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Design of Russian Language Recognition System

Design of Russian Language Recognition System

引用

2022 IEEE international conference on Advances in Electrical Engineering and computer Applications, AEECA 2022

作者： Zhang, Shuang Heilongjiang University of Technology The School of International Education Jixi China

ISBN: (数字)9781665480901

ISBN: (纸本)9781665480901

Language identification is widely used in network security, authentication, multilingual information services and other fields. Nowadays, the increasingly open international environment and the rapid development of the Internet have brought new challenges to language recognition. This paper has completed the design of the Russian language identification system, using the hierarchical design scheme of high cohesion and low coupling. Starting from the system requirements, the overall design and hierarchical design of the language identification system have been completed. The system design includes five layers: data acquisition layer, data transmission layer, algorithm analysis layer, system management layer and system visualization layer. The data transmission layer is responsible for transmitting voice data and system signaling to the business background, and then the business background calls the language recognition algorithm of the algorithm analysis layer to classify and recognize the voice data. The system management layer and system visualization layer support users and devices to access the system more friendly and manage language identification data. Finally, a detailed test and analysis are carried out. The results show that the system can complete the whole language recognition task in a short time, and the recognition accuracy is relatively high. © 2022 IEEE.

关键词： Data visualization

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Human Pose Estimation: Benchmarking Deep Learning-based Methods

Human Pose Estimation: Benchmarking Deep Learning-based Meth...

引用

2022 IEEE conference on Interdisciplinary Approaches in technology and Management for Social Innovation, IATMSI 2022

作者： Lovanshi, Mayank Tiwari, Vivek International Institute of Information Technology Department of Computer Science & Engineering Naya Raipur India

ISBN: (纸本)9781665477192

Human posture estimation (HPE) aims to detect human body parts and generate a demonstration of the human body with incoming data such as images and video. HPE has lately gained much attention as shown numerous applications in the real world. State-of-the-art human posture estimation methods may benefit from deep learning. In this view, this study aims to examine and assess deep learning-based human posture estimation algorithms. To describe and analyze recent research, multiple deep learning-based human posture estimation approaches (OpenPose, ViTPose-B, HRNet, AlphaPose, DenseNet, EfficientPose, DensePose, Hourglass, 4RSN-50) deployed on the COCO and MPII datasets. This paper examined human posture using evaluation measures like average precision (AP) and probability of correct key points (PCK). Therefore, ViTPose-B methods outperform AP on the COCO dataset & Hourglass achieves better PCK on MPII. The comparative analysis helps to understand the applicability of different techniques for human pose estimation. Furthermore, unresolved issues and future research challenges of the human pose are mentioned. © 2022 IEEE.

关键词： computer vision

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The Advancement of Biomedical Relation Extraction through Cloud Computing and Big Data Analysis 2

The Advancement of Biomedical Relation Extraction through Cl...

引用

2nd IEEE international conference on Electronic technology, Communication and Information, ICETCI 2022

作者： Wang, Zishuo Fujian Agriculture and Forestry University International College Fuzhou350007 China

ISBN: (数字)9781728181158

ISBN: (纸本)9781728181158

Due to the development of biology and medicine, more and more biomedical relationships are collected in the literature. Meanwhile, with the completion of the human genome project, the post-genome era is beginning. Proteomics, metabolomics, and other interdisciplinary disciplines formed by biology, medicine, and information technology are constantly emerging. In this case, Biomedical information extraction brings a faster and more convenient retrieval mechanism for medicine, which can make doctors consult the interaction between drugs more quickly to avoid adverse drug reactions. Specifically, it brings a more efficient pre-experiment to the field of biological research because a large number of protein interactions are presented on the computer without the need for researchers to extract them from the literature. However, this emerging field often faces significant challenges. (1) The imbalance of data sets makes computers deceive themselves and produce a tendentious result. (2) Annotating the training dataset is a waste of human and material resources. (3) The accuracy of the results obtained by different algorithms is too low. This paper summarizes the research progress in this field in recent years including most classical methods, e.g., rule-based, supervised and unsupervised, On this basis, the existing problems in this field are demonstrated and corresponding feasible solutions are proposed. Finally, through the basic knowledge and development momentum of biology and computer science, this field's future development direction and improvement direction are discussed. Overall, these results shed light on the progress of biomedical research, the practical operation norms of biology and medicine and the improvement of research efficiency. © 2022 IEEE.

关键词： Big data

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Forecasting of Bank Performance using Hybrid Machine Learning Techniques

Forecasting of Bank Performance using Hybrid Machine Learnin...

引用

2022 international conference on Innovations in Science, Engineering and technology, ICISET 2022

作者： Islam, Ummana Anjum, Nishat Masum, Abdul Kadar Muhammad Arafat, Abm Yasir International Islamic University Computer Science and Engineering Chittagong Bangladesh

ISBN: (纸本)9781665483971

Banks are the most powerful economic stimulants, and they play a crucial role in state economic development since they control the supply of currency in circulation to a substantial extent. To sustain a healthy financial system and an efficient economy, the forecasting of the bank can be considered as an important part of their financial planning process in the banking industry. So, to achieve this essential requirement of the banking sector, previously many attempts have been made where people have made forecasting systems using Binary Gradual Analysis, Data Envelopment Analysis (DEA), Linear Discriminant Analysis, optimizing machine learning approaches, specifically Particle Swarm Optimization (PSO) and Random Search (RS), etc. In our study, predictive analysis is carried out on 100 banks' performance datasets using Support Vector Machine (SVM), Decision Tree (DT), and Artificial Neural network (ANN) techniques. The dataset includes the value of 29 performance indicators that measure performance from 2000 to 2020. In addition, we attempted to create a few models by hybridizing SVM, ANN, and DT techniques such as SVM_ANN, SVM_DT, DT_ANN, and SVM_DT_ANN. Finally, a comparative analysis was done between hybridization models and their standard versions. © 2022 IEEE.

关键词： Support vector machines

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Deep Learning for Depression Symptomatic Activity Recognition

Deep Learning for Depression Symptomatic Activity Recognitio...

引用

2022 international conference on Innovations in Science, Engineering and technology, ICISET 2022

作者： Anjum, Fariha Alam, Sadia Bahadur, Erfanul Hoque Masum, Abdul Kadar Muhammad Rahman, Md. Ziaur International Islamic University Dept. of Computer Science and Engineering Chittagong Bangladesh

ISBN: (纸本)9781665483971

Discovering solutions to a handful of hazards associated with human health through analyzing physical activities have elevated the aspiration of prognosis of various diseases. This novel approach employs Human Activity Recognition (HAR) to devise depression symptomatic activities as a repercussion. The accumulation of data is conducted versatilely, including outdoor and indoor activities, maintaining all desirable postures while the smartphone is located in the slash pocket. Concerning the augmentation of the original dataset, synthetic sensors data are generated by the Generative Adversarial network (GAN) *** preprocessing, including Butterworth low pass filter, enhanced the dataset. Considering the type of data being sequential, deep learning models like Long Short Term Memory (LSTM) and Gated Recurrent Unit (GRU) were employed. The evaluation process is held in three stages. Firstly, LSTM outperformed GRU on the amalgamation of actual and generated data with an accuracy of 96.48%. Secondly, the selected dataset was further experimented to recognize the significance of the Butterworth low pass filter, where it achieved greater accuracy of 2.01% with its presence. Finally, two publicly available datasets WISDM and MHEALTH were compared with the acquired dataset based on the two proposed models, where the combination of the depression dataset and LSTM model acquired higher accuracy of 1.80%. © 2022 IEEE.

关键词： Health hazards

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A Two-Stage DDoS Attacks Detection Method in SDN 3

A Two-Stage DDoS Attacks Detection Method in SDN

引用

3rd international conference on computer Communication and network Security, CCNS 2022

作者： Li, Yan Qin, Bin Zhang, YunSheng Nie, Wei College of Electronics and Information Engineering Shenzhen University 3688 Nanhai Avenue Shenzhen518060 China Information Center Shenzhen University 3688 Nanhai Avenue Shenzhen518060 China College of International Educational Exchange Shenzhen Institute of Information Technology 2188 Longxiang Boulevard Shenzhen518172 China

ISBN: (纸本)9781510660113

Distributed Denial of Service (DDoS) attacks are one of the dangers of Software Defined networking (SDN). In order to detect DDoS attacks quickly and accurately, this paper proposes a two-stage DDoS attacks detection method based on SDN, which combines a preliminary detection method based on information entropy and K-Nearest Neighbors (KNN) regression algorithm, and a depth detection method based on Gated Recurrent Unit (GRU). First, the two-stage detection method extracts their necessary data features from the flow table information respectively through the feature extraction module. Second, the preliminary detection module uses the information entropy of the six-tuple as feature vectors to train the KNN regression model. Third, the depth detection module uses the GRU neural network to fully learn the sequence features. Finally, the method proposed in this paper is verified in the experimental environment based on Mininet. When the first-stage preliminary detection module determines that the SDN network environment is suspected of being attacked by DDoS, the second-stage depth detection module is called for further detection. The experimental results demonstrate that the method proposed in this paper can effectively detect DDoS attacks in SDN. © 2022 SPIE.

关键词： Denial-of-service attack

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Predictive Bearing Maintenance Based on Transfer Learning with Preprocessing and Machine Learning Models Analysis 4

Predictive Bearing Maintenance Based on Transfer Learning wi...

引用

4th international conference on technology and Policy in Energy and Electric Power, ICT-PEP 2022

作者： Amornsrivarakul, Pornnapat Loahavilai, Phatham School of Information Computer and Communication Technology Sirindhorn International Institute of Technology Thammasat University Pathum-Thani Thailand Pathum-Thani Thailand Tsinghua University Department of Engineering Physics Beijing China

ISBN: (数字)9798350310276

ISBN: (纸本)9798350310276

In energy and power systems, the bearing is a crucial part of machineries such as generators and motors. The analysis of preprocessing methods and machine learning models is presented through validating bearing conditions classification. Two types of bearing (drive end and fan end) conditions are obtained from Case Western Reserve University Bearing Data Center, in which the drive end condition is used to train a basic model. The corresponding model is then used to evaluate the fan end condition (namely transfer learning). The features for machine learning are generated from a series of preprocessing: pre-normalization, envelope, skewness, kurtosis, root mean square, standard deviation, Fourier transform, DC removal, post-normalization, and frequency-domain features extractions. Repeatable preprocessing and machine learning algorithms are explored. Numerical preprocessing methods for time-domain and frequency-domain feature extractions are suggested. The model could predict faults from different locations using data from only a single location. © 2022 IEEE.

关键词： Numerical methods

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A survey of phishing detection: from an intelligent countermeasures view

A survey of phishing detection: from an intelligent counterm...

引用

2022 IEEE conference on Telecommunications, Optics and computer Science, TOCS 2022

作者： Wang, Yifei International College of Chongqing University of Posts and Telecommunications Chongqing China

ISBN: (纸本)9781665470537

The number of network attacks is increasing at an unprecedented speed, and online phishing is the most common form of network attacks and the most close to our life. In order to enable users to better understand what kind of online phishing they have suffered, and enable scholars and researchers to more quickly customize detection for different types of online phishing detection. We need an in-depth summary of the existing types of online phishing and various detection methods. This paper classifies and summarizes all kinds of phishing, mainly introduces six kinds of phishing, including E-mail phishing and search engine phishing. For phishing detection methods, this paper also summarizes and classifies existing and widely used detection methods into seven categories such as list-based heuristic machine learning, and introduces them in detail. Then the paper analyzes the big data frontier technology and detection channel direction of assisted anomaly detection, and divides it into stream processing technology, Hadoop DPI parsing keyword recognition, web crawler recognition, etc. Finally the paper discusses and thinks deeply about the methods and techniques mentioned above as well as the current situation of phishing detection, and puts forward some forward-looking suggestions on the interpretation of data processing model selection and general detection architecture. © 2022 IEEE.

关键词： Web crawler

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Path Planning of Mobile Robot Based on Improved Genetic Algorithm 3

Path Planning of Mobile Robot Based on Improved Genetic Algo...

引用

2022 3rd international conference on Internet of Things, Artificial Intelligence and Mechanical Automation, IoTAIMA 2022

作者： Wei, Yongdong Feng, Jihe Huang, Yuming Liu, Kaiwei Ren, Bin School of Computer Science and Technology Dongguan University of Technology Dongguan523000 China International School of Microelectronics Dongguan University of Technology Dongguan523000 China

With the continuous development of science and technology, mobile robots are gradually applied to people's routine life. As one of the key technologies of mobile robots, the performance of path planning algorithms is a significant factor affecting its efficiency. On the basis of using a traditional Genetic Algorithm for path planning, combined with specific application scenarios, this paper proposes a new path planning algorithm for mobile robots. The path smoothness and the number of path corners are added to the fitness function to evaluate the path, so as to select a more superior path. Simulation experiments in MATLAB show the validity and feasibility of the modified method. By comparison to the traditional GA and other classical path planning algorithms, the optimal path selected has fewer turning angles under the same path length, which has a better application effect and greatly improves the working efficiency and stability. © 2022 Institute of Physics Publishing. All rights reserved.

关键词： Mobile robots

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Design of international Railway Logistics Management System Based on MDA and Smart Contract 3

Design of International Railway Logistics Management System ...

引用

3rd international conference on Green Communication, network, and Internet of Things, CNIoT 2023

作者： Wang, Zi Wang, Keming School of Information Science and Technology Southwest Jiaotong University Chengdu611756 China School of Computer and Artificial Intelligence Southwest Jiaotong University Chengdu611756 China

ISBN: (纸本)9781510668867

The international railway logistics management system with a centralized system has problems such as information security, difficulty tracking logistics information, complex operating procedures, and information sharing and exchange. However, the decentralized, tamper-proof, and traceable features of blockchain technology can effectively enhance the data security and credibility of the system. Nonetheless, the development of smart contracts based on blockchain lacks a disciplined, standardized, and mature development process. On the other hand, adopting a Model-Driven Architecture (MDA) development approach can reduce human coding errors and improve development efficiency. This study proposed a modeling method based on UML class diagrams and state machine diagrams to analyze the static structure and dynamic behavior of the smart contract in the international railway logistics management system. By developing a conversion algorithm and combining the source and target metamodels, the platform-independent model (PIM) was converted into a platform-specific model (PSM), which was eventually transformed into code using a model-to-code transformation algorithm. The feasibility and correctness of this method were validated through experiments. © 2023 SPIE.

关键词： Smart contract

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