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An Ensemble Method to Classify Telugu Idiomatic Sentences using Deep learning Models 6

An Ensemble Method to Classify Telugu Idiomatic Sentences us...

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6th international conference on Inventive Computation Technologies, ICICT 2023

作者： Briskilal, J. Sai Praneeth, Ch.V.M. Chaitanya, Ch. Jaya Karthik, M. Purnachandra Reddy, P. Department of Computing Technologies SRM Institute of Science and Technology Kattankulathur Chennai603203 India

ISBN: (纸本)9798350398496

Text classification is a requirement for every text processing application because the web contains a vast amount of text data. Intent detection, information extraction, sentiment analysis, and spam detection involves text categorization. Since text classification uses idioms, metaphors, and polysemic words, intent detection can be difficult. It is challenging to automatically identify idioms in Natural Language Processing applications such as Information Retrieval, machine Translation, and chatbots. In all these applications, automatic idiom recognition is crucial. In this work, idiomatic and literals sentences are being classified. Idioms are typical expressions with new meanings. this research proposes an ensemble model using pre-trained deep learning models to make model with more predictive nature. the models are trained and tested using in-house dataset. Moreover, an in-house dataset that contains 1040 idiomatic and literal sentences is suggested. the experimental results demonstrate the effectiveness of the proposed approach, achieving an accuracy of 86% on the test dataset. © 2023 IEEE.

关键词： Classification (of information)

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Optimizing the pattern recognitions Accuracy by Neural Network Methods 6

Optimizing the Pattern Recognitions Accuracy by Neural Netwo...

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6th international Symposium on Multidisciplinary Studies and Innovative Technologies, ISMSIT 2022

作者： Safar, Ali Abdulrahman Murshid, Asim M. College of Computer Science and Information Technology University of Kirkuk Department of Computer Science Kirkuk Iraq University of Kirkuk Software Department at College of Computer Science and Information Technology Kirkuk Iraq

ISBN: (数字)9781665470131

ISBN: (纸本)9781665470131

Due to the development of technology and computer science, especially in the field of artificial intelligence and machine learning, today information technologies have become the most important element in most fields. the developments in these technologies included building models and systems that depend mainly on machine learning. One of the most important areas that we need to develop is pattern recognition and classification. the term patterns include giving the computer the ability to recognize and classify images, shapes, sounds, and digital and textual data into different classes. these systems depend on building a model based on artificial intelligence and a trained neural network to analyze and classify patterns. In this article, we develop neural network algorithms based on five different datasets to build an intelligent pattern classification and recognition system by providing a neural network method for pattern recognition (NNM - PR). Using these algorithms, a high accuracy scale was obtained compared to previous studies. © 2022 IEEE.

关键词： pattern recognition

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Deep learning-Based Detection of Lung Lesions in Medical Images 6

Deep Learning-Based Detection of Lung Lesions in Medical Ima...

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6th IEEE international conference on pattern recognition and Artificial Intelligence, PRAI 2023

作者： Feng, Siling Zhang, Guirong Tong, Tong He, Zhanping Hainan University School of Information and Communication Engineering Haikou China Guangdong Ocean University College of Mathematics and Computer Zhanjiang China Hainan University School of Information and Communication Engineering Radiology Department Haikou People's Hospital Haikou China

ISBN: (纸本)9798350325485

this paper designs a target detection algorithm based on Transformer network architecture. Swin Transformer was used to extract features of lesion areas in lung medical images based on zero basic training, and the extracted feature images were embedded into the detection segmentation network Mask R-CNN to accurately locate and classify all suspected lesion areas in lung medical images. Multi-scale feature extraction and feature fusion are carried out through network, which solves the problems of focus region recognition caused by single resolution, weak communication between different layers and lack of attention fusion. After submitting the test on the open data set, the mAP accuracy can reach 0.118, which is better than the mainstream target detection algorithm and provides a reference scheme for the application of computer-Aided diagnosis system. © 2023 IEEE.

关键词： Medical imaging

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Work with Free Response Implementation Process Analysis Based on Sequential pattern mining in Engineering Education 6

Work with Free Response Implementation Process Analysis Base...

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6th international conference on Information Technologies in Engineering Education, Inforino 2022

作者： Latypova, V. Ufa State Aviation Technical University Faculty of Informatics and Robotics Ufa Russia

ISBN: (纸本)9781665405775

learning process, its dynamics are one of the points of focus of researchers which conduct studies in the field of education. the widespread use of learning management systems and MOOC platforms allows to create and store records of student activities in educational system (logs). In educational process special systems for implementing assignments of a particular type, for example, software development, construction of concept maps, can be used together with MOOC platforms or learning management systems. these special systems can also keep logs of student activities. the logs are an analytical issue in applying educational data mining, in particular in sequential pattern mining. the use of sequential pattern mining makes it possible to identify typical patterns of student activities during the learning process. these patterns may be used in defining potential problems for students and courses with a goal of improving educational process through course corrections or through providing special feedback to students with problems. However, the usage of sequential pattern mining approach is limited to the majority of works with free response, widely applied in engineering education. A method of work with free response implementation process analysis based on sequential pattern mining is proposed in the paper. the method was tested in training future engineers in the field of information technologies in Ufa State Aviation Technical University on the results of distance term paper preparation in the course the design of automated systems for information processing and control. the use of the suggested method allows to extract undesirable patterns of implementing works with free response and reasons for their presence. Also, the method allows to define typical strategies of implementing such works and their proportions. © 2022 IEEE.

关键词： Training learning management systems Electronic learning Information processing Course correction Software data mining

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Deep Saliency Map Generators for Multispectral Video Classification 26

Deep Saliency Map Generators for Multispectral Video Classif...

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26th international conference on pattern recognition / 8th international Workshop on Image mining - theory and Applications (IMTA)

作者： Bayer, Jens Muench, David Arens, Michael Fraunhofer Ctr Machine Learning & Fraunhofer IOSB Ettlingen Germany

ISBN: (数字)9781665490627

ISBN: (纸本)9781665490627

Despite their black box nature, deep neural networks have been successfully used in practical applications lately. In areas where the results of these applications can lead to safety hazards or decisions of ethical relevance, the application provider is accountable for the resulting decisions and should therefore be able to explain, how, and why a specific decision was made. For image processing networks, saliency map generators are a possible solution. A saliency map gives a visual hint on what is of special importance for the network's decision, can reveal possible dataset biases and give a more profound insight in the decision process of the black box. this paper investigates how 2D saliency map generators need to be adapted for 3D input data, and additionally, how the methods behave when applied not only to ordinary video input but rather multispectral 3D input data. this is exemplarily shown on 3D video input data in human action recognition in the infrared and visual spectrum and evaluated by using the insertion and deletion metrics. the dataset used in this work is the Multispectral Action dataset, where each scene is available in the long-wave infrared as well as the visual spectrum. To be able to draw a more general conclusion, the two investigated networks, 3D-ResNet 18 and Persistent Appearance Network (PAN), follow a different mindset. It could be shown, that the saliency methods can also be applied to 3D input data with remarkable results. the results show that a combined training with both, infrared and RGB 3D input data, lead to more focused saliency maps and outperform a training with only RGB or infrared data.

关键词： Measurement Training Visualization three-dimensional displays TV Neural networks Generators

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Diabetes Classification Problem with CatBoost Method and Optuna Gradient Boosting Optimization 6

Diabetes Classification Problem with CatBoost Method and Opt...

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6th international conference on Information and Communications Technology, ICOIACT 2023

作者： Kurniawan, Safar Dwi Purwono, Purwono Ma'arif, Alfian Suwarno, Iswanto Politeknik Harapan Bersama Department of Computer Engineering Tegal Indonesia Universitas Harapan Bangsa Department of Informatics Purwokerto Indonesia Universitas Ahmad Dahlan Department of Electrical Engineering Yogyakarta Indonesia Universitas Muhammadiyah Yogyakarta Department of Engineer Professional Program Yogyakarta Indonesia

ISBN: (纸本)9798350315639

Diabetes mellitus is a dangerous global epidemic that can cause kidney failure, heart attack, blindness, and death. the main cause of this disease is too high blood sugar levels due to the irrelevant work of pancreatic beta cells. Rapid computer technology advances and implementations based on artificial intelligence have penetrated various fields, especially the health sector. data mining and machine learning are technologies that continue to grow, are reliable, and can be used as research support tools in the medical field. this technology is considered capable of helping to automate diabetes prediction. Based on related research, our contribution to this research is to utilize the CatBoost method with Optuna optimization. In the data preprocessing stage, all 768 data sets have gone through removing outliers, normalizing features to reducing dimensions. the dataset is divided into 85\% composition for training data and 15\% for test data. At the feature selection stage with Correlation Pearson's, we found that Glucose correlated with diabetes outcome or disease because it had a value range of 0.47. the results' accuracy is considered less than optimal because it only produces an accuracy value below 80%, even after optimization with Optuna. It can be seen that there was only an increase of 7\% after optimizing with Optuna. © 2023 IEEE.

关键词： data mining

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Implementation of Face patterns and Smile recognition for Intelligent Class Attendance Systems 6

Implementation of Face Patterns and Smile Recognition for In...

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6th international conference on Computer and Informatics Engineering, IC2IE 2023

作者： Miftakhurrokhmat Suparyanto, Teddy Elwirehardja, Gregorius Natanael Karimah, Shofiyati Nur Pardamean, Bens Bina Nusantara University Bioinformatics and Data Science Research Center Jakarta Indonesia School of Computer Science Bina Nusantara University Computer Science Department Jakarta Indonesia Universitas Amikom Yogyakarta Faculty of Computer Science Department of Informatics Yogyakarta Indonesia Bina Nusantara University Binus Graduate Program - Master of Computer Science Computer Science Department Jakarta Indonesia

ISBN: (纸本)9798350345162

Students 'attendance in class is one important success parameter in face-to-face learning processes. Conventional attendance systems, such as paper-based attendance sheets or identity card systems, require a long time in the manual recapitulation process. Without additional verifications, even computer vision-based methods are prone to fraudulent practices by the students instead of gaining their excitement and attention in a class. To stimulate students' attention in a class, this work designs an intelligent class attendance system, in which facial pattern and smile recognition are implemented by using the latter as an additional task-based verification to reduce the risks of fake attendance. For the face recognition module, this pilot study used FaceNet as a feature extractor combined with SVM for classification, whereas the Haar cascade algorithm is used for recognizing smiles. this face recognition pipeline was implemented as a service installed on minicomputers or Internet of things (IoT) devices in each classroom and connected to an IP camera. Every recorded attendance will be sent as a notification to a mobile application for students that requires their active participation to confirm it with a smiling self-photo. the proposed pipeline obtained 92.86% accuracy on the test data, and 66.67% accuracy when evaluated in a real-life simulation setting through the implemented system. the lower accuracy in the simulation indicated that further improvements are indispensable, especially since the model obtained 28.57% False Negative Rate. Future studies will need to acquire more data and experiment with more efficient methods of attendance verification. © 2023 IEEE.

关键词： class attendance convolutional neural network deep learning face recognition smile

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Factorization Approach for Sparse Spatio-Temporal Brain-Computer Interface 26

Factorization Approach for Sparse Spatio-Temporal Brain-Comp...

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26th international conference on pattern recognition / 8th international Workshop on Image mining - theory and Applications (IMTA)

作者： Lee, Byeong-Hoo Cho, Jeong-Hyun Kwon, Byoung-Hee Lee, Seong-Whan Korea Univ Dept Brain & Cognit Engn Seoul South Korea Korea Univ Dept Artificial Intelligence Seoul South Korea

ISBN: (数字)9781665490627

ISBN: (纸本)9781665490627

Recently, advanced technologies have unlimited potential in solving various problems with a large amount of data. However, these technologies have yet to show competitive performance in brain-computer interfaces (BCIs) which deal with brain signals. Basically, brain signals are difficult to collect in large quantities, in particular, the amount of information would be sparse in spontaneous BCIs. In addition, we conjecture that high spatial and temporal similarities between tasks increase the prediction difficulty. We define this problem as sparse condition. To solve this, a factorization approach is introduced to allow the model to obtain distinct representations from latent space. To this end, we propose two feature extractors: A class-common module is trained through adversarial learning acting as a generator;Class-specific module utilizes loss function generated from classification so that features are extracted with traditional methods. To minimize the latent space shared by the class-common and class-specific features, the model is trained under orthogonal constraint. As a result, EEG signals are factorized into two separate latent spaces. Evaluations were conducted on a single-arm motor imagery dataset. From the results, we demonstrated that factorizing the EEG signal allows the model to extract rich and decisive features under sparse condition.

关键词： Training Face recognition Feature extraction Brain modeling Electroencephalography Brain-computer interfaces Adversarial machine learning

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Boosting the Discriminant Power of Naive Bayes 26

Boosting the Discriminant Power of Naive Bayes

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26th international conference on pattern recognition / 8th international Workshop on Image mining - theory and Applications (IMTA)

作者： Wang, Shihe Ren, Jianfeng Lian, Xiaoyu Bai, Ruibin Jiang, Xudong Univ Nottingham Sch Comp Sci Ningbo Peoples R China Ningbo Beilun Taihe High Sch Ningbo Peoples R China Nanyang Technol Univ Sch Elect & Elect Engn Singapore Singapore

ISBN: (数字)9781665490627

ISBN: (纸本)9781665490627

Naive Bayes has been widely used in many applications because of its simplicity and ability in handling both numerical data and categorical data. However, lack of modeling of correlations between features limits its performance. In addition, noise and outliers in the real-world dataset also greatly degrade the classification performance. In this paper, we propose a feature augmentation method employing a stack auto-encoder to reduce the noise in the data and boost the discriminant power of naive Bayes. the proposed stack auto-encoder consists of two auto-encoders for different purposes. the first encoder shrinks the initial features to derive a compact feature representation in order to remove the noise and redundant information. the second encoder boosts the discriminant power of the features by expanding them into a higher-dimensional space so that different classes of samples could be better separated in the higher-dimensional space. By integrating the proposed feature augmentation method with the regularized naive Bayes, the discrimination power of the model is greatly enhanced. the proposed method is evaluated on a set of machine-learning benchmark datasets. the experimental results show that the proposed method significantly and consistently outperforms the state-of-the-art naive Bayes classifiers.

关键词： Correlation Codes Benchmark testing Feature extraction Boosting data models Numerical models

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Air Quality Index Predictions with a Hybrid Forecasting Model: Combining Series Decomposition and Deep learning Techniques 6

Air Quality Index Predictions with a Hybrid Forecasting Mode...

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6th IEEE international conference on pattern recognition and Artificial Intelligence, PRAI 2023

作者： Cao, Juxin Bhatti, Uzair Aslam Feng, Siling Huang, Mengxing Hasnain, Ahmad Hainan University School of Information and Communication Engineering Haikou China Nanjing Normal University School of Geography Nanjing China

ISBN: (纸本)9798350325485

As China's industrialization process and people's living standards continue to improve, the importance placed on air quality has gradually increased. Precise prediction of the Air Quality Index (AQI) is vital for proactively mitigating the risks posed by air pollution. However, air quality levels can fluctuate significantly over a certain period. Even the interval models used in recent research face the challenge of not being able to retain essential information regarding air quality status. the prediction of the Air Quality Index (AQI) is crucial for controlling air pollution. Furthermore, while pursuing longer prediction times, the accuracy of the prediction must also be ensured. this paper proposes a model based on the fusion of EEMD-CEEMDAN and LSTM. EEMD-CEEMDAN is used to decompose the raw data to reduce its complexity, improve the accuracy and stability of data analysis, and then LSTM is used to predict the AQI. the experiments show that the model proposed in this paper demonstrates excellent experimental results on the real-world collected weather quality dataset. © 2023 IEEE.

关键词： Air quality

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