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Type-I Heavy-Tailed Burr XII Distribution with Applications to Quality Control,Skewed Reliability Engineering Systems and Lifetime Data 认领 引用
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作者 Okechukwu J.Obulezi Hatem E.Semary +4 位作者 Sadia Nadir Chinyere P.Igbokwe Gabriel O.Orji A.S.Al-Moisheer Mohammed Elgarhy 《Computer Modeling in Engineering & Sciences》 SCIE EI 2025年第9期2991-3027,共37页
This study introduces the type-I heavy-tailed Burr XII(TIHTBXII)distribution,a highly flexible and robust statistical model designed to address the limitations of conventional distributions in analyzing data character... This study introduces the type-I heavy-tailed Burr XII(TIHTBXII)distribution,a highly flexible and robust statistical model designed to address the limitations of conventional distributions in analyzing data characterized by skewness,heavy tails,and diverse hazard behaviors.We meticulously develop the TIHTBXII’s mathematical foundations,including its probability density function(PDF),cumulative distribution function(CDF),and essential statistical properties,crucial for theoretical understanding and practical application.A comprehensive Monte Carlo simulation evaluates four parameter estimation methods:maximum likelihood(MLE),maximum product spacing(MPS),least squares(LS),and weighted least squares(WLS).The simulation results consistently show that as sample sizes increase,the Bias and RMSE of all estimators decrease,with WLS and LS often demonstrating superior and more stable performance.Beyond theoretical development,we present a practical application of the TIHTBXII distribution in constructing a group acceptance sampling plan(GASP)for truncated life tests.This application highlights how the TIHTBXII model can optimize quality control decisions by minimizing the average sample number(ASN)while effectively managing consumer and producer risks.Empirical validation using real-world datasets,including“Active Repair Duration,”“Groundwater Contaminant Measurements,”and“Dominica COVID-19 Mortality,”further demonstrates the TIHTBXII’s superior fit compared to existing models.Our findings confirm the TIHTBXII distribution as a powerful and reliable alternative for accurately modeling complex data in fields such as reliability engineering and quality assessment,leading to more informed and robust decision-making. 展开更多
关键词 Acceptance sampling heavy-tailed models parameter estimation reliability engineering
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iPAFAR: An Adaptive Pareto-Based NS-AAA Energy-Stable Fuzzy Clustering and Routing Framework for Smart City IoT-Enabled WSNs 认领 引用
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作者 Bhanu Talwar Puneet Thapar +3 位作者 Tahani Alsubait Mai Alduailij Ateeq Ur Rehman Salil Bharany 《Computers, Materials & Continua》 SCIE EI 2026年第8期785-805,共21页
Wireless Sensor Networks(WSNs)play a vital role in smart city Internet of Things(IoT)applications,including environmental monitoring,intelligent transportation,and infrastructure management.However,limited battery cap... Wireless Sensor Networks(WSNs)play a vital role in smart city Internet of Things(IoT)applications,including environmental monitoring,intelligent transportation,and infrastructure management.However,limited battery capacity,uneven energy consumption,and inefficient clustering and routing mechanisms significantly reduce network lifetime,reliability,and scalability,especially in large-scale IoT deployments.Traditional routing protocols often rely on single-objective optimization or static clustering strategies,which fail to maintain long-term energy balance and stable communication performance.To address these challenges,this paper proposes iPAFAR,a Pareto-based multi-objective clustering and routing framework designed for IoT-enabled WSNs.The proposed model formulates cluster-head selection as a multi-objective optimization problem that considers residual energy,node centrality,load variance,and fairness.A Non-Dominated Sorting Artificial Algae Algorithm(NS-AAA)is used to obtain Pareto-optimal cluster-head configurations,followed by a fuzzy inference system for refined decision-making.To ensure long-term energy stability,a Lyapunov-based routing model is incorporated,and an adaptive re-clustering mechanism is introduced to reduce unnecessary control overhead under dynamic network conditions.The performance of the proposed framework is evaluated through MATLAB-based simulations and compared with existing protocols,including LEACH-M,ME-LEACH,FQA,MKNDPC,RANP-PSO,and BKA-TOA.Experimental results show that iPAFAR achieves approximately 40%lower end-to-end delay,15%–20%higher packet delivery ratio,and 45%–50%improvement in residual energy while maintaining nearly twice the number of active nodes after 1000 simulation rounds.These results confirm that the proposed framework provides improved energy efficiency,load balancing,and routing stability,making it suitable for long-term smart city IoT deployments. 展开更多
关键词 Wireless sensor networks smart cities multi-objective optimization artificial algae algorithm energy-aware clustering pareto optimization IoT routing
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Mitigating and Monitoring Smart City Using Internet of Things 认领 引用 被引量：4
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作者 Sudan Jha Lewis Nkenyereye +1 位作者 Gyanendra Prasad Joshi Eunmok Yang 《Computers, Materials & Continua》 SCIE EI 2020年第11期1059-1079,共21页
The present trends in smart world reflects the extensive use of limited resources through information and communication technology.The limited resources like space,mobility,energy,etc.,have been consumed rigorously to... The present trends in smart world reflects the extensive use of limited resources through information and communication technology.The limited resources like space,mobility,energy,etc.,have been consumed rigorously towards creating optimized but smart instances.Thus,a new concept of IoT integrated smart city vision is yet to be proposed which includes a combination of systems like noise and air loss monitoring,web monitoring and fire detection systems,smart waste bin systems,etc.,that have not been clearly addressed in the previous researches.This paper focuses on developing an effective system for possible monitoring of losses,traffic management,thus innovating smart city at large with digitalized and integrated systems and software for fast and effective implementations.In our proposed system,a real time data analysis is performed.These data are collected by various sensors to analyze different factors that are responsible for such losses.The proposed work is validated on a real case study. 展开更多
关键词 Internet of Things smart city environmental impairments pollution temperature
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A Novel Deep Learning Based Healthcare Model for COVID-19 Pandemic Stress Analysis 认领 引用 被引量：1
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作者 Ankur Dumka Parag Verma +5 位作者 Rajesh Singh Anil Kumar Bisht Divya Anand Hani Moaiteq Aljahdali Irene Delgado Noya Silvia Aparicio Obregon 《Computers, Materials & Continua》 SCIE EI 2022年第9期6029-6044,共16页
Coronavirus(COVID-19)has impacted nearly every person across the globe either in terms of losses of life or as of lockdown.The current coronavirus(COVID-19)pandemic is a rare/special situation where people can express... Coronavirus(COVID-19)has impacted nearly every person across the globe either in terms of losses of life or as of lockdown.The current coronavirus(COVID-19)pandemic is a rare/special situation where people can express their feelings on Internet-based social networks.Social media is emerging as the biggest platform in recent years where people spend most of their time expressing themselves and their emotions.This research is based on gathering data from Twitter and analyzing the behavior of the people during the COVID-19 lockdown.The research is based on the logic expressed by people in this perspective and emotions for the suffering of COVID-19 and lockdown.In this research,we have used a Long Short-Term Memory(LSTM)network model with Convolutional Neural Network using Keras python deep-learning library to determine whether social media platform users are depressed in terms of positive,negative,or neutral emotional out bust based on their Twitter posts.The results showed that the model has 88.14%accuracy(representation of the correct prediction over the test dataset)after 10 epochs which most tweets showed had neutral polarity.The evaluation shows interesting results in positive(1),negative(–1),and neutral(0)emotions through different visualization. 展开更多
关键词 COVID-19 lockdown stress analysis depression analysis sentiment analysis social media COVID-19 twitter dataset coronavirus
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A Systematic Review on the Internet of Medical Things:Techniques,Open Issues,and Future Directions 认领 引用 被引量：4
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作者 Apurva Sonavane Aditya Khamparia Deepak Gupta 《Computer Modeling in Engineering & Sciences》 SCIE EI 2023年第11期1525-1550,共26页
IoT usage in healthcare is one of the fastest growing domains all over the world which applies to every age group.Internet of Medical Things(IoMT)bridges the gap between the medical and IoT field where medical devices... IoT usage in healthcare is one of the fastest growing domains all over the world which applies to every age group.Internet of Medical Things(IoMT)bridges the gap between the medical and IoT field where medical devices communicate with each other through a wireless communication network.Advancement in IoMT makes human lives easy and better.This paper provides a comprehensive detailed literature survey to investigate different IoMT-driven applications,methodologies,and techniques to ensure the sustainability of IoMT-driven systems.The limitations of existing IoMTframeworks are also analyzed concerning their applicability in real-time driven systems or applications.In addition to this,various issues(gaps),challenges,and needs in the context of such systems are highlighted.The purpose of this paper is to interpret a rigorous review concept related to IoMT and present significant contributions in the field across the research fraternity.Lastly,this paper discusses the opportunities and prospects of IoMT and discusses various open research problems. 展开更多
关键词 Internet ofMedical Things(IoMT) healthcare systems sensors preprocessing RFID remote monitoring devices networks
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Technologies Behind the Smart Grid and Internet of Things: A System Survey 认领 引用 被引量：2
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作者 Kuldeep Sharma Arun Malik +3 位作者 Isha Batra A.S.M.Sanwar Hosen Md Abdul Latif Sarker Dong Seog Han 《Computers, Materials & Continua》 SCIE EI 2023年第6期5049-5072,共24页
Electric smart grids enable a bidirectional flow of electricity and information among power system assets.For proper monitoring and con-trolling of power quality,reliability,scalability and flexibility,there is a need... Electric smart grids enable a bidirectional flow of electricity and information among power system assets.For proper monitoring and con-trolling of power quality,reliability,scalability and flexibility,there is a need for an environmentally friendly system that is transparent,sustainable,cost-saving,energy-efficient,agile and secure.This paper provides an overview of the emerging technologies behind smart grids and the internet of things.The dependent variables are identified by analyzing the electricity consumption patterns for optimal utilization and planning preventive maintenance of their legacy assets like power distribution transformers with real-time parameters to ensure an uninterrupted and reliable power supply.In addition,the paper sorts out challenges in the traditional or legacy electricity grid,power generation,transmission,distribution,and revenue management challenges such as reduc-ing aggregate technical and commercial loss by reforming the existing manual or semi-automatic techniques to fully smart or automatic systems.This article represents a concise review of research works in creating components of the smart grid like smart metering infrastructure for postpaid as well as in prepaid mode,internal structure comparison of advanced metering methods in present scenarios,and communication systems. 展开更多
关键词 Electricity consumption bidirectional advanced meter infrastructure energy internet of things smart grid smart meter
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Effective and Efficient Video Compression by the Deep Learning Techniques 认领 引用
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作者 Karthick Panneerselvam K.Mahesh +1 位作者 V.L.Helen Josephine A.Ranjith Kumar 《Computer Systems Science & Engineering》 SCIE EI 2023年第5期1047-1061,共15页
Deep learning has reached many successes in Video Processing.Video has become a growing important part of our daily digital interactions.The advancement of better resolution content and the large volume offers serious... Deep learning has reached many successes in Video Processing.Video has become a growing important part of our daily digital interactions.The advancement of better resolution content and the large volume offers serious challenges to the goal of receiving,distributing,compressing and revealing highquality video content.In this paper we propose a novel Effective and Efficient video compression by the Deep Learning framework based on the flask,which creatively combines the Deep Learning Techniques on Convolutional Neural Networks(CNN)and Generative Adversarial Networks(GAN).The video compression method involves the layers are divided into different groups for data processing,using CNN to remove the duplicate frames,repeating the single image instead of the duplicate images by recognizing and detecting minute changes using GAN and recorded with Long Short-Term Memory(LSTM).Instead of the complete image,the small changes generated using GAN are substituted,which helps with frame-level compression.Pixel wise comparison is performed using K-nearest Neighbours(KNN)over the frame,clustered with K-means and Singular Value Decomposition(SVD)is applied for every frame in the video for all three colour channels[Red,Green,Blue]to decrease the dimension of the utility matrix[R,G,B]by extracting its latent factors.Video frames are packed with parameters with the aid of a codec and converted to video format and the results are compared with the original video.Repeated experiments on several videos with different sizes,duration,Frames per second(FPS),and quality results demonstrated a significant resampling rate.On normal,the outcome delivered had around a 10%deviation in quality and over half in size when contrasted,and the original video. 展开更多
关键词 Convolutional neural networks(CNN) generative adversarial network(GAN) singular value decomposition(SVD) K-nearest neighbours(KNN) stochastic gradient descent(SGD) long short-term memory(LSTM)
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Automatic PV Grid Fault Detection System with IoT and LabVIEW as Data Logger 认领 引用
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作者 Rohit Samkria Mohammed Abd-Elnaby +4 位作者 Rajesh Singh Anita Gehlot Mamoon Rashid Moustafa H.Aly Walid El-Shafai 《Computers, Materials & Continua》 SCIE EI 2021年第11期1709-1723,共15页
Fault detection of the photovoltaic(PV)grid is necessary to detect serious output power reduction to avoid PV modules’damage.To identify the fault of the PV arrays,there is a necessity to implement an automatic syste... Fault detection of the photovoltaic(PV)grid is necessary to detect serious output power reduction to avoid PV modules’damage.To identify the fault of the PV arrays,there is a necessity to implement an automatic system.In this IoT and LabVIEW-based automatic fault detection of 3×3 solar array,a PV system is proposed to control and monitor Internet connectivity remotely.Hardware component to automatically reconfigure the solar PV array from the series-parallel(SP)to the complete cross-linked array underneath partial shading conditions(PSC)is centered on the Atmega328 system to achieve maximum power.In the LabVIEW environment,an automated monitoring system is developed.The automatic monitoring system assesses the voltage drop losses present in the DC side of the PV generator and generates a decimal weighted value depending on the defective solar panels and transmits this value to the remote station through an RF modem,and provides an indicator of the faulty solar panel over the built-in Interface LabVIEW.The managing of this GUI indicator helps the monitoring system to generate a panel alert for damaged panels in the PV system.Node MCU in the receiver section enables transmission of the fault status of PV arrays via Internet connectivity.The IoT-based Blynk app is employed for visualizing the fault status of the 3×3 PV array.The dashboard of Blynk visualizes every array with the status. 展开更多
关键词 Blynk app IoT LabVIEW node MCU PV array RF modem WSN
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Handling Class Imbalance in Online Transaction Fraud Detection 认领 引用
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作者 Kanika Jimmy Singla +3 位作者 Ali Kashif Bashir Yunyoung Nam Najam UI Hasan Usman Tariq 《Computers, Materials & Continua》 SCIE EI 2022年第2期2861-2877,共17页
With the rise of internet facilities,a greater number of people have started doing online transactions at an exponential rate in recent years as the online transaction system has eliminated the need of going to the ba... With the rise of internet facilities,a greater number of people have started doing online transactions at an exponential rate in recent years as the online transaction system has eliminated the need of going to the bank physically for every transaction.However,the fraud cases have also increased causing the loss of money to the consumers.Hence,an effective fraud detection system is the need of the hour which can detect fraudulent transactions automatically in real-time.Generally,the genuine transactions are large in number than the fraudulent transactions which leads to the class imbalance problem.In this research work,an online transaction fraud detection system using deep learning has been proposed which can handle class imbalance problem by applying algorithm-level methods which modify the learning of the model to focus more on the minority class i.e.,fraud transactions.A novel loss function named Weighted Hard-Reduced Focal Loss(WH-RFL)has been proposed which has achieved maximum fraud detection rate i.e.,True PositiveRate(TPR)at the cost of misclassification of few genuine transactions as high TPR is preferred over a high True Negative Rate(TNR)in fraud detection system and same has been demonstrated using three publicly available imbalanced transactional datasets.Also,Thresholding has been applied to optimize the decision threshold using cross-validation to detect maximum number of frauds and it has been demonstrated by the experimental results that the selection of the right thresholding method with deep learning yields better results. 展开更多
关键词 Class imbalance deep learning fraud detection loss function thresholding
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Fuzzy Decision-Based Clustering for Efficient Data Aggregation in Mobile UWSNs 认领 引用
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作者 Aadil Mushtaq Pandith Manni Kumar +5 位作者 Naveen Kumar Nitin Goyal Sachin Ahuja Yonis Gulzar Rashi Rastogi Rupesh Gupta 《Computers, Materials & Continua》 SCIE EI 2025年第4期259-279,共21页
Underwater wireless sensor networks(UWSNs)rely on data aggregation to streamline routing operations by merging information at intermediate nodes before transmitting it to the sink.However,many existing data aggregatio... Underwater wireless sensor networks(UWSNs)rely on data aggregation to streamline routing operations by merging information at intermediate nodes before transmitting it to the sink.However,many existing data aggregation techniques are designed exclusively for static networks and fail to reflect the dynamic nature of underwater environments.Additionally,conventional multi-hop data gathering techniques often lead to energy depletion problems near the sink,commonly known as the energy hole issue.Moreover,cluster-based aggregation methods face significant challenges such as cluster head(CH)failures and collisions within clusters that degrade overall network performance.To address these limitations,this paper introduces an innovative framework,the Cluster-based Data Aggregation using Fuzzy Decision Model(CDAFDM),tailored for mobile UWSNs.The proposed method has four main phases:clustering,CH selection,data aggregation,and re-clustering.During CH selection,a fuzzy decision model is utilized to ensure efficient cluster head selection based on parameters such as residual energy,distance to the sink,and data delivery likelihood,enhancing network stability and energy efficiency.In the aggregation phase,CHs transmit a single,consolidated set of non-redundant data to the base station(BS),thereby reducing data duplication and saving energy.To adapt to the changing network topology,the re-clustering phase periodically updates cluster formations and reselects CHs.Simulation results show that CDAFDM outperforms current protocols such as CAPTAIN(Collection Algorithm for underwater oPTical-AcoustIc sensor Networks),EDDG(Event-Driven Data Gathering),and DCBMEC(Data Collection Based on Mobile Edge Computing)with a packet delivery ratio increase of up to 4%,an energy consumption reduction of 18%,and a data collection latency reduction of 52%.These findings highlight the framework’s potential for reliable and energy-efficient data aggregation mobile UWSNs. 展开更多
关键词 Clustering data aggregation data collection fuzzy model monitoring UWSN
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Advances in Hyperspectral Image Classification Based on Convolutional Neural Networks: A Review 认领 引用 被引量：9
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作者 Somenath Bera Vimal K.Shrivastava Suresh Chandra Satapathy 《Computer Modeling in Engineering & Sciences》 SCIE EI 2022年第11期219-250,共32页
Hyperspectral image(HSI)classification has been one of themost important tasks in the remote sensing community over the last few decades.Due to the presence of highly correlated bands and limited training samples in H... Hyperspectral image(HSI)classification has been one of themost important tasks in the remote sensing community over the last few decades.Due to the presence of highly correlated bands and limited training samples in HSI,discriminative feature extraction was challenging for traditional machine learning methods.Recently,deep learning based methods have been recognized as powerful feature extraction tool and have drawn a significant amount of attention in HSI classification.Among various deep learning models,convolutional neural networks(CNNs)have shown huge success and offered great potential to yield high performance in HSI classification.Motivated by this successful performance,this paper presents a systematic review of different CNN architectures for HSI classification and provides some future guidelines.To accomplish this,our study has taken a few important steps.First,we have focused on different CNN architectures,which are able to extract spectral,spatial,and joint spectral-spatial features.Then,many publications related to CNN based HSI classifications have been reviewed systematically.Further,a detailed comparative performance analysis has been presented between four CNN models namely 1D CNN,2D CNN,3D CNN,and feature fusion based CNN(FFCNN).Four benchmark HSI datasets have been used in our experiment for evaluating the performance.Finally,we concluded the paper with challenges on CNN based HSI classification and future guidelines that may help the researchers to work on HSI classification using CNN. 展开更多
关键词 Convolutional neural network deep learning feature fusion hyperspectral image classification review spectralspatial feature
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Load Balancing Algorithm for Migrating Switches in Software-Dened Vehicular Networks 认领 引用 被引量：4
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作者 Himanshi Babbar Shalli Rani +3 位作者 Mehedi Masud Sahil Verma Divya Anand Nz Jhanjhi 《Computers, Materials & Continua》 SCIE EI 2021年第4期1301-1316,共16页
In Software-Dened Networks(SDN),the divergence of the control interface from the data plane provides a unique platform to develop a programmable and exible network.A single controller,due to heavy load trafc triggered... In Software-Dened Networks(SDN),the divergence of the control interface from the data plane provides a unique platform to develop a programmable and exible network.A single controller,due to heavy load trafc triggered by different intelligent devices can not handle due to it’s restricted capability.To manage this,it is necessary to implement multiple controllers on the control plane to achieve quality network performance and robustness.The ow of data through the multiple controllers also varies,resulting in an unequal distribution of load between different controllers.One major drawback of the multiple controllers is their constant conguration of the mapping of the switch-controller,quickly allowing unequal distribution of load between controllers.To overcome this drawback,Software-Dened Vehicular Networking(SDVN)has evolved as a congurable and scalable network,that has quickly achieved attraction in wireless communications from research groups,businesses,and industries administration.In this paper,we have proposed a load balancing algorithm based on latency for multiple SDN controllers.It acknowledges the evolving characteristics of real-time latency vs.controller loads.By choosing the required latency and resolving multiple overloads simultaneously,our proposed algorithm solves the loadbalancing problems with multiple overloaded controllers in the SDN control plane.In addition to the migration,our algorithm has improved 25%latency as compared to the existing algorithms. 展开更多
关键词 Software-dened networking load balancing multiple controllers ryu controller mininet
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Modified Bat Algorithm for Optimal VM’s in Cloud Computing 认领 引用 被引量：4
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作者 Amit Sundas Sumit Badotra +2 位作者 Youseef Alotaibi Saleh Alghamdi Osamah Ibrahim Khalaf 《Computers, Materials & Continua》 SCIE EI 2022年第8期2877-2894,共18页
All task scheduling applications need to ensure that resources are optimally used,performance is enhanced,and costs are minimized.The purpose of this paper is to discuss how to Fitness Calculate Values(FCVs)to provide... All task scheduling applications need to ensure that resources are optimally used,performance is enhanced,and costs are minimized.The purpose of this paper is to discuss how to Fitness Calculate Values(FCVs)to provide application software with a reliable solution during the initial stages of load balancing.The cloud computing environment is the subject of this study.It consists of both physical and logical components(most notably cloud infrastructure and cloud storage)(in particular cloud services and cloud platforms).This intricate structure is interconnected to provide services to users and improve the overall system’s performance.This case study is one of the most important segments of cloud computing,i.e.,Load Balancing.This paper aims to introduce a new approach to balance the load among Virtual Machines(VM’s)of the cloud computing environment.The proposed method led to the proposal and implementation of an algorithm inspired by the Bat Algorithm(BA).This proposed Modified Bat Algorithm(MBA)allows balancing the load among virtual machines.The proposed algorithm works in two variants:MBA with Overloaded Optimal Virtual Machine(MBAOOVM)and Modified Bat Algorithm with Balanced Virtual Machine(MBABVM).MBA generates cost-effective solutions and the strengths of MBA are finally validated by comparing it with Bat Algorithm. 展开更多
关键词 Bat algorithm cloud computing fitness value calculation load balancing modified bat algorithm
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A Formal Testing Model for Operating Room Control System Using Internet of Things 认领 引用 被引量：3
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作者 Moez Krichen Seifeddine Mechti +4 位作者 Roobaea Alroobaea Elyes Said Parminder Singh Osamah Ibrahim Khalaf Mehedi Masud 《Computers, Materials & Continua》 SCIE EI 2021年第3期2997-3011,共15页
Technological advances in recent years have significantly changed the way an operating room works.This work aims to create a platformto solve the problems of operating room occupancy and prepare the rooms with an envi... Technological advances in recent years have significantly changed the way an operating room works.This work aims to create a platformto solve the problems of operating room occupancy and prepare the rooms with an environment that is favorable for all operations.Using this system,a doctor can control all operation rooms,especially before an operation,and monitor their temperature and humidity to prepare for the operation.Also,in the event of a problem,an alert is sent to the nurse responsible for the room and medical stuff so that the problem can be resolved.The platformis tested using a Raspberry PI card and sensors.The sensors are connected to a cloud layer that collects and analyzes the temperature and humidity values obtained from the environment during an operation.The result of experimentations is visualized through a web application and an Android application.The platform also considers the security aspects such as authorization to access application functionalities for the Web and the mobile applications.We can also test and evaluate the system’s existing problems and vulnerabilities using the IEEE and owasp IoT standards.Finally,the proposed framework is extended with a model based testing technique that may be adopted for validating the security aspects. 展开更多
关键词 Communication Internet of Things control rooms sensors cloud robot
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A Direct Data-Cluster Analysis Method Based on Neutrosophic Set Implication 认领 引用 被引量：2
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作者 Sudan Jha Gyanendra Prasad Joshi +2 位作者 Lewis Nkenyereya Dae Wan Kim Florentin Smarandache 《Computers, Materials & Continua》 SCIE EI 2020年第11期1203-1220,共18页
Raw data are classified using clustering techniques in a reasonable manner to create disjoint clusters.A lot of clustering algorithms based on specific parameters have been proposed to access a high volume of datasets... Raw data are classified using clustering techniques in a reasonable manner to create disjoint clusters.A lot of clustering algorithms based on specific parameters have been proposed to access a high volume of datasets.This paper focuses on cluster analysis based on neutrosophic set implication,i.e.,a k-means algorithm with a threshold-based clustering technique.This algorithm addresses the shortcomings of the k-means clustering algorithm by overcoming the limitations of the threshold-based clustering algorithm.To evaluate the validity of the proposed method,several validity measures and validity indices are applied to the Iris dataset(from the University of California,Irvine,Machine Learning Repository)along with k-means and threshold-based clustering algorithms.The proposed method results in more segregated datasets with compacted clusters,thus achieving higher validity indices.The method also eliminates the limitations of threshold-based clustering algorithm and validates measures and respective indices along with k-means and threshold-based clustering algorithms. 展开更多
关键词 Data clustering data mining neutrosophic set k-means validity measures cluster-based classification hierarchical clustering
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Three-Dimensional Distance-Error-Correction-Based Hop Localization Algorithm for IoT Devices 认领 引用
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作者 Deepak Prashar Gyanendra Prasad Joshi +2 位作者 Sudan Jha Eunmok Yang Kwang Chul Son 《Computers, Materials & Continua》 SCIE EI 2021年第2期1529-1549,共21页
The Internet of Things(IoT)is envisioned as a network of various wireless sensor nodes communicating with each other to offer state-of-the-art solutions to real-time problems.These networks of wireless sensors monitor... The Internet of Things(IoT)is envisioned as a network of various wireless sensor nodes communicating with each other to offer state-of-the-art solutions to real-time problems.These networks of wireless sensors monitor the physical environment and report the collected data to the base station,allowing for smarter decisions.Localization in wireless sensor networks is to localize a sensor node in a two-dimensional plane.However,in some application areas,such as various surveillances,underwater monitoring systems,and various environmental monitoring applications,wireless sensors are deployed in a three-dimensional plane.Recently,localization-based applications have emerged as one of the most promising services related to IoT.In this paper,we propose a novel distributed range-free algorithm for node localization in wireless sensor networks.The proposed three-dimensional hop localization algorithm is based on the distance error correction factor.In this algorithm,the error decreases with the localization process.The distance correction factor is used at various stages of the localization process,which ultimately mitigates the error.We simulated the proposed algorithm using MATLAB and verified the accuracy of the algorithm.The simulation results are compared with some of the well-known existing algorithms in the literature.The results show that the proposed three-dimensional error-correctionbased algorithm performs better than existing algorithms. 展开更多
关键词 3D localization DV-hop algorithm IoT PSO wireless sensor networks
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Intelligent Approach for Clustering Mutations’ Nature of COVID-19 Genome 认领 引用
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作者 Ankur Dumka Parag Verma +5 位作者 Rajesh Singh Anuj Bhardwaj Khalid Alsubhi Divya Anand Irene Delgado Noya Silvia Aparicio Obregon 《Computers, Materials & Continua》 SCIE EI 2022年第9期4453-4466,共14页
In December 2019,a group of people in Wuhan city of Hubei province of China were found to be affected by an infection called dark etiology pneumonia.The outbreak of this pneumonia infection was declared a deadly disea... In December 2019,a group of people in Wuhan city of Hubei province of China were found to be affected by an infection called dark etiology pneumonia.The outbreak of this pneumonia infection was declared a deadly disease by the China Center for Disease Control and Prevention on January 9,2020,named Novel Coronavirus 2019(nCoV-2019).This nCoV-2019 is now known as COVID-19.There is a big list of infections of this coronavirus which is present in the form of a big family.This virus can cause several diseases that usually develop with a serious problem.According to the World Health Organization(WHO),2019-nCoV has been placed as the modern generation of Severe Acute Respiratory Syndrome(SARS)and Middle East Respiratory Syndrome(MERS)coronaviruses,so COVID-19 can repeatedly change its internal genome structure to extend its existence.Understanding and accurately predicting the mutational properties of the genome structure of COVID-19 can form a good leadership role in preventing and fighting against coronavirus.In this research paper,an analytical approach has been presented which is based on the k-means cluster technique of machine learning to find the clusters over the mutational properties of the COVID-19 viruses’complete genome.This method would be able to act as a promising tool to monitor and track pathogenic infections in their stable and local genetics/hereditary varieties.This paper identifies five main clusters of mutations with k=5 as best in most cases in the coronavirus that could help scientists and researchers develop disease control vaccines for the transformation of coronaviruses. 展开更多
关键词 nCoV-2019 SARS-CoV-2 COVID-19 genome structure etiology COVID-19 mutations COVID-19 genomes
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A Comprehensive Review on Medical Diagnosis Using Machine Learning 认领 引用 被引量：4
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作者 Kaustubh Arun Bhavsar Ahed Abugabah +3 位作者 Jimmy Singla Ahmad Ali AlZubi Ali Kashif Bashir Nikita 《Computers, Materials & Continua》 SCIE EI 2021年第5期1997-2014,共18页
The unavailability of sufficient information for proper diagnosis,incomplete or miscommunication between patient and the clinician,or among the healthcare professionals,delay or incorrect diagnosis,the fatigue of clin... The unavailability of sufficient information for proper diagnosis,incomplete or miscommunication between patient and the clinician,or among the healthcare professionals,delay or incorrect diagnosis,the fatigue of clinician,or even the high diagnostic complexity in limited time can lead to diagnostic errors.Diagnostic errors have adverse effects on the treatment of a patient.Unnecessary treatments increase the medical bills and deteriorate the health of a patient.Such diagnostic errors that harm the patient in various ways could be minimized using machine learning.Machine learning algorithms could be used to diagnose various diseases with high accuracy.The use of machine learning could assist the doctors in making decisions on time,and could also be used as a second opinion or supporting tool.This study aims to provide a comprehensive review of research articles published from the year 2015 to mid of the year 2020 that have used machine learning for diagnosis of various diseases.We present the various machine learning algorithms used over the years to diagnose various diseases.The results of this study show the distribution of machine learning methods by medical disciplines.Based on our review,we present future research directions that could be used to conduct further research. 展开更多
关键词 Diagnostic system machine learning medical diagnosis healthcare applications
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MCBC-SMOTE:A Majority Clustering Model for Classification of Imbalanced Data 认领 引用 被引量：2
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作者 Jyoti Arora Meena Tushir +4 位作者 Keshav Sharma Lalit Mohan Aman Singh Abdullah Alharbi Wael Alosaimi 《Computers, Materials & Continua》 SCIE EI 2022年第12期4801-4817,共17页
Datasets with the imbalanced class distribution are difficult to handle with the standard classification algorithms.In supervised learning,dealing with the problem of class imbalance is still considered to be a challe... Datasets with the imbalanced class distribution are difficult to handle with the standard classification algorithms.In supervised learning,dealing with the problem of class imbalance is still considered to be a challenging research problem.Various machine learning techniques are designed to operate on balanced datasets;therefore,the state of the art,different undersampling,over-sampling and hybrid strategies have been proposed to deal with the problem of imbalanced datasets,but highly skewed datasets still pose the problem of generalization and noise generation during resampling.To overcome these problems,this paper proposes amajority clusteringmodel for classification of imbalanced datasets known as MCBC-SMOTE(Majority Clustering for balanced Classification-SMOTE).The model provides a method to convert the problem of binary classification into a multi-class problem.In the proposed algorithm,the number of clusters for themajority class is calculated using the elbow method and the minority class is over-sampled as an average of clustered majority classes to generate a symmetrical class distribution.The proposed technique is cost-effective,reduces the problem of noise generation and successfully disables the imbalances present in between and within classes.The results of the evaluations on diverse real datasets proved to provide better classification results as compared to state of the art existing methodologies based on several performance metrics. 展开更多
关键词 Imbalance class problem classification SMOTE k-means clustering sampling
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Enhanced Marathi Speech Recognition Facilitated by Grasshopper Optimisation-Based Recurrent Neural Network 认领 引用 被引量：2
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作者 Ravindra Parshuram Bachate Ashok Sharma +3 位作者 Amar Singh Ayman AAly Abdulaziz HAlghtani Dac-Nhuong Le 《Computer Systems Science & Engineering》 SCIE EI 2022年第11期439-454,共16页
Communication is a significant part of being human and living in the world.Diverse kinds of languages and their variations are there;thus,one person can speak any language and cannot effectively communicate with one w... Communication is a significant part of being human and living in the world.Diverse kinds of languages and their variations are there;thus,one person can speak any language and cannot effectively communicate with one who speaks that language in a different accent.Numerous application fields such as education,mobility,smart systems,security,and health care systems utilize the speech or voice recognition models abundantly.Though,various studies are focused on the Arabic or Asian and English languages by ignoring other significant languages like Marathi that leads to the broader research motivations in regional languages.It is necessary to understand the speech recognition field,in which the major concentrated stages are feature extraction and classification.This paper emphasis developing a Speech Recognition model for the Marathi language by optimizing Recurrent Neural Network(RNN).Here,the preprocessing of the input signal is performed by smoothing and median filtering.After preprocessing the feature extraction is carried out using MFCC and Spectral features to get precise features from the input Marathi Speech corpus.The optimized RNN classifier is used for speech recognition after completing the feature extraction task,where the optimization of hidden neurons in RNN is performed by the Grasshopper Optimization Algorithm(GOA).Finally,the comparison with the conventional techniques has shown that the proposed model outperforms most competing models on a benchmark dataset. 展开更多
关键词 Deep learning grasshopper optimization algorithm recurrent neural network speech recognition word error rate
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