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Real-Time Prediction of Urban Traffic Problems Based on Artificial Intelligence-Enhanced Mobile Ad Hoc Networks(MANETS)

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computers, Materials & Continua 2024年第5期79卷 1903-1923页

作者： Ahmed Alhussen Arshiya S.Ansari Department of computer Engineering College of Computer and Information SciencesMajmaah UniversityAl-Majmaah11952Saudi Arabia Department of information Technology College of Computer and Information SciencesMajmaah UniversityAl-Majmaah11952Saudi Arabia

Traffic in today’s cities is a serious problem that increases travel times,negatively affects the environment,and drains financial *** study presents an Artificial Intelligence(AI)augmentedMobile Ad Hoc Networks(MANETs)based real-time prediction paradigm for urban traffic *** are wireless networks that are based on mobile devices and may *** distributed nature of MANETs and the power of AI approaches are leveraged in this framework to provide reliable and timely traffic congestion *** study suggests a unique Chaotic Spatial Fuzzy Polynomial Neural Network(CSFPNN)technique to assess real-time data acquired from various sources within *** framework uses the proposed approach to learn from the data and create predictionmodels to detect possible traffic problems and their severity in real ***-time traffic prediction allows for proactive actions like resource allocation,dynamic route advice,and traffic signal optimization to reduce *** framework supports effective decision-making,decreases travel time,lowers fuel use,and enhances overall urban mobility by giving timely information to pedestrians,drivers,and urban *** simulations and real-world datasets are used to test the proposed framework’s prediction accuracy,responsiveness,and *** results show that the suggested framework successfully anticipates urban traffic issues in real-time,enables proactive traffic management,and aids in creating smarter,more sustainable cities.

关键词： Mobile AdHocNetworks(MANET) urban traffic prediction artificial intelligence(AI) traffic congestion chaotic spatial fuzzy polynomial neural network(CSFPNN)

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Throughput and BER of wireless powered DF relaying in Nakagami-m fading

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Science China(information sciences) 2017年第10期60卷 204-216页

作者： Yan GAO Yunfei CHEN Aiqun HU School of Electronic information Nanjing College of Information Technology college of computer and information Hohai University college of information Science and Engineering Southeast University

Energy harvesting provides a promising solution to the extra energy requirement at the relay due to relaying. In this paper, the throughput and bit error rate of a decode-and-forward relaying system are studied using power splitting wireless power. Three different transmission scenarios are considered: instantaneous transmission, delay-or error-constrained transmission and delay-or error-tolerant transmission. For each scenario,exact expressions for the throughput and bit error rate are derived. Numerical results show that, for instantaneous transmission, the optimum splitting factor is not sensitive to the channel gain of the source-to-relay *** delay-or error-constrained transmissions, the optimum splitting factor increases with the quality of the source-to-relay link and decreases with the quality of the relay-to-destination link. For delay-or error-tolerant transmissions, the optimum splitting factor is insensitive to the quality of the source-to-relay link.

关键词： decode-and-forward energy harvesting power-splitting relaying

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Feature selection on probabilistic symbolic objects

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Frontiers of computer Science 2014年第6期8卷 933-947页

作者： Djamal ZIANI information Systems Department College of Computer and Information Sciences King Saud University Riyadh 11543 Saudi Arabia

In data analysis tasks, we are often confronted to very high dimensional data. Based on the purpose of a data analysis study, feature selection will find and select the relevant subset of features from the original features. Many feature selection algorithms have been proposed in classical data analysis, but very few in symbolic data analysis （SDA） which is an extension of the classical data analysis, since it uses rich objects instead to simple matrices. A symbolic object, compared to the data used in classical data analysis can describe not only individuals, but also most of the time a cluster of individuals. In this paper we present an unsupervised feature selection algorithm on probabilistic symbolic objects （PSOs）, with the purpose of discrimination. A PSO is a symbolic object that describes a cluster of individuals by modal variables using relative frequency distribution associated with each value. This paper presents new dissimilarity measures between PSOs, which are used as feature selection criteria, and explains how to reduce the complexity of the algorithm by using the discrimination matrix.

关键词： symbolic data analysis feature selection probabilistic symbolic object discrimination criteria data and knowledge visualization.

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Modified Anam-Net Based Lightweight Deep Learning Model for Retinal Vessel Segmentation

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computers, Materials & Continua 2022年第10期73卷 1501-1526页

作者： Syed Irtaza Haider Khursheed Aurangzeb Musaed Alhussein college of computer and information sciences King Saud UniversityRiyadh11543Saudi Arabia Department of computer Engineering College of Computer and Information SciencesKing Saud UniversityRiyadh11543Saudi Arabia

The accurate segmentation of retinal vessels is a challenging taskdue to the presence of various pathologies as well as the low-contrast ofthin vessels and non-uniform illumination. In recent years, encoder-decodernetworks have achieved outstanding performance in retinal vessel segmentation at the cost of high computational complexity. To address the aforementioned challenges and to reduce the computational complexity, we proposea lightweight convolutional neural network (CNN)-based encoder-decoderdeep learning model for accurate retinal vessels segmentation. The proposeddeep learning model consists of encoder-decoder architecture along withbottleneck layers that consist of depth-wise squeezing, followed by fullconvolution, and finally depth-wise stretching. The inspiration for the proposed model is taken from the recently developed Anam-Net model, whichwas tested on CT images for COVID-19 identification. For our lightweightmodel, we used a stack of two 3 × 3 convolution layers (without spatialpooling in between) instead of a single 3 × 3 convolution layer as proposedin Anam-Net to increase the receptive field and to reduce the trainableparameters. The proposed method includes fewer filters in all convolutionallayers than the original Anam-Net and does not have an increasing numberof filters for decreasing resolution. These modifications do not compromiseon the segmentation accuracy, but they do make the architecture significantlylighter in terms of the number of trainable parameters and computation *** proposed architecture has comparatively fewer parameters (1.01M) thanAnam-Net (4.47M), U-Net (31.05M), SegNet (29.50M), and most of the otherrecent works. The proposed model does not require any problem-specificpre- or post-processing, nor does it rely on handcrafted features. In addition,the attribute of being efficient in terms of segmentation accuracy as well aslightweight makes the proposed method a suitable candidate to be used in thescreening platforms at

关键词： Anam-Net convolutional neural network cross-database training data augmentation deep learning fundus images retinal vessel segmentation semantic segmentation

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Impact of preprocessing on medical data classification

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Frontiers of computer Science 2016年第6期10卷 1082-1102页

作者： Sarab ALMUHAIDEB Mohamed El Bachir MENAI computer Science Department College of Computer and Information Sciences Prince Sultan University Riyadh 11586 Saudi Arabia

The significance of the preprocessing stage in any data mining task is well known. Before attempting medical data classification, characteristics of medical datasets, including noise, incompleteness, and the existence of multiple and possibly irrelevant features, need to be addressed. In this paper, we show that selecting the right combination of prepro- cessing methods has a considerable impact on the classification potential of a dataset. The preprocessing operations con- sidered include the discretization of numeric attributes, the selection of attribute subset（s）, and the handling of missing values. The classification is performed by an ant colony optimization algorithm as a case study. Experimental results on 25 real-world medical datasets show that a significant relative improvement in predictive accuracy, exceeding 60% in some cases, is obtained.

关键词： classification ant colony optimization medical data classification preprocessing feature subset selection,discretization

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MIoT Based Skin Cancer Detection Using Bregman Recurrent Deep Learning

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computers, Materials & Continua 2022年第12期73卷 6253-6267页

作者： Nithya Rekha Sivakumar Sara Abdelwahab Ghorashi Faten Khalid Karim Eatedal Alabdulkreem Amal Al-Rasheed Department of computer sciences College of Computer and Information SciencesPrincess Nourah Bint Abdulrahman UniversityRiyadh11671Saudi Arabia Department of information Systems College of Computer and Information SciencesPrincess Nourah Bint Abdulrahman UniversityRiyadh11671Saudi Arabia

Mobile clouds are the most common medium for aggregating,storing,and analyzing data from the medical Internet of Things(MIoT).It is employed to monitor a patient’s essential health signs for earlier disease diagnosis and *** the various disease,skin cancer was the wide variety of cancer,as well as enhances the endurance *** recent years,many skin cancer classification systems using machine and deep learning models have been developed for classifying skin tumors,including malignant melanoma(MM)and other skin ***,accurate cancer detection was not performed with minimum time *** order to address these existing problems,a novel Multidimensional Bregman Divergencive Feature Scaling Based Cophenetic Piecewise Regression Recurrent Deep Learning Classification(MBDFS-CPRRDLC)technique is introduced for detecting cancer at an earlier *** MBDFS-CPRRDLC performs skin cancer detection using different layers such as input,hidden,and output for feature selection and *** patient information is composed of *** patient information was stored in mobile clouds server for performing predictive *** collected data are sent to the recurrent deep learning *** the first hidden layer,the feature selection process is carried out using the Multidimensional Bregman Divergencive Feature Scaling technique to find the significant features for disease identification resulting in decreases time *** by,the disease classification is carried out in the second hidden layer using cophenetic correlative piecewise regression for analyzing the testing and training *** process is repeatedly performed until the error gets *** this way,disease classification is accurately performed with higher *** evaluation is carried out for factors namely Accuracy,precision,recall,F-measure,as well as cancer detection time,by the amount of patient *** observed result confirms that the pr

关键词： MIoT skin cancer detection recurrent deep learning classification multidimensional bregman divergencive scaling cophenetic correlative piecewise regression

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Real and Altered Fingerprint Classification Based on Various Features and Classifiers

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computers, Materials & Continua 2023年第1期74卷 327-340页

作者： Saif Saad Hameed Ismail Taha Ahmed Omar Munthir Al Okashi college of computer sciences and information Technology University of AnbarAnbarIraq

Biometric recognition refers to the identification of individuals through their unique behavioral features(e.g.,fingerprint,face,and iris).We need distinguishing characteristics to identify people,such as fingerprints,which are world-renowned as the most reliablemethod to identify *** recognition of fingerprints has become a standard procedure in forensics,and different techniques are available for this *** current techniques lack interest in image enhancement and rely on high-dimensional features to generate classification ***,we proposed an effective fingerprint classification method for classifying the fingerprint image as authentic or altered since criminals and hackers routinely change their fingerprints to generate fake *** order to improve fingerprint classification accuracy,our proposed method used the most effective texture features and *** Analysis(DCA)and Gaussian Discriminant Analysis(GDA)are employed as classifiers,along with Histogram of Oriented Gradient(HOG)and Segmentation-based Feature Texture Analysis(SFTA)feature vectors as *** performance of the classifiers is determined by assessing a range of feature sets,and the most accurate results are *** proposed method is tested using a Sokoto Coventry Fingerprint Dataset(SOCOFing).The SOCOFing project includes 6,000 fingerprint images collected from 600 African people whose fingerprints were taken ten *** distinct degrees of obliteration,central rotation,and z-cut have been performed to obtain synthetically altered replicas of the genuine *** proposal achieved massive success with a classification accuracy reaching 99%.The experimental results indicate that the proposed method for fingerprint classification is feasible and *** experiments also showed that the proposed SFTA-based GDA method outperformed state-of-art approaches in feature dimension and classification accuracy.

关键词： Fingerprint classification HOG SFTA discriminant analysis(DCA)classifier gaussian discriminant analysis(GDA)classifier SOCOFing

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Smart Healthcare Activity Recognition Using Statistical Regression and Intelligent Learning

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computers, Materials & Continua 2024年第1期78卷 1189-1205页

作者： K.Akilandeswari Nithya Rekha Sivakumar Hend Khalid Alkahtani Shakila Basheer Sara Abdelwahab Ghorashi Department of computer sciences Government Arts College(Autonomous)Salem636007India Department of computer sciences College of Computer and Information SciencesPrincess Nourah Bint Abdulrahman UniversityRiyadh11671Saudi Arabia Department of information Systems College of Computer and Information SciencesPrincess Nourah Bint Abdulrahman UniversityRiyadh11671Saudi Arabia

In this present time,Human Activity Recognition(HAR)has been of considerable aid in the case of health monitoring and *** exploitation of machine learning with an intelligent agent in the area of health informatics gathered using HAR augments the decision-making quality and *** many research works conducted on Smart Healthcare Monitoring,there remain a certain number of pitfalls such as time,overhead,and falsification involved during ***,this paper proposes a Statistical Partial Regression and Support Vector Intelligent Agent Learning(SPR-SVIAL)for Smart Healthcare *** first,the Statistical Partial Regression Feature Extraction model is used for data preprocessing along with the dimensionality-reduced features extraction ***,the input dataset the continuous beat-to-beat heart data,triaxial accelerometer data,and psychological characteristics were acquired from IoT wearable *** attain highly accurate Smart Healthcare Monitoring with less time,Partial Least Square helps extract the dimensionality-reduced *** that,with these resulting features,SVIAL is proposed for Smart Healthcare Monitoring with the help of Machine Learning and Intelligent Agents to minimize both analysis falsification and *** evaluation is carried out for factors such as time,overhead,and false positive rate accuracy concerning several *** quantitatively analyzed results indicate the better performance of our proposed SPR-SVIAL method when compared with two state-of-the-art methods.

关键词： Internet of Things smart health care monitoring human activity recognition intelligent agent learning statistical partial regression support vector

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Firefly-CDDL:A Firefly-Based Algorithm for Cyberbullying Detection Based on Deep Learning

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computers, Materials & Continua 2023年第4期75卷 19-34页

作者： Monirah Al-Ajlan Mourad Ykhlef Department of information Systems College of Computer and Information SciencesKing Saud UniversityRiyadhKSA

There are several ethical issues that have arisen in recent years due to the ubiquity of the Internet and the popularity of social media and community *** them is cyberbullying,which is defined as any violent intentional action that is repeatedly conducted by individuals or groups using online channels against victims who are not able to react *** alarmingly high percentage of people,especially teenagers,have reported being cyberbullied in recent years.A variety of approaches have been developed to detect cyberbullying,but they require time-consuming feature extraction and selection ***,no approach to date has examined the meanings of words and the semantics involved in *** past work,we proposed an algorithm called Cyberbullying Detection Based on Deep Learning(CDDL)to bridge this *** eliminates the need for feature engineering and generates better predictions than traditional approaches for detecting *** was accomplished by incorporating deep learning—specifically,a convolutional neural network(CNN)—into the detection *** this algorithm shows remarkable improvement in performance over traditional detection mechanisms,one problem with it persists:CDDL requires that many parameters(filters,kernels,pool size,and number of neurons)be set prior to *** parameters play a major role in the quality of predictions,but a method for finding a suitable combination of their values remains *** address this issue,we propose an algorithm called firefly-CDDL that incorporates a firefly optimisation algorithm into CDDL to automate the hitherto-manual trial-and-error hyperparameter *** proposed method does not require features for its predictions and its detection of cyberbullying is fully *** firefly-CDDL outperformed prevalent methods for detecting cyberbullying in experiments and recorded an accuracy of 98%within acceptable polynomial time.

关键词： Firefly optimization convolutional neural network(CNN) cyberbullying cyberbullying detection text classification

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computerized Detection of Limbal Stem Cell Deficiency from Digital Cornea Images

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computer Systems Science & Engineering 2022年第2期40卷 805-821页

作者： Hanan A.Hosni Mahmoud Doaa S.Khafga Amal H.Alharbi Department of computer sciences College of Computer and Information SciencesPrincess Nourah Bint Abdulrahman UniversityRiyadh11047KSA

Limbal Stem Cell Deficiency(LSCD)is an eye disease that can cause corneal opacity and *** its advanced stage it can lead to a degree of visual *** involves the changing in the semispherical shape of the cornea to a drooping shape to downwards *** is hard to be diagnosed at early *** color and texture of the cornea surface can provide significant information about the cornea affected by *** such as shape and texture are very crucial to differentiate normal from LSCD *** several medical approaches exist,most of them requires complicated procedure and medical ***,in this paper,we pursued the development of a LSCD detection technique(LDT)utilizing image processing *** diagnosis of LSCD is very crucial for physicians to arrange for effective *** the proposed technique,we developed a method for LSCD detection utilizing frontal eye images.A dataset of 280 eye images of frontal and lateral LSCD and normal patients were used in this ***,the cornea region of both frontal and lateral images is segmented,and the geometric features are extracted through the automated active contour model and the spline *** the texture features are extracted using the feature selection *** experimental results exhibited that the combined features of the geometric and texture will exhibit accuracy of 95.95%,sensitivity of 97.91% and specificity of 94.05% with the random forest classifier of n=*** a result,this research developed a Limbal stem cell deficiency detection system utilizing features’fusion using image processing techniques for frontal and lateral digital images of the eyes.

关键词： Feature extraction corneal opacity geometric features computerized detection image processing

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