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Sepsis Prediction Using CNNBDLSTM and Temporal Derivatives Feature Extraction in the ioT Medical Environment

Computers, Materials & Continua 2024年第4期79卷 1157-1185页

作者： Sapiah Sakri Shakila Basheer Zuhaira Muhammad Zain Nurul Halimatul Asmak ismail Dua’Abdellatef Nassar Manal Abdullah Alohali Mais Ayman Alharaki department of information Systems College of Computer and Information SciencesPrincess Nourah Bint Abdulrahman UniversityRiyadh11671Kingdom of Saudi Arabia department of Computer Science and information technology Applied CollegePrincess Nourah Bint Abdulrahman UniversityRiyadh11671Kingdom of Saudi Arabia

Background:Sepsis,a potentially fatal inflammatory disease triggered by infection,carries significant healthimplications *** detection is crucial as sepsis can rapidly escalate if left *** in deep learning(DL)offer powerful tools to address this ***:Thus,this study proposeda hybrid CNNBDLSTM,a combination of a convolutional neural network(CNN)with a bi-directional long shorttermmemory(BDLSTM)model to predict sepsis *** the proposed model provides a robustframework that capitalizes on the complementary strengths of both architectures,resulting in more accurate andtimelier ***:The sepsis prediction method proposed here utilizes temporal feature extraction todelineate six distinct time frames before the onset of *** time frames adhere to the sepsis-3 standardrequirement,which incorporates 12-h observation windows preceding sepsis *** models were trained usingthe Medical information Mart for intensive Care iii(MiMiC-iii)dataset,which sourced 61,522 patients with 40clinical variables obtained from the ioT medical *** confusion matrix,the area under the receiveroperating characteristic curve(AUCROC)curve,the accuracy,the precision,the F1-score,and the recall weredeployed to evaluate ***:The CNNBDLSTMmodel demonstrated superior performance comparedto the benchmark and other models,achieving an AUCROC of 99.74%and an accuracy of 99.15%one hour beforesepsis *** results indicate that the CNNBDLSTM model is highly effective in predicting sepsis onset,particularly within a close proximity of one ***:The results could assist practitioners in increasingthe potential survival of the patient one hour before sepsis onset.

关键词： Temporal derivatives hybrid deep learning predicting sepsis onset MiMiC iii machine learning(ML) deep learning

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Design of double protograph LDPC codes based JSCC systems via the ACE-PEG algorithm

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Science China(information Sciences) 2024年第7期67卷 339-340页

作者： Yijie LV Jiguang HE Shaohua HONG department of information and Communication Engineering Xiamen University technology innovation institute Centre for Wireless Communications Oulu University

in this study, we present a state-of-the-art design of double protograph low-density parity-check (DP-LDPC) codes based on progressive edge-growth (PEG) algorithm with approximated cycle extrinsic message degree (ACE)... 详细信息

关键词： Benchmarking

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Security of quantum key distribution with virtual mutually unbiased bases

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Science China(Physics,Mechanics & Astronomy) 2024年第7期67卷 13-21页

作者： Hong-Wei Li Chen-Peng Hao Zhi-Jiang Chen Li Gong Yi-Fei Lu Yang Wang Jia-Ji Li Chun-Mei Zhang Rong Wang Zhen-Qiang Yin Qing-Yu Cai Henan Key Laboratory of Quantum information and Cryptography SSF IEUZhengzhou 450000China institute of Quantum information and technology Nanjing University of Posts and TelecommunicationsNanjing 210003China department of Physics University of Hong KongHong Kong 999077China Key Laboratory of Quantum information University of Science and Technology of ChinaHefei 230026China School of information and Communication Engineering Hainan UniversityHaikou 570228China

in a perfect quantum key distribution(QKD)protocol,quantum states should be prepared and measured with mutually unbiased bases(MUBs).However,in a practical QKD system,quantum states are generally prepared and measured with imperfect MUBs using imperfect devices,possibly reducing the secret key rate and transmission *** analyze the security of a QKD system with imperfect MUBs,we propose virtual MUBs to characterize the quantum channel against collective attack,and analyze the corresponding secret key rate under imperfect state preparation and measurement *** generally,we apply the advantage distillation method for analyzing the security of QKD with imperfect MUBs,where the error tolerance and transmission distance can be sharply *** analysis method can be applied to benchmark and standardize a practical QKD system,elucidating the security analysis of different QKD protocols with imperfect devices.

关键词： quantum key distribution mutually unbiased bases advantage distillation

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Mode-dependent dynamic gain of all-fiber FM-EDFA under various pump manipulation

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Chinese Optics Letters 2024年第2期22卷 128-134页

作者：方翼鸿张心怡黄锡恒曾研许鸥李建平付松年秦玉文 institute of Advanced Photonics technology School of Information EngineeringGuangdong University of TechnologyGuangzhou 510006China Guangdong Provincial Key Laboratory of information Photonics technology Guangdong University of TechnologyGuangzhou 510006China Key Laboratory of Photonic technology for integrated Sensing and Communication Ministry of EducationGuangdong University of TechnologyGuangzhou 510006China

The dynamic gain of a few-mode erbium-doped fiber amplifier(FM-EDFA)is vital for the long-haul mode division multiplexing(MDM)***,we investigate the mode-dependent dynamic gain of an FM-EDFA under various manipulations of the pump ***,we numerically calculate the gain variation with respect to the input signal power,where a modedependent saturation input power occurs under different pump *** under the fixed intensity profile of the pump laser,the saturation input power of each spatial mode is ***,high-order mode pumping leads to a compression of the linear amplification region,even though it is beneficial for the mitigation of the differential modal gain(DMG)arising in all guided ***,we develop an all-fiber 3-mode EDFA,where the fundamental mode of the pump laser can be efficiently converted to the LP_(11)mode using the all-fiber mode-selective coupler(MSC).in comparison with the traditional LP_(01)pumping scheme,the DMG at 1550 nm can be mitigated from 1.61 dB to 0.97 dB under the LP_(11)mode pumping,while both an average gain of 19.93 dB and a DMG of less than 1 dB can be achieved from 1530 nm to 1560 ***,the corresponding signal input saturation powers are reduced by 0.3 dB for the LP_(01)mode and 1.6 dB for the LP_(11)mode,*** theoretical and experimental results indicate that a trade-off occurs between the DMG mitigation and the extension of the linear amplification range when the intensity profile of pump laser is manipulated.

关键词： few-mode erbium-doped fiber amplifier differential modal gain saturation input power

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Adaptive Optimal Discrete-Time Output-Feedback Using an internal Model Principle and Adaptive Dynamic Programming

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iEEE/CAA Journal of Automatica Sinica 2024年第1期11卷 131-140页

作者： Zhongyang Wang Youqing Wang Zdzisław Kowalczuk the College of information Science and technology Beijing University of Chemical TechnologyBeijing 100029China iEEE the department of Robotics and Decision Systems Faculty of ElectronicsTelecommunications and InformaticsGdańsk University of TechnologyGdansk 80-233Poland

in order to address the output feedback issue for linear discrete-time systems, this work suggests a brand-new adaptive dynamic programming(ADP) technique based on the internal model principle(iMP). The proposed method, termed as iMP-ADP, does not require complete state feedback-merely the measurement of input and output data. More specifically, based on the iMP, the output control problem can first be converted into a stabilization problem. We then design an observer to reproduce the full state of the system by measuring the inputs and outputs. Moreover, this technique includes both a policy iteration algorithm and a value iteration algorithm to determine the optimal feedback gain without using a dynamic system model. it is important that with this concept one does not need to solve the regulator equation. Finally, this control method was tested on an inverter system of grid-connected LCLs to demonstrate that the proposed method provides the desired performance in terms of both tracking and disturbance rejection.

关键词： Adaptive dynamic programming(ADP) internal model principle(iMP) output feedback problem policy iteration(Pi) value iteration(Vi)

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AMachine Learning Approach to Cyberbullying Detection in Arabic Tweets

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Computers, Materials & Continua 2024年第7期80卷 1033-1054页

作者： Dhiaa Musleh Atta Rahman Mohammed Abbas Alkherallah Menhal Kamel Al-Bohassan Mustafa Mohammed Alawami Hayder Ali Alsebaa Jawad Ali Alnemer Ghazi Fayez Al-Mutairi May issa Aldossary Dalal A.Aldowaihi Fahd Alhaidari department of Computer Science College of Computer Science and Information TechnologyImam Abdulrahman Bin Faisal UniversityP.O.Box 1982Dammam31441Saudi Arabia department of Computer information Systems College of Computer Science and Information TechnologyImam Abdulrahman Bin Faisal UniversityP.O.Box 1982Dammam31441Saudi Arabia department of Networks and Communications College of Computer Science and Information TechnologyImam Abdulrahman Bin Faisal UniversityP.O.Box 1982Dammam31441Saudi Arabia

With the rapid growth of internet usage,a new situation has been created that enables practicing *** has increased over the past decade,and it has the same adverse effects as face-to-face bullying,like anger,sadness,anxiety,and *** the anonymity people get on the internet,they tend to bemore aggressive and express their emotions freely without considering the effects,which can be a reason for the increase in cyberbullying and it is the main motive behind the current *** study presents a thorough background of cyberbullying and the techniques used to collect,preprocess,and analyze the ***,a comprehensive review of the literature has been conducted to figure out research gaps and effective techniques and practices in cyberbullying detection in various languages,and it was deduced that there is significant room for improvement in the Arabic *** a result,the current study focuses on the investigation of shortlisted machine learning algorithms in natural language processing(NLP)for the classification of Arabic datasets duly collected from Twitter(also known as X).in this regard,support vector machine(SVM),Naive Bayes(NB),Random Forest(RF),Logistic regression(LR),Bootstrap aggregating(Bagging),Gradient Boosting(GBoost),Light Gradient Boosting Machine(LightGBM),Adaptive Boosting(AdaBoost),and eXtreme Gradient Boosting(XGBoost)were shortlisted and investigated due to their effectiveness in the similar ***,the scheme was evaluated by well-known performance measures like accuracy,precision,Recall,and ***,XGBoost exhibited the best performance with 89.95%accuracy,which is promising compared to the state-of-the-art.

关键词： Supervised machine learning ensemble learning cyberbullying Arabic tweets NLP

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Sensitivity of ghost imaging compared to conventional imaging [invited]

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Chinese Optics Letters 2024年第6期22卷 15-32页

作者： Long-Kun Du Shuai Sun Yao-Kun Xu Hui-Zu Lin Ning-Yi Leng Wei-Tao Liu institute for Quantum Science and technology College of ScienceNational University of Defense TechnologyChangsha 410073China interdisciplinary Center of Quantum information National University of Defense TechnologyChangsha 410073China Hunan Key Laboratory of Mechanism and technology of Quantum information Changsha 410073China College of Electronic Science and technology National University of Defense TechnologyChangsha 410073China department of Mathematics College of ScienceNational University of Defense TechnologyChangsha 410073China

Ghost imaging has been attracting more and more attention, which provides a way to obtain images of objects with only a single-pixel detector. Considering possible applications, it becomes urgent to clarify the sensitivity of ghost imaging. Due to the unique characteristics of single-pixel detectors, which collect photons without distributing them to multiple pixels,often outperforming array sensors, ghost imaging is believed to be more sensitive than conventional imaging. However,a systematic analysis on the sensitivity of ghost imaging is yet to be completed. in this paper, we present a method for quantitatively assessing the sensitivity of ghost imaging. A detailed comparison is provided between ghost imaging and conventional imaging, taking into account the particle nature of photons and the noise of detection. With the settings of the two imaging methods being the same to the most extent, the minimal required number of detected photons for images of a certain quality is considered. For the thermal source version, ghost imaging demonstrates enhanced sensitivity under practical situations, with noise *** an entangled source, ghost imaging surpasses conventional imaging techniques in terms of sensitivity obviously. in one word, ghost imaging promises higher sensitivity at low photon flux and noisy situations.

关键词： sensitivity ghost imaging conventional imaging

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Predicting Energy Consumption Using Stacked LSTM Snapshot Ensemble

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Big Data Mining and Analytics 2024年第2期7卷 247-270页

作者： Mona Ahamd Alghamdi Abdullah S.A.L-Malaise AL-Ghamdi Mahmoud Ragab information Systems department Faculty of Computing and Information Technology(FCIT)King Abdulaziz University(KAU)Jeddah 21589Kingdom of Saudi Arabia information Systems department FCITKAUJeddah 21589Kingdom of Saudi Arabiaand also with School of EngineeringComputing and DesignDar Al-Hekma UniversityJeddah 22246Kingdom of Saudi Arabia information technology department FCITKAUJeddah 21589Kingdom of Saudi Arabiaand also with Mathematics DepartmentFaculty of ScienceAl-Azhar UniversityNaser City 11884Egypt

The ability to make accurate energy predictions while considering all related energy factors allows production plants,regulatory bodies,and governments to meet energy demand and assess the effects of energy-saving *** energy consumption falls within normal parameters,it will be possible to use the developed model to predict energy consumption and develop improvements and mitigating measures for energy *** objective of this model is to accurately predict energy consumption without data limitations and provide results that are easily *** proposed model is an implementation of the stacked Long Short-Term Memory(LSTM)snapshot ensemble combined with the Fast Fourier Transform(FFT)and *** and Berard’s individual Household Electric-Power Consumption(iHEPC)dataset incorporated with weather data are used to analyse the model’s accuracy with predicting energy *** model is trained,and the results measured using Root Mean Square Error(RMSE),Mean Absolute Error(MAE),Mean Absolute Percentage Error(MAPE),and coefficient of determination(R^(2))metrics are 0.020,0.013,0.017,and 0.999,*** stacked LSTM snapshot ensemble performs better than the compared models based on prediction accuracy and minimized *** results of this study show that prediction accuracy is high,and the model’s stability is high as *** model shows that high levels of accuracy prove accurate predictive ability,and together with high levels of stability,the model has good interpretability,which is not typically accounted for in ***,this study shows that it can be inferred.

关键词： energy consumption prediction Artificial intelligence(Ai) Deep Learning(DL) snapshot ensemble

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A Survey of Edge Caching:Key issues and Challenges

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Tsinghua Science and technology 2024年第3期29卷 818-842页

作者： Hanwen Li Mingtao Sun Fan Xia Xiaolong Xu Muhammad Bilal School of Computer Science Nanjing University of Information Science and TechnologyNanjing 210044China Shandong Provincial University Laboratory for Protected Horticulture Weifang University of Science and TechnologyWeifang 262700China Reading Academy Nanjing University of Information Science and TechnologyNanjing 210044China School of Software Nanjing University of Information Science and TechnologyNanjing 210044China department of Computer Engineering Hankuk University of Foreign StudiesYongin-si 17035Republic of Korea

With the rapid development of mobile communication technology and intelligent applications,the quantity of mobile devices and data traffic in networks have been growing exponentially,which poses a great burden to networks and brings huge challenge to servicing user *** caching,which utilizes the storage and computation resources of the edge to bring resources closer to end users,is a promising way to relieve network burden and enhance user *** this paper,we aim to survey the edge caching techniques from a comprehensive and systematic *** first present an overview of edge caching,summarizing the three key issues regarding edge caching,i.e.,where,what,and how to cache,and then introducing several significant caching *** then carry out a detailed and in-depth elaboration on these three issues,which correspond to caching locations,caching objects,and caching strategies,*** particular,we innovate on the issue“what to cache”,interpreting it as the classification of the“caching objects”,which can be further classified into content cache,data cache,and service ***,we discuss several open issues and challenges of edge caching to inspire future investigations in this research area.

关键词： edge caching edge computing caching location caching object caching strategy 5G networkarchitecture internet of Things(ioT)

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TFN-FiCFM:sEMG-Based Gesture Recognition Using Temporal Fusion Network and Fuzzy integral-based Classifier Fusion

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Journal of Bionic Engineering 2024年第4期21卷 1878-1891页

作者： Fo Hu Kailun He Mengyuan Qian Mohamed Amin Gouda College of information Engineering Zhejiang University of TechnologyHangzhou310023China department of Mechanical Engineering and Automation Northeastern UniversityShenyang110819China

Surface electromyography(sEMG)-based gesture recognition is a key technology in the field of human–computer ***,existing gesture recognition methods face challenges in effectively integrating discriminative temporal feature representations from sEMG *** this paper,we propose a deep learning framework named TFN-FiCFM comprises a Temporal Fusion Network(TFN)and Fuzzy integral-Based Classifier Fusion method(FiCFM)to improve the accuracy and robustness of gesture ***,we design a TFN module,which utilizes an attention-based recurrent multi-scale convolutional module to acquire multi-level temporal feature representations and achieves deep fusion of temporal features through a feature pyramid ***,the deep-fused temporal features are utilized to generate multiple sets of gesture category prediction confidences through a feedback ***,we employ FiCFM to perform fuzzy fusion on prediction confidences,resulting in the ultimate *** study conducts extensive comparisons and ablation studies using the publicly available datasets Ninapro DB2 and *** demonstrate that the TFN-FiCFM model outperforms state-of-the-art methods in classification *** research can serve as a benchmark for sEMG-based gesture recognition and related deep learning modeling.

关键词： Gesture recognition sEMG Deep learning Temporal fusion Fuzzy fusion

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