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secure Channel Estimation Using Norm Estimation Model for 5G Next Generation Wireless Networks

Computers, Materials & Continua 2025年第1期82卷 1151-1169页

作者： Khalil Ullah Song Jian Muhammad Naeem Ul Hassan Suliman Khan Mohammad Babar Arshad Ahmad Shafiq Ahmad Faculty of Information Engineering and Automation Kunming University of Science and TechnologyKunming650500China department of Computer Science Abbottabad University of Science and TechnologyHavelianAbbottabad22500Pakistan department of Computing and Electronics Engineering Middle East CollegeMuscat124Oman department of cs&IT Pak-Austria Fachhochschule Institute of Applied Sciences and Technology MangHaripur22621Pakistan Industrial Engineering department College of EngineeringKing Saud UniversityRiyadh11421Saudi Arabia

The emergence of next generation networks(NextG),including 5G and beyond,is reshaping the technological landscape of cellular and mobile *** networks are sufficiently scaled to interconnect billions of users and *** in academia and industry are focusing on technological advancements to achieve highspeed transmission,cell planning,and latency reduction to facilitate emerging applications such as virtual reality,the metaverse,smart cities,smart health,and autonomous *** continuously improves its network functionality to support these *** input multiple output(MIMO)technology offers spectral efficiency,dependability,and overall performance in *** article proposes a secure channel estimation technique in MIMO topology using a norm-estimation model to provide comprehensive insights into protecting NextG network components against adversarial *** technique aims to create long-lasting and secure NextG networks using this extended *** viability of MIMO applications and modern AI-driven methodologies to combat cybersecurity threats are explored in this ***,the proposed model demonstrates high performance in terms of reliability and accuracy,with a 20%reduction in the MalOut-RealOut-Diff metric compared to existing state-of-the-art techniques.

关键词： Next generation networks massive mimo communication network artificial intelligence 5G adversarial attacks channel estimation information security

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Context Based Adoption of Ranking and Indexing Measures for Cricket Team Ranks

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Computers, Materials & Continua 2020年第11期65卷 1113-1136页

作者： Raja Sher Afgun Usmani Syed Muhammad Saqlain Shah Muhammad Sher Ramzan Abdullah Saad A.L-Malaise AL-Ghamdi Anwar Ghani Imran Khan Farrukh Saleem department of cs&se International Islamic UniversityH-10 SectorIslamabad44000Pakistan department of Information System Faculty of Computing and Information TechnologyKing Abdulaziz UniversityJeddah21589Saudi Arabia

There is an international cricket governing body that ranks the expertise of all the cricket playing nations,known as the International Cricket Council(ICC).The ranking system followed by the ICC relies on the winnings and defeats of the *** model used by the ICC to implement rankings is deficient in certain key *** ignores key factors like winning margin and strength of the *** measures of the ranking concept are presented in this *** proposed methods adopt the concepts of h-Index and PageRank for presenting more comprehensive ranking *** proposed approaches not only rank the teams on their losing/winning stats but also take into consideration the margin of winning and the quality of the *** cricket team ranking techniques are presented i.e.,(1)Cricket Team-Index(ct-index),(2)Cricket Team Rank(CTR)and(3)Weighted Cricket Team Rank(WCTR).The proposed metrics are validated through the collection of cricket dataset,extracted from Cricinfo,having instances for all the three formats of the game i.e.,T20 International(T20i),One Day International(ODI)and Test *** comparative analysis between the proposed and existing techniques,for all the three formats,is presented as well.

关键词： Indexing ranking cricket teams runs and wickets

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Deep Learning Method to Detect the Road Cracks and Potholes for Smart Cities

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Computers, Materials & Continua 2023年第4期75卷 1863-1881页

作者： Hong-Hu Chu Muhammad Rizwan Saeed Javed Rashid Muhammad Tahir Mehmood Israr Ahmad Rao Sohail Iqbal Ghulam Ali College of Civil Engineering Hunan UniversityChangsha410082China department of cs University of OkaraOkara56310Pakistan department of cs&se Islamic International UniversityIslamabad44000Pakistan department of IT services University of OkaraOkara56310Pakistan department of cs Government College UniversityFaisalabad38000Pakistan department of Automation Science Beihang UniversityBeijing100191China

The increasing global population at a rapid pace makes road trafficdense;managing such massive traffic is challenging. In developing countrieslike Pakistan, road traffic accidents (RTA) have the highest mortality percentageamong other Asian countries. The main reasons for RTAs are roadcracks and potholes. Understanding the need for an automated system forthe detection of cracks and potholes, this study proposes a decision supportsystem (DSS) for an autonomous road information system for smart citydevelopment with the use of deep learning. The proposed DSS works in layerswhere initially the image of roads is captured and coordinates attached to theimage with the help of global positioning system (GPS), communicated tothe decision layer to find about the cracks and potholes in the roads, andeventually, that information is passed to the road management informationsystem, which gives information to drivers and the maintenance *** the decision layer, we projected a CNN-based model for pothole crackdetection (PCD). Aimed at training, a K-fold cross-validation strategy wasused where the value of K was set to 10. The training of PCD was completedwith a self-collected dataset consisting of 6000 images from Pakistani *** proposed PCD achieved 98% of precision, 97% recall, and accuracy whiletesting on unseen images. The results produced by our model are higher thanthe existing model in terms of performance and computational cost, whichproves its significance.

关键词： Road cracks and potholes CNN smart cities pothole crack detection decision support system

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Real-Time Multiple Guava Leaf Disease Detection from a Single Leaf Using Hybrid Deep Learning Technique

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Computers, Materials & Continua 2023年第1期74卷 1235-1257页

作者： Javed Rashid Imran Khan Ghulam Ali Shafiq ur Rehman Fahad Alturise Tamim Alkhalifah department of cs&se Islamic International UniversityIslamabad44000Pakistan department of IT services University of OkaraOkara56310Pakistan department of cs University of OkaraOkara56310Pakistan department of Botany University of OkaraOkara56310Pakistan department of Computer College of Science and Arts in Ar RassQassim UniversityAr RassQassim52571Saudi Arabia

The guava plant has achieved viable significance in subtropics and tropics owing to its flexibility to climatic environments,soil conditions and higher human *** is cultivated in vast areas of Asian and Non-Asian countries,including *** guava plant is vulnerable to diseases,specifically the leaves and fruit,which result in massive crop and profitability *** existing plant leaf disease detection techniques can detect only one disease from a ***,a single leaf may contain symptoms of multiple *** study has proposed a hybrid deep learning-based framework for the real-time detection of multiple diseases from a single guava leaf in several ***,Guava Infected Patches Modified MobileNetV2 and U-Net(GIP-MU-NET)has been proposed to segment the infected guava *** proposed model consists of modified MobileNetv2 as an encoder,and the U-Net model’s up-sampling layers are used as a decoder ***,the Guava Leaf segmentationModel(GLSM)is proposed to segment the healthy and infected *** the final step,the Guava Multiple Leaf Diseases Detection(GMLDD)model based on the YOLOv5 model detects various diseases from a guava *** self-collected datasets(the Guava Patches Dataset and the Guava Leaf Diseases Dataset)are used for training and *** proposed method detected the various defects,including five distinct classes,i.e.,anthracnose,insect attack,nutrition deficiency,wilt,and *** average,the GIP-MU-Net model achieved 92.41%accuracy,the GLSM gained 83.40%accuracy,whereas the proposed GMLDD technique achieved 73.3%precision,73.1%recall,71.0%mAP@0.5 and 50.3 mAP@0.5:0.95 scores for all the aforesaid classes.

关键词： Guava leaf diseases guava leaf segmentation guava patches segmentation multiple leaf diseases guava leaf diseases dataset

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Exploration on the Long-term Education System of Software Engineering

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计算机教育 2020年第12期 151-158页

作者： Mei Hong Feng Lin Jing Yu Hunan Wang Mingzheng Hou College of cs&se SichuanUniversityChengduChina610065

It is a challenge to cultivate senior and comprehensive talents in software *** promote the quality of software engineering education,this paper proposes a new education model of long-term education system and combines Bachelor-Master degree program by mirroring the experience of higher engineering education in the world and integrating with the current situation of universities and industries in *** proposed model joins the higher degree education with vocational education by carrying out the interdisciplinary and university-enterprise joint *** demonstration shows that this education model has its necessity and feasibility in China higher education,and provides a reference to improve the education quality for the higher engineering education in China.

关键词： software engineering major long-term education system the combined Bachelor-Master degree program engineering talent cultivation emerging engineering interdisciplinary

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Human Behavior Classification Using Geometrical Features of Skeleton and Support Vector Machines

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Computers, Materials & Continua 2019年第8期61卷 535-553页

作者： Syed Muhammad Saqlain Shah Tahir Afzal Malik Robina khatoon SyedSaqlain Hassan Faiz Ali Shah department of cs&se International Islamic UniversityH-10 SectorIslamabad44000Pakistan department of Management Information Systems Ibn Rushd College of Management SciencesAbhaSaudi Arabia department of Computer Science Bahria UniversityIslamabad44000Pakistan Institute of Computer Science University of TartuTartuEstonia

Classification of human actions under video surveillance is gaining a lot of attention from computer vision *** this paper,we have presented methodology to recognize human behavior in thin crowd which may be very helpful in *** have mostly focused the problem of human detection in thin crowd,overall behavior of the crowd and actions of individuals in video *** based Human behavior modeling is a complex task as it involves human detection,tracking,classifying normal and abnormal *** proposed methodology takes input video and applies Gaussian based segmentation technique followed by post processing through presenting hole filling algorithm i.e.,fill hole inside objects *** detection is performed by presenting human detection algorithm and then geometrical features from human skeleton are extracted using feature extraction *** classification task is achieved using binary and multi class support vector *** proposed technique is validated through accuracy,precision,recall and F-measure metrics.

关键词： Human behavior classification segmentation human detection support vector machine

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A Novel Framework for Multi-Classification of Guava Disease

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Computers, Materials & Continua 2021年第11期69卷 1915-1926页

作者： Omar Almutiry Muhammad Ayaz Tariq Sadad Ikram Ullah Lali Awais Mahmood Najam Ul Hassan Habib Dhahri College of Applied Computer Sciences King Saud University(Almuzahmiyah Branch)Riyadh11564KSA department of Computer Science University of Central PunjabLahore54000Pakistan department of cs&se International Islamic UniversityIslamabad44000Pakistan Division of Science and Technology University of EducationLahore54000Pakistan department of Electrical and Computer Engineering Dhofar UniversitySalalahOman

Guava is one of the most important fruits in Pakistan,and is gradually boosting the economy of *** production can be interrupted due to different diseases,such as anthracnose,algal spot,fruit fly,styler end rot and *** diseases are usually detected and identified by visual observation,thus automatic detection is required to assist *** this research,a new technique was created to detect guava plant diseases using image processing techniques and computer *** automated system is developed to support farmers to identify major diseases in *** collected healthy and unhealthy images of different guava diseases from the *** image labeling was done with the help of an expert to differentiate between healthy and unhealthy *** local binary pattern(LBP)was used for the extraction of features,and principal component analysis(PCA)was used for dimensionality *** classification was carried out using multiple classifiers,including cubic support vector machine,Fine K-nearest neighbor(F-KNN),Bagged Tree and RUSBoosted Tree algorithms and achieved 100%accuracy for the diagnosis of fruit flies disease using Bagged ***,the findings indicated that cubic support vector machines(C-SVM)was the best classifier for all guava disease mentioned in the dataset.

关键词： Classification guava disease image processing machine learning

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A Hybrid Deep Learning Approach to Classify the Plant Leaf Species

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Computers, Materials & Continua 2023年第9期76卷 3897-3920页

作者： Javed Rashid Imran Khan Irshad Ahmed Abbasi Muhammad Rizwan Saeed Mubbashar Saddique Mohamed Abbas department of cs&se Islamic International UniversityIslamabad44000Pakistan department of IT services University of OkaraOkara56310Pakistan Faculty of Science and Arts Belqarn University of BishaSabtul Alaya61985Saudi Arabia department of cs University of OkaraOkara56310Pakistan department of Computer Science&Engineering University of Engineering&Technology LahoreNarowalCampusNarowal51601Pakistan Electrical Engineering department College of EngineeringKing Khalid UniversityAbha61421Saudi Arabia Research Center for Advanced Materials Sciences(RCAMS) King Khalid UniversityAbhaSaudi Arabia

Many plant species have a startling degree of morphological similarity,making it difficult to split and categorize them *** plant species can be challenging to classify and segment using deep *** using deep learning architectures has helped improve classification accuracy,the resulting models often need to be more flexible and require a large dataset to *** the sake of taxonomy,this research proposes a hybrid method for categorizing guava,potato,and java *** new approaches are used to formthe hybridmodel suggested *** guava,potato,and java plum plant species have been successfully segmented using the first model built on the MobileNetV2-UNET *** a second model,we use a Plant Species Detection Stacking Ensemble Deep Learning Model(PSD-se-DLM)to identify potatoes,java plums,and *** proposed models were trained using data collected in Punjab,Pakistan,consisting of images of healthy and sick leaves from guava,java plum,and *** datasets are known as PLSD and *** levels of 99.84%and 96.38%were achieved for the suggested PSD-se-DLM and MobileNetV2-UNET models,respectively.

关键词： Plant leaf species stacking ensemble model guava potato java plum MobileNetV2-UNET hybrid deep learning segmentation

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Changes of plasma fasting carnitine ester profile in patients with ulcerative colitis

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World Journal of Gastroenterology 2006年第1期12卷 110-113页

作者： Judit Bene Katalin Komlósi Viktória Havasi Gábor Talián Beáta Gasztonyi Krisztina Horváth Gyula Mózsik Béla Hunyady Béla Melegh Mária Figler MTA PTE Clinical Genetics Research Group of Hungarian Academy of Sciences at the University of Pécs PécsHungary Department of Medical Genetics and Child DevelopmentUniversity of PécsPécsHungary department of Medical Genetics and Child Development University of PécsPécsHungary department of Medicine School of MedicineUniversity of PécsPécsHungary

AIM： To determine the plasma carnitine ester profile in adult patients with ulcerative culitis （UC） and compared with healthy control subjects. METHOD： Using ESI triple quadrupole tandem mass spectrometry, the carnitine ester profile was measured in 44 patients with UC and 44 age- and sex-matched healthy controls. RESULTS： There was no significant difference in the fasting free carnitine level between the patients with UC and the healthy controls. The fasting propionyl- （0.331 ± 0.019 vs 0.392 ± 0.017 μmol/L）, butyryl- （0.219 ± 0.014 vs 0.265 ± 0.012）, and isovalerylcarniUne （0.111 ± 0.008 vs 0.134 ± 0.008） levels were decreased in the UC patients. By contrast, the level of octanoyl- （0.147 ± 0.009 vs 0.114 ± 0.008）, decanoyl- （0.180 ± 0.012 vs 0.137 ± 0.008）, myristoyl- （0.048 ± 0.003 vs 0.039 ± 0.003）, palmitoyl- （0.128 ± 0.006 vs 0.109 ± 0.004）, palmitoleyl- （0.042±0.003 vs 0.031 ± 0.002） and oleylcarnitine （0.183 ± 0.007 vs 0.163 ± 0.007; P 〈 0.05 in all comparisons） were increased in the patients with UC. CONCLUSION： Our data suggest selective involvement of the carnitine esters in UC patients, probably due to their altered metabolism.

关键词： Carnitine Carnitine ester profile Ulcerativecolitis

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severe dystrophy in DiGeorge syndrome

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World Journal of Gastroenterology 2009年第11期15卷 1391-1393页

作者： Barnabás Rózsai kos Kiss Gyrgyi csábi Márta Czakó Tamás Decsi department of Pediatrics University of PécsPécs H-7623 Hungary department of Medical Genetics and Child Development University of PécsPécs H-7623Hungary

We present the case history of a 3-year-old girl who was examined because of severe *** the background,cow’s milk allergy was found,but her body weight was unchanged after eliminating milk from her *** types of malabsorption were *** on nasal regurgitation and facial dysmorphisms,the possibility of DiGeorge syndrome was suspected and was confirmed by fluorescence in situ *** authors suggest a new feature associated with DiGeorge syndrome.

关键词： DiGeorge syndrome Dystrophy Cow's milkallergy Nasal regurgitation Hypoparathyroidism

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