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Defect Detection Model Using Time Series data Augmentation and Transformation

Computers, Materials & Continua 2024年第2期78卷 1713-1730页

作者： Gyu-Il Kim Hyun Yoo Han-Jin Cho Kyungyong Chung department of Computer Science Kyonggi UniversitySuwon-si16227Korea Contents Convergence Software Research Institue Kyonggi UniversitySuwon-si16227Korea department of Energy IT Engineering Far East UniversityEumsung-gun27601Korea Division of ai Computer Science and Engineering Kyonggi UniversitySuwon-si16227Korea

Time-series data provide important information in many fields,and their processing and analysis have been the focus of much ***,detecting anomalies is very difficult due to data imbalance,temporal dependence,and ***,methodologies for data augmentation and conversion of time series data into images for analysis have been *** paper proposes a fault detection model that uses time series data augmentation and transformation to address the problems of data imbalance,temporal dependence,and robustness to *** method of data augmentation is set as the addition of *** involves adding Gaussian noise,with the noise level set to 0.002,to maximize the generalization performance of the *** addition,we use the Markov Transition Field(MTF)method to effectively visualize the dynamic transitions of the data while converting the time series data into *** enables the identification of patterns in time series data and assists in capturing the sequential dependencies of the *** anomaly detection,the PatchCore model is applied to show excellent performance,and the detected anomaly areas are represented as heat *** allows for the detection of anomalies,and by applying an anomaly map to the original image,it is possible to capture the areas where anomalies *** performance evaluation shows that both F1-score and Accuracy are high when time series data is converted to ***,when processed as images rather than as time series data,there was a significant reduction in both the size of the data and the training *** proposed method can provide an important springboard for research in the field of anomaly detection using time series ***,it helps solve problems such as analyzing complex patterns in data lightweight.

关键词： Defect detection time series deep learning data augmentation data transformation

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Cloud datacenter Selection Using Service Broker Policies:A Survey

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Computer Modeling in Engineering & Sciences 2024年第4期139卷 1-41页

作者： Salam Al-E’mari Yousef Sanjalawe Ahmad Al-Daraiseh Mohammad Bany Taha Mohammad Aladaileh department of Information Security Faculty of ITUniversity of PetraAmman11196Jordan department of Cybersecurity School of ITAmerican University of Madaba(AUM)Amman11821Jordan department of Computer Science School of ITAmerican University of Madaba(AUM)Amman11821Jordan department of data Science and Artificial Intelligence School of ITAmerican University of Madaba(AUM)Amman11821Jordan

Amid the landscape of Cloud Computing(CC),the Cloud datacenter(DC)stands as a conglomerate of physical servers,whose performance can be hindered by bottlenecks within the realm of proliferating CC services.A linchpin in CC’s performance,the Cloud Service Broker(CSB),orchestrates DC *** to adroitly route user requests with suitable DCs transforms the CSB into a bottleneck,endangering service *** tackle this,deploying an efficient CSB policy becomes imperative,optimizing DC selection to meet stringent Qualityof-Service(QoS)*** numerous CSB policies,their implementation grapples with challenges like costs and *** article undertakes a holistic review of diverse CSB policies,concurrently surveying the predicaments confronted by current *** foremost objective is to pinpoint research gaps and remedies to invigorate future policy ***,it extensively clarifies various DC selection methodologies employed in CC,enriching practitioners and researchers *** synthetic analysis,the article systematically assesses and compares myriad DC selection *** analytical insights equip decision-makers with a pragmatic framework to discern the apt technique for their *** summation,this discourse resoundingly underscores the paramount importance of adept CSB policies in DC selection,highlighting the imperative role of efficient CSB policies in optimizing CC *** emphasizing the significance of these policies and their modeling implications,the article contributes to both the general modeling discourse and its practical applications in the CC domain.

关键词： Cloud computing cloud service broker datacenter selection quality-of-service user request

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data-driven slicing for dimension reduction in regressions:A likelihood-ratio approach

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Science China Mathematics 2024年第3期67卷 647-664页

作者： Peirong Xu Tao Wang Lixing Zhu department of Statistics Shanghai Jiao Tong UniversityShanghai 200240China Center for Statistics and data Science Beijing Normal University at ZhuhaiZhuhai 519087China

To efficiently estimate the central subspace in sufficient dimension reduction,response discretization via slicing its range is one of the most used methodologies when inverse regression-based methods are ***,existing slicing schemes are almost all ad hoc and not widely ***,how to define datadriven schemes with certain optimal properties is a longstanding problem in this *** research described here is then ***,we introduce a likelihood-ratio-based framework for dimension reduction,subsuming the popularly used methods including the sliced inverse regression,the sliced average variance estimation and the likelihood acquired ***,we propose a regularized log likelihood-ratio criterion to obtain a data-driven slicing scheme and derive the asymptotic properties of the estimators.A simulation study is carried out to examine the performance of the proposed method and that of existing methods.A data set concerning concrete compressive strength is also analyzed for illustration and comparison.

关键词： full-likelihood approach adaptive slicing regularization second-order method

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Polo-like kinase 1 as a biomarker predicts the prognosis and immunotherapy of breast invasive carcinoma patients

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Oncology Research 2024年第2期32卷 339-351页

作者： JUAN SHEN WEIYU ZHANG QINQIN JIN FUYU GONG HEPING ZHANG HONGLIANG XU JIEJIE LI HUI YAO XIYA JIANG YINTING YANG LIN HONG JIE MEI YANG SONG SHUGUANG ZHOU School of big data and Artificial Intelligence Anhui Xinhua UniversityHefei230088China department of Gynecology and Obstetrics Maternity and Child Healthcare Hospital Affiliated to Anhui Medical UniversityAnhui Province Maternity and Child Healthcare HospitalHefei230001China department of Gynecology and Obstetrics The Fifth Clinical College of Anhui Medical UniversityHefei230032China departments of Breast Surgery Fuyang Women and Children’s HospitalFuyang236000China departments of Pathology Anhui Province Maternity and Child Health HospitalHefei230001China department of Pain The First Affiliated Hospital of Anhui Medical UniversityHefei230032China department of Gynecology and Obstetrics Linquan Maternity and Child Healthcare HospitalFuyang236400China

Invasive breast carcinoma(BRCA)is associated with poor prognosis and high risk of ***,it is critical to identify novel biomarkers for the prognostic assessment of ***:The expression data of polo-like kinase 1(PLK1)in BRCA and the corresponding clinical information were extracted from TCGA and GEO ***1 expression was validated in diverse breast cancer cell lines by quantitative real-time polymerase chain reaction(qRT-PCR)and western *** sample gene set enrichment analysis(ssGSEA)was performed to evaluate immune infiltration in the BRCA microenvironment,and the random forest(RF)and support vector machine(SVM)algorithms were used to screen for the hub infiltrating cells and calculate the immunophenoscore(IPS).The RF algorithm and COX regression model were applied to calculate survival risk scores based on the PLK1 expression and immune cell ***,a prognostic nomogram was constructed with the risk score and pathological stage,and its clinical potential was evaluated by plotting calibration charts and DCA *** application of the nomogram was further validated in an immunotherapy ***:PLK1 expression was significantly higher in the tumor samples in TCGA-BRCA ***,PLK1 expression level,age and stage were identified as independent prognostic factors of *** the IPS was unaffected by PLK1 expression,the TMB and MATH scores were higher in the PLK1-high group,and the TIDE scores were higher for the PLK1-low *** also identified 6 immune cell types with high infiltration,along with 11 immune cell types with low infiltration in the PLK1-high tumors.A risk score was devised using PLK1 expression and hub immune cells,which predicted the prognosis of BRCA *** addition,a nomogram was constructed based on the risk score and pathological staging,and showed good predictive ***:PLK1 expression and immune cell infiltration can predict post-immunotherapy pr

关键词： Breast invasive carcinoma(BRCA) Polo-like kinase 1(PLK 1) Random forest(RF) Support vector machine(SVM) Immune infiltration

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Feature deformation network with multi-range feature enhancement for agricultural machinery operation mode identification

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International Journal of Agricultural and Biological Engineering 2024年第4期17卷 265-275页

作者： Weixin Zhai Zhi Xu Jinming Liu Xiya Xiong Jiawen Pan Sun-Ok Chung Dionysis Bochtis Caicong Wu College of Information and Electrical Engineering China Agricultural UniversityBeijing 100083China Key Laboratory of Agricultural Machinery Monitoring and big data Application Ministry of Agriculture and Rural AffairsBeijing 100083China department of Smart Agricultural Systems Graduate SchoolChungnam National UniversityDaejeon 34134Republic of Korea Institute for Bio-Economy and Agri-Technology(iBO) Centre for Research and Technology-Hellas(CERTH)Thessaloniki 57001Greece

Utilizing the spatiotemporal features contained in extensive trajectory data for identifying operation modes of agricultural machinery is an important basis task for subsequent agricultural machinery trajectory *** the present study,to effectively identify agricultural machinery operation mode,a feature deformation network with multi-range feature enhancement was ***,a multi-range feature enhancement module was developed to fully explore the feature distribution of agricultural machinery trajectory ***,to further enrich the representation of trajectories,a feature deformation module was proposed that can map trajectory points to high-dimensional space to form feature ***,EfficientNet-B0 was used to extract features of different scales and depths from the feature map,select features highly relevant to the results,and finally accurately predict the mode of each trajectory *** validate the effectiveness of the proposed method,experiments were conducted to compare the results with those of other methods on a dataset of real agricultural *** the corn and wheat harvester trajectory datasets,the model achieved accuracies of 96.88%and 96.68%,as well as F1 scores of 93.54%and 94.19%,exhibiting improvements of 8.35%and 9.08%in accuracy and 20.99%and 20.04%in F1 score compared with the current state-of-the-art method.

关键词： road-field trajectory classification efficientNet feature deformation network multi-range feature enhancement agricultural machinery operation mode recognition

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Audio Mixing Inversion via Embodied Self-supervised Learning

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Machine Intelligence Research 2024年第1期21卷 55-62页

作者： Haotian Zhou Feng Yu Xihong Wu department of ai Music and Music Information Technology Central Conservatory of MusicBeijing 100031China Laboratory of Music Artificial Intelligence Laboratory of Philosophy and Social SciencesMinistry of EducationBeijing 100031China School of Intelligence Science and Technology Peking UniversityBeijing 100871China

Audio mixing is a crucial part of music *** analyzing or recreating audio mixing,it is of great importance to conduct research on estimating mixing parameters used to create mixdowns from music recordings,i.e.,audio mixing ***,approaches of audio mixing inversion are rarely explored.A method of estimating mixing parameters from raw tracks and a stereo mixdown via embodied self-supervised learning is *** this work,several commonly used audio effects including gain,pan,equalization,reverb,and compression,are taken into *** method is able to learn an inference neural network that takes a stereo mixdown and the raw audio sources as input and estimate mixing parameters used to create the mixdown by iteratively sampling and *** the sampling step,the inference network predicts a set of mixing parameters,which is sampled and fed to an audio-processing framework to generate audio data for the training *** the training step,the same network used in the sampling step is optimized with the sampled data generated from the sampling *** method is able to explicitly model the mixing process in an interpretable way instead of using a black-box neural network model.A set of objective measures are used for *** experimental results show that this method has better performance than current state-of-the-art methods.

关键词： Audio mixing inversion intelligent audio mixing self-supervised learning audio signal processing deep learning

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A Hybrid and Lightweight Device-to-Server Authentication Technique for the Internet of Things

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Computers, Materials & Continua 2024年第3期78卷 3805-3823页

作者： Shaha Al-Otaibi Rahim Khan Hashim Ali Aftab Ahmed Khan Amir Saeed Jehad Ali department of Information Systems College of Computer and Information SciencesPrincess Nourah bint Abdulrahman UniversityP.O.Box 84428Riyadh11671Saudi Arabia department of Computer Science Abdul Wali Khan University MardanMardan23200Pakistan department of Computer Science and IT UET PeshawarJalozai Campus PeshawarPeshawar24240Pakistan department of ai Convergence Network Ajou UniversitySuwon16499South Korea

The Internet of Things(IoT)is a smart networking infrastructure of physical devices,i.e.,things,that are embedded with sensors,actuators,software,and other technologies,to connect and share data with the respective server *** IoTs are cornerstones in different application domains,the device’s authenticity,i.e.,of server(s)and ordinary devices,is the most crucial issue and must be resolved on a priority ***,various field-proven methodologies were presented to streamline the verification process of the communicating devices;however,location-aware authentication has not been reported as per our knowledge,which is a crucial metric,especially in scenarios where devices are *** paper presents a lightweight and location-aware device-to-server authentication technique where the device’s membership with the nearest server is subjected to its location information along with other ***,Media Access Control(MAC)address and Advance Encryption Scheme(AES)along with a secret shared key,i.e.,λ_(i) of 128 bits,have been utilized by Trusted Authority(TA)to generate MaskIDs,which are used instead of the original ID,for every device,i.e.,server and member,and are shared in the offline ***,TA shares a list of authentic devices,i.e.,server S_(j) and members C_(i),with every device in the IoT for the onward verification process,which is required to be executed before the initialization of the actual communication ***,every device should be located such that it lies within the coverage area of a server,and this location information is used in the authentication process.A thorough analytical analysis was carried out to check the susceptibility of the proposed and existing authentication approaches against well-known intruder attacks,i.e.,man-in-the-middle,masquerading,device,and server impersonations,etc.,especially in the IoT ***,proposed authentication and existing state-of-the-art approaches have been s

关键词： Internet of things authenticity security location communication

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Prediction of three-dimensional ocean temperature in the South China Sea based on time series gridded data and a dynamic spatiotemporal graph neural network

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Acta Oceanologica Sinica 2024年第7期 26-39页

作者： Feng Nan Zhuolin Li Jie Yu Suixiang Shi Xinrong Wu Lingyu Xu department of Computer Engineering and Science Shanghai University Key Laboratory of Digital Ocean National Marine Data and Information Service

Ocean temperature is an important physical variable in marine ecosystems, and ocean temperature prediction is an important research objective in ocean-related fields. Currently, one of the commonly used methods for ocean temperature prediction is based on data-driven, but research on this method is mostly limited to the sea surface,with few studies on the prediction of internal ocean temperature. Existing graph neural network-based methods usually use predefined graphs or learned static graphs, which cannot capture the dynamic associations among data. In this study, we propose a novel dynamic spatiotemporal graph neural network(DSTGN) to predict threedimensional ocean temperature(3D-OT), which combines static graph learning and dynamic graph learning to automatically mine two unknown dependencies between sequences based on the original 3D-OT data without prior knowledge. Temporal and spatial dependencies in the time series were then captured using temporal and graph convolutions. We also integrated dynamic graph learning, static graph learning, graph convolution, and temporal convolution into an end-to-end framework for 3D-OT prediction using time-series grid data. In this study, we conducted prediction experiments using high-resolution 3D-OT from the Copernicus global ocean physical reanalysis, with data covering the vertical variation of temperature from the sea surface to 1 000 m below the sea surface. We compared five mainstream models that are commonly used for ocean temperature prediction,and the results showed that the method achieved the best prediction results at all prediction scales.

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ThyroidNet:A Deep Learning Network for Localization and Classification of Thyroid Nodules

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Computer Modeling in Engineering & Sciences 2024年第4期139卷 361-382页

作者： Lu Chen Huaqiang Chen Zhikai Pan Sheng Xu Guangsheng Lai Shuwen Chen Shuihua Wang Xiaodong Gu Yudong Zhang Ultrasonic department Zhongda Hospital Affiliated to Southeast UniversityNanjing210009China School of Physics and Information Engineering Jiangsu Second Normal UniversityNanjing211200China School of Computing and Mathematical Sciences University of LeicesterLeicesterLE17RHUK department of Information Systems Faculty of Computing and Information TechnologyKing Abdulaziz UniversityJeddah21589Saudi Arabia State Key Laboratory of MillimeterWaves Southeast UniversityNanjing210096China Jiangsu Province Engineering Research Center of Basic Education big data Application Jiangsu Second Normal UniversityNanjing211200China School of Software Engineering Quanzhou Normal UniversityQuanzhou362000China department of Biological Sciences Xi’an Jiaotong-Liverpool UniversitySuzhou215123China

aim:This study aims to establish an artificial intelligence model,ThyroidNet,to diagnose thyroid nodules using deep learning techniques ***:A novel method,ThyroidNet,is introduced and evaluated based on deep learning for the localization and classification of thyroid ***,we propose the multitask TransUnet,which combines the TransUnet encoder and decoder with multitask ***,we propose the DualLoss function,tailored to the thyroid nodule localization and classification *** balances the learning of the localization and classification tasks to help improve the model’s generalization ***,we introduce strategies for augmenting the ***,we submit a novel deep learning model,ThyroidNet,to accurately detect thyroid ***:ThyroidNet was evaluated on private datasets and was comparable to other existing methods,including U-Net and *** results show that ThyroidNet outperformed these methods in localizing and classifying thyroid *** achieved improved accuracy of 3.9%and 1.5%,***:ThyroidNet significantly improves the clinical diagnosis of thyroid nodules and supports medical image analysis *** research directions include optimization of the model structure,expansion of the dataset size,reduction of computational complexity and memory requirements,and exploration of additional applications of ThyroidNet in medical image analysis.

关键词： ThyroidNet deep learning TransUnet multitask learning medical image analysis

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Evaluating Pharmacological and Rehabilitation Strategies for Effective Management of Bipolar Disorder: A Comprehensive Clinical Study

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Advances in Bioscience and Biotechnology 2024年第7期15卷 406-431页

作者： Rocco de Filippis Abdullah Al Foysal department of Neuroscience Institute of Psychopathology Rome Italy department of Computer Engineering (ai) University of Genova Genova Italy

Bipolar disorder presents significant challenges in clinical management, characterized by recurrent episodes of depression and mania often accompanied by impairment in functioning. This study investigates the efficacy of pharmacological interventions and rehabilitation strategies to improve patient outcomes and quality of life. Utilizing a randomized controlled trial with multiple treatment arms, participants will receive pharmacotherapy, polypharmacotherapy, rehabilitation interventions, or combination treatments. Outcome measures will be assessed using standardized scales, including the Hamilton Depression Scale, Yale-Brown Obsessive Compulsive Scale (Y-BOCS), and Mania Scale. Preliminary data suggest improvements in symptom severity and functional outcomes with combination treatments. This research aims to inform clinical practice, guide treatment decisions, and ultimately enhance the quality of care for individuals living with bipolar disorder. Findings will be disseminated through peer-reviewed journals and scientific conferences to advance knowledge in this field.

关键词： Bipolar Disorder (BD) Pharmacotherapy (PT) Rehabilitation Interventions (RI) Hamilton Depression Scale (HAM-D) Yale-Brown Obsessive Compulsive Scale (Y-BOCS) Mania Scale (MS) Machine learning (ML) and Artificial Intelligence (ai).

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