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11th international conference on Modelling, Identification and Control, ICMIC 2019

ISBN: (纸本)9789811504730

the proceedings contain 128 papers. the special focus in this conference is on Modelling, Identification and Control. the topics include: Industrial Robot Control Systems: A Review;the Research on Aircraft Anti-skid Braking Switching Control with Road Online Identification Based on Fuzzy Algorithm;Automatic Sleep Staging Based on XGBOOST Physiological Signals;research on Unmanned Aerial Vehicle Water Combined Quality Detection and Early-Warning System;an Improved Lidar data Segmentation Algorithm Based on Euclidean Clustering;RLV Guidance and Control System Design for Terminal Area Energy Management Phase;a Compound Attitude Control System for a High Coupling Reentry Vehicle;An Improved FCM Algorithm Based on the Firework Algorithm for Liquid Rocket Engine Fault Detection;two-Stage Least Squares Based Iterative Parameter Identification Method for Time-Delay Systems;modeling Correlated Wind Speeds by Trigonometric Archimedean Copulas;trajectory Tracking of Unmanned Ground Vehicle Based on Iterative learning Model Predictive Control;attack-Time Cooperative Guidance of Multi-missile System Based on Bessel Curve;flight Control Experimental Platform of Transport Aircraft Based on FlightGear/Matlab;a Dynamic Buffer Reservation Method Based on Markov Chain to Solve Deadlock Problem in Scheduling;robust Adaptive Position/Force Control for Flexible-Link with Flexible-Joint Manipulator;Identification of Flowrates and Pressures in HVAC Distribution Network Based on Collective Intelligence System;neural Network Sliding Mode Control for Pneumatic Servo System Based on Particle Swarm Optimization;delay Efficient D2D Communications over 5G Edge-Computing Mobile Networks;data Optimization for Spatial data Mining and Classification in Marine Geochemical Exploration;single Machine Due Date Assignment Scheduling with Deterioration and learning Effect.

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A data-driven deep learning model to pattern recognition for medical diagnosis, by using model aggregation and model selection 11

A data-driven deep learning model to pattern recognition for...

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11th international conference on Emerging Ubiquitous Systems and Pervasive Networks, EUSPN 2020

作者： Djellali, Choukri Adda, Mehdi Moutacalli, Mohamed Tarik Department of Computer Science and Engineering University of Quebec At Rimouski Rimouski Canada

In recent years, pattern recognition becomes a research area with increasing importance using several techniques. One of the most common techniques used is Deep learning. this paper presents a new Deep learning model to pattern recognition for medical diagnosis. the uncovering of hidden structures is performed by feature selection, model aggregation, and model selection. Our Deep learning model has the ability to reach the optimal solution and create complex decision boundaries when used to look for and diagnose breast cancer. the evaluation, based on 10-fold cross-validation, showed that the proposed model, which is named BaggingSMF, yielded good results and performed better than Radial Basis Function, Bidirectional Associative Memory, and ELMAN Neural Networks. Experimental studies demonstrate the multidisciplinary applications of our model. © 2020 the Authors. Published by Elsevier B.V.

关键词： Diagnosis

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Study on classification of electricity consumption behavior of power users based on ACO-ELM 11

Study on classification of electricity consumption behavior ...

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11th international conference on intelligent Human-Machine Systems and Cybernetics (IHMSC)

作者： Zhai Guangxin He Chunguang Shao Hua An Jiakun Han Jinglin Hu Shiyao Sun Pengfei State Grid Hebei Econ Res Inst Shijiazhuang Hebei Peoples R China

ISBN: (纸本)9781728118598

Aiming at the classification problem of power users' behavior under the background of big data, a classification method of power users' behavior based on ant colony optimize extreme learning machine (ACO-ELM) was proposed. First, the problem of optimizing the initial input weight and hidden layer neuron threshold of the extreme learning machine is transformed into the problem of ants seeking the best path in the ant colony algorithm. through the updating, mutation and heredity of ant colony algorithm, the extreme learning machine is trained, and the input weight and threshold with the minimum error are selected to improve its prediction accuracy. ACO-ELM was used to classify power users according to power load data. Experimental simulation verifies that ACO-ELM has better prediction accuracy and accurate classification of power users.

关键词： user electricity behavior ant colony optimization extreme learning machine big data

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Vehicular data offloading by road-side units using intelligent software defined network 11

Vehicular data offloading by road-side units using intellige...

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11th international conference on Emerging Ubiquitous Systems and Pervasive Networks, EUSPN 2020

作者： Guntuka, Sony Shakshuki, Elhadi M. Yasar, Ansar Gharrad, Hana Jodrey School of Computer Science Acadia University WolfvilleNSB4P2R6 Canada Transportation Research Institute Hasselt University HasseltB-3500 Belgium

the evolution of wide variety of applications that are used by vehicular users includes a lot of data hungry applications. this increases the workload on the cellular networks, thereby delivering poor service to the users. We can overcome this problem by sharing this workload with open wireless networks. As such, this improves the Quality of Service provided by cellular networks. Road-Side Units (RSU) are a wireless network which plays a major role in data offloading. Our approach discusses switching the communication network from cellular to RSU whenever there is an opportunity for a vehicle to offload vehicles data. Busy roads/urban traffic consists of several RSUs with many users. In urban environment, the vehicular user needs to choose an RSU from several available RSUs within the vehicle communication proximity. For seamless connectivity, the delay in network communication because of selecting the best RSU and frequent switching of connection between vehicles and RUSs must be minimized. In this paper, we propose a Smart Ranking based data Offloading (SRDO) algorithm for selecting an RSU and to improve the Quality of Service. In SRDO algorithm, Q-learning is utilized for RSU selection. this algorithm is modelled in Software Defined Network controller to deal with the problem of choosing the RSU in an intelligent way for data offloading. © 2020 the Authors. Published by Elsevier B.V.

关键词： Reinforcement learning

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Performance Analysis of Incremental learning Strategy in Image Classification

Performance Analysis of Incremental Learning Strategy in Ima...

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international conference on Confluence the Next Generation Information Technology Summit (Confluence)

作者： Gaurav Jaiswal ICT Research Lab University of Lucknow Lucknow India

ISBN: (数字)9781665414517

ISBN: (纸本)9781665430340

Deep learning-based image classification model learns from the fixed and specific training dataset. For the generalization and adaptation of human learning behaviour, some of these models adapted incremental learning to enhance the learning and knowledge from updated and incremented dataset. An incremented dataset can be in form of increment of examples or new class dataset images. this incremented dataset is learned by deep learning models by two incremental learning strategies i.e. sample-wise and class-wise. this paper proposes a performance analysis methodology and experimentally analyze the performance of these incremental learning strategies in CNN based image classification model on prepared incremented dataset. the evaluation of performance of these deep image classification model on classification performance metrics such as accuracy, precision, recall and f1 score shows that these model's learning and classification capabilities are increased with incremental learning on the incremented dataset. Two different incremental learning shows the relation between performances of model to the increment of dataset.

关键词： Deep learning Training Measurement Adaptation models Analytical models Performance analysis Image classification

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Comparison of Machine learning and Deep learning Approaches for Decoding Brain Computer Interface: An fNIRS Study 11th

Comparison of Machine Learning and Deep Learning Approaches ...

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11th IFIP TC 12 international conference on intelligent Information Processing, IIP 2020

作者： Lu, Jiahao Yan, Hongjie Chang, Chunqi Wang, Nizhuan School of Biomedical Engineering Health Science Center Shenzhen University Shenzhen China Department of Neurology Affiliated Lianyungang Hospital of Xuzhou Medical University Lianyungang China Laboratory School of Computer Engineering Jiangsu Ocean University Lianyungang China

ISBN: (纸本)9783030469306

Recently, deep learning has gained great attention in decoding the neuro-physiological signal. However, which one (classical machine learning or deep learning) has better performance for decoding the functional near-infrared spectroscopy (fNIRS) signal is still lack of full verification. thus, in this paper, we systematically compared the performance of many classical machine learning methods and deep learning methods in fNIRS data processing for decoding the mental arithmetic task. the classical machine learning methods such as decision tree, linear discriminant analysis (LDA), support vector machine (SVM), K-Nearest Neighbor (KNN) and ensemble methods with strict feature extraction and screening, were used for performance comparison, while the long short-term memory-fully convolutional network (LSTM-FCN) method as a representative of deep leaning methods was applied. Results showed that the classification performance of SVM was the best among the classical machine learning methods, achieving that the average accuracy of the subject-related/unrelated were 91.0% and 83.0%, respectively. Furthermore, the classification accuracy of deep learning was significantly better than that of the involved classical machine learning methods, where the accuracy of deep learning could reach 95.3% with subject-related condition and 97.1% with subject-unrelated condition, respectively. thus, this paper has totally showed the excellent performance of LSTM-FCN as a representative of deep learning in decoding brain signal from fNIRS dataset, which has outperformed many classical machine learning methods. © 2020, IFIP international Federation for Information Processing.

关键词： Support vector machines

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Understanding Brand Authenticity Sentiments using Big data Analytics

Understanding Brand Authenticity Sentiments using Big Data A...

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international conference on Confluence the Next Generation Information Technology Summit (Confluence)

作者： Harmit Singh Akanksha Yadav Rishabh Bansal Shuchi Mala Amity School of Engineering and Technology (Amity University) Noida India Iowa State University Iowa USA

ISBN: (数字)9781665414517

ISBN: (纸本)9781665430340

Social media is the major place where people share their feelings in reviews and opinions about anything such as events, products, people, article and many more. these sentiments have great influence on readers who read those opinions and construct their own opinions in the matters. the unstructured data collected from varied sources needs to be analyzed and well-structured to address specific market questions and for this purpose sentiment analysis has received considerable attention over the years. Sentiment analysis is used to decipher the mind-set or feelings of a writer from his review. Many scholars have discussed the ability of deep learning models to resolve the question of knowing consumer emotions. In this paper, we are interested in handling the sentiment analysis task using various deep learning models such as Simple Neural Network, Convolutional Neural Network (CNN) and two Recurrent Neural Networks (RNN) -Long Short Term Memory (LSTM) and Gated Recurrent Unit (GRU).

关键词： Deep learning Sentiment analysis Recurrent neural networks Text categorization Convolutional neural networks Task analysis Long short term memory

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Comparison of Machine learning Algorithms for Tumor Detection in Breast Microwave Imaging

Comparison of Machine Learning Algorithms for Tumor Detectio...

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international conference on Confluence the Next Generation Information Technology Summit (Confluence)

作者： Priyam Patel Anant Raina Institute of Technology Nirma University Ahmedabad India

ISBN: (数字)9781665414517

ISBN: (纸本)9781665430340

Conventional breast imaging methods like Magnetic Resonance Imaging, ultrasound and X-Ray are relied upon by most clinics and doctors worldwide. Breast microwave imaging (BMI) is an alternative imaging technology which uses nonionizing radiation which safer for the body and has a lower cost. A pre-clinical BMI system using breast phantoms is used to create the open source University of Manitoba- BMI dataset (UM-BMID). In this paper, we explore the usability of the dataset, implement different machine learning classification algorithms for tumor detection on UM-BMID and compare our findings with the previously published results. the accuracy achieved was a maximum of 94% which shows great promise for use of machine learning techniques in breast microwave imaging.

关键词： Training Computational modeling Imaging Usability X-ray imaging Microwave imaging Testing

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DRAC: Diabetic Retinopathy Analysis Challenge with Ultra-Wide Optical Coherence Tomography Angiography Images

arXiv

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arXiv 2023年

作者： Qian, Bo Chen, Hao Wang, Xiangning Che, Haoxuan Kwon, Gitaek Kim, Jaeyoung Choi, Sungjin Shin, Seoyoung Krause, Felix Unterdechler, Markus Hou, Junlin Feng, Rui Li, Yihao Daho, Mostafa El Habib Wu, Qiang Zhang, Ping Yang, Xiaokang Cai, Yiyu Li, Huating Jia, Weiping Sheng, Bin Department of Computer Science and Engineering Shanghai Jiao Tong University Shanghai China Department of Computer Science and Engineering The Hong Kong University of Science and Technology Hong Kong Department of Chemical and Biological Engineering The Hong Kong University of Science and Technology Hong Kong Department of Ophthalmology Shanghai Jiao Tong University Affiliated Sixth People’s Hospital Shanghai China VUNO Inc. Seoul Korea Republic of AI/DX Convergence Business Group KT Korea Republic of Johannes Kepler University Linz Linz Austria School of Computer Science Shanghai Key Laboratory of Intelligent Information Processing Fudan University China Academy for Engineering and Technology Fudan University China LaTIM UMR 1101 Inserm Brest France University of Western Brittany Brest France Department of Computer Science and Engineering The Ohio State University Ohio United States Department of Biomedical Informatics The Ohio State University Ohio United States Translational Data Analytics Institute The Ohio State University Ohio United States MoE Key Lab of Artificial Intelligence Artificial Intelligence Institute Shanghai Jiao Tong University Shanghai China School of Mechanical and Aerospace Engineering Nanyang Technological University Singapore Department of Endocrinology and Metabolism Shanghai Jiao Tong University Affiliated Sixth People’s Hospital Shanghai China Shanghai Diabetes Institute Shanghai Clinical Center for Diabetes Shanghai China

Computer-assisted automatic analysis of diabetic retinopathy (DR) is of great importance in reducing the risks of vision loss and even blindness. Ultra-wide optical coherence tomography angiography (UW-OCTA) is a non-invasive and safe imaging modality in DR diagnosis system, but there is a lack of publicly available benchmarks for model development and evaluation. To promote further research and scientific benchmarking for diabetic retinopathy analysis using UW-OCTA images, we organized a challenge named "DRAC - Diabetic Retinopathy Analysis Challenge" in conjunction with the 25th international conference on Medical Image Computing and Computer Assisted Intervention (MICCAI 2022). the challenge consists of three tasks: segmentation of DR lesions, image quality assessment and DR grading. the scientific community responded positively to the challenge, with 11, 12, and 13 teams from geographically diverse institutes submitting different solutions in these three tasks, respectively. this paper presents a summary and analysis of the top-performing solutions and results for each task of the challenge. the obtained results from top algorithms indicate the importance of data augmentation, model architecture and ensemble of networks in improving the performance of deep learning models. these findings have the potential to enable new developments in diabetic retinopathy analysis. the challenge remains open for post-challenge registrations and submissions for benchmarking future methodology developments. Copyright © 2023, the Authors. All rights reserved.

关键词： Optical tomography

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Social Network measures association with social and intelligent behaviors in Dolphin network

Social Network measures association with social and intellig...

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international conference on Confluence the Next Generation Information Technology Summit (Confluence)

作者： Sangita Garg Tapan K. Gandhi B.K. Panigrahi Indian Institute Of Technology Delhi New Delhi India

ISBN: (数字)9781665414517

ISBN: (纸本)9781665430340

the need for sociality in animals manifests in formation of communities with likeminded individuals. Social network analysis approach is a way to quantify the social and intelligent behaviors as displayed by animals in social structures. the study analyzes some of such network measures like modularity-based algorithms for community formation, intergroup and intragroup dynamics. Other network measures such as Degree centrality, betweenness centrality, closeness centrality, authority and hub have the potential to be mapped to some of the key leadership skills such as influencing, listening, information passing, resourcefulness, broadcasting and the extent of gregariousness abilities. the outcome of the analysis could be further extended in identifying animal threat, fitness and conservation of wild life. the study also opens avenues to explore similarities with human social interactions in organization networks.

关键词： Visualization Social networking (online) Heuristic algorithms Wildlife Organizations Broadcasting Safety

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