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检索条件"任意字段=2022 International Conference on Algorithms, Network and Computer Technology, ICANCT 2022"

共 1025 条记录，以下是511-520 订阅

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Application of different Machine Learning models for Supply Chain Demand Forecasting: Comparative Analysis 2

Application of different Machine Learning models for Supply ...

引用

2nd international conference on Innovative Practices in technology and Management, ICIPTM 2022

作者： Singha, D. Panse, Chetan Symbiosis Institute Of Business Management Symbiosis International Deemed University Maharashtara Pune India

ISBN: (纸本)9781665466431

Demand is defined as the propensity or willingness of customers to pay a certain amount of price for a product or service they desire. Business entities use various forecasting techniques to anticipate customer demands in advance to make crucial strategic decisions related to various aspects of the supply chain, such as customer service level, inventory management, manufacturing planning, and control, etc. But the error related to forecasting models creates uncertainty and poses a great challenge to decision-makers. With the ever-changing market dynamics, it is becoming more and more important for businesses to minimize those forecasting errors. In this paper, we have analysed the application of various advanced Machine Learning algorithms like Multi-layered Perceptron model (MLP), Convolution Neural network (CNN), Long-Short Term Memory (LSTM) networks, etc. in Time Series Forecasting, thereafter, performed a comparative analysis to understand which of them yields the better result. To perform the analysis, we have considered the 'Store Item Demand Forecasting' dataset available at Kaggle. © 2022 IEEE.

关键词： Supply chains

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GOSGAN: An Adaptive Atomic Force Microscope Imaging Repair Model for Cell Scanning 3

GOSGAN: An Adaptive Atomic Force Microscope Imaging Repair M...

引用

3rd international conference on computer Science and Management technology, ICCSMT 2022

作者： Li, Jingxin Xu, Hongmei Cui, Haobo Yuan, Lixin Changchun University of Science and Technology International Research Centre for Nano Handling and Manufacturing of China Changchun China

ISBN: (纸本)9798350320404

In recent years, Atomic Force Microscopy (AFM) has played an irreplaceable role in cell biology as it can measure the local mechanical information of individual cells while observing the surface morphology of cells. To address the problems of long scanning time and poor image quality, this paper attempts to introduce a super-resolution method to optimize the images in order to reduce the imaging time, reduce probe loss and improve the image quality. The proposed method is based on the existing super-resolution method of generative adversarial networks and is designed to introduce a self-attentive information fusion module to enhance the organic fusion of a priori and input while improving the richness of the a priori information encapsulated in the network, and to propose GOSGAN. In addition, a large-scale data generation model and a targeted image degradation model are introduced to address the problem of difficult access to AFM data. The final experiment achieves a peak signal-to-noise ratio (PSNR) and structural similarity ratio (SSIM) of 26.8 dB and 0.92, respectively, on its own validation set. © 2022 IEEE.

关键词： Image enhancement

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18th international Symposium on Algorithmics of Wireless network, ALGOSENSORS 2022

18th International Symposium on Algorithmics of Wireless Net...

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18th international Symposium on Algorithmics of Wireless network, ALGOSENSORS 2022

ISBN: (纸本)9783031220494

The proceedings contain 10 papers. The special focus in this conference is on Algorithmics of Wireless network. The topics include: Optimal and Heuristic algorithms for Data Collection by Using an Energy- and Storage-Constrained Drone;blackout-Tolerant Temporal Spanners;molecular Robots with Chirality on Grids;centralised Connectivity-Preserving Transformations by Rotation: 3 Musketeers for All Orthogonal Convex Shapes;triangle Evacuation of 2 Agents in the Wireless Model: (Extended Abstract);resource Time-Sharing for IoT Applications with Deadlines;fault-Tolerant Graph Realizations in the Congested Clique;the Complexity of Growing a Graph.

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Design of a Dual-band Antenna Array for CubeSat Application

Design of a Dual-band Antenna Array for CubeSat Application

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2022 international conference on Smart Applications, Communications and networking, SmartNets 2022

作者： Ramahatla, Kebonyethebe Mosalaosi, Modisa Yahya, Abid Basutli, Bokamoso Botswana International University of Science and Technology Department of Electrical Computer and Telecommunications Palapye Botswana

ISBN: (纸本)9781665487580

This work demonstrates the design and simulation of a compact, dual-band antenna which enables frequency adaptation and eliminates the need for multiple antennas in a CubeSat platform. The antenna follows a transmission line design model and is fed by a stripline feed network. It has a size of 64 × 64 mm2 with a total height of 3.693 mm. At S-band, the antenna is linearly polarized with gain of 4.31 dBi and right hand circularly polarized at X-band with an axial ratio of 2.5 and realized gain of 7.7 dBi. It is shown through simulation that the proposed design achieves the required performance needs at the specified frequency bands. © 2022 IEEE.

关键词： Radiation efficiency

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ESEEG: An Efficient Epileptic Seizure Detection using EEG signals based on Machine Learning algorithms 2

ESEEG: An Efficient Epileptic Seizure Detection using EEG si...

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2nd international Mobile, Intelligent, and Ubiquitous Computing conference, MIUCC 2022

作者： Abdelminaam, Diaa Salama Fahmy, Andrew Gamal Ali, Youssef Mohamed El-Din, Omar Ahmed Diaa Aly, Ahmed Raouf Heidar, Mahmoud Misr International University Faculty of Computer Science Cairo Egypt

ISBN: (纸本)9781665466776

Epileptic seizure Disease is a substantial health burden and cause of death worldwide. For epileptic seizure localization and classification,It's critical to be able to detect recorded epileptic seizure activity in EEG segments. Seizure progression is usually a dynamic and non-stationary process involving signals of multiple frequency., therefore visual and traditional frequency-based techniques are restricted in their utility. Researchers are attempting to integrate artificial intelligence (AI) and machine learning (ML) approaches to improve medical practise as a result of advancements in AI and ML methodology. Early disease identification and prediction is a critical goal in healthcare in order to provide timely preventive actions. This is especially true in the case of epilepsy, which is characterised by frequent and unexpected convulsions. We present a comprehensive overview of existing machine learning strategies for early seizure prediction using EEG data. We will highlight the research's shortcomings, difficulties, and pitfalls, as well as make recommendations for future directions. The following findings (accuracy) were obtained: KNN : 94%, Decision Tree : 94%, Random forrest: 96%, and Convolutional Neural network (CNN) : 96.9% © 2022 IEEE.

关键词： Location awareness Visualization Machine learning algorithms Epilepsy Machine learning Medical services Ubiquitous computing

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Automatic Life Event Tree Generation for Older Adults 1

引用

24th international conference on Human-computer Interaction, HCII 2022

作者： Gui, Fang Wu, Xi Hu, Min Yang, Jiaoyun School of Computer Science and Information Engineering Hefei University of Technology Hefei China Key Laboratory of Knowledge Engineering with Big Data of Ministry of Education Hefei University of Technology Hefei China National Smart Eldercare International S&T Cooperation Base Hefei University of Technology Hefei China Laboratory of Affective Computing and Advanced Intelligent Machine Hefei University of Technology Hefei China Intelligent Interconnected Systems Laboratory of Anhui Province Hefei University of Technology Hefei China

ISBN: (数字)9783031179020

ISBN: (纸本)9783031179013

Studies have shown that learning personal stories could help provide individualized eldercare services. However, personal stories are often disordered because of the scattered collection, including informal interviews or daily interactions, which brings difficulties in acquiring valuable information quickly. One solution to this problem is to extract events from personal stories and automatically organize them in chronological order. Events extracted by current methods from social media or news corpus are mainly organized in a linear structure. These works usually focus on the event time and ignore the consistency of event contents when organizing events. This paper aims to organize events into a tree structure based on an event network, with stem nodes representing key event topics and branch nodes representing detailed events. Social workers or caregivers can clarify the life experience of the older adults quickly through the event tree and have a preliminary understanding of them. The experiments show that the event tree generated by our method has a better performance in consistency than current event organization methods. A survey study shows that our method achieves the highest logical coherence for the event tree branches compared with other algorithms. © 2022, Springer Nature Switzerland AG.

关键词： Trees (mathematics)

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Design of Automatic Russian Language Recognition System 6

Design of Automatic Russian Language Recognition System

引用

6th international conference on Digital technology in Education, ICDTE 2022

作者： Wang, Yang School of International Education Heilongjiang Institute of Technology JiXi China

ISBN: (纸本)9781450398091

Language recognition is also called language recognition. Its purpose is to enable the computer to automatically recognize the language type according to an input speech. Language identification is widely used in the fields of network security, authentication, multilingual information services and so on. Nowadays, the increasingly open international environment and the rapid development of the Internet have brought new challenges to language recognition. Based on the previous research results, this paper designs and implements a language recognition system based on Gaussian mixture model unified background model (GMM UBM), mainly from the feature extraction module, UBM model training module, language model training module and language recognition module. © 2022 Association for Computing Machinery.

关键词： Support vector machines

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Vision: Overview of Vehicle Body Violation Target Detection algorithms 2

Vision: Overview of Vehicle Body Violation Target Detection ...

引用

2nd IEEE international conference on Electronic technology, Communication and Information, ICETCI 2022

作者： Liang, Chenghao Lu, Yanzhen Yu, Xingkuan Samuel Ginn College of Engineering Auburn University AuburnAL36849 United States International Baccalaureate Diploma Program Guangdong Country Garden School Foshan Guangdong528311 China College of Mount Saint Vincent RiverdaleNY10471 United States

ISBN: (数字)9781728181158

ISBN: (纸本)9781728181158

The rapid development of the information field has made people's lives more convenient. To prevent drivers from violating traffic laws, a deep learning model has been developed to detect vehicle violations. The main function is to detect if the vehicle body touches the lane line and occupies more than one lane. The increasing accuracy of vehicle body recognition measurements calls for consideration of the impact of the camera on the vehicle body and lane line detection. The specific objective of the article is to analyze the vehicle dataset model;the calculation formula for lane line detection;and the precision, accuracy, and recall rate of the model of convolutional neural network (CNN). In addition to improving the detection of illegal vehicles by the traffic police system, this can provide information for autonomous driving in the coming years. © 2022 IEEE.

关键词： Deep learning

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Data Mining for Predicting and Finding Factors of Bankruptcy

Data Mining for Predicting and Finding Factors of Bankruptcy

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2022 international conference on Innovations in Science, Engineering and technology, ICISET 2022

作者： Hossain, Tasrin Ferdous, Tashrifa Bahadur, Erfanul Hoque Masum, Abdul Kadar Muhammad Yasirarafat, Abm International Islamic University Chittagong Dept. of Computer Science and Engineering Chattagram Bangladesh

ISBN: (纸本)9781665483971

The financial crisis, since the pandemic outbreak due to COVID-19, the dissemination and invasive systemic risk in the global financial environment have drawn the attention to organizations' solvency monitoring methods. Inevitably, in this paper we have looked at the both bankruptcy prediction and the factors that lead to bankruptcy. The dataset for this study was acquired from Kaggle, which was based on the Taiwan Economic Journal, from 1999 to 2009. The corporate statutes of the Taiwan Stock Exchange were utilized to determine a company's bankruptcy. It was a highly imbalanced dataset having 220 Non-bankrupt and 6,599 bankrupt data. We have used Random Forest, Support Vector Machine, Artificial Neural network, XGBoost, and LightGBM classifiers regarding bankruptcy prediction. On the other hand, to find the factors that lead to bankruptcy, we did an empiric analysis for which we calculated fourteen statistical values of both bankrupted and non-bankrupted features and saw their cosine similarities. These factors will help any financial company to plan its financial ratios for preventing bankruptcy. Here we got the best performance from the Artificial Neural network with 98.64% accuracy. And we found four factors that were responsible for the bankruptcy in our dataset. Here, the factors determining bankruptcy are crucial because by finding these factors and the likelihood of bankruptcy, companies can take the necessary steps to plan their financial ratios and ensure the solvency of their businesses. © 2022 IEEE.

关键词： Forecasting

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Automated Robot (Car) using Artificial Intelligence 12

Automated Robot (Car) using Artificial Intelligence

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12th IEEE Integrated STEM Education conference, ISEC 2022

作者： Mishra, Satyam Minh, Cao Sy Chuc, Hoang Thi Long, Tran Viet Nguyen, Thanh Tung Vietnam National University International School Informatics and Computer Engineering Hanoi Viet Nam Vietnam National University International School Management and Information System Hanoi Viet Nam Vietnam National University International School Department of Science and Technology Hanoi Viet Nam

ISBN: (纸本)9781665484299

This paper discusses about researching an automated robot car using artificial intelligence;training its neural network using AlexNet model, using YOLO (you only look once algorithm) for object detection phase and for practical deduction and judging component we have used Open Neural network Exchange (ONNX) format. Our Robot Car model is agile and cost efficient. It detects objects efficiently in front of it and movement of it is smooth. It moves through sensors in motors which makes it different than other models in the world. © 2022 IEEE.

关键词： Stochastic systems

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