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2nd International Conference on Computational Intelligence, Communication technology and Networking, CICTN 2025

作者： Sharma, Pratyaksha Gupta, Sumita Bhavya, Boddu Asmitha Sharma, Khushi Dhir, Saru Gambhir, Sapna Department of Computer Science & Engineering Amity School of Engineering & Technology Amity University UP India Department of Information Technology Amity School of Engineering & Technology Amity University UP India Dept. of Computer Science College of Engineering and Computing George Mason University United States

ISBN: (纸本)9798331530389

With the advancement of technologies, different methods are currently being used for converting spoken language into text. These systems offer a hands-free alternative to traditional input methods, especially for individuals with physical disabilities or those requiring efficient documentation of spoken content. The goal of this study is to review recent advancements in this field and highlight gaps in current research, with a focus on accuracy, language adaptability, ease of use, and the integration of various technologies. We also suggest future directions for enhancing and developing this technology further. © 2025 IEEE.

关键词： Hidden Markov models

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Enhanced photoluminescence of monolayer MoS_(2)on stepped gold structure

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Chinese Physics B 2022年第8期31卷 639-644页

作者： Yu-Chun Liu Xin Tan Tian-Ci Shen Fu-Xing Gu School of Optical-Electrical and Computer Engineering University of Shanghai for Science and TechnologyShanghai 200093China

Different Mo S_(2)/Au heterostructures can play an important role in tuning the photoluminescence(PL)and optoelectrical properties of monolayer Mo S_(2).Previous studies of PL of Mo S_(2)/Au heterostructures were mainly limited to the PL enhancement by using different Au nanostructures and PL quenching of monolayer Mo S_(2)on flat Au ***,we demonstrate the enhanced excitonic PL emissions of monolayer Mo S_(2)/Au heterostructures on Si/Si O_(2)*** transferring the continuous monolayer Mo S_(2)onto a stepped Au structure consisting of 60-nm and 100-nm Au films,the Mo S_(2)/Au-60 and Mo S_(2)/Au-100 heterostructures exhibit enhanced PL emissions,each with a blue-shifted PL peak in comparison with the Mo S_(2)/Si O_(2).Furthermore,the PL intensity of Mo S_(2)/Au-60 is about twice larger than that of Mo S_(2)/*** different enhanced excitonic PL emissions in Mo S_(2)/Au heterostructures can be attributed to the different charge transfer effects modified by the stepped Au *** work may provide an insight into the excitonic PL and charge transfer effect of Mo S_(2)on Au film and yield novel phenomena in Mo S_(2)/Au heterostructures for further study of PL tuning and optoelectrical properties.

关键词： MoS_(2) stepped gold heterostructures enhanced photoluminescence

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Applying SVR-PPSO, SVR-SSO, and SVR-BBO to estimate california bearing capacity of stabilized pond ash using admixtures

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Journal of Intelligent and Fuzzy Systems 2024年第4期46卷 8311-8327页

作者： Teng, Wei Li, Yan Sun, Hongxing Chen, Haojie School of Computer Science and Software Engineering University of Science and Technology Liaoning Liaoning Anshan China School of Electronic and Information Engineering University of Science and Technology Liaoning Liaoning Anshan China School of Information Technology Nanchang Vocational University Jiangxi Nanchang China School of Intelligent Construction Luzhou Vocational and Technical College Sichuan Luzhou China

In the present study, three hybrid models include support vector regression-salp swarm optimization (SVR-SSO), support vector regression-biogeography-based (SVR-BBO), and support vector regression-phasor particle swarm optimization (SVR- PPSO) was applied to forecast pond ash's CBR value modified with lime sludge (LS) and lime (LI). In the developed models, five variables were selected as inputs. It can result that the developed integrated models have R2 bigger than 0.9952. It means the agreement between observed and forecasted values by hybrid models is mainly similar to represent the highest accuracy. In both the training and testing stages, PSO-SVR results from better performance than the BBO-SVR model, with R2, RMSE, MAE, and PI equal to 0.9983, 0.6439, 0.3181, and 0.0081 for training data, and 0.9975, 0.7319, 0.4135, and 0.0141 for testing data, respectively. So, by considering the OBJ index, the OBJ value for PSO-SVR is 12.966, lower than BBO-SVR at 16.9957. Therefore, the PSO-SVR model outperforms another model to estimate the CBR of pond ash modified with LI and LS, consequently being recognized as the proposed model that makes it to be used for practical applications. © 2024 - IOS Press. All rights reserved.

关键词： Lime

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Named Entity Recognition in Electronic Medical Records Incorporating Pre-trained and Multi-Head Attention

IAENG International Journal of Computer Science

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IAENG International Journal of computer Science 2024年第4期51卷 401-408页

作者： Yang, Haotian Wang, Li Yang, Yanpeng School of Computer Science and Software Engineering University of Science and Technology Liaoning CO Anshan114051 China College of Computer Science and Software Engineering University of Science and Technology Liaoning CO Anshan114051 China Network Information Centre University of Science and Technology Liaoning CO Anshan114051 China

Chinese Named Entity Recognition (NER) for Electronic Medical Records (EMR) is a fundamental task in building a digital hospital and is widely considered to be a sequence annotation problem in the Natural Language Processing domain. (NLP). However, existing deep learning sequence annotation models cannot fully use the large amount of unannotated data for Chinese EMRs that contain a vast number of professional unregistered words, named entities, and inter-of-entity relationships carrying rich professional knowledge. Moreover, the syntactic structure of EMR sentences is complex, and the text is long;the features of the EMR documents often cannot be captured deeply. Aiming at these two problems, this paper proposes a deep learning method that combines Multi-Head Attention with a pre-trained language model. (BERT-BiGRU-Att-CRF). The method uses the BERT pre-trained model to obtain dynamic word vectors combined with contextual information, extracts global semantic features through a Bidirectional Gated Recurrent Unit (BiGRU), obtains augmented semantic features by using the Multi-Head Attention. Finally, using Conditional Random Field (CRF) decoding, outputs the globally optimal label sequence with greatest probability. The model is trained using the CCKS2019 Chinese EMR dataset containing six types of entities: anatomical sites, surgeries, diseases and diagnoses, medicines, laboratory tests, and imaging tests, and good results are achieved with an F1 score of 86.97%. © (2024) International Association of Engineers.

关键词： Random processes

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Characterizing the app recommendation relationships in the iOS app store: a complex network's perspective

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Science China(Information Sciences) 2025年第4期68卷 284-299页

作者： Gang HUANG Fuqi LIN Yun MA Haoyu WANG Qingxiang WANG Gareth TYSON Xuanzhe LIU Key Lab of High Confidence Software Technologies (Peking University) Ministry of Education School of Computer Science Peking University Institute for Artificial Intelligence Peking University School of Cyber Science and Engineering Huazhong University of Science and Technology Alibaba Group Hong Kong University of Science and Technology (Guangzhou)

Mobile apps have become widely adopted in our daily lives. To facilitate app discovery, most app markets provide recommendations for users, which may significantly impact how apps are accessed. However, little has been known about the underlying relationships and how they reflect(or affect) user behaviors. To fill this gap, we characterize the app recommendation relationships in the i OS app store from the perspective of the complex network. We collect a dataset containing over 1.3 million apps and 50 million app recommendations. This dataset enables us to construct a complex network that captures app recommendation relationships. Through this, we explore the recommendation relationships between mobile apps and how these relationships reflect or affect user behavior patterns. The insights gained from our research can be valuable for understanding typical user behaviors and identifying potential policy-violating apps.

关键词： mobile app recommendation complex network user behavior policy-violating app

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Smart Monitoring and Irrigation Regulation via IoT and Cloud 8

Smart Monitoring and Irrigation Regulation via IoT and Cloud

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8th International Conference on I-SMAC (IoT in Social, Mobile, Analytics and Cloud), I-SMAC 2024

作者： Sumalatha, Konatham Harshdeep, Kumar Vellore Institute of Technology School of Computer Science & Engineering Department of Database Systems Vellore India Vellore Institute of Technology Computer Science and Engineering Vellore India

ISBN: (纸本)9798350376425

The increasing demand for agricultural produce and the strain on global water resources highlight the need for innovative solutions to improve water efficiency in farming. This work introduces an IoT-powered Smart Irrigation System designed to optimize water usage, ensuring sustainable crop cultivation. Traditional irrigation methods often lead to inefficiencies, wasting water and increasing costs, as they fail to consider varying soil moisture and environmental conditions across fields. This can result in suboptimal crop growth and resource depletion. To overcome these challenges, the proposed system integrates IoT technology with smart irrigation practices. Sensors deployed in the field collect real-time data on soil moisture, temperature, humidity, and light intensity, which a central controller then processes. Using cloud-based analytics, the controller adjusts irrigation schedules and water distribution to match current conditions precisely. The system's primary goal is to create an eco-friendly, cost-effective solution that reduces water waste, lowers operational costs, and minimizes environmental impact. Scalable and adaptable to various crops and environments, this smart irrigation system promises to improve crop yields, promote sustainable agriculture, and address water scarcity challenges globally. © 2024 IEEE.

关键词： Cloud Computing Real-Time Data Analytics Smart Irrigation Soil Moisture Sensors

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Network Traffic Anomaly Detection Based on DGBi-SA Model

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engineering Letters 2025年第3期33卷 612-619页

作者： Wang, Xin Dai, Hong Huang, Zheng Han, Yue University of Science and Technology Liaoning Liaoning CO Anshan114051 China School of Computer Science and Software Engineering University of Science and Technology Liaoning Liaoning CO Anshan114051 China

Network traffic anomaly detection plays a crucial role in today's network security and performance management. In response to the challenges in current network traffic data processing, such as insufficient structuring and dynamics, the paper proposes a network traffic anomaly detection approach based on DGBi-SA model, which integrates Dynamic Graph Convolutional Networks (DyGCN), Bi-directional Long Short-Term Memory (Bi-LSTM), and the Self-Attention mechanism. To solve the problem of insufficient structuring, the model employs DyGCN to capture network traffic's dynamic graph structural characteristics proficiently. Simultaneously, combining Bi-LSTM and the Self-Attention mechanism enhances the model's ability to capture temporal and spatial dependencies. The main objective of the research in the paper is to improve the accuracy and detection efficiency of network traffic prediction, thereby achieving effective detection and timely response to abnormal behaviors in complex network environments. Through a sequence of experiments conducted on the CICIDS-2017 dataset, the paper verifies that the DGBi-SA model can effectively solve the above problems. The experimental results indicate that the model outperforms existing methods in key performance indicators such as AUC, precision, recall, and F1 score, thereby proving that the DGBi-SA model has important application value and scientific significance in network traffic anomaly detection. © 2025, International Association of Engineers. All rights reserved.

关键词： Long short-term memory

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A Sentiment-Support Graph Convolutional Network for Aspect-Level Sentiment Analysis 10

A Sentiment-Support Graph Convolutional Network for Aspect-L...

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10th IEEE International Conference on Cyber Security and Cloud Computing and 9th IEEE International Conference on Edge Computing and Scalable Cloud, CSCloud-EdgeCom 2023

作者： Gao, Rui-Ding Jiang, Lei Zou, Zi-Wei Li, Yuan Hu, Yu-Rong Hunan University of Science and Technology School of Computer Science and Engineering Xiangtan China Jingchu University of Technology School of Computer Engineering Jingmen China

ISBN: (纸本)9798350312461

The task of aspect-level sentiment analysis is to identify the sentiment polarity of sentences when expressed in different aspects. The attention mechanism-based approach allows for attentional interaction between the target and context, but it only combines sentences from a semantic perspective, overlooking the syntactic information present in the sentences. Although graph convolutional networks are capable of handling syntactic information well, they are still unable to effectively combine semantic and syntactic information. This paper proposes a sentiment-supported graph convolutional network (SSGCN), which first extracts the semantic information of words using aspect-aware attention and self-attention. Then, the grammar mask matrix and graph convolutional network are used to combine semantic and grammatical information. The features are then split into two parts - one part extracts semantic and syntactic information related to aspect words, and the other part extracts features related to sentiment-supportive words. Finally, the results from the two parts are concatenated to effectively combine semantic and syntactic information. Experimental results show that the proposed model outperforms the benchmark models in terms of accuracy and macro F1 values on three public datasets. © 2023 IEEE.

关键词： Sentiment analysis

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Gene selection and clustering of single-cell data based on Fisher score and genetic algorithm

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Journal of Supercomputing 2023年第7期79卷 7067-7093页

作者： Feng, Junhong Zhang, Jie Zhu, Xiaoshu Wang, Jian-Hong School of Computer and Information Engineering Guangdong Songshan Polytechnic Guangdong Shaoguan512126 China School of Computer Science and Engineering Yulin Normal University Guangxi Yulin537000 China Department of Computer Science and Information Engineering National Chin-Yi University of Technology Taichung Taiwan

Huge amounts of genes in single-cell RNA sequencing (scRNA-seq) data may influence the performance of data clustering. To obtain high-quality genes for data clustering, the study proposes a novel gene selection algorithm based on Fisher score and genetic algorithms with dynamic crossover (abbreviated as FDCGA). To reduce time and space complexity, FDCGA first employs Fisher score to gain the preliminary candidate genes and then utilizes genetic algorithms with dynamic crossover to select beneficial genes to data clustering and analysis. The experimental results conducted on several publicly real-world scRNA-seq datasets demonstrate that FDCGA outperforms the other several competitors in terms of both NMI and ARI metrics and possesses significant optimization performances. The experimental convergence shows that the fitness of FDCGA can increase and converge to a fixed state versus the number of iterations. The statistical analysis demonstrates that FDCGA statistically significantly outperforms the other competing methods. © 2022, The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature.

关键词： Genetic algorithms

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Enhanced Multi-Scale Quantum Harmonic Oscillator Algorithm with Opposition-based Learning 13

Enhanced Multi-Scale Quantum Harmonic Oscillator Algorithm w...

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13th IEEE Congress on Evolutionary Computation, CEC 2024

作者： Ye, Xinggui Li, Jianping Wang, Peng School of Computer Science and Engineering University of Electronic Science and Technology of China Chengdu China School of Computer Science and Technology Southwest Minzu University Chengdu China

ISBN: (纸本)9798350308365

Multi-scale Quantum Harmonic Oscillator Algorith-m (MQHOA) is a recently proposed metaheuristic algorithm (MA), which simulates the photoelectron moves from high energy level down to the ground energy level. It requires few parameters, yet verified effective and efficient to solve numerical problems. However, it is easy to get trapped into local optima and suffer from premature convergence. This work proposes an enhanced MQHOA with opposition-based learning (OMQHOA) to balance the exploration and exploitation. An adaptive scaling strategy and a jumping rate scheme are proposed to enhance the diversity of the particles. The performance of the proposed algorithm is validated by evaluating on several benchmark problems with different dimensions, including the success rate of finding the global optimum over multiple independent trials, trajectory of convergence, and Wilcoxon rank-sum tests. The empirical results are compared with MQHOA, recent variants of MQHOA, and some state-of-the-art MAs, which show the superiority or at least competitiveness of the proposed approach. © 2024 IEEE.

关键词： Contrastive Learning

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