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Application of pattern recognition Techniques for MathE Questions Difficulty Level Definition 1

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3rd international conference on Optimization, Learning Algorithms and Applications (OL2A)

作者： Azevedo, Beatriz Flamia de Souza, Roberto Molina Pacheco, Maria F. Fernandes, Florbela P. Pereira, Ana I. Inst Politecn Braganca Res Ctr Digitalizat & Intelligent Robot CeDRI P-5300252 Braganca Portugal Inst Politecn Braganca Lab Associado Sustentabil & Tecnol Regioes Montan P-5300253 Braganca Portugal Univ Tecnol Fed Parana BR-80230901 Curitiba Parana Brazil

ISBN: (数字)9783031530258

ISBN: (纸本)9783031530241;9783031530258

Active learning is a modern educational strategy that involves students in the learning process through diverse interactive and participatory activities. The MathE platform is an international online platform created to support students and lecturers in the Mathematics teaching and learning process. This platform offers a tool to aid and engage students, ensuring new and creative ways to encourage them to improve their mathematical skills. The study proposed in this paper refers to a comprehensive investigation of the patterns that may exist within the set of questions available on the MathE platform. The objective is to investigate how to evaluate the student's opinions about the question's difficulty levels based on the variables extracted from student answers collected through surveys applied among the platform's users. Moreover, a comparative study between variables is performed using correlation and hypothesis tests. Furthermore, based on the results obtained for samples of different sizes, it was possible to define the most appropriate number of answers that should be considered to categorize the question's difficulty level. The results demonstrated that the variables extracted could be used to carry out the question level, and 30 answers are the most appropriate number of questions that must be used to categorize the question level.

关键词： active learning e-learning higher education Mathematics

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3rd international conference on Computational Electronics for Wireless Communications, ICCWC 2023

3rd International Conference on Computational Electronics fo...

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3rd international conference on Computational Electronics for Wireless Communications, ICCWC 2023

ISBN: (纸本)9789819719457

The proceedings contain 35 papers. The special focus in this conference is on Computational Electronics for Wireless Communications. The topics include: Design of a Four-Port MIMO Antenna for C-, X-, and Ku-Band Applications;Exploration of Diverse Techniques for Design and Optimization of QCA-Based Multiplexer Circuits;melanoma Detection Via Deep Convolutional Neural Network;pattern Diversity mm-Wave Rectangular Ring Shaped MIMO Antenna for 5G Communication and Internet of Things Applications;Flexible Two/Eight-Port MIMO Antenna Configuration for LTE B41/WLAN/Wi-Fi 6E and C-Band Applications;Improvement in Machining Ability of Tungsten Carbide Tool Insert Through Microwave Hybrid Heating During Turning of EN 24 Steel;optimizing Airfare Pricing: A Data-Driven Approach for Affordable Travel Planning;advancements in Emotion recognition: Systematic Review and Research Roadmap;epoc-Based Electroencephalography Signals Analysis of Different Stress Levels;Two-Element Tilted Rectangular MIMO UWB Antenna for Underwater Communications;deep Learning-Based Maize Crop Disease Detection and Remedial Recommendation System;design of Splash Plate Dielectric Hat Feed for Ka-Band Tracking Radar Applications;soft Computing Paradigms for Load Balancing in Cloud Computing;strategies for Effective Network Congestion Control: Insights from Parameter-Based Analysis;BICC: Optimizing Sensor Network Performance Via an Efficient Bioinspired Iterative Approach with Congestion Control;Leakage Power Reduction and Stability Analysis of 5 nm Node GAA CNTFET SRAMs;bandwidth Enhancement and Improvement of Directivity of a Composite Conical Structure in the Presence of a Circular Loop Antenna;development of Compact Dual Polarized Corrugated Feed Horn Antenna with Low Cross-Polarization for Doppler Weather Radar Applications in C-Band;A Review on Recent Breakthroughs and Accomplishments in the Development of FeFET.

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Islanding Detection Using pattern recognition by HOG Features with Ensemble Classifier 3

Islanding Detection Using Pattern Recognition by HOG Feature...

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3rd IEEE international conference on Electrical Power and Energy Systems, ICEPES 2024

作者： Pal, Kumaresh Goswami, Subhadip Bhuyan, Anshuman Koley, Santanu Roy, Soumya Bhakat, Rishika National Institute of Technology Department of Electrical Engineering Jamshedpur India Gandhi Institute for Education & Technology Department of Electrical Engineering Bhubaneswar India Haldia Institute of Technology Department of Computer Science Engineering Haldia India Brainware University Department of Computational Sciences West Bengal India Cambridge Institute of Technology Department of Management Ranchi India

ISBN: (纸本)9798350390728

This study presents a novel approach for detecting islanding based upon image categorisation using Ensemble Classifier is proposed. Images are used to derive the histogram of oriented gradient (HOG) characteristics so as to identify non-islanding as well as islanding events. The ever-increasing need for power encourages the growth of Distributed Generation (DG). Islanding is one of the key challenges associated with excessive penetration of DG sources and can cause harm to the clients and their equipment. The islanding needs to be detected in two seconds, according to the IEEE 1547 DG interconnection rules, and the DG must be turned off. In the suggestive technique, spectrogram images are acquired through the time-series signals that are derived from the point of common coupling (PCC). The features are extracted from these images using the histogram of oriented gradient (HOG). The resulting feature vector is then utilised as an input for both the training and testing processes of the ensemble classifier. For deriving the spectrogram image, the rate of change of voltage is utilised as a parameter. In order to evaluate the performance of the ensemble classifier, 5, 10, 20, and 25-fold cross-validations are performed. The findings from the classification analysis indicate that the utilisation of an ensemble classifier in conjunction with the histogram of oriented gradient feature for image classification yielded remarkable outcomes, including an accuracy of 97.45% and a validation error of 0.188. © 2024 IEEE.

关键词： Graphic methods

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3rd international conference on Computational Electronics for Wireless Communications, ICCWC 2023

3rd International Conference on Computational Electronics fo...

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3rd international conference on Computational Electronics for Wireless Communications, ICCWC 2023

ISBN: (纸本)9789819719426

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Thunderstorm Predictions Using Artificial Neural Network with Radiosonde Indices in Saumlaki Area 3rd

Thunderstorm Predictions Using Artificial Neural Network wit...

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3rd international conference on Radioscience, Equatorial Atmospheric Science and Environment, INCREASE 2023

作者： Indra Putra, Richard Mahendra BMKG Mathilda Batlayeri Meteorological Station Tanimbar Island Saumlaki Indonesia BMKG Aviation Meteorology Center Jakarta Capital City Government Jakarta Indonesia

ISBN: (纸本)9789819707393

Thunderstorms are hydro-meteorological disasters that need to be vigilant. In response, various studies have been conducted to determine suitable prediction methods for forecasting thunderstorms, one of which involves using upper air data. However, the use of atmospheric stability threshold values as a reference may not apply universally to all locations due to differences in the characteristics of each region. In meteorology, especially for predicting specific events, machine learning methods such as artificial neural networks are widely used. Therefore, this study employs an artificial neural network approach to predict thunderstorms in the Saumlaki region based on radiosonde index data. The research compares the performance of machine learning models using the pattern recognition Neural Network (PNN) technique in predicting thunderstorms, specifically between models with a single hidden layer and double hidden layers with a certain number of neurons in each hidden layer, as previously used in other studies. Through this comparison, the study aims to provide insights into how accurately the PNN model can predict thunderstorms in the Saumlaki region. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.

关键词： pattern recognition

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Integrating IoT and Machine Learning for Enhanced Forest Fire Detection and Temperature Monitoring 3

Integrating IoT and Machine Learning for Enhanced Forest Fir...

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3rd international conference on Innovative Mechanisms for Industry Applications, ICIMIA 2023

作者： Varun, M. Kesavraj, K. Suman, S. Raj, X Suman Saveetha Engineering College Department of ECE Tamilnadu Chennai India

ISBN: (纸本)9798350343632

Forest fires have become more frequent and intense in recent years, which has caused serious problems for public safety and environmental preservation. In the situation, combining Internet of Things (IoT) devices with cutting-edge machine learning algorithms offers a viable approach for early temperature and forest fire detection. In order to identify forest fires and track temperature swings in real-time, an intelligent system that integrates IoT sensors and machine learning algorithms is designed, developed, and evaluated in detail in it. The proposed methodology improves the effectiveness of early fire detection while also offering insightful information for efficient wildfire management and prevention methods. The report illustrates the system's efficacy and dependability via thorough experimentation and research, demonstrating its potential for wider use in wooded regions across the world. The paper also analyzes implementation difficulties, such as data privacy and system scalability, and offers suggestions for resolving these problems. The findings in the work add to the body of research on IoT-based environmental monitoring systems and provide new avenues for investigation and advancement in the field of intelligent forest fire detection and prevention. © 2023 IEEE.

关键词： early warning system environmental monitoring forest fire detection Internet of Things (IoT) pattern recognition remote areas

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Utilising Deep Learning as a Law Enforcement Ally 3

Utilising Deep Learning as a Law Enforcement Ally

引用

3rd international conference on Smart Data Intelligence, ICSMDI 2023

作者： Ramani, D. Roja Nirmala, M. Sourabh, V.N. Chaudhary, Shreyas Kumar, Deepesh New Horizon College of Engineering Department of Computer Science and Engineering Bengaluru India

ISBN: (纸本)9781665464871

Finding fugitive offenders after they have committed a crime or an illegal act takes time and effort. It is challenging for law enforcement authorities to complete this work on their own given the rising population density and the size of any nation's landmass. Thus, public participation becomes crucial, revolutionary, and beneficial. This cycle is both times and works seriously. In this paper, we tried to suggest a different framework for criminal Distinguishing & recognition using Deep learning and Heroku Cloud, i.e. Cloud Computing, which, assuming it is used by our Crime Control Organizations, would help them catch criminals from CCTV images or images uploaded by the public if seen anywhere. This system is in place to assist in capturing criminals and anyone who can upload information indicating that they saw the relevant individual at a specific location and time. In India, where conditions are always changing due to things like light, weather, and specific directions, existing solutions use conventional face acknowledgement computations, which might be problematic because there is no open public contribution. Our research paper employs LBPH, Deep Learning, and Heroku Cloud technologies to construct the system. © 2023 IEEE.

关键词： Local binary pattern

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Modal Analysis System of Emotion First in Vocal Art Under the Background of Sound Wave recognition and Matching 3

Modal Analysis System of Emotion First in Vocal Art Under th...

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3rd international conference on Electronics and Sustainable Communication Systems, ICESC 2022

作者： Niu, Minghui Hebei University of Economics and Business College of Art Shijiazhuang050000 China

ISBN: (数字)9781665479714

ISBN: (纸本)9781665479714

This paper focuses on the back-end pattern matching algorithm, and introduces the fuzzy-mean clustering algorithm in the model training process, which makes the recognition effect significantly improved. This algorithm has high robustness in clustering. It is introduced into the training algorithm to replace the commonly used hard clustering algorithm to find the initial point of the model. The importance of emotional expression in vocal performance art is analyzed, and then it is discussed. The measures to improve the emotional expression level of vocal performance art hope to bring help to the majority of colleagues to improve their artistic level. Correspondingly starts n sub-threads for concurrent computing. When the improved feature coefficients are applied to the Android platform, the equivalence rate is reduced by 7.24%, the recognition rate is increased by 6.8%, and the robustness in noisy environments has also been significantly improved. After adding 3 sub-threads, the computation time of feature extraction is shortened by about 35%. © 2022 IEEE.

关键词： pattern matching

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Spam recognition Model Based on TextCNN 3

Spam Recognition Model Based on TextCNN

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3rd international conference on pattern recognition and Machine Learning, PRML 2022

作者： Yixuan, Gao Yuanyuan, Zhang Ye, Cheng Fuzhou University Maynooth International Engineering College Fuzhou China Fuzhou University College of Computer and Data Science Fuzhou China

ISBN: (数字)9781665499507

ISBN: (纸本)9781665499507

With the advancement of modern Internet technology, email is widely used in people's daily lives and has become one of the common communication tools. However, at the same time, email-based spam has also been widely spread, flooding people's lives and not only wasting public resources but also infringing on people's legitimate rights and interests. Based on this, we propose a Spam recognition model based on TextCNN by combining Convolutional Neural Network and Natural Language Processing, where natural language processing techniques are used to pre-process the text in the spam corpus to obtain text features, which are then imported into the constructed Convolutional Neural Network analysis model. A CNN spam recognition model is then designed and analyzed hierarchically using the TensorFlow framework. After extracting the features from the email dataset, the model was fed into the convolutional neural network again for several iterations of training. The results indicated that the model achieved an accuracy of 97.7% in spam recognition, which is an exceptionally significant improvement in accuracy and stability compared to the SVM model used previously, demonstrating good filtering performance. © 2022 IEEE.

关键词： Convolutional neural networks

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Shop Signboards Detection Using the ShoS Dataset 3rd

Shop Signboards Detection Using the ShoS Dataset

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3rd international conference on pattern recognition and Artificial Intelligence, ICPRAI 2022

作者： Almuhajri, Mrouj Suen, Ching Y. Centre for Pattern Recognition and Machine Intelligence Concordia University MontrealQCH3G 2W1 Canada Department of Computer Science Saudi Electronic University Riyadh Saudi Arabia

ISBN: (纸本)9783031092817

This work was driven by the possibility of enabling beneficial applications for humans and municipal agencies utilizing deep learning techniques to detect shop signboards. Data is the foundation of successful AI-based applications. Despite the abundance of data, providing datasets for such applications is a challenge as they have to be refined and characterized accurately for better performance. This paper describes the process of collecting the ShoS dataset from Google Street Views for the detection of the signboards of shops on streets. A total of 10k storefront signboards were captured and fully annotated within 7500 images. The ShoS datasets were tested by running different baseline methodology using one-stage detectors YOLO and SSD. Out of the tested methods and variances, the YOLOv3 yields the highest performance reaching 94.23% (mAP@0.5). © 2022, Springer Nature Switzerland AG.

关键词： Object detection

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