Automated recognition of violent activities from videos is vital for public safety,but often raises significant privacy concerns due to the sensitive nature of the footage.Moreover,resource constraints often hinder th...Automated recognition of violent activities from videos is vital for public safety,but often raises significant privacy concerns due to the sensitive nature of the footage.Moreover,resource constraints often hinder the deployment of deep learning-based complex video classification models on edge devices.With this motivation,this study aims to investigate an effective violent activity classifier while minimizing computational complexity,attaining competitive performance,and mitigating user data privacy concerns.We present a lightweight deep learning architecture with fewer parameters for efficient violent activity recognition.We utilize a two-stream formation of 3D depthwise separable convolution coupled with a linear self-attention mechanism for effective feature extraction,incorporating federated learning to address data privacy concerns.Experimental findings demonstrate the model’s effectiveness with test accuracies from 96%to above 97%on multiple datasets by incorporating the FedProx aggregation strategy.These findings underscore the potential to develop secure,efficient,and reliable solutions for violent activity recognition in real-world scenarios.展开更多
Offensive messages on social media,have recently been frequently used to harass and criticize people.In recent studies,many promising algorithms have been developed to identify offensive texts.Most algorithms analyze ...Offensive messages on social media,have recently been frequently used to harass and criticize people.In recent studies,many promising algorithms have been developed to identify offensive texts.Most algorithms analyze text in a unidirectional manner,where a bidirectional method can maximize performance results and capture semantic and contextual information in sentences.In addition,there are many separate models for identifying offensive texts based on monolin-gual and multilingual,but there are a few models that can detect both monolingual and multilingual-based offensive texts.In this study,a detection system has been developed for both monolingual and multilingual offensive texts by combining deep convolutional neural network and bidirectional encoder representations from transformers(Deep-BERT)to identify offensive posts on social media that are used to harass others.This paper explores a variety of ways to deal with multilin-gualism,including collaborative multilingual and translation-based approaches.Then,the Deep-BERT is tested on the Bengali and English datasets,including the different bidirectional encoder representations from transformers(BERT)pre-trained word-embedding techniques,and found that the proposed Deep-BERT’s efficacy outperformed all existing offensive text classification algorithms reaching an accuracy of 91.83%.The proposed model is a state-of-the-art model that can classify both monolingual-based and multilingual-based offensive texts.展开更多
Brain tumors pose a significant threat to human lives and have gained increasing attention as the tenth leading cause of global mortality.This study addresses the pressing issue of brain tumor classification using Mag...Brain tumors pose a significant threat to human lives and have gained increasing attention as the tenth leading cause of global mortality.This study addresses the pressing issue of brain tumor classification using Magnetic resonance imaging(MRI).It focuses on distinguishing between Low-Grade Gliomas(LGG)and High-Grade Gliomas(HGG).LGGs are benign and typically manageable with surgical resection,while HGGs are malignant and more aggressive.The research introduces an innovative custom convolutional neural network(CNN)model,Glioma-CNN.GliomaCNN stands out as a lightweight CNN model compared to its predecessors.The research utilized the BraTS 2020 dataset for its experiments.Integrated with the gradient-boosting algorithm,GliomaCNN has achieved an impressive accuracy of 99.1569%.The model’s interpretability is ensured through SHapley Additive exPlanations(SHAP)and Gradient-weighted Class Activation Mapping(Grad-CAM++).They provide insights into critical decision-making regions for classification outcomes.Despite challenges in identifying tumors in images without visible signs,the model demonstrates remarkable performance in this critical medical application,offering a promising tool for accurate brain tumor diagnosis which paves the way for enhanced early detection and treatment of brain tumors.展开更多
The writer identification system identifies individuals based on their handwriting is a frequent topic in biometric authentication and verification systems.Due to its importance,numerous studies have been conducted in...The writer identification system identifies individuals based on their handwriting is a frequent topic in biometric authentication and verification systems.Due to its importance,numerous studies have been conducted in various languages.Researchers have established several learning methods for writer identification including supervised and unsupervised learning.However,supervised methods require a large amount of annotation data,which is impossible in most scenarios.On the other hand,unsupervised writer identification methods may be limited and dependent on feature extraction that cannot provide the proper objectives to the architecture and be misinterpreted.This paper introduces an unsupervised writer identification system that analyzes the data and recognizes the writer based on the inter-feature relations of the data to resolve the uncertainty of the features.A pairwise architecturebased Autoembedder was applied to generate clusterable embeddings for handwritten text images.Furthermore,the trained baseline architecture generates the embedding of the data image,and the K-means algorithm is used to distinguish the embedding of individual writers.The proposed model utilized the IAM dataset for the experiment as it is inconsistent with contributions from the authors but is easily accessible for writer identification tasks.In addition,traditional evaluation metrics are used in the proposed model.Finally,the proposed model is compared with a few unsupervised models,and it outperformed the state-of-the-art deep convolutional architectures in recognizing writers based on unlabeled data.展开更多
The early identification and treatment of tomato leaf diseases are crucial for optimizing plant productivity,efficiency and quality.Misdiagnosis by the farmers poses the risk of inadequate treatments,harming both toma...The early identification and treatment of tomato leaf diseases are crucial for optimizing plant productivity,efficiency and quality.Misdiagnosis by the farmers poses the risk of inadequate treatments,harming both tomato plants and agroecosystems.Precision of disease diagnosis is essential,necessitating a swift and accurate response to misdiagnosis for early identification.Tropical regions are ideal for tomato plants,but there are inherent concerns,such as weather-related problems.Plant diseases largely cause financial losses in crop production.The slow detection periods of conventional approaches are insufficient for the timely detection of tomato diseases.Deep learning has emerged as a promising avenue for early disease identification.This study comprehensively analyzed techniques for classifying and detecting tomato leaf diseases and evaluating their strengths and weaknesses.The study delves into various diagnostic procedures,including image pre-processing,localization and segmentation.In conclusion,applying deep learning algorithms holds great promise for enhancing the accuracy and efficiency of tomato leaf disease diagnosis by offering faster and more effective results.展开更多
Selecting techniques is a crucial aspect of disease detection analysis,particularly in the convergence of computer vision and agricultural technology.Maintaining crop disease detection in a timely and accurate manner ...Selecting techniques is a crucial aspect of disease detection analysis,particularly in the convergence of computer vision and agricultural technology.Maintaining crop disease detection in a timely and accurate manner is essential to maintaining global food security.Deep learning is a viable answer to meet this need.To proceed with this study,we have developed and evaluated a disease detection model using a novel ensemble technique.We propose to introduce DenseNetMini,a smaller version of DenseNet.We propose combining DenseNetMini with a learning resizer in ensemble approach to enhance training accuracy and expedite learning.Another unique proposition involves utilizing Gradient Product(GP)as an optimization technique,effectively reducing the training time and improving the model performance.Examining images at different magnifications reveals noteworthy diagnostic agreement and accuracy improvements.Test accuracy rates of 99.65%,98.96%,and 98.11%are seen in the Plantvillage,Tomato leaf,and Appleleaf9 datasets,respectively.One of the research's main achievements is the significant decrease in processing time,which suggests that using the GP could improve disease detection in agriculture's accessibility and efficiency.Beyond quantitative successes,the study highlights Explainable Artificial Intelligence(XAl)methods,which are essential to improving the disease detection model's interpretability and transparency.XAI enhances the interpretability of the model by visually identifying critical areas on plant leaves for disease identification,which promotes confidence and understanding of the model's functionality.展开更多
基金Supported by the Research Chair of Online Dialogue and Cultural Communication,King Saud University,Saudi Arabia.
文摘Automated recognition of violent activities from videos is vital for public safety,but often raises significant privacy concerns due to the sensitive nature of the footage.Moreover,resource constraints often hinder the deployment of deep learning-based complex video classification models on edge devices.With this motivation,this study aims to investigate an effective violent activity classifier while minimizing computational complexity,attaining competitive performance,and mitigating user data privacy concerns.We present a lightweight deep learning architecture with fewer parameters for efficient violent activity recognition.We utilize a two-stream formation of 3D depthwise separable convolution coupled with a linear self-attention mechanism for effective feature extraction,incorporating federated learning to address data privacy concerns.Experimental findings demonstrate the model’s effectiveness with test accuracies from 96%to above 97%on multiple datasets by incorporating the FedProx aggregation strategy.These findings underscore the potential to develop secure,efficient,and reliable solutions for violent activity recognition in real-world scenarios.
文摘Offensive messages on social media,have recently been frequently used to harass and criticize people.In recent studies,many promising algorithms have been developed to identify offensive texts.Most algorithms analyze text in a unidirectional manner,where a bidirectional method can maximize performance results and capture semantic and contextual information in sentences.In addition,there are many separate models for identifying offensive texts based on monolin-gual and multilingual,but there are a few models that can detect both monolingual and multilingual-based offensive texts.In this study,a detection system has been developed for both monolingual and multilingual offensive texts by combining deep convolutional neural network and bidirectional encoder representations from transformers(Deep-BERT)to identify offensive posts on social media that are used to harass others.This paper explores a variety of ways to deal with multilin-gualism,including collaborative multilingual and translation-based approaches.Then,the Deep-BERT is tested on the Bengali and English datasets,including the different bidirectional encoder representations from transformers(BERT)pre-trained word-embedding techniques,and found that the proposed Deep-BERT’s efficacy outperformed all existing offensive text classification algorithms reaching an accuracy of 91.83%.The proposed model is a state-of-the-art model that can classify both monolingual-based and multilingual-based offensive texts.
基金This research is funded by the Researchers Supporting Project Number(RSPD2024R1027),King Saud University,Riyadh,Saudi Arabia.
文摘Brain tumors pose a significant threat to human lives and have gained increasing attention as the tenth leading cause of global mortality.This study addresses the pressing issue of brain tumor classification using Magnetic resonance imaging(MRI).It focuses on distinguishing between Low-Grade Gliomas(LGG)and High-Grade Gliomas(HGG).LGGs are benign and typically manageable with surgical resection,while HGGs are malignant and more aggressive.The research introduces an innovative custom convolutional neural network(CNN)model,Glioma-CNN.GliomaCNN stands out as a lightweight CNN model compared to its predecessors.The research utilized the BraTS 2020 dataset for its experiments.Integrated with the gradient-boosting algorithm,GliomaCNN has achieved an impressive accuracy of 99.1569%.The model’s interpretability is ensured through SHapley Additive exPlanations(SHAP)and Gradient-weighted Class Activation Mapping(Grad-CAM++).They provide insights into critical decision-making regions for classification outcomes.Despite challenges in identifying tumors in images without visible signs,the model demonstrates remarkable performance in this critical medical application,offering a promising tool for accurate brain tumor diagnosis which paves the way for enhanced early detection and treatment of brain tumors.
文摘The writer identification system identifies individuals based on their handwriting is a frequent topic in biometric authentication and verification systems.Due to its importance,numerous studies have been conducted in various languages.Researchers have established several learning methods for writer identification including supervised and unsupervised learning.However,supervised methods require a large amount of annotation data,which is impossible in most scenarios.On the other hand,unsupervised writer identification methods may be limited and dependent on feature extraction that cannot provide the proper objectives to the architecture and be misinterpreted.This paper introduces an unsupervised writer identification system that analyzes the data and recognizes the writer based on the inter-feature relations of the data to resolve the uncertainty of the features.A pairwise architecturebased Autoembedder was applied to generate clusterable embeddings for handwritten text images.Furthermore,the trained baseline architecture generates the embedding of the data image,and the K-means algorithm is used to distinguish the embedding of individual writers.The proposed model utilized the IAM dataset for the experiment as it is inconsistent with contributions from the authors but is easily accessible for writer identification tasks.In addition,traditional evaluation metrics are used in the proposed model.Finally,the proposed model is compared with a few unsupervised models,and it outperformed the state-of-the-art deep convolutional architectures in recognizing writers based on unlabeled data.
文摘The early identification and treatment of tomato leaf diseases are crucial for optimizing plant productivity,efficiency and quality.Misdiagnosis by the farmers poses the risk of inadequate treatments,harming both tomato plants and agroecosystems.Precision of disease diagnosis is essential,necessitating a swift and accurate response to misdiagnosis for early identification.Tropical regions are ideal for tomato plants,but there are inherent concerns,such as weather-related problems.Plant diseases largely cause financial losses in crop production.The slow detection periods of conventional approaches are insufficient for the timely detection of tomato diseases.Deep learning has emerged as a promising avenue for early disease identification.This study comprehensively analyzed techniques for classifying and detecting tomato leaf diseases and evaluating their strengths and weaknesses.The study delves into various diagnostic procedures,including image pre-processing,localization and segmentation.In conclusion,applying deep learning algorithms holds great promise for enhancing the accuracy and efficiency of tomato leaf disease diagnosis by offering faster and more effective results.
文摘Selecting techniques is a crucial aspect of disease detection analysis,particularly in the convergence of computer vision and agricultural technology.Maintaining crop disease detection in a timely and accurate manner is essential to maintaining global food security.Deep learning is a viable answer to meet this need.To proceed with this study,we have developed and evaluated a disease detection model using a novel ensemble technique.We propose to introduce DenseNetMini,a smaller version of DenseNet.We propose combining DenseNetMini with a learning resizer in ensemble approach to enhance training accuracy and expedite learning.Another unique proposition involves utilizing Gradient Product(GP)as an optimization technique,effectively reducing the training time and improving the model performance.Examining images at different magnifications reveals noteworthy diagnostic agreement and accuracy improvements.Test accuracy rates of 99.65%,98.96%,and 98.11%are seen in the Plantvillage,Tomato leaf,and Appleleaf9 datasets,respectively.One of the research's main achievements is the significant decrease in processing time,which suggests that using the GP could improve disease detection in agriculture's accessibility and efficiency.Beyond quantitative successes,the study highlights Explainable Artificial Intelligence(XAl)methods,which are essential to improving the disease detection model's interpretability and transparency.XAI enhances the interpretability of the model by visually identifying critical areas on plant leaves for disease identification,which promotes confidence and understanding of the model's functionality.
