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Congruent Feature Selection Method to Improve the Efficacy of Machine Learning-Based Classification in Medical Image Processing

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computer Modeling in Engineering & sciences 2025年第1期142卷 357-384页

作者： Mohd Anjum Naoufel Kraiem Hong Min Ashit Kumar Dutta Yousef Ibrahim Daradkeh Department of computer Engineering Aligarh Muslim UniversityAligarh202002India College of computer Science King Khalid UniversityAbha61413Saudi Arabia School of Computing Gachon UniversitySeongnam13120Republic of Korea Department of computer Science and information Systems College of Applied SciencesAlMaarefa UniversityRiyadh13713Saudi Arabia Department of computer Engineering and information College of Engineering in Wadi AlddawasirPrince Sattam bin Abdulaziz UniversityAl-Kharj16273Saudi Arabia

Machine learning(ML)is increasingly applied for medical image processing with appropriate learning *** applications include analyzing images of various organs,such as the brain,lung,eye,etc.,to identify specific flaws/diseases for *** primary concern of ML applications is the precise selection of flexible image features for pattern detection and region *** of the extracted image features are irrelevant and lead to an increase in computation ***,this article uses an analytical learning paradigm to design a Congruent Feature Selection Method to select the most relevant image *** process trains the learning paradigm using similarity and correlation-based features over different textural intensities and pixel *** similarity between the pixels over the various distribution patterns with high indexes is recommended for disease ***,the correlation based on intensity and distribution is analyzed to improve the feature selection ***,the more congruent pixels are sorted in the descending order of the selection,which identifies better regions than the ***,the learning paradigm is trained using intensity and region-based similarity to maximize the chances of ***,the probability of feature selection,regardless of the textures and medical image patterns,is *** process enhances the performance of ML applications for different medical image *** proposed method improves the accuracy,precision,and training rate by 13.19%,10.69%,and 11.06%,respectively,compared to other models for the selected *** mean error and selection time is also reduced by 12.56%and 13.56%,respectively,compared to the same models and dataset.

关键词： computer vision feature selection machine learning region detection texture analysis image classification medical images

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Latent Space Representational Learning of Deep Features for Acute Lymphoblastic Leukemia Diagnosis

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computer Systems Science & Engineering 2023年第4期45卷 361-376页

作者： Ghada Emam Atteia information Technology Department College of Computer&Information SciencesPrincess Nourah bint Abdulrahman UniversityRiyadh11461Saudi Arabia

Acute Lymphoblastic Leukemia(ALL)is a fatal malignancy that is featured by the abnormal increase of immature lymphocytes in blood or bone *** prognosis of ALL is indispensable for the effectual remediation of this *** screening of ALL is conducted through manual examination of stained blood smear microscopic images,a process which is time-consuming and prone to ***,many deep learning-based computer-aided diagnosis(CAD)systems have been established to automatically diagnose *** paper proposes a novel hybrid deep learning system for ALL diagnosis in blood smear *** introduced system integrates the proficiency of autoencoder networks in feature representational learning in latent space with the superior feature extraction capability of standard pretrained convolutional neural networks(CNNs)to identify the existence of ALL in blood *** augmented set of deep image features are formed from the features extracted by GoogleNet and Inception-v3 CNNs from a hybrid dataset of microscopic blood smear images.A sparse autoencoder network is designed to create an abstract set of significant latent features from the enlarged image feature *** latent features are used to perform image classification using Support Vector Machine(SVM)*** obtained results show that the latent features improve the classification performance of the proposed ALL diagnosis system over the original image ***,the classification performance of the system with various sizes of the latent feature set is *** retrieved results reveal that the introduced ALL diagnosis system superiorly compete the state of the art.

关键词： Autoencoder deep learning CNN leukemia diagnosis computeraided diagnosis

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Evaluation of Spatiotemporal Dynamics of Simulated Land Use/Cover in China Using a Probabilistic Cellular Automata-Markov Model

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Pedosphere 2013年第2期23卷 243-255页

作者： CHEN Xu YU Shi-Xiao ZHANG Ya-Ping Department of Ecology School of Life Sciences and State Key Laboratory of BiocontrolSun Yat-sen University computer&information Science Department Southwest Forestry University faculty of computer Science&information Technology Yunnan Normal University

Using the fuzzy rule-based classification method, normalized difference vegetation index (NDVI) images acquired from 1982 to 1998 were classified into seventeen phases. Based on these classification images, a probabilistic cellular automata-Markov Chain model was developed and used to simulate a land cover scenario of China for the year 2014. Spatiotemporal dynamics of land use/cover in China from 1982 to 2014 were then analyzed and evaluated. The results showed that the change trends of land cover type from 1998 to 2014 would be contrary to those from 1982 to 1998. In particular, forestland and grassland areas decreased by 1.56% and 1.46%, respectively, from 1982 to 1998, and should increase by 1.5% and 2.3% from 1998 to 2014, respectively.

关键词： classification image fuzzy rule-based classification normalized difference vegetation index remote sensing

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An Integrated Incentive Framework for Mobile Crowdsourced Sensing

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Tsinghua Science and Technology 2016年第2期21卷 146-156页

作者： Wei Dai Yufeng Wang Qun Jin Jianhua Ma College of Telecommunications and information Engineering Nanjing University of Posts and TelecommunicationsNanjing 210003China. Department of Human Informatics and Cognitive sciences Waseda UniversitySaitama 359-1192Japan. faculty of computer&information sciences Hosei UniversityTokyo 184-8584Japan

Currently, mobile devices （e.g., smartphones） are equipped with multiple wireless interfaces and rich builtin functional sensors that possess powerful computation and communication capabilities, and enable numerous Mobile Crowdsourced Sensing （MCS） applications. Generally, an MCS system is composed of three components： a publisher of sensing tasks, crowd participants who complete the crowdsourced tasks for some kinds of rewards, and the crowdsourcing platform that facilitates the interaction between publishers and crowd participants. Incentives are a fundamental issue in MCS. This paper proposes an integrated incentive framework for MCS, which appropriately utilizes three widely used incentive methods： reverse auction, gamification, and reputation updating. Firstly, a reverse-auction-based two-round participant selection mechanism is proposed to incentivize crowds to actively participate and provide high-quality sensing data. Secondly, in order to avoid untruthful publisher feedback about sensing-data quality, a gamification-based verification mechanism is designed to evaluate the truthfulness of the publisher＇s feedback. Finally, the platform updates the reputation of both participants and publishers based on their corresponding behaviors. This integrated incentive mechanism can motivate participants to provide high-quality sensed contents, stimulate publishers to give truthful feedback, and make the platform profitable.

关键词： mobile crowdsourced sensing incentive mechanism reverse auction gamification reputation updating

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Multi-Stage-Based Siamese Neural Network for Seal Image Recognition

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computer Modeling in Engineering & sciences 2025年第1期142卷 405-423页

作者： Jianfeng Lu Xiangye Huang Caijin Li Renlin Xin Shanqing Zhang Mahmoud Emam School of computer Science and Technology Hangzhou Dianzi UniversityHangzhou310018China Shangyu Institute of Science and Engineering Hangzhou Dianzi UniversityShaoxing312300China faculty of Artificial Intelligence Menoufia UniversityShebin El-Koom32511Egypt

Seal authentication is an important task for verifying the authenticity of stamped seals used in various domains to protect legal documents from tampering and *** seal inspection is commonly audited manually to ensure document ***,manual assessment of seal images is tedious and laborintensive due to human errors,inconsistent placement,and completeness of the *** image recognition systems are inadequate enough to identify seal types accurately,necessitating a neural network-based method for seal image ***,neural network-based classification algorithms,such as Residual Networks(ResNet)andVisualGeometryGroup with 16 layers(VGG16)yield suboptimal recognition rates on stamp ***,the fixed training data categories make handling new categories to be a challenging *** paper proposes amulti-stage seal recognition algorithmbased on Siamese network to overcome these ***,the seal image is pre-processed by applying an image rotation correction module based on Histogram of Oriented Gradients(HOG).Secondly,the similarity between input seal image pairs is measured by utilizing a similarity comparison module based on the Siamese ***,we compare the results with the pre-stored standard seal template images in the database to obtain the seal *** evaluate the performance of the proposed method,we further create a new seal image dataset that contains two subsets with 210,000 valid labeled pairs in *** proposed work has a practical significance in industries where automatic seal authentication is essential as in legal,financial,and governmental sectors,where automatic seal recognition can enhance document security and streamline validation ***,the experimental results show that the proposed multi-stage method for seal image recognition outperforms state-of-the-art methods on the two established datasets.

关键词： Seal recognition seal authentication document tampering siamese network spatial transformer network similarity comparison network

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Deep Learning Based Cyber Event Detection from Open-Source Re-Emerging Social Data

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computers, Materials & Continua 2023年第8期76卷 1423-1438页

作者： Farah Mohammad Saad Al-Ahmadi Jalal Al-Muhtadi Center of Excellence in information Assurance(CoEIA) King Saud UniversityRiyadh11543Saudi Arabia College of computer&information sciences King Saud UniversityRiyadh11543Saudi Arabia

Social media forums have emerged as the most popular form of communication in the modern technology era,allowing people to discuss and express their *** increases the amount of material being shared on social media *** is a wealth of information about the threat that may be found in such open data *** security of already-deployed software and systems relies heavily on the timely detection of newly-emerging threats to their safety that can be gleaned from such *** the fact that several models for detecting cybersecurity events have been presented,it remains challenging to extract security events from the vast amounts of unstructured text present in public data *** majority of the currently available methods concentrate on detecting events that have a high number of *** is because the unstructured text in open data sources typically contains a large number of ***,to react to attacks quicker than they can be launched,security analysts and information technology operators need to be aware of critical security events as soon as possible,regardless of how often they are *** research provides a unique event detection method that can swiftly identify significant security events from open forums such as *** proposed work identified new threats and the revival of an attack or related event,independent of the volume of mentions relating to those events on *** this research work,deep learning has been used to extract predictive features from open-source *** proposed model is composed of data collection,data transformation,feature extraction using deep learning,Latent Dirichlet Allocation(LDA)based medium-level cyber-event detection and final Google Trends-based high-level cyber-event *** proposed technique has been evaluated on numerous *** results show that the proposed method outperforms existing methods in detecting cyber events by giving 95.96

关键词： Social media twitter cyber events deep learning

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RoGRUT: A Hybrid Deep Learning Model for Detecting Power Trapping in Smart Grids

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computers, Materials & Continua 2024年第5期79卷 3175-3192页

Electricity theft is a widespread non-technical issue that has a negative impact on both power grids and electricity *** hinders the economic growth of utility companies,poses electrical risks,and impacts the high energy costs borne by *** development of smart grids is crucial for the identification of power theft since these systems create enormous amounts of data,including information on client consumption,which may be used to identify electricity theft using machine learning and deep learning ***,there also exist different solutions such as hardware-based solutions to detect electricity theft that may require human resources and expensive ***-based solutions are presented in the literature to identify electricity theft but due to the dimensionality curse,class imbalance issue and improper hyper-parameter tuning of such models lead to poor *** this research,a hybrid deep learning model abbreviated as RoGRUT is proposed to detect electricity theft as amalicious and non-malicious *** key steps of the RoGRUT are data preprocessing that covers the problem of class imbalance,feature extraction and final theft *** advanced-level models like RoBERTa is used to address the curse of dimensionality issue,the near miss for class imbalance,and transfer learning for *** effectiveness of the RoGRUTis evaluated using the dataset fromactual smartmeters.A significant number of simulations demonstrate that,when compared to its competitors,the RoGRUT achieves the best classification *** performance evaluation of the proposed model revealed exemplary results across *** accuracy achieved was 88%,with precision at an impressive 86%and recall reaching 84%.The F1-Score,a measure of overall performance,stood at 85%.Furthermore,themodel exhibited a noteworthyMatthew correlation coefficient of 78%and excelled with an area under the curve of 91%.

关键词： Electricity theft smart grid RoBERTa GRU transfer learning

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Utilizing engineered extracellular vesicles as delivery vectors in the management of ischemic stroke:a special outlook on mitochondrial delivery

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Neural Regeneration Research 2025年第8期20卷 2181-2198页

作者： Jiali Chen Yiyang Li Xingping Quan Jinfen Chen Yan Han Li Yang Manfei Zhou Greta Seng Peng Mok Ruibing Wang Yonghua Zhao Institute of Chinese Medical sciences State Key Laboratory of Quality Research in Chinese MedicineUniversity of MacaoTaipaMacao Special Administrative RegionChina Department of Pharmacy Hunan Provincial People’s Hospitalthe First Affiliated Hospital of Hunan Normal UniversityChangshaHunan ProvinceChina Department of Electrical and computer Engineering University of MacaoTaipaMacao Special Administrative RegionChina Department of Pharmaceutical sciences Faculty of Health SciencesUniversity of MacaoTaipaMacao Special Administrative RegionChina

Ischemic stroke is a secondary cause of mortality worldwide,imposing considerable medical and economic burdens on *** vesicles,serving as natural nanocarriers for drug delivery,exhibit excellent biocompatibility in vivo and have significant advantages in the management of ischemic ***,the uncertain distribution and rapid clearance of extracellular vesicles impede their delivery *** utilizing membrane decoration or by encapsulating therapeutic cargo within extracellular vesicles,their delivery efficacy may be greatly ***,previous studies have indicated that microvesicles,a subset of large-sized extracellular vesicles,can transport mitochondria to neighboring cells,thereby aiding in the restoration of mitochondrial function post-ischemic *** extracellular vesicles have also demonstrated the capability to transfer mitochondrial components,such as proteins or deoxyribonucleic acid,or their sub-components,for extracellular vesicle-based ischemic stroke *** this review,we undertake a comparative analysis of the isolation techniques employed for extracellular vesicles and present an overview of the current dominant extracellular vesicle modification *** the complex facets of treating ischemic stroke,we also delineate various extracellular vesicle modification approaches which are suited to different facets of the treatment ***,given the burgeoning interest in mitochondrial delivery,we delved into the feasibility and existing research findings on the transportation of mitochondrial fractions or intact mitochondria through small extracellular vesicles and microvesicles to offer a fresh perspective on ischemic stroke therapy.

关键词： delivery engineering extracellular vesicles identification ischemic stroke isolation mitochondria targeting strategy therapeutic effects

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Stereo Matching Method Based on Space-Aware Network Model

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computer Modeling in Engineering & sciences 2021年第4期127卷 175-189页

作者： Jilong Bian Jinfeng Li College of information&computer Engineering Northeast Forestry UniversityHarbin150040China College of computer&information Technology Mudanjiang Normal UniversityMudanjiang157011China

The stereo matching method based on a space-aware network is proposed, which divides the network into threesections: Basic layer, scale layer, and decision layer. This division is beneficial to integrate residue network and densenetwork into the space-aware network model. The vertical splitting method for computing matching cost by usingthe space-aware network is proposed for solving the limitation of GPU RAM. Moreover, a hybrid loss is broughtforward to boost the performance of the proposed deep network. In the proposed stereo matching method, thespace-aware network is used to calculate the matching cost and then cross-based cost aggregation and semi-globalmatching are employed to compute a disparity map. Finally, a disparity-post processing method is utilized suchas subpixel interpolation, median filter, and bilateral filter. The experimental results show this method has a goodperformance on running time and accuracy, with a percentage of erroneous pixels of 1.23% on KITTI 2012 and1.94% on KITTI 2015.

关键词： Deep learning stereo matching space-aware network hybrid loss

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Efficient computation of Hash Hirschberg protein alignment utilizing hyper threading multi-core sharing technology

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CAAI Transactions on Intelligence Technology 2022年第2期7卷 278-291页

作者： Muhannad Abu-Hashem Adnan Gutub Department of Geomatics Faculty of Architecture and PlanningKing Abdulaziz UniversityJeddahSaudi Arabia Department of computer Engineering College of Computer&Information SystemsUmm Al-Qura UniversityMakkahSaudi Arabia

Due to current technology enhancement,molecular databases have exponentially grown requesting faster efficient methods that can handle these amounts of huge ***-fore,Multi-processing CPUs technology can be used including physical and logical processors(Hyper Threading)to significantly increase the performance of ***,sequence comparison and pairwise alignment were both found contributing significantly in calculating the resemblance between sequences for constructing optimal *** research used the Hash Table-NGram-Hirschberg(HT-NGH)algo-rithm to represent this pairwise alignment utilizing hashing *** authors propose using parallel shared memory architecture via Hyper Threading to improve the performance of molecular dataset protein pairwise *** proposed parallel hyper threading method targeted the transformation of the HT-NGH on the datasets decomposition for sequence level efficient utilization within the processing units,that is,reducing idle processing unit *** authors combined hyper threading within the multicore architecture processing on shared memory utilization remarking perfor-mance of 24.8%average speed up to 34.4%as the highest boosting *** benefit of this work improvement is shown preserving acceptable accuracy,that is,reaching 2.08,2.88,and 3.87 boost-up as well as the efficiency of 1.04,0.96,and 0.97,using 2,3,and 4 cores,respectively,as attractive remarkable results.

关键词： computational biology high-performance computing Hyper Threading pairwise sequence alignment parallel design sequence alignment shared-memory

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