检索结果-内蒙古大学图书馆

Role of Artificial Intelligence and Deep Learning in Skin Disease Prediction: A Systematic Review and Meta-analysis

Annals of Data science 2024年第6期11卷 2109-2139页

作者： Nancy, V. Auxilia Osvin Prabhavathy, P. Arya, Meenakshi S. Computer Science and Engineering College of Engineering and Technology SRM Institute of Science and Technology Vadapalani Tamil Nadu Chennai India Institute of Transportation IOWA State University IowaIA United States

Skin is a most essential and extraordinary part of the human structure. Exposure to chemicals such as nitrates, sunlight, arsenic, and UV rays due to pollution and depletion of the ozone layer is causing various skin diseases to spread rapidly. Digital healthcare offers many opportunities to reduce time, and human error, and improve clinical outcomes. However, the automatic recognition of skin disease is a major challenge due to high visual similarity between different skin diseases, low contrast, and large inter variation. Early detection of skin cancer can prevent death. Thus, Artificial intelligence (AI) and Machine Learning (ML) helps the physicians to improve clinical judgment or change manual perception. For skin cancer diagnostics, the ML/AI algorithm can outperform or match professional dermatologists in multiple studies. Different pre-trained architectures such as ResNet152, AlexNet, VGGNet, etc. are used for fusing different skin disease features such as texture, color, etc. and they are also utilized for conducting segmentation tasks. The variations in reflection, lesion size, shape, illumination, etc. often make automatic skin disease classification a complex task. ISIC 2019 and HAM 10000 are the widely used public datasets for skin disease prediction. More technical paper on skin cancer diagnosis is compared in this study. This report examines the majority of technical papers published between 2018 and October 2022 in order to appreciate current trends in the disciplines of skin cancer prediction. A study that combined clinical patient data with deep learning models (DL) increased the accuracy of predicting skin cancer. This article presents a visually attractive and well-organized summary of the current study findings. © The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature 2024.

关键词： Deep learning

来源：评论

学校读者我要写书评

暂无评论

Dynamic-EC:an efficient dynamic erasure coding method for permissioned blockchain systems

引用

Frontiers of computer science 2025年第1期19卷 15-28页

作者： Mizhipeng ZHANG Chentao WU Jie LI Minyi GUO Department of Computer Science and Engineering Shanghai Jiao Tong UniversityShanghai 200200China Yancheng Blockchain Research Institute Hengyang 421200China

Blockchain as a decentralized storage technology is widely used in many *** has extremely strict requirements for reliability because there are many potentially malicious ***,blockchain is a chain storage structure formed by interconnecting blocks1),which are stored by full replication method,where each node stores a replica of all blocks and the data consistency is maintained by the consensus *** decrease the storage overhead,previous approaches such as BFT-Store and Partition Chain store blocks via erasure ***,existing erasure coding based methods utilize static encoding schema to tolerant f malicious nodes,but in the typical cases,the number of malicious nodes is much smaller than f as described in previous *** redundant parities to tolerate excessive malicious nodes introduces unnecessary storage *** solve the above problem,we propose Dynamic-EC,which is a Dynamic Erasure Coding method in permissioned blockchain *** key idea of Dynamic-EC is to reduce the storage overhead by dynamically adjusting the total number of parities according to the risk level of the whole system,which is determined by the number of perceived malicious nodes,while ensuring the system *** demonstrate the effectiveness of Dynamic-EC,we conduct several experiments on an open source blockchain software *** results show that,compared to the state-of-the-art erasure coding methods,Dynamic-EC reduces the storage overhead by up to 42%,and decreases the average write latency of blocks by up to 25%,respectively.

关键词： blockchain Byzantine Fault Tolerance(BFT) erasure coding consensus reputation evaluation

来源：评论

学校读者我要写书评

暂无评论

Cellular Automaton–Based Sentiment Analysis for Bipolar Classification of Reviews

引用

Complex Systems 2025年第1期34卷 29-57页

作者： Elizabeth, M.J. Chaudhuri, Parimal Pal Hazari, Raju Department of Computer Science and Engineering National Institute of Technology Kerala Calicut India Indian Institute of Technology Kharagpur India

One of the main goals of sentiment analysis is to analyze human perception to continuously adapt to each person’s demands. The information gathered is structured to understand the mood or emotional tone of the reviews and comments. In every aspect of society, there is always an opportunity for opinions and suggestions from the public. So, the quality of the services provided by various resources can considerably increase if we can determine whether those thoughts are positive, negative or neutral. In this paper, we employ cellular automata (CAs) to categorize the reviews as positive or negative. The fundamental advantage of the suggested technique is that there is less need to be concerned about the linguistic characteristics of human languages. The proposed method performs better by leveraging a variety of parameters that are extracted by analyzing cycle length (CL) graphs generated using CL signals produced by elementary CAs (ECAs) without employing any form of pre-trained language model. We evaluated the effectiveness of the proposed cellular automaton–based machine learning (CAML) classifier by assessing certain parameters such as recall, precision, F1 score and accuracy. Our method achieves a maximum accuracy of 92.44% for the sentiment tweet dataset. We compared the proposed technique with all existing machine learning models, as well as recent methodologies employed in the field. The suggested method performs well in predicting the positive and negative classes of sentiments using CAs. © 2025, Complex Systems Publications, Inc. All rights reserved.

关键词： Emotion Recognition

来源：评论

学校读者我要写书评

暂无评论

Knowledge Graph Representation Learning based on Entity Semantic Distance Classification

引用

computer-Aided Design and Applications 2024年第S7期21卷 256-269页

作者： Zhang, Yi Cao, Wanhua Liu, Juntao Wang, Yuanbin Rao, Ziyun College of Computer Science and Technology Harbin Engineering University Harbin China Wuhan Digital Engineering Research Institute Wuhan China

In recent years, significant progress has been made in knowledge graph representation learning, which has shown promising results in knowledge computing applications such as relation extraction and knowledge reasoning. However, the unbalanced distribution of relations and entities in knowledge graphs leads to low training efficiency. To address this issue, this paper proposes a novel knowledge representation learning method based on entity distance classification. This method classifies entities based on their semantic distance on a specific relational plane, and employs different training strategies to increase the training opportunities for entities with low semantic distance differentiation. Moreover, the loss function is adjusted by introducing different residual weights, which allows for the allocation of different training opportunities to negative samples. The effectiveness of our approach is demonstrated by comparing it with mainstream knowledge representation models on various benchmark datasets. © 2024 U-turn Press LLC,.

关键词： Knowledge graph

来源：评论

学校读者我要写书评

暂无评论

A survey on blockchain architecture and consensus mechanism: design vulnerability and security analysis

引用

International Journal of Cloud Computing 2024年第5期13卷 485-547页

作者： Sharma, Shashikant Kushwaha, Dharmender Singh Department of Computer Science and Engineering Motilal Nehru National Institute of Technology Prayagraj Allahabad India

Today for an organisation, data security is the most crucial topic. An organisation needs to protect its information against cyberattacks. Cryptography, DLT, and blockchain technology provide higher security for data storage and prevent any cyberattack. The most prominent reasons for using this technology are its specific properties, such as decentralisation, transparency, autonomy (without human interaction), and robustness. This paper discusses the performance and limitations of the existing blockchain architecture, consensus mechanism, and the security aspect of the consensus mechanism in an organised way. This survey presents systematic reviews of blockchain architectures, consensus mechanisms, the performance analysis of the current blockchain consensus mechanisms, and the vulnerabilities and types of attacks. The aim is to provide a comprehensive state-of-the-art platform where a beginner can swiftly move on to research aspects. Copyright © 2024 Inderscience Enterprises Ltd.

关键词： Distributed ledger

来源：评论

学校读者我要写书评

暂无评论

A Deep Learning-Based Ultra-Lightweight Architecture for Atrial Fibrillation Detection Using Single-Lead ECG Recordings

引用

IEEE Access 2025年 13卷 86474-86486页

作者： Khan, Abbas Mughal, Muhammad Rizwan Ali Irtaza, Syed Khan, Mohsin Tahir, Madiha Ali, Ahtisham Saeed, Zoraize Ghulam Ishaq Khan Institute of Engineering Science and Technology Topi23640 Pakistan Sultan Qaboos University Electrical and Computer Engineering Department Muscat123 Oman Institute of Space Technology Electrical Engineering Department Islamabad44000 Pakistan FAST National University of Computer Science Department of Artificial Intelligence Islamabad44000 Pakistan Institute of Space Technology Computer Science Department Islamabad44000 Pakistan

Atrial fibrillation (A-Fib), a common cardiac arrhythmia, is linked to serious complications like heart failure and stroke, which early diagnosis could prevent. However, its paroxysmal and often asymptomatic nature complicates detection. In this paper, we propose a novel deep-learning model for single-lead ECG signal analysis, combining a convolutional neural network (CNN) to extract localized features and a long short-term memory (LSTM) network to capture temporal dependencies. To address data imbalance in the PhysioNet/CinC Challenge 2017 dataset, we employ a filtering-based approach with Neurokit, BioSPPy, and EngZeeMod for synthetic data generation. Simulation results show the model achieves a 99.56% F1 score, a 102.25 dB gain in efficiency, a 1.06% performance gain over the state-of-the-art method, and real-time diagnosis with a 0.48 ×10-3 second inference time. These simulation results confirm the superior performance of the proposed model. © 2013 IEEE.

关键词： Electrocardiography

来源：评论

学校读者我要写书评

暂无评论

Text Simplification Using T5 Model and BART Model 8

Text Simplification Using T5 Model and BART Model

引用

8th International Conference on Electronics, Materials engineering and Nano-Technology, IEMENTech 2025

作者： Das, Soma Basak, Dipan Bhattacharjee, Abir Institute of Engineering and Management Department of Computer Science and Engineering Kolkata India

ISBN: (纸本)9798331520762

Text simplification is an important part of Natural Language Processing. It focuses on transforming complex text into simpler versions while preserving the meaning. This study examines the performance of two advanced models, T5 (Text-to-Text Transfer Transformer) and BART (Bidirectional and Auto-Regressive Transformers), in simplifying text. The models are trained on a dataset containing complex and simplified text pairs. Their performance is evaluated using BLEU, ROUGE, and SARI scores. The results show that both models perform well, but BART achieves better BLEU and ROUGE scores. © 2025 IEEE.

关键词： Natural language processing systems

来源：评论

学校读者我要写书评

暂无评论

Query based biomedical document retrieval for clinical information access with the semantic similarity

引用

Multimedia Tools and Applications 2024年第18期83卷 55305-55317页

作者： Gupta, Supriya Sharaff, Aakanksha Nagwani, Naresh Kumar Nagpur India Department of Computer Science and Engineering National Institute of Technology Raipur Raipur India

The amount of exploration done for the available medical literature is quite less and at the same time, there is less awareness of information mining in this specific field. The accessibility of immense quantity of biomedical literature has opened up additional opportunities to apply Information Retrieval and NLP methods for mining existing archives. Therefore, a query based retrieval application (QBR) based on hybrid similarity of string and semantic similarity can help medical professionals in their ongoing research. There are multiple benefits of utilizing various NLP applications, for example, information retrieval engine, and clinical diagnosis frameworks for decision support in medical field. These applications depend on the capacity to gauge Hybrid textual similarity (HTS) and N-Gram similarity. Hybrid similarity is the combination of weighting function and word embedding models providing similarity scores with optimum results. In this work, the main focus is on building of a new biomedical document retrieval model which can pull relevant literature for clinical decision support system based on the specific query. There is also an attempt to compare the statistical and NLP based approaches of query based biomedical document retrieval with the baseline systems. Analysis of the proposed method inclusive of semantic word embeddings shows promising results for both of the suggested similarities. © The Author(s), under exclusive licence to Springer science+Business Media, LLC, part of Springer Nature 2023.

关键词： Information retrieval

来源：评论

学校读者我要写书评

暂无评论

Sign-Based Distributed Learning with Byzantine Resilience based on Audit Mechanism

引用

IEEE Transactions on Information Forensics and Security 2025年 20卷 5774-5788页

作者： Li, Chengxi Xiao, Ming Skoglund, Mikael KTH Royal Institute of Technology Division of Information Science and Engineering School of Electrical Engineering and Computer Science Stockholm 10044 Sweden

In this paper, we study the problem of distributed learning (DL) with devices transmitting sign information of the local gradients to the server under communication constraints, where the devices are susceptible to Byzantine attacks. For this problem, a sign-based gradient descent method with majority vote and stochastic 1-bit quantization (Sign-M-stochastic) has been proposed very recently. However, the Byzantine resilience of Sign-M-stochastic is inherently limited, based on the fact that all Byzantine devices and honest devices participate equally in the training process. To overcome this drawback and enhance the resilience to Byzantine attacks, inspired by the audit-based distributed detection systems, we propose a novel DL method with an audit mechanism (DL-AM). In each iteration, the sign information of the local gradients are obtained by the devices from stochastic 1-bit quantization. All devices, partitioned into groups, send the sign information to the server through multiple paths, both directly and via other devices in the same group. This approach provides the server with additional information about the identities of the devices, which enables the server to form the global model update by aggregating the sign information of different devices with varying weights. We analyze the convergence performance of the proposed method from a theoretical perspective. Finally, numerical results demonstrate the superiority of DL-AM over the baseline methods. © 2005-2012 IEEE.

关键词： Audit mechanism Byzantine resilience convergence analysis distributed learning sign information

来源：评论

学校读者我要写书评

暂无评论

A Novel Fuzzy-Based Modified GAN and Faster RCNN for Classification of Banana Leaf Disease

引用

Journal of The Institution of Engineers (India): Series A 2023年第3期104卷 529-540页

作者： Raja, N. Bharathi Rajendran, P. Selvi Department of Computer Science and Engineering Hindustan Institute of Technology and Science Tamil Nadu Kelambakkam India

In the global marketplace, agriculture plays an important role. However, diseases produced in plants mostly affect the financial system. Pest occurrence and climatic changes are the leading trouble that the banana plant leaf faces, which influences the quality of fruit and crop yields. To predict the leaf disease symptoms in an early stage, the automatic banana leaf disease detection technique is more significant. The traditional deep neural network methods used in banana plant leaf detection need more parameters and utilize complex architectures. To address such problems, modified generative adversarial networks–modified faster region-based convolutional neural networks with fuzzy (MGAN–MFRCNN with Fuzzy) is proposed for identifying banana leaf disease. The most effective framework in deep learning is GAN. The autoencoder is incorporated within the fundamental GAN design for developing a modified GAN model;it is used for data augmentation. To perform feature extraction from high-resolution networks, an MFRCNN is used in our proposed method. The improved and more accurate classification scores are obtained by combining fuzzy with the ensemble model. The experimentation is carried out using a banana plant leaf dataset in which the healthy leaf, xanthomonas-affected leaf, and sigatoka-affected leaf diseases are detected and classified. The proposed model reached 98% accuracy, 97% precision, 96% F1-score, 98% specificity, and 98% sensitivity, which is better than other existing methods. © 2023, The Institution of Engineers (India).

关键词： Generative adversarial networks

来源：评论

学校读者我要写书评

暂无评论

建议与咨询留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

时间限定

文献类型

馆藏选择

核心期刊

语言

文献类型

帮助

文字说明：

检索规则说明：

检索范例：

分类表

所选分类

限定检索结果

文献类型

馆藏范围

日期分布

学科分类号

主题

机构

作者

语言

请选择保存的检索档案：

请选择收藏分类：

通借通还

建议与咨询 留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

时间限定

文献类型

馆藏选择

核心期刊

语言

文献类型

帮助

文字说明：

检索规则说明：

检索范例：

分类表

所选分类

限定检索结果

文献类型

馆藏范围

日期分布

学科分类号

主题

机构

作者

语言

请选择保存的检索档案： 新增检索档案 确定 取消

请选择收藏分类： 新增自定义分类 确定 取消

通借通还

建议与咨询留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

请选择保存的检索档案：

请选择收藏分类：