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检索条件"任意字段=2022 3rd International Conference on Signal Processing and Computer Science, SPCS 2022"

共 761 条记录，以下是601-610 订阅

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Meta-heuristic Algorithm for Energy-Efficient Task Scheduling in Fog Computing 3rd

Meta-heuristic Algorithm for Energy-Efficient Task Schedulin...

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3rd international conference on VLSI, Communication and signal processing, VCAS 2020

作者： AL-Amodi, Saeed Patra, Sudhansu Shekhar Bhattacharya, Suman Mohanty, Jnyana Ranjan Kumar, Vinay Barik, Rabindra Kumar School of Computer Engineering KIIT Deemed To Be University Bhubaneswar India School of Computer Applications KIIT Deemed To Be University Bhubaneswar India CAAS KIIT Deemed To Be University Bhubaneswar India Department of ECE MN National Institute of Technology Allahabad India

ISBN: (纸本)9789811627606

Due to the rapid growth of the Industrial IoT (IIoT), social media, digitization, and wireless communication technology in various sectors, the volume of data is increasing very rapidly. For handling and processing of the huge volume of data, cloud computing is an emerging solution with the assistance of fog computing. It is a soars of means to improve the quality of services provided to users through cloud computing, which has being more overwhelmed by the massive flow of data. Transmitting all the data to the cloud and getting back from cloud causes high latency and requires high network bandwidth. In the IIoT applications, there is a sufficient amount of energy required in the fog layer which is promising area to be handled by the cloud service providers. An important factor which contributes to the energy consumption in fog servers is the task scheduling. In this paper, we proposed an energy saving task scheduling algorithm based on a meta-heuristic named Harris Hawks optimization technique to improve the QoS in parallel with service level agreement (SLA). The suggested algorithm outperforms in comparison to the other existing algorithms such as PSO, TLBO in terms of energy consumption and other QoS parameters. © 2022, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Fog

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Low-Area, High-Throughput Field-Programmable Gate Array Implementation of Microprocessor Without Interlocked Pipeline Stages 3rd

Low-Area, High-Throughput Field-Programmable Gate Array Impl...

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3rd international conference on VLSI, Communication and signal processing, VCAS 2020

作者： Sikka, Prateek Asati, Abhijit R. Shekhar, Chandra Electrical and Electronics Engineering Department Birla Institute of Technology and Science Vidya Vihar Campus Rajasthan Pilani India

ISBN: (纸本)9789811627606

Microprocessors without interlocked stages (MIPS) are based on the reduced instruction set computer architecture. These processors have been in use for years and remain in wide use today in applications such as automation, processing, and communication. Many of these applications must be run on smaller, low-cost target boards with limited resources. Field-programmable gate arrays (FPGAs) are also gaining importance in the very large scale integration design flows because their parallel architecture makes them very fast. Some low-cost FPGAs also offer limited on chip resources such as lookup tables and flip flops. Furthermore, high-level synthesis is gaining popularity among designers because it offers a higher level of design abstraction along with continued verification during the design flow. In this paper, we propose a low-area MIPS processor with high throughput. We use high-level synthesis to generate register transfer level code for the 32-bit MIPS core and target the same for Virtex 7 FPGA. We optimize the design for area and performance using selected high-level synthesis directives, which produces results superior to those reported in the literature. © 2022, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Field programmable gate arrays (FPGA)

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ANN-Based Integrated Multi-Objective Investment Decision Model 3

ANN-Based Integrated Multi-Objective Investment Decision Mod...

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3rd international conference on computers, Information processing and Advanced Education, CIPAE 2022

作者： Qiu, Ziao Zhang, Hang Liu, Rungen School of Computer Science and Engineering Northeastern University Liaoning Shenyang110819 China

ISBN: (纸本)9781665468121

Gold and Bitcoin are two volatile assets that market traders frequently purchase. In order to maximize returns, market traders want us to develop a model that uses past daily price data to determine daily scenarios for buying, holding, or selling assets in their portfolios. This paper focuses on a portfolio investment strategy for gold and bitcoin, providing a comprehensive model for investment forecasting and decision making, combining a variety of economic indicators, and using sensitivity analysis and hierarchical analysis to analyze and evaluate the model. This article divides the investment model into two parts[1]: the prediction and decision models. For the prediction model, we constructed a time series model based on artificial neural network (ANN), and after training a large amount of data, the model has excellent prediction accuracy with the mean absolute percentage error (MAPE) of 2.842%, and root mean square error (RMSE) of 7.203, which is much more accurate than other models. For the decision model, this article introduced evaluation indexes such as relative strength index (RSI), confidence interval of normal distribution, and prediction risk rate as the basis for decision making, and gave a generic logic for judging buying and selling operations, a detailed classification of all possible situations, and a treatment plan for each situation. In order to evaluate our model, we introduce five evaluation indicators: RMSE, MAPE, average annualized return, final return, and Sharpe ratio. The average annualized return of the model is 26.165%, the Sharpe ratio is 1.147, and the final return of the investment is 2945.48, which is an excellent performance. In addition, we also calculated the evaluation indexes of backpropagation neural network (BP) and support vector machines(SVM) participation forecasting models and used hierarchical analysis to evaluate these three forecasting models. After calculation and comparison, the final ANN forecasting model scored 0.407

关键词： Time series

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Numerical Analysis of Transversely Porous Core PCF with Square Airhole Cladding 3rd

Numerical Analysis of Transversely Porous Core PCF with Squa...

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3rd international conference on VLSI, Communication and signal processing, VCAS 2020

作者： Upadhyay, Anurag Prajapati, Y.K. Tripathi, Rajeev Department of Applied Science and Humanities Rajkiya Engineering College U. P Azamgarh India Department of Electronics and Communication Engineering Motilal Nehru National Institute of Technology U. P Prayagraj India

ISBN: (纸本)9789811627606

In this manuscript, a PCF is proposed with cladding of square-shaped airholes in the form of circular rings. Moreover, two circular airholes have been perforated in the core along the transverse axis of PCF structure. The entire structure is analyzed with finite element method-based simulation software and optimized for pitch of the core airholes. Tremendous results have been achieved from simulation. Remarkable high values of birefringence and nonlinearity of 4.2 × 10–3 and 204 W−1 km−1 have been achieved from simulation results. Moreover, large negative dispersion of −800 ps/nm/km and very low confinement of 10−11 dB/m is also obtained. Henceforth, the proposed PCF can be applicable in frequency conversion, supercontinuum generation, low loss waveguide, polarization maintaining fiber, polarization splitter and in sensing application. © 2022, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Birefringence

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Scene Change Captioning in Real Scenarios 3rd

Scene Change Captioning in Real Scenarios

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3rd international conference on Artificial Intelligence in HCI (AI-HCI) Held as Part of the 24th international conference on Human-computer Interaction (HCII)

作者： Qiu, Yue Nakashima, Kodai Satoh, Yutaka Suzuki, Ryota Iwata, Kenji Kataoka, Hirokatsu Natl Inst Adv Ind Sci & Technol Tsukuba Ibaraki 3058560 Japan

ISBN: (纸本)9783031056437;9783031056420

This paper discusses the scene change captioning task that describes scene changes using natural language for real scenarios. Most current three-dimensional understanding tasks focus on recognizing static scenes. Despite its importance in a variety of real environment applications, scene change understanding remains less discussed. Existing change understanding methods discussed in robotics focus on change detection and lack the ability to perform detailed recognition of scene changes. Most previous experiments on change captioning methods were conducted on simulation datasets with limited visual complexity, limiting their availability for real scenarios. To solve the above issues, we propose a scene change captioning dataset with scenes photographed using RGB-D cameras. We also propose an automatic simulation dataset generation process, aiming for training models transferring to real scenarios. We conducted experiments with various input modalities and proposed a method that integrates different input modalities using an attention mechanism over modalities and dynamic attention to select related information during the sentence generation process. The experimental results show that models trained on the proposed simulation dataset obtained promising results on real scenario dataset, indicating the proposed dataset generation process's practicality in real scenarios. The proposed multimodality integrating method can generate change captions with high change type and object attribute accuracy while showing robustness in real scenarios. We hope our work can open a door for future research on scene change understanding in real scenarios.

关键词： computer vision Deep learning Natural language processing

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An Effective Machine Learning Approach for Clustering Categorical Data with High Dimensions 3rd

An Effective Machine Learning Approach for Clustering Catego...

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3rd international conference on Artificial Intelligence and Speech Technology, AIST 2021

作者： Umar, Syed Deressa, Tadele Debisa Yadesa, Tariku Birhanu Beshan, Gemechu Boche Mosisa, Endal Kachew Gole, Nilesh T. Department of Computer Science Wollega University Nekemte Ethiopia Department of Information Technology Wollega University Nekemte Ethiopia Department of Mechanical Engineering Wollega University Nekemte Ethiopia Department of Computer Science and Engineering RTMNU Nagpur India

ISBN: (纸本)9783030957100

Many modern real world databases include redundant quantities of categorical data that contribute in data processing and efficient decision-making with their advances in database technology. However, for the reasons that they are identical to measurements the clustering algorithms are only devised for numerical results. An immense amount of work is being performed on the clustering of categorical data using a specifically defined similarity measure over categorical data. Thereby, the dynamic issue with real-world domain, which does not clearly take the predictive form, is the inner function. The function is based on both unseen and transonic perspective. This then offers a detailed and inventive collaboration with categorical results. The paper describes a stratified, immune-based approach with a new similarity metric, in order to reduce distance function, for clustering CAIS categorical data. For successful exploration of clusters over categorical results, CAIS adopts an immunology focused approach. It also selects subsistent nomadic characteristics as a representative entity and organize them into clusters that quantify affinity. To minimize database throughput, CAIS is segmented into several attributes. The analytical findings show that the proposed solution yields greater mining performance on different categorical datasets and outperforms EM on categorical datasets. © 2022, Springer Nature Switzerland AG.

关键词： Clustering algorithms

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Counterfactual Faithful Data Generation Based on Disentangled Representation for Compound Fault Diagnosis of Rolling Bearings 3

Counterfactual Faithful Data Generation Based on Disentangle...

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3rd international conference on Sensing, Measurement and Data Analytics in the Era of Artificial Intelligence, ICSMD 2022

作者： Ding, Xu Zhu, Qiang Wang, Song Zhang, Yiqi Wang, Hui Xu, Juan Institute of Industry and Equipment Technology Hefei University of Technology Hefei China School of Mechanical Engineering Hefei University of Technology Hefei China Bengbu Triumph Engineering Technology Co. Ltd Bengbu China Hefei University of Technology Key Laboratory of Knowledge Engineering with Big Data School of Computer and Information Hefei China

ISBN: (纸本)9781665492812

Recently, deep learning and human-out-of-the-loop methods enjoy their prosperous applications in mechanical fault diagnosis. Nonetheless, the None-IID(independent and identically distributed) issue radicated in acquired data severely limits the stability and accuracy of compound fault diagnosis of rolling bearings. This paper proposes a sample augmentation method for generating simulated signals based on the concept of counterfactuals. Firstly, disentangled representations and counterfactual faithful theory are applied to classify the original signal into two categories of properties. One is the fault semantics encoded from the original vibration signal. And the other is the sample attribute encoded by the encoder of Variational Autoencoders (VAEs). Secondly, the counterfactual faithful pseudo-samples are conjured through the Generative Adversarial Network(GAN) using the seeds of the 'factual' sample attributes and 'counterfactual' fault semantics to compensate for the drawback of distribution shift. Finally, the original samples and pseudo-samples are used as the CNN classifier dataset to realize bearing fault diagnosis. Experiments show that this method can generate counterfactual signals that are highly consistent with the original data distribution and can achieve better classification accuracy after balancing the dataset. © 2022 IEEE.

关键词： counterfactual faithfulness fault diagnosis rolling bearing structural causal model VAEGAN

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Detection of Alzheimer's Disease Using Machine Learning Models: The Impact of Pre-processing 3

Detection of Alzheimer's Disease Using Machine Learning Mode...

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3rd international conference on Distributed Sensing and Intelligent Systems, ICDSIS 2022

作者： Al Zoubi, Rouaa Elnagar, Ashraf Dept. of Computer Science University of Sharjah Sharjah United Arab Emirates

ISBN: (纸本)9781839538186

Alzheimer's disease is a brain ailment that causes memory loss, disorientation, personality changes, and a loss of independence over time. People with this disease initially experience minor memory loss and confusion. This is referred to as cognitive deterioration. However, as time passes, these symptoms get more acute. On the other hand, as there is no cure for this disease, medical professionals concentrate on detecting dementia in its early stages, this helps them determine the best treatments and allows patients to take precautionary measures before any damage happens to their brain which is irreversible. As a result, researchers focus on using machine learning models that can give significant results in identifying symptoms of dementia in the early stages. In this paper, we employed machine learning methods to identify the various phases of dementia using brain MRI scans. We used multiple traditional algorithms such as XGBoost, AdaBoost, Gradient Boosting, Decision trees, SVM, Logistic regression, Random forest, and KNearest Neighbor. We compared the performance of these algorithms with and without the use of preprocessing techniques. Our best model was the SVM model with an accuracy of 100% after applying preprocessing. In addition, we used a transfer learning model that achieves an accuracy of 90%. Thus, our work demonstrated the effect of preprocessing on enhancing the results of the traditional algorithms and transfer learning models in identifying the stages of dementia. © IET conference Proceedings 2022.

关键词： Deterioration

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3rd international conference on Neural Computing for Advanced Applications, NCAA 2022

3rd International Conference on Neural Computing for Advance...

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3rd international conference on Neural Computing for Advanced Applications, NCAA 2022

ISBN: (纸本)9789811961342

The proceedings contain 77 papers. The special focus in this conference is on Neural Computing for Advanced Applications. The topics include: A Multi-channel Fusion Method Based on Tensor for Rolling Bearing Fault Diagnosis;a Perception Method Based on Point Cloud processing in Autonomous Driving;many-Objective Artificial Bee Colony Algorithm Based on Decomposition and Dimension Learning;LCSW: A Novel Indoor Localization System Based on CNN-SVM Model with WKNN in Wi-Fi Environments;large Parallax Image Stitching via Structure Preservation and Multi-matching;traffic Congestion Event Mining Based on Trajectory Data;many-Objective Evolutionary Algorithm Based on Dominance and Objective Space Decomposition;a New Unified Control Approach for Finite-/Fixed-Time Synchronisation of Multi-weighted Dynamical Networks;aperiodic Sampling Based Event-Triggering for Synchronization of Nonlinear Systems;two-Stream 3D MobileNetV3 for Pedestrians Intent Prediction Based on Monocular Camera;Research on Non-intrusive Household Load Identification Method Applying LightGBM;design of Portrait System for Road Safety Based on a Dynamic Density Clustering Algorithm;an Ensemble Deep Learning Model Based on Transformers for Long Sequence Time-Series Forecasting;multi-layer Integrated Extreme Learning Machine for Mechanical Fault Diagnosis of High-Voltage Circuit Breaker;wind Power Forecast Based on Multiple Echo States;UAV-Assisted Blind Area Pedestrian Detection via Terminal-Edge-Cloud Cooperation in VANETs;a Survey of Optimal Design of Antenna (Array) by Evolutionary Computing Methods;a Span-Based Joint Model for Measurable Quantitative Information Extraction;bayesian Optimization Based Seq2Seq Network Models for Real Estate Price Prediction in Hong Kong;ensemble Learning for Crowdfunding Dynamics: JingDong Crowdfunding Projects;Cage Mass Center Capture for Whirl Analysis Using an Improved MultiResUNet from the Multimodal Biomedical Image Segmentation;master Multiple Real-Time Strategy Game

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3rd international conference on Neural Computing for Advanced Applications, NCAA 2022

3rd International Conference on Neural Computing for Advance...

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3rd international conference on Neural Computing for Advanced Applications, NCAA 2022

ISBN: (纸本)9789811961410

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