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Position Vectors Based Efcient Indoor Positioning System

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Computers, Materials & Continua 2021年第5期67卷 1781-1799页

作者： Ayesha Javed Mir Yasir Umair Alina Mirza Abdul Wakeel fazli subhan Wazir Zada Khan Department of Electrical Engineering Military College of SignalsNational University of Sciences and TechnologyIslamabad44000Pakistan Department of Computer Engineering National University of Modern Languages(NUML)Islamabad44000Pakistan Department of Computer Science and Information Security Jazan UniversityJazan45142Saudi Arabia

With the advent and advancements in the wireless technologies,Wi-Fi ngerprinting-based Indoor Positioning System(IPS)has become one of the most promising solutions for localization in indoor *** the outdoor environment,the lack of line-of-sight propagation in an indoor environment keeps the interest of the researchers to develop efcient and precise positioning systems that can later be incorporated in numerous applications involving Internet of Things(IoTs)and green *** this paper,we have proposed a technique that combines the capabilities of multiple algorithms to overcome the complexities experienced ***,in the database development phase,Motley Kennan propagation model is used with Hough transformation to classify,detect,and assign different attenuation factors related to the types of ***,important parameters for system accuracy,such as,placement and geometry of Access Points(APs)in the coverage area are also *** algorithm for deployment of an additional AP to an already existing infrastructure is proposed by using Genetic Algorithm(GA)coupled with Enhanced Dilution of Precision(EDOP).Moreover,classication algorithm based on k-Nearest Neighbors(k-NN)is used to nd the position of a stationary or mobile user inside the given coverage *** k-NN to provide low localization error and reduced space dimensionality,three APs are required to be selected *** this paper,we have suggested an idea to select APs based on Position Vectors(PV)as an input to the localization *** from our comprehensive investigations,it is revealed that the accuracy of indoor positioning system using the proposed technique unblemished the existing solutions with signicant improvements.

关键词： Indoor positioning systems Internet of Things access points position vectors genetic algorithm k-nearest neighbors

Enhanced Fingerprinting Based Indoor Positioning Using Machine Learning

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Computers, Materials & Continua 2021年第11期69卷 1631-1652页

作者： Muhammad Waleed Pasha Mir Yasir Umair Alina Mirza Faizan Rao Abdul Wakeel Safia Akram fazli subhan Wazir Zada Khan Department of Electrical Engineering National University of Sciences and TechnologyIslamabad44000Pakistan Department of Computer Engineering National University of Modern Languages(NUML)Islamabad44000Pakistan Faculty of Computing Capital University of Science and TechnologyIslamabad44000Pakistan

Due to the inability of the Global Positioning System(GPS)signals to penetrate through surfaces like roofs,walls,and other objects in indoor environments,numerous alternative methods for user positioning have been *** those,the Wi-Fi fingerprinting method has gained considerable interest in Indoor Positioning Systems(IPS)as the need for lineof-sight measurements is minimal,and it achieves better efficiency in even complex indoor *** and online are the two phases of the fingerprinting *** researchers have highlighted the problems in the offline phase as it deals with huge datasets and validation of Fingerprints without pre-processing of data becomes a *** learning is used for the model training in the offline phase while the locations are estimated in the online *** researchers have considered the concerns in the offline phase as it deals with huge datasets and validation of Fingerprints becomes an *** learning algorithms are a natural solution for winnowing through large datasets and determining the significant fragments of information for localization,creating precise models to predict an indoor *** training sets are a key for obtaining better results in machine learning ***,an existing WLAN fingerprinting-based multistory building location database has been used with 21049 samples including 19938 training and 1111 testing *** proposed model consists of mean and median filtering as pre-processing techniques applied to the database for enhancing the accuracy by mitigating the impact of environmental dispersion and investigated machine learning algorithms(kNN,WkNN,FSkNN,and SVM)for estimating the *** proposed SVM with median filtering algorithm gives a reduced mean positioning error of 0.7959 m and an improved efficiency of 92.84%as compared to all variants of the proposed method for 108703 m^(2) area.

关键词： Indoor positioning system fingerprinting received signal strength indicator mean position error support vector machine

Performance Evaluation of Supervised Machine Learning Techniques for Efficient Detection of Emotions from Online Content

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Computers, Materials & Continua 2020年第6期63卷 1093-1118页

作者： Muhammad Zubair Asghar fazli subhan Muhammad Imran Fazal Masud Kundi Adil Khan Shahboddin Shamshirband Amir Mosavi Peter Csiba Annamaria RVarkonyi Koczy Institute of Computing and Information Technology Gomal UniversityDIKhan29050Pakistan National University of Modern Languages IslamabadPakistan Higher Education Commission Khyber PakhtunkhwaPakistan Department for Management of Science and Technology Development Ton Duc Thang UniversityHo Chi MinhVietnam Faculty of Information Technology Ton Duc Thang UniversityHo Chi MinhVietnam Kando Kalman Faculty of Electrical Engineering Obuda UniversityBudapestHungary Institute of Structural Mechanics Bauhaus-Universität WeimarWeimar99423Germany Department of Mathematics and Informatics J.Selye UniversityKomarno94501Slovakia

Emotion detection from the text is a challenging problem in the text *** opinion mining experts are focusing on the development of emotion detection applications as they have received considerable attention of online community including users and business organization for collecting and interpreting public ***,most of the existing works on emotion detection used less efficient machine learning classifiers with limited datasets,resulting in performance *** overcome this issue,this work aims at the evaluation of the performance of different machine learning classifiers on a benchmark emotion *** experimental results show the performance of different machine learning classifiers in terms of different evaluation metrics like precision,recall ad ***,a classifier with the best performance is recommended for the emotion classification.

关键词： Emotion classification machine learning classifiers ISEAR dataset performance evaluation

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基于深度学习的漏洞检测框架与实现

基于深度学习的漏洞检测框架与实现

作者： subhan fazli（苏晗）扬州大学

学位级别：硕士

软件漏洞会给软件安全造成极大的危害,带来巨大的经济和社会损失,严重威胁国家安全。目前,自动化漏洞检测是软件安全领域的一个研究热点。传统基于静态检测和动态检测技术主要依赖专家的先验知识来定义特征,机器学习和深度学习的快速发... 详细信息

软件漏洞会给软件安全造成极大的危害,带来巨大的经济和社会损失,严重威胁国家安全。目前,自动化漏洞检测是软件安全领域的一个研究热点。传统基于静态检测和动态检测技术主要依赖专家的先验知识来定义特征,机器学习和深度学习的快速发展缓解了手动定义特征的负担以及由于人们的主观性而造成的准确性较低的问题,自动化漏洞检测（AVP）引起了软件安全领域研究人员的大量关注。目前已经提出一些自动化漏洞检测方法:如基于机器学习（ML）的漏洞检测方法（VPMs）、基于软件度量的漏洞检测方法和基于深度学习的漏洞检测方法。需要设计一种客观的表征方法来提取漏洞特征,本文基于代码度量来表征代码,从而进行检测漏洞,进一步提出了一种方法（VULNDETECT）,一种基于深度学习的漏洞检测模型,它使用CNN、CNN-LSTM和简单DNN的混合神经网络进行特征提取和代码度量的学习。我们通过实验将VULNDETECT与现有的基于深度学习的漏洞检测方法进行比较,实验结果显示,我们的方法在F1、召回率和准确率三个评价指标上分别达到了 0.92、0.99和0.91,F1-score相比之前的工作提高了 18%。

关键词：代码度量深度学习神经网络漏洞检测机器学习卷积神经网络长短期记忆

A Survey of Lung Nodules Detection and Classification from CT Scan Images

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Computer Systems Science & Engineering 2024年第6期48卷 1483-1511页

作者： Salman Ahmed fazli subhan Mazliham Mohd Su’ud Muhammad Mansoor Alam Adil Waheed Faculty of Computing and Technology IQRA UniversityIslamabad44000Pakistan Faculty of Engineering and Computer Science National University of Modern LanguagesIslamabad44000Pakistan Faculty of Computing and Informatics Multimedia UniversityCyberjaya63100Malaysia Faculty of Computing Riphah International UniversityIslamabad44000Pakistan Department of Information Sciences Division of Sciences and TechnologyUniversity of EducationLahore54770Pakistan

In the contemporary era,the death rate is increasing due to lung ***,technology is continuously enhancing the quality of *** improve the survival rate,radiologists rely on Computed Tomography(CT)scans for early detection and diagnosis of lung *** paper presented a detailed,systematic review of several identification and categorization techniques for lung *** analysis of the report explored the challenges,advancements,and future opinions in computer-aided diagnosis CAD systems for detecting and classifying lung nodules employing the deep learning(DL)*** findings also highlighted the usefulness of DL networks,especially convolutional neural networks(CNNs)in elevating sensitivity,accuracy,and specificity as well as overcoming false positives in the initial stages of lung cancer *** paper further presented the integral nodule classification stage,which stressed the importance of differentiating between benign and malignant nodules for initial cancer ***,the findings presented a comprehensive analysis of multiple techniques and studies for nodule classification,highlighting the evolution of methodologies from conventional machine learning(ML)classifiers to transfer learning and integrated ***,while accepting the strides formed by CAD systems,the review addressed persistent challenges.

关键词： Lung nodules computed tomography scans lung cancer deep learning

A STUDY OF GENOTYPIC FREQUENCY OF VARIOUS BLOOD GROUP ALLELES

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A STUDY OF GENOTYPIC FREQUENCY OF VARIOUS BLOOD GROUP ALLELE...

Mycorrhiza and Phosphate Solubilizing Bacteria:Potential Bioagents for Sustainable Phosphorus Management in Agriculture

The 1st Session of 2004 Workshop of Pakistan Academy of Sciences

作者： Mohammad Shoaib Khan Faheem Tahir Mubashir Ahmed Sheikh fazli subhan Birjees Mazhar Kazi Athar Saeed Dil Fariyal Deepa Sikandar Sultan Irshad Ali Musa Kaleem Baloch Public Health Division National Institute of HealthIslamabadPakistan Department of Clinical Pathology Jinnah Postgraduate Medical CentreKarachiPakistan Department of Chemistry Gomal UniversityD.I. KhanPakistan

A random population sample from urban and rural areas of Bannu region, Northwest Frontier Province（NWFP） of Pakistan was screened for distribution of blood groups. The objective was to identify the genotypic frequency of blood groups in the region, in order to comprehend the allelic diversity. Blood grouping was carried out over a period of 16 months from January 2002 to April 2003, and encompassed 2581 subjects, in which 57.09% were male and 42.9% female. These were categorized according to ABO/Rh system. Allele frequency was computed according to HardyWeinberg law. The distribution of phenotypes in the total sample was 0.3623, 0.3103, 0.2507 and0.0767 for groups B, A, O and AB, respectively, with 0.672 Rh-positive（R） and 0.328 Rh-negative（r）. B group was dominant in both the genders, and AB was rare in both males as well as females. The distribution of the alleles in the total sample was 0.345, 0.378 and 0.277 for I A, I B and i, *** studied population exhibited a predominance of group B, in the order of B＞A＞O＞AB, as well as Rh-positive antigen for both male（90.26%） and female（87.98%） subjects within the population,with Rh-negative men and women being 9.74% and 12.02% respectively.

关键词： Alleles gene frequency blood groups Rh factor transfusion Bannu Pakistan

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Phyton-International Journal of Experimental Botany 2022年第2期91卷 257-278页

作者： fazli Wahid Muhammad Sharif Shah Fahad Amjad Ali Muhammad Adnan Rafiullah Shah Saud subhan Danish Muhammad Arif Ali Niaz Ahmed Hüseyin Arslan Doğan Arslan Murat Erman Ayman EL Sabagh Fatemeh Gholizadeh Rahul Datta Department of Agriculture University of SwabiSwabi23561Pakistan Department of Soil and Environmental Sciences The University of AgriculturePeshawar25130Pakistan Department of Agronomy The University of HaripurHaripur22620Pakistan School of Environmental and Municipal Engineering Xi’an University of Architecture and TechnologyXi’an710055China Department of Horticulture Northeast Agricultural UniversityHarbin150030China Department of Soil Science Faculty of Agricultural Sciences and TechnologyBahauddin Zakariya UniversityMultan60800Pakistan Department of Field Crops Faculty of AgricultureHarranÜniversityOsmanbey CampusŞanliurfa63050Turkey Department of Field Crops Faculty of AgricultureSiirt UniversitySiirt56100Turkey Department of Agronomy Faculty of AgricultureKafrelsheikh UniversityKafrelsheikh33156Egypt Department of Plant Production and Genetics Faculty of AgricultureUniversity of KurdistanSanandaj66177-15175Iran Department of Geology and Pedology Faculty of Forestry and Wood TechnologyMendel University in BrnoBrno61300Czech Republic

Phosphorus(P)is a critical nutrient that plays an essential role in improving soil fertility for optimum plant growth and *** is one of the most deficient macro-nutrients in agricultural soils after nitrogen and is considered inadequate for plant growth and *** P availability in soils,the farmers are applying huge amounts of synthetic P fertilizers that adversely affect the wider environment,groundwater,soil fertility and microbial *** beneficial microbes are known to release and supply soluble P for improving growth and yield of a variety of plants in a sustainable manner in P deficient ***,inoculation of these microbes,including arbuscular mycorrhizal fungi(AMF)and phosphate solubilizing bacteria(PSB)to soil to enhance crop production without harming the environment,is an alternative approach to chemical *** combined role of AMF and PSB in P solubilization is not well understood and the application and mode of action of these microbial groups are often naive due to variation in the ***,the current review article would develop a better understanding of the interactive role and mechanisms of AMF and PSB in improving P availability from both organic and inorganic sources in a sustainable crop production ***,the current review would loop out further avenues for researchers interested to commercially produce effective AMF and PSB-based biofertilizers for sustainable management of phosphorus over a wide range of agricultural crops worldwide.

关键词： Beneficial microbes soil inoculation phosphorus mineralization crop production