to mitigate the domain variance;however, their performance is still limited since no target domain supervision is introduced. To make the pulmonary nodule detection algorithm more applicable in clinical practice and further boost the performance across domains, we propose a human-in-the-loop method in a semi-supervised fashion to enhance the model generalization ability when transferred from source domain to target domain. Specifically, we first train a detector model on source domain, and then the pre-trained detector is utilized with our proposed uncertainty-guided sample selection scheme (USSS) to find a few target domain samples worth annotating most and obtain their human annotations. Finally, the annotated and the rest unlabeled target domain samples are used together to refine the pre-trained model via our proposed zoom-in and zoom-out constraint (ZZC) strategy. We evaluate our method on the Nodule Analysis 2016 (LUNA16) and TianChi datasets. Experimental results show that our method surpasses recent competitive methods on source domain and also achieves surprising performance on target domain. © The Author(s) 2024.

关键词： Domain adaptation Human-in-the-loop Pulmonary nodule detection Semi-supervised learning

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Effective Intrusion Detection Model Using Raptor Optimized Deep Convolutional Neural Network

Effective Intrusion Detection Model Using Raptor Optimized D...

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2023 IEEE Guwahati Subsection Conference, GCON 2023

作者： Mahanta, Kshyamasagar Maringanti, Hima Bindu Bhale, Pradeepkumar Maharaja Sriram Chandra Bhanja Deo University Department of Computer Application Odisha Mayurbhanj757003 India Indian Institute of Technology Department of Computer Science and Engineering Guwahati Guwahati781039 India

ISBN: (纸本)9798350337785

Malicious behavior in the system must be recognized for ensure security because the occurrence of the intrusions can threaten the valuable data and threatens the quality of the network. An important area of research in network security is network intrusion detection. In recent years the intrusion detection model faced several problems due to low accuracy, data imbalance, lack of optimization and so on. To overcome this issue, in this research, a raptor-optimized deep CNN is used to develop the intrusion detection model, where the occurrence of intrusions is effectively detected. The deep CNN classifier effectively learns the features, and the enabling of the raptor optimization optimizes the weights and bias parameters to enhance the performance of the deep CNN classifier. The raptor optimization improves the classifier convergence time and global search capability, and the enabling of Synthetic Minority Oversampling Technique (SMOTE) resolved the issues of data imbalance, which reduced the complexity of the system. By evaluating the parameters of accuracy, sensitivity, and specificity, the research's superiority is demonstrated, and the suggested raptor-optimized deep CNN classifier achieved values of 96.51%, 90.23%, and 97.71% for dataset 1. The proposed raptor optimization also achieved values of 95.39%, 95.83%, and 95.15% for dataset 2, which is highly efficient compared to other existing methods. © 2023 IEEE.

关键词： Network security

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Detection of Gastric Cancer in Endoscopic Images Using Deep Convolutional Neural Networks

Detection of Gastric Cancer in Endoscopic Images Using Deep ...

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2024 IEEE International Conference on Communication, Computing and Signal Processing, IICCCS 2024

作者： Pauranik, Abhinav Nishanthi, R. Ramesh, U Veera Riad Al-Fatlawy, Ramy Kaur, Jasmeet Gandhi, Vaibhav C. Ies College of Technology Department of Computer Science & Engineering M.P. Bhopal India Prince Shri Venkateshwara Padmavathy Engineering College Chennai India Aditya Engineering College Department of Computer Science & Engineering Surampalem India The Islamic University College of Technical Engineering Department of Computers Techniques Engineering Najaf Iraq The Islamic University of Al Diwaniyah College of Technical Engineering Department of Computers Techniques Engineering Al Diwaniyah Iraq Chandigarh Group of Colleges Chandigarh Engineering College Department of Computer Application Punjab Jhanjeri Mohali140307 India Department of Computer Engineering Gujarat India

ISBN: (纸本)9798350390759

In finding features of gastric cancer in endoscopic images, difficulties arise from early manifestations and variance in the appearance of lesions. The objective of this study is to design an automobile deep convolutional neural network (CNN) that would help enhance the rate of accurate diagnosis of gastric cancer. In the current work, using the dataset of endoscopic images reviewed by senior gastroenterologists, transfer learning is applied on the base of the ResNet-50 based deep CNN the result of data augmentation is extended. By evaluating the model on measures of training, validation, and testing sets, high performance has been realized as depicted by accuracy of 87. 4%, precision of 87. 5%, recall of 87%, and f1-score of 87%. Research on the comparison of the proposed method with the conventional CAD systems and machine learning models confirmed the efficiency of the deep learning method. The utilization of Grad-CAM visualizations brought interpretability in improving match between regions of focus and clinically relevant areas therefore improving the trust in the model among the clinicians. To foster efficiency, the implementation incorporated Python and deep learning tools, including TensorFlow, the model of which was trained on GPUs. Thus, this paper demonstrates the feasibility of the studied CNNs in promoting the early diagnosis of gastric cancer, suggesting the adaptation of AI tools as complementary tools for clinicians in everyday practice. Future studies should follow up on how false negative results may occur;additionally, future studies should devise better approaches on how to increase the generalizability of these models across different patients. © 2024 IEEE.

关键词： Convolutional neural networks

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