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Citrus Diseases Recognition Using Deep Improved Genetic Algorithm

Computers, Materials & Continua 2022年第5期71卷 3667-3684页

作者： Usra Yasmeen Muhammad Attique Khan Usman Tariq junaid Ali Khan Muhammad Asfand EYar ChAvais Hanif Senghour Mey Yunyoung Nam Department of Computer Science HITEC University TaxilaTaxilaPakistan College of Computer Engineering and Sciences Prince Sattam Bin Abdulaziz UniversityAl-KhrajSaudi Arabia Department of Computer Science Bahria UniversityIslamabadPakistan Department of EE HITEC University TaxilaTaxilaPakistan Department of ICT Convergence Soonchunhyang UniversityKorea Department of Computer Science and Engineering Soonchunhyang UniversityKorea

Agriculture is the backbone of each country,and almost 50%of the population is directly involved in *** Pakistan,several kinds of fruits are produced and exported the other *** is an important fruit,and its production in Pakistan is higher than the other ***,the diseases of citrus fruits such as canker,citrus scab,blight,and a few more impact the quality and quantity of this *** manual diagnosis of these diseases required an expert person who is always a time-consuming and costly *** the agriculture sector,deep learning showing significant success in the last five *** research work proposes an automated framework using deep learning and best feature selection for citrus diseases *** the proposed framework,the augmentation technique is applied initially by creating more training data from existing *** were then modifying the two pre-trained models named Resnet18 and Inception *** modified models are trained using an augmented dataset through transfer *** are extracted for each model,which is further selected using Improved Genetic Algorithm(ImGA).The selected features of both models are fused using an array-based approach that is finally classified using supervised learning classifiers such as Support Vector Machine(SVM)and name a few *** experimental process is conducted on three different datasets-Citrus Hybrid,Citrus Leaf,and Citrus *** these datasets,the best-achieved accuracy is 99.5%,94%,and 97.7%,*** proposed framework is evaluated on each step and compared with some recent techniques,showing that the proposed method shows improved performance.

关键词： Citrus diseases data augmentation deep learning features selection features fusion

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Effective Classification of Synovial Sarcoma Cancer Using Structure Features and Support Vectors

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Computers, Materials & Continua 2022年第8期72卷 2521-2543页

作者： P.Arunachalam N.Janakiraman junaid Rashid Jungeun Kim Sovan Samanta Usman Naseem Arun Kumar Sivaraman A.Balasundaram Department of Electronics and Communication Engineering K.L.N.College of EngineeringPottapalayam630612Tamil NaduIndia Department of Computer Science and Engineering Kongju National UniversityCheonan 31080Korea Department of Mathematics Tamralipta MahavidyalayaTamluk721636West Bengal School of Computer Science University of SydneyNSW 2006Australia School of Computer Science and Engineering Vellore Institute of TechnologyChennai600127India School of Computer Science and Engineering Centre for Cyber Physical SystemsVellore Institute of TechnologyChennai600127India

In this research work,we proposed a medical image analysis framework with two separate releases whether or not Synovial Sarcoma(SS)is the cell structure for *** this framework the histopathology images are decomposed into a third-level sub-band using a two-dimensional Discrete Wavelet ***,the structure features(SFs)such as PrincipalComponentsAnalysis(PCA),Independent ComponentsAnalysis(ICA)and Linear Discriminant Analysis(LDA)were extracted from this subband image representation with the distribution of wavelet *** SFs are used as inputs of the Support Vector Machine(SVM)***,classification of PCA+SVM,ICA+SVM,and LDA+SVM with Radial Basis Function(RBF)kernel the efficiency of the process is differentiated and compared with the best classification ***,data collected on the internet from various histopathological centres via the Internet of Things(IoT)are stored and shared on blockchain technology across a wide range of image distribution across secure data IoT *** to this,the minimum and maximum values of the kernel parameter are adjusted and updated periodically for the purpose of industrial application in device ***,these resolutions are presented with an excellent example of a technique for training and testing the cancer cell structure prognosis methods in spindle shaped cell(SSC)histopathological imaging *** performance characteristics of cross-validation are evaluated with the help of the receiver operating characteristics(ROC)curve,and significant differences in classification performance between the techniques are *** combination of LDA+SVM technique has been proven to be essential for intelligent SS cancer detection in the future,and it offers excellent classification accuracy,sensitivity,specificity.

关键词： Principal components analysis independent components analysis linear discriminant analysis support vector machine blockchain technology IoT application industry application

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Contribution of public places in proliferation of dengue vectors in Penang Island,Malaysia

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Asian Pacific Journal of Tropical Biomedicine 2017年第3期7卷 183-187页

作者： Alhaji Hamisu Maimusa Abu Hassan Ahmad Nur Faeza Abu Kassim Hamdan Ahmad Hamady Dieng junaid Rahim School of Biological Sciences Universiti Sains Malaysia11800Pulau PinangMalaysia Vector Control Research Unit School of Biological SciencesUniversiti Sains Malaysia11800Pulau PinangMalaysia Institute of Biodiversity and Environmental Conservation Universiti Malaysia Sarawak94300Kota SamarahanSarawakMalaysia

Objective: To determine abundance, distribution and diversity of potential breeding container habitats of the dengue vectors in public places including schools, restaurants,mosques and parks in southwest areas of Penang Island, ***: Premises at restaurants, schools, parks and mosques were surveyed simultaneously and inspected visually for container habitats and production of immature mosquitoes from March 2015 to March 2016. Abundance(mean ± SE) of breeding containers between sites was compared using One-way ANOVA. Independent sample ttest was used to compare total number of Aedes albopictus(Ae. albopictus) and Aedes aegypti(Ae. aegypti) ***: The surveyed locations yielded a total of 3 741 breeding containers and 19 537 immature mosquitoes from four areas. Concurrent artificial and natural containers produced 78.4% immature Ae. albopictus and 6.3% Ae. aegypti mosquitoes in wet season,with 14.2% Ae. albopictus and 1.1% Ae. aegypti mosquitoes in dry season. Artificial containers accounted for 98.1% of the total containers recorded, with restaurants being the most productive locations(8 012) and schools being the least productive(2 234).Conclusions: It was concluded that public places are good sources of potential container habitats of Aedes mosquitoes in Penang Island, Malaysia and Ae. albopictus has exclusively replaced the home-grown Ae. aegypti even in urban areas. Therefore, treatment of artificial containers in such locations is critical in Aedes mosquito control campaigns during dengue outbreaks.

关键词： Public places Breeding containers Abundance Aedes albopictus Aedes aegypti

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Classification Using Two Layer Neural Network Back Propagation Algorithm

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Circuits and Systems 2016年第8期7卷 1207-1212页

作者： K. A. Mohamed junaid Department of Electronics & Instrumentation Engineering R.M.K. Engineering College Chennai India

Worldwide breast cancer is the most common form of cancer death occurring in 12.6% of women. This paper presents a cost effective approach to classify the normal, malignant and benign tumor using two layer neural network back propagation algorithm. Back propagation algorithm is used to train the neural network. Parallelization techniques speed up the computation process and as a result two layer neural networks outperform the previous work in terms of accuracy. Breast cancer tumor database used for the testing purpose is from the CIA machine learning repository. The highest accuracy of 97.12% is achieved using the two layer neural network back propagation algorithm.

关键词： Breast Cancer Back Propagation Neural Network Mammogram Sonography

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Modelling of debris-flow susceptibility and propagation: a case study from Northwest Himalaya

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Journal of Mountain Science 2024年第1期21卷 200-217页

作者： Hamza DAUD Javed Iqbal TANOLI Sardar Muhammad ASIF Muhammad QASIM Muhammad ALI junaid KHAN Zahid Imran BHATTI Ishtiaq Ahmad Khan JADOON Badong National Observation and Research Station of Geohazards China University of GeosciencesWuhan 430074China Department of Earth Sciences COMSATS University IslamabadAbbottabad Campus 22010Pakistan Department of Earth Sciences Sultan Qaboos UniversityAl-KhoudMascat 123Oman Faculty of Engineering China University of Geosciences(Wuhan)Wuhan 430074China Department of Earth Sciences Abbottabad University of Science and TechnologyHavelian 22500Pakistan

The geological and geographical position of the Northwest Himalayas makes it a vulnerable area for mass movements particularly landslides and debris flows. Mass movements have had a substantial impact on the study area which is extending along Karakorum Highway(KKH) from Besham to Chilas. Intense seismicity, deep gorges, steep terrain and extreme climatic events trigger multiple mountain hazards along the KKH, among which debris flow is recognized as the most destructive geohazard. This study aims to prepare a field-based debris flow inventory map at a regional scale along a 200 km stretch from Besham to Chilas. A total of 117 debris flows were identified in the field, and subsequently, a point-based debris-flow inventory and catchment delineation were performed through Arc GIS analysis. Regional scale debris flow susceptibility and propagation maps were prepared using Weighted Overlay Method(WOM) and Flow-R technique sequentially. Predisposing factors include slope, slope aspect, elevation, Topographic Roughness Index(TRI), Topographic Wetness Index(TWI), stream buffer, distance to faults, lithology rainfall, curvature, and collapsed material layer. The dataset was randomly divided into training data(75%) and validation data(25%). Results were validated through the Receiver Operator Characteristics(ROC) curve. Results show that Area Under the Curve(AUC) using WOM model is 79.2%. Flow-R propagation of debris flow shows that the 13.15%, 22.94%, and 63.91% areas are very high, high, and low susceptible to debris flow respectively. The propagation predicated by Flow-R validates the naturally occurring debris flow propagation as observed in the field surveys. The output of this research will provide valuable input to the decision makers for the site selection, designing of the prevention system, and for the protection of current infrastructure.

关键词： North Pakistan Debris flow Flow-R Propagation Susceptibility mapping Debris-flow inventory Weighted Overlay Method

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微塑料和全氟辛烷磺酸类物质共暴露对翡翠贻贝滤食率和抗氧化系统的影响

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北京大学学报（自然科学版） 2021年第5期57卷 894-902页

作者：徐澎 Muhammad junaid 刘燕陈裕鹏毕春晴许楠深圳市重金属污染控制与资源化重点实验室北京大学深圳研究生院环境与能源学院深圳518055

为了探索微塑料和全氟辛烷磺酸类物质(PFOS)在海洋生物体内的复合污染毒性机制,以翡翠贻贝为研究对象,在聚苯乙烯微塑料(0.2μm)4.55×10^(8)个/L以及PFOS浓度为10,100和1000μg/L的条件下,研究PFOS或微塑料单独暴露以及二者复合暴露对... 详细信息

为了探索微塑料和全氟辛烷磺酸类物质(PFOS)在海洋生物体内的复合污染毒性机制,以翡翠贻贝为研究对象,在聚苯乙烯微塑料(0.2μm)4.55×10^(8)个/L以及PFOS浓度为10,100和1000μg/L的条件下,研究PFOS或微塑料单独暴露以及二者复合暴露对翡翠贻贝滤食率、活性氧水平(ROS)、丙二醛(MDA)含量、超氧化物歧化酶(SOD)活性、过氧化氢酶(CAT)活性、谷胱甘肽巯基转移酶(GST)活性和谷胱甘肽还原酶(GR)活性的影响。结果表明,与溶剂对照组相比,暴露于微塑料和PFOS后翡翠贻贝的滤食率均没有显著变化。在翡翠贻贝的鳃、内脏团和性腺中,微塑料或PFOS的胁迫都会引起酶响应的显著变化。与PFOS单独暴露组相比,微塑料和1000μg/L PFOS共同暴露时,ROS水平在鳃和性腺中显著提升,在内脏团中显著下降;MDA含量在鳃和内脏团中显著提升,在性腺中显著下降;GST和GR活性在鳃中显著下降,在性腺中显著提高。研究结果说明微塑料会改变翡翠贻贝对PFOS的氧化应激响应。

关键词： PFOS 海洋微塑料共暴露氧化应激滤食率翡翠贻贝

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Deep Learning and Improved Particle Swarm Optimization Based Multimodal Brain Tumor Classicatio

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Computers, Materials & Continua 2021年第7期68卷 1099-1116页

作者： Ayesha Bin T.Tahir Muhamamd Attique Khan Majed Alhaisoni junaid Ali Khan Yunyoung Nam Shui-Hua Wang Kashif Javed Department of Computer Science HITEC UniversityTaxila47040Pakistan College of Computer Science and Engineering University of Ha’ilHa’ilSaudi Arabia Department of Computer Science and Engineering Soonchunhyang UniversityAsanKorea School of Architecture Building and Civil Engineering Loughborough UniversityLoughboroughLE113TUUK Department of Robotics SMME NUSTIslamabadPakista

Background:A brain tumor reects abnormal cell ***:Surgery,radiation therapy,and chemotherapy are used to treat brain tumors,but these procedures are painful and *** resonance imaging(MRI)is a non-invasive modality for diagnosing tumors,but scans must be interpretated by an expert ***:We used deep learning and improved particle swarm optimization(IPSO)to automate brain tumor *** scan contrast is enhanced by ant colony optimization(ACO);the scans are then used to further train a pretrained deep learning model,via transfer learning(TL),and to extract features from two dense *** fused the features of both layers into a single,more informative *** IPSO algorithm selected the optimal features,which were classied using a support vector ***:We analyzed high-and low-grade glioma images from the BRATS 2018 dataset;the identication accuracies were 99.9%and 99.3%,***:The accuracy of our method is signicantly higher than existing techniques;thus,it will help radiologists to make diagnoses,by providing a“second opinion.”

关键词： Brain tumor contrast enhancement deep learning feature selection classication

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Stable and oscillatory hypoxia differentially regulate invasibility of breast cancer associated fibroblasts

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Mechanobiology in Medicine 2024年第3期2卷 83-93页

作者： Wenqiang Du Ashkan Novin Yamin Liu junaid Afzal Shaofei Liu Yasir Suhail Kshitiz Department of Biomedical Engineering University of Connecticut HealthFarmingtonCTUSA Center for Cell Analysis and Modeling University of Connecticut HealthFarmingtonCTUSA Department of Cardiology University of California San FranciscoSan FranciscoCAUSA NEAG Comprehensive Cancer Center University of Connecticut HealthFarmingtonCTUSA

As local regions in the tumor outstrip their oxygen supply,hypoxia can develop,affecting not only the cancer cells,but also other cells in the microenvironment,including cancer associated fibroblasts(CAFs).Hypoxia is also not necessarily stable over time,and can fluctuate or *** Inducible Factor-1 is the master regulator of cellular response to hypoxia,and can also exhibit oscillations in its *** understand how stable,and fluctuating hypoxia influence breast CAFs,we measured changes in gene expression in CAFs in normoxia,hypoxia,and oscillatory hypoxia,as well as measured change in their capacity to resist,or assist breast cancer *** show that hypoxia has a profound effect on breast CAFs causing activation of key pathways associated with fibroblast activation,but reduce myofibroblast activation and traction force *** also found that oscillatory hypoxia,while expectedly resulted in a“sub-hypoxic”response in gene expression,it resulted in specific activation of pathways associated with actin polymerization and actomyosin *** traction force microscopy,and a nanopatterned stromal invasion assay,we show that oscillatory hypoxia increases contractile force generation vs stable hypoxia,and increases heterogeneity in force generation response,while also additively enhancing invasibility of CAFs to MDA-MB-231 *** data show that stable and unstable hypoxia can regulate many mechnobiological characteristics of CAFs,and can contribute to transformation of CAFs to assist cancer dissemination and onset of metastasis.

关键词： invasion breast unstable

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Obstructive Jaundice Caused by Mucinous Cystic Tumor of Gallbladder:A Case Report and Literature Review

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Journal of Clinical and Translational Hepatology 2021年第4期9卷 598-602页

作者： Sulai Liu Zhihua Zhang Chao Guo Zhangtao Yu Siyuan He junaid Khan Bo Jiang Yinghui Song Chuang Peng Department of Hepatobiliary Surgery/Hunan Research Center of Biliary Disease Hunan Provincial People’s Hospital/The First Affiliated Hospital of Hunan Normal UniversityChangshaHunanChina Biliary Disease Research Laboratory of Hunan Provincial People’s Hospital Key Laboratory of Hunan Normal UniversityChangshaHunanChina Clinical Medical Technology Research Center of Hunan Provincial for Biliary Disease Prevention and Treatment ChangshaHunanChina

Mucinous cystic tumor of the gallbladder is an extremely rare benign tumor,with potential for malignant *** cystic tumors of the cystic duct are divided into mucinous cystadenoma and mucinous ***,cystadenoma is generally considered to be a precancerous lesion of *** present,there are few cases reported worldwide,and there are no relevant guidelines for diagnosis and treatment of this *** article presents the collected clinical data of a patient with mucinous cystic tumor of the gallbladder who was admitted to the First Affiliated Hospital of Hunan Normal University,with the characteristics of the disease summarized in combination with a focused literature review.

关键词： Mucinous cystic tumor Jaundice Gallbladder Case report Litera-ture review

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Crop Yield Prediction Using Machine Learning Approaches on a Wide Spectrum

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Computers, Materials & Continua 2022年第9期72卷 5663-5679页

作者： SVinson Joshua ASelwin Mich Priyadharson Raju Kannadasan Arfat Ahmad Khan Worawat Lawanont Faizan Ahmed Khan Ateeq Ur Rehman Muhammad junaid Ali Department of Electronics and Communication Engineering Vel Tech Rangarajan Dr.Sagunthala R&D Institute of Science and TechnologyChennai600062India Department of Electrical and Electronics Engineering Sri Venkateswara College of EngineeringSriperumbudur602117India Suranaree University of Technology Nakhon Ratchasima30000Thailand University of Central Punjab Lahore54000Pakistan Government College University Lahore54000Pakistan Virtual University of Pakistan Islamabad Campus45550Pakistan

The exponential growth of population in developing countries likeIndia should focus on innovative technologies in the Agricultural processto meet the future crisis. One of the vital tasks is the crop yield predictionat its early stage;because it forms one of the most challenging tasks inprecision agriculture as it demands a deep understanding of the growth patternwith the highly nonlinear parameters. Environmental parameters like rainfall,temperature, humidity, and management practices like fertilizers, pesticides,irrigation are very dynamic in approach and vary from field to field. In theproposed work, the data were collected from paddy fields of 28 districts in widespectrum of Tamilnadu over a period of 18 years. The Statistical model MultiLinear Regression was used as a benchmark for crop yield prediction, whichyielded an accuracy of 82% owing to its wide ranging input data. Therefore,machine learning models are developed to obtain improved accuracy, namelyBack Propagation Neural Network (BPNN), Support Vector Machine, andGeneral Regression Neural Networks with the given data set. Results showthat GRNN has greater accuracy of 97% (R2 = 0.97) with a normalizedmean square error (NMSE) of 0.03. Hence GRNN can be used for crop yieldprediction in diversified geographical fields.

关键词： Machine learning crop yield prediction computer simulation and modelling

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