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Image Caption Generation Using Contextual Information Fusion With Bi-LSTM-s

ieee ACCESS 2023年 11卷 134-143页

作者： Zhang, Huawei Ma, Chengbo Jiang, Zhanjun Lian, Jing Lanzhou Jiaotong Univ Elect & Informat Engn Lanzhou 730000 Peoples R China

The image caption generation algorithm necessitates the expression of image content using accurate natural language. Given the existing encoder-decoder algorithm structure, the decoder solely generates words one by one in a front-to-back order and is unable to analyze integral contextual information. This paper employs a Bi-LSTM (Bi-directional Long Short-Term Memory) structure, which not only draws on past information but also captures subsequent information, resulting in the prediction of image content subject to the context clues. The visual information is respectively fed into the F-LSTM decoder (forward LSTM decoder) and B-LSTM decoder (backward LSTM decoder) to extract semantic information, along with complementing semantic output. Specifically, the subsidiary attention mechanism S-Att acts between F-LSTM and B-LSTM, while the semantic information of B-LSTM and F-LSTM is extracted using the attention mechanism. Meanwhile, the semantic interaction is extracted pursuant to the similarity while aligning the hidden states, resulting in the output of the fused semantic information. We adopt a Bi-LSTM-s model capable of extracting contextual information and realizing finer-grained image captioning effectively. In the end, our model improved by 9.7% on the basis of the original LSTM. In addition, our model effectively solves the problem of inconsistent semantic information in the forward and backward direction of the simultaneous order, and gets a score of 37.5 on BLEU-4. The superiority of this approach is experimentally demonstrated on the MSCOCO dataset.

关键词： Feature extraction Semantics visualization data mining Decoding Task analysis Logic gates Bi-LSTM image caption generation semantic fusion semantic similarity

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Integrating Cloud Computing with IoT for Battery Monitoring in Electric Vehicles 2

Integrating Cloud Computing with IoT for Battery Monitoring ...

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2nd International Conference on Smart Technologies for Smart Nation, SmartTechCon 2023

作者： Selvi, J. Sathyanathan, P. Priya, S. Balaji Damodhar, T.S. Sujatha, S. Department of Civil Engineering Shree Sathyam College of Engineering and Technology Sankari Tamil Nadu Salem India Department of Electrical and Electronics Engineering VelTech Rangarajan Dr.Sagunthala R&D Institute of Science and Technology Tamil Nadu Chennai India Deemed to be University Tamil Nadu Chennai India Department of Electrical and Electronics Engineering Madanapalle Institute of Technology & Science Andhra Pradesh Madanapalle India Department of Biomedical Engineering Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences Saveetha University Tamil Nadu Chennai India

ISBN: (纸本)9798350305418

Cloud computing and the Internet of Things (IoT) for battery monitoring in electric vehicles (EVs) can improve battery performance and efficiency. EV batteries, IoT devices, cloud infrastructure, data transmission, storage, processing, analysis, visualization, user interface, and integration with EV management systems are used in this integration. These components are integrated to provide a complete system for real-time monitoring, predictive maintenance, and performance improvement of EV batteries. Sensors are essential to this system. The battery's electrical characteristics, temperature, environmental conditions, and potential concerns are collected via voltage, current, and temperature sensors. The technology improves battery performance, lifespan, real-time monitoring and alarms, predictive maintenance insights, and charging and discharging techniques. Stakeholders may make informed decisions and take proactive steps to enhance EV battery efficiency and lifetime using cloud infrastructure, data analysis and visualization tools, and IoT. Advancing electric mobility and sustainability, the system monitors, manages, and improves battery performance. © 2023 ieee.

关键词： Battery monitoring Cloud computing Electric vehicle IoT Sensors

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IoT-Based Real Time data Acquisition of PV Panel

IoT-Based Real Time Data Acquisition of PV Panel

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2023 International Conference on Engineering, science and Advanced Technology, ICESAT 2023

作者： Al-Naib, Ahmed M. T. Ibraheem Mohammed, Majeed Ismail Technical Engineering College/ Mosul Northern Technical University Dept. of Electrical Power Eng. Techniques Mosul Iraq

ISBN: (纸本)9798350344400

Renewable energy is becoming more popular because of the world's increasing demand for electricity as a result of rising energy consumption and the depletion of traditional energy sources. The most important and most efficient of these is solar energy, and to get the maximum value of this energy with the highest efficiency, there should be direct monitoring of the performance of these solar panels. So, in this work, a data acquisition system for a PV panel using a Programmable Logic Controller was proposed and implemented, and the collected data was remotely monitored based on the Internet of Things (IoT). The proposed PV monitoring system, which consists of a PV panel, various sensors, a PLC (a Siemens S7-1200 type), and a load, was experimentally tested in Kirkuk City for approximately 10 hours to visualize the five parameters of PV terminal voltage, load current, power, temperature, and radiation. Three visualization methods were employed for presenting the results: The first, locally direct method, is in SIMATIC WinCC on the computer. The second method is that the collected data was saved in Excel file format. The final and most important technique is smart online wireless remote monitoring on the Ubidots platform, which utilizes the router unit to display the parameters on the smart device. The proposed system has a low price, a simple design, real-time measurements, the ability to be expanded, high efficiency, accuracy, and reliability, and can be built quickly. © 2023 ieee.

关键词： data acquisition

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Hot Spots and Trends of Computational Thinking Research in China CiteSpace visualization and analysis based on CNKI data 13

Hot Spots and Trends of Computational Thinking Research in C...

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13th International Conference on Information Technology in Medicine and Education, ITME 2023

作者： Wang, Yiwei Xu, Liancheng School of Information Science and Engineering Shandong Normal University Jinan China

ISBN: (纸本)9798350319156

With the rapid development of computer technology, computational thinking, as one of the core literacy elements of information technology majors, has been widely noticed, and at the same time, it has gradually become one of the core competencies cultivated by education and teaching. This paper uses CiteSpace to visualize and analyze the Chinese core papers related to computational thinking included in CNKI. By studying the current situation of domestic research on computational thinking and related hot keywords, it aims to explore the prospects for the application of computational thinking in secondary education, try to incorporate the educational model of C elements in STEM education, and provide research ideas for the cultivation of computational thinking ability of secondary students. © 2023 ieee.

关键词： Education computing

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Research and Prospect of Early Warning and Prediction Model of Public Health Emergencies Based on Big data Computation 3

Research and Prospect of Early Warning and Prediction Model ...

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3rd ieee International Conference on Electronic Communications, Internet of Things and Big data, ICEIB 2023

作者： Hu, Songnian Chen, Dan Li, Hongrong Xingshan People's Hospital Yichang China School of Management Hubei University of Chinese Medicine Wuhan China Key Projects Sciences Research Base of Humanities and Social in Hubei Province the Development Research Center of Traditional Chinese Medicine Wuhan China Hubei University of Chinese Medicine Wuhan China

ISBN: (纸本)9798350333862

The systematic review method was used to analyze the distribution of literature for understanding the trend of early warning methods. Regression analysis using spatiotemporal models such as ARIMA models and propagation dynamics models was used in this study. Geographic visualization techniques and neural networks, and even traditional statistical models were combined with big data and geographic information systems for early warning. 72 articles were reviewed, and the result showed that public health emergencies have gradually changed from traditional models In practical applications, it is necessary to comprehensively consider the characteristics of the disease, data types, and model characteristics to select the most appropriate predictive model. The model can be compared with a variety of methods to provide technical support. © 2023 ieee.

关键词： Big data

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Contrastive Graph Representations for Logical Formulas Embedding

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ieee TRANSACTIONS ON KNOWLEDGE AND data ENGINEERING 2023年第4期35卷 3563-3574页

作者： Lin, Qika Liu, Jun Zhang, Lingling Pan, Yudai Hu, Xin Xu, Fangzhi Zeng, Hongwei Xi An Jiao Tong Univ Sch Comp Sci & Technol Xian 710049 Shaanxi Peoples R China Xi An Jiao Tong Univ Shaanxi Prov Key Lab Satellite & Terr Network Tech Natl Engn Lab Big Data Analyt Xian 710049 Shaanxi Peoples R China

Currently, the non-transparent computing process of deep learning has become a significant reason hindering its further development. The Neural-Symbolic (NS) system formed by integrating logic rules into neural networks has attracted increasing attention owing to its direct interpretability. Embedding symbolic logical formulas into a low-dimensional continuous space provides an effective way for the NS system. However, current studies are all constrained by the modeling ability for its syntactic structure and fail to preserve the intrinsic semantics in embeddings, which causes poor performance on downstream reasoning tasks. To this end, this paper proposes a novel method of Contrastive Graph Representations (ConGR) for logical formulas embedding. First, to improve the modeling ability for the syntactic structure, ConGR introduces a densely connected graph convolutional network (GCN) with an attention mechanism to process syntax parsing graphs of formulas. In this way, discriminative local and global embeddings of formulas are obtained at the syntax level. Second, the contrastive instances (positive or negative) for each anchor formula are generated by the transformation under the guidance of logical properties. To preserve semantic information, two types of contrast, global-local and global-global, are carried out to refine formula embeddings. Extensive experiments demonstrate that ConGR obtains superior performance against state-of-the-art baselines on entailment checking and premise selection datasets.

关键词： Syntactics Semantics Task analysis visualization Transforms Recurrent neural networks Deep learning Formulas embedding graph representation contrastive learning graph convolutional network

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Evaluation and Analysis data from Twitter data by Using Hybrid CNN & LTSM 5

Evaluation and Analysis Data from Twitter Data by Using Hybr...

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5th International Congress on Human-Computer Interaction, Optimization and Robotic Applications, HORA 2023

作者： Abdullah Aswad, Salma College of Science University of Duhok Department of Computer Science Duhok42001 Iraq

ISBN: (纸本)9798350337525

The central theme for this thesis is the design of an aspect-based sentiment analysis model for the classification of online Italian automotive forums' comments. The work starts with designing a strategy for collecting information about target forums to make it possible to develop a machine learning-based sentiment classification model. The study involved applying the CNN and LTSM model, a state-of-the-art solution based on a parametric model that will improve the performance of a baseline algorithm, especially in case of very noisy data like the ones where this tool is supposed to be to work on. This work has been designed as a two-stage CNN and LTSM classifier in all its parts. It was compared with a one-step classifier to detect the pertinence about some topics, and eventually, the sentiment achieved an accuracy of 96.78% for all comments. The current problem passed from a typical three degrees' polarity sentiment analysis to a four labels text classification, where it will be introduced an additional category for determining whether the text is pertinent to a particular topic or not. Presenting this information, the models must be enhanced, and a cascade classification solution will be proposed. The final model is then utilized for a real-world use case. New data have been classified concerning some selected topics, finally presented exploiting a data visualization but still not satisfactory, thus making sentiment analysis an ongoing and open research subject. © 2023 ieee.

关键词： Sentiment analysis

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Global Patent Landscape of Decision Support System in The Business: An Overview 8

Global Patent Landscape of Decision Support System in The Bu...

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8th International Conference on Information Management and Technology, ICIMTech 2023

作者： Irawan, Michelia Champaca Salsabila Purnomo, Agung Sanjaya, Angelie Natalia Meiryani Ubud, Sahnaz Maulana, Fairuz Iqbal Binus Business School Undergraduate Program Bina Nusantara University Entrepreneurship Department Jakarta11480 Indonesia School of Accounting Bina Nusantara University Accounting Department Jakarta11480 Indonesia School of Computer Science Bina Nusantara University Computer Science Department Jakarta11480 Indonesia

ISBN: (纸本)9798350326093

The study of decision support systems in business and entrepreneurial ecosystems continues to grow and play an essential role. However, few studies have not comprehensively reviewed decision support system studies from the patent perspective. This research attempts to review the global patent landscape and the main trends of patents on decision support systems in business. Research using patent landscapes analysis was intended to clarify data visualization, identification, and analysis. This study uses 6,713 patent documents and 3,492 simple patent families over 35 years from the *** database. The study results reveal increased trends regarding developments to patent activity related to decision support systems in the business. There were business opportunities that business parties could use to the potential for increasing patent filings. The CPC codes that dominate decision support system patents in the business were G (physics). The United States was the most prolific country regarding owners and inventors. © 2023 ieee.

关键词： Decision support systems

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Memorial GAN With Joint Semantic Optimization for Unpaired Image Captioning

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ieee TRANSACTIONS ON CYBERNETICS 2023年第7期53卷 4388-4399页

作者： Song, Peipei Guo, Dan Zhou, Jinxing Xu, Mingliang Wang, Meng Hefei Univ Technol Key Lab Knowledge Engn Big Data Intelligent Interconnected Syst Lab Anhui Prov Minist EducSch Comp Sci & Informat Engn Hefei 230601 Peoples R China Hefei Univ Technol Sch Artificial Intelligence Hefei 230601 Peoples R China Zhengzhou Univ Sch Informat Engn Zhengzhou 450001 Peoples R China

Most works of image captioning are implemented under the full supervision of paired image-caption data. Limited to expensive cost of data collection, the task of unpaired image captioning has attracted researchers' attention. In this article, we propose a novel memorial GAN (MemGAN) with the joint semantic optimization for unpaired image captioning. The core idea is to explore implicit semantic correlation between disjointed images and sentences through building a multimodal semantic-aware space (SAS). Concretely, each modality is mapped into a unified multimodal SAS, where SAS includes the semantic vectors of image I, visual concepts O, unpaired sentence S, and the generated caption C. We adopt the memory unit based on multihead attention and relational gate as a backbone to preserve and transit crucial multimodal semantics in the SAS for image caption generation and sentence reconstruction. Then, the memory unit is embedded into a GAN framework to exploit the semantic similarity and relevance in SAS, that is, imposing a joint semantic-aware optimization on SAS without supervision clues. To summarize, the proposed MemGAN learns the latent semantic relevance of SAS's multimodalities in an adversarial manner. Extensive experiments and qualitative results demonstrate the effectiveness of MemGAN, achieving improvements over state of the arts on unpaired image captioning benchmarks.

关键词： Semantics Synthetic aperture sonar visualization Task analysis Optimization Generative adversarial networks Correlation Generative adversarial network memory network semantic-aware space (SAS) unpaired image captioning

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A Machine Learning based Platform for Remote Management of Heart Failure Patients 19

A Machine Learning based Platform for Remote Management of H...

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19th ieee International Conference on Intelligent Computer Communication and Processing Conference, ICCP 2023

作者： Man, Rares-Ioan Anghel, Ionut Technical University of Cluj-Napoca Computer Science Department Cluj-Napoca Romania

ISBN: (纸本)9798350370355

Heart failure is a growing concern due to its high incidence nowadays, also representing a major cause of morbidity and mortality worldwide. In this paper we propose a web-based platform that incorporates both the clinical data prediction aspect and the continuous monitoring of the heart health. We implement multiple machine learning models that can support the doctors in the process of classification between a healthy and unhealthy situation. The platform benefits from an ETL (Extract, Transform, Load) sub-system that processes biometric data from smart wearables and displays it in customizable dashboards for a more illustrative visualization. The prediction service integrates three Machine Learning (ML) techniques, namely Logistic Regression, Naïve Bayes Classifier, and a custom Artificial Neural Network responsible for making classifications on the monitored data. The results illustrate that the proposed solution's usage for performing remote monitoring and heart health assessment is feasible, obtaining promising accuracies with the aid of a public heart failure dataset (best accuracy of 88.5%). © 2023 ieee.

关键词： Machine learning

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