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2024 IEEE Conference on Standards for Communications and Networking, CSCN 2024

作者： Katzis, Konstantinos Evetovic, Nina Cloteaux, Brian Sayrafian, Kamran Bajic, Dragana European University Cyprus Department of Computer Science and Engineering Nicosia Cyprus University of Novi Sad Department of Communications & Signal Processing Novi Sad Serbia Information Technology Laboratory National Institute of Standards & Technology MD United States

ISBN: (纸本)9798331507428

Lack of sufficient patient monitoring data patterns and scenarios are among the challenges for accurate traffic modeling and network performance optimization when considering IoT medical devices. This paper examines how health data-traffic models can be used to investigate the capability and proper configuration of Long-Range Wide Area Networks (LoRaWAN) for remotely monitoring patients in a hospital environment or at home. For the hospital environment, it is assumed that there are two types of patients at any given time i.e., patients in the intensive care unit (ICU) or surgical wards and those located in the general medical wards. The transmission times of the health data from patients in the ICU/Surgical wards and those in general wards are randomly distributed within one and four-hour periods, respectively. For personal health monitoring devices that people use at their homes, a two-state Markov chain is used to model the generated traffic pattern. Preliminary results indicate that LoRaWAN may be used to support a significant number of nodes with sufficient reliability in both scenarios. © 2024 IEEE.

关键词： Electronic health record

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Efficient Segmentation Approach for Different Medical Image Modalities

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computers, Materials & Continua 2022年第11期73卷 3119-3135页

作者： Walid El-Shafai Amira A.Mahmoud El-Sayed M.El-Rabaie Taha E.Taha Osama F.Zahran Adel S.El-Fishawy Naglaa F.Soliman Amel A.Alhussan Fathi E.Abd El-Samie Department Electronics and Electrical Communications Faculty of Electronic EngineeringMenoufia UniversityMenouf32952Egypt Security Engineering Laboratory Department of Computer SciencePrince Sultan UniversityRiyadh11586Saudi Arabia Department of Information Technology College of Computer and Information SciencesPrincess Nourah Bint Abdulrahman UniversityRiyadh11671Saudi Arabia Department of Computer Sciences College of Computer and Information SciencesPrincess Nourah Bint Abdulrahman UniversityRiyadhSaudi Arabia

This paper presents a study of the segmentation of medical *** paper provides a solid introduction to image enhancement along with image segmentation *** the first step,the morphological operations are employed to ensure image detail protection and *** objective of using morphological operations is to remove the defects in the texture of the ***,the Fuzzy C-Means(FCM)clustering algorithm is used to modify membership function based only on the spatial neighbors instead of the distance between pixels within local spatial neighbors and cluster *** proposed technique is very simple to implement and significantly fast since it is not necessary to compute the distance between the neighboring pixels and the cluster *** is also efficient when dealing with noisy images because of its ability to efficiently improve the membership partition *** results are performed on different medical image ***(Us),X-ray(Mammogram),Computed Tomography(CT),Positron Emission Tomography(PET),and Magnetic Resonance(MR)images are the main medical image modalities used in this *** obtained results illustrate that the proposed technique can achieve good results with a short time and efficient image *** results on different image modalities show that the proposed technique can achieve segmentation accuracies of 98.83%,99.71%,99.83%,99.85%,and 99.74%for Us,Mammogram,CT,PET,and MRI images,respectively.

关键词： Image segmentation ultrasonic mammogram CT PET MRI morphological operations FCM active contours

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Hybrid Segmentation Approach for Different Medical Image Modalities

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computers, Materials & Continua 2022年第11期73卷 3454-3471页

作者： Walid El-Shafai Amira A.Mahmoud El-Sayed M.El-Rabaie Taha E.Taha Osama F.Zahran Adel S.El-Fishawy Naglaa F.Soliman Amel A.Alhussan Fathi E.Abd El-Samie Department Electronics and Electrical Communications Faculty of Electronic EngineeringMenoufia UniversityMenouf32952Egypt Security Engineering Laboratory Department of Computer SciencePrince Sultan UniversityRiyadh11586Saudi Arabia Department of Information Technology College of Computer and Information SciencesPrincess Nourah bint Abdulrahman UniversityRiyadh11671Saudi Arabia Department of Computer Sciences College of Computer and Information SciencesPrincess Nourah bint Abdulrahman UniversityRiyadhSaudi Arabia

The segmentation process requires separating the image region into sub-regions of similar *** sub-region has a group of pixels having the same characteristics,such as texture or *** paper suggests an efficient hybrid segmentation approach for different medical image modalities based on particle swarm optimization(PSO)and improved fast fuzzy C-means clustering(IFFCM)*** extensive comparative study on different medical images is presented between the proposed approach and other different previous segmentation *** existing medical image segmentation techniques incorporate clustering,thresholding,graph-based,edge-based,active contour,region-based,and watershed *** paper extensively analyzes and summarizes the comparative investigation of these ***,a prediction of the improvement involves the combination of these techniques is *** obtained results demonstrate that the proposed hybrid medical image segmentation approach provides superior outcomes in terms of the examined evaluation metrics compared to the preceding segmentation techniques.

关键词： Image segmentation ultrasonic images X-ray images CT images PET images MR images fuzzy c-mean morphological operations active contour

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Exact Statistics and Tight Approximations for RIS-Assisted Communications in Generalized Fading Environments 100

Exact Statistics and Tight Approximations for RIS-Assisted C...

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100th IEEE Vehicular Technology Conference, VTC 2024-Fall

作者： Luna Alvarado, Maria Cecilia Rafael Nogueira Da Silva, Carlos Simmons, Nidhi Sofotasios, Paschalis C. Cotton, Simon L. Yacoub, Michel Daoud University of Campinas Wireless Technology Laboratory School of Electrical and Computer Engineering Campinas Brazil Federal University of Triângulo Mineiro Department of Electrical Engineering Uberaba Brazil Belfast United Kingdom Khalifa University C2PS and 6G Center Department of Computer and Information Engineering Abu Dhabi United Arab Emirates

ISBN: (纸本)9798331517786

Reconfigurable Intelligent Surfaces (RISs) are considered a key candidate technology for next-generation 6G wireless systems, promising to extend network coverage, enhance spectral efficiency, and effectively mitigate interference. RISs will achieve this by controlling the propagation environment, enabled by carefully altering the reflective properties of their elements. In this context, this work presents a general framework for deriving the exact probability density function (PDF) and higher-order moments of the signal-to-ratio (SNR) in a RIS system, considering the direct transmission and RIS-assisted links from source to destination. Recognizing the analytical challenges associated with obtaining exact statistics for RIS-assisted systems, which persist in both common and more generalized fading models, the proposed contribution is realized with the aid of the suitable α-μ fading model. To that end, we derive accurate approximations for the κ-μ and the extended η-μ distributions, which constitute effective and versatile multipath fading models. Based on this, the achieved results are virtually indistinguishable from those obtained through simulations for various environmental settings. This renders the proposed framework a valuable tool for evaluating the performance of RIS systems, particularly in terms of the corresponding average symbol error rate (ASER). Tractable closed-form asymptotic expressions are also derived and utilized to provide a deeper understanding of the system's behavior. © 2024 IEEE.

关键词： Multipath fading

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An effective global learning framework for hyperspectral image classification based on encoder-decoder architecture

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International Journal of Digital Earth 2022年第1期15卷 1350-1376页

作者： Lanxue Dang Chongyang Liu Weichuan Dong Yane Hou Qiang Ge Yang Liu Henan Key Laboratory of Big Data Analysis and Processing Henan UniversityKaifengChina School of Computer and Information Engineering Henan UniversityKaifengChina Department of Geography Kent State UniversityKentOHUSA

Most deep learning methods in hyperspectral image(HSI)classification use local learning methods,where overlapping areas between pixels can lead to spatial redundancy and higher computational *** paper proposes an efficient global learning(EGL)framework for HSI *** EGL framework was composed of universal global random stratification(UGSS)sampling strategy and a classification model *** UGSS sampling strategy was used to solve the problem of insufficient gradient variance resulted from limited training *** fully extract and explore the most distinguishing feature representation,we used the modified linear bottleneck structure with spectral attention as a part of the BrsNet network to extract spectral spatial *** a type of spectral attention,the shuffle spectral attention module screened important spectral features from the rich spectral information of HSI to improve the classification accuracy of the ***,we also designed a double branch structure in BrsNet that extracted more abundant spatial information from local and global perspectives to increase the performance of our classification *** were conducted on three famous datasets,IP,PU,and *** with other classification methods,our proposed method produced competitive results in training time,while having a greater advantage in test time.

关键词： Deep learning global learning feature representation hyperspectral image classification spectral attention

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Construction and Research on Learning State Analysis in Classroom Teaching 5th

Construction and Research on Learning State Analysis in C...

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5th International Conference on computer Science and Educational Informatization, CSEI 2023

作者： Zhou, Qingquan Shen, Xiner Du, Xiaoyu Han, Daojun Qiao, Baojun College of Computer and Information Engineering Henan University Kaifeng475004 China Department of Government and Public Administration The Chinese University of Hong Kong New Territories Hong Kong Special Administrative Region Hong Kong Henan Engineering Laboratory of Spatial Information Processing Kaifeng475004 China

ISBN: (纸本)9789819994984

The learning state of students in the classroom is affected by many aspects of influence. According to the neural network and equation of state, this paper analyzes effect of classroom in the University. This paper first puts forward the improvement scheme of classroom teaching and constructs a simplified mathematical model of the students’ brain in the classroom, then points out the main information and its description signals in the classroom, and finally under the stimulation of these signals, mathematical language was used to qualitatively describe the excited state of the brain. According to the analysis of the mathematical model, this paper summarizes the teaching scheme of tacit cooperation between teachers and students to improve the effect. © 2024, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Students

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Deep Learning Hybrid Models for Multilingual Cyberbullying Detection: Insights from Bangla and Chittagonian Languages 26

Deep Learning Hybrid Models for Multilingual Cyberbullying D...

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26th International Conference on computer and information Technology, ICCIT 2023

作者： Mahmud, Tanjim Ptaszynski, Michal Masui, Fumito Kitami Institute of Technology Text Information Processing Laboratory Hokkaido090-8507 Japan Rangamati Science and Technology University Department of Computer Science and Engineering Rangamati4500 Bangladesh

ISBN: (纸本)9798350359015

Cyberbullying is a serious problem that affects online communication, and stopping it is essential to building safer digital spaces. The goal of this study is to address the problem of identifying cyberbullying content within the different linguistic contexts of Bangla and Chittagonian languages by utilizing deep learning techniques, with a particular focus on hybrid multilingual models. We provide a thorough analysis of both traditional machine learning (ML) models, which in our study reached accuracy values between 0.63 and 0.711 (with SVM as the highest performer), and deep learning (DL) models, which produced accuracy values between 0.69 and 0.811 (with CNN as the top performer) when trained and tested on both languages together. We also propose a set of combined hybrid network-based models, which improve accuracy. Among the models implemented, including BiLSTM+GRU (achieving an accuracy of 0.799), CNN+LSTM (with an accuracy of 0.801), CNN+BiLSTM (yielding an accuracy of 0.78), and CNN+GRU (attaining an accuracy of 0.804), especially the most complex model ((CNN+LSTM)+BiLSTM) achieved the highest score of 0.82. In comparison, mBERT, a popular multilingual transformer model, reached 0.79 accuracy. This research emphasizes high-performing hybrid neural networks' significance in multilingual contexts, particularly for low-resource languages, contributing to effective cyberbullying detection in diverse populations. © 2023 IEEE.

关键词： Deep learning Hybrid models Low-resource languages Machine learning Multilingual models

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Modeling and Clustering of Parabolic Granular Data

IEEE Transactions on Artificial Intelligence

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IEEE Transactions on Artificial Intelligence 2024年第7期5卷 3728-3742页

作者： Tang, Yiming Gao, Jianwei Pedrycz, Witold Hu, Xianghui Xi, Lei Ren, Fuji Hu, Min Hefei University of Technology Anhui Province Key Laboratory of Affective Computing and Advanced Intelligent Machine School of Computer and Information Hefei230601 China University of Alberta Department of Electrical and Computer Engineering EdmontonABT6R 2V4 Canada Hefei University of Technology School of Computer and Information Hefei230601 China University of Alberta Department of Electrical and Computer Engineering EdmontonABT6G 2R3 Canada Polish Academy of Sciences Systems Research Institute Warsaw00-901 Poland Istinye University Research Center of Performance and Productivity Analysis Istanbul34010 Turkey Southeast University School of Computer Science and Engineering Nanjing211189 China University of Electronic Science and Technology of China School of Computer Science and Engineering Sichuan Chengdu611731 China

At present, there exist some problems in granular clustering methods, such as lack of nonlinear membership description and global optimization of granular data boundaries. To address these issues, in this study, revolving around the parabolic granular data, we propose an overall architecture for parabolic granular modeling and clustering. To begin with, novel coverage and specificity functions are established, and then a parabolic granular data structure is proposed. The fuzzy c-means (FCM) algorithm is used to obtain the numeric prototypes, and then particle swarm optimization (PSO) is introduced to construct the parabolic granular data from the global perspective under the guidance of principle of justifiable granularity (PJG). Combining the advantages of FCM and PSO, we propose the parabolic granular modeling and optimization (PGMO) method. Moreover, we put forward attribute weights and sample weights as well as a distance measure induced by the Gaussian kernel similarity, and then come up with the algorithm of weighted kernel fuzzy clustering for parabolic granularity (WKFC-PG). In addition, the assessment mechanism of parabolic granular clustering is discussed. In summary, we set up an overall architecture including parabolic granular modeling, clustering, and assessment. Finally, comparative experiments on artificial, UCI, and high-dimensional datasets validate that our overall architecture delivers a good improvement over previous strategies. The parameter analysis and time complexity are also given for WKFC-PG. In contrast with related granular clustering algorithms, it is observed that WKFC-PG performs better than other granular clustering algorithms and has superior stability in handling outliers, especially on high-dimensional datasets. © 2020 IEEE.

关键词： Fuzzy clustering

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Semantic and spatial‐spectral feature fusion transformer network for the classification of hyperspectral image

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CAAI Transactions on Intelligence Technology 2023年第4期8卷 1308-1322页

作者： Erxin Xie Na Chen Jiangtao Peng Weiwei Sun Qian Du Xinge You Hubei Key Laboratory of Applied Mathematics Faculty of Mathematics and StatisticsHubei UniversityWuhanChina Department of Geography and Spatial Information Techniques Ningbo UniversityNingboChina Department of Electrical and Computer Engineering Mississippi State UniversityMississippi StateMississippiUSA School of Electronic Information and Communications Huazhong University of Science and TechnologyWuhanChina

Recently,transformer‐based networks have been introduced for the classification of hyperspectral image(HSI).Although transformer‐based methods can well capture spectral sequence information,their ability to fuse different types of information contained in HSI is still *** exploit rich spectral,spatial and semantic information in HSI,a novel semantic and spatial‐spectral feature fusion transformer(S3FFT)network is proposed in this *** the proposed S3FFT method,spatial attention and efficient channel attention(ECA)modules are employed for the extraction of shallow spatialspectral ***,a transformer‐based module is designed to extract advanced fused features and to produce the pseudo‐label and class probability of each pixel for semantic feature ***,the semantic,spatial and spectral features are combined by the transformer for *** with traditional deep learning methods and recently transformer‐based methods,the proposed S3FFT shows relatively better results on three HSI datasets.

关键词： image classification machine learning

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A Survey on Channel Measurements and Models for Underground MIMO Communication Systems

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China Communications 2023年第8期20卷 103-119页

作者： Asad Saleem Yejun He Guoxin Zheng Zhining Chen State Key Laboratory of Radio Frequency Heterogeneous Integration Guangdong Engineering Research Center of Base Station Antennas and PropagationShenzhen Key Laboratory of Antennas and PropagationCollege of Electronics and Information EngineeringShenzhen UniversityShenzhen 518060China Key Laboratory of Specialty Fiber Optics and Optical Access Networks Shanghai UniversityShanghai 200444China Department of Electrical and Computer Engineering National University of SingaporeSingapore

The high reliability of the communication system is critical in metro and mining applications for personal safety,channel optimization,and improving operational *** paper surveys the progress of wireless communication systems in underground environments such as tunnels and mines from 1920 to 2022,including the evolution of primitive technology,advancements in channel modelling,and realization of various wireless propagation *** addition,the existing and advanced channel modeling strategies,which include the evolution of different technologies and their applications;mathematical,analytical,and experimental techniques for radio propagation;and significance of the radiation characteristics,antenna placement,and physical environment of multiple-input multiple-output(MIMO)communication systems,are *** given study introduces leaky coaxial cable(LCX)and distributed antenna system(DAS)designs for improving narrowband and wideband channel *** paper concludes by figuring out open research areas for the future technologies.

关键词： leaky coaxial cable(LCX) long-term evolution for metro(LTE-M) multiple-input multipleoutput(MIMO)systems propagation modeling

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