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CINS 2024 - 2nd international conference on Computational intelligence and Network Systems

CINS 2024 - 2nd International Conference on Computational In...

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2nd international conference on Computational intelligence and Network Systems, CINS 2024

ISBN: (纸本)9798331504106

The proceedings contain 74 papers. The topics discussed include: domain-specific software system testing by using intelligent approaches;machine learning for misbehavior detection in intelligent transportation systems using BSM data;comparative analysis of analog, digital, and hybrid beamforming techniques for enhanced MIMO wireless communication systems: radiation pattern and normalized power;an extremely lightweight driver distraction detection system using recurrent neural network for enhanced road safety;tumor detection using deep learning and explainable artificial intelligence;rotation-invariant self-supervised representation learning for object detection in autonomous driving;and enhancing secure medical image transmission using visually meaningful medical image encryption.

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Deep Learning Based K-Line Chart Recognition for Financial Quantitative Investment analysis 18th

Deep Learning Based K-Line Chart Recognition for Financial Q...

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18th international conference on Neural Networks (ISNN)

作者： Luo, Yamei Zhang, Zhijun Jiang, Rongzhun Liu, Yu South China Univ Technol 381 Wushan Rd Guangzhou 510000 Peoples R China

ISBN: (纸本)9789819743988;9789819743995

In financial quantitative investment analysis field, most of existing artificial intelligence analysis methods are based on sequential data, and very few research apply expert intuitive image experience based intelligence method to analyze many K-line patterns. In this paper, a Faster R-CNN based image recognition method is proposed to analyze and predict the financial image (e.g., w-bottom patterns, pivots in entanglement theory, trend and consolidation patterns, etc.). The proposed method can intuitively recognize the K-line pattern images effectively and accurately. To do so, a Faster R-CNN is firstly constructed. The financial data images are then fed into the constructed neural network for training. What is more, the trained network model is used to predict some new financial images. The proposed method can not only recognize a financial K-line pattern feature, but also recognize multiple-features image patterns. The experimental results show that the recognition algorithm based on Faster -R-CNN greatly improves the accuracy of K-line pattern recognition, and the method is effective.

关键词： Neural Network Data analysis K-line pattern Image Faster R-CNN Image Recognition

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2nd international conference on Artificial intelligence, robotics, and Communication, ICAIRC 2022

2nd International Conference on Artificial Intelligence, Rob...

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2nd international conference on Artificial intelligence, robotics, and Communication, ICAIRC 2022

ISBN: (纸本)9789819945535

The proceedings contain 27 papers. The special focus in this conference is on international conference on Artificial intelligence, robotics, and Communication. The topics include: Pilot Design for Compressed Sensing Based OFDM Channel Estimation;a Review of the Development of Artificial intelligence Electronic Circuit Technology;Stock’s Closing Price Prediction Based on GRU Neural Network;random Forest Algorithm for Forest Fire Prediction;underwater Image Clearing Algorithm Based on the Laplacian Edge Detection Operator;multi-models Study on the Influence of Space–Time Factors on the Shared Bike Usage;application of Collaborative Robot in Cigarette Production Line for Automatic Distribution of Packaging Materials;comparison of Data Processing Performance of Hadoop and Spark Based on Huawei Cloud and Serverless;structured Model of "Three Flows in One" Emergency Preplan Based on Knowledge Graph;the Advance and Performance analysis of MapReduce;an Efficient Model for Dorsal Hand Vein Recognition Based on Combination of Squeeze-and-Excitation Block and Vanilla ResNet;design and Implementation of Online Book Sale System;review of Tobacco Planting Area Estimation Based on Machine Learning and Multi-source Remote Sensing Data;an Innovative Information System for Health and Nutrition Guidance;Research on Energy Saving Scene of 5G Base Stations Based on SOM + K-Means Two-Stage Clustering Algorithm;Smart Substation Synthetical Smart Prevent Mishandling System Based on Topology Model and Intelligent IOT;fresh Products Application Information Management System;a Novel Action Recognition Method Based on Attention Enhancement and Relative Entropy;Application of Combinatorics Based on Discrete analysis in WCET Embedded Software Testing Technology.

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Rise of AI: Prediction of Job Replacements Based on the Evolution of Artificial intelligence and Robots Intensification 3

Rise of AI: Prediction of Job Replacements Based on the Evol...

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international conference on Advances in Computing, Communication and Applied Informatics (ACCAI)

作者： Ramachandran, K. K. Raju, Valliappan Karthick, K. K. Lakshmi Gnanakumar, Baba P. Deepa, M. DR GRD Coll Sci Coimbatore Coimbatore Tamil Nadu India Perdana Univ Kuala Lumpur Malaysia Dr GR Damodaran Coll Sci Dept Management Sci Coimbatore Tamil Nadu India Dwaraka Doss Goverdhan Doss Vaishnav Coll Dept English Chennai Tamil Nadu India Kristu Jayanti Coll Bengaluru India Kumaraguru Coll Technol KCT Business Sch Coimbatore Tamil Nadu India

ISBN: (纸本)9798350389432;9798350389449;9798350389456

This research aims to identify the principal factors that foster innovation among students engaged in Work-integrated learning (WIL). Concerns are escalating that robotics and artificial intelligence may displace numerous job roles. In response to this evolving employment landscape, future workers must cultivate innovation skills, identify opportunities, revolutionize industries, and devise inventive solutions to global challenges. Work-integrated learning (WIL) has been recognized as a pivotal educational strategy to develop such attributes, in which the proposed model is cross-validated with the conventional learning scheme called Random Forest (RF) to evaluate the efficiency of the proposed scheme. Diverging from the predominantly qualitative or snapshot-based approaches of previous research, this study employs a quantitative, longitudinal method to assess student capabilities both before and after their WIL placements within businesses. Through confirmatory factor analysis, it compares the pre- and post-placement skills of students. The findings indicate that critical thinking, problem-solving, communication, and teamwork significantly influence the cultivation of innovative skills, which are essential in the age of artificial intelligence.

关键词： AI Job Replacements Artificial intelligence Robots Intensification Work Integrated Learning WIL Random Forest RF

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An EMG-based objective function for human-in-the-loop optimization

An EMG-based objective function for human-in-the-loop optimi...

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international conference on Rehabilitation robotics (ICORR)

作者： Diaz, Maria Alejandra De Bock, Sander Beckerle, Philipp Babic, Jan Verstraten, Tom De Pauw, Kevin Vrije Univ Brussel Human Physiol & Sports Physiotherapy Res Grp Brussels Belgium Vrije Univ Brussel Brussels Human Robot Res Ctr Brussels Belgium Vrije Univ Brussel & Flanders Make Robot & Multibody Mech Res Grp Brussels Belgium Friedrich Alexander Univ Erlangen Nurnberg Inst Autonomous Syst & Mech Dept Elect Engn Erlangen Germany Friedrich Alexander Univ Erlangen Nurnberg Dept Artificial Intelligence Biomed Engn Erlangen Germany Jozef Stefan Inst Dept Automat Biocybernet & Robot Lab Neuromech & Biorobot Ljubljana 1000 Slovenia Univ Ljubljana Fac Elect Engn Ljubljana Slovenia

ISBN: (纸本)9798350342758

Advancements in wearable robots aim to improve the users' motion, performance, and comfort by optimizing, mainly, energetic cost (EC). However, EC is a noisy measurement with a physiological delayed response that requires long evaluation periods and wearing an uncomfortable mask. This study aims to estimate and minimize an EMG-based objective function that describes the natural energetic expenditure of individuals walking. This objective is assessed by combining multiple electromyography (EMG) variables from the EMG intensity and muscle synergies. To evaluate this objective function simply and repeatedly, we prescribed step frequency (SF) via a metronome and optimized this frequency to minimize muscle activity demands. Further, a linear mixed-effects model was fitted for EC, with the EMG variables as fixed-effects and a random intercept that varies by participant. After the model was fitted to the data, a cubic polynomial was used to identify the optimal SF that reduces the overall EMG-based objective function. Our analysis outlines that the proposed objective function is comparable to the EC during walking, the primary objective function used in human-in-the-loop optimization. Thus, this EMG-based objective function could be potentially used to optimize wearable robots and improve human-robot interaction.

关键词： Muscle

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NSGA-II for qualitative phase analysis of powder X-ray diffraction patterns with domain knowledge

NSGA-II for qualitative phase analysis of powder X-ray diffr...

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2024 international conference on Machine intelligence for GeoAnalytics and Remote Sensing, MIGARS 2024

作者： Cong, Tian Francis, Neil Lampinen, Heta Csiro Mineral Resources Perth Australia

ISBN: (纸本)9798350389678

An evolutionary algorithm, NSGA-II, together with an unmixing approach, is used to automate the interpretation of powder X-ray Diffraction (pXRD) patterns. This work incorporates domain knowledge, such as information about crystal structure similarity, and forward modelling of the instrumentation and pXRD profiles. The method not only provides an interpretation of the pXRD pattern, but also addresses uncertainty in minerals identification, by generating multiple solutions that can be further refined in the subsequent analysis. Additionally, this method is more efficient than brute force approaches to sparse solutions that are sometimes applied in unmixing. © 2024 IEEE.

关键词： Domain Knowledge

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A User-customized Automatic Music Composition System 39

A User-customized Automatic Music Composition System

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IEEE international conference on robotics and Automation (ICRA)

作者： Mo, Fan Ji, Xiaoqiang Qian, Huihuan Xu, Yangsheng Chinese Univ Hong Kong Sch Sci & Engn Shenzhen Guangdong Peoples R China Chinese Univ Hong Kong Shenzhen Inst Artificial Intelligence & Robot Soc Shenzhen Guangdong Peoples R China

ISBN: (纸本)9781728196817

This paper introduces an intelligent system which composes music following the users' instructions. Current automatic music generation models are lack of stability. Meanwhile, they cannot satisfy the preference of different people. To overcome these challenges, we train a Transformer-based neural network to generate short music segments using a dataset. A user can compose music pieces by interacting with a well-trained generator. Our system collects the user's feedback during the interactions, and fine-tunes the neural network to optimize the generator. After a large number of interactions, our system can learn the musical taste of the user and customize a personal automatic music composer for him or her. Our work enhances the application value of generative models significantly, which enables people to compose music with the assistance of artificial intelligence.

关键词： Automation Neural networks Music Entertainment industry Transformers Generators Stability analysis

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The Unspoken Aspect of Socially Shared Regulation in Collaborative Learning: AI-Driven Learning Analytics Unveiling ′Silent Pauses′ 24

The Unspoken Aspect of Socially Shared Regulation in Collabo...

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14th Annual international conference on Learning Analytics and Knowledge (LAK) - Learning Analytics in the Age of Artificial intelligence

作者： Dang, Belle Nguyen, Andy Jarvela, Sanna Univ Oulu Oulu Finland

ISBN: (纸本)9798400716188

Socially Shared Regulation (SSRL) contributes to collaborative learning success. Recent advancements in Artificial intelligence (AI) and Learning Analytics (LA) have enabled examination of this phenomenon ' s temporal and cyclical complexities. However, most of these studies focus on students ' verbalised interactions, not accounting for the intertwined ' silent pauses ' that can index learners ' internal cognitive and emotional processes, potentially offering insight into regulation ' s core mental processes. To address this gap, we employed AI-driven LA to explore the deliberation tactics among ten triads of secondary students during a face-to-face collaborative task (2,898 events). Discourse was coded for deliberative interactions for SSRL. With the micro-annotation of ' silent pause ' added, sequences were analysed with the Optimal Matching algorithm, Ward ' s Clustering and Lag Sequential analysis. Three distinct deliberation tactics with different patterns and characteristics involving silent pauses emerged: i) Elaborated deliberation, ii) Coordinated deliberation, and iii) Solitary deliberation. Our findings highlight the role of ' silent pauses ' in revealing not only the pattern but also the dynamics and characteristics of each deliberative interaction. This study illustrates the potential of AI-driven LA to tap into granular data points that enrich discourse analysis, presenting theoretical, methodological, and practical contributions and implications.

关键词： Socially Shared Regulation Collaborative Learning Learning Analytics Artificial intelligence Deliberative Interactions

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An EEG Signal analysis Method Based on Common Spatial pattern and Support Vector Machine Algorithm 7

An EEG Signal Analysis Method Based on Common Spatial Patter...

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7th international conference on pattern Recognition and Artificial intelligence, PRAI 2024

作者： Wang, Yuxin Liu, Bao Tang, Yuqi Qingdao China The 22nd Research Institute of China Electronics Technology Group Corporation Qingdao China

ISBN: (纸本)9798350350890

To solve the problem of EEG signal on feature extraction and low recognition rate, we analyze the characteristics of EEG signal and propose an EEG signal analysis method based on common spatial pattern (CSP) and support vector machine (SVM) algorithms. In this method, we first preprocess the EEG signal by wavelet packet transform (WPT) and extract the specific frequency band signal in the EEG signal. Then the features of the EEG signal are extracted by constructing pair-wise CSP (PW-CSP) filters. Finally, the recognition of the EEG signal was realized by the support vector machine algorithm which optimizes parameter selection by particle swarm optimization (PSO), and the 2008 BCI competition 2A data set was used to verify the classification effect of the algorithm. The experimental results indicated that the classification accuracy of the method may reach up to 93.07%, and the average accuracy is higher than the results of the top two in the 2008 brain-computer interface (BCI) competition. The EEG signal features extracted by this method can reflect the signal characteristics of EEG well, and improve the accuracy of classification, which may assist in the development and application of BCI. © 2024 IEEE.

关键词： Particle swarm optimization (PSO)

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Comparative analysis of Partial Discharge pattern Recognition Using Deep Learning and Machine Learning 10

Comparative Analysis of Partial Discharge Pattern Recognitio...

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10th international conference on Condition Monitoring and Diagnosis, CMD 2024

作者： Hong, Tae-Yun. Ahn, Hyun-Mo. Jang, Hyun-Jae. Park, Jun-Kyu. Sun, Jong-Ho. Kim, Jin-Gyu. Changwon Korea Republic of Kyungpook National University Daegu Korea Republic of

ISBN: (纸本)9788986510225

Partial Discharge (PD) defect type analysis is important for evaluating insulation performance. A machine learning feature extraction algorithm is presented for AC PD pattern data collected in the laboratory, along with deep learning algorithms for PD pattern images and PD time series data. In addition, data is collected under conditions different from those used for artificial intelligence (AI) training, and algorithm performance is evaluated and compared. © 2024 The Korean Institute of Electrical Engineers (KIEE).

关键词： Adversarial machine learning

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