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NAMAQ - Arabic Handwriting recognition Using Deep learning, AI, and ML with Sentiment Analysis

NAMAQ - Arabic Handwriting Recognition Using Deep Learning, ...

引用

Computing and Information Technology (ICCIT-1441), 2020 international conference on

作者： Wareef Yousuf Aljomaee Saeed M Alshahrani Nayyar Ahmed Khan Department of Computer Science College of Computing and Information Technology Shaqra University Shaqra Saudi Arabia

ISBN: (数字)9798350353839

ISBN: (纸本)9798350353846

the Arabic language is one of the delicate languages in terms of usage. Many characters in the language make it difficult for management to identify the script quickly. the holy book of the Muslims, the Quran, is written in Arabic. Several valuable resources from ancient and present times have helpful information on Arabic. the handwritten scripts vary from person to person, and it is not simple to understand and realize what is mentioned inside the scripts. In this study, the main emphasis is on recognizing Arabic handwritten words. the convolution neural network (CNN) is used, and feature extraction is done with the help of CNN for the Arabic text images uploaded into the system. With the help of machine learning classifiers, these handwritten scripts are recognized for similarity from the training data set. A recognition rate of up to 96.4% for 29 classes is achieved. the proposed model is tested using large datasets of characters and formations in the Arabic language. the identified features are further passed on to the machine learning classification model. Various researchers have worked on several models that yield variable rates of recognition. the highest rate is received at 96.3%. the next phase of the model proposed in this study focuses on the sentiment analysis of the scripts or texts classified in the recognition phase. the author's sentiment is identified for classifying the results.

关键词： Deep learning Handwriting recognition Sentiment analysis Text recognition Convolution Text categorization Neural networks Training data Feature extraction Information technology

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Proceedings of the international conference on Computer and Applications, ICCA 2022 - Proceedings

Proceedings of the International Conference on Computer and ...

引用

4th international conference on Computer and Applications, ICCA 2022

ISBN: (纸本)9781665452946

the proceedings contain 37 papers. the topics discussed include: improve data mining techniques with a high-performance cluster;optimum operation and cost scenarios of a hybrid wind/PV/battery in a radial network using firefly algorithm and surrogate optimization;tibia player profiling and modeling using machine learning;an advanced deep learning medical image recognition and diagnosis of respiratory system viruses;measuring google classroom's usability level from an expert perspective;a novel non-inverting quadratic double switch buck boost converter for charging lithium-ion batteries in electrical vehicles;the influential factors for ICT adoption: survey of teachers in the higher educational institutions in Bauchi state;ranking of SADC countries e-government information services based on citizen satisfaction;and machine learning methods for dysgraphia screening with online handwriting features

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data Augmentation for EEG-based SSVEP Decoding

Data Augmentation for EEG-based SSVEP Decoding

引用

Computer Applications and Information Technology (ISCAIT), international Symposium on

作者： Ruoxuan Xiao Yuzhu Guo Chang’an University CHD Xi’an Shaanxi Province China School of Automation Science and Electrical Engineering Beihang University Beijing China

ISBN: (数字)9798331542856

ISBN: (纸本)9798331542863

Steady-state visual evoked potential (SSVEP)based neural decoding (BCIs) play a crucial role in brain-computer interfaces (BCIs). However, traditional machine learning algorithms often face SSVEP decoding stability challenges due to the inherent multi-class small-sample pattern recognition, along with external factors such as noise interference or subject fatigue, resulting in unsatisfied performance. To solve this limitation, a data augmented decoding algorithm is proposed by applying Gaussian noise to SSVEP signals to enhance the robustness of traditional machine learning-based SSVEP decoding algorithms. this method was evaluated on selected subjects with initially low decoding accuracy in the public SSVEP dataset, showing significant performance improvement, with an average accuracy increase of $8.33 \%$. the proposed approach effectively improves the robustness of SSVEP decoding with a straightforward manner and facilitates the broader application of SSVEP in practical BCIs.

关键词： Visualization machine learning algorithms Accuracy Gaussian noise Interference data augmentation Robustness Stability analysis Decoding Steady-state

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Diagnosis and Identification of Citrus Canker Growth Rate Using machine learning

Diagnosis and Identification of Citrus Canker Growth Rate Us...

引用

pattern recognition and machine learning (PRML), IEEE international conference on

作者： Naureen Zainab Hammad Afzal Fahim Arif Abdul Ghafoor Naima Iltaf Muhammad Zakria Muhammad Javed Hyder Rafia Ahsan Department of Computer Software Engineering National University of Sciences and Technology Islamabad Pakistan National Agricultural Research Centre (NARC) Park Road Crop Diseases Research Institute (CDRI) Islamabad Pakistan Department of Mechanical Engineering Capital University of Science and Technology (CUST) Islamabad Pakistan

Citrus canker is a bacterial disease caused by Xanthomonas citri pv citri. Mainly the research in the field of Citrus canker has been done on fruits; early detection of disease on leaves can help in adopting preventive measures at early stage. the present study focuses on the detection of Citrus canker in leaves and classification of six different growth stages/levels of disease: water soaking, yellow initiation, chlorosis, and blister formation, canker infection (50%), canker infection (100%). the primary dataset was developed by taking images of citrus leaves infected by X. citri for the specified time. the proposed strategy is based on the detection of the lesion spot on citrus leaves and the classification of citrus disease stages/level was done to evaluate the growth rate of citrus canker through six distinct growth levels and evaluate affected area of leaf with each passing day. the entire processing includes Pre-processing, Segmentation, Features extraction and perform Classification using four different classifiers which include SVM, KNN, Naive Bayes, and Neural Network. the efficiency of different classifiers was 96.77% by SVM, followed by KNN 90%, NB 84% and NN 82% respectively. the lowest execution time was achieved by NB 0.011s, followed by KNN 0.015s, SVM 0.021s, and N.N. 0.216s.

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A Close Look at Deep learning with Small data 25

A Close Look at Deep Learning with Small Data

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25th international conference on pattern recognition (ICPR)

作者： Brigato, Lorenzo Iocchi, Luca Sapienza Univ Rome Dept Comp Control & Management Engn Rome Italy

ISBN: (纸本)9781728188089

In this work, we perform a wide variety of experiments with different deep learning architectures on datasets of limited size. According to our study, we show that model complexity is a critical factor when only a few samples per class are available. Differently from the literature, we show that in some configurations, the state of the art can be improved using low complexity models. For instance, in problems with scarce training samples and without data augmentation, low-complexity convolutional neural networks perform comparably well or better than state-of-the-art architectures. Moreover, we show that even standard data augmentation can boost recognition performance by large margins. this result suggests the development of more complex data generation/augmentation pipelines for cases when data is limited. Finally, we show that dropout, a widely used regularization technique, maintains its role as a good regularizer even when data is scarce. Our findings are empirically validated on the sub-sampled versions of popular CIFAR-10, Fashion-MNIST and, SVHN benchmarks.

关键词： Convolutional neural networks

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Artificial Intelligence Based Emotion recognition from Fuzzy EEG Signals: A Comprehensive Review

Artificial Intelligence Based Emotion Recognition from Fuzzy...

引用

Sentiment Analysis and Deep learning (ICSADL), international conference on

作者： Indraneel K Trinath Basu Miriyala Department of Computer Science and Enginnering Koneru Lakshmaiah Education Foundation Hyderabad Telangana India

ISBN: (数字)9798331523923

ISBN: (纸本)9798331523930

Affective Computing, a type of artificial intelligence that can detect and interpreting human emotions with an advancement technologies like non-invasive sensing, and brain-computer interfaces. Academics have been fascinated by the idea of developing systems that can identify human emotions using various facial expressions, gestures, and physiological signals. the first three techniques are typically ineffective because people may hide what they're feeling unconsciously or consciously. Emotion recognition can be improved using physiological signals. these signals are more sensitive to the changes in the emotional states of people. Implementing emotional recognition systems face several challenges including data acquisition, feature extraction, model development, model training, and generalization. Adoption of AI techniques can help in improving accuracy and robustness using fuzzy EEG signals. Advancements in deep learning and machine learning have made it possible to recognize, analyze, and interpret various emotional states. the present work reviews the available datasets and techniques such as the extraction of features, selection/reduction, and machine learning for eliciting emotional states. It also explores different types of neural networks with long-term memory or convolutional systems. the review also describes electroencephalogram signals that are associated with certain emotions and the relationship among various parts of the brain. the review also helps future research to gain insights towards efficient emotion recognition capturing human emotions.

关键词： Deep learning Emotion recognition Reviews Convolution Face recognition Feature extraction Electroencephalography Physiology data models Brain-computer interfaces

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4th international conference on Advanced Network Technologies and Intelligent Computing, ANTIC 2024

4th International Conference on Advanced Network Technologie...

引用

4th international conference on Advanced Network Technologies and Intelligent Computing, ANTIC 2024

ISBN: (纸本)9783031837890

the proceedings contain 109 papers. the special focus in this conference is on Advanced Network Technologies and Intelligent Computing. the topics include: Exploratory Study on Different Transformer Models;visionLingua: Empowering Multimodal Understanding with Cutting-Edge Language Models;human Action recognition Using Mediapipe Holistic Keypoints: A Deep learning Approach;machine learning for Waste-to-Energy: Optimization and Predictive Analytics;enhanced Traffic Forecasting for Urban Planning Using DeepWalk Embeddings and Spatio-Temporal Graph Ordinary Differential Equation Model;fault Detection in Semiconductor Manufacturing Using Advanced machine learning Techniques with Interpretability;fine-Tuning RetinaNet for Few-Shot Fruit Detection;Smart Street: AI-Powered Traffic Flow Enhancement with Adaptive Signal Control;a Hybrid machine learning Approach for Accurate Milk Quality Classification;Designing VoiceGPT Chatterbox Concierge: A Personal AI Assistant for Daily Services Using Supervised machine learning Based Speech recognition Techniques;improved Classification Techniques Using Convolutional Neural Networks for Rice Plant Disease Detection;Enhancing Customer Segmentation and Behavior Prediction through machine learning and Medium Gaussian SVM;Grapevine SDLC Model for Real-Time Fake News Classification;Detecting AI-Generated Text Using Fine-Tuned Transformers: A Study on Academic Integrity;MoReco: AI Model Recommendation and Optimization for B5G Networks;file Prefetching Accuracy Enhancement Using Deep learning Approaches;Mamba Based Conditional GAN for Optical Cloud Removal of Satellite Images;a Hybrid Quantum machine learning Model for Multi-class Classifier;adaptive Resource Management in Massive machine-Type Communications Using Reinforcement learning;Advanced machine learning and Features Based data Analytics of Electricity Consumption in USA and India;cross-Domain Adversarial Attacks and Robust Defense Mechanisms for Multimodal Neural Networks;deep Featu

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Social Media Analysis for Criminal Behavior Detection: Methods, Application and Challenge

Social Media Analysis for Criminal Behavior Detection: Metho...

引用

Sentiment Analysis and Deep learning (ICSADL), international conference on

作者： Prachi Patel Kush Bhushanwar Hemlata Patel Computer Engineering Department PIET Parul University Vadodara Gujarat India Computer Science & Engineering Department PIET Parul University Vadodara Gujarat India

ISBN: (数字)9798331523923

ISBN: (纸本)9798331523930

Social media websites have provided rich contextual data but its misuse for criminal activities creates lot of problems. this paper seeks to solve the problem of criminal behavioral analysis on social media platforms using machine learning, deep learning and Natural Language Processing (NLP) methodologies. the goal is to assess the effectiveness of classification algorithms such as SVM, Navier Bayes, Random Forest, Convolutional Neural Network, Recurrent Neural Network, and the Natural Language Processing (NLP) methodology that include sentiment analysis, topic modelling and entity recognition for detecting irregularities. Specifically, this research study discusses about the recent progress in these methods while considering essential problems, including data accessibility, model flexibility, and the topic of ethics.