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检索条件"任意字段=Proceedings of the 9th Knowledge-Based Software Engineering Conference"

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A State Graph-based Improved Framework for Monkey GUI Testing for EDA Desktop Applications 13

A State Graph-Based Improved Framework for Monkey GUI Testin...

引用

13th International conference on software Technology and engineering, ICSTE 2023

作者： Ali, Sarah Fayek, Magda Cairo University Faculty of Engineering Computer Engineering Department Giza Egypt

ISBN: (纸本)9798350371475

Monkey testing is one of the autonomous Graphical User Interface (GUI) automation testing techniques. Due to the complex GUI of Electronic Design Automation (EDA) software applications, applying Monkey Testing in a timely manner is very challenging. By making use of the GUI regressions knowledge about the application we can have a faster and improved Monkey testing approach. Yet directing the improved Monkey flow to have better coverage needs enhancements so this paper addresses this by building a state graph while running to guide traversing new states using image processing and weighted random selection technique. A state is represented as an image and the improved Monkey testing flow is guided to avoid already hit windows and actions the paper proves that the state graph-based Monkey testing approach reaches higher coverage than the improved Monkey testing approach. © 2023 IEEE.

关键词： Automation

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A Probability-based Distributionally Robust Optimization Approach for Transmission and Distribution Networks Coordinated Dispatch 9

A Probability-Based Distributionally Robust Optimization App...

引用

9th Asia conference on Power and Electrical engineering, ACPEE 2024

作者： Yu, Haoyang Liu, Dong Wu, Xiong Li, Xiaofei Qin, Jishuo Guo, Guodong State Grid Economic and Technological Research Institute Co. Ltd. Changping District Beijing102209 China Xi'An Jiaotong University School of Electrical Engineering Shaanxi Xi'an710049 China

ISBN: (纸本)9798350309638

the development of information interconnection technology has enabled the sharing of information between different levels of power grids. the co-operation of transmission and distribution networks, abbreviated as coordinated T&D operation, is an effective approach for achieving optimal allocation of generation resources in multi-level power grids, thereby enhancing the utilization of distributed renewable energy. However, the uncertainty surrounding distributed renewable energy presents a major challenge to the coordinated allocation. Traditional operating methods often lead to additional adjustment costs for over-conservatism or empirical errors due to limited historical date. In this paper, a probability-based distributionally robust optimal (DRO) method is introduced to the coordinated T&D operation model to deal with uncertainty. the proposed DRO model aims to strike a balance between conservatism and optimism. And the co-operation problem is addressed by a novel column-and-constraint generation (C&CG) algorithm. Simulation results show that the DRO-based coordinated T&D operation model can efficiently coordinate the generation resources in multi-level power system and achieve better economic benefits. © 2024 IEEE.

关键词： Electric power distribution

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Multi-objective optimization of the swing frame of a belt conveyor based on workbench 9

Multi-objective optimization of the swing frame of a belt co...

引用

9th International conference on Electromechanical Control Technology and Transportation, ICECTT 2024

作者： Hu, Kaiyu Li, Gang Taiyuan Research Institute China Coal Technology & Engineering Group Taiyuan030006 China Shanxi Tiandi Coal Machinery Equipment Co. Ltd. Taiyuan030006 China National Engineering Laboratory for Coal Mining & Excavation Machinery Equipment Taiyuan030006 China

ISBN: (纸本)9781510682399

In response to the reliability of key components of transportation equipment and the difficulty in controlling welding deformation in coal mine, a local response surface model of the swing frame was constructed by using ANSYS Workbench finite element software, taking the swing frame of a belt conveyor used in coal mining tunnels as an example. A multi-objective optimization design was carried out on the structural size and thickness of the swing frame using a built-in professional optimization module Design Exploration, and design recommendations were provided, the results show that the optimized swing frame not only reduces the design weight, but also meets the structural strength requirements of the specifications, and has high economic and feasibility. © 2024 SPIE.

关键词： Belt conveyors

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Comparative Analysis of Single and Ensemble Support Vector Regression Methods for software Development Effort Estimation 16

Comparative Analysis of Single and Ensemble Support Vector R...

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16th International conference on knowledge Discovery and Information Retrieval, KDIR 2024 as part of 16th International Joint conference on knowledge Discovery, knowledge engineering and knowledge Management, IC3K 2024

作者： Hosni, Mohamed MOSI Research Team LM2S3 Laboratory ENSAM Moulay Ismail Iniversity of Meknes Meknes Morocco

ISBN: (纸本)9789897587160

Providing an accurate estimation of the effort required to develop a software project is crucial for its success. these estimates are essential for managers to allocate resources effectively and deliver the software product on time and with the desired quality. Over the past five decades, various effort estimation techniques have been developed, including machine learning (ML) techniques. ML methods have been applied in software development effort estimation (SDEE) for the past three decades and have demonstrated promising levels of accuracy. Numerous ML methods have been explored, including the Support Vector Regression (SVR) technique, which has shown competitive performance compared to other ML techniques. However, despite the plethora of proposed methods, no single technique has consistently outperformed the others in all situations. Prior research suggests that generating estimations by combining multiple techniques in ensembles, rather than relying solely on a single technique, can be more effective. Consequently, this research paper proposes estimating SDEE using both individual ML techniques and ensemble methods based on SVR. Specifically, four variations of the SVR technique are employed, utilizing four different kernels: polynomial, linear, radial basis function, and sigmoid. Additionally, a homogeneous ensemble is constructed by combining these four variants using two types of combiners. An empirical analysis is conducted on six well-known datasets, evaluating performance using eight unbiased criteria and the Scott-Knott statistical test. the results suggest that both single and ensemble SVR techniques exhibit similar predictive capabilities. Furthermore, the SVR variant with the polynomial kernel is deemed the most suitable for SDEE. Regarding the combiner rule, the nonlinear combiner yields superior accuracy for the SVR ensemble. © 2024 by SCITEPRESS – Science and Technology Publications, Lda.

关键词： Support vector regression

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BAMGAN: A KBQA method based GAN and bi-directional attention 6

BAMGAN: A KBQA method based GAN and bi-directional attention

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6th International conference on Advanced Electronic Materials, Computers, and software engineering, AEMCSE 2023

作者： Guo, Jiazhi Faculty of Information Engineering and Automation Kunming University of Science and Technology Kunming Yunnan650504 China

ISBN: (纸本)9781510668249

knowledge-based question answering(KBQA) involves using knowledge base technology to generate answers to natural language processing(NLP) questions. KBQA is one of the most challenging tasks in the field of NLP. Answer selection plays a vital role in KBQA, as it requires selecting the correct answer from a pool of candidate answers. However, knowledge bases still struggle to identify the correct answer from multiple candidate answers. To address this issue, In this paper, we propose BAMGAN, a KBQA method based on generative adversarial networks. this method uses generative adversarial networks (GANs) to improve answer selection accuracy in deep neural networks. Experimental results show the effectiveness of the BAMGAN. © COPYRIGHT SPIE. Downloading of the abstract is permitted for personal use only.

关键词： Generative adversarial networks

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Exploration of Automatic Spam/Ham Message Classifier Using NLP 9

Exploration of Automatic Spam/Ham Message Classifier Using N...

引用

9th IEEE International conference for Convergence in Technology, I2CT 2024

作者： Asmitha, M. Kavitha, C.R. Amrita Vishwa Vidyapeetham Amrita School of Computing Dept. of Computer Science And Engineering Bengaluru India

ISBN: (数字)9798350394474

ISBN: (纸本)9798350394474

Text classification holds great significance for analyzing textual information in one or more predetermined categories. Text classification tools allows to arrange all different kinds of texts, emails, court records, advertisements, databases, and other materials. this saves time in making informed decisions based on accurate information. A text classifier is software functionality designed to segment unresolved text into a number of preordained groups within a given text. Such software is frequently incorporated into various applications, including sentiment analysis, online content moderation, spam detection in emails, and language detection. Classifying a text is an essential but complex undertaking, as it requires a level of language comprehension akin to that of humans. Consequently, text classification has emerged as a subject of ongoing research interest. this study's primary goal is to investigate how cutting-edge natural language processing techniques are used to automatically detect spam messages. the proposed model will examine two distinct datasets to construct and categorize the disaster tweets. One dataset was obtained from the NUS SMS Corpus (NSC), while the other was specifically generated for this study using Grumbletext. When a spam email turns up, the system will inevitably identify the category using procedures like GloVe and BERT. the methodology, Naive Bayes DTM and XGBoost functionalities are used. the system's performance will be assessed through metrics such as precision, recall, f-score, FNR, FPR. © 2024 IEEE.

关键词： Sentiment analysis

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Experimental Evaluation of Brain Cerebral Palsy Disease Prediction Using Artificial Intelligence Assisted Learning Methodology 9

Experimental Evaluation of Brain Cerebral Palsy Disease Pred...

引用

9th International conference on Science, Technology, engineering and Mathematics, ICONSTEM 2024

作者： Mohan, P. Pattabhirama Ramkumar, Govindaraj Saveetha University Department of Electronics And Communication Engineering Chennai602105 India

ISBN: (纸本)9798350365092

Cerebral Palsy (CP) is a complex neurodevelopmental disorder affecting movement, muscle tone, and posture. Early detection and intervention are crucial for managing CP effectively. this abstract presents a novel approach for predicting CP using Artificial Intelligence Assisted Learning Methodology (AIALM) with a Cascaded LeNet architecture. the proposed methodology leverages advancements in artificial intelligence and deep learning to enhance the accuracy and efficiency of CP prediction. the Cascaded LeNet architecture, inspired by convolutional neural networks (CNNs), is designed to extract relevant features from brain images associated with CP. Data preprocessing techniques are applied to prepare the input data, including normalization and augmentation, ensuring the robustness of the model. the AIALM framework integrates the cascaded LeNet model with intelligent learning mechanisms, enabling continuous adaptation and optimization based on incoming data. this dynamic learning approach enhances the model's ability to adapt to diverse patient profiles and imaging variations, ultimately improving prediction accuracy. Extensive experimentation and evaluation demonstrate the effectiveness of the proposed methodology. the model achieves an impressive accuracy of 96.4% in predicting CP from brain images, outperforming existing approaches. Furthermore, the model's performance is validated through rigorous cross-validation and comparison with clinical assessments. © 2024 IEEE.

关键词： Forecasting

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Vision-based Drug Classification System Using Transfer Learning Approach 9

Vision-Based Drug Classification System Using Transfer Learn...

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9th International conference on Business and Industrial Research, ICBIR 2024

作者： Laohakijvitoon, Patpon Nilwong, Sivapong Thai-Nichi Institute of Technology Master of Engineering Program in Engineering Technology Bangkok Thailand Thai-Nichi Institute of Technology Department of Computer Engineering and Artificial Intelligence Bangkok Thailand

ISBN: (纸本)9798350383010

Dispensing generic drugs instead of original drugs from pharmacies may cause adverse drug events to patients. It is difficult to separate between generic drugs and originals due to their similarities, except for imprints on the tablets. In this paper, a vision-based medicine tablets classification system using Convolutional Neural Networks (CNN) with a transfer learning approach is proposed. the proposed classification system aims to helps its user distinguish between the original drugs and generic drugs based on imprints on the medicine tablets. Multiple CNN models are created and tested as the classifier for the drug classification system. three well-known CNN models are chosen as base models for creating CNNs with the transfer learning, including VGG16, Inception-V3, and ResNet50-V2. the developed CNN models are tested with images of medicine tablet taken from smartphones. Results from the experiments are evaluated through accuracies, precisions, recalls, and F-1 scores. Experimental results show satisfying performances of the proposed system in classifying original drugs and generic drugs using visual information. © 2024 IEEE.

关键词： convolutional neural network deep learning image classification Medicine tablet transfer learning

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Enhanced Stain Normalization Method for Accurate Mitosis Detection in Histopathological Images 9

Enhanced Stain Normalization Method for Accurate Mitosis Det...

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9th International conference on Computer Science and engineering, UBMK 2024

作者： Samet, Refik Nemati, Nooshin Hançer, Emrah Sak, Serpil Kirmizi, Bilge Ayca Ankara University Department of Computer Engineering Ankara Turkey Victoria University of Wellington Center for Data Science and Artificial Intelligence Wellington New Zealand Ankara University Department of Medical Pathology Ankara Turkey

Mitosis on H&E-stained (Hematoxylin and Eosin) images is an important prognostic marker for evaluating the tumor9;s aggressiveness and providing comprehensive and reliable information for accurate diagnosis and... 详细信息

ISBN: (纸本)9798350365887

Mitosis on H&E-stained (Hematoxylin and Eosin) images is an important prognostic marker for evaluating the tumor's aggressiveness and providing comprehensive and reliable information for accurate diagnosis and treatment. the variability in dosage rate and different fading rates in H&E stains lead to challenges in histopathological analysis. Standardizing the staining process and understanding the factors that influence stain stability is critical for consistency and accuracy in diagnosis. this paper proposes an improved stain normalization method with DDPM-based (Denoising Diffusion Probabilistic Models) diffusion model to detect mitosis with high accuracy in histopathological images. One of the challenges in the DDPM-based diffusion model is incorrect transfer, where the model may confuse hematoxylin with eosin due to the high diversity nature of DDPM-based diffusion models. A stain separation method using CD-SNMF (Color Deconvolution-Sparse Non- Negative Matrix Factorization) is proposed to address this issue. this method normalizes the mitosis in images in CCMCT (Canine Mast Cell Tumors) and MIDOG21 (Mitosis Domain Generalization Challenge 2021) datasets by this stain normalization pathway with decent performance. © 2024 IEEE.

关键词： Non-negative matrix factorization

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KG-ADS: A Log Anomaly Detection Assisted Decision-Making with Support of knowledge Graph and Reinforcement Learning 9

KG-ADS: A Log Anomaly Detection Assisted Decision-Making wit...

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9th International conference on Computer and Communications, ICCC 2023

作者： Lai, Guoping Hu, Hao Tang, Hailin Zhou, Hongwei Chen, Guang Yuan, Jinhui Information Engineering University Zhengzhou China Zhongyuan University of Technology Zhengzhou China

ISBN: (纸本)9798350317251

there are various methods for log anomaly detection, and different methods have different performances in accuracy, recall rate, and F1 score when they process the different logs. there is often a difficulty in decision-making for log anomaly detection. this paper proposes KG-Ads which is an assisted decision-making with the support of knowledge graph and reinforcement learning , which is designed to reduce the difficulty of applying log anomaly detection. With the support of knowledge graphs, KG-Ads holds a log anomaly detection knowledge which contain 22015 entities, and 11837 relationships, and 118 relationship models. KG-Ads builds an inference engine based on reinforcement learning, and produce recommended policy with the support of the log anomaly detection knowledge base. Our experiments show that KG-Ads is able to provide the suitable policies which are comparable to known policies in the existing work. © 2023 IEEE.

关键词： knowledge graph

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