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2021智慧城市与智能建造高端论坛

作者： Xiaosong Ding Hu Liu Xi Chen International Business School Beijing Foreign Studies University 100089 BeijingChina DECIDE Group D International Business School Beijing Foreign Studies University 100089 BeijingChina

The immediate delivery problem is a variant of vehicle routing problem with stochastic demand,in which not all customers are known in advance,but are revealed while the system *** this paper,we investigate a multi-period immediate delivery problem with a comprehensive indicator for order *** particular,we model the order selection as a sequential decision-making *** the immediacy of delivery tasks,the mismatch between demand and capacity,and the autonomy of the delivery staff,we propose herein a new comprehensive indicator consisting of dis-tance,time pressure,and order revenue as the order selection criterion to help drivers choose the right *** the nearest neighbor algorithm is adopted to optimize routes ***,extensive simulation ex-periments show that the proposed indicator performs stable and better than the other single indicators.

关键词： Vehicle Routing Problem Dynamic Requests Multi Period Order Selec-tion nearest neighbor algorithm

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nearest neighbor Classifier for Vision Based Unstructured Road Detection

Nearest Neighbor Classifier for Vision Based Unstructured Ro...

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International Conference on Power Electronics, Control and Automation (ICPECA)

作者： Altaf Alam Zainul Abdin Jaffery Department of Electrical Engineering Jamia Millia Islamia New Delhi

ISBN: (纸本)9781728139593

Driver can drive a vehicle on road very smoothly, if he knows about the road and non road region in surrounding environments. Accurate road detection is an important and difficult task for the development of vision based autonomous land vehicle. Therefore this research work proposed an algorithm which can separate road and non road region effectively. nearest neighbor algorithm has trained with chromatic information to differentiate the road and surrounding environments. Created classifier divide the image of road region into three classes such as road region, near background region and far background region. Classifier training process utilized chromatic information from that color space which has more resemblance with human color perception. System performance is evaluated on the basis of precision, recall and required training time. Achieved results signify that overall performance of the system is worthy.

关键词： Unstructured road detection L*a*b color space nearest neighbor classification Autonomous vehicle Road Autonomous Vehicles nearest neighbor chromatic information color spaces Sight Classifiers nearest neighbor algorithm System performance

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Hate Speech Detection in Indonesian Language on Instagram Comment Section Using K-nearest neighbor Classification Method

Hate Speech Detection in Indonesian Language on Instagram Co...

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IEEE International Conference on Internet of Things and Intelligence System (IOTAIS)

作者： Annisa Briliani Budhi Irawan Casi Setianingsih School of Electrical Engineering Telkom University Bandung Indonesia

ISBN: (纸本)9781728125176

Instagram is a social media that is widely used by people in the world, especially Indonesia. Instagram has a feature in delivering opinions in the Instagram photo comment section. Comments given can be positive or negative comments. Negative comments on Instagram include hate speech. Hate speech is one of the big problems and very difficult to overcome by the authorities. Because of that, this research will create a system that will detect hate speech or not on the Instagram comment section with the K-nearest neighbor classification method. Accuracy generated from this research is 98.13%, 98% of precision, recall and f1-score using K-nearest neighbor with K=3.

关键词： natural language processing nearest neighbour methods pattern classification social networking (online) Natural Language Processing Pattern recognition nearest neighbor algorithm Hate Indonesian Languages neighbor Speech Classification Methods Social Media Comment

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A RFF Access Authentication Technology Based on K-nearest neighbor Method

A RFF Access Authentication Technology Based on K-Nearest Ne...

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International Conference on Intelligent Computing, Automation and Systems (ICICAS)

作者： Weifeng Luo Xinchen Xu Wei Deng Shan He Man Li Feiyi Xie Hong Wen Shenzhen Power Supply Bureau Co. Ltd Shenzhen China University of Electronic Science and Technology of China Chengdu China

ISBN: (数字)9781728161068

ISBN: (纸本)9781728161075

Access security of wireless device is a great challenge to wireless communication networks, especially for radio frequency identification (RFID) and some other devices with limited computing power, which are faced with effective high-strength security challenges, and as an important non-cryptographic authentication technology, radio frequency fingerprinting (RFF) technology, for its full use of non-imitation radio frequency characteristics, attracts more and more attention. The generation mechanism of radio frequency fingerprint and the basic model of recognition system are described, and A RFF access authentication technology based on k-nearest neighbor (kNN) approach also is analyzed. In addition, the existing problems of radio frequency fingerprint recognition technology and the research direction of further improvement are pointed out.

关键词： cryptography message authentication radiofrequency identification telecommunication security Radio frequency identification telecommunication security message authentication nearest neighbor algorithm cryptography reset flip-flop radiofrequency Wireless networks computational power recognition system neighbor Authentication basic model

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SAR Target Recognition Based On Variational Autoencoder

SAR Target Recognition Based On Variational Autoencoder

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IEEE MTT-S International Microwave Biomedical Conference (IMBioC)

作者： Xu, Yanbing Zhang, Gong Wang, Ke Leung, Henry Minist Educ Key Lab Radar Imaging & Microware Photon Nanjing Peoples R China Nanjing Univ Aeronaut & Astronaut 29 Jiangjun Ave Nanjing 211106 Peoples R China Univ Calgary Dept Elect & Comp Engn Calgary AB Canada

ISBN: (纸本)9781538673959

Recently, with the rapid development of machine learning, varieties of new thoughts have flooded into the field of the synthetic aperture radar automatic target recognition. Convolutional neural networks are mostly used in this area. Nonetheless, the features extract from convolutional neural networks learned through back propagation may discard the features helpless for recognition like azimuth. Unlike supervised learning, our model based on probabilistic generative model in an unsupervised way learns the distribution inside the input data. Features of synthetic aperture radar images learned by our model are more fruitful and interpretable, which are more effective than traditional hand-crafted features in SAR image processing. Meanwhile, tested on the MSTAR data set, our model gets high accuracy in classify task under KNN and SVM.

关键词： convolutional neural nets feature extraction nearest neighbour methods probability radar computing radar imaging radar target recognition support vector machines synthetic aperture radar Synthetic aperture radar radar computing Radar target recognition Support Vector Network Radar imaging Feature extraction nearest neighbor algorithm Radar polarimetry Target recognition Rescue Specific absorption rate SAFETY ANALYSIS REPORTS

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Testing Suicide Risk Prediction algorithms Using Phone Measurements With Patients in Acute Mental Health Settings: Feasibility Study

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JMIR MHEALTH AND UHEALTH 2020年第6期8卷 e15901页

作者： Haines-Delmont, Alina Chahal, Gurdit Bruen, Ashley Jane Wall, Abbie Khan, Christina Tara Sadashiv, Ramesh Fearnley, David Manchester Metropolitan Univ Fac Hlth Psychol & Social Care Manchester Lancs England CLARA Analyt CLARA Labs Santa Clara CA USA Univ Liverpool Hlth Serv Res Liverpool Merseyside England Stanford Univ Sch Med Stanford CA 94305 USA Mersey Care NHS Fdn Trust Prescot England

Background: Digital phenotyping and machine learning are currently being used to augment or even replace traditional analytic procedures in many domains, including health care. Given the heavy reliance on smartphones and mobile devices around the world, this readily available source of data is an important and highly underutilized source that has the potential to improve mental health risk prediction and prevention and advance mental health globally. Objective: This study aimed to apply machine learning in an acute mental health setting for suicide risk prediction. This study uses a nascent approach, adding to existing knowledge by using data collected through a smartphone in place of clinical data, which have typically been collected from health care records. Methods: We created a smartphone app called Strength Within Me, which was linked to Fitbit, Apple Health kit, and Facebook, to collect salient clinical information such as sleep behavior and mood, step frequency and count, and engagement patterns with the phone from a cohort of inpatients with acute mental health (n=66). In addition, clinical research interviews were used to assess mood, sleep, and suicide risk. Multiple machine learning algorithms were tested to determine the best fit. Results: K-nearest neighbors (KNN;k=2) with uniform weighting and the Euclidean distance metric emerged as the most promising algorithm, with 68% mean accuracy (averaged over 10,000 simulations of splitting the training and testing data via 10-fold cross-validation) and an average area under the curve of 0.65. We applied a combined 5x2 F test to test the model performance of KNN against the baseline classifier that guesses training majority, random forest, support vector machine and logistic regression, and achieved F statistics of 10.7 (P=.009) and 17.6 (P=.003) for training majority and random forest, respectively, rejecting the null of performance being the same. Therefore, we have taken the first steps in prototyping a syst

关键词： suicide suicidal ideation smartphone cell phone machine learning nearest neighbor algorithm digital phenotyping

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Indoor Tracking with Bluetooth Low Energy Devices Using K-nearest Neighbour algorithm

Indoor Tracking with Bluetooth Low Energy Devices Using K-Ne...

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International Conference on Computer Applications and Industrial Electronics (ICCAIE)

作者： Koon Kee Lie Kwok Shien Yeo Alvin Kee Ngoh Ting David Heng Tze Chieng Faculty of Engieering and Technology Tunku Abdul Rahman University College Kuala Lumpur Setapak Malaysia Wireless Innovation MIMOS Berhad Technology Park Malaysia Kuala Lumpur Malaysia

ISBN: (数字)9781728150338

ISBN: (纸本)9781728150345

In this paper we discuss the design of an indoor positioning system (IPS) using Bluetooth Low Energy (BLE) scanners and beacons. We deployed the prototype system in a laboratory where its dimension is measured at 990 × 770 cm 2 , Range test has been carried out to study the relationship between distance and Received Signal Strength (RSSI) of the BLE devices. Using the highest RSSI values received from 3 of the scanners, we use K-nearest Neighbour algorithm to predict the region where the beacon is possibly located. We further demonstate that our system is able to estimate the location region of the target beacon with good accuracy.

关键词： Indoor tracking localization Bluetooth Low Energy K-nearest Neighbour INDOOR TRACKS Beacons Bluetooth RANGE TESTS Scanner neighbor Wheat LOW ENERGY Received Signal Strength Indicator nearest neighbor algorithm

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Machine Learning Methods Applying for Hydraulic System States Classification

Machine Learning Methods Applying for Hydraulic System State...

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Systems of Signals Generating and Processing in the Field of on Board Communications Conference

作者： Bykov, A. D. Voronov, V., I Voronova, L., I Moscow Tech Univ Commun & Informat Moscow Russia

The article describes the analysis of a dataset on the states of the hydraulic system of the aircraft and an attempt to classify error in valve switching modes for further research of the possibility of predicting fai... 详细信息

ISBN: (纸本)9781728106069

关键词： machine learning classification gradient boosting support vector machine nearest neighbor algorithm hydraulic system sensors comparison

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Multivariate Time Series nearest neighbor Search: A Case Study on Solar Flare Prediction

Multivariate Time Series Nearest Neighbor Search: A Case Stu...

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Artificial Intelligence and Knowledge Engineering (AIKE)

作者： Soukaina Filali Boubrahimi Rafal Angryk Departement of Computer Science Georgia State University Atlanta GA

ISBN: (纸本)9781538695562;9781538660843

Accurate solar flare prediction is crucial for the safety of the infrastructures both in space and on the ground. In this paperm, we examined the solar flare prediction problem from a multivariate time series perspective. We use a feature-free multivariate time series nearest neighbor search classifier applied on the solar flare data. We compared different data extraction priors and spans, and ranked the importance for different solar magnetic parameters.

关键词： nearest neighbour methods pattern classification search problems solar flares solar magnetism time series Solar flares solar magnetism tabu search Pattern recognition nearest neighbor algorithm Time series nearest neighbor query Case studies

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Mining Student Feedback to Improve the Quality of Higher Education through Multi Label Classification, Sentiment Analysis, and Trend Topic

Mining Student Feedback to Improve the Quality of Higher Edu...

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International Conference on Information Technology, Information Systems and Electrical Engineering (ICITISEE)

作者： Calandra Alencia Hariyani Achmad Nizar Hidayanto Nur Fitriah Zaenal Abidin Theresia Wati Fakultas Ilmu Komputer Universitas Indonesia Fakultas Ilmu Komputer UPN “Veteran” Jakarta Jakarta Indonesia

ISBN: (数字)9781728151182

ISBN: (纸本)9781728151199

This research carried out the label aspect classification, sentiment analysis, and topic trends on the Open-Ended Question (OEQ) section for Student Feedback Questionnaire (SFQ). Multi-Class aspect label classification for SFQ will choose the best classification model by comparing the results of the evaluation of accuracy, precision, recall, and Flscore for each feature combination and comparison of four classification algorithms namely Decision Tree (DT), Naive Bayes (NB), K-nearest neighbor (KNN), and Support Vector Machine (SVM). The results of this research are Classification Techniques using a combination of features of TFIDF, Unigranb and Bigram with the SVM algorithm which is the best Multi-Class classification model for labeling SFQ aspects. In addition, the SentiStrenghtID algorithm used to get sentiments and also the LDA (Latent Dirichlet Allocation) used to get annual topic trends on each survey aspect label. The findings can help Higher Education to support decision making in taking proactive actions towards improvement for self-evaluation and quality.

关键词： Bayes methods computer aided instruction data mining decision making decision trees further education learning (artificial intelligence) nearest neighbour methods pattern classification support vector machines Web sites Continuing Education Support Vector Network computer-assisted instruction Decision Trees Bayes methods Pattern recognition higher education websites data mining nearest neighbor algorithm RSFQ Taxonomy decision making

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