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Effective Return Rate Prediction of Blockchain Financial Products Using Machine Learning

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Computers, Materials & Continua 2023年第1期74卷 2303-2316页

作者： K.Kalyani Velmurugan Subbiah Parvathy Hikmat A.M.Abdeljaber T.Satyanarayana Murthy Srijana Acharya Gyanendra Prasad Joshi Sung Won Kim PG and Research Department of Computer Science Marudupandiyar College(Affiliated to Bharathidasan UniversityTiruchirappalli)Thanjavur613403India Department of Electronics and Communication Engineering Kalasalingam Academy of Research and EducationKrishnankoil626126TamilnaduIndia Department of Computer Science Faculty of Information TechnologyApplied Science Private UniversityAmmanJordan Department of Information Technology Chaitanya Bharathi Institute of TechnologyHyderabadTelangana500075India Department of Convergence Science Kongju National UniversityGongju32588Korea Department of Computer Science and Engineering Sejong UniversitySeoul05006Korea Department of Information and Communication Engineering Yeungnam UniversityGyeongsan-siGyeongbuk-do38541Korea

In recent times,financial globalization has drastically increased in different ways to improve the quality of services with advanced *** successful applications of bitcoin Blockchain(BC)techniques enable the stockholders to worry about the return and risk of financial *** stockholders focused on the prediction of return rate and risk rate of financial ***,an automatic return rate bitcoin prediction model becomes essential for BC financial *** newly designed machine learning(ML)and deep learning(DL)approaches pave the way for return rate predictive *** study introduces a novel Jellyfish search optimization based extreme learning machine with autoencoder(JSO-ELMAE)for return rate prediction of BC financial *** presented JSO-ELMAE model designs a new ELMAE model for predicting the return rate of financial ***,the JSO algorithm is exploited to tune the parameters related to the ELMAE model which in turn boosts the classification *** application of JSO technique assists in optimal parameter adjustment of the ELMAE model to predict the bitcoin return *** experimental validation of the JSO-ELMAE model was executed and the outcomes are inspected in many *** experimental values demonstrated the enhanced performance of the JSO-ELMAE model over recent state of art approaches with minimal RMSE of 0.1562.

关键词： Financial products blockchain return rate prediction model machine learning parameter optimization

Artificial Intelligence-Based Fusion Model for Paddy Leaf Disease Detection and Classification

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Computers, Materials & Continua 2022年第7期72卷 1391-1407页

作者： Ahmed SAlmasoud Abdelzahir Abdelmaboud Taiseer Abdalla Elfadil Eisa Mesfer Al Duhayyim Asma Abbas Hassan Elnour Manar Ahmed Hamza Abdelwahed Motwakel Abu Sarwar Zamani Department of Information Systems College of Computer and Information SciencesPrince Sultan UniversityRiyadh11586Saudi Arabia Department of Information Systems College of Science and ArtsKing Khalid UniversityMuhayel Aseer62529Saudi Arabia Department of Information Systems-Girls Section King Khalid UniversityMuhayel Aseer62529Saudi Arabia Department of Natural and Applied Sciences College of Community-AflajPrince Sattam bin Abdulaziz UniversityAflaj16828Saudi Arabia Computer Science Department Community College-Girls SectionKing Khalid UniversityMuhayel Aseer62529Saudi Arabia Department of Computer and Self Development Preparatory Year DeanshipPrince Sattam bin Abdulaziz UniversityAl-Kharj16278Saudi Arabia

In agriculture,rice plant disease diagnosis has become a challenging issue,and early identification of this disease can avoid huge loss incurred from less crop *** of the recently-developed computer vision and Deep Learning(DL)approaches can be commonly employed in designing effective models for rice plant disease detection and classification *** this motivation,the current research work devises an Efficient Deep Learning based FusionModel for Rice Plant Disease(EDLFM-RPD)detection and *** aim of the proposed EDLFM-RPD technique is to detect and classify different kinds of rice plant diseases in a proficient *** addition,EDLFM-RPD technique involves median filtering-based preprocessing and K-means segmentation to determine the infected *** study also used a fusion of handcrafted Gray Level Co-occurrence Matrix(GLCM)and Inception-based deep features to derive the ***,Salp Swarm optimization with Fuzzy Support Vector Machine(FSVM)model is utilized for *** order to validate the enhanced outcomes of EDLFM-RPD technique,a series of simulations was *** results were assessed under different *** obtained values infer the improved performance of EDLFM-RPD technique over recent approaches and achieved a maximum accuracy of 96.170%.

关键词： Rice plant disease classification model artificial intelligence deep learning fusion model parameter optimization

Vision Based Real Time Monitoring System for Elderly Fall Event Detection Using Deep Learning

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Computer Systems Science & Engineering 2022年第7期42卷 87-103页

作者： G.Anitha S.Baghavathi Priya Department of Information Technology Rajalakshmi Engineering CollegeChennai602105India Department of Computer Science and Engineering Rajalakshmi Engineering CollegeChennai602105India

Human fall detection plays a vital part in the design of sensor based alarming system,aid physical therapists not only to lessen after fall effect and also to save human *** and timely identiﬁcation can offer quick medical ser-vices to the injured people and prevent from serious *** vision-based approaches have been developed by the placement of cameras in diverse everyday *** present times,deep learning(DL)models par-ticularly convolutional neural networks(CNNs)have gained much importance in the fall detection *** this motivation,this paper presents a new vision based elderly fall event detection using deep learning(VEFED-DL)*** proposed VEFED-DL model involves different stages of operations namely pre-processing,feature extraction,classiﬁcation,and parameter ***-ily,the digital video camera is used to capture the RGB color images and the video is extracted into a set of *** improving the image quality and elim-inate noise,the frames are processed in three levels namely resizing,augmenta-tion,and min–max based ***,MobileNet model is applied as a feature extractor to derive the spatial features that exist in the preprocessed *** addition,the extracted spatial features are then fed into the gated recur-rent unit(GRU)to extract the temporal dependencies of the human ***,a group teaching optimization algorithm(GTOA)with stacked autoenco-der(SAE)is used as a binary classiﬁcation model to determine the existence of fall or non-fall *** GTOA is employed for the parameter optimization of the SAE model in such a way that the detection performance can be *** order to assess the fall detection performance of the presented VEFED-DL model,a set of simulations take place on the UR fall detection dataset and multi-ple cameras fall *** experimental outcomes highlighted the superior per-formance of the presented method over the recent methods.

关键词： Computer vision elderly people fall detection deep learning metaheuristics object detection parameter optimization

AID4I:An Intrusion Detection Framework for Industrial Internet of Things Using Automated Machine Learning

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Computers, Materials & Continua 2023年第8期76卷 2121-2143页

作者： Anil Sezgin Aytug Boyacı ATASAREN National Defence UniversityIstanbul34334Turkey Software in Motion Department Siemens AdvantaSiemens Corporate TechnologyIstanbul34870Turkey Department of Computer Engineering National Defence University Air Force AcademyIstanbul34149Turkey

By identifying and responding to any malicious behavior that could endanger the system,the Intrusion Detection System(IDS)is crucial for preserving the security of the Industrial Internet of Things(IIoT)*** benefit of anomaly-based IDS is that they are able to recognize zeroday attacks due to the fact that they do not rely on a signature database to identify abnormal *** order to improve control over datasets and the process,this study proposes using an automated machine learning(AutoML)technique to automate the machine learning processes for *** groundbreaking architecture,known as AID4I,makes use of automatic machine learning methods for intrusion *** automation of preprocessing,feature selection,model selection,and hyperparameter tuning,the objective is to identify an appropriate machine learning model for intrusion *** studies demonstrate that the AID4I framework successfully proposes a *** integrity,security,and confidentiality of data transmitted across the IIoT network can be ensured by automating machine learning processes in the IDS to enhance its capacity to identify and stop threatening *** a comprehensive solution that takes advantage of the latest advances in automated machine learning methods to improve network security,AID4I is a powerful and effective instrument for intrusion *** preprocessing module,three distinct imputation methods are utilized to handle missing data,ensuring the robustness of the intrusion detection system in the presence of incomplete *** selection module adopts a hybrid approach that combines Shapley values and genetic *** parameter optimization module encompasses a diverse set of 14 classification methods,allowing for thorough exploration and optimization of the parameters associated with each *** carefully tuning these parameters,the framework enhances its adaptability and accuracy in identifying potential

关键词： Automated machine learning intrusion detection system industrial internet of things parameter optimization

optimization of Interval Type-2 Fuzzy Logic System Using Grasshopper optimization Algorithm

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Computers, Materials & Continua 2022年第5期71卷 3513-3531页

作者： Saima Hassan Mojtaba Ahmadieh Khanesar Nazar Kalaf Hussein Samir Brahim Belhaouari Usman Amjad Wali Khan Mashwani Institute of Computing Kohat University of Science and TechnologyKohatPakistan Department of Mechanical Materials and Manufacturing EngineeringFaculty of EngineeringUniversity of NottinghamUK College of Computer Sciences and Mathematics Tikrit UniversityIraq Division of Information and Computing Technology College of Science and EngineeringHamad Bin Khalifa UniversityQatar Department of Computer Science Sir Syed University of Engineering and TechnologyPakistan Institute of Numerical Sciences Kohat University of Science and TechnologyKohatPakistan

The estimation of the fuzzy membership function parameters for interval type 2 fuzzy logic system(IT2-FLS)is a challenging task in the presence of uncertainty and *** optimization algorithm(GOA)is a fresh population based meta-heuristic algorithm that mimics the swarming behavior of grasshoppers in nature,which has good convergence ability towards *** main objective of this paper is to apply GOA to estimate the optimal parameters of the Gaussian membership function in an *** antecedent part parameters(Gaussian membership function parameters)are encoded as a population of artificial swarm of grasshoppers and optimized using its *** of the consequent part parameters are accomplished using extreme learning *** optimized IT2-FLS(GOAIT2FELM)obtained the optimal premise parameters based on tuned consequent part parameters and is then applied on the Australian national electricity market data for the forecasting of electricity loads and *** forecasting performance of the proposed model is compared with other population-based optimized IT2-FLS including genetic algorithm and artificial bee colony optimization *** of the performance,on the same data-sets,reveals that the proposed GOAIT2FELM could be a better approach for improving the accuracy of the IT2-FLS as compared to other variants of the optimized IT2-FLS.

关键词： parameter optimization grasshopper optimization algorithm interval type-2 fuzzy logic system extreme learning machine electricity market forecasting

Dendritic Cell Algorithm with Bayesian optimization Hyperband for Signal Fusion

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Computers, Materials & Continua 2023年第8期76卷 2317-2336页

作者： Dan Zhang Yu Zhang Yiwen Liang School of Computer Science Wuhan UniversityWuhan430072China GNSS Research Center Wuhan UniversityWuhan430079China

The dendritic cell algorithm(DCA)is an excellent prototype for developing Machine Learning inspired by the function of the powerful natural immune *** many parameters increase complexity and lead to plenty of criticism in the signal fusion procedure of *** loss function of DCA is ambiguous due to its *** reduce the uncertainty,several researchers simplified the algorithm program;some introduced gradient descent to optimize parameters;some utilized searching methods to find the optimal parameter ***,these studies are either time-consuming or need to be revised in the case of non-convex *** overcome the problems,this study models the parameter optimization into a black-box optimization problem without knowing the information about its loss *** study hybridizes bayesian optimization hyperband(BOHB)with DCA to propose a novel DCA version,BHDCA,for accomplishing parameter optimization in the signal fusion *** BHDCA utilizes the bayesian optimization(BO)of BOHB to find promising parameter configurations and applies the hyperband of BOHB to allocate the suitable budget for each potential *** experimental results show that the proposed algorithm has significant advantages over the otherDCAexpansion algorithms in terms of signal fusion.

关键词： Dendritic cell algorithm signal fusion parameter optimization bayesian optimization hyperband

Intelligent Modulation Recognition of Communication Signal for Next-Generation 6G Networks

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Computers, Materials & Continua 2023年第3期74卷 5723-5740页

作者： Mrim M.Alnfiai Department of Information Technology College of Computers and Information TechnologyTaif UniversityP.O.Box 11099Taif21944Saudi Arabia

In recent years,the need for a fast,efficient and a reliable wireless network has increased *** 5G networks have already been tested while a few are in the early stages of *** noncooperative communication scenarios,the recognition of digital signal modulations assists people in identifying the communication targets and ensures an effective management over *** recent advancements in both Machine Learning(ML)and Deep Learning(DL)models demand the development of effective modulation recognition models with self-learning *** this background,the current research article designs aDeep Learning enabled Intelligent Modulation Recognition of Communication Signal(DLIMR-CS)technique for next-generation *** aim of the proposed DLIMR-CS technique is to classify different kinds of digitally-modulated *** addition,the fractal feature extraction process is appliedwith the help of the Sevcik Fractal Dimension(SFD)***,the extracted features are fed into the Deep Variational Autoencoder(DVAE)model for the classification of the modulated *** order to improve the classification performance of the DVAE model,the Tunicate Swarm Algorithm(TSA)is used to finetune the hyperparameters involved in DVAE model.A wide range of simulations was conducted to establish the enhanced performance of the proposed DLIMR-CS *** experimental outcomes confirmed the superior recognition rate of the DLIMR-CS model over recent state-of-the-art methods under different evaluation parameters.

关键词： 6G networks communication signal modulation recognition deep learning machine learning parameter optimization

An Optimized Convolution Neural Network Architecture for Paddy Disease Classification

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Computers, Materials & Continua 2022年第6期71卷 6053-6067页

作者： Muhammad Asif Saleem Muhammad Aamir Rosziati Ibrahim Norhalina Senan Tahir Alyas Faculty of Computer Science Universiti Tun Hussein Onn MalaysiaBatu Pahat54000Malaysia School of Electronics Computing and MathematicsUniversity of DerbyDerbyDE221GBUnited Kingdom Department of Computer Science Lahore Garrison UniversityLahore54000Pakistan

Plant disease classification based on digital pictures is *** learning approaches and plant image categorization technologies such as deep learning have been utilized to recognize,identify,and diagnose plant diseases in the previous *** the yield quantity and quality of rice forming is an important cause for the paddy production ***,some diseases that are blocking the improvement in paddy production are considered as an ominous *** Neural Network(CNN)has shown a remarkable performance in solving the early detection of paddy leaf diseases based on its images in the fast-growing era of science and ***,the significant CNN architectures construction is dependent on expertise in a neural network and domain *** approach is time-consuming,and high computational resources are *** this research,we propose a novel method based on Mutant Particle swarm optimization(MUT-PSO)Algorithms to search for an optimum CNN architecture for Paddy leaf disease *** results show that Mutant Particle swarm optimization Convolution Neural Network(MUTPSO-CNN)can find optimumCNNarchitecture that offers better performance than existing hand-crafted CNN architectures in terms of accuracy,precision/recall,and execution time.

关键词： Deep learning optimum CNN architecture particle swarm optimization convolutional neural network parameter optimization

Deer Hunting optimization with Deep Learning Model for Lung Cancer Classification

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Computers, Materials & Continua 2022年第10期73卷 533-546页

作者： Mahmoud Ragab Hesham A.Abdushkour Alaa F.Nahhas Wajdi H.Aljedaibi Information Technology Department Faculty of Computing and Information TechnologyKing Abdulaziz UniversityJeddah 21589Saudi Arabia Center of Artificial Intelligence for Precision Medicines King Abdulaziz UniversityJeddah 21589Saudi Arabia Mathematics Department Faculty of ScienceAl-Azhar UniversityNaser City 11884CairoEgypt Nautical Science Department Faculty of Maritime StudiesKing Abdulaziz UniversityJeddah21589Saudi Arabia Biochemistry Department Faculty of ScienceKing Abdulaziz UniversityJeddah21589Saudi Arabia Computer Science Department Faculty of Computing and Information TechnologyKing Abdulaziz UniversityJeddah 21589Saudi Arabia

Lung cancer is the main cause of cancer related death owing to its destructive nature and postponed detection at advanced *** recognition of lung cancer is essential to increase the survival rate of persons and it remains a crucial problem in the healthcare *** aided diagnosis(CAD)models can be designed to effectually identify and classify the existence of lung cancer using medical *** recently developed deep learning(DL)models find a way for accurate lung nodule classification ***,this article presents a deer hunting optimization with deep convolutional neural network for lung cancer detection and classification(DHODCNNLCC)*** proposed DHODCNN-LCC technique initially undergoes pre-processing in two stages namely contrast enhancement and noise ***,the features extraction process on the pre-processed images takes place using the Nadam optimizer with RefineDet *** addition,denoising stacked autoencoder(DSAE)model is employed for lung nodule ***,the deer hunting optimization algorithm(DHOA)is utilized for optimal hyper parameter tuning of the DSAE model and thereby results in improved classification *** experimental validation of the DHODCNN-LCC technique was implemented against benchmark dataset and the outcomes are assessed under various *** experimental outcomes reported the superior outcomes of the DHODCNN-LCC technique over the recent approaches with respect to distinct measures.

关键词： Lung cancer image classification computer aided diagnosis deep learning medical imaging parameter optimization