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检索条件"丛书名=Computer engineering series"

共 367966 条记录，以下是91-100 订阅

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A decision support system for customer behavior analysis through social media data mining 16

A decision support system for customer behavior analysis thr...

引用

16th International Conference on computer and Automation engineering (ICCAE)

作者： Kiadeh, Zahra Sarmast Hasan Nikookar, Ethan Shokouhyar, Sajjad Sinclair, Andrew Aminbeidokhti, Kayvan Shahid Beheshti Univ Dept Management & Accounting Tehran Iran Univ Wollongong Sch Business Wollongong NSW Australia Australian Inst Business Dept Supply Chain & Operat Management Adelaide SA Australia Wilmington Univ Coll Business New Castle DE USA

ISBN: (纸本)9798350370058;9798350370164

Elevating product quality is the most straightforward way to raise customer satisfaction. The most effective solution to investigate necessary points of improvement is to explore customers' feedback and complaints. Due to the growing use of social media channels for sharing ideas and reviews about products and services, the current study focused on social media data analysis to investigate products' frequent failures and forecast customer behavior using machine learning techniques. In this regard, warranty services are complementary services to raise the customer's satisfaction using a product-service approach. The study will support the warranty service providers to be prepared for flaws before a claim on a warranty occurs. We used the QFD house to prioritise the most defective components of the product. We validated the model using the claimed data of a laptop gathered by a LENOVO warranty service and compared them with the data gained from social media. Regarding the results, this paper verifies the role of social media data in revealing the most defective components and the prediction of customer behavior.

关键词： social media machine learning QFD data mining customer behavior

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Implementation of a Single Cycle Datapath for the Design of a Low-process Control in XC3S500E 16

Implementation of a Single Cycle Datapath for the Design of ...

引用

16th International Conference on computer and Automation engineering (ICCAE)

作者： Lazaro, Jose B., Jr. Pabiania, Maribelle D. Bayonito, Cedric Lawrenze P. Cabit, Jhed Patrick M. Fernando, Kate Carolyn L. Jocson, John Rafael L. Park, Adrian Kuk Chong S. Pinera, Clarenzz Bryan S. Roll, Angelica C. Solis, Kim Kerwin L. Mapua Malayan Coll Laguna Cabuyao City Philippines

ISBN: (纸本)9798350370058;9798350370164

this paper outlines the design of a low-process control system for embedded systems applications, utilizing a RISC-V processor. The design incorporates functional units of a single-cycle Datapath and a control unit developed in Verilog HDL. Focus on testing RISC-V instructions to achieve low-process control in embedded systems applications by employing proper instruction mnemonics. The approach enhances the low-process control and efficiency in embedded systems applications and system design through single-cycle control. The designed low-process control proves valuable by computing the processor time (T) needed in instruction execution. Low-process control 1 exhibits an 88.21% improvement over an AVR processor control, and low-process control 2 demonstrates a 49.43% improvement, thereby ensuring efficient process control. The findings contribute to the understanding of RISC-V architecture implementation in an FPGA platform, featuring LED visualization to depict equivalent machine code generated in a single-cycle process. This study advances the theoretical framework of system optimization and control with the use of FPGA platforms.

关键词： risc-v single-cycle datapath spartan 3e xc3s500e fpga avr low-process control

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Robust Control of Fleet Systems Under External Disturbances 16

Robust Control of Fleet Systems Under External Disturbances

引用

16th International Conference on computer and Automation engineering (ICCAE)

作者： Fan, Hongli Wei, Cui Li, Zhengyi Chen, Ye-Hwa Nanjing Tech Univ Coll Elect Engn & Control Sci Nanjing Peoples R China Nanjing Tech Univ Coll Intelligent Mfg Nanjing Peoples R China Georgia Inst Technol Geroge Woodrufi Sch Mech Engn Atlanta GA USA

ISBN: (纸本)9798350370058;9798350370164

In this work, a fleet system composed of N+1 vehicles is studied with consideration of uncertainty and possibly unknown external disturbances. Uncertainty may be caused by factors such as parameter changes, aerodynamics, and network malfunction, which change over time and are nonlinear. The system is designed aiming to avoid collision and form formation in the case of external disturbances. In this study, a unique robust control strategy is proposed and examined to ensure that the fleet system satisfies uniform boundedness and uniform ultimate boundedness. Finally, an experimental simulation is conducted using a fleet system consisting of four vehicles to verify the feasibility of the proposed robust control.

关键词： fleet system robust control external disturbances network malfunction constraint-following

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Power Loss Investigation of series-connected Current Source Inverters with Different Modulations

Power Loss Investigation of Series-connected Current Source ...

引用

IEEE Canadian Conference on Electrical and computer engineering (CCECE)

作者： Jiang, Bowen Wang, Zijian Wei, Qiang Lakehead Univ Elect Engn Thunder Bay ON Canada

ISBN: (纸本)9798350371635;9798350371628

Various modulation schemes such as the trapezoidal pulse-width modulation (TPWM), the selective harmonics elimination (SHE), and the space vector modulation (SVM) have been developed for series-connected current source inverters (SC-CSIs). However, the power loss of the SC-CSIs with different modulation schemes has not been studied yet. In this paper, the power loss of SC-CSIs using different modulations is investigated. The optimal modulation index resulting in the best power loss performance is selected. The results have been verified by MATLAB simulations.

关键词： Modulation power loss series-connected current source inverters

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Deep Learning-Based Face Mask Recognition System with YOLOv8 16

Deep Learning-Based Face Mask Recognition System with YOLOv8

引用

16th International Conference on computer and Automation engineering (ICCAE)

作者： Dewi, Christine Manongga, Danny Hendry Mailoa, Evangs Satya Wacana Christian Univ Dept Informat Technol Salatiga Indonesia

ISBN: (纸本)9798350370058;9798350370164

A system for recognizing face masks is a technological solution that employs computer vision and machine learning methodologies to detect and ascertain the presence or absence of a face mask on an individual. The utilization of face masks has garnered substantial significance and prevalence in contemporary times, primarily attributable to the COVID-19 pandemic, wherein it has emerged as a pivotal measure to impede the transmission of the virus. The facial mask recognition system is commonly comprised of two primary stages, namely face detection and mask classification. During the face detection stage, the system can detect and localize the existence of a human face within an image or video frame. The categorization of masks can be executed through a range of machine learning methodologies, including deep learning algorithms. The algorithms are trained using a substantial dataset of labeled facial images, which includes both masked and unmasked faces. This training enables the algorithms to discern the distinctive characteristics between the two categories. This work utilizes the You Only Look Once (YOLO) v8 algorithm to discern the presence or absence of a mask on a given subject, subsequently classifying them into two distinct categories based on the extent to which they are wearing a mask, namely "no_mask" and "mask". The present investigation utilized the Face Mask Dataset as the primary data source for our empirical analysis. The assessments and appraisals of these models incorporate essential criteria. Based on the available data, it can be inferred that YOLOv8s has attained the maximum mean average precision (96.1%) in the "mask" category.

关键词： YOLOv8 Face Mask Identification CNN COVID-19

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Anomaly Detection-Based Multilevel Ensemble Learning for CPU Prediction in Cloud Data Centers

Anomaly Detection-Based Multilevel Ensemble Learning for CPU...

引用

IEEE Canadian Conference on Electrical and computer engineering (CCECE)

作者： Daraghmeh, Mustafa Agarwal, Anjali Jararweh, Yaser Concordia Univ Elect & Comp Engn Montreal PQ Canada Jordan Univ Sci & Technol Comp Sci Irbid Jordan

ISBN: (纸本)9798350371635;9798350371628

In today's cloud computing era, accurate forecasting of CPU usage is crucial to maximize performance and energy efficiency in data centers. As cloud data centers become more complex and larger in scale, traditional predictive models may require enhancements to incorporate more sophisticated and comprehensive solutions. This paper presents a sophisticated multilevel learning framework specifically tailored to address the requirements of contemporary cloud data centers. The proposed framework synergistically combines anomaly detection and multilevel ensemble learning-based regression prediction to improve CPU usage prediction within cloud data centers. Various anomaly detection techniques are explored in the preliminary data processing stage to identify and address anomalies within the CPU usage trace. Subsequent phases employ multilevel ensemble-based prediction models for accurate data-driven forecasts. By conducting thorough assessments, our model exhibits substantial improvements in both the accuracy of predictions and its resilience to the inherent volatility of cloud environments. Our research provides the foundation for an improved method of predicting CPU utilization, paving the way for advancements in cloud computing resource management.

关键词： Anomaly Detection Ensemble Learning Multilevel Learning CPU Usage Prediction

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Revisiting Temporal Inconsistency and Feature Extraction for Android Malware Detection

Revisiting Temporal Inconsistency and Feature Extraction for...

引用

IEEE Canadian Conference on Electrical and computer engineering (CCECE)

作者： Tanha, Maryam Singh, Arunab Knoke, Gavin British Columbia Inst Technol Sch Comp & Acad Studies Burnaby BC Canada

ISBN: (纸本)9798350371635;9798350371628

The growing popularity and ease of access have turned Android applications into prime targets for malicious attackers. Within the security research community, machine learning has become an essential instrument for conducting Android malware detection and analysis. However, there are potential threats to validity of existing studies, mainly resulting from their used datasets. One of the primary issues is temporal inconsistency (also called temporal bias) that is caused by incorrect time splits of training and testing sets or using imprecise indicators for release time of apps. This paper investigates the use of Google Play Store upload year of an app as a precise indicator of its release time to address temporal bias in machine learning-based Android malware detection. Using this approach is made possible by AndroZoo's December 2023 data release. Through a three-layer filtering process, we demonstrate the unreliability of the commonly used dex date as the release time of an app and propose a more accurate approach for creating temporally-consistent datasets based on an app's upload year. Additionally, we have open-sourced our data and feature extraction process for Android malware analysis, supporting both server-side and on-device extraction, to enhance research reproducibility and facilitate community access.

关键词： Android malware

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Adapting Large Language Models for Automatic Annotation of Radiology Reports for Metastases Detection

Adapting Large Language Models for Automatic Annotation of R...

引用

IEEE Canadian Conference on Electrical and computer engineering (CCECE)

作者： Barabadi, Maede Ashofteh Chan, Wai Yip Zhu, Xiaodan Simpson, Amber L. Do, Richard K. G. Queens Univ Dept Elect & Comp Engn Kingston ON Canada Queens Univ Ingenu Labs Res Inst Kingston ON Canada Queens Univ Sch Comp Kingston ON Canada Queens Univ Dept Biomed & Mol Sci Kingston ON Canada Mem Sloan Kettering Canc Ctr Dept Radiol 1275 York Ave New York NY 10021 USA

ISBN: (纸本)9798350371635;9798350371628

Automatic identification of metastatic sites in cancer patients from electronic health records is a challenging yet crucial task with significant implications for diagnosis and treatment. In this study, we propose a method to detect metastases from non-structured radiology report texts by accessing only their impression section. We build models based on pre-trained large language models and parameter-efficient fine-tuning. We compare model performances between utilizing non-structured reports and reports following institutional-level templates. By incorporating patient historical data and their timeline into the model, we bridge the gap between structured and non-structured reports. Our experiments are conducted on data gathered at Memorial Sloan Kettering Cancer Center (MSKCC) which have been annotated for metastases presence in three organs: liver, lung, and adrenal glands. Our results suggest that access to previous reports significantly improves model performance, with an average improvement of 7.7 points in terms of F1-score over all datasets. Additionally, incorporating temporal information enhances the accuracy of metastasis detection by 0.4 and 1.1 points on liver and adrenal glands data, respectively. Our method shows potential for automating radiology report labeling on a large scale in an efficient manner, with the potential to deploy on low-cost hardware.

关键词： metastases detection natural language processing parameter-efficient tuning large language models

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Hierarchical Federated Learning for Predicting Water Levels: A Case Study in Thailand 16

Hierarchical Federated Learning for Predicting Water Levels:...

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16th International Conference on computer and Automation engineering (ICCAE)

作者： Thonglek, Kundjanasith Maipradit, Arnan Osaka Univ Osaka Japan Rajamangala Univ Technol Thanyaburi Pathum Thani Thailand

ISBN: (纸本)9798350370058;9798350370164

Water is one of the most essential natural resources for the sustenance of humankind. Despite its renewable nature, water faces considerable challenges arising from urban growth, climate change, and deforestation. Effectively managing water to meet the demands of agricultural activities remains a formidable task due to the inherent difficulty in predicting water quantities. This study addresses these concerns by tapping into the rapid advancement of computing power on edge devices and employing federated learning techniques. Deploying and training prediction models on resource-limited devices allows for rapid prediction and response to water levels, outpacing conventional deployment and training on centralized servers. However, the distribution of water level stations in Thailand presents a problem in transferring prediction models from edge devices for aggregation on a central server due to communication cost constraints. Consequently, this paper applied hierarchical federated learning to predict water levels in Thailand. This approach aims to reduce communication overhead and enhance scalability. The study examined the clustering of water level stations in Thailand based on Harversine distance, aiming to balance between reducing communication costs and minimizing accuracy loss. As a result, the proposed approach clusters the water level stations into ten clusters. This clustering yields a reduction in distance from local water level stations of 78.02%.

关键词： Edge Computing Hydrological Time-series Forecasting Federated Learning Sustainable Agricultural Development Water Resources Management

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Reducing the Computational Complexity of the Volterra series Using Permutation/Product Maps

Reducing the Computational Complexity of the Volterra Series...

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IEEE Canadian Conference on Electrical and computer engineering (CCECE)

作者： Fonseca, Aaron J. Bolstad, Andrew K. Dickerson, Julie A. Iowa State Univ Dept Elect & Comp Engn Ames IA 50011 USA

ISBN: (纸本)9798350371635;9798350371628

The Volterra series is often used to model nonlinear systems in the fields of system identification and adaptive filtering. One means of computing the response of Volterra series-based filters is via input product vector generation. This method is beneficial for applications which utilize the input product vector in addition to the filter's response. However, the problem of input vector generation as well as the more general problem of Volterra series filtering is computationally complex-as the number of terms within the kernel grows exponentially with the model's memory and order. This paper proposes a process of Volterra input product vector generation which utilizes the minimal number of multiplication instructions required. This is done by computing permutation and product maps which relate terms in the current iteration's input product vector to terms within the previous iteration. A MATLAB implementation of the proposed method is applied to the task of Volterra filtering. Its performance is then compared to (a) the MATLAB implementation of a similar, reduced (though not-minimal) multiplication method of input product generation and (b) a "Fast Volterra Filtering" script listed at the MATLAB Central File Exchange. The results show that the proposed method performs comparably to or outperforms both of the other methods for increasing memories and orders of the underlying Volterra kernel.

关键词： Computational complexity Nonlinear systems Signal processing algorithms System kernels

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