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Osteoporosis prediction for Trabecular Bone using Machine Learning: A Review

Computers, Materials & Continua 2021年第4期67卷 89-105页

作者： Marrium Anam vasaki a/p ponnusamy Muzammil Hussain Muhammad Waqas Nadeem Mazhar Javed Hock Guan Goh Sadia Qadeer Department of Computer Science Government College UniversityFaisalabadPakistan Faculty of Information and Communication Technology(FICT) Universiti Tunku Abdul Rahman(UTAR)KamparMalaysia Department of Computer Science School of Systems and TechnologyUniversity of Management and TechnologyLahorePakistan Department of Computer Science Lahore Garrison UniversityLahorePakistan

Trabecular bone holds the utmost importance due to its significance regarding early bone *** like osteoporosis greatly affect the structure of the Trabecular bone which results in different outcomes like high risk of *** objective of this paper is to inspect the characteristics of the Trabecular Bone by using the Magnetic Resonance Imaging(MRI)*** characteristics prove to be quite helpful in studying different studies related to Trabecular bone such as *** things that were considered before the selection of the articles for the systematic review were language,research field,and electronic *** those articles written in the English language were selected as it is the most prominent language used in scientific,engineering,computer science,and biomedical *** literature review was conducted on the articles published between 2006 and 2020.A total of 62 research papers out of 1050 papers were extracted which were according to our topic of review after screening abstract and article content for the title and abstract *** findings from those researches were compiled at the end of the result *** systematic literature review presents a comprehensive report on scientific researches and studies that have been done in the medical area concerning trabecular bone.

关键词： Magnetic resonance imaging high resolution trabecular bone(TB) bone structure machine learning

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Fusion-Based Machine Learning Architecture for Heart Disease prediction

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Computers, Materials & Continua 2021年第5期67卷 2481-2496页

作者： Muhammad Waqas Nadeem Hock Guan Goh Muhammad Adnan Khan Muzammil Hussain Muhammad Faheem Mushtaq vasaki a/p ponnusamy Faculty of Information and Communication Technology(FICT) Universiti Tunku Abdul Rahman(UTAR)KamparPerak31900Malaysia Department of Computer Science Lahore Garrison UniversityLahore54000Pakistan Department of Computer Science Faculty of ComputingRiphah International UniversityLahore CampusLahore54000Pakistan Department of Computer Science School of Systems and TechnologyUniversity of Management and TechnologyLahore54000Pakistan Department of Information Technology Khwaja Fareed University of Engineering and Information TechnologyRahim Yar Khan64200Pakistan

The contemporary evolution in healthcare technologies plays a considerable and signicant role to improve medical services and save human *** disease or cardiovascular disease is the most fatal and complex disease which it is hardly to be detected through our naked eyes,as numerous people have been suffering from this disease *** attacks occur when the ranges of vital signs such as blood pressure,pulse rate,and body temperature exceed their normal *** efcient diagnosis of heart diseases could play a substantial role in the eld of cardiology,while diagnostic time could be *** has been a key challenge for researchers and medical experts to diagnose heart diseases accurately and ***,machine learning-based techniques are used for the diagnosis with higher accuracy,using datasets compiled from former medical patients’*** recent years,numerous studies have been presented in the literature propose machine learning techniques for diagnosing heart ***,the existing techniques have some limitations in terms of their *** this paper,a novel Support Vector Machine(SVM)based architecture for heart disease prediction,empowered with a fuzzy based decision level fusion,is *** SVMbased architecture has improved the accuracy signicantly as compared to existing solutions,where 96.23%accuracy has been achieved.

关键词： Heart disease machine learning support vector machine fuzzy logic fusion cardiovascular

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IoT Wireless Intrusion Detection and Network Traffic Analysis

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Computer Systems Science & Engineering 2022年第3期40卷 865-879页

作者： vasaki ponnusamy Aun Yichiet NZ Jhanjhi Mamoona humayun MaramFahhad Almufareh University Tunku Abdul Rahman Kampar31900Malaysia School of Computer Science and Engineering(SCE) Taylor’s UniversitySelangorMalaysia Department of Information Systems College of Computer and Information SciencesJouf UniversityAl-JoufSaudi Arabia

Enhancement in wireless networks had given users the ability to use the Internet without a physical connection to the *** every Internet of Things(IoT)devices such as smartphones,drones,and cameras use wireless technology(Infrared,Bluetooth,IrDA,IEEE 802.11,etc.)to establish multiple interdevice connections *** the flexibility of the wireless network,one can set up numerous ad-hoc networks on-demand,connecting hundreds to thousands of users,increasing productivity and profitability ***,the number of network attacks in wireless networks that exploit such flexibilities in setting and tearing down networks has become very *** can launch attacks since there is no first line of defense in an ad hoc network setup besides the standard IEEE802.11 WpA2 *** feasible countermeasure is to deploy intrusion detection systems at the edge of these ad hoc networks(Network-based IDS)or at the node level(Host-based IDS).The challenge here is that there is no readily available benchmark data available for IoT network *** this benchmark data is very tedious as IoT can work on multiple platforms and networks,and crafting and labelling such dataset is very *** research aims to study the characteristics of existing datasets available such as KDD-Cup and NSL-KDD,and their suitability for wireless IDS *** hypothesize that network features are parametrically different depending on the types of network and assigning weight dynamically to these features can potentially improve the subsequent threat *** paper analyses packet and flow features for the data packet captured on a wireless network rather than a wired *** domain heuristcs and early classification results,the paper had identified 19 header fields exclusive to wireless network that contain high information gain to be used as ML features in Wireless IDS.

关键词： IoT machine learning traffic features IDS KDD-CUp NSL-KDD

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Intrusion Detection Systems in Internet of Things and Mobile Ad-Hoc Networks

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Computer Systems Science & Engineering 2022年第3期40卷 1199-1215页

作者： vasaki ponnusamy Mamoona Humayun NZJhanjhi Aun Yichiet Maram Fahhad Almufareh Faculty of Information and Communication Technology Universiti Tunku Abdul RahmanKamparMalaysia Department of Information Systems College of Computer and Information SciencesJouf UniversityAl-JoufSaudi Arabia School of Computer Science and Engineering(SCE) Taylor’s UniversitySelangorMalaysia

Internet of Things(IoT)devices work mainly in wireless mediums;requiring different Intrusion Detection System(IDS)kind of solutions to leverage 802.11 header information for intrusion ***-specific traffic features with high information gain are primarily found in data link layers rather than application layers in wired *** survey investigates some of the complexities and challenges in deploying wireless IDS in terms of data collection methods,IDS techniques,IDS placement strategies,and traffic data analysis *** paper’s main finding highlights the lack of available network traces for training modern machine-learning models against IoT specific ***,the Knowledge Discovery in Databases(KDD)Cup dataset is reviewed to highlight the design challenges of wireless intrusion detection based on current data attributes and proposed several guidelines to future-proof following traffic capture methods in the wireless network(WN).The paper starts with a review of various intrusion detection techniques,data collection methods and placement *** main goal of this paper is to study the design challenges of deploying intrusion detection system in a wireless *** detection system deployment in a wireless environment is not as straightforward as in the wired network environment due to the architectural *** this paper reviews the traditional wired intrusion detection deployment methods and discusses how these techniques could be adopted into the wireless environment and also highlights the design challenges in the wireless *** main wireless environments to look into would be Wireless Sensor Networks(WSN),Mobile Ad Hoc Networks(MANET)and IoT as this are the future trends and a lot of attacks have been targeted into these *** it is very crucial to design an IDS specifically to target on the wireless networks.

关键词： Internet of Things MANET intrusion detection systems wireless networks

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DDoS Detection in SDN using Machine Learning Techniques

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Computers, Materials & Continua 2022年第4期71卷 771-789页

作者： Muhammad Waqas Nadeem Hock Guan Goh vasaki ponnusamy Yichiet Aun Faculty of Information and Communication Technology(FICT) Universiti Tunku Abdul Rahman(UTAR)Jalan UniversitiBandar Barat31900 KamparPerakMalaysia

Software-defined network(SDN)becomes a new revolutionary paradigm in networks because it provides more control and network operation over a network *** SDN controller is considered as the operating system of the SDN based network infrastructure,and it is responsible for executing the different network applications and maintaining the network services and *** all its tremendous capabilities,the SDN face many security issues due to the complexity of the SDN *** denial of services(DDoS)is a common attack on SDN due to its centralized architecture,especially at the control layer of the SDN that has a network-wide *** learning is now widely used for fast detection of these *** this paper,some important feature selection methods for machine learning on DDoS detection are *** selection of optimal features reflects the classification accuracy of the machine learning techniques and the performance of the SDN controller.A comparative analysis of feature selection and machine learning classifiers is also derived to detect SDN *** experimental results show that the Random forest(RF)classifier trains the more accurate model with 99.97%accuracy using features subset by the Recursive feature elimination(RFE)method.

关键词： Machine learning software-defined network distributed denial of services feature selection protection artificial neural network decision trees naïve bayes security

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Toward Secure Software-Defined Networks Using Machine Learning: A Review, Research Challenges, and Future Directions

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Computer Systems Science & Engineering 2023年第11期47卷 2201-2217页

作者： Muhammad Waqas Nadeem Hock Guan Goh Yichiet Aun vasaki ponnusamy Faculty of Information and Communication Technology Universiti Tunku Abdul RahmanKamparPerakMalaysia Higher Colleges of Technology Fujairah Men’s CampusFujairahUAE

Over the past few years,rapid advancements in the internet and communication technologies have led to increasingly intricate and diverse networking *** a result,greater intelligence is necessary to effectively manage,optimize,and maintain these *** to their distributed nature,machine learning models are challenging to deploy in traditional ***,Software-Defined Networking(SDN)presents an opportunity to integrate intelligence into networks by offering a programmable architecture that separates data and control *** provides a centralized network view and allows for dynamic updates of flow rules and softwarebased traffic *** the programmable nature of SDN makes it easier to deploy machine learning techniques,the centralized control logic also makes it vulnerable to *** address these issues,recent research has focused on developing powerful machine-learning methods for detecting and mitigating attacks in SDN *** paper highlighted the countermeasures for cyberattacks on SDN and how current machine learningbased solutions can overcome these emerging *** also discuss the pros and cons of using machine learning algorithms for detecting and mitigating these ***,we highlighted research issues,gaps,and challenges in developing machine learning-based solutions to secure the SDN controller,to help the research and network community to develop more robust and reliable solutions.

关键词： Botnet attack deep learning distributed denial of service machine learning network security software-defined network

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WAVE pROpAGATION IN A TRANSVERSELY ISOTROpIC THERMOELASTIC SOLID CYLINDER OF ARBITRARY CROSS-SECTION

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Acta Mechanica Solida Sinica 2011年第6期24卷 527-538页

作者： p. ponnusamy M. Rajagopal Department of Mathematics Govt. Arts College (Autonomous) Coimbatore-641 018 Tamil Nadu India

The wave propagation in an infinite, homogeneous, transversely isotropic solid cylin- der of arbitrary cross-section is studied using Fourier expansion collocation method, within the frame work of linearized, three-dimensional theory of thermoelasticity. Three displacement po- tential functions are introduced, to uncouple the equations of motion and the heat conduction. The frequency equations are obtained for longitudinal and flexural （symmetric and antisymmet- ric） modes of vibration and are studied numerically for elliptic and parabolic cross-sectional zinc cylinders. The computed non-dimensional wave numbers are presented in the form of dispersion curves.

关键词： vibration of thermal cylinder thermoelastic stress three-dimensional stress anal-ysis wave propagation in an arbitrary cross-section

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Koebe Type Theorems and pre-Schwarzian of Harmonic K-quasiconformal Mappings,and Their Applications

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Acta Mathematica Sinica,English Series 2022年第11期38卷 1965-1980页

作者： Shao Lin CHEN Saminathan ponnusamy College of Mathematics and Statistics Hengyang Normal UniversityHengyang 421002P.R.China Hunan provincial Key Laboratory of Intelligent Information processing and Application Hengyang 421002P.R.China Department of Mathematics Indian Institute of Technology MadrasChennai-600036India

In this article,we first establish an asymptotically sharp Koebe type covering theorem for harmonic K-quasiconformal *** we use it to obtain an asymptotically Koebe type distortion theorem,a coefficients estimate,a Lipschitz characteristic and a linear measure distortion theorem of harmonic K-quasiconformal *** last,we give some characterizations of the radial John disks with the help of pre-Schwarzian of harmonic mappings.

关键词： Harmonic K-quasiconformal mapping Koebe type covering theorem Koebe type distortion theorem Radial John disk

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Detection of Diabetic Retinopathy from Retinal Images Using DenseNet Models

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Computer Systems Science & Engineering 2023年第4期45卷 279-292页

作者： R.Nandakumar p.Saranya Vijayakumar ponnusamy Subhashree Hazra Antara Gupta Department of ECE K S R Institute for Engineering and TechnologyTiruchengode637215India Department of Computer Science and Engineering SRM Institute of Science and TechnologyChennai603203India Department of Electronics and Communication and Engineering SRM Institute of Science and TechnologyChennai603203India

A prevalent diabetic complication is Diabetic Retinopathy(DR),which can damage the retina’s veins,leading to a severe loss of *** treated in the early stage,it can help to prevent vision *** since its diagnosis takes time and there is a shortage of ophthalmologists,patients suffer vision loss even before ***,early detection of DR is the necessity of the *** primary purpose of the work is to apply the data fusion/feature fusion technique,which combines more than one relevant feature to predict diabetic retinopathy at an early stage with greater *** procedures for diabetic retinopathy analysis are fundamental in taking care of these *** profound learning for parallel characterization has accomplished high approval exactness’s,multi-stage order results are less noteworthy,especially during beginning phase *** Connected Convolutional Networks are suggested to detect of Diabetic Retinopathy on retinal *** presented model is trained on a Diabetic Retinopathy Dataset having 3,662 images given by *** results suggest that the training accuracy of 93.51%0.98 precision,0.98 recall and 0.98 F1-score has been achieved through the best one out of the three models in the proposed *** same model is tested on 550 images of the Kaggle 2015 dataset where the proposed model was able to detect No DR images with 96%accuracy,Mild DR images with 90%accuracy,Moderate DR images with 89%accuracy,Severe DR images with 87%accuracy and proliferative DR images with 93%accuracy.

关键词： Convolutional Neural Networks vision loss pathogenic blood vessels DenseNet AlexNet ResNet

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Intra-population genetic variance for grain iron and zinc contents and agronomic traits in pearl millet

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The Crop Journal 2016年第1期4卷 48-54页

作者： Mahalingam Govindaraj Kedar N.Rai ponnusamy Shanmugasundaram International Crops Research Institute for the Semi-arid Tropics (ICRISAT) Centre for plant Breeding and Genetics Tamil Nadu Agricultural University

Crop biofortification is a sustainable approach for fighting micronutrient malnutrition in the world. The estimation of variance components in genetically broad-based populations provides information about their genetic architecture, allowing the design of an appropriate biofortification breeding method for cross-pollinated crops such as pearl millet. The objective of this study was to estimate intra-population genetic variance using self(S1) and half-sib(HS) progenies in two populations, AIMp92901 and ICMR312. Field trials were evaluated in two contrasting seasons(2009 rainy and 2010 summer; otherwise called environments) in Alfisols at ICRISAT, patancheru. Analyses of variance showed highly significant variation for S1 s and HS progenies, reflecting high within-population genetic variation for both micronutrients and other key traits. However, the HS showed narrow ranges and lower genetic variances than the S1 for all of the traits. The micronutrients were highly positively correlated in S1(r = 0.77 to 0.86; p p progenies of both populations, implying concurrent genetic improvement for both micronutrients. The genetic variance component was different among populations for Fe and Zn contents across environments, with AIMp92901 showing a greater proportion of dominance and ICMR312 greater additive variance for these micronutrients. The estimates of variance(additive and dominance) were specific for each population, given their dependence on the additive and dominance effects of the segregating loci, which also differ among populations. The possible causes for such differences were discussed. The results showed that the expression of these micronutrients in pearl millet shows largely additive variance, so that breeding high-iron hybrids will require incorporation of these micronutrient traits into both parental lines.

关键词： Genetic variance Iron Micronutrient malnutrition pearl millet Zinc

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