检索结果-内蒙古大学图书馆

4th International Conference on Advancement in Electronics and Communication engineering, AECE 2024

作者： Surendran, R. Sree, C. Pranitha Sumathy, K. Saveetha Institute of Medical and Technical Sciences Saveetha School of Engineering Department of Computer Science and Engineering Chennai India Saveetha Institution of Medical and Technical Sciences Saveetha School of Engineering Department of Computer Science Engineering Chennai India

ISBN: (纸本)9798350364729

This work aims to improve plant disease prediction through the implementation of an intriguing and revolutionary model that combines refined algorithms for machine learning with predictive analytics strategies. The goal is to strengthen utilities' resilience by providing precise and trustworthy forecasts of the potential classification of diseases. This study aims to push the limits of present prediction skills, analyze cutting-edge technology, and test unique features to improve the accuracy and timeliness of plant pathogen prophecies significantly. The ultimate goal in a rapidly evolving technological environment is to contribute to the development of proactive and effective mitigation techniques that reduce the impact of electrical glitches on equipment, infrastructure, and system-wide consistency. Materials required: The core database for the present investigation was obtained from the reputable data-gathering and analytics website Kaggle. The dataset includes a wide range of line voltages and currents for various failure scenarios. In Group 1, the convolutional neural network approach was applied. The statistical power of the 48 sample sizes utilized in the study was 0.8, the alpha value was 0.05, and the beta value was 0.2. ResNet is used by Group 2. Clinical is the method used to compute these figures. To run the accurate prediction software, Google Colab is employed. It analyzes data and displays accuracy as a percentage and in a graphical format for simple interpretation. Result: The convolutional neural network technique yielded an accuracy of 78.65% percent for Group 1, while the ResNet strategy yielded an accuracy of 70.61% for Group 2. SPSS data analysis program is employed for data set statistical analysis and correctness. A two-tailed test is used to appraise to what extent there is a statistically significant discrepancy in accuracy between both sets of data. Conclusion: Convolutional neural networks are more accurate than ResNet algorithms. © 2024 I

关键词： Convolutional neural networks

来源：评论

学校读者我要写书评

暂无评论

Spatial Correlation Module for Classification of Multi-Label Ocular Diseases Using Color Fundus Images

引用

computers, Materials & Continua 2023年第7期76卷 133-150页

作者： Ali Haider Khan Hassaan Malik Wajeeha Khalil Sayyid Kamran Hussain Tayyaba Anees Muzammil Hussain Department of Software Engineering Faculty of Computer SciencesLahore Garrison UniversityLahorePakistan Department of Computer Science School of Systems and TechnologyUniversity of Management and TechnologyLahore54000Pakistan Department of Computer Science&Information Technology University of Engineering and Technology(UET)PeshawarPakistan Department of Computer Science TIMES InstituteMultan60000Pakistan Department of Software Engineering School of Systems and TechnologyUniversity of Management and TechnologyLahore54000Pakistan

To prevent irreversible damage to one’s eyesight,ocular diseases(ODs)need to be recognized and treated *** fundus imaging(CFI)is a screening technology that is both effective and *** to CFIs,the early stages of the disease are characterized by a paucity of observable symptoms,which necessitates the prompt creation of automated and robust diagnostic *** traditional research focuses on image-level diagnostics that attend to the left and right eyes in isolation without making use of pertinent correlation data between the two sets of *** addition,they usually only target one or a few different kinds of eye diseases at the same *** this study,we design a patient-level multi-label OD(PLML_ODs)classification model that is based on a spatial correlation network(SCNet).This model takes into consideration the relevance of patient-level diagnosis combining bilateral eyes and multi-label ODs ***_ODs is made up of three parts:a backbone convolutional neural network(CNN)for feature extraction i.e.,DenseNet-169,a SCNet for feature correlation,and a classifier for the development of classification *** DenseNet-169 is responsible for retrieving two separate sets of attributes,one from each of the left and right *** then,the SCNet will record the correlations between the two feature sets on a pixel-by-pixel *** the attributes have been analyzed,they are integrated to provide a representation at the patient *** the whole process of ODs categorization,the patient-level representation will be *** efficacy of the PLML_ODs is examined using a soft margin loss on a dataset that is readily accessible to the public,and the results reveal that the classification performance is significantly improved when compared to several baseline approaches.

关键词： Ocular disease multi-label spatial correlation CNN eye disease

来源：评论

学校读者我要写书评

暂无评论

MIMO performance enhancement of MIMO arrays using PCS-based near-field optimization technique

引用

Science China(Information Sciences) 2023年第6期66卷 170-185页

作者： Yipeng WANG Xiaoming CHEN Huiling PEI Wei E.I.SHA Yi HUANG Ahmed A.KISHK School of Information and Communications Engineering Xi'an Jiaotong University Key Laboratory of Micro-Nano Electronic Devices and Smart Systems of Zhejiang Province College of Information Science and Electronic EngineeringZhejiang University Department of Electrical Engineering and Electronics University of Liverpool Department of Electrical and Computer Engineering Concordia University

Extensive efforts have been made in designing large multiple-input multiple-output(MIMO)arrays. Nevertheless, improvements in conventional antenna characteristics cannot ensure significant MIMO performance improvement in realistic multipath environments. Array decorrelation techniques have been proposed, achieving correlation reductions by either tilting the antenna beams or shifting the phase centers away from each other. Hence, these methods are mainly limited to MIMO terminals with small arrays. To avoid such problems, this work proposes a decorrelation optimization technique based on phase correcting surface(PCS)that can be applied to large MIMO arrays, enhancing their MIMO performances in a realistic(non-isotropic)multipath environment. First, by using a near-field channel model and an optimization algorithm, a near-field phase distribution improving the MIMO capacity is obtained. Then the PCS(consisting of square elements)is used to cover the array's aperture, achieving the desired near-field phase *** examples demonstrate the effectiveness of this PCS-based near-field optimization technique. One is a1 × 4 dual-polarized patch array(working at 2.4 GHz)covered by a 2 × 4 PCS with 0.6λ center-to-center distance. The other is a 2 × 8 dual-polarized dipole array, for which a 4 × 8 PCS with 0.4λ center-to-center distance is designed. Their MIMO capacities can be effectively enhanced by 8% and 10% in single-cell and multi-cell scenarios, respectively. The PCS has insignificant effects on mutual coupling, matching, and the average radiation efficiency of the patch array, and increases the antenna gain by about 2.5 dB while keeping broadside radiations to ensure good cellular coverage, which benefits the MIMO performance of the *** proposed technique offers a new perspective for improving large MIMO arrays in realistic multipath in a statistical sense.

关键词： antenna correlation array antenna multiple-input multiple-output (MIMO) capacity

来源：评论

学校读者我要写书评

暂无评论

Twitter Based Emotion Recognition Using Bi-LSTM 2

Twitter Based Emotion Recognition Using Bi-LSTM

引用

2nd IEEE International Conference on Trends in Quantum Computing and Emerging Business Technologies, TQCEBT 2024

作者： Anbazhagan, K. Kurlekar, Supriya Brindha, T.V. Sudhish Reddy, D. Saveetha School of Engineering Department of Computer Science and Engineering Chennai India Department of Electronics and Telecommunication Engineering JSPM Narhe Technical Campus Pune India Sri Venkateshwara College of Engineering and Technology Department of Computer Science and Engineering Tiruvallur India Rmd Engineering College Department of Computer Science and Engineering Tiruvallur India

ISBN: (纸本)9798350384277

Twitter and other social media platforms have evolved into crucial sources of information and communication for billions of people across the world. The massive amount of data collected on these platforms enables insights into people's emotions and feelings. Emotion identification, in particular, has gotten a lot of attention because of its potential uses in industries like marketing, healthcare, and social study. "Emotional states of individuals, also known as moods, are central to expressing thoughts, ideas, and opinions, which influence attitudes and behavior."In this study, we present a method for identifying the emotion or mood of a tweet and classifying it into the appropriate emotional category. The work mentioned in this paper aims to identify and analyze the mood and emotion displayed by people in their text tweets to offer ideas. We gathered tweets and replies on a particular set of themes to create a dataset with text, user, emotion, sentiment, and other data. We used the dataset to compute user impact ratings based on various user and tweet-related factors to analyze feelings and emotions in tweets and their replies. Our model used a brand-new bidirectional long short-term memory (BiLSTM) and previously learned word embedding vectors. Finally, using the latest data, we made generalized and specific consumer suggestions based on Twitter's conduct. According to the experimental findings, the suggested model has 95.56% and 99.16% prediction accuracy. Modern performance improvement was achieved for the model. © 2024 IEEE.

关键词： Tweets

来源：评论

学校读者我要写书评

暂无评论

Monitoring the pH fluctuation of lysosome under cell stress using a near-infrared ratiometric fluorescent probe

引用

Chinese Chemical Letters 2023年第3期34卷 409-414页

作者： Lijuan Gui Kaizhen Wang Yuxin Wang Jun Yan Xian Liu Jingxuan Guo Ji Liu Dawei Deng Haiyan Chen Zhenwei Yuan Department of Biomedical Engineering School of EngineeringChina Pharmaceutical UniversityNanjing 210009China Department of Health Technology and Informatics Faculty of Health and Social SciencesThe Hong Kong Polytechnic UniversityHong KongChina

Cell stress responses are associated with numerous diseases including diabetes, neurodegenerative diseases, and cancer. Several events occur under cell stress, in which, are protein expression and organellespecific pH fluctuation. To understand the lysosomal pH variation under cell stress, a novel NIR ratiometric pH-responsive fluorescent probe(BLT) with lysosomes localization capability was *** quinoline ring of BLT combined with hydrogen ion which triggered the rearrangement of π electrons conjugated at low pH medium, meanwhile, the absorption and fluorescent spectra of BLT showed a red-shifts, which gived a ratiometric signal. Moreover, the probe BLT with a suitable p Kavalue has the potential to discern changes in lysosomal pH, either induced by heat stress or oxidative stress or acetaminophen-induced(APAP) injury stress. Importantly, this ratiometric fluorescent probe innovatively tracks pH changes in lysosome in APAP-induced liver injury in live cells, mice, and zebrafish. The probe BLT as a novel fluorescent probe possesses important value for exploring lysosomal-associated physiological varieties of drug-induced hepatotoxicity.

关键词： APAP-induced liver injury Ratiometric fluorescent probe Lysosomal pH Cell stress

来源：评论

学校读者我要写书评

暂无评论

Forecasting Stock Market Alternations Using Social Media Sentiment Analysis and Deep Neural Networks 14

Forecasting Stock Market Alternations Using Social Media Sen...

引用

14th International Conference on Information, Intelligence, Systems and Applications, IISA 2023

作者： Saravanos, Christina Kanavos, Andreas University of Patras Computer Engineering and Informatics Department Patras Greece Ionian University Department of Informatics Corfu Greece

ISBN: (纸本)9798350318067

Forecasting stock market volatility is a challenging task, primarily due to the influence of non-financial factors such as public opinion and sentiment. Social media platforms, particularly Twitter, have the potential to sway public sentiment and impact stock market prices. In this paper, we present a novel approach for predicting the volatility of multiple indices in the U.S. stock market by integrating historical closing prices with sentiment analysis of Twitter data using deep neural networks. Our approach involves extracting features from tweet metadata and analyzing the overall presence of authors on the platform to estimate future changes in index closing prices. Through our experiments, we observed a strong correlation between the predicted closing prices of an index, the sentiment expressed in tweets, and the extracted features. Despite the sparsity of the dataset, our applied neural networks exhibited robust per-formance. This study contributes to the expanding research on predicting stock market volatility through the utilization of social media sentiment analysis and deep learning techniques. The proposed approach holds promise for applications in financial decision-making and risk management. © 2023 IEEE.

关键词： Sentiment analysis

来源：评论

学校读者我要写书评

暂无评论

EfficientNetB0-based Automated Diabetic Retinopathy Classification in Fundus Images 3

EfficientNetB0-based Automated Diabetic Retinopathy Classifi...

引用

3rd International Conference on Automation, Computing and Renewable Systems, ICACRS 2024

作者： Majaw, Ezekiel Alaric Sundar, G. Naveen Narmadha, D. Thangavel, Senthil Kumar Venkatesan Ajibesin, Adeyemi Abel Department of Computer Science and Engineering Karunya Institute of Technology and Sciences Coimbatore India Department of Computer Science and Engineering Amrita School of Computing Coimbatore India Department of Information Technology Faculty of Informatics and Design Cape Peninsula University of Technology South Africa

ISBN: (纸本)9798331532420

The most common cause of blindness among diabetics is diabetic retinopathy. This disease must be detected in its early stages as delaying treatment can result in permanent blindness. In today’s modern world with the development of technologies, automated techniques have improved the accuracy and efficiency of detecting and classifying this disease. This study suggests a deep learning model utilizing EfficientNetB0 that focuses on designing a neural network structure that requires fewer parameters and computations compared to traditional architectures. Further to help in increasing the accuracy, the image is being processed with methods like CLAHE which is an image processing algorithm that helps in enhancing the contrast while keeping the noise of the images as low as possible. Also, augmentation is being done on the images and SMOTE is applied to handle class imbalance. We collected the APTOS Blindness Detection dataset consisting of retinal fundus images. The accuracy that is being obtained from the proposed methodology is 98.78% indicating the model's effectiveness in detection and classifying between the various stages of diabetic retinopathy. © 2024 IEEE.

关键词： Contrastive Learning

来源：评论

学校读者我要写书评

暂无评论

Ternary Clock Tree Synthesis for 3D ICs 20

Ternary Clock Tree Synthesis for 3D ICs

引用

20th International Conference on Synthesis, Modeling, Analysis and Simulation Methods and Applications to Circuit Design, SMACD 2024

作者： Pavlidis, Christos Sketopoulos, Nikos Sotiriou, Christos P. Pavlidis, Vasilis F. Aristotle University of Thessaloniki School of Electrical and Computer Engineering Greece University of Thessaly Department of Electrical and Computer Engineering Greece

ISBN: (纸本)9798350351927

A 3D clock tree topology generation algorithm is presented based on ternary trees. In addition, a method that leads to an improved stopping criterion for algorithms based on nearest neighbor is proposed. A new grid assignment approach for vertical interconnects that further contributes to low skew clock trees is also presented. By leveraging ternary tree formations, wirelength is significantly reduced, while assigning vertical interconnects with the new approach reduces the number of inserted clock buffers. Results on IBM benchmarks exhibit a 2 4 % decrease in skew alongside a 1 7 % decrease in wirelength on average compared to 3D-MMM. © 2024 IEEE.

关键词： Trees (mathematics)

来源：评论

学校读者我要写书评

暂无评论

Mutual Retinex: Combining Transformer and CNN for Image Enhancement

引用

IEEE Transactions on Emerging Topics in Computational Intelligence 2024年第3期8卷 2240-2252页

作者： Jiang, Kui Wang, Qiong An, Zhaoyi Wang, Zheng Zhang, Cong Lin, Chia-Wen Harbin Institute of Technology School of Computer Science and Technology Harbin150001 China Wuhan University National Engineering Research Center for Multimedia Software School of Computer Science Wuhan430072 China Wuhan Polytechnic University School of Mathematics and Computer Science Wuhan430024 China National Tsing Hua University Department of Electrical Engineering Institute of Communications Engineering Hsinchu30013 Taiwan

Images captured in low-light or underwater environments are often accompanied by significant degradation, which can negatively impact the quality and performance of downstream tasks. While convolutional neural networks (CNNs) and Transformer architectures have made significant progress in computer vision tasks, there are few efforts to harmonize them into a more concise framework for enhancing such images. To this end, this study proposes to aggregate the individual capability of self-attention (SA) and CNNs for accurate perturbation removal while preserving background contents. Based on this, we carry forward a Retinex-based framework, dubbed as Mutual Retinex, where a two-branch structure is designed to characterize the specific knowledge of reflectance and illumination components while removing the perturbation. To maximize its potential, Mutual Retinex is equipped with a new mutual learning mechanism, involving an elaborately designed mutual representation module (MRM). In MRM, the complementary information between reflectance and illumination components are encoded and used to refine each other. Through the complementary learning via the mutual representation, the enhanced results generated by our model exhibit superior color consistency and naturalness. Extensive experiments have shown the significant superiority of our mutual learning based method over thirteen competitors on the low-light task and ten methods on the underwater image enhancement task. In particular, our proposed Mutual Retinex respectively surpasses the state-of-the-art method MIRNet-v2 by 0.90 dB and 2.46 dB in PSNR on the LOL 1000 and FIVEK datasets, while with only 19.8% model parameters. © 2017 IEEE.

关键词： Image enhancement

来源：评论

学校读者我要写书评

暂无评论

Semi-Supervised Machine Learning for Fault Detection and Diagnosis of a Rooftop Unit

引用

Big Data Mining and Analytics 2023年第2期6卷 170-184页

作者： Mohammed G.Albayati Jalal Faraj Amy Thompson Prathamesh Patil Ravi Gorthala Sanguthevar Rajasekaran Department of Mechanical Engineering School of EngineeringUniversity of ConnecticutStorrsCT 06269USA Department of Computer Science and Engineering University of ConnecticutStorrsCT 06269USA Department of Mechanical and Industrial Engineering Tagliatela College of EngineeringUniversity of New HavenWest HavenCT 06516USA.

Most heating,ventilation,and air-conditioning(HVAC)systems operate with one or more faults that result in increased energy consumption and that could lead to system failure over ***,most building owners are performing reactive maintenance only and may be less concerned or less able to assess the health of the system until catastrophic failure *** is mainly because the building owners do not previously have good tools to detect and diagnose these faults,determine their impact,and act on *** available fault detection and diagnostics(FDD)tools have been developed to address this issue and have the potential to reduce equipment downtime,energy costs,maintenance costs,and improve occupant comfort and system ***,many of these tools require an in-depth knowledge of system behavior and thermodynamic principles to interpret the *** this paper,supervised and semi-supervised machine learning(ML)approaches are applied to datasets collected from an operating system in the field to develop new FDD methods and to help building owners see the value proposition of performing proactive *** study data was collected from one packaged rooftop unit(RTU)HVAC system running under normal operating conditions at an industrial facility in *** paper compares three different approaches for fault classification for a real-time operating RTU using semi-supervised learning,achieving accuracies as high as 95.7%using few-shot learning.

关键词： semi-supervised machine learning fault classification fault detection and diagnostics heating,ventilation,and air-conditioning data-driven modeling energy efficiency

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：