Acadlore Transactions on AI and Machine Learning最新文献_第2页

Automated Identification of Insect Pests: A Deep Transfer Learning Approach Using ResNet 害虫的自动识别:使用ResNet的深度迁移学习方法

Acadlore Transactions on AI and Machine Learning Pub Date : 2023-11-13 DOI: 10.56578/ataiml020402

Christine Dewi, Henoch Juli Christanto, Guowei Dai

引用次数: 0

Augmenting Diabetic Retinopathy Severity Prediction with a Dual-Level Deep Learning Approach Utilizing Customized MobileNet Feature Embeddings 利用自定义MobileNet特征嵌入的双层次深度学习方法增强糖尿病视网膜病变严重程度预测

Acadlore Transactions on AI and Machine Learning Pub Date : 2023-10-12 DOI: 10.56578/ataiml020401

Jyostna Devi Bodapati, Rajasekhar Konda

{"title":"Augmenting Diabetic Retinopathy Severity Prediction with a Dual-Level Deep Learning Approach Utilizing Customized MobileNet Feature Embeddings","authors":"Jyostna Devi Bodapati, Rajasekhar Konda","doi":"10.56578/ataiml020401","DOIUrl":"https://doi.org/10.56578/ataiml020401","url":null,"abstract":"Diabetic retinopathy, a severe ocular disease correlated with elevated blood glucose levels in diabetic patients, carries a significant risk of visual impairment. The essentiality of its timely and precise severity classification is underscored for effective therapeutic intervention. Deep learning methodologies have been shown to yield encouraging results in the detection and categorisation of severity levels of diabetic retinopathy. This study proposes a dual-level approach, wherein the MobileNetV2 model is modified for a regression task, predicting retinopathy severity levels and subsequently fine-tuned on fundus images. The refined MobileNetV2 model is then utilised for learning feature embeddings, and a Support Vector Machine (SVM) classifier is trained for grading retinopathy severity. Upon implementation, this dual-level approach demonstrated remarkable performance, achieving an accuracy rate of 87% and a kappa value of 93.76% when evaluated on the APTOS19 benchmark dataset. Additionally, the efficacy of data augmentation and the handling of class imbalance issues were explored. These findings suggest that the novel dual-level approach provides an efficient and highly effective solution for the detection and classification of diabetic retinopathy severity levels.","PeriodicalId":292575,"journal":{"name":"Acadlore Transactions on AI and Machine Learning","volume":"27 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-10-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135924872","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Convolutional Neural Network-Assisted Scattering Inversion in Diverse Noise Environments 不同噪声环境下卷积神经网络辅助散射反演

Acadlore Transactions on AI and Machine Learning Pub Date : 2023-09-26 DOI: 10.56578/ataiml020305

Jiabao Zhuang, Pinchao Meng

{"title":"Convolutional Neural Network-Assisted Scattering Inversion in Diverse Noise Environments","authors":"Jiabao Zhuang, Pinchao Meng","doi":"10.56578/ataiml020305","DOIUrl":"https://doi.org/10.56578/ataiml020305","url":null,"abstract":"In addressing the challenge of obstacle scattering inversion amidst intricate noise conditions, a model predicated on convolutional neural networks (CNN) has been proposed, demonstrating high precision. Five distinct noise scenarios, encompassing Gaussian white noise, uniform distribution noise, Poisson distribution noise, Laplace noise, and impulse noise, were evaluated. Far-field data paired with the Fourier coefficients of obstacle boundary curves were employed as network input and output, respectively. Through the convolutional processes inherent to the CNN, salient features within the far-field data related to obstacles were adeptly identified. Concurrently, the statistical characteristics of the noise were assimilated, and its perturbing effects were diminished, thus facilitating the inversion of obstacle shape parameters. The intrinsic capacity of CNNs to intuitively learn and differentiate salient features from data eradicates the necessity for external intervention or manually designed feature extractors. This adaptability confers upon CNNs a significant edge in tackling obstacle scattering inversion challenges, particularly in light of fluctuating data distributions and feature variability. Numerical experiments have substantiated that the aforementioned CNN model excels in addressing scattering inversion complications within multifaceted noise conditions, consistently delivering solutions with remarkable precision.","PeriodicalId":292575,"journal":{"name":"Acadlore Transactions on AI and Machine Learning","volume":"7 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-09-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"134960812","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Multi-Variable Time Series Decoding with Long Short-Term Memory and Mixture Attention 基于长短期记忆和混合注意的多变量时间序列解码

Acadlore Transactions on AI and Machine Learning Pub Date : 2023-09-22 DOI: 10.56578/ataiml020304

Soukaina Seddik, Hayat Routaib, Routaib Elhaddadi

{"title":"Multi-Variable Time Series Decoding with Long Short-Term Memory and Mixture Attention","authors":"Soukaina Seddik, Hayat Routaib, Routaib Elhaddadi","doi":"10.56578/ataiml020304","DOIUrl":"https://doi.org/10.56578/ataiml020304","url":null,"abstract":"The task of interpreting multi-variable time series data, while also forecasting outcomes accurately, is an ongoing challenge within the machine learning domain. This study presents an advanced method of utilizing Long Short-Term Memory (LSTM) recurrent neural networks in the analysis of such data, with specific attention to both target and exogenous variables. The novel approach aims to extract hidden states that are unique to individual variables, thereby capturing the distinctive dynamics inherent in multi-variable time series and allowing the elucidation of each variable's contribution to predictive outcomes. A pioneering mixture attention mechanism is introduced, which, by leveraging the aforementioned variable-specific hidden states, characterizes the generative process of the target variable. The study further enhances this methodology by formulating associated training techniques that permit concurrent learning of network parameters, variable interactions, and temporal significance with respect to the target prediction. The effectiveness of this approach is empirically validated through rigorous experimentation on three real-world datasets, including the 2022 closing prices of three major stocks - Apple (AAPL), Amazon (AMZN), and Microsoft (MSFT). The results demonstrated superior predictive performance, attributable to the successful encapsulation of the diverse dynamics of different variables. Furthermore, the study provides a comprehensive evaluation of the interpretability outcomes, both qualitatively and quantitatively. The presented framework thus holds substantial promise as a comprehensive solution that not only enhances prediction accuracy but also aids in the extraction of valuable insights from complex multi-variable datasets.","PeriodicalId":292575,"journal":{"name":"Acadlore Transactions on AI and Machine Learning","volume":"44 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-09-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"136061530","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 1

An Efficient Descriptor-Based Approach for Dominant Point Detection in Shape Contours 一种基于描述子的形状轮廓优势点检测方法

Acadlore Transactions on AI and Machine Learning Pub Date : 2023-09-21 DOI: 10.56578/ataiml020303

Mohammad T. Parvez

引用次数: 0

A Comparative Study on AI-Based Algorithms for Cost Prediction in Pharmaceutical Transport Logistics 基于人工智能的药品运输物流成本预测算法比较研究

Acadlore Transactions on AI and Machine Learning Pub Date : 2023-09-01 DOI: 10.56578/ataiml020302

Fadwa Farchi, Chayma Farchi, Badr Touzi, C. Mabrouki

引用次数: 3

Advanced Hybrid Segmentation Model Leveraging AlexNet Architecture for Enhanced Liver Cancer Detection 利用AlexNet架构的高级混合分割模型增强肝癌检测

Acadlore Transactions on AI and Machine Learning Pub Date : 2023-08-22 DOI: 10.56578/ataiml020301

Venkata Raja Sekhar Reddy Nagireddy, Khaja Shareef Shaik

引用次数: 0

Artificial Intelligence in Cervical Cancer Research and Applications 人工智能在宫颈癌研究中的应用

Acadlore Transactions on AI and Machine Learning Pub Date : 2023-06-14 DOI: 10.56578/ataiml020205

Chunhui Liu, Jiahui Yang, Y. Liu, Ying Zhang, Shuang Liu, Tetiana Chaikovska, C. Liu

引用次数: 1

Enhancing Face Spoofing Attack Detection: Performance Evaluation of a VGG-19 CNN Model 增强人脸欺骗攻击检测:VGG-19 CNN模型的性能评估

Acadlore Transactions on AI and Machine Learning Pub Date : 2023-06-13 DOI: 10.56578/ataiml020204

T. Ayanwola, A. Oludele, M. Agbaje

引用次数: 0

Detecting False Data Injection Attacks in Industrial Internet of Things Using an Optimized Bidirectional Gated Recurrent Unit-Swarm Optimization Algorithm Model 基于优化的双向门控循环单元群优化算法模型检测工业物联网虚假数据注入攻击

Acadlore Transactions on AI and Machine Learning Pub Date : 2023-06-06 DOI: 10.56578/ataiml020203

Nadella Sree Divya, Ramesh Vatambeti

引用次数: 1