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Research on the application of Children's Reading Analysis Based on Artificial Intelligence - - Take Small "raccoon Reading" and "jiao Jiao Reading" as examples 4

Research on the Application of Children's Reading Analysis B...

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2021 4th international conference on Advanced Algorithms and Control Engineering, ICAACE 2021

作者： Nini, Huang Kong, Dehua Faculty of Literature and Law Wuhan Donghu University Hubei Province Wuhan430212 China School of Computer Science Wuhan Donghu University Hubei Province Wuhan430212 China

Artificial intelligence technology is widely used in the field of education, and the development of children's Chinese reading field is increasingly mature. Using the boundary awareness of AI algorithm for reference, the Chinese reading class should also awaken the boundary awareness of Chinese subject. In the AI algorithm, only the annotated data are valid data. the reading text can become effective learning materials through natural language processing only when it is "annotated"out of ability points. through machine learning, AI constantly generates self-learning data sets based on trial-and-error feedback. the feedback mechanism, trial-and-error opportunity and Chinese teaching design are transformed into each other to build student portrait and specifically improve students' independent Chinese learning ability. this article analyzes the application of artificial intelligence technology in the development of children's Chinese reading services by taking the examples of [[CHECK_DOUBLEQUOT_ENT]] Small raccoon Reading"and "Jiao Jiao Reading. © Published under licence by IOP Publishing Ltd.

关键词： Students

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6th international conference on Optimization and learning, OLA 2023

6th International Conference on Optimization and Learning, O...

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6th international conference on Optimization and learning, OLA 2023

ISBN: (纸本)9783031340192

the proceedings contain 32 papers. the special focus in this conference is on Optimization and learning. the topics include: We Won’t Get Fooled Again: When Performance Metric Malfunction Affects the Landscape of Hyperparameter Optimization Problems;condition-Based Maintenance Optimization Under Large Action Space with Deep Reinforcement learning Method;an application of machine learning Tools to Predict the Number of Solutions for a Minimum Cardinality Set Covering Problem;adaptative Local Search for a Pickup and Delivery Problem Applied to Large Parcel Distribution;GRAPH Reinforcement learning for Operator Selection in the ALNS Metaheuristic;multi-objective Optimization of Adhesive Bonding Process in Constrained and Noisy Settings;evaluating Surrogate Models for Robot Swarm Simulations;interactive Job Scheduling with Partially Known Personnel Availabilities;multi-armed Bandit-Based Metaheuristic Operator Selection: the Pendulum Algorithm Binarization Case;diagonal Barzilai-Borwein Rules in Stochastic Gradient-Like Methods;binary Black Widow with Hill Climbing Algorithm for Feature Selection;optimization of Fuzzy C-Means with Alternating Direction Method of Multipliers;partial K-Means with M Outliers: Mathematical Programs and Complexity Results;An Optimization Approach for Optimizing PRIM’s Randomly Generated Rules Using the Genetic Algorithm;A Fast Methodology to Find Decisively Strong Association Rules (DSR) by Mining Datasets of Security Records;characterization and Categorization of Software Programs on X86 Architectures;robot-Assisted Delivery Problems and their Exact Solutions;modeling and Analysis of Organizational Network Analysis Graphs Based on Employee Data;time Series Forecasting for Parking Occupancy: Case Study of Malaga and Birmingham Cities;e-scooters Routes Potential: Open Data Analysis in Current Infrastructure. Malaga Case;algorithm Selection for Large-Scale Multi-objective Optimization;automatic Generation of Subtitles for Videos of the Governm

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7th international conference on Soft Computing in Data Science, SCDS 2023

7th International Conference on Soft Computing in Data Scien...

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7th international conference on Soft Computing in Data Science, SCDS 2023

ISBN: (纸本)9789819904044

the proceedings contain 21 papers. the special focus in this conference is on Soft Computing in Data Science. the topics include: Short-Time Fourier Transform with Optimum Window Type and Length: An application for Sag, Swell and Transient;cox Point Process with Ridge Regularization: A Better Approach for Statistical Modeling of Earthquake Occurrences;discovering Popular Topics of Sarawak Gazette (SaGa) from Twitter Using Deep learning;Comparison Analysis of LSTM and CNN Variants with Embedding Word Methods for Sentiment Analysis on Food Consumption Behavior;towards Robust Underwater Image Enhancement;a Comparative Study of machine learning Classification Models on Customer Behavior Data;performance Evaluation of Deep learning Algorithms for Young and Mature Oil Palm Tree Detection;object Detection Based Automated Optical Inspection of Printed Circuit Board Assembly Using Deep learning;the Characterization of Rainfall Data Set Using Persistence Diagram and Its Relation to Extreme Events: Case Study of three Locations in Kemaman, Terengganu;fault Diagnosis Methods of Deep Convolutional Dynamic Adversarial Networks;Clustering Stock Prices of Industrial and Consumer Sector Companies in Indonesia Using Fuzzy C-Means and Fuzzy C-Medoids Involving ACF and PACF;zero-Inflated Time Series Model for Covid-19 Deaths in Kelantan Malaysia;carbon-Energy Composite Flow for Transferred Multi-searcher Q-learning Algorithm with Reactive Power Optimization;multi-source Heterogeneous Data Fusion Algorithm Based on Federated learning;dynamic Micro-cluster-Based Streaming Data Clustering Method for Anomaly Detection;federated Ensemble Algorithm Based on Deep Neural Network;performance Comparison of Feature Selection Methods for Prediction in Medical Data.

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Research on Rotation Detection of Aircraft Glass Canopy Defects Based on Deep learning 22

Research on Rotation Detection of Aircraft Glass Canopy Defe...

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proceedings of the 2022 5th international conference on machine learning and machine Intelligence

作者： Jing Wang Kexin Wang Jianhong Li School of Computer Science and Technology Xidian University China

ISBN: (纸本)9781450397551

Aircraft Glass Canopy (AGC) is an important part of human-aircraft interface, and efficient and real-time AGC surface defect detection has become a prerequisite for pilots to perceive the external field of vision normally. While existing deep learning models are sufficient for routine defect detection, AGC surface defects are characterized by tiny and high aspect ratios, which make accurate defect detection challenging. therefore, this paper proposes an improved YOLOv4 rotating target detection model. Specifically, we firstly employs the decoupled head to improve the model performance and convergence efficiency on the basis of the original YOLOv4 model. Secondly, we construct a multi-dimensional coordinate structure for high aspect ratio defects, and achieve rotation detection by optimizing the loss function. Finally, we utilize Fuzzy C-means (FCM) to cluster AGC surface defects to obtain the most suitable initial anchor box size. Experimental results show that the mAP value is improved by 6.16% compared to the original YOLOv4 model, which proves the effectiveness of our proposed model.

关键词： Aircraft Glass Canopy FCM YOLOv4 defect detection rotation detection

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Achieving Accurate Trajectory Control While Training Legged Robots Using machine learning 1

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Computing Science, Communication and Security - 4th international conference, COMS2 2023, Revised Selected Papers

作者： Biswas, Amit Chaubey, Neha N. Chaubey, Nirbhay Kumar Triassic Robotics Pvt Ltd. Delhi India Dharmsinh Desai University Gujarat India Ganpat University Gujarat India

ISBN: (数字)9783031405648

ISBN: (纸本)9783031405631

Legged robots are a class of robotic systems designed to move and navigate using leg mechanisms, similar to how animals with legs move. Legged robots differ from other mobile robots primarily in their mode of locomotion. they can traverse various types of terrain, uneven surfaces and human made structures like stairs, steps etc. they have significant advantages over other forms of robotic locomotion, such as wheels or tracks, but they are complex to build and control. Designing and controlling legged robots can be complex due to the need for stability, balance, and coordination among multiple legs. Building these control mechanisms have always been a challenging task. In this paper we discuss about a four-legged robot that we built and how we trained and controlled its motion. We discuss how we can use latest techniques in machine learning (ML) to train locomotion skills to legged robots. We evaluate training directly in the cartesian space as compared to the more popular approach of using joint space. We also look at the challenges we faced in trajectory control and how we solved them. We discuss how we achieved accurate tracking of trajectories and explain the importance of accurate trajectory tracking in legged locomotion. We describe our experimental setup including the simulation environment we used, the tools and techniques used in setting up the experiments and how we built a real robot and used it for our experiments. © 2023, the Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： Reinforcement learning

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Surface Defect Detection of Transparent Parts Based on Improved YOLOv4 Model 22

Surface Defect Detection of Transparent Parts Based on Impro...

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proceedings of the 2022 5th international conference on machine learning and machine Intelligence

作者： Jing Wang Jianhong Li Siwen Wei School of Computer Science and Technology Xidian University China

ISBN: (纸本)9781450397551

With the development of science and technology, transparent parts are almost used in various industries and play an indispensable role. It is meaningful to measure the surface quality of transparent parts. To improve the detection accuracy of surface defects on transparent parts, an improved YOLOv4 target detection model is proposed in this paper. Firstly, to expand the amount of dataset, we adopt Mosaic strategy combined with Copy-Pasting strategy for data augmentation. Secondly, the Canopy + K-means clustering algorithm is employed to select the initial anchor box size. Finally, to improve the feature representation ability of the model, Squeeze-and-Excitation Networks (SENet) with an attention mechanism is embedded to the model. Experiments show that the mAP value of the improved YOLOv4 model on the test set reaches 91.07%, which is 6.4% higher than that of Faster R-CNN and 4.15% higher than that of the original YOLOv4 model, which proves the effectiveness of our model.

关键词： SENet YOLOv4 defect detection transparent parts

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An application Programming Interface to Recognize Emotion using Speech Features

An Application Programming Interface to Recognize Emotion us...

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Sustainable Technologies for Industry 4.0 (STI), international conference on

作者： Shimul Chandra Das Md. Benajir Alam Most. Sumiya Akter Moon Md. Solaiman Mia Department of Computer Science and Engineering Green University of Bangladesh Bangladesh

With the recent increasing interest in the study of the emotional component of speech signals, a number of methods have been put forth to ascertain the emotional content of uttered words. Often, we can tell how someone is feeling by looking at their faces. Another method is to detect someone’s emotions by auditory cues or speech. With a more straightforward architecture and fewer learnable parameters, the aim of this paper is to identify someone’s emotion from an audio input. three aspects have been suggested in the proposed methodology named MFCC (Mel-frequency Cepstral Coefficients), Mel-spectrogram and Chromagram, and MLP (Multilayer Perceptron). these features have been employed in this paper, because they perform better. MLP is a supervised machine learning, which is frequently used in various research to categorize human voice recognition. In addition, MLPs are appropriate for classification prediction issues where inputs are given as a class or label. they are also appropriate for regression prediction issues in which a real-valued quantity is forecasted from a collection of inputs. With the help of our proposed methods, we are able to achieve an accuracy of 80% on the RAVDESS Dataset.

关键词： Human computer interaction Emotion recognition Speech recognition machine learning Multilayer perceptrons Nonhomogeneous media Fourth Industrial Revolution

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machine learning-Based Android Malware Detection

Machine Learning-Based Android Malware Detection

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international conference on Data Science and Advanced Analytics (DSAA)

作者： David Ojo Nusayer Masud Siddique Carson K. Leung Connor C.J. Hryhoruk Department of Computer Science University of Manitoba Winnipeg MB Canada

the use of mobile phones, particularly smartphones, has been growing exponentially in recent times. From 2016 to 2021, smartphone users increased by more than 70%. With the increase in the popularity of smartphones, smartphones have become the prime target for criminal hackers. As a result, Android malware samples are coming to the market at an alarming rate. A study shows that there are more than 4 million malicious Android apps in the market, and each day around 11,000 new malwares add to this number. To combat this mass number of malware, we need a malware detection system that is efficient in detecting malicious Android apps. there are numerous existing malware detection systems, but most of them require countless features from both dynamic and static analysis. thus, they are not scalable, lightweight, and efficient in detecting malware. Additionally, most studies that used limited features like only permission data, had done their research on much older dataset. Hence, there is a need for new research on this topic. In this paper, we build a permission-based malware detector for Android application with a new dataset and significantly less permissions. Initially, we used support vector machine (SVM) and all the extracted permission data as features to build our classification model. the model accuracy, precision, recall and F1 score were 97.41 percent, which is higher than the other state of the art similar approaches done on an older dataset. Next, we replicated this similar study with a few different machine learning algorithms: random forest, decision tree and logistic regression, and observed they all give similar results. However, tree-based algorithm performs a little better than the other algorithms. Finally, to achieve a lightweight malware detection system, we reduced the number of permissions or the features on a two-step process, and found only a slight difference in results. In the first step, even after reducing the number of permissions by about 9

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12th international conference on High-Performance Computing Systems and Technologies in Scientific Research, Automation of Control and Production, HPCST 2022

12th International Conference on High-Performance Computing ...

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12th international conference on High-Performance Computing Systems and Technologies in Scientific Research, Automation of Control and Production, HPCST 2022

ISBN: (纸本)9783031237430

the proceedings contain 23 papers. the special focus in this conference is on High-Performance Computing Systems and Technologies in Scientific Research, Automation of Control and Production. the topics include: Ontology-Based Data Mining Platform for Diagnosing Sowing Quality of Wheat Seeds;algorithm for the Classification of Coronary Heart Disease Based on the Use of Symptom Complexes in the Cardiovascular Environment;business Process Designing of the Institutional Ranking System of Higher Education of the Kyrgyz Republic;evaluation of the Efficiency of the Vaccination Against Chickenpox Based on the Results of Simulation Modeling;hierarchical Volume Mesh Model of Heterogeneous Media Based on Non-Destructive Imaging Data;modeling a Potential Plant Habitat by Ensemble machine learning;adaptive Traffic Signal Control Based on a Macroscopic Model of the Transport Network;optimization and Robustization of Angular Motion Control of an Automatic Maneuverable Aerial Vehicle;synthesis of the Rational Analyzing Function for Feature Extraction of Signals from the Electrostatic Location System;Architecture and CAD Software Solutions for PCB Design;application of computer Numerical Control in Eddy-Current Study Methods;Open-Source Solution for Identification and Blocking of Anomalous BGP-4 Routing Information;Simulation of DDoS Attacks on LTE and LoRaWAN Protocols in the NS-3 Network Simulator;transaction-Oriented Approach to the Design of Information Systems Based on Formal Grammar;frequency-Domain Generalized Phase Transform Method in Pipeline Leaks Locating;inverse Problem Regularization of Parametric Identification of the Particle Temperature Distribution of Gas-thermal Flow with Optimization of Its Solution;application of the Heterogeneous Multiscale Finite Element Method for Modelling the Compressibility of Porous Media.

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Epilepsy Detection through EEG Signal Classification Using machine learning Methods

Epilepsy Detection Through EEG Signal Classification Using M...

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international conference on ICT in Business Industry & Government (ICTBIG)

作者： Vivek Deshpande P. Gajendran Varun Ojha Latha B Ishika Soni Arunkumar D T Department of Computer Engineering Vishwakarma Institute of Technology Pune India Department of Electrical and Electronics Engineering Karpagam College of Engineering Coimbatore Centre of Research Impact and Outcome Chitkara University Rajpura Punjab India Department of IT Prince Shri Venkateshwara Padmavathy Engineering College Chennai Chitkara Centre for Research and Development Chitkara University Himachal Pradesh India School of Engineering and Technology Mechaical Engineering JAIN (Deemed to be University) Bangalore Karnataka India

ISBN: (数字)9798331518981

ISBN: (纸本)9798331518998

In this exertion demonstrates the application of a deep learning algorithm to the analysis of focal and non-focal EEG data for the finding of epilepsy. this research presents a strategy for automating the diagnosis of epilepsy by making use of a soft computing resource. By using the ANFIS classification technique, focal and non-focal samples may be distinguished. Structures computation, features classification, and features transformation are the three pillars upon which this technique rests. For an electroencephalogram (EEG) data, the Approximate and Directional bands are produced when the suggested NSCT is used. NSDF and Non-Subsampled Pyramid Filter are the two main components of this NSCT. these fall into two categories: low pass, which includes power frequencies, and high pass, which includes high frequency noise. To do this, we construct directed subbands and reroute the high-pass signal using NSDF. Step two of decomposition involves creating high and low frequency sub bands by passing the low pass sub band signal via the Non-Sub Sampled Pixel Frequency (NSPF). the directed sub bands that are created are achieved by retransmitting the high-frequency sub band signal via NSDF. Using the statistical properties obtained from these sub-bands, the ANFIS classification method divides the overall EEG signal into focal and non-focal samples. Applying the proposed method to a collection of EEG signal extraction measurements allows for validation.

关键词： Deep learning Analytical models Time-frequency analysis Sensitivity Noise Epilepsy Pattern classification Brain modeling Electroencephalography Data models

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