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检索条件"任意字段=8th IEEE International Conference on Software Engineering and Computer Systems, ICSECS 2023"

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A New Spatio-Temporal Neural Architecture with Bi-LSTM for Multimodal Emotion Recognition 8

A New Spatio-Temporal Neural Architecture with Bi-LSTM for M...

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8th international conference on Communication and Electronics systems, ICCES 2023

作者： Jothimani, S. Sangeethaa, S.N. Premalatha, K. Sathishkannan, R. Bannari Amman Institute of Technology Department of Computer Science and Engineering Erode Sathyamangalam638401 India Bannari Amman Institute of Technology Computer Science and Engineering Erode Sathyamangalam638401 India

ISBN: (纸本)9798350396638

Emotional recognition uses implicit annotation to detect the user's emotional response to multimedia. this helps create efficient user-centric services. Researchers are increasingly using physiologically-based ways to convey emotions objectively. Traditional methods for addressing the problem of emotion recognition have mostly concentrated their efforts on the extraction of various sorts of manually-created characteristics. Nevertheless, features that are hand-crafted always require domain expertise for the individual job, and the process of building the appropriate features may take more time. As a result, determining the physiologically based temporal feature representation that is the most useful for emotion identification has become the primary focus of the majority of recent research. A variety of different applications require emotional identification in conversations as a first step. these applications include opinion mining over chat history, threads on social media, debates, argumentation mining, and interpreting customer feedback in live discussions, amongst others. Currently, systems do not adapt to the various individuals in a debate by considering each comment as if it were made by a distinct individual. Integrating information from multiple modalities requires careful selection and fusion of relevant features. this can be a complex task, as different modalities may have different temporal and spatial characteristics, and may provide redundant or conflicting information. this study's objective is to describe a unique strategy based on deep learning, Convolutional Neural Networks, and Bidirectional Long Short-Term Memory that keeps track of the independent party systems across a whole conversation in order to identify emotional responses using the data gathered from such provinces. For two separate datasets RAVDESS and SAVEE, our model achieves much better results 100% than the current state-of-the-art solution. © 2023 ieee.

关键词： Deep neural networks

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Automated Diagnosis of Marigold Leaf Diseases using a Hybrid CNN-SVM Model 8

Automated Diagnosis of Marigold Leaf Diseases using a Hybrid...

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8th international conference on Communication and Electronics systems, ICCES 2023

作者： Banerjee, Deepak Kukreja, Vinay Sharma, Vandana Jain, Vishal Hariharan, Shanmugasundaram Chitkara University Institute of Engineering and Technology Chitkara University Punjab India Amity University Amity Institute of Information Technology Noida Campus India Sharda University Sharda School of Engineering and Technology Department of Computer Science and Engineering UP Greater Noida India

ISBN: (纸本)9798350396638

For healthy yields of crops and to maintain food security, it is essential to identify and classify plant diseases. In this research, the aimed to create a convolutional neural network-based deep learning algorithm for accurately classifying various marigold leaf diseases. 2000 images of marigold leaves were included in the dataset, with 200 images for each of the ten groups. (nine diseases and one healthy leaf). Six layers of convolution, six maximum-pooling layers, and two fully linked layers with regularisation and ReLU function activation made up the suggested CNN model. the efficiency of the model was optimized through hyperparameter tuning. Using the Keras framework, the model was trained and evaluated on the preprocessed dataset, and its performance was assessed using metrics like precision, recall, F1-score, as well as accuracy. According to the findings, the developed model classified the various classes of marigold leaves with an overall accuracy of 92%, with precision, recall, as well as F1-score metrics varying from 50% to 71.79% depending on the class. the confusion matrix gave a thorough summary of how well the model did at identifying each of the various marigold leaf classes. the created model can be applied to the early discovery of diseases affecting marigold leaves, aiding in both disease containment and yield protection. this research also emphasizes how well deep learning models work at accurately identifying and categorizing various plant diseases. © 2023 ieee.

关键词： Convolutional neural networks

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Classification of Motor Unit Action Potential Using Transfer Learning for the Diagnosis of Neuromuscular Diseases 8

Classification of Motor Unit Action Potential Using Transfer...

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8th international conference on Smart Structures and systems, ICSSS 2022

作者： Somani, Vikas Rahman, A. Nisam Verma, Devvret Chandan, Radha Raman Vidhya, R.G. Vijayan, Vinodh P Sangam University Department of Computer Science Rajastan Bhilwara India Innovation and Research Society Mavelipuram Kerala India Graphic Era Deemed to Be University Department of Biotechnology Uttarakhand Dehradun India Shambhunath Group of Institutions Department of Computer Science and Engineering Prayagraj India Hkbk College of Engineering Department of Electronics and Communication Engineering Karnataka Bengaluru India Mangalam College of Engineering Kerala Kottayam India

ISBN: (纸本)9781665497619

In the assessment of neuromuscular illnesses, the motor unit action potentials (MUPs) in an electromyographic (EMG) signal are an important information source. Many methodologies in the time and frequency domains have been employed for quantitative research of EMG data since recent improvements in software EMG technology. the use of several feature extraction methods to describe MUP morphology is investigated in this article. Single classifier characteristics were used to investigate classification algorithms. To predict the class label for each MUAP, a distance weighted K-nearest neighbour (KNN) classifier was applied (Myopathic, Neuropathic, or Normal). the proposed techniques perform brilliantly in terms of overall classification accuracy, according to an exhaustive analysis of the clinical EMG database for the categorization of neuromuscular disorders. © 2022 ieee.

关键词： Action potentials Neuromuscular Transfer learning Morphology Electromyography software Classification algorithms

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Testing Approach for IoT System (Case Study: Air Quality Monitoring System) 8

Testing Approach for IoT System (Case Study: Air Quality Mon...

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8th international conference on Information Management and Technology, ICIMTech 2023

作者： Rochadiani, theresia Herlina Soeparno, Haryono Gaol, Ford Lumban Arifin, Yulyani Bina Nusantara University Binus Graduate Program - Doctor of Computer Science Computer Science Department Jakarta11480 Indonesia Pradita University Informatics Department Tangerang15810 Indonesia

ISBN: (纸本)9798350326093

the Internet of things (IoT) was evaluated as part of this study. the IoT system in this study is the air quality monitoring system. the system consists of three layers, three end devices with air quality monitoring sensors as the perception layer, the LoRaWAN network as the network layer, and the monitoring website as the presentation layer. Before being made available to the public, IoT hardware and software must undergo testing. For IoT systems, there are no established standards or quality criteria. this study's goal is to evaluate the methodology for an air quality monitoring system. Website speed is measured using Pingdom tools. the website received a performance grade of B. Pingdom offers advice for enhancing website performance. Sucuri is utilized as a security test tool to evaluate the website's security, and consequently, no malware was discovered. the latency and data dependability are monitored to assess the system's performance fully. the outcome demonstrates that all system components consistently and accurately receive data with a latency of less than 1 s. © 2023 ieee.

关键词： Internet of things

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Applied Gamification to Enhance Customer Loyalty for Fintech Industry 8

Applied Gamification to Enhance Customer Loyalty for Fintech...

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8th ieee international conference on engineering Technologies and Applied Sciences, ICETAS 2023

作者： Qureshi, Muhammad Ali Ghulamani, Sumbul Abdallah, Ssekamanya Siraje thartori, Voltisa Khowaja, Kamran Mahdi, Ghulam Shah, Asadullah Szabist University Department of Computer Science Hyderabad Pakistan Department of Educational Psychology and Counseling Kulliyyah of Education Malaysia Unitar International University Petaling Jaya Malaysia Kimberley Lark Corporation RoswellGA United States Kulliyah of Information and Communication Technology International Islamic Malaysia

ISBN: (纸本)9798350327090

In this study, a mobile application prototype for fintech industry is proposed which aims to addresses users' problems and engage them based on incentivized mechanism through applied gamification used for the users. Users will perform transactions and in return get some points as rewards, which they can use for cashback rewards or send these points to someone they want. For this research, Figma is used as a tool for designing wireframes and prototypes which later included in UseBerry to perform usability tests from users to collect their responses. Around 50 participants have been invited to collect their responses based on using the prototype and answering a few questions based on that. Results have shown an enormously positive response from users who really want to get incentives through their daily transactions and can get whatever benefits they are provided. However, this research is limited to a few features and more features and testing can be done based on the current response from the users which will directly impact user's daily life and improve conversions for the banking and fintech industry. © 2023 ieee.

关键词： software prototyping

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Joint Subcarrier and Power Allocation in Mobile Scenario of the OFDM systems Based on Deep Reinforcement Learning 8

Joint Subcarrier and Power Allocation in Mobile Scenario of ...

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8th international conference on computer and Communication systems, ICCCS 2023

作者： Li, Xiaodong Zhou, Weixi Zhang, Hongjie Zhao, Jing Zhao, Dongcai Dong, Zhicheng School of Information Science and Technology Tibet University Lhasa China School of Computer Science Sichuan Normal University Chengdu China School of Network & Communication Engineering Chengdu Technological University Chengdu China

ISBN: (纸本)9781665456128

the increasing number of base station (BS) service users has made spectrum resources more valuable, leading to the need for efficient resource allocation. To address this issue, this paper proposes a novel deep reinforcement learning (DRL) architecture that solves the subcarrier-user matching and subcarrier power allocation problem in orthogonal frequency division multiple (OFDM) systems. this approach improves the system spectral efficiency (SE) by reducing inter-subcarrier interference (ICI) through subcarrier matching and power allocation, while ensuring that the minimum rate requirements for users (mobile or stationary) are met. We approach this problem by dividing it into two interconnected components: the subcarrier matching part (SMP) and the subcarrier power allocation part (SPAP). To address these two parts, the paper proposes a DRL method based on joint allocation of resources by three agents (JAoR-TA). In SMP, the REINFORCE algorithm is used to match subcarriers, and the results are then given to SPAP. In SPAP, two Actor-Critic network frameworks are proposed to address the subcarrier power allocation problem. Based on the simulation results, it has been observed that the JAoR-TA algorithm outperforms the REINFORCE algorithm in mobile scenarios, as it achieves a higher system SE. © 2023 ieee.

关键词： Reinforcement learning

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Ensemble Learning for Mobility-Aware of Edge User 8

Ensemble Learning for Mobility-Aware of Edge User

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8th international conference on Information Management and Technology, ICIMTech 2023

作者： Rayuda Dewa, Gilang Raka Satrya, Wahyu Fadli Aprilliyani, Ria Seoul National University of Science and Technology Computer Science and Engineering Seoul Korea Republic of Binus Online Learning Bina Nusantara University Computer Science Department Jakarta11480 Indonesia Universitas Pertahanan Electrical Engineering Bogor Indonesia

ISBN: (纸本)9798350326093

the mobility of edge users in cellular networks critically affects throughput and signal reception performance. the mobile manner of users can deteriorate network connection, causing a significant delay in data delivery. In addition, a limited number of cellular base stations also increases the signal blocking for far-sight users, making it difficult to achieve optimum signal reception for ubiquitous locations. To maintain throughput performance, the network coordinator needs to identify the mobility type;thus, it can provide a priority resource for the high-mobility user. Accordingly, this paper proposes an ensemble learning to classify the mobility type of edge users by utilizing throughput, distance, and delay as features. the proposed ensemble learning combines multiple weak models, i.e., Random Forest and XGBoost, to construct more robust and established models. the verification results show that ensemble learning predicts a model with more than 97% accuracy. Moreover, the evaluation results confirm that the proposed algorithm outperforms conventional algorithms in precision, recall, and F1-score for every feature. © 2023 ieee.

关键词： Learning systems

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Research on UAV Detection and Classification Based on Time-Frequency Analysis and Support Vector Machine 8

Research on UAV Detection and Classification Based on Time-F...

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8th international conference on computer and Communication systems, ICCCS 2023

作者： Deng, Honggao Wang, Haichuan Ji, Yuanfa Sun, Xiyan Cao, Xun Natl. Loc. Jt. Engineering Research Center of Satellite Navigation Positioning and Location Service Guilin University of Electronic Technology Guilin China Information and Communication School Guilin University of Electronic Technology Guilin China Natl. Loc. Jt. Engineering Research Center of Satellite Navigation Positioning and Location Service GUET-Nanning E-Tech Research Institute Co. Ltd Guilin China Natl. Loc. Jt. Engineering Research Center of Satellite Navigation Positioning and Location Service Information and Communication School Guilin China

ISBN: (纸本)9781665456128

In recent years, unmanned aerial vehicles (UAVs) have been widely used in various fields, bringing many conveniences to our lives but also posing a threat. therefore, the control of UAVs is imminent. In this paper, we propose a method based on short-time Fourier transform (STFT) and support vector machine (SVM) to identify UAVs in complex environments. First, the remote control signal of the UAV is collected by the self-made signal acquisition equipment, then the two-stage haar wavelet transform is used to de-noise and compress the data, and the signal is processed by STFT transform. the interference of the fixed frequency signal is eliminated by the time-frequency spectrum cancellation method. Finally, the instantaneous frequency, signal duration point, and signal bandwidth of the frequency hopping signal are extracted to form a feature set and classified by SVM. the method is used to test three UAVs. the algorithm has an average recognition rate of 98.43% for three UAVs under the condition of SNR of 5dB. © 2023 ieee.

关键词： Support vector machines

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A Segmented D2D Caching Strategy Based on User Preferences and Overall Popularity 8

A Segmented D2D Caching Strategy Based on User Preferences a...

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8th international conference on computer and Communication systems, ICCCS 2023

作者： Wu, Qunying Huang, Peiyao Zheng, Qiaoe Song, Hui South China Normal University Department of Electronics and Information Engineering Foshan China

ISBN: (纸本)9781665456128

To reduce the burden on the core network, a technique for putting cached files in several geographic locations is being implemented and is developing into Mobile Edge Caching technology. In this paper, the mobile edge caching problem with device mobility and user preferences for D2D content-sharing scenarios is studied. First, we construct a Markov chain-based device mobility model and a user preferences prediction model based on social proximity, file topic classification, and user historical request records. then we design a caching policy that combines user preferences and overall popularity by dividing the user cache space into two segments, caching files from the library sorted by overall popularity and the library sorted by user preferences, respectively. Furthermore, we maximize the system cache hit probability and prove that it is a non-convex nonlinear problem. Consequently, a particle swarm optimization with adaptive inertia weights is presented to discover the optimal segmentation point of the file library and user cache space. the convergence speed of this algorithm has been significantly increased. the simulation results demonstrate that the caching strategy outperforms even traditional caching strategies. © 2023 ieee.

关键词： Particle swarm optimization (PSO)

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DMCNET: Data-Driven Multi-Pilot Convolution Neural Network for Mimo-Ofdm Receiver 8

DMCNET: Data-Driven Multi-Pilot Convolution Neural Network f...

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8th ieee international conference on Network Intelligence and Digital Content, IC-NIDC 2023

作者： Xin, Yutong Peng, Jingyi Lu, Zejian Lee, Yong Yang, Yang China Industrial Control Systems Cyber Emergency Response Team 100029 China China Academic of Electronics and Information Technology 100041 China School of Artificial Intelligence Beijing University of Posts & Telecommunications 100876 China Collage of Electrical and Computer Engineering Sungkyunkwan University 16419 Korea Republic of

ISBN: (纸本)9798350317923

this paper focuses on studying the deep learning (DL) application of neural networks to solve the reception of single-antenna OFDM signals. Specifically, in multi-antenna scenarios, the channel model is more complex compared to single-antenna cases. By leveraging the characteristics of DL, such as automatic learning of parameters using deep neural networks, we treat the reception process of MIMOOFDM signals as a black box and utilize neural networks to accomplish the signal reception task. Moreover, we propose the data-driven multi-pilot convolution neural network for MIMO-OFDM receiver (DMCNet). By incorporating complex convolution and complex fully connected structures, we design a receiver network to recover the transmitted signals from the received signals. We validate the accuracy and robustness of DMCNet under different channel conditions, comparing the bit error rates with different schemes. Additionally, we discuss the factors influencing various channel effects. Experimental results demonstrate that the DL-based reception scheme exhibits promising feasibility in terms of accuracy and interference resistance when compared to traditional approaches. © 2023 ieee.

关键词： MIMO systems

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