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2022 International Conference on Artificial Intelligence, Virtual Reality, and Visualization, AIVRV 2022

作者： Cui, Xi-Shuo Shi, Tian-Wei Cui, Wen-Hua Tao, Ye School of Electronic and Information Engineering University of Science and Technology Liaoning Liaoning Anshan114051 China School of Computer Science and Software Engineering University of Science and Technology Liaoning Liaoning Anshan114051 China

ISBN: (纸本)9781510662988

In recent years, facial landmark detection has assumed an important role in various fields. However, the current facial landmark detection algorithms are still lacking in recognition accuracy. In order to solve the above problem, this paper uses Ghost bottleneck to replace the original bottleneck on the basis of the original model of PFLD model, and adds and improves the CBAM attention mechanism. The improved PFLD model increases the ability of the model to extract facial landmark and improves the accuracy of the algorithm. The improved model has high accuracy and low parametric number and improves the accuracy of facial landmark detection. It also provides a new idea for facial landmark detection task. © 2023 SPIE. All rights reserved.

关键词： Face recognition

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Submarine Hunter: Efficient and Secure Multi-Type Unmanned Vehicles

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computers, Materials & Continua 2023年第7期76卷 573-589页

作者： Halah Hasan Mahmoud Marwan Kadhim Mohammed Al-Shammari Gehad Abdullah Amran Elsayed Tag eldin Ala R.Alareqi Nivin A.Ghamry Ehaa A.Lnajjar Esmail Almosharea Computer Center University of BaghdadBaghdad6751Iraq Department of Management Science and Engineering Dalian University of TechnologyLiaoningDalian116024China Department of Information Technology Faculty of Computer Sciences and Information Technology AL Razi UniversitySana’aYemen Faculty of Engineering and Technology Future University in EgyptNew Cairo11835Egypt Faculty of Engineering Mechanics Dalian University of TechnologyLiaoningDalian116024China Faculty of Computers and Artificial Intelligence Cairo UniversityGizaEgypt Department of Business Administration Dalian University of TechnologyLiaoningDalian116024China College of Software Engineering Dalian University of TechnologyChina

Utilizing artificial intelligence(AI)to protect smart coastal cities has become a novel vision for scientific and industrial *** of these AI technologies is using efficient and secure multi-environment Unmanned Vehicles(UVs)for anti-submarine *** study’s contribution is the early detection of a submarine assault employing hybrid environment UVs that are controlled using swarm optimization and secure the information in between UVs using a decentralized cybersecurity *** Dragonfly Algorithm is used for the orientation and clustering of the UVs in the optimization approach,and the Re-fragmentation strategy is used in the Network layer of the TCP/IP protocol as a cybersecurity *** research’s noteworthy findings demonstrate UVs’logistical capability to promptly detect the target and address the problem while securely keeping the drone’s geographical *** results suggest that detecting the submarine early increases the likelihood of averting a *** dragonfly strategy of sensing the position of the submersible and aggregating around it demonstrates the reliability of swarm intelligence in increasing access *** communication between Unmanned Aerial Vehicles(UAVs)improves the level of secrecy necessary for the *** swarm navigation is based on a peer-to-peer system,which allows each UAV to access information from its ***,in turn,helps the UAVs to determine the best route to take and to avoid collisions with other *** dragonfly strategy also increases the speed of the mission by minimizing the time spent finding the target.

关键词： Unmanned vehicles unmanned aerial vehicles unmanned underwater vehicles high altitude unmanned aerial vehicles anti-submarine warfare re-fragmentation dragonfly algorithm

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Live Streaming Data Analysis using Distributed Stochastic Bi-LSTM Model

Live Streaming Data Analysis using Distributed Stochastic Bi...

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2022 International Conference on Communication, Computing and Internet of Things, IC3IoT 2022

作者： Kavitha Nair, R. Mathew, Jimsha K Deepika, R. Sharanyaa, S. Therasa, M. Computer Science and Engineering Acharya Institute of Technology Bangalore India Acharya Institute of Technology Information Science and Engineering Bangalore India Computer Science and Engineering Sri Sairam Engineering College Chennai India Panimalar Engineering College Information Technology Chennai India Panimalar Institute of Technology Computer Science and Engineering Chennai India

ISBN: (纸本)9781665479950

In the real world, currently there are more than millions bytes of social media data being created. With the help of modern computational capacity in addition to advanced research in the field of artificial intelligence, a great improvement is shown in natural language processing using deep learning techniques. These AI techniques including machine learning algorithms and deep learning algorithms make it possible to analyze the given tons of data. In addition to it, it also helps us to have a deep dive knowledge in people's mindset towards the current trend in media application. There is another problem of cyber bullying wherein many people start sharing and posting anonymous and fake information to make a significant impact by making riots just as fake heroes. By continuously monitoring these types of online riots will provide a clean and peaceful environment which in turn supports the government in a better way. This article focuses on performing sentiment analysis over a given real time social media data by categorizing it. The effective analysis of live streaming from social media applications is done with word2vec embedding method in natural language processing and trained using bi-directional LSTM model. The performance is compared with the LSTM model, and traditional machine learning techniques like naïve bayes and linear regression model and Bi-LSTM model outperforms the other techniques with 80%-85% accuracy with live streaming unconventional social media data. © 2022 IEEE.

关键词： Learning algorithms

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IoT-Based Portable ECG and Heart Rate Monitoring System with Minimal Cost 1

IoT-Based Portable ECG and Heart Rate Monitoring System with...

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1st International Conference on AIML-Applications for engineering and technology, ICAET 2025

作者： Tonmoy, Achinta Brata Roy Sultan, Selim Bristy, Suparna Rani University of Wyoming Electrical Engineering and Computer Science Department LaramieWY United States Department of Electrical and Electronics Engineering Jain Bangalore India Mawlana Bhashani Science and Technology University Department of Information and Communication Technology Tangail Santosh1902 Bangladesh

ISBN: (纸本)9798350355611

Cardiovascular disease is a major cause of mortality globally, and early detection through electrocardiograms (ECG) is crucial for effective treatment. ECGs measure the heart's electrical impulses, enabling detection of various cardiac irregularities. In recent years, there has been a push to develop low-cost, IoT-based portable ECG monitoring systems to make healthcare more accessible. However, existing solutions are often constrained by limited Bluetooth connectivity (under 100 meters) or rely on expensive processors like Raspberry Pi, which restricts use in low-resource settings. This research proposes a novel IoT-based ECG monitoring system designed to be both affordable and efficient, overcoming these limitations with a minimal hardware setup. The system utilizes the ESP8266 module as a cost-effective microcontroller, enabling real-time ECG data transmission over the internet and allowing healthcare professionals to monitor patients remotely without range restrictions. At a cost of only ∃16, this system reduces expenses by 88.49% compared to similar existing models, offering a practical, scalable solution for telemedicine. The system architecture includes an ECG detecting node, IoT cloud platform, and a user-friendly interface, with the AD8232 sensor gathering ECG data processed by the ESP8266 and transmitted to a secure cloud platform(Ubidots). Authorized healthcare providers can access this data, with alerts for any detected irregularities, supporting timely intervention. The system's performance has been validated on multiple patients, achieving a 92% accuracy that aligns well with clinical ECG standards. This low-cost and precise IoT-based ECG monitoring technology signifies a significant progression in telemedicine, facilitating efficient remote monitoring and broadening access to vital healthcare services. © 2025 IEEE.

关键词： Telemedicine

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GSAGE2defect: An Improved Approach to Software Defect Prediction based on Inductive Graph Neural Network 35

GSAGE2defect: An Improved Approach to Software Defect Predic...

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35th International Conference on Software engineering and Knowledge engineering, SEKE 2023

作者： Ma, Ju Sun, Yi-Yang He, Peng Zeng, Zhang-Fan School of Computer Science and Information Engineering Hubei University Wuhan China School of Cyber Science and Technology Hubei University Wuhan China

Graph neural network is an effective deep learning framework for learning graph data. Existing research has introduced different variants of graph neural networks into the field of software defects and has achieved promising results. However, the graph neural network model based on the previous research is essentially transductive, is applied to a single fixed graph, and often ignores the direction and weight of the edges when modeling the network. In practice, software systems are dynamically evolving. Furthermore, in software network modeling, the direction and weight of edges are factors that are worth considering. Based on an inductive graph neural network, we proposed an improved defect prediction method named GSAGE2defect. We first constructed the class dependency network of the program and then used node2vec for embedding learning to automatically obtain the structural features of the network. Then we combined the learned structural features with traditional software code features to initialize the properties of nodes in the class dependency network. Next, we fed the dependency network to GraphSAGE for a deeper class representation. Finally, we evaluated the proposed method based on eight open-source programs and demonstrated that GSAGE2defect achieves an average improvement of 2.09%-26.69% over state-of-the-art methods in terms of F-measure. © 2023 Knowledge Systems Institute Graduate School. All rights reserved.

关键词： Graph neural networks

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A Novel Metaheuristic Algorithm: The Team Competition and Cooperation Optimization Algorithm

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computers, Materials & Continua 2022年第11期73卷 2879-2896页

作者： Tao Wu Xinyu Wu Jingjue Chen Xi Chen Amir Homayoon Ashrafzadeh School of Computer Science Chengdu University of Information TechnologyChengdu610225China School of Computer Science and Engineering Southwest Minzu UniversityChengdu610041China CSIT Department School of ScienceRMIT UniversityMelbourne3058Australia

Metaheuristic algorithm is a generalization of heuristic algorithm that can be applied to almost all optimization *** optimization problems,metaheuristic algorithm is one of the methods to find its optimal solution or approximate solution under limited *** of the existing metaheuristic algorithms are designed for serial ***,existing algorithms still have a lot of room for improvement in convergence speed,robustness,and *** address these issues,this paper proposes an easily parallelizable metaheuristic optimization algorithm called team competition and cooperation optimization(TCCO)inspired by the process of human team cooperation and *** proposed algorithm attempts to mathematically model human team cooperation and competition to promote the optimization process and find an approximate solution as close as possible to the optimal solution under limited *** order to evaluate the performance of the proposed algorithm,this paper compares the solution accuracy and convergence speed of the TCCO algorithm with the Grasshopper Optimization Algorithm(GOA),Seagull Optimization Algorithm(SOA),Whale Optimization Algorithm(WOA)and Sparrow Search Algorithm(SSA).Experiment results of 30 test functions commonly used in the optimization field indicate that,compared with these current advanced metaheuristic algorithms,TCCO has strong competitiveness in both solution accuracy and convergence speed.

关键词： Optimization metaheuristic algorithm

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Optimizing Lane Configuration and ETC Adoption to Minimize Congestion and Costs at Toll Plazas 17th

Optimizing Lane Configuration and ETC Adoption to Minimize C...

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17th International Conference on Multi-disciplinary Trends in Artificial Intelligence, MIWAI 2024

作者： Klaykul, Pattarapon Lee, Wilaiporn Prayote, Akara Department of Computer and Information Science King Mongkut’s University of Technology North Bangkok Bangkok10800 Thailand Department of Electrical and Computer Engineering King Mongkut’s University of Technology North Bangkok Bangkok10800 Thailand

ISBN: (纸本)9789819606917

At toll plazas, congestion frequently occurs during peak hours due to inadequate lane management and misalignment of toll payment lanes with traffic volume and payment patterns. This study proposes a model to determine the optimal number of Manual Toll Collection (MTC) lanes to open or close and to optimize the proportion of Electronic Toll Collection (ETC) lanes. The objective is to minimize average queue lengths while striving to meet secondary-level service requirements. The current system often exceeds these standards, and this study aims to simulate the effects of increasing ETC usage and ETC lanes to improve queue management. The analysis shows that ETC lanes are more efficient and can handle a higher traffic volume than MTC lanes, leading to shorter average queue lengths based on queueing theory. It was observed that lane adjustments are only feasible during off-peak periods, optimizing queue lengths to meet standards. During peak hours, simulations show significant reductions in average queue lengths for various ETC usage scenarios, with reductions of up to 91.96% depending on lane configuration. Additionally, converting one MTC lane to ETC reduces hourly operating costs per lane from 73.17 Baht to 5.42 Baht, a cost reduction of approximately 92.59%. These results emphasize the importance of strategic planning and scheduling for dynamic lane adjustments and advocate for increased ETC adoption through targeted policies and incentives to enhance overall toll plaza efficiency. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Queueing theory

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Predicting Gold Prices with Rolling Average Representation and Machine Learning 1

Predicting Gold Prices with Rolling Average Representation a...

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1st International Conference on Innovative engineering sciences and Technological Research, ICIESTR 2024

作者： Kannan, Rajkumar Andres, Frederic Muthu, Kalaiarasi Sonai Anutariya, Chutiporn Department of Computer Science Tiruchirappalli India National Institute of Informtics Digital Content and Media Science Tokyo Japan Multimedia University Faculty of Information Science and Technology Malaysia Asian Institute of Technology School of Engineering and Technology Pathum Thani Thailand

ISBN: (纸本)9798350348637

Price prediction and forecasting have been important topics that have been long studied in the financial world. Commodity price prediction is one such highly popular application in stock markets across the world. Among all commodities such as gold, silver, platinum and others, gold has continued to be the prime and forefront commodity which decides the price movements of other commodities. Moving average approach and its variants are the important technical indicators which help financial institutions, investors and traders to understand the movement of gold prices throughout the year. Some of the popular technical indicators are simple moving averages, cumulative moving averages and exponential moving averages. This paper proposes a unique gold price prediction methodology by building popular ensemble machine learning models utilizing all three types of moving average values as feature representation and next day price as the target for prediction. The predicted values based on the three moving average features are presented to users, investors and traders, so that investors and traders will know the gold price for the next day. Extensive experiments are conducted on the Indian gold price daily data from December 2011 to March 2024 by comparing six prominent machine learning models and ARIMA model. We also present a detailed study of different feature importance. Experimental results on the gold price daily data have confirmed that polynomial regression models have given superior performance in terms of regression accuracy and error metrics namely MSE, RMSE and MAE © 2024 IEEE.

关键词： Prediction models

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Channel-Aware Direct Localization Using One-Bit Quantized Measurements 32

Channel-Aware Direct Localization Using One-Bit Quantized Me...

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32nd European Signal Processing Conference, EUSIPCO 2024

作者： Zhang, Guoxin Liang, Yunfei Yi, Wei Ngo, Hien Quoc Matthaiou, Michail Varshney, Pramod K. School of Information and Communication Engineering University of Electronic Science and Technology of China China Queen’s University Belfast United Kingdom Department of Electrical Engineering and Computer Science Syracuse University United States

ISBN: (纸本)9789464593617

To address the constraints of limited transmission bandwidth and power consumption in wireless sensor networks (WSNs), this paper proposes a direct localization method using one-bit quantized measurements. Specifically, the received signal is quantized into its one bit version at the local sensor before being transmitted to the fusion center (FC) for target localization. Recognizing the imperfections of the wireless channel between the sensors and the FC, a channel-aware position estimator using one-bit quantized measurements is proposed. Furthermore, the Cramér-Rao lower bound (CRLB) of the proposed channel-aware estimator is also derived to theoretically assess the localization performance. Simulation results demonstrate that the proposed channel-aware position estimator approaches its theoretical performance bounds and is superior compared to that of the channel-unaware estimator. © 2024 European Signal Processing Conference, EUSIPCO. All rights reserved.

关键词： Cramer-Rao bounds

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Facial Expression Recognition Using Deep Learning Model on Crowd

Facial Expression Recognition Using Deep Learning Model on C...

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2024 International Conference on Advances in Computing Research on science engineering and technology, ACROSET 2024

作者： Shakyawar, Prashant Singh, Upendra Prestige Institute of Engineering Management & Research Department of Computer Science and Engg. Indore India Shri G. S. Institute of Technology and Science Department of Information Technology Indore India

ISBN: (纸本)9798350388800

In the field of computer vision, Facial Expression Recognition (FER) is an essential problem that automates emotion analyzing and understanding in human facial expressions. Facial occlusions, changing lighting conditions and overlapping facial attributes in crowded environments make it difficult to recognize the expression of emotions. In this research we are solving a problem of group friendly facial expression recognition using deep neural network models. We also tested a number of complex deep neural network architectures to learn fine-grained patterns and representations in human face videos or images. In training and testing these models, resources such as AffectNet which encompasses a broad spectrum of people with various facial expressions is utilized to make them robust against most general cases in crowds. The results of the deep neural network model are presented, revealing its ability to detect facial expressions efficiently with high accuracy and recall. This is supported by the performance of their models being around 80-94 % in terms of accuracy and F1-score, based on different studies. Their benefits involve deploying state-of-the-art architectures and performing robustly on large scale datasets;fusing multi-modal for improved recognition, learning temporal relationships will be well established technologies;scalability to resource-constrained environments. © 2024 IEEE.

关键词： Convolutional neural networks

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