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Examining How IP can be used to Find patterns for Industrial Automation

Examining How IP can be used to Find Patterns for Industrial...

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international conference on Contemporary computing and Informatics (IC3I)

作者： Alka Kumari Pooja Sapra Department of Computer Science ARKA JAIN University Jharkhand India Department of IT PIET Parul University Gujarat India

Visual input and output require image processing. IP inspired the technology’s name. This platform supports imaging, signal processing, image enhancement, and voice signal processing. Recognizable photos and videos have a distinct object or attribute. CBIR finds patterns in this study. IP in industrial automation was another topic. Industrial photo processing makes high-speed recording flawless. Automatic inspection systems use industrial image processing. Camera trigger sensors and rails are industrial imaging accessories.

关键词： Rails Visualization Automation Sensor phenomena and characterization Inspection pattern recognition Recording

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7th international conference on Computational Science and Technology, ICCST 2020

7th International Conference on Computational Science and Te...

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7th international conference on Computational Science and Technology, ICCST 2020

ISBN: (纸本)9789813340688

The proceedings contain 47 papers. The special focus in this conference is on Computational Science and Technology. The topics include: The Most Potential Decision Tree Technique to Classify the Large Dataset of Students;attention Models for Sentiment Analysis Using Objectivity and Subjectivity Word Vectors;a Question-Answering System that Can Count;contactless Patient Authentication for Registration Using Face recognition Technology;drawing and Recognising Simple Shapes with Real-Time Feedback Using pattern recognition;information Technology Students’ Preferences on Blended Learning;preface;building a Knowledge Graph of Vietnam Tourism from Text;improved Facial recognition Algorithms Based on Dragonfly and Grasshopper Optimization;optimization on the Financial Management of Banks with Two-Stage Goal Programming Model;evaluating the Performance of Selected Mortality Forecasting Models: A Malaysia Case Study;assessing Python Programming Through Personalised Learning Styles Model;the Programming Learning Assessment Model for Measuring Student Performance;design and Functionality of a University Academic Advisor Chatbot as an Early Intervention to Improve Students’ Academic Performance;Multiprocessing Implementation for Building a DNA q-gram Index Hash Table;predicting Chart Difficulty in Rhythm Games Through Classification Using Chart pattern Derived Attributes;nasheed Song Classification by Fuzzy soft-Set Approach;Hybrid SDN Deployment Using Machine Learning;technology Adoption Models: Users’ Online Social Media Behavior Towards Visual Information;LED Lighting Assessment for High-Performance Stadium Illuminance;split Balancing (sBal)—A Data Preprocessing Sampling Technique for Ensemble Methods for Binary Classification in Imbalanced Datasets;DyslexiAR: Augmented Reality Game Based Learning on Reading, Spelling and Numbers for Dyslexia User’s;applying Transfer Learning in Stock Prediction Based on Financial News.

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Identifying Sequential Influence in Predicting Engagement of Online Social Marketing for Video Games 6th

Identifying Sequential Influence in Predicting Engagement of...

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6th international conference on soft computing in Data Science (SCDS)

作者： Chen, Joseph Chia Wei Ais, Nurulhuda Firdaus Binti Mohd Azmi Univ Teknol Malaysia Kuala Lumpur UTM KL Razak Fac Technol & Informat Kuala Lumpur 54100 Malaysia

ISBN: (纸本)9789811673344;9789811673337

Advancement of online social networks has seen digital marketing use platforms like YouTube and Twitch as key levers for video games marketing. Identifying key influencer factors in these emerging platforms can both deliver better understanding of user behavior in consumption and engagement towards marketing on social platforms and deliver great business value towards video game makers. However, data sparsity and topic maturity has made it difficult to identify user behavior over a sequence of different marketing videos, with a key challenge being identifying key features and distinguishing their contribution to the measure that defines sustained engagement over sequential marketing. This paper presents a method to understand sequential behavioral patterns by extracting features from marketing frameworks and develop a supervised model that takes all the features into consideration to identify the best contributing features to predicting engagement that delivers sustained interest for the next video in a series of marketing videos on YouTube. Experiment results on dataset demonstrate the proposed model is effective within constraint.

关键词： Online social marketing Regression prediction Video game trailers Machine learning Sequential pattern

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softer Pruning, Incremental Regularization

Softer Pruning, Incremental Regularization

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international conference on pattern recognition

作者： Linhang Cai Zhulin An Chuanguang Yang Yongjun Xu University of Chinese Academy of Sciences Beijing China Institute of Computing Technology Chinese Academy of Sciences Beijing China

Network pruning is widely used to compress Deep Neural Networks (DNNs). The soft Filter Pruning (SFP) method zeroizes the pruned filters during training while updating them in the next training epoch. Thus the trained information of the pruned filters is completely dropped. To utilize the trained pruned filters, we proposed a softeR Filter Pruning (SRFP) method and its variant, Asymptotic softeR Filter Pruning (ASRFP), simply decaying the pruned weights with a monotonic decreasing parameter. Our methods perform well across various networks, datasets and pruning rates, also transferable to weight pruning. On ILSVRC-2012, ASRFP prunes 40% of the parameters on ResNet-34 with 1.63% top-1 and 0.68% top-5 accuracy improvement. In theory, SRFP and ASRFP are an incremental regularization of the pruned filters. Besides, We note that SRFP and ASRFP pursue better results while slowing down the speed of convergence.

关键词： Training Neural networks Information filters Filtering theory pattern recognition softening Convergence

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The Delivery Strategy in Cross Docking Operations with Overlapped Temperature Region and Time Windows Constraints

The Delivery Strategy in Cross Docking Operations with Overl...

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IEEE international conference on Industrial Engineering and Engineering Management (IEEM)

作者： Su, T. S. Liao, T. W. Chaoyang Univ Technol Dept Mkt & Logist Management Taichung 41349 Taiwan Louisiana State Univ Dept Mech & Ind Engn Baton Rouge LA 70803 USA

ISBN: (纸本)9781665437714

In the past decade, the retailer trade shifted from brick-and-mortar store to multi-channel, making time-based logistics strategies a necessity to shorten the delivery time for warm foods, fresh product, and fast-moving consumer goods. In this study, a task dispatching approach is developed to sort and consolidate omni-channel commodities for the purpose of mutual delivery in consideration of time windows constraints. The hidden pattern and correlation of data can be mined from uncertain, incomplete, or vague consumer preferences within channels analyzed by the measurement table and reducts using the rough set theory. As the result, valuable dispatching rules are revealed to simulate the sorting, consolidation, and packing processes of commodities with overlapping region of temperature for delivery. To balance the workload of staff and material handling facilities within zones in the cross-dock, the advanced receiving activities is arranged based on the information decision table and the simulated annealing metaheuristic is used to design goods assignment towards outbound docks. It is our hope that the proposed method from this study can assist e-commerce to run their logistics operation more efficiently in the last mile delivery of commodities.

关键词： Cross-docking overlapping region of temperature time windows rough set soft computing

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recognition of the Cognitive State in the Visual Search Task

Recognition of the Cognitive State in the Visual Search Task

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10th international conference on Applied Human Factors and Ergonomics (AHFE) / AHFE international conference on Neuroergonomics and Cognitive Engineering / AHFE international conference on Industrial Cognitive Ergonomics and Engineering Psychology

作者： Zhang, Shuomo Lu, Yanyu Fu, Shan Shanghai Jiao Tong Univ Sch Elect Informat & Elect Engn Shanghai Peoples R China

ISBN: (纸本)9783030204730;9783030204723

Electroencephalogram (EEG) is a research subject that has been studied constantly. By the analysis of EEG signals, the mental state of the humans can be detected, so it would contribute to the design of human-machine interaction (HMI) systems. In this paper, we intend to study the cognitive state using EEG signals when the subject is performing a visual search task. We tried to obtain the different patterns of the EEG signals when the subject is performing differently in the task. Several pattern recognition algorithms on the signal are conducted to find the principal features in the EEG signals. We can see that the features of EEG signals can present the differences between cognitive states and this result will be beneficial to the recognition of the cognitive state of the operators in the complex systems.

关键词： EEG Signal processing Cognitive state pattern recognition

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Image Classification Using Convolutional Neural Networks 4th

Image Classification Using Convolutional Neural Networks

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4th international conference on Advanced Informatics for computing Research, ICAICR 2020

作者： Handa, Nishit Kaushik, Yash Sharma, Nikhil Dixit, Muskaan Garg, Monika Manav Rachna International Institute of Research and Studies Faridabad India

ISBN: (纸本)9789811636592

The term Deep Learning can be termed as the subset of artificial intelligence with multiple network layers forming neural patterns. People’s interest in having the knowledge of deep hidden layers have recently boosted and have begun to takeover various classical strategical performances in numerous fields;especially in pattern recognition and image recognition. One of the most talked-about and important part in deep learning and neural networks is about the Convolutional Neural Network (CNN). We have briefly described about how CNNs plays an integral role in the field of image recognition. Starting from the concepts of Deep Learning and Neural Networks we make our way for *** have given the idea of how CNN works along with convolutional layers and convolutional filters in a lay-man language and the simplest way possible. Further, how a convolutional filter classifies objects and shapes is also explained in the paper. Later in the paper we have also discussed the advantages of CNN over any other neural network technique. © 2021, Springer Nature Singapore Pte Ltd.

关键词： Image classification

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Inferior Alveolar Nerve Segmentation in CBCT Images Using Connectivity-Based Selective Re-training

Inferior Alveolar Nerve Segmentation in CBCT Images Using C...

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2nd international ToothFairy2: Multi-Structure Segmentation in CBCT Volumes, ToothFairy 2024, 3D Teeth Landmarks Detection Challenge, 3DTeethLand 2024, Semi-supervised Teeth Segmentation, STS 2024 held in conjunction with the 27th international conference on Medical Image computing and Computer-Assisted Intervention, MICCAI 2024

作者： Liu, Yusheng Xin, Rui Yang, Tao Wang, Lisheng Institute of Image Processing and Pattern Recognition Department of Automation Shanghai Jiao Tong University Shanghai200240 China

ISBN: (纸本)9783031889769

Inferior Alveolar Nerve (IAN) canal detection in CBCT is an important step in many dental and maxillofacial surgery applications to prevent irreversible damage to the nerve during the procedure. The ToothFairy2023 Challenge aims to establish a 3D maxillofacial dataset comprising both sparse and partially dense labels, with the goal of improving the accuracy of automatic IAN segmentation. In this work, in order to avoid the negative impact brought by sparse labeling, we transform the mixed supervised problem into a semi-supervised problem. Inspired by self-training via pseudo labeling, we propose a selective re-training framework based on IAN connectivity. Our method is quantitatively evaluated on the ToothFairy verification cases, achieving the dice similarity coefficient (DSC) of 0.7956, and 95% hausdorff distance (HD95) of 4.4905, and wining the champion in the competition. Code is available at https://***/GaryNico517/SSL-IAN-Retraining. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Supervised learning

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Application of bioinspired algorithms for solving transcomputational tasks 23

Application of bioinspired algorithms for solving transcompu...

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23rd international conference on soft computing and Measurement, SCM 2020

作者： Kureichik, V.V. Kursitys, I.O. Kuliev, E.V. Gerasimenko, E.M. Southern Federal University Rostov-on-Don344006 Russia

The paper investigates the pattern recognition task as one of the most important transcomputational problems. pattern recognition is applied for statistical data analysis, signal processing, image analysis, bioinformatics, machine learning, and many other fields. The area of application is related to automated pattern detection in the data using computer algorithms and data classification in terms of different categories. The development of new ideas in the processing of large volumes of information creates a new trend in the pattern recognition area. A specific field is formed by the bioinspired algorithms as mathematical reorganization, that transform the initial information into the results based on simulating the evolution methods, natural analogies, and statistic approach. In terms of computer modelling based on the bioinspired algorithms, we can create and develop difficult concepts, for which we have no analytical description. The authors present the optimal bioinspired algorithms based on the ant, monkey, and bat behavior in nature and develop software based on the principle of pattern recognition using the uploaded photo and downloading the results into the file. The experiments demonstrate the effectiveness of the proposed approach. © 2020 Institute of Physics Publishing. All rights reserved.

关键词： pattern recognition

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Role of soft computing Techniques in software Effort Estimation: An Analytical Study 1st

Role of Soft Computing Techniques in Software Effort Estimat...

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1st international conference on Computational Intelligence in pattern recognition, CIPR 2019

作者： Suresh Kumar, P. Behera, H.S. Department of Information Technology Veer Surendra Sai University of Technology Burla768018 India

ISBN: (纸本)9789811390418

In developing software, software effort estimation plays an important role in the success of a software project. Inaccurate, inconsistent, and unreliable estimation of a software leads to failure. Because of various special specifications and changes in the requirements, accurate software Effort Estimation (SEE) for developing software is a difficult task. This software effort estimation must be calculated effectively to avoid unforeseen results. At early development stages, these inabilities to maintain certainty, inaccurate, unreliability are the limitations of expert judgment and algorithmic effort estimation models. After that, attention was turned to machine learning and soft computing methods. soft computing is an association with the methodologies centering on fuzzy logic, artificial neural networks, and evolutionary computation. These methods will provide flexible information processing capability for handling real-life situations. The main aim of the study is to provide an in-depth review of software effort estimation from the initial stages that included expert judgment-based SEE to the latest techniques of soft computing. © 2020, Springer Nature Singapore Pte Ltd.

关键词： soft computing

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