New Generation Computing最新文献_第3页

Networks of Classical Conditioning Gates and Their Learning 经典条件门网络及其学习

IF 2.6 4区计算机科学

New Generation Computing Pub Date : 2024-04-30 DOI: 10.1007/s00354-024-00256-3

Shun-ichi Azuma, Dai Takakura, Ryo Ariizumi, Toru Asai

引用次数: 0

Binary and Ternary Classifiers to Detect COVID-19 Patients Using Chest X-ray Images: An Efficient Layered CNN Approach 使用胸部 X 光图像的二元和三元分类器检测 COVID-19 患者：一种高效的分层 CNN 方法

IF 2.6 4区计算机科学

New Generation Computing Pub Date : 2024-04-27 DOI: 10.1007/s00354-024-00254-5

Mamta Mittal, Nitin Kumar Chauhan, Adrija Ghansiyal, D. Jude Hemanth

{"title":"Binary and Ternary Classifiers to Detect COVID-19 Patients Using Chest X-ray Images: An Efficient Layered CNN Approach","authors":"Mamta Mittal, Nitin Kumar Chauhan, Adrija Ghansiyal, D. Jude Hemanth","doi":"10.1007/s00354-024-00254-5","DOIUrl":"https://doi.org/10.1007/s00354-024-00254-5","url":null,"abstract":"Coronavirus disease 2019, i.e., COVID-19, an emerging contagious disease with human-to-human transmission, first appeared at the end of year 2019. The sudden demand for disease diagnostic kits prompted researchers to shift their focus toward developing solutions that could assist in identifying COVID-19 using available resources. Therefore, it is imperative to develop a high-accuracy system that makes use of Artificial Intelligence and its tools considering its contribution to computer vision. The time consumed to diagnose test outcomes is to be taken care of as a crucial aspect of an efficient model. To address the global challenges faced by the COVID-19 pandemic, this study proposed two deep learning models developed for automatic COVID-19 detection and distinguish it from pneumonia, another common lung disease. The proposed designs implement layered convolutional neural networks and are trained on a data set of 1824 chest X-rays for binary classification (COVID-19 and normal) and 2736 chest X-rays for ternary classification (COVID-19, normal, and pneumonia). The input images and hyper-parameters in the convolution layers are fine-tuned during the model training phase. The observations show that the proposed models have achieved a better performance as compared to their earlier contemporaries’ approaches, resulting in accuracy, precision, recall, and F-score of 98.91%, 98.5%, 98.5%, and 99% for binary-class and 95.99%, 96.3%, 96%, and 96.33% for ternary-class classifiers, respectively. The presented architectures have been built from scratch, thus with the implemented convolutional layered architecture, they were successful in providing more efficient and early diagnosis of the disease.","PeriodicalId":54726,"journal":{"name":"New Generation Computing","volume":"18 1","pages":""},"PeriodicalIF":2.6,"publicationDate":"2024-04-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140809180","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Efficient Classification of Hallmark of Cancer Using Embedding-Based Support Vector Machine for Multilabel Text 使用基于嵌入的支持向量机对多标签文本进行高效的癌症特征分类

IF 2.6 4区计算机科学

New Generation Computing Pub Date : 2024-03-12 DOI: 10.1007/s00354-024-00248-3

Shikha Verma, Aditi Sharan, Nidhi Malik

{"title":"Efficient Classification of Hallmark of Cancer Using Embedding-Based Support Vector Machine for Multilabel Text","authors":"Shikha Verma, Aditi Sharan, Nidhi Malik","doi":"10.1007/s00354-024-00248-3","DOIUrl":"https://doi.org/10.1007/s00354-024-00248-3","url":null,"abstract":"The Hallmark of Cancers consists of various biological capabilities of the tumor cell which help the medical experts to understand the development and identification of these cells during various stages of the cancer disease. The hallmark of cancer classification is a widely accepted framework that characterizes the fundamental biological capabilities of cancer cells. This classification is based on the work of Hanahan and Weinberg, who identified 10 hallmark capabilities that collectively enable the development and progression of cancer. The hallmark of cancer classification provides a comprehensive framework for understanding the biological basis of cancer development and progression. It helps researchers to identify the key molecular and cellular pathways that are involved in the disease, which can inform the development of new diagnostic tools and therapies. Multi-label classification aims to assign a set of labels to the samples under study. This paper focuses on creating an improved model by hybridizing the biomedical domain-specific embeddings for all the extracted biomedical features on the machine learning model. The use of domain-specific embeddings adds semantics to the vector-represented text. More specifically the study has tried to improve the efficacy of the multi-label classification as compared with other state-of-art methods using BioWordVec and the MeSH embeddings. The experimental work showed a significant improvement in the performance of our model which is being trained on the machine learning algorithm Support Vector Machine (SVM). The paper also focuses on understanding the label correlation which is studied by conducting a case study with medical domain experts and is also analyzed with the proposed model.","PeriodicalId":54726,"journal":{"name":"New Generation Computing","volume":"42 1","pages":""},"PeriodicalIF":2.6,"publicationDate":"2024-03-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140116295","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

IRI_Debug: An Ontology Evaluation Tool IRI_Debug：本体评估工具

IF 2.6 4区计算机科学

New Generation Computing Pub Date : 2024-03-12 DOI: 10.1007/s00354-024-00246-5

Vishal Lama, Archana Patel, Narayan C. Debnath, Sarika Jain

引用次数: 0

Deep Convolutional Neural Network for Knowledge-Infused Text Classification 用于知识注入文本分类的深度卷积神经网络

IF 2.6 4区计算机科学

New Generation Computing Pub Date : 2024-03-09 DOI: 10.1007/s00354-024-00245-6

Sonika Malik, Sarika Jain

引用次数: 0

Transfer Learning Fusion and Stacked Auto-encoders for Viral Lung Disease Classification 用于病毒性肺病分类的迁移学习融合和堆叠自动编码器

IF 2.6 4区计算机科学

New Generation Computing Pub Date : 2024-03-04 DOI: 10.1007/s00354-024-00247-4

{"title":"Transfer Learning Fusion and Stacked Auto-encoders for Viral Lung Disease Classification","authors":"","doi":"10.1007/s00354-024-00247-4","DOIUrl":"https://doi.org/10.1007/s00354-024-00247-4","url":null,"abstract":"<h3>Abstract</h3> The objective of this research endeavor is to identify an effective model for the classification of multiple viral respiratory diseases, encompassing COVID-19. The feature extraction phase from medical images constitutes a formidable challenge in achieving optimal disease classification outcomes. In this work, a selection of the best models among several popular transfer learning (TL) models is realized. The concatenation of the best models for better features extraction is used; the deep learning (DL) methods for deep features extraction and deep data reduction were applied for an optimal classification. This paper includes two studies, the first was applied to binary classification (COVID-19/Normal) and the second is concerned with multi-classification (COVID-19/Normal/VPneumonia). The validation of the proposed approaches is made on a big datasets: (i) binary classification 4800 COVID-19 and 4803 Normal images for the two cases Chest X-Ray (CXR) and Computed Tomography (CT) scans, and (ii) multi-class classification 3931 COVID-19, 3931 Normal, and 4273 Viral Pneumonia (VP) images for CXR. This study hypothesized that the proposed approach might be able to extract specific graphical features of COVID-19 and provide a clinical diagnosis ahead of the pathogenic test. Experimental results achieved in binary classification a high: Val_accuracy = 99.87% and 98.41%, Test_accuracy = 100% and 99.21%, Test_time = 0.002 s and 0.008 s per image for CT scans and CXR images, respectively, and in multi-classification: Val_accuracy = 97.48%, Test_accuracy = 92.96% with Test_time = 0.006 s per image for CXR images.","PeriodicalId":54726,"journal":{"name":"New Generation Computing","volume":"22 1","pages":""},"PeriodicalIF":2.6,"publicationDate":"2024-03-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140037246","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Incorporating Domain-Specific Traits into Personality-Aware Recommendations for Financial Applications 将特定领域的特质纳入金融应用的人格感知推荐中

IF 2.6 4区计算机科学

New Generation Computing Pub Date : 2024-02-25 DOI: 10.1007/s00354-024-00241-w

Takehiro Takayanagi, Kiyoshi Izumi

{"title":"Incorporating Domain-Specific Traits into Personality-Aware Recommendations for Financial Applications","authors":"Takehiro Takayanagi, Kiyoshi Izumi","doi":"10.1007/s00354-024-00241-w","DOIUrl":"https://doi.org/10.1007/s00354-024-00241-w","url":null,"abstract":"The general personality traits, notably the Big-Five personality traits, have been increasingly integrated into recommendation systems. The personality-aware recommendations, which incorporate human personality into recommendation systems, have shown promising results in general recommendation areas including music, movie, and e-commerce recommendations. On the other hand, the number of research delving into the applicability of personality-aware recommendations in specialized domains such as finance and education remains limited. In addition, these domains have unique challenges in incorporating personality-aware recommendations as domain-specific psychological traits such as risk tolerance and behavioral biases play a crucial role in explaining user behavior in these domains. Addressing these challenges, this study addresses an in-depth exploration of personality-aware recommendations in the financial domain, specifically within the context of stock recommendations. First, this study investigates the benefits of deploying general personality traits in stock recommendations through the integration of personality-aware recommendations with user-based collaborative filtering approaches. Second, this study further verifies whether incorporating domain-specific psychological traits along with general personality traits enhances the performance of stock recommender systems. Thirdly, this paper introduces a personalized stock recommendation model that incorporates both general personality traits and domain-specific psychological traits as well as transaction data. The experimental results show that the proposed model outperformed baseline models in financial stock recommendations.","PeriodicalId":54726,"journal":{"name":"New Generation Computing","volume":"39 1","pages":""},"PeriodicalIF":2.6,"publicationDate":"2024-02-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139978366","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

The Impact of Sentiment Scores Extracted from Product Descriptions on Customer Purchase Intention 从产品描述中提取的情感分数对客户购买意向的影响

IF 2.6 4区计算机科学

New Generation Computing Pub Date : 2024-02-24 DOI: 10.1007/s00354-024-00242-9

引用次数: 0

Asymmetric Short-Text Clustering via Prompt 通过提示进行非对称短文聚类

IF 2.6 4区计算机科学

New Generation Computing Pub Date : 2024-02-19 DOI: 10.1007/s00354-024-00244-7

Zhi Wang, Yi Zhu, Yun Li, Jipeng Qiang, Yunhao Yuan, Chaowei Zhang

{"title":"Asymmetric Short-Text Clustering via Prompt","authors":"Zhi Wang, Yi Zhu, Yun Li, Jipeng Qiang, Yunhao Yuan, Chaowei Zhang","doi":"10.1007/s00354-024-00244-7","DOIUrl":"https://doi.org/10.1007/s00354-024-00244-7","url":null,"abstract":"Short-text clustering, which has attracted much attention with the rapid development of social media in recent decades, is a great challenge due to the feature sparsity, high ambiguity, and massive quantity. Recently, pre-trained language models (PLMs)-based methods have achieved fairly good results on this task. However, two main problems still hang in the air: (1) the significant gap of objective forms in pretraining and fine-tuning, which restricts taking full advantage of knowledge in PLMs. (2) Most existing methods require a post-processing operation for clustering label learning, potentially leading to label estimation errors for different data distributions. To address these problems, in this paper, we propose an Asymmetric Short-Text Clustering via Prompt (short for ASTCP), the features learned with our ASTCP are denser and constricted for clustering. Specifically, a subset text of the corpus is first selected by an asymmetric prompt-tuning network, which aims to obtain predicted label as a clustering center. Then, by the propagation of predicted-label information, a fine-tuned model is designed for representation learning. Thus, a clustering module, such as K-means, is built to directly output clustering labels on top of these representations. Extensive experiments conducted on three datasets have demonstrated that our ASTCP can significantly and consistently outperform other SOTA clustering methods. The source code is available at https://github.com/zhuyi_yzu/ASTCP.","PeriodicalId":54726,"journal":{"name":"New Generation Computing","volume":"34 1","pages":""},"PeriodicalIF":2.6,"publicationDate":"2024-02-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139918625","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Neuro-Symbolic Reasoning for Multimodal Referring Expression Comprehension in HMI Systems 用于人机界面系统中多模态参考表达理解的神经符号推理

IF 2.6 4区计算机科学

New Generation Computing Pub Date : 2024-02-15 DOI: 10.1007/s00354-024-00243-8

Aman Jain, Anirudh Reddy Kondapally, Kentaro Yamada, Hitomi Yanaka

{"title":"Neuro-Symbolic Reasoning for Multimodal Referring Expression Comprehension in HMI Systems","authors":"Aman Jain, Anirudh Reddy Kondapally, Kentaro Yamada, Hitomi Yanaka","doi":"10.1007/s00354-024-00243-8","DOIUrl":"https://doi.org/10.1007/s00354-024-00243-8","url":null,"abstract":"Conventional Human–Machine Interaction (HMI) interfaces have predominantly relied on GUI and voice commands. However, natural human communication also consists of non-verbal communication, including hand gestures like pointing. Thus, recent works in HMI systems have tried to incorporate pointing gestures as an input, making significant progress in recognizing and integrating them with voice commands. However, existing approaches often treat these input modalities independently, limiting their capacity to handle complex multimodal instructions requiring intricate reasoning of language and gestures. On the other hand, multimodal tasks requiring complex reasoning are being challenged in the language and vision domain, but these typically do not include gestures like pointing. To bridge this gap, we explore one of the challenging multimodal tasks, called Referring Expression Comprehension (REC), within multimodal HMI systems incorporating pointing gestures. We present a virtual setup in which a robot shares an environment with a user and is tasked with identifying objects based on the user’s language and gestural instructions. Furthermore, to address this challenge, we propose a hybrid neuro-symbolic model combining deep learning’s versatility with symbolic reasoning’s interpretability. Our contributions include a challenging multimodal REC dataset for HMI systems, an interpretable neuro-symbolic model, and an assessment of its ability to generalize the reasoning to unseen environments, complemented by an in-depth qualitative analysis of the model’s inner workings.","PeriodicalId":54726,"journal":{"name":"New Generation Computing","volume":"41 1","pages":""},"PeriodicalIF":2.6,"publicationDate":"2024-02-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139769251","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0