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ParsBERT topic modeling of Persian scientific articles about COVID-19

Informatics in Medicine Unlocked 2023年 36卷 101144页

作者： Dehghani, Mohammad Ebrahimi, Fezzeh School of Electrical and Computer Engineering University of Tehran Tehran Iran Department of Knowledge and Information Science University of Isfahan Isfahan Iran

Purpose: The COVID-19 pandemic has indisputably impacted every aspect of human life, and a host of studies have investigated its different aspects. This paper models the contents of Persian literature on COVID-19. Method: This is a descriptive-exploratory study in which 815 articles were collected from the Magiran database. The articles were published before March 2022. The abstracts and titles were used in the modeling. The modeling was performed by combining the latent Dirichlet allocation (LDA) algorithm with ParsBERT. Findings: Topic modeling indicated ten major topics, including medicine, psychology, humanities, politics, management, biology, economics, culture, engineering, and religion. The articles under the category of medicine had the largest cluster (42.3%), while engineering and religion had the smallest clusters (1.1% each). Conclusion: The found topics in the created clusters have structural relationships. The COVID-19 effect on physical and mental health (medical and psychological topics) is the most crucial factor. These clusters provide evidence that COVID-19 affects all facets of human society at three levels: the individual, family, and society. Aside from the ten critical clusters in the humanities field, the utmost disorder is related to teaching and learning. For the first time, this research has presented a model of scientific communication in the field of COVID-19 based on the data collected from a Persian database – Magiran. © 2022 The Authors

关键词： Corona COVID-19 LDA ParsBERT Topic modeling

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Emotion Recognition System via Facial Expressions and Speech Using Machine Learning and Deep Learning Techniques

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SN computer science 2023年第4期4卷 363页

作者： Chaudhari, Aayushi Bhatt, Chintan Nguyen, Thanh Thi Patel, Nisarg Chavda, Kirtan Sarda, Kalind U & P U. Patel Department of Computer Engineering Chandubhai S. Patel Institute of Technology Charotar University of Science And Technology (CHARUSAT) Gujarat Changa 388421 India Department of Computer Science and Engineering School of Technology Pandit Deendayal Energy University Knowledge Corridor Gujarat Gandhinagar 382007 India School of Information Technology Faculty of Science Engineering and Built Environment Deakin University Victoria 3216 Australia Bishop University 2600 College St Sherbrooke J1M 1Z7 QC Canada Crest Data Systems Private Limited Gandhinagar Highway Makarba Gujarat Ahmedabad 380051 India Programmer Analyst Meditab Software Pvt Ltd Kalasagar Mall Ghatlodiya Gujarat Ahmedabad 380061 India

Patients in hospitals frequently exhibit psychological issues such as sadness, pessimism, eccentricity, and anxiety. However, hospitals normally lack tools and facilities to continuously monitor the psychological health of patients. It is desirable to identify depression in patients so that it can be managed by instantly providing better therapy. This can be possible by advances in machine learning for image processing with notable applications in the domain of emotion recognition using facial expressions. In this paper, we have proposed two different methods, i.e. facial expression detection and voice analysis, to predict emotions. For facial expression recognition, we have used two approaches, one is the use of Gabor filters for feature extraction with support vector machine for classification and another is using convolutional neural network (CNN). For voice analysis, we extracted mel-frequency cepstral coefficients from speech data and, based on those features, predicted the emotions of the speech using a CNN model. Experimental results show that our proposed emotion recognition methods obtained high accuracy and thus could be potentially deployed to real-world applications. © 2023, The Author(s), under exclusive licence to Springer Nature Singapore Pte Ltd.

关键词： CNN Deep learning Expressions Facial emotion Machine learning Speech SVM

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Fine-grained Cross-modal Fusion based Refinement for Text-to-Image Synthesis

arXiv

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arXiv 2023年

作者： Haoran, Sun Yang, Wang Haipeng, Liu Biao, Qian Meng, Wang Key Laboratory of Knowledge Engineering With Big Data Ministry of Education Hefei University of Technology Hefei China Department of Computer Science and Information Engineering Hefei University of Technology Hefei China

Text-to-image synthesis refers to generating visual-realistic and semantically consistent images from given textual descriptions. Previous approaches generate an initial low-resolution image and then refine it to be high-resolution. Despite the remarkable progress, these methods are limited in fully utilizing the given texts and could generate text-mismatched images, especially when the text description is complex. We propose a novel Fine-grained text-image Fusion based Generative Adversarial Networks, dubbed FF-GAN, which consists of two modules: Fine-grained text-image Fusion Block (FF-Block) and Global Semantic Refinement (GSR). The proposed FF-Block integrates an attention block and several convolution layers to effectively fuse the fine-grained word-context features into the corresponding visual features, in which the text information is fully used to refine the initial image with more details. And the GSR is proposed to improve the global semantic consistency between linguistic and visual features during the refinement process. Extensive experiments on CUB-200 and COCO datasets demonstrate the superiority of FF-GAN over other state-of-the-art approaches in generating images with semantic consistency to the given *** is available at htt s:// ithub com/haoranhfut/FF-GAN Copyright © 2023, The Authors. All rights reserved.

关键词： Generative adversarial networks

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Short Tail:taming tail latency for erasure-code-based in-memory systems

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Frontiers of Information Technology & Electronic engineering 2022年第11期23卷 1646-1657页

作者： Yun TENG Zhiyue LI Jing HUANG Guangyan ZHANG College of Computer Science and Technology Jilin UniversityChangchun 130012China Department of Computer Science and Technology Tsinghua UniversityBeijing 100084China Key Laboratory of Symbolic Computation and Knowledge Engineering of Ministry of Education Jilin UniversityChangchun 130012China Beijing National Research Center for Information Science and Technology(Tsinghua University) Beijing 100084China

In-memory systems with erasure coding(EC)enabled are widely used to achieve high performance and data ***,as the scale of clusters grows,the server-level fail-slow problem is becoming increasingly frequent,which can create long tail *** influence of long tail latency is further amplified in EC-based systems due to the synchronous nature of multiple EC *** this paper,we propose an EC-enabled in-memory storage system called ShortTail,which can achieve consistent performance and low latency for both reads and ***,ShortTail uses a lightweight request monitor to track the performance of each memory node and identify any fail-slow ***,ShortTail selectively performs degraded reads and redirected writes to avoid accessing fail-slow ***,ShortTail posts an adaptive write strategy to reduce write amplification of small *** implement ShortTail on top of Memcached and compare it with two baseline *** experimental results show that ShortTail can reduce the P99 tail latency by up to 63.77%;it also brings significant improvements in the median latency and average latency.

关键词： Erasure code In-memory system Node fail-slow Small write Tail latency

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A Comparative Study on Cyber security Technology in Big data Cloud Computing Environment 5

A Comparative Study on Cyber security Technology in Big data...

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5th International Conference on Contemporary Computing and Informatics, IC3I 2022

作者： Jain, Amit Kumar Misra, Tripti Tyagi, Neha Suresh Kumar, M.V. Pant, Bhasker Delhi University Department of Computer Science Delhi India Deutche Telecom Digital Labs Haryana Gurugram India G. L Bajaj Institute of Technology & Management Knowledge Park 3 Department of IT Greater Noida India Koneru Lakshmaiah Education Foundation Department of Law Green Fields Guntur Vaddeswaram India Graphic Era Deemed to Be University Department of Computer Science & Engineering Uttarakhand Guntur Dehradun India

ISBN: (纸本)9798350398267

Protection of networks, programs and systems from cyberattacks is the practice of cybersecurity technology. The cyberattacks are capable of gaining access for deleting or altering of data that are sensitive, demanding money from the users and obstruction of regular operations of corporates. In the present situation many devices are getting smarter than that of the hackers and humans that makes the tasks difficult for implementing the measures of cybersecurity. In terms of cyber security, the ability of collecting huge amount of data is known as big data analytics. The functions are performed through displaying, interpretation and extraction of the insights of future that can enable early detection of catastrophic cyber threats and attacks. Organizations can better understand all the activities and acts that could potentially result in cyber-attacks by having a stronger and more effective cyber defensive posture. As offices became more remote, cloud computing became crucial for gaining access to important documents, programs, and computing resources. Although connection can be both a benefit and a curse, as easily accessible files can become an easy target for hostile actors, we expect the global economy to transfer even more into the cloud over time. Therefore, efforts in strengthening cybersecurity measures are vital to safeguard distant vaults of sensitive data. The management depends on a combination of technology and advice to secure the cloud. It involves managing the framework, information applications, safe-secure guidelines, consistency leads, and secure infrastructure data applications that pertain to cloud computing. The same principles that underpin security for modern cloud computing platforms and historic knowledge centers are secrecy, integrity, and usability. The novel method of DDoS attack detection is proposed. © 2022 IEEE.

关键词： Cybersecurity

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AI-Driven Privacy Analysis of IoT Services in Edge Computing

AI-Driven Privacy Analysis of IoT Services in Edge Computing

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Emerging Research in Computational science (ICERCS), International Conference on

作者： Maki Mahdi Abdulhasan Ahmed Hussain Mariam Fadel Ali Mustafa Zuhaer Nayef Al-Dabagh Jawad Radhi Rustum Al-Assal Al-Nisour University College Baghdad Iraq Department of Medical Instruments Engineering Techniques Al-Farahidi University Baghdad Iraq Department of Radiology Techniques Uruk University Baghdad Iraq Department of Computer Science College of Science Knowledge University Erbil Iraq Department of Petroleum Engineering Al Kitab University Kirkuk Iraq

ISBN: (数字)9798331534967

ISBN: (纸本)9798331534974

Inevitably, IoT equipment generates more data, but fog computing's concept of transferring all data to the cloud for computation has failed to meet real-time needs. Cutting-edge edge computing delivers all data from the cloud to edge devices, enhancing service quality for IoT applications that need delay. However, Edge devices are more vulnerable to attacks than endpoint technologies due to low processing power and storage. Edge computing IoT services face growing dangers, making security and privacy solutions crucial. This article proposes AI-SPM to optimally solve IoT security and privacy issues in edge computing devices. AI's better learning skills help the proposed system detect harmful intrusions more precisely. The proposed solution was compared to other IoT security and privacy management methods to prove its efficacy. The proposed framework paradigm updates security and gives edge computing devices the most robust defences against IoT service cyberattacks. The AI-SPM achieved 99.54% consumer fulfilment, 99.88% security, 99.98% system reliability, and 5.34% latency compared to other techniques.

关键词： Privacy Scientific computing Real-time systems Internet of Things Security Reliability Protection Interoperability Faces Edge computing

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Deep Hierarchical Learning for Estimating High Penetration of Renewable Energy Sources in Smart Grid Environments

Deep Hierarchical Learning for Estimating High Penetration o...

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Emerging Research in Computational science (ICERCS), International Conference on

作者： Mohammed Abed Jawad Waleed Al-Azzawi Mariam Fadel Ali Noor Kaylan Hamid Marwan Aziz Mohammed Al-Nisour University College Baghdad Iraq Department of Medical Instruments Engineering Techniques Al-Farahidi University Baghdad Iraq Department of Radiology Techniques Uruk University Baghdad Iraq Department of Computer Education Al Kitab University Kirkuk Iraq Department of Computer Science College of Science Knowledge University Erbil Iraq Erbil Iraq

ISBN: (数字)9798331534967

ISBN: (纸本)9798331534974

A prototype model for deep hierarchical learning and information for highly penetrating renewable energy resources (HPRES) is presented in this study. Because the stochastic nature of renewable energy increases the potential for hazardous situations, the growing deployment of renewable generators has posed a significant challenge to power system stability. As a result, a learning algorithm is used based on labelled databases and other complex data to learn how to reliably predict HPRES in real time without knowing the incoming renewable power data. The suggested method uses various historical data, such as wind direction, sunshine duration, and power distribution operations data, to produce modest, real-time results on the predictability of the data. MA does a rudimentary spectral analysis before starting to build the training dataset. The strategy's effectiveness is evaluated using a typical 16-machine, 64-bus testing setup.

关键词： Deep learning Training Renewable energy sources Stochastic processes Real-time systems Power system reliability Reliability Wind forecasting Spectral analysis Testing

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Energy Efficient UAV-assisted Communications via Collaborative Beamforming

Energy Efficient UAV-assisted Communications via Collaborati...

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IEEE International Symposium on Personal, Indoor and Mobile Radio Communications (PIMRC)

作者： Tingting Zheng Yanheng Liu Geng Sun Mushu Li Conghao Zhou Xuemin Sherman Shen College of Computer Science and Technology Jilin University Changchun China Department of Electrical and Computer Engineering University of Waterloo Waterloo ON Canada Key Laboratory of Symbolic Computation and Knowledge Engineering of Ministry of Education Jilin University Changchun China

In this paper, we propose collaborative beamforming (CB) in unmanned aerial vehicle (UAV)-assisted communication networks to improve transmission data rate with minimum energy consumption. Specifically, CB allows a group of UAVs forming a virtual element antenna array (VEAA) and transmitting data collaboratively in a synchronous manner through a high-gain mainlobe (ML) beam. The goal is to optimize the deployment locations of UAVs in the VEAA and excitation current weights for performing CB transmissions considering the energy cost for UAV deployment. Accordingly, we formulate an Energy-Efficient Communication Multi-objective Optimization Problem (EECMOP) to jointly maximize the transmission rate and minimize the maximum sidelobe level (SLL) as well as UAV energy consumption. Then, we propose an Enhanced Multi Objective Ant Lion Optimizer (EMOALO) algorithm which incorporates a chaos theory to develop chaotic initialization and adjustable mode operators for solving the problem. Simulation results demonstrate the effectiveness of the EMOALO algorithm in improving energy efficiency for UAV-assisted communication networks.

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Face recognition using pca implemented on raspberry pi 11th

Face recognition using pca implemented on raspberry pi

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11th National Technical Symposium on Unmanned System Technology, NUSYS 2019

作者： Mohammed, Ibrahim Majid Al-Dabagh, Mustafa Zuhaer Nayef Ahmad, Muhammad Imran Isa, Mohd Nazrin Md School of Computer and Communication Engineering University Malaysia Perlis ArauPerlis Malaysia School of Microelectronic Engineering University Malaysia Perlis ArauPerlis Malaysia Department of Computer Science College of Science Knowledge University ErbilKurdistan Region Iraq

ISBN: (纸本)9789811552809

Currently, many consumers electronic are utilizing personal identification technique such as ID, fingerprint, face, Iris and palmprint recognition for security reason. Among other biometric traits, face image is easy to capture using Pi Camera. This paper proposes facial recognition using principle component analysis (PCA) method implemented using Raspberry Pi embedded processor. The algorithm is model using PC based method to identify the best eigenvector to represent facial image. The pre-calculated statistical parameter is then used to implement the PCA algorithm in Raspberry Pi embedded processor. OpenCV image processing libraries is use to support the basic task of image pre-processing such as cropping, resize and color conversion. In the propose work, the whole system is implemented in low cost processor to evaluate the performance in terms of recognition rates and processing time. The proposed method includes three main phases. The pre-processing phase processes facial ORL image and to collect significant information. The second phase extracts important features from image and decreases the size of the image using the PCA method. This phase uses a linear projection technique to decrease redundancies and remove noise from the image. Moreover, this strategy also improves the strength of discrimination power in the feature space. The Euclidean distance classifier is utilized in the third phase of classification. The best recognition rates achieved using the propose method is 82.5% implemented using low cost embedded system. © Springer Nature Singapore Pte Ltd 2021.

关键词： Python

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Revolutionizing Internet of Things with Blockchain for Enhanced Security and Data Management

Revolutionizing Internet of Things with Blockchain for Enhan...

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International Conference on Contemporary Computing and Informatics (IC3I)

作者： G Anitha K Gopal Alok Jain Rafal Emad Nagarjuna Thandra Preeti Tewari Department of Computer Science and Engineering Institute of Aeronautical Engineering Hyderabad Telangana India Department of Mechanical Engineeieng New Horizon College of Engineering Bangalore India Lovely Professional University Phagwara India Hilla University College Babylon Iraq Department of Computer Science and Engineering MLR Institute of Technology Hyderabad Telangana India Lloyd Law College Plot No. 11 Knowledge Park II Greater Noida Uttar Pradesh

ISBN: (数字)9798350350067

ISBN: (纸本)9798350350074

This paper introduces a paradigm that might transform the Internet of Things. Blockchain technology makes this system workable. Five approaches comprise the framework. Each is crucial for a powerful, automated, and scalable Internet of Things ecosystem. The framework has five techniques. Making a permanent record is the first step. This record is unchangeable. The second program handles steps. The third way secures messages. The fourth program prioritizes customer privacy. The fifth technique addresses expansion and interoperability. Comparison testing and graphic presentations reveal that the technique outperforms all key qualities. The reason is: Since reviews are detailed, the technology is supposed to transform data management and security in the fast-growing Internet of Things. This modification will likely be major.

关键词： Data privacy Ecosystems Smart contracts Transforms Software Blockchains Software reliability Internet of Things Interoperability Testing

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