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Estimation of Genetic Divergence and Character Association Studies in Local and Exotic Diversity Panels of Soybean(Glycine max L.)Genotypes

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Phyton-International Journal of Experimental Botany 2023年第6期92卷 1887-1906页

作者： Syed Ali Zafar Muhammad Aslam Haroon Zaman Khan Sehrish Sarwar rao saad rehman Mariam Hassan Ramala Masood Ahmad Rafaqat AGill Basharat Ali Ibrahim Al-Ashkar Abdullah Ibrahim Md Atikur Rahman Ayman El Sabagh Oilseeds Research Institute Ayub Agricultural Research InstituteFaisalabadPakistan Department of Plant Breeding and Genetics University of AgricultureFaisalabadPakistan Department of Agronomy University of AgricultureFaisalabadPakistan College of Plant Science and Technology Huazhong Agricultural UniversityWuhanChina Key Laboratory of Biology and Genetic Improvement of Oil Crops The Ministry of Agriculture and Rural AffairsOil Crops Research Institute of Chinese Academy of Agricultural SciencesWuhan430062China Department of Agricultural Engineering Khwaja Fareed University of Engineering and Information TechnologyRahim Yar KhanPakistan Department of Plant Production College of Food and Agriculture SciencesKing Saud UniversityRiyadhSaudi Arabia Grassland and Forage Division National Institute of Animal ScienceRural Development AdministrationCheonanKorea Department of Agronomy Faculty of AgricultureUniversity of KafrelsheikhKafr ElsheikhEgypt

The availability of favorable genetic diversity is a thriving vitality for the success of a breeding *** provides a firm basis of selecting superior breeding lines for the development of high yielding crop *** this context,present investigation aimed to generate information on genetic divergence and character association in a diversity panel of 123 local and exotic soybean *** of variance revealed significant response of the evaluated genotypes based on studied *** depicted the probability of selecting desirable soybean genotypes by focusing on character association studies and genetic diversity *** analysis revealed that seed yield per plant showed high positive correlation with 100-seed weight followed by pods per plant and plant ***,path coefficient analysis exposed that pods per plant had maximum direct contribution in seed yield per plant followed by 100-seed weight,days to flowering and SPAD *** named“G-10”showed maximum yield per plant followed by 24607,G-52,24593,Arisoy,24566,17426,A-3127,24570 and *** diversity analysis grouped the evaluated germplasm into 17 *** clusters showed zero intra-cluster variability;while inter-cluster divergence ranged from 9.00 to *** V showed maximum inter-cluster distance with cluster XII followed by that of between V and ***,cluster IV with superior genotypes(G-10,24607,24593 and 24566),VI(17426 and 24567),XIII(24570)and X(Arisoy and G-52)showed above mean values for most of the studied ***,the results of hybridization between the superior genotypes of these cluster pairs might be useful for soybean breeding with improving agronomic traits and adaptability.

关键词： Genetic variability correlation path analysis genetic diversity analysis soybean selective breeding

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A Hybrid Duo-Deep Learning and Best Features Based Framework for Action Recognition

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Computers, Materials & Continua 2022年第11期73卷 2555-2576页

作者： Muhammad Naeem Akbar Farhan Riaz Ahmed Bilal Awan Muhammad Attique Khan Usman Tariq saad rehman National University of Sciences and Technology(NUST) Islamabad46000Pakistan Department of Computer Science HITEC UniversityTaxilaPakistan College of Computer Engineering and Science Prince Sattam Bin Abdulaziz UniversityAl-Kharaj 11942Saudi Arabia

Human Action Recognition(HAR)is a current research topic in the field of computer vision that is based on an important application known as video *** in computer vision have introduced various intelligent methods based on deep learning and machine learning,but they still face many challenges such as similarity in various actions and redundant *** proposed a framework for accurate human action recognition(HAR)based on deep learning and an improved features optimization algorithm in this *** deep learning feature extraction to feature classification,the proposed framework includes several critical *** training fine-tuned deep learning models–MobileNet-V2 and Darknet53–the original video frames are *** feature extraction,pre-trained deep models are used,which are fused using the canonical correlation *** that,an improved particle swarm optimization(IPSO)-based algorithm is used to select the best *** that,the selected features were used to classify actions using various *** experimental process was performed on six publicly available datasets such as KTH,UT-Interaction,UCF Sports,Hollywood,IXMAS,and UCF YouTube,which attained an accuracy of 98.3%,98.9%,99.8%,99.6%,98.6%,and 100%,*** comparison with existing techniques,it is observed that the proposed framework achieved improved accuracy.

关键词： Action recognition deep learning features fusion features selection recognition

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Fast Intra Mode Selection in HEVC Using Statistical Model

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Computers, Materials & Continua 2022年第2期70卷 3903-3918页

作者： Junaid Tariq Ayman Alfalou Amir Ijaz Hashim Ali Imran Ashraf Hameedur Rahman Ammar Armghan Inzamam Mashood saad rehman Department of Computer Science&Engineering HITEC UniversityTaxilaPakistan L@BISEN Equipe LSLYncrea Oust20 rue Cuirasse BretagneBrest29200France Department of IT and CS Pak-Austria FachhochschuleHaripurPakistan Department of Electrical Engineering Jouf UniversitySakakaKSA

Comprehension algorithms like High Efficiency Video Coding(HEVC)facilitates fast and efficient handling of multimedia *** algorithms involve various computation modules that help to reduce the size of content but preserve the same subjective viewing ***,the brute-force behavior of HEVC is the biggest hurdle in the communication of multimedia ***,a novel method will be presented here to accelerate the encoding process of HEVC by making early intra mode decisions for the ***,the HEVC applies 35 intra modes to every block of the frame and selects the best among them based on the RD-cost(rate-distortion).Firstly,the proposed work utilizes neighboring blocks to extract available information for the current *** this information is converted to the probability that tells which intra mode might be best in the current *** proposed model has a strong foundation as it is based on the probability rule-2 which says that the sum of probabilities of all outcomes should be ***,it is also based on optimal stopping theory(OST).Therefore,the proposed model performs better than many existing OST and classical *** proposed algorithms expedited the encoding process by 30.22%of the HEVC with 1.35%Bjontegaard Delta Bit Rate(BD-BR).

关键词： Statistical HEVC angular mode reduce time

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Energy Efficiency Trade-off with Spectral Efficiency in MIMO Systems

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Computers, Materials & Continua 2022年第3期70卷 5889-5905页

作者： rao Muhammad Asif Mustafa Shakir Jamel Nebhen Ateeq Ur rehman Muhammad Shafiq Jin-Ghoo Choi Department of Electrical Engineering Superior UniversityLahore54000Pakistan College of Computer Engineering and Sciences Prince Sattam Bin Abdulaziz UniversityAlkharj11942Saudi Arabia Department of Electrical Engineering Government College UniversityLahore54000Pakistan Department of Information and Communication Engineering Yeungnam UniversityGyeongsan38541Korea

5G technology can greatly improve spectral efficiency(SE)and throughput of wireless *** this regard,multiple inputmultiple output(MIMO)technology has become the most influential technology using huge antennas and user equipment(UE).However,the use of MIMO in 5G wireless technology will increase circuit power consumption and reduce energy efficiency(EE).In this regard,this article proposes an optimal solution for weighing SE and throughput tradeoff with energy *** research work is based on theWyner model of uplink(UL)and downlink(DL)transmission under the multi-cell model *** SE-EE trade-off is carried out by optimizing the choice of antenna and UEs,while the approximation method based on the logarithmic function is used for *** this paper,we analyzed the combination of UL and DL power consumption models and precoding schemes for all actual circuit power consumption models to optimize the trade-off between EE and *** simulation results show that the SE-EE trade-off has been significantly improved by developing UL and DL transmission models with the approximation method based on logarithmic *** is also recognized that the throughput-EE trade-off can be improved by knowing the total actual power consumed by the entire network.

关键词： Energy efficiency spectral efficiency throughput massive MIMO downlink uplink base stations power consumption

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Improving Thyroid Disorder Diagnosis via Ensemble Stacking and Bidirectional Feature Selection

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Computers, Materials & Continua 2024年第3期78卷 4225-4241页

作者： Muhammad Armghan Latif Zohaib Mushtaq saad Arif Sara rehman Muhammad Farrukh Qureshi Nagwan Abdel Samee Maali Alabdulhafith Yeong Hyeon Gu Mohammed A.Al-masni Department of Computer and Information System Cleveland State UniversityOhio44115USA Department of Electrical Electronics and Computer SystemsCollege of Engineering and TechnologyUniversity of SargodhaSargodha40100Pakistan Department of Mechanical Engineering College of EngineeringKing Faisal UniversityAl-Ahsa31982Saudi Arabia Department of Biomedical Engineering Riphah International UniversityIslamabad44000Pakistan Department of Electrical Engineering Riphah International UniversityIslamabad44000Pakistan Department of Information Technology College of Computer and Information SciencesPrincess Nourah bint Abdulrahman UniversityP.O.Box 84428Riyadh11671Saudi Arabia Department of Artificial Intelligence College of Software and Convergence TechnologySejong UniversitySeoul05006Korea

Thyroid disorders represent a significant global health challenge with hypothyroidism and hyperthyroidism as two common conditions arising from dysfunction in the thyroid *** and timely diagnosis of these disorders is crucial for effective treatment and patient *** research introduces a comprehensive approach to improve the accuracy of thyroid disorder diagnosis through the integration of ensemble stacking and advanced feature selection *** forward feature selection,sequential backward feature elimination,and bidirectional feature elimination are investigated in this *** ensemble learning,random forest,adaptive boosting,and bagging classifiers are *** effectiveness of these techniques is evaluated using two different datasets obtained from the University of California Irvine-Machine Learning Repository,both of which undergo preprocessing steps,including outlier removal,addressing missing data,data cleansing,and feature *** experimentation demonstrates the remarkable success of proposed ensemble stacking and bidirectional feature elimination achieving 100%and 99.86%accuracy in identifying hyperthyroidism and hypothyroidism,*** enhancing detection accuracy,the ensemble stacking model also demonstrated a streamlined computational complexity which is pivotal for practical medical *** significantly outperformed existing studies with similar objectives underscoring the viability and effectiveness of the proposed *** research offers an innovative perspective and sets the platform for improved thyroid disorder diagnosis with broader implications for healthcare and patient well-being.

关键词： Ensemble learning random forests boosting dimensionality reduction machine learning smart healthcare computer aided diagnosis

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