检索结果-内蒙古大学图书馆

An Iterative PRISMA Review of GAN Models for Image Processing, Medical Diagnosis, and Network Security

Computers, Materials & Continua 2025年第2期82卷 1757-1810页

作者： Uddagiri Sirisha Chanumolu Kiran Kumar Sujatha Canavoy Narahari Parvathaneni Naga Srinivasu Department of Computer Science and Engineering Prasad V Potluri Siddhartha Institute of TechnologyVijayawada520007India Department of Computer Science and Engineering Koneru Lakshmaiah Education FoundationVaddeswaram522302India Department of Electronics and Communication Engineering Sreenidhi Institute of Science and TechnologyHyderabad501301India Amrita School of Computing Amrita Vishwa VidyapeethamAmaravati522503India Department of Teleinformatics Engineering Federal University of CearáFortaleza60455-970Brazil Inti International University Nilai71800Malaysia

The growing spectrum of Generative Adversarial Network (GAN) applications in medical imaging, cyber security, data augmentation, and the field of remote sensing tasks necessitate a sharp spike in the criticality of review of Generative Adversarial Networks. Earlier reviews that targeted reviewing certain architecture of the GAN or emphasizing a specific application-oriented area have done so in a narrow spirit and lacked the systematic comparative analysis of the models’ performance metrics. Numerous reviews do not apply standardized frameworks, showing gaps in the efficiency evaluation of GANs, training stability, and suitability for specific tasks. In this work, a systemic review of GAN models using the PRISMA framework is developed in detail to fill the gap by structurally evaluating GAN architectures. A wide variety of GAN models have been discussed in this review, starting from the basic Conditional GAN, Wasserstein GAN, and Deep Convolutional GAN, and have gone down to many specialized models, such as EVAGAN, FCGAN, and SIF-GAN, for different applications across various domains like fault diagnosis, network security, medical imaging, and image segmentation. The PRISMA methodology systematically filters relevant studies by inclusion and exclusion criteria to ensure transparency and replicability in the review process. Hence, all models are assessed relative to specific performance metrics such as accuracy, stability, and computational efficiency. There are multiple benefits to using the PRISMA approach in this setup. Not only does this help in finding optimal models suitable for various applications, but it also provides an explicit framework for comparing GAN performance. In addition to this, diverse types of GAN are included to ensure a comprehensive view of the state-of-the-art techniques. This work is essential not only in terms of its result but also because it guides the direction of future research by pinpointing which types of applications require some

关键词： GAN CGAN WGAN DCGAN image analysis

来源：评论

学校读者我要写书评

暂无评论

GAIT based human Parkinson’s disease detection using fused features with multi-kernel support vector machine

引用

International Journal of Information Technology (Singapore) 2025年第3期17卷 1387-1395页

作者： Jhapate, Arun Kumar Shrivastava, Hemang Department of Computer Science and Engineering Sagar Institute of Research and Technology Madhya Pradesh Bhopal India Institute of Advance Computing SAGE University Madhya Pradesh Indore India

Gait analysis plays a pivotal role in the detection and monitoring of Parkinson’s disease (PD), offering insights into motor impairments characteristic of the condition. This study proposes a novel approach employing advanced feature extraction and machine learning algorithms to enhance the classification of PD depends on gait features. In this study, both local as well as global features are extracted from the gait images to facilitate a classification process. Local feature extraction is achieved using an optimized convolutional neural network (OCNN), while global feature extraction is done by the Swin Transformer architecture. To further improve feature extraction, an Adaptive Glowworm Swarm Optimization (AGSO) algorithm is applied to improve the performance of the CNN. The fused local and global features are then fed into a Multi-Kernel Support Vector Machine (MK-SVM) classifier, which serves as the core of the proposed model. The performance of the model is evaluated using the GAIT-IT datasets, assessing metrics such as accuracy, precision, recall, and F-measure. As a result of the proposed model, performance is superior to that of traditional methods in terms of accuracy and reliability, offering a promising approach for PD detection based on gait analysis. © Bharati Vidyapeeth's institute of Computer Applications and Management 2024.

关键词： Gait analysis Global features Local features Machine learning Multi kernel SVM Optimized CNN Parkinson’s disease Swin transformer

来源：评论

学校读者我要写书评

暂无评论

Demand Forecasting in Supply Chain Management using CNN-LSTM Hybrid Model 14

Demand Forecasting in Supply Chain Management using CNN-LSTM...

引用

14th International Conference on computing Communication and Networking Technologies, ICCCNT 2023

作者： Naveen Parthasarathy, M.K. Rastogi, Shrey Alice, K. SRM Institute of Science and Technology Computer Science & Engineering Chennai India SRM Institute of Science and Technology Computing Technologies Chennai India

ISBN: (纸本)9798350335095

Efficient supply chain management is necessary to meet customer demands. Demand forecasting is a predictive analysis that estimates how much of a product or service a customer will need in the future. Accurate demand forecasting relies on past sales data and statistical estimates from test markets. Deep neural networks have gained popularity as a demand forecasting technique in supply chain management because of their high level of accuracy. The objective of this study is to use Long Short-Term Memory (LSTM) neural networks with hyperparameter tuning, as well as a hybrid model comprising Long Short-Term Memory and 1D Convolutional Neural Networks (CNNs), to predict the demand for eight medicinal drugs. The results obtained from the hybrid model will be compared with the LSTM models to select the best model for long-term forecasting. Demand forecasting has several applications, including inventory control, production planning, and market entry decisions © 2023 IEEE.

关键词： Long short-term memory

来源：评论

学校读者我要写书评

暂无评论

Custom Transformer-Based Approach for Enhanced Bengali Automatic Speech Recognition 1

Custom Transformer-Based Approach for Enhanced Bengali Autom...

引用

1st International Conference on Innovations in Communications, Electrical and Computer engineering, ICICEC 2024

作者： Deepa, R. Jayalakshmi, V. Thilakavathy, P. Manikandan, G. Surendran, R. SRM Institute of Science and Technology School of Computing Department of Computing Technologies Chennai India SRM Institute of Science and Technology Department of Computer Science and Applications Ramapuram Chennai India Vels Institute of Science and Engineering Vels Institute of Science Technology and Advanced Studies Chennai India Anna University St. Joseph's College of Engineering Department of Artificial Intelligence and Machine Learning Chennai India Saveetha Institute of Medical and Technical Sciences Saveetha School of Engineering Department of Computer Science and Engineering Chennai India

ISBN: (数字)9798350376517

ISBN: (纸本)9798350376517

Automated speech recognition (ASR) systems struggle with Bengali, which is the fifth most spoken language. Bengali has a varied morphology, many dialects, and limited high-quality annotated voice data. Traditional voice recognition techniques work well however struggle with Bengali phonetics and syntax. This method eliminates the need for large amounts of tagged data and the difficulties of differentiating sounds in different languages, both issues are addressed by this procedure. The research in this paper offers an Intellectual Bengali Speech Recognition System using Deep Learning Techniques (IBSRS-DLT). The model in this system uses transformer architecture and attention processes to manage Bengali speech pattern long-range dependencies. The IBSRS-DLT outperforms standard models in recognition accuracy using a pre-trained transformer model modified for phonetic and lexical correctness and fine-tuned on Bengali datasets. After training on a vast Bengali speech corpus, real-world datasets and rigorous simulations confirm the system. IBSRS-DLT can be used for automatic transcription, voice-controlled devices, and Bengali virtual assistants, such applications are efficient and accessible. The simulation showed a considerable boost in recognition speed and a decrease in keyword mistakes compared to current methods. This paper shows that deep learning can solve linguistic issues in ASR systems, enabling scalable regional language detection. © 2024 IEEE.

关键词： Deep learning

来源：评论

学校读者我要写书评

暂无评论

Transformer-Enhanced U-Net with Transfer Learning for Accurate Detection of Eye Coloboma 4

Transformer-Enhanced U-Net with Transfer Learning for Accura...

引用

4th International Conference on Power, Energy, Control and Transmission Systems, ICPECTS 2024

作者： Dhayanithi, A. Bala, K. Bharath Institute of Higher Education and Research School of Computing Department of Computer Science and Engineering Tamil Nadu Chennai600073 India Bharath Institute of Higher Education and Research School of Computing Department of Computer Science and Engineering Tamil Nadu Chennai600073 India

ISBN: (纸本)9798331508845

Eye coloboma is a type of congenital eye abnormality that causes developmental abnormalities of the eye which may culminate in eye disorders that fundamentally derange eyesight. Fractional and timely diagnosis remains highly important to its proper managing and treating. This work proposes a model with a Transformer-Enhanced U-Net design implemented with Transfer Learning to enhance the ability of the model to detect and segment eye coloboma from medical imaging data. This work builds upon the benefits of U-Net which has shown promising results especially in the biomedical image segmentation problem through addition of transformer modules that introduce long range dependencies and contextual information. This enhancement makes helps the model to pay more attention to such minor aspects as coloboma and general diagnostic accuracy consequently. Thanks to Transfer Learning with a pretrained backbone, for example Efficient Net the system is provided with Feature Representations recovered from large datasets that are already available and training time is drastically cut short while the performance of the system is not compromised at *** approach includes fine-tuning of Transformer-Enhanced U-Net model using a selected set of eye images, labelled for coloboma existence. By including the self-attention mechanism, the model is capable of focusing on the critical regions in the image, increasing its responsiveness to any form of coloboma. Preliminary tests show that this scheme performs better than the standard convolutional networks for segmenting complex regions by minimizing false negatives. Further, the model's flexibility to different imaging conditions demonstrates its suitability in realistic clinical applications. The objective of this research is to come up with a dependable, Auto-Generated diagnostic tool that will help the ophthalmologists in the early diagnosis of the eye related illnesses hence making increased positive outcomes for eye patients. The Transf

关键词： Image segmentation

来源：评论

学校读者我要写书评

暂无评论

High Accurate Gold Prize Rate Prediction Using Random Forest Regression Algorithm 7

High Accurate Gold Prize Rate Prediction Using Random Forest...

引用

7th IET Smart Cities Symposium, SCS 2023

作者： Sivasangari, A. Deepa, R. Geetha Rani, K. Surendran, R. Tamilvizhi, T. School of Computing Sathyabama Institute of Science and Technology Chennai India Department of Computing Technologies School of Computing College of Engineering and Technology SRM Institute of Science and Technology Kattankulathur Chennai India Bangalore India Department of Computer Science and Engineering Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences Chennai India Department of Computer Science and Engineering Panimalar Engineering College Chennai600123 India

ISBN: (纸本)9781839539831

In the past, one of the precious resources utilized to fund trading purchases was gold. The nations that have gold resources are regarded as rich countries. The nation's gold reserves show how strong its economy is. Not only have government departments participated in gold reserves, but also a number of private individuals and businesses. The routine of the world's top financial prudence affects gold rates. An increase in investors has been drawn to gold due to the recent fluctuations in price. Transactions involving gold are risky, and price fluctuations in gold are unpredictable due to the unpredictability of the gold market. The suggested uses machine learning to forecast the price of gold. Generally, machine learning is used to predict economic variables. To train a prediction model, we use supervised learning algorithms such as multiple linear regression and random forest regression. © The Institution of engineering & Technology 2023.

关键词： Multiple linear regression

来源：评论

学校读者我要写书评

暂无评论

Thermal conductivity of GeTe crystals based on machine learning potentials

引用

Chinese Physics B 2024年第4期33卷 104-107页

作者：张健张昊春李伟峰张刚 School of Energy Science and Engineering Harbin Institute of TechnologyHarbin 150001China Institute of High Performance Computing Agency for ScienceTechnology and ResearchSingapore 138632Singapore School of Physics&State Key Laboratory of Crystal Materials Shandong UniversityJinan 250100China

GeTe has attracted extensive research interest for thermoelectric *** this paper,we first train a neuroevolution potential(NEP)based on a dataset constructed by ab initio molecular dynamics,with the Gaussian approximation potential(GAP)as a *** phonon density of states is then calculated by two machine learning potentials and compared with density functional theory results,with the GAP potential having higher ***,the thermal conductivity of a GeTe crystal at 300 K is calculated by the equilibrium molecular dynamics method using both machine learning potentials,and both of them are in good agreement with the experimental results;however,the calculation speed when using the NEP potential is about 500 times faster than when using the GAP ***,the lattice thermal conductivity in the range of 300 K-600 K is calculated using the NEP *** lattice thermal conductivity decreases as the temperature increases due to the phonon anharmonic *** study provides a theoretical tool for the study of the thermal conductivity of GeTe.

关键词： machine learning potentials thermal conductivity molecular dynamics

来源：评论

学校读者我要写书评

暂无评论

Exploring Machine Learning Models for Duplicate Question Detection in Online Communities 5

Exploring Machine Learning Models for Duplicate Question Det...

引用

5th IEEE International Conference on Advances in Electronics, Computers and Communications, ICAECC 2023

作者： Bharathi Mohan, G. Kumar, R. Prasanna Elakkiya, R. Veer, V. Druva Amrita Vishwa Vidyapeetham School of Computing Computer Science and Engineering Chennai India Institute of Technology and Science School of Computing Computer Science and Engineering Birla Dubai345055 United Arab Emirates

ISBN: (纸本)9798350305449

Duplicate question detection is a crucial task in question-and-answer platforms, aiming to improve user experience and minimize redundancy. This project utilizes NLP techniques and machine learning models to predict whether a pair of questions are duplicates accurately. The project involves comprehensive data pre-processing, including tokenization, stopword removal, and lemmatization, to prepare the textual data for analysis. Here we are using various feature engineering techniques, such as bag-of-words, TF-IDF, and word embedding, are applied to extract meaningful representations of the questions. Multiple machine learning models, including Logistic Regression, Random Forest, SVM, XGBoost, and a Combined model, are trained on labeled data. Evaluation metrics focus on precision and recall minimizing misclassification costs. New sample data is used to validate the models. Predictions are made to classify question pairs as duplicates or non-duplicates, showcasing the model's generalization capabilities. The results highlight the potential of NLP techniques and machine learning models in accurately identifying duplicate question pairs, improving knowledge sharing, and reducing redundancy. This project provides valuable insights and methodologies for future research in duplicate question detection, contributing to the field of NLP and enhancing user experiences on question-and-answer platforms. © 2023 IEEE.

关键词： Embeddings

来源：评论

学校读者我要写书评

暂无评论

Glaucoma Stage Classification Using Image Empirical Mode Decomposition (IEMD) and Deep Learning from Fundus Images 4th

Glaucoma Stage Classification Using Image Empirical Mode Dec...

引用

4th EAI International Conference on Cognitive computing and Cyber Physical Systems, IC4S 2023

作者： Shankar, D. Harsha, I. Sri Madhuri, P. Shyamala Naidu, J. N. S. S. Janardhana Madhuri, P. Krishna Cherukuvada, Srikanth Department of Computer Science and Engineering Vishnu Institute of Technology Kovvada India Department of Networking and Communications School of Computing SRM Institute of Science and Technology Chennai India

ISBN: (纸本)9783031488870

Glaucoma is an ocular pathology characterized by the gradual deterioration of neural cells in the eye, which is attributed to elevated intra ocular pressure within the retina. Glaucoma takes the second spot in terms of its prevalence as a neurodegenerative eye disease, failure to diagnose glaucoma at an early stage can lead to complete blindness. This underlying issue requires a streamlined system that uses experienced medical experts, little equipment, and less time. To categories the stages of glaucoma, a Computer-Aided Diagnosis (CAD) method is used called Image Empirical Mode Decomposition (IEMD). Segmentation-based algorithms utilizing features like cup-to-disc ratio (CDR) and textural characteristics close to the optic disc region can distinguish glaucoma. To capture pixel variations for this investigation, the pre-processed fundus images are separated and transformed into a diverse range of intrinsic mode function (IMFs). The study employs deep learning-based framework for finding the optic nerve head from coloured fundus photographs, to transform the fundus images into a delimited region of interest (ROI) and utilizes multiple deep networks like RFCN and RCNN classifiers identified glaucoma stages separately. The CAD system serves as an automated tool for retinal image processing and demonstrates superior performance over the RFCN classifier, achieving an impressive accuracy of 96% with the RCNN classifier in classifying glaucoma stages. © 2024, ICST institute for Computer sciences, Social Informatics and Telecommunications engineering.

关键词： Computer aided diagnosis

来源：评论

学校读者我要写书评

暂无评论

Compressive sector selection and channel estimation for optimizing throughput and delay in IEEE 802.11ad WLAN

引用

International Journal of Information Technology (Singapore) 2025年第2期17卷 987-998页

作者： Venkatachalam, Ilayaraja Palaniappan, Senthilnathan Ameerjohn, Sirajudeen School of Computer Science and Engineering Vellore Institute of Technology Tamilnadu Vellore India School of Computing Science and Engineering VIT Bhopal University Bhopal-Indore Highway Kothrikalan Madhya Pradesh Sehore 466114 India

To identify the best beam pair in IEEE 802,11ad Wireless Local Area Network (WLAN), it is necessary to evaluate every potential pair's spectrum responsiveness and Signal-to-noise Ratio (SNR). During beam monitoring, nevertheless, the pilot expenditure associated with SNR or channels predictions have been successfully minimized. Therefore, it is crucial to reduce the aided pilot in beam monitoring. In this study, we present a methodology for IEEE 802,11ad WLAN performance and latency optimization called Compressive Sector Selection and Channel Estimation (CSS-CE). 60 GHz transmissions suffer from scarce channel in beams synchronization. Thus, to lower the sample rate, Compressed Sensing (CS) is used. The next step is to recreate the compressed data utilizing Orthogonal Pairing Pursuit (OPP). Because different antenna sectors share similar spatial characteristics, compressive sensing typically probes portions of antenna sector areas. Next, the most suitable region is chosen to meet the additional transmission's latency as well as throughput limitations. The suggested CSS-CE paradigm outperforms the current methods in terms of performance and interruption, according to data from experiments. © Bharati Vidyapeeth's institute of Computer Applications and Management 2024.

关键词： Beam tracking Channel estimation Compressive sensing IEEE 802.11ad WLAN Orthogonal matching pursuit

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：