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Deep learning based isolated Arabic scene character recognition

Deep learning based isolated Arabic scene character recognit...

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international workshop on Arabic Script Analysis and recognition (ASAR)

作者： Saad Bin Ahmed Saeeda Naz Muhammad Imran Razzak Rubiyah Yousaf King Saud bin Abdulaziz University for Health Sciences Riyadh Saudi Arabia Malaysia Japan Institute of Information Technology (MJIIT) Universiti Teknologi Malaysia KualaLumpur Malaysia GGPGC No.1 Abbottabad Khyber Pakhtunkhua (KPK) Pakistan

ISBN: (纸本)9781509066292

The technological advancement and sophistication in cameras and gadgets prompt researchers to have focus on image analysis and text understanding. The deep learning techniques demonstrated well to assess the potential for classifying text from natural scene images as reported in recent years. There are variety of deep learning approaches that prospects the detection and recognition of text, effectively from images. In this work, we presented Arabic scene text recognition using Convolutional Neural Networks (ConvNets) as a deep learning classifier. As the scene text data is slanted and skewed, thus to deal with maximum variations, we employ five orientations with respect to single occurrence of a character. The training is formulated by keeping filter size 3 × 3 and 5 × 5 with stride value as 1 and 2. During text classification phase, we trained network with distinct learning rates. Our approach reported encouraging results on recognition of Arabic characters from segmented Arabic scene images.

关键词： Text recognition Feature extraction Image segmentation Image recognition Optical character recognition software Character recognition machine learning

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GBCI: Adaptive Frequency Band learning for Gender recognition in Brain-Computer Interfaces 1st

GBCI: Adaptive Frequency Band Learning for Gender Recogniti...

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1st CAAI international Conference on Artificial Intelligence, CICAI 2021

作者： Wang, Pengpai Zhou, Yueying Li, Zhongnian Zhang, Daoqiang College of Computer Science and Technology MIIT Key Laboratory of Pattern Analysis and Machine Intelligence Nanjing University of Aeronautics and Astronautics Nanjing211106 China

ISBN: (纸本)9783030930455

In recent years, with the rapid development of brain-computer interface (BCI) technology, various applications based on BCI have generated significant interest. A motivating application of BCI is predicting human gender by electroencephalogram (EEG) analysis. However, most of the recent researches only identify the gender of EEG in the resting state. In real-life practice, it is difficult for the subjects to be at a full resting state when it mixes with motor imagery (MI). Therefore, we acquired two kinds of EEG activities data including 9 subjects (5 males and 4 females) with two state (i.e., resting state and MI) based on Chinese sign language. In this paper, to recognize gender form two state, an improved adaptive variational mode decomposition with long short term memory (AVMD-LstM) network is developed to construct a hybrid state learning framework. Besides, the sample entropy (SE) is employed to select the channel of EEG data as the input of AVMD-LstM. The recognition accuracy of gender classification was 89.21 %. Comparing with the task of complete resting state, the model is validated with more robustness. Furthermore, the proposed algorithm has many applications, including biometrics, healthcare, and online entertainment advertising. © 2021, Springer Nature Switzerland AG.

关键词： Variational mode decomposition

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Graph-Based Exercise- and Knowledge-Aware learning Network for student Performance Prediction 1st

Graph-Based Exercise- and Knowledge-Aware Learning Network ...

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1st CAAI international Conference on Artificial Intelligence, CICAI 2021

作者： Liu, Mengfan Shao, Pengyang Zhang, Kun Key Laboratory of Knowledge Engineering with Big Data Hefei University of Technology Hefei China School of Computer Science and Information Engineering Hefei University of Technology Hefei China

ISBN: (纸本)9783030930455

Predicting student performance is a fundamental task in Intelligent Tutoring Systems (ITSs), by which we can learn about students’ knowledge level and provide personalized teaching strategies for them. Researchers have made plenty of efforts on this task. They either leverage educational psychology methods to predict students’ scores according to the learned knowledge proficiency, or make full use of Collaborative Filtering (CF) models to represent latent factors of students and exercises. However, most of these methods either neglect the exercise-specific characteristics (e.g., exercise materials), or cannot fully explore the high-order interactions between students, exercises, as well as knowledge concepts. To this end, we propose a Graph-based Exercise- and Knowledge-Aware learning Network for accurate student score prediction. Specifically, we learn students’ mastery of exercises and knowledge concepts respectively to model the two-fold effects of exercises and knowledge concepts. Then, to model the high-order interactions, we apply graph convolution techniques in the prediction process. Extensive experiments on two real-world datasets prove the effectiveness of our proposed Graph-EKLN. © 2021, Springer Nature Switzerland AG.

关键词： data mining

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Support vector learning for gender classification using audio and visual cues: A comparison 1st

Support vector learning for gender classification using audi...

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1st international workshop on pattern recognition with Support Vector machines, SVM 2002

作者： Walawalkar, L. Yeasin, Mohammad Narasimhamurthy, Anand M. Sharma, Rajeev Department of Computer Science and Engineering The Pennsylvania State University University ParkPA16802 United States

ISBN: (纸本)354044016X

Computer vision systems for monitoring people and collecting valuable demographics in a social environment will play an increasingly important role in enhancing user’s experience and can significantly improve the intelligibility of a human computer interaction (HCI) system. For example, a robust gender classification system is expected to provide a basis for passive surveillance and access to a smart building using demographic information or can provide valuable consumer statistics in a public place. The option of an audio cue in addition to the visual cue promises a robust solution with high accuracy and ease-of-use in human computer interaction systems. This paper investigates the use of Support Vector machines(SVMs) for the purpose of gender classification. Both visual (thumbnail frontal face) and audio (features from speech data) cues were considered for designing the classifier and the performance obtained by using each cue was compared. The performance of the SVM was compared with that of two simple classifiers namely, the nearest prototype neighbor and the k-nearest neighbor on all feature sets. It was found that the SVM outperformed the other two classifiers on all datasets. The best overall classification rates obtained using the SVM for the visual and speech data were 95. 31% and 100%, respectively. © Springer-Verlag Berlin Heidelberg 2002.

关键词： Support vector machines

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Toward a Practical Process Model for Anomaly Detection Systems

Toward a Practical Process Model for Anomaly Detection Syste...

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workshop on Software Engineering for Cognitive Services (SE4COG)

作者： Nils Schwenzfeier Volker Gruhn paluno - The Ruhr Institute for Software Technology University of Duisburg-Essen

ISBN: (纸本)9781538662564

Process models are an important tool for software engineers to produce reliable software within schedule and budget. Especially technically challenging domains like machine learning need a supportive process model to guide the developers and stakeholders during the development process. One major problem type of machine learning is anomaly detection. Its goal is to identify anomalous data points (outlier) between the normal data instances. Anom- aly detection has a wide scope of applications in industrial and scienti c areas. Detecting intruders in computer networks, distin- guishing between cancerous and healthy tissue in medical images, cleaning data from disturbing outliers for further evaluation and many more. The cross-industry standard process for data mining (CRISP-DM) has been developed to support developers with all kinds of data mining applications. It describes a generic model of six phases that covers the whole development cycle. The generality of the CRISP-DM model is as much a strength as it is a weakness, since the particularities of di erent problem types like anomaly detection can not be addressed without making the model overly complex. There is a need for a more practical, specialised process model for anomaly detection applications. We demonstrate this issue and outline an approach towards a practical process model tailored to the development of anomaly detection systems.

关键词： Anomaly detection Task analysis data models Software machine learning Software engineering

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Ensemble of 3D CNN Regressors with data Fusion for Fluid Intelligence Prediction 1

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1st Challenge in Adolescent Brain Cognitive Development Neurocognitive Prediction, ABCD-NP 2019, held in conjunction with the 22nd international Conference on Medical Image Computing and Computer Assisted Intervention, MICCAI 2019

作者： Pominova, Marina Kuzina, Anna Kondrateva, Ekaterina Sushchinskaya, Svetlana Burnaev, Evgeny Yarkin, Vyacheslav Sharaev, Maxim Skolkovo Institute of Science and Technology Moscow Russia

ISBN: (数字)9783030319014

ISBN: (纸本)9783030319007

In this work, we aimed at predicting children’s fluid intelligence scores based on structural T1-weighted MR images from the largest long-term study of brain development and child health. The target variable was regressed on a data collection site, sociodemographic variables, and brain volume, thus being independent to the potentially informative factors, which were not directly related to the brain functioning. We investigated both feature extraction and deep learning approaches as well as different deep CNN architectures and their ensembles. We proposed an advanced architecture of VoxCNNs ensemble, which yields MSE (92.838) on a blind test. © 2019, Springer Nature Switzerland AG.

关键词： Forecasting

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MaxMin Linear Initialization for Fuzzy C-Means 1

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14th international Conference on machine learning and data mining in pattern recognition, MLDM 2018

作者： Öztürk, Aybüke Lallich, stéphane Darmont, Jérôme Waksman, Sylvie Yona ERIC EA 3083 Université de Lyon Lyon 2 5 avenue Pierre Mendès France Bron Cedex69676 France ArAr UMR 5138 Université de Lyon Lyon 2 7 rue Raulin Lyon Cedex 769365 France

ISBN: (数字)9783319961361

ISBN: (纸本)9783319961354

Clustering is an extensive research area in data science. The aim of clustering is to discover groups and to identify interesting patterns in datasets. Crisp (hard) clustering considers that each data point belongs to one and only one cluster. However, it is inadequate as some data points may belong to several clusters, as is the case in text categorization. Thus, we need more flexible clustering. Fuzzy clustering methods, where each data point can belong to several clusters, are an interesting alternative. Yet, seeding iterative fuzzy algorithms to achieve high quality clustering is an issue. In this paper, we propose a new linear and efficient initialization algorithm MaxMin Linear to deal with this problem. Then, we validate our theoretical results through extensive experiments on a variety of numerical real-world and artificial datasets. We also test several validity indices, including a new validity index that we propose, Transformed standardized Fuzzy Difference (TSFD). © Springer international Publishing AG, part of Springer Nature 2018.

关键词： Iterative methods

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Negative Emotion recognition using Deep learning for Thai Language 5

Negative Emotion Recognition using Deep Learning for Thai La...

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5th Joint international Conference on Digital Arts, Media and Technology with 3rd ECTI Northern Section Conference on Electrical, Electronics, Computer and Telecommunications Engineering, ECTI DAMT and NCON 2020

作者： Mekruksavanich, Sakorn Jitpattanakul, Anuchit Hnoohom, Narit University of Phayao Department of Computer Engineering Phayao Thailand Faculty of Applied Science King Mongkut's University of Technology Intelligent and Nonlinear Dynamic Innovations Research Center North Bangkok Thailand Faculty of Engineering Mahidol University Image Information and Intelligence Laboratory Nakorn Pathom Thailand

ISBN: (数字)9781728163987

ISBN: (纸本)9781728163987

Over the last decade, speech emotion recognition (SER) has become an interesting and challenging topic in the human behavior analysis research field. The objective of this area of research is to classify the emotional states of people based on the speech patterns of humans. Currently, the focus of this research field is on the identification of the effectiveness of automatic classifiers that can enhance the efficiency of the classification in practical applications, particularly those used in telecommunication services. Negative emotions, such as sadness, anger, disgust, and fear, can provide a significant amount of beneficial data to both the user of the quality assurance platform and the customer. This paper examines the complicated task involving recognition of negative emotions in human speech data by employing a deep learning technique. Four open emotional speech datasets are used in this research in order to identify a deep learning classifier that provides good efficiency for use with negative emotion speech data. Furthermore, the classifier with the best performance was also tested with a Thai language dataset. Based on the experimental results, the one-dimensional convolution neural network was determined to be the classifier that has the most outstanding performance level for tasks involving negative emotion recognition in the Thai language with a level of accuracy at 96.60%. © 2020 IEEE.

关键词： Emotion recognition

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Leveraging AI & MI for Predictive Analytics in stress Level of Private Insurance Employees : Comprehensive Review 1

Leveraging AI & MI for Predictive Analytics in Stress Level ...

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1st international Conference on Artificial Intelligence for Innovations in Healthcare Industries, ICAIIHI 2023

作者： Vij, Renu Nazir, Hena Chandigarh University Usb Department of AIT-Management Mohali India

ISBN: (纸本)9798350330915

This comprehensive study delves into the revolutionary possibilities of AI and ML in predictive analytics for gauging private insurance industry employees' stress levels. Recognizing the root causes and anticipating stress patterns becomes critical as the industry faces increasing workplace stress. The use of AI and ML allows us to explore several aspects of stress, including stress in the workplace and personal factors that affect stress levels. starting with an overview of the present state of affairs in the private insurance sector, we underscore the critical nature of resolving this matter immediately. stress event forecasting, pattern recognition in historical data, and early indication identification are areas where AI and ML are finding practical use. In order to reduce the negative effects of stress, AI-driven algorithms can quickly determine which employees are at high risk and then intervene proactively. Additionally, this article discusses possible biases in AI systems, data privacy, and ethical concerns. The substantial contribution is found in the exhaustive analysis of the revolutionary role of AI and ML in changing the insurance industry, namely in determining which employees pose a high risk and in developing specific interventions to improve their health. Industry experts, lawmakers, and researchers will find this research paper useful since it sheds light on the complex nature of stress and provides concrete suggestions for creating more positive workplaces. © 2023 IEEE.

关键词： AI-driven Models Artificial Intelligence Bias data Privacy Employee Satisfaction Employee Well-being Ethical Considerations machine learning Predictive Analytics Private Insurance Productivity stress Levels Work Environments Workplace stress

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Music Tagging and Similarity Analysis for Recommendation System 1st

Music Tagging and Similarity Analysis for Recommendation Sys...

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

作者： Kumar, Laxman Mitra, Anirban Mittal, Mamta Sanghvi, Vatsal Roy, Sudipta Setua, Sanjit Kumar Department of Computer Science and Engineering U.V. Patel College of Engineering Ganpat University Mehsana Kherva384012 India Department of Computer Science and Engineering Academy of Technology AdisaptagramWest Bengal712121 India Department of Computer Science and Engineering Calcutta University Technology Campus JD-2 Sector-III Salt Lake Kolkata700098 India Department of Computer Science & Engineering G. B. Pant Government Engineering College Okhla New Delhi110020 India Mallinckrodt Institute of Radiology Department Washington University in St. Louis 510 South Kingshighway Boulevard St. LouisMO63110-1076 United States

ISBN: (纸本)9789811390418

Many of the websites follow the system of retrieving and recommending music based on the metadata. Metadata is generally a text file that attached to the music file has title and genre. Without attached metadata, it is very difficult for such websites to recommend or retrieve music. A regularly utilized rundown of the fundamental components incorporates pitch, timber, surface, volume, span, and frame. In the proposed methodology to process such a vast amount of data, the distributed storage and data processing systems like Hadoop and Spark has been used. Hadoop Distributed File System has been used for storing the music files and extracting feature information. Kafka queues has been used for asynchronous feature extraction in the background and finally Spark has been used for feature analysis using machine learning algorithms. This Proposed automated system for assigning genres for music provides very promising accuracy with a high true positive value. © 2020, Springer Nature Singapore Pte Ltd.

关键词： Metadata

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