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Improving Multiple Sclerosis Disease Prediction Using Hybrid Deep Learning Model

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Computers, Materials & Continua 2024年第10期81卷 643-661页

作者： stephen ojo Moez Krichen Meznah A.Alamro Alaeddine Mihoub Gabriel Avelino Sampedro Jaroslava Kniezova Department of Electrical and Computer Engineering College of EngineeringAnderson UniversityAndersonSC 29621USA Faculty of Computing and Information Al-Baha UniversityAl-Baha65528Saudi Arabia ReDCAD Laboratory University of SfaxSfax3038Tunisia Department of Information Technology College of Computer&Information SciencePrincess Nourah Bint Abdul Rahman UniversityRiyadh11564Saudi Arabia Department of Management Information Systems College of Business and EconomicsQassim UniversityP.O.Box 6640Buraidah51452Saudi Arabia School of Management and Information Technology De La Salle-College of Saint BenildeManila1004Philippines Department of Information Management and Business Systems Faculty of ManagementComenius University BratislavaBratislava 2582005Slovakia

Myelin damage and a wide range of symptoms are caused by the immune system targeting the central nervous system in Multiple Sclerosis(MS),a chronic autoimmune neurological *** disrupts signals between the brain and body,causing symptoms including tiredness,muscle weakness,and difficulty with memory and *** methods for detecting MS are less precise and time-consuming,which is a major gap in addressing this *** gap has motivated the investigation of new methods to improve MS detection consistency and *** paper proposed a novel approach named FAD consisting of Deep Neural Network(DNN)fused with an Artificial Neural Network(ANN)to detect MS with more efficiency and accuracy,utilizing regularization and combat *** use gene expression data for MS research in the GEO GSE17048 *** dataset is preprocessed by performing encoding,standardization using min-max-scaler,and feature selection using Recursive Feature Elimination with Cross-Validation(RFECV)to optimize and refine the ***,for experimenting with the dataset,another deep-learning hybrid model is integrated with different ML models,including Random Forest(RF),Gradient Boosting(GB),XGBoost(XGB),K-Nearest Neighbors(KNN)and Decision Tree(DT).Results reveal that FAD performed exceptionally well on the dataset,which was evident with an accuracy of 96.55%and an F1-score of 96.71%.The use of the proposed FAD approach helps in achieving remarkable results with better accuracy than previous studies.

关键词： Multi Sclerosis(MS) machine learning deep learning artificial neural network healthcare

A Review of Road Accidents Detection through Wireless Technology—5G, MIMO and Internet of Things

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Open Journal of Applied Sciences 2022年第12期12卷 1968-1978页

作者： stephen ojo Samuel Olugbade Taiwo P. ojo College of Engineering Anderson University Anderson USA Department of Tourism and Hotel Management Faculty of Social Sciences Near East University Nicosia Turkey Computer Science Department Florida A & M University Tallahassee USA

Road transport is been used for moving people and all kinds of goods throughout the world. However, it is one mode of transportation that is prone to accidents and it faces a plethora of never-ending challenges, such as the frequent loss of lives and valuables when accident occurs. The best course of action to handle these issues is to set up an autonomous incident detection system using wireless communication, 5G technologies and the Internet of Things. IoT is a seamless technology that increases the connectivity between humans and machines. It is web-based, and improves communication between vehicle to vehicle, vehicle to infrastructures, transfer of data and information to predict incident occurrences through various networks and frameworks such as eCall, OneM2M and integration of mobile broadband. Additionally, internet of things is being adopted for public safety;for instance, it can speed up first responders’ response times to situations by displaying the best routes to a scene of an accident. The rapid development of 5G is happening in parallel with developments of internet of things (IoT), artificial intelligence (AI), and smart platforms for novel applications such as mission-critical communications. 5G is a new generation technology that operates on the Ultra High Spectrum Band UHSB. It is an innovation that uses the pedestrians-vehicle-road-cloud, and the communication between vehicle locations and temperature of high-quality connection. It is essential for intelligent transport systems because it allows for information sharing, prediction of incidences as safety is the primary concern of road transport. This review examines accident detection through 5G technology, integrated mobile broadband, and multiple inputs multiple outputs (MIMO) wireless system. Finally, we conclude by examining recent technology, challenges, present and future research trends.

关键词： 5G Internet of Thing (IoT) Wireless Communication Technology Road Transportation

Path Loss Modeling: A Machine Learning Based Approach Using Support Vector Regression and Radial Basis Function Models

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Open Journal of Applied Sciences 2022年第6期12卷 990-1010页

作者： stephen ojo Arif Sari Taiwo P. ojo College of Engineering Anderson University Anderson SC USA Department of Management Information Systems Girne American University Girne Cyprus Computer Science Department Florida A&M University Tallahassee FL USA

Path loss prediction models are vital for accurate signal propagation in wireless channels. Empirical and deterministic models used in path loss predictions have not produced optimal results. In this paper, we introduced machine learning algorithms to path loss predictions because it offers a flexible network architecture and extensive data can be used. We introduced support vector regression (SVR) and radial basis function (RBF) models to path loss predictions in the investigated environments. The SVR model was able to process several input parameters without introducing complexity to the network architecture. The RBF on its part provides a good function approximation. Hyperparameter tuning of the machine learning models was carried out in order to achieve optimal results. The performances of the SVR and RBF models were compared and result validated using the root-mean squared error (RMSE). The two machine learning algorithms were also compared with the Cost-231, SUI, Egli, Freespace, Cost-231 W-I models. The analytical models overpredicted path loss. Overall, the machine learning models predicted path loss with greater accuracy than the empirical models. The SVR model performed best across all the indices with RMSE values of 1.378 dB, 1.4523 dB, 2.1568 dB in rural, suburban and urban settings respectively and should therefore be adopted for signal propagation in the investigated environments and beyond.

关键词： Support Vector Regression Radial Basis Function Machine Learning Path Loss Empirical Deterministic

A Fuzzy-Logic Based Path Loss Model at 3.4 GHz for LTE Networks

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Open Journal of Applied Sciences 2022年第7期12卷 1271-1283页

作者： stephen ojo Taiwo P. ojo Victor ojong Etta College of Engineering Anderson University Anderson USA Department of Management Information Systems Girne American University Girne Northern Cyprus Computer Science Department Florida A & M University Tallahassee USA

Empirical and deterministic models have not proven to be effective in path loss predictions because of the problems of computational complexities, low accuracies, and inability to generalize. To solve these problems relating to path loss predictions, this article presents an optimal path loss propagation model developed at 3.4 GHz with the use of fuzzy logic. We introduced Fuzzy logic to accurately represent all forms of uncertainties in the data spectrum as the signal propagates from the transceiver to the receiver, thereby producing accurate results. Experimental data were collected across Cyprus at 3.4 GHz and compared with three existing path loss models. The fuzzy-logic path loss prediction model was then developed and compared with the experimental data and with each of the theoretical empirical models, the newly developed model predicted signal loss with the greatest accuracy as it gives the lowest root-mean-square error. The newly developed model is very efficient for signal propagation and path loss prediction.

关键词： Signal Loss Fuzzy-Logic Machine Learning Signal Propagation Accuracy Empirical Deterministic

Call for Papers Special Issue on Artificial Intelligence Powered Internet of Healthcare Things(IoHT):Data Science,Emerging Trends and Applications

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Big Data Mining and Analytics 2022年第2期5卷 161-161页

作者： Yousef FARHAOUI stephen ojo Lateef Adesola Akinyemi Agbotiname Lucky Imoize FST Moulay Ismail University College of Engineering Anderson University Faculty of Engineering Lagos State University Institute of Digital Communication Ruhr UniversityGermany

The Internet of Things(IoT)is one of the most prominent technologies emerged in recent *** has a broad spectrum of applications in diverse *** IoT is evolving as a significant area of future technology and gaining much attention from various walks of *** has revolutionized various application domains,such as home automation,industrial automation,medical aids,mobile healthcare,elderly assistance,intelligent energy management and smart grids,automotive,traffic management,and many *** applications will make use of the potentially enormous amount and variety of data generated by such objects to provide new services to citizens,companies,and public administrations.

关键词： IoT companies services

Effect of functional composite coating developed via sulphate and chloride process parameter on the UNS G10150 steel for structural and wear mitigation in defence application

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Defence Technology（防务技术） 2018年第3期14卷 196-203页

作者： ojo Sunday Isaac Fayomi Department of Mechanical Engineering Covenant University Department of Chemical Metallurgical and Materials Engineering Tshwane University of Technology

The major engineering challenge of materials in defence technologies is the vulnerability of based metals to structural and wears deformation in service. In this paper, structural formation, mechanical and thermal stability behavior of developed composite coating of Zn-30 Al-7%Ti/Sn chloride bath and Zn-30 Al-7%Ti/Sn sulphate bath was investigated and compared to provide mitigation against failure. The thermal ageing property was done for 2 h at 600C via isothermal furnace. The structural, interfacial effect and stability behaviors of the co-deposited alloys were evaluated using scanning electron microscope equipped with energy dispersive spectrometer(SEM/EDS), atomic force microscope(AFM) and Xray diffractometer(XRD). The hardness and wear properties of the deposited coatings were examined with diamond base micro-hardness tester and reciprocating sliding tester respectively. The result shows that Zn-30 Al-7%Ti/Sn sulphate co-deposition contributed to increase hardness and wear resistance than Zn-30 Al-7%Ti/Sn chloride bath alloy. The stable crystal growth and significant performance of Zn-30 Al-7%Ti/Sn sulphate are link to the intermetallic phase hybrid of Zn Al, Zn4 Ti Al2, Zn3 Al Ti. Besides, it was observed that Zn-30 Al-7%Ti/Sn sulphate has excellent thermomechanical stability at harsh temperature,due to the deposition of Sn/Ti on steel; leading to formation of super-hard interface. However, it was established that co-deposition of mild steel with Zn-30 Al-7%Ti/Sn in sulphate bath significantly improved the structural and wear performance. It was shown that the hardness and wear of the developed composite Zn-30 Al-7%Ti/Sn is increased by about 80% compared to as received sample and about 25% compared with Zn-30 Al-7%Ti/Sn chloride coating developed. The improvement was proved to be an interference of zinc-composite growth. Thus, this work shows that sulphate induced Zn-30 Al-7%Ti/Sn via generation of controllable process parameter can provide significant improv

关键词：防卫技术硫酸盐氯化物低碳钢结构涂层合成 UNS

Additive manufacturing of 18%nickel maraging steels:Defect,structure and mechanical properties:A review

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Journal of Materials Science & Technology 2022年第25期120卷 227-252页

作者： Lulu Guo Lina Zhang Joel Andersson Olanrewaju ojo Department of Mechanical Engineering University of ManitobaWinnipeg R3T 5V6Canada Department of Engineering Science University WestTrollh tan 46186Sweden

This paper reviews the latest research progress in the additive manufacturing(AM)process of 18%nickel maraging steels,which involves laser-based powder bed fusion(L-PBF),laser-based directed energy deposition(L-DED),and wire arc additive manufacturing(WAAM).The emphasis is on the defects,structure,and mechanical properties of the additive manufactured 18%nickel maraging *** first,this article outlines the influences of the processing parameters of AM techniques on the defects formed in the fabricated parts,in terms of the L-PBF and *** macrostructure and microstructure characteristics of as-built and heat-treated 18%nickel maraging steel are then described in ***,the mechanical properties of as-built and heat-treated 18%nickel maraging steel are assessed,such as their tensile,hardness,impact toughness,and fatigue ***,future directions for work on the AM of 18%nickel maraging steel are provided.

关键词： Additive manufacturing 18%Nickel maraging steel Defects Macrostructure Microstructure Mechanical properties

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Defence Technology（防务技术） 2021年第2期17卷 531-544页

作者： Gui-ju Zhang Cai-yuan Xiao Olatunj Oladimeji ojo Key Laboratory of Hunan Province for Efficient Power System and Intelligent Manufacturing Shaoyang422000HunanChina College of Mechanical and Energy Engineering Shaoyang UniversityShaoyang422000HunanChina Department of Industrial and Production Engineering Federal University of Technology AkureAkureNigeria

This paper studies the friction stir spot welding of AA2024-T3/AA7075-T6 Al alloys in the ambient and underwater environments by clarifying the nugget features,microstructure,fracture and mechanical properties of the *** results show that the water-cooling medium exhibits a significant heat absorption capacity in the AA2024-T3/AA7075-T6 welded *** features such as stir zone width,circular imprints,average grain sizes,and angular inter-material hooking are reduced by the watercooling effect in the *** whitish(intercalated structures)bands are formed in the underwater joints while Mg2Si and Al2CuMg precipitates are formed in the ambient and the underwater welded joints *** increase in tool rotational speed(600e1400 rpm)and plunge depth(0.1 e0.5 mm)increases the tensile-shear force of the welded AA2024-T3/AA7075-T6 joints in both the ambient and underwater *** maximum tensile-shear forces of 5900 N and 6700 N were obtained in the ambient and the underwater welds respectively.

关键词： Friction stir spot welding Underwater friction stir spot welding Nugget characterization Microstructure Mechanical properties Fracture

Anti-Corrosion and Tribo-Mechanical Properties of Co-deposited Zn–SnO_2 Composite Coating

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Acta Metallurgica Sinica(English Letters) 2015年第4期28卷 521-530页

作者： ojo Sunday Isaac Fayomi Abimbola Patricia Idowu Popoola Department of Chemical Metallurgical and Materials EngineeringTshwane University of Technology Department of Mechanical Engineering Covenant University

Zn-SnO2 composite coatings were prepared by direct potential using electrolytic co-deposition technique from sulfate solution. The effect of Zn2＋ and SnO2 concentrations in deposited bath on the mechanical properties and mor- phological characteristics of the composite coatings were examined. The characterizations of the sample were analyzed using scanning electron microscopy couple with energy dispersive spectroscopy （SEM/EDS）, X-ray diffraction （XRD） and atomic force microscopy （AFM）. The electrochemical degradation behavior of the samples in 3.65 wt.% NaCl solution was studied using potentiodynamic polarization technique and characterized by high-resolution optical microscope. From all the fabricated composite coatings, obvious diffraction peaks were observed with Zn-7Sn-S-0.3V film with Zn2Sn7, Sn, Zn2Sn5 and Zn phases, confirming the presence and formation of Zn-SnO2 coating. The XRD pattern shows that the presences of SnO2 particle remarkably play a major role in the precipitation and orientation of the alloy matrix. From the SEM/EDS and AFM results, the deposits show that composite particle and proper bath composition have strong influence on the microstructure. An enhanced corrosion resistance was attained as a result of the induced particles.

关键词： Metal matrix composite Co-deposition Mechanical property Corrosion resistance