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检索条件"任意字段=8th International Conference on Data Science and Machine Learning Applications, CDMA 2025"

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A Vision Transformer-Based Framework for Detection and Classification of Soybean Leaf Diseases 8

A Vision Transformer-Based Framework for Detection and Class...

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8th international conference on data science and machine learning applications, cdma 2025

作者： Shahzore, Usama Akbar, Shahzad Shoukat, Ijaz Ali Rehman, Amjad Saba, Tanzila Sikder, Tanjin Riphah College of Computing Riphah International University Faisalabad Pakistan Prince Sultan University AIDA Lab. CCIS Riyadh Saudi Arabia Ajman University Department of Engineering and IT Ajman United Arab Emirates

ISBN: (纸本)9798331539696

Soybean is among most cultivated oil seed crop worldwide. Leaf diseases pose a serious threat to the yield of soybean crops which could lead to huge economic losses. It is essential to diagnose these diseases for effective management and mitigation. Traditional methods involved physical inspection of leaves which often led to errors and inconsistencies. Various deep learning models were developed using Convolutional Neural Networks but lacked comprehensive feature extraction and attention mechanisms. this research established a methodology for detecting and classifying soybean leaf diseases using pre-trained Vision Transformer. Research was performed using the Auburn Soybean Disease Image dataset, Soybean Diseased Leaf and SoyNet. the datasets were divided into 70% training, 10% validation and 20% testing sets and evaluated using standard performance metrics. For the Auburn Soybean Disease Image dataset, the proposed model achieved an accuracy of 98.7% with precision, recall and F1-score values of 99.4%, 99.2% and 99.3% respectively. On the Soybean Diseased Leaf dataset, the proposed model achieved an accuracy of 98.9% with a 98.6% recall precision of 98.4% and an F1-score of 98.5%. For the SoyNet dataset, despite a larger and more complex dataset, the model achieved an accuracy of 97.4%, with precision, recall and F1-score values of 97.0%, 97.2% and 97.1 % respectively. the training and validation losses of model decreased consistently across epochs, indicating effective learning and classification. the proposed model will help farmers classify soybean leaf diseases with potential applications in precision agriculture and plant health monitoring. © 2025 IEEE.

关键词： Convolutional neural networks

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Enhancing Zero-Shot Question Answering in Arabic Language with Fine-Tuned AraGPT Model 8

Enhancing Zero-Shot Question Answering in Arabic Language wi...

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8th international conference on data science and machine learning applications, cdma 2025

作者： Aljebreen, Jwaher Alhargan, Ashwaq Nagro, Shimaa Saudi Electronic University Computer Science Department Riyadh Saudi Arabia

ISBN: (纸本)9798331539696

Zero-shot question answering is a recent natural language processing evolution. It aims to equip models with the ability to infer answers to questions they have never encountered during training. this research aims to contribute to the ongoing development of language models for Arabic natural language processing tasks, emphasizing the importance of fine-tuning to optimize Question Answering (QA) model performance. the pre-trained AraGPT-2 model is utilized and fine-tuned on the TyDi QA dataset. the fine-tuned model is then evaluated using multiple metrics, including perplexity, F1 score, exact match, BLEU score, and cosine similarity. these metrics are compared to those of the base model to assess the improvements achieved through fine-tuning. the results of the evaluation indicate that the fine-tuned model exhibits an enhanced performance compared to the base model in producing exact-match answers. the perplexity of the base model is measured at 29.8, whereas the fine-tuned model achieves a lower perplexity of 16.5. the F1 score for the base model is 14.7%, and the fine-tuned model achieves a slightly lower F1 score of 13.4%. the exact match score for the base model is 3.92%, while the fine-tuned model shows a slight improvement with an exact match score of 4.14%. Additionally, the BLEU evaluation, conducted solely on the fine-tuned model, yields a score of 0.43. the cosine similarity metric also applied only to the fine-tuned model, produces a score of 0.75. these findings demonstrate the effectiveness of fine-tuning the AraGPT-2 model for enhancing zero-shot QA in the Arabic language. While the improvements achieved may not be significant, they indicate areas for further investigation and improvement in future research endeavors. © 2025 IEEE.

关键词： Zero-shot learning

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Retinal Image Analysis for Detection of Diabetic Macular Edema Using Transfer learning 8

Retinal Image Analysis for Detection of Diabetic Macular Ede...

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8th international conference on data science and machine learning applications, cdma 2025

作者： Asim, Tayyaba Akbar, Shahzad Shahzore, Usama Rehman, Amjad Saba, Tanzila Ayesha, Noor Riphah College of Computing Riphah International University Faisalabad Pakistan Prince Sultan University AIDA Lab. CCIS Riyadh Saudi Arabia Zhengzhou University Zhengzhou China Henan450001 China

ISBN: (纸本)9798331539696

Diabetic Macular Edema (DME) is a disease of the eye's retina and it's a major factor of causing vision problems and leads towards blindness if it is undiagnosed. Early detection of DME can prevent vision loss and may reduce diabetic-related problems like cardiovascular issues. therefore, this study presents a method for detecting DME through Optical Coherence Tomography (OCT) and fundus images using transfer learning. the proposed method is based on four stages: Pre-processing, augmentation, segmentation through DeepLabV3+, and binary classification of DME by using two (02) publicly available datasets;the first dataset is Messidor-2, which contains fundus images, and the second dataset is Retinal Images of Optical Coherence Tomography (OCT). In the Messidor-2 dataset, the total number of images is 1744, and Retinal OCT Images dataset consists of 84,495 images total, separated into four categories (CNV, DME, DRUSEN, NORMAL). In the proposed method, Convolutional Neural Networks (CNN) architectures ResNet50 and VGG-19 have been used for the detection of the DME. Convolutional Neural Networks (CNNs) have been extensively used in medical imaging analysis and classification. Using the well-known ResNet50 architecture for classification of each dataset, the proposed model yielded an accuracy of 98.79%, 99% of F1 Score, 98.43% of Precision, and recall of 98.89%. By using VGG-19, the proposed model gives an accuracy of 98.81%, 98.94% of Fl Score, 98.1 % of Precision and recall of 98.73%. When both models (ResNet50 and VGG-19) were compared, the VGG-19 gave the best accuracy. © 2025 IEEE.

关键词： Convolutional neural networks

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Adversarial learning of Summary Statistics for Approximate Bayesian Computation 24

Adversarial Learning of Summary Statistics for Approximate B...

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8th international conference on Advances in Artificial Intelligence, ICAAI 2024

作者： Adel, Tameem Cambridge United Kingdom

ISBN: (纸本)9798400718014

Estimating influential parameters in real-world applications can be challenging, especially in problems where the model likelihood is intractable. To perform inference, the machine learning model should be able to understand the underlying phenomena and conditions which have led to the observations in hand. Estimating the posterior distribution of the influential parameters given observations (the inverse problem) can therefore be onerous, especially when the likelihood is intractable. the Approximate Bayesian Computation (ABC) paradigm is the standard statistical methodology to address such likelihood-free problems. In a nutshell, ABC methods establish a proxy for the likelihood based on data simulations. Two main bottlenecks of any ABC technique are the comparisons between observed and simulated datasets, and the manner via which data must be summarized in order to make the eventual data comparisons optimally fit for the inference purpose. We propose an ABC algorithm that learns how to perform data summarization in a data-driven manner, which is not based on pre-selecting summary statistics a priori. the proposed ABC algorithm is based on a neural network which is trained via adversarial learning. We show how the proposed ABC algorithm can infer the posterior distributions of parameters with high fidelity, and with low bias and variance. We apply the algorithm to two different applications and demonstrate its efficacy and robustness with respect to parameter inference. © 2024 Copyright held by the owner/author(s).

关键词： Adversarial machine learning

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Multi-agent Sequential learning via Gradient Ascent Credit Assignment 24

Multi-agent Sequential Learning via Gradient Ascent Credit A...

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8th international conference on Computer science and Artificial Intelligence, CSAI 2024

作者： Sabri, Oussama Lehéricy, Luc Muzy, Alexandre Hamburg Germany Laboratoire JAD CNRS Universite Cote d’Azur Nice France Laboratoire I3S CNRS Universite Cote d’Azur Sophia Antipolis France

ISBN: (纸本)9798400718182

We consider a multi-agent learning problem with two layers: first, split up the tasks among the agents, and then have them solve their allocated task. A set of algorithms is proposed based on a policy gradient method with different reward functions and models of policy parameterizations. the algorithms are proved to converge. their performances are assessed and compared on several synthetic data sets. the proposed algorithms prove to be more efficient than a naive multi-armed bandit algorithm. © 2024 Copyright held by the owner/author(s).

关键词： Adversarial machine learning

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Research on the Detection Methods for Changes in the Mangroves of Dongzhaigang, Hainan from 2019 to 2023 24

Research on the Detection Methods for Changes in the Mangrov...

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8th international conference on Electronic Information Technology and Computer Engineering, EITCE 2024

作者： Zhang, Xianyao Lu, Huajun Qiu, Shuailong Liu, Lijin Wu, Zhongqiang School of Information Science and Technology Hainan Normal University Hainan Haikou China National Engineering Laboratory for Big Data System Computing Technology Shenzhen University Guangdong Shenzhen China

ISBN: (纸本)9798400710094

this study introduces the research methods for detecting changes in the mangroves of Dongzhaigang, Hainan from 2019 to 2023. Mangrove ecosystems play a crucial role in providing habitats and have ecological and economic value, but in recent years, they have faced degradation and management issues. In order to effectively monitor mangrove changes, researchers have utilized remote sensing techniques and machine learning methods for mangrove cover information extraction and classification. the paper describes the methods of estimating mangrove cover using remote sensing satellite imagery and spectral indices and explores the applications of clustering, decision trees, support vector machines, and deep learning in mangrove change detection. the research results indicate that the support vector machine algorithm performs well in classifying mangrove and non-mangrove areas. this study provides a basis for the management and decision-making of Dongzhaigang Mangrove Nature Reserve in Hainan Province and highlights the future research potential in the application of multi-source and multi-resolution data and deep learning techniques. © 2024 Copyright held by the owner/author(s).

关键词： Adversarial machine learning

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CSAI 2024 - Proceedings of 2024 8th international conference on Computer science and Artificial Intelligence

CSAI 2024 - Proceedings of 2024 8th International Conference...

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8th international conference on Computer science and Artificial Intelligence, CSAI 2024

ISBN: (纸本)9798400718182

the proceedings contain 87 papers. the topics discussed include: poor posture detection based on behavioral and environmental data using machine learning;fine-grained zero-shot object detection via semantic decoupling;wearable photoplethysmography-based bipolar disorder detection;cosine similarity guided joint modeling of foreground and background for incremental object detection in drone images;a novel 3D Keypoint detection method based on self-supervised learning;convolutional neural network-based accelerator for hyperspectral image classification;stochastic variance-reduced iterative hard thresholding in graph sparsity optimization;depth-aware image captioning with bus dataset for visually impaired individuals;and social robot recognition based on E-GraphCTAGE model: integration of facial expression and multi-feature processing.

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Strengthening Deep learning through Morphological Analysis for an Arabic Lemmatizer Development 8th

Strengthening Deep Learning Through Morphological Analysis f...

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8th international conference on Arabic Language Processing

作者： Belayachi, Samir Mazroui, Azzeddine Mohammed First Univ Dept Comp Sci Fac Sci BV Mohammed VI Oujda 60000 Morocco

ISBN: (纸本)9783031791635;9783031791642

Morphological analysis is an essential step in many natural language processing applications. Indeed, information retrieval and processing algorithms often use a morphological analyzer as a text pre-processing step. the rich morphology of the Arabic language, combined with the frequent absence of diacritical marks in texts, make morphological analysis particularly complex. Numerous morphological analyzers have been developed for the Arabic language using machine learning techniques such as Hidden Markov Models and support vector machines. In this article, we present a new Arabic lemmatizer that combines deep learning and out-of-context morphological analysis using the Alkhalil Morpho Sys analyzer. the tests carried out demonstrate the effectiveness of this approach, and an accuracy of the order of 98% was achieved in the test set.

关键词： Natural Language Processing Arabic language Morphological analysis Lemmatizer Deep learning

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Short Paper: Effects of Poisoning Attacks on Causal Deep Reinforcement learning 19th

Short Paper: Effects of Poisoning Attacks on Causal Deep R...

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19th international Workshop on data Privacy Management, DPM 2024, 8th international Workshop on Cryptocurrencies and Blockchain Technology, CBT 2024 and 10th Workshop on the Security of Industrial Control Systems and of Cyber-Physical Systems, CyberICPS 2024 which were held in conjunction with the 29th European Symposium on Research in Computer Security, ESORICS 2024

作者： Lodeweegs, Patrick Tajalli, Behrad Picek, Stjepan Radboud University Nijmegen Netherlands

ISBN: (纸本)9783031823619

machine learning models are widely used for diverse applications, including image classification, natural language analysis, and creating personalized recommendations. To generate personalized recommendations, recommender systems can use supervised or reinforcement learning to predict which items interest a user. At the same time, machine learning models are vulnerable to adversarial attacks. Such attacks attempt to intentionally reduce a model’s performance, for instance, by applying specific changes to an input. Causal machine learning is a promising approach to machine learning that may improve a model’s robustness by learning causal relations instead of correlations. In this paper, the robustness of causal machine learning models against data poisoning attacks has been compared with statistical machine learning models in the context of recommender systems. Based on our results, the causal deep reinforcement learning model CDT4Rec is not more robust against reward poisoning attacks than the evaluated statistical reinforcement learning model DDPG. © the Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Adversarial machine learning

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Intelligent Waste Classification System Integrating Deep learning and Robotic Arm Technology 24

Intelligent Waste Classification System Integrating Deep Lea...

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8th international conference on Electronic Information Technology and Computer Engineering, EITCE 2024

作者： Duan, Yixuan Nanjing University Of Posts And Telecommunications Jiangsu Nanjing China

ISBN: (纸本)9798400710094

the paper proposes an intelligent garbage classification system that combines deep learning models with robotic arm technology, aiming to enhance the automation level and accuracy of the classification process. To overcome the limitations of traditional garbage classification methods, the study introduces data augmentation in data preprocessing, improving the model’s generalization ability and its effectiveness in recognizing complex images. DenseNet121 was selected as the primary model for the experiments, and after hyperparameter optimization, the model performed excellently in both training and validation phases. Additionally, through robotic arm simulation experiments, the system successfully achieved automatic identification and classification of different types of waste, demonstrating its effectiveness and robustness in real-world application scenarios. the experimental results show that the proposed system not only significantly improves the efficiency and accuracy of garbage classification but also provides a solid technical foundation and theoretical support for future large-scale and more complex environmental applications. © 2024 Copyright held by the owner/author(s).

关键词： Adversarial machine learning

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