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| 1 | Simulation, Modeling, and Optimization of Intelligent Kidney Disease Predication Empowered with Computational Intelligence Approaches显示文摘Artificial intelligence(AI)is expanding its roots in medical diagnostics.Various acute and chronic diseases can be identified accurately at the initial level by using AI methods to prevent the progression of health complications.Kidney diseases are producing a high impact on global health and medical practitioners are suggested that the diagnosis at earlier stages is one of the foremost approaches to avert chronic kidney disease and renal failure.High blood pressure,diabetes mellitus,and glomerulonephritis are the root causes of kidney disease.Therefore,the present study is proposed a set of multiple techniques such as simulation,modeling,and optimization of intelligent kidney disease prediction(SMOIKD)which is based on computational intelligence approaches.Initially,seven parameters were used for the fuzzy logic system(FLS),and then twenty-five different attributes of the kidney dataset were used for the artificial neural network(ANN)and deep extreme machine learning(DEML).The expert system was proposed with the assistance of medical experts.For the quick and accurate evaluation of the proposed system,Matlab version 2019 was used.The proposed SMOIKD-FLSANN-DEML expert system has shown 94.16%accuracy.Hence this study concluded that SMOIKD-FLS-ANN-DEML system is effective to accurately diagnose kidney disease at initial levels. | Abdul Hannan Khan Muhammad Adnan Khan Sagheer Abbas Shahan Yamin Siddiqui Muhammad Aanwar Saeed Majed Alfayad Nouh Sabri Elmitwally | 2021 | Computers, Materials & Continua2021，，5： | 2 |
| 2 | Intelligent Forecasting Model of COVID-19 Novel Coronavirus Outbreak Empowered with Deep Extreme Learning Machine显示文摘An epidemic is a quick and widespread disease that threatens many lives and damages the economy.The epidemic lifetime should be accurate so that timely and remedial steps are determined.These include the closing of borders schools,suspension of community and commuting services.The forecast of an outbreak effectively is a very necessary but difficult task.A predictive model that provides the best possible forecast is a great challenge for machine learning with only a few samples of training available.This work proposes and examines a prediction model based on a deep extreme learning machine(DELM).This methodology is used to carry out an experiment based on the recent Wuhan coronavirus outbreak.An optimized prediction model that has been developed,namely DELM,is demonstrated to be able to make a prediction that is fairly best.The results show that the new methodology is useful in developing an appropriate forecast when the samples are far from abundant during the critical period of the disease.During the investigation,it is shown that the proposed approach has the highest accuracy rate of 97.59%with 70%of training,30%of test and validation.Simulation results validate the prediction effectiveness of the proposed scheme. | Muhammad Adnan Khan Sagheer Abbas Khalid Masood Khan Mohammed AAl Ghamdi Abdur Rehman | 2020 | Computers, Materials & Continua2020，，9： | 1 |
| 3 | First report on the molecular prevalence of Mycoplasma capricolum subspecies capripneumoniae (Mccp) in goats the cause of contagious caprine pleuropneumonia (CCPP) in Balochistan province of Pakistan显示文摘 | Mohammad Arif Awan Ferhat Abbas Masoom Yasinzai Robin A. J. Nicholas Shakeel Babar Roger D. Ayling Mohammad Adnan Attique Zafar Ahmed Abdul Wadood Faisal Ameer Khan | 2010 | Molecular Biology Reports2010，，7： | 1 |
| 4 | Alzheimer Disease Detection Empowered with Transfer Learning显示文摘Alzheimer’s disease is a severe neuron disease that damages brain cells which leads to permanent loss of memory also called dementia.Many people die due to this disease every year because this is not curable but early detection of this disease can help restrain the spread.Alzheimer’s ismost common in elderly people in the age bracket of 65 and above.An automated system is required for early detection of disease that can detect and classify the disease into multiple Alzheimer classes.Deep learning and machine learning techniques are used to solvemanymedical problems like this.The proposed system Alzheimer Disease detection utilizes transfer learning on Multi-class classification using brain Medical resonance imagining(MRI)working to classify the images in four stages,Mild demented(MD),Moderate demented(MOD),Non-demented(ND),Very mild demented(VMD).Simulation results have shown that the proposed systemmodel gives 91.70%accuracy.It also observed that the proposed system gives more accurate results as compared to previous approaches. | Taher M.Ghazal Sagheer Abbas Sundus Munir M.A.Khan Munir Ahmad Ghassan F.Issa Syeda Binish Zahra Muhammad Adnan Khan Mohammad Kamrul Hasan | 2022 | Computers, Materials & Continua2022，，3： | 1 |
| 5 | Tailoring the optical and magnetic properties of La-BaM hexaferrites by Ni substitution显示文摘We investigate the impact of Ni insertion on the structural,optical,and magnetic properties of Ba_(0.8)La_(0.2)Fe_(12-x)Ni_(x)O_(19)hexaferrites(Ni substituted La-BaM hexaferrites).Samples were prepared using the conventional co-precipitation method and sintered at 1000℃for 4 hours to assist the crystallization process.An analysis of the structure of the samples was carried out using an x-ray diffraction(XRD)spectrometer.The M-type hexagonal structure of all the samples was confirmed using XRD spectra.The lattice parameters a and c were found to be in the ranges of 5.8925±0.001 nm–5.8952±0.001 nm and 23.2123±0.001 nm–23.2219±0.001 nm,respectively.The M-type hexagonal nature of the prepared samples was also indicated by the presence of corresponding FT-IR bands and Raman modes in the FT-IR and Raman spectra,respectively.EDX results confirmed the successful synthesis of the samples according to the required stoichiometric ratio.A UV-vis spectrometer was used to record the absorption spectra of the prepared samples in the wavelength range of 200 nm–1100 nm.The optical energy bandgap of the samples was found to be in the range of 1.21 eV–3.39 eV.The M–H loops of the samples were measured at room temperature at an applied magnetic field range of 0 kOe–60 kOe.A high saturation magnetization of 99.92 emu/g was recorded in the sample with x=0 at a microwave operating frequency of 22.2 GHz.This high value of saturation magnetization is due to the substitution of La3+ions at the spin-up(12k,2a,and 2b)sites.The Ni substitution is proven to be a potential candidate for the tuning of the optical and magnetic parameters of M-type hexaferrites.Therefore,we suggest that the prepared samples are suitable for use in magneto-optic applications. | Hafiz T.Ali MRamzan M Imran Arshad Nicola A.Morley M.Hassan Abbas Mohammad Yusuf Atta Ur Rehman Khalid Mahmood Adnan Ali Nasir Amin MAjaz-un-Nabi | 2022 | Chinese Physics B2022，31，2： | 1 |
| 6 | A Water Management Support System for Amman Zarqa Basin in Jordan显示文摘 | Abbas Al-Omari Saleh Al-Quraan Adnan Al-Salihi | 2009 | Water Resource Management2009，23，： | 1 |
| 7 | Autonomous Parking-Lots Detection with Multi-Sensor Data Fusion Using Machine Deep Learning Techniques显示文摘The rapid development and progress in deep machine-learning techniques have become a key factor in solving the future challenges of humanity.Vision-based target detection and object classification have been improved due to the development of deep learning algorithms.Data fusion in autonomous driving is a fact and a prerequisite task of data preprocessing from multi-sensors that provide a precise,well-engineered,and complete detection of objects,scene or events.The target of the current study is to develop an in-vehicle information system to prevent or at least mitigate traffic issues related to parking detection and traffic congestion detection.In this study we examined to solve these problems described by(1)extracting region-of-interest in the images(2)vehicle detection based on instance segmentation,and(3)building deep learning model based on the key features obtained from input parking images.We build a deep machine learning algorithm that enables collecting real video-camera feeds from vision sensors and predicting free parking spaces.Image augmentation techniques were performed using edge detection,cropping,refined by rotating,thresholding,resizing,or color augment to predict the region of bounding boxes.A deep convolutional neural network F-MTCNN model is proposed that simultaneously capable for compiling,training,validating and testing on parking video frames through video-camera.The results of proposed model employing on publicly available PK-Lot parking dataset and the optimized model achieved a relatively higher accuracy 97.6%than previous reported methodologies.Moreover,this article presents mathematical and simulation results using state-of-the-art deep learning technologies for smart parking space detection.The results are verified using Python,TensorFlow,OpenCV computer simulation frameworks. | Kashif Iqbal Sagheer Abbas Muhammad Adnan Khan Atifa Ather Muhammad Saleem Khan Areej Fatima Gulzar Ahmad | 2021 | Computers, Materials & Continua2021，，2： | 1 |
| 8 | Projection of precipitation extremes over South Asia from CMIP6 GCMs显示文摘Extreme precipitation events are one of the most dangerous hydrometeorological disasters,often resulting in significant human and socio-economic losses worldwide.It is therefore important to use current global climate models to project future changes in precipitation extremes.The present study aims to assess the future changes in precipitation extremes over South Asia from the Coupled Model Intercomparison Project Phase 6(CMIP6)Global Climate Models(GCMs).The results were derived using the modified Mann-Kendall test,Sen's slope estimator,student's t-test,and probability density function approach.Eight extreme precipitation indices were assessed,including wet days(RR1mm),heavy precipitation days(RR10mm),very heavy precipitation days(RR20mm),severe precipitation days(RR50mm),consecutive wet days(CWD),consecutive dry days(CDD),maximum 5-day precipitation amount(RX5day),and simple daily intensity index(SDII).The future changes were estimated in two time periods for the 21^(st) century(i.e.,near future(NF;2021-2060)and far future(FF;2061-2100))under two Shared Socioeconomic Pathway(SSP)scenarios(SSP2-4.5 and SSP5-8.5).The results suggest increases in the frequency and intensity of extreme precipitation indices under the SSP5-8.5 scenario towards the end of the 21^(st) century(2061-2100).Moreover,from the results of multimodel ensemble means(MMEMs),extreme precipitation indices of RR1mm,RR10mm,RR20mm,CWD,and SDII demonstrate remarkable increases in the FF period under the SSP5-8.5 scenario.The spatial distribution of extreme precipitation indices shows intensification over the eastern part of South Asia compared to the western part.The probability density function of extreme precipitation indices suggests a frequent(intense)occurrence of precipitation extremes in the FF period under the SSP5-8.5 scenario,with values up to 35.00 d for RR1mm and 25.00-35.00 d for CWD.The potential impacts of heavy precipitation can pose serious challenges to the study area regarding flooding,soil erosion,water resource management,food security,and agriculture development. | Adnan ABBAS Asher S BHATTI Safi ULLAH Waheed ULLAH Muhammad WASEEM ZHAO Chengyi DOU Xin Gohar ALI | 2023 | Journal of Arid Land2023，15，3： | 1 |
| 9 | Foliar application of micronutrients enhances crop stand, yield and the biofortification essential for human health of different wheat cultivars显示文摘Globally about half of the world’s population is under micronutrient malnutrition due to poor quality food intake.To overcome this problem,fortification and biofortification techniques are often used.Biofortification is considered a better option than fortification due to the easy control of nutrient deficiencies present in daily food.This field experiment was conducted to evaluate the effects of foliar application of a micronutrient mixture(MNM)consisting of zinc(Zn),iron(Fe),copper(Cu),manganese(Mn)and boron(B)on yield and flour quality of wheat.The results show the effectiveness of foliar feeding for growth and yield parameters,in addition to the enriching of wheat grains with Zn,Cu,Fe,Mn and B.Compared to the control without foliar feeding,foliar application on wheat crop increased tillering ability,spike length,grain yield and the contents of Zn,Cu,Mn,Fe and B by 21,47,22,22 and 25%in wheat flour,respectively.Therefore,foliar feeding of micronutrients could be an effective approach to enrich wheat grains with essential nutrients for correcting malnutrition. | Muhammad Zahir Aziz Muhammad Yaseen Tanveer Abbas Muhammad Naveed Adnan Mustafa Yasir Hamid Qudsia Saeed XU Ming-gang | 2019 | Journal of Integrative Agriculture2019，18，6： | 1 |
| 10 | 菠菜品质安全参数的拉曼点扫描快速检测方法显示文摘针对蔬菜品质安全无损伤检测的实际市场需求,结合叶菜表面农药残留等品质安全参数的不均一性,以实验室自行搭建的拉曼光谱硬件系统为基础,开发了叶菜气吸平整装置,编写了基于LabVIEW开发平台的GUI应用程序。该系统通过设定扫描步长等参数,实现了整个菠菜样品的拉曼光谱自动点扫描检测,包括对所有扫描点的拉曼信号进行自动采集、显示和存储。检测过程中,系统软件实时监控相机、二维平移台的运行状态。同时,针对菠菜原始光谱特性编写了基于有效峰线性拟合基线校准方法的拉曼光谱荧光剔除程序,实现了对样品所有扫描点数据的自动基线校准及叠加平均处理。菠菜样品的点扫描实验结果显示,一次扫描不仅可以获得菠菜样品每一扫描点的叶绿素含量及毒死蜱农药残留等品质安全参数的分布情况,而且还可以获得整个样品各参数的平均信息。该点扫描拉曼系统有效提高了不均匀样品的品质安全参数的检测精度。 | 徐田锋 彭彦昆 李永玉 翟晨 郑晓春 乔璐 Adnan Abbas | 2016 | 光谱学与光谱分析2016，36，6： | 1 |
| 11 | Convolutional Neural Network Based Intelligent Handwritten Document Recognition显示文摘This paper presents a handwritten document recognition system based on the convolutional neural network technique.In today’s world,handwritten document recognition is rapidly attaining the attention of researchers due to its promising behavior as assisting technology for visually impaired users.This technology is also helpful for the automatic data entry system.In the proposed systemprepared a dataset of English language handwritten character images.The proposed system has been trained for the large set of sample data and tested on the sample images of user-defined handwritten documents.In this research,multiple experiments get very worthy recognition results.The proposed systemwill first performimage pre-processing stages to prepare data for training using a convolutional neural network.After this processing,the input document is segmented using line,word and character segmentation.The proposed system get the accuracy during the character segmentation up to 86%.Then these segmented characters are sent to a convolutional neural network for their recognition.The recognition and segmentation technique proposed in this paper is providing the most acceptable accurate results on a given dataset.The proposed work approaches to the accuracy of the result during convolutional neural network training up to 93%,and for validation that accuracy slightly decreases with 90.42%. | Sagheer Abbas Yousef Alhwaiti Areej Fatima Muhammad A.Khan Muhammad Adnan Khan Taher M.Ghazal Asma Kanwal Munir Ahmad Nouh Sabri Elmitwally | 2022 | Computers, Materials & Continua2022，，3： | 1 |
| 12 | A Fused Machine Learning Approach for Intrusion Detection System显示文摘The rapid growth in data generation and increased use of computer network devices has amplified the infrastructures of internet.The interconnectivity of networks has brought various complexities in maintaining network availability,consistency,and discretion.Machine learning based intrusion detection systems have become essential to monitor network traffic for malicious and illicit activities.An intrusion detection system controls the flow of network traffic with the help of computer systems.Various deep learning algorithms in intrusion detection systems have played a prominent role in identifying and analyzing intrusions in network traffic.For this purpose,when the network traffic encounters known or unknown intrusions in the network,a machine-learning framework is needed to identify and/or verify network intrusion.The Intrusion detection scheme empowered with a fused machine learning technique(IDS-FMLT)is proposed to detect intrusion in a heterogeneous network that consists of different source networks and to protect the network from malicious attacks.The proposed IDS-FMLT system model obtained 95.18%validation accuracy and a 4.82%miss rate in intrusion detection. | Muhammad Sajid Farooq Sagheer Abbas Atta-ur-Rahman Kiran Sultan Muhammad Adnan Khan Amir Mosavi | 2023 | Computers, Materials & Continua2023，，2： | 0 |
| 13 | Personality Detection Using Context Based Emotions in Cognitive Agents显示文摘Detection of personality using emotions is a research domain in artificial intelligence.At present,some agents can keep the human’s profile for interaction and adapts themselves according to their preferences.However,the effective method for interaction is to detect the person’s personality by understanding the emotions and context of the subject.The idea behind adding personality in cognitive agents begins an attempt to maximize adaptability on the basis of behavior.In our daily life,humans socially interact with each other by analyzing the emotions and context of interaction from audio or visual input.This paper presents a conceptual personality model in cognitive agents that can determine personality and behavior based on some text input,using the context subjectivity of the given data and emotions obtained from a particular situation/context.The proposed work consists of Jumbo Chatbot,which can chat with humans.In this social interaction,the chatbot predicts human personality by understanding the emotions and context of interactive humans.Currently,the Jumbo chatbot is using the BFI technique to interact with a human.The accuracy of proposed work varies and improve through getting more experiences of interaction. | Nouh Sabri Elmitwally Asma Kanwal Sagheer Abbas Muhammad A.Khan Muhammad Adnan Khan Munir Ahmad Saad Alanazi | 2022 | Computers, Materials & Continua2022，，3： | 0 |
| 14 | Modelling Intelligent Driving Behaviour Using Machine Learning显示文摘In vehicular systems,driving is considered to be the most complex task,involving many aspects of external sensory skills as well as cognitive intelligence.External skills include the estimation of distance and speed,time perception,visual and auditory perception,attention,the capability to drive safely and action-reaction time.Cognitive intelligence works as an internal mechanism that manages and holds the overall driver’s intelligent system.These cognitive capacities constitute the frontiers for generating adaptive behaviour for dynamic environments.The parameters for understanding intelligent behaviour are knowledge,reasoning,decision making,habit and cognitive skill.Modelling intelligent behaviour reveals that many of these parameters operate simultaneously to enable drivers to react to current situations.Environmental changes prompt the parameter values to change,a process which continues unless and until all processes are completed.This paper model intelligent behaviour by using a‘driver behaviour model’to obtain accurate intelligent driving behaviour patterns.This model works on layering patterns in which hierarchy and coherence are maintained to transfer the data with accuracy from one module to another.These patterns constitute the outcome of different modules that collaborate to generate appropriate values.In this case,accurate patterns were acquired using ANN static and dynamic non-linear autoregressive approach was used and for further accuracy validation,time-series dynamic backpropagation artificial neural network,multilayer perceptron and random sub-space on real-world data were also applied. | Qura-Tul-Ain Khan Sagheer Abbas Muhammad Adnan Khan Areej Fatima Saad Alanazi Nouh Sabri Elmitwally | 2021 | Computers, Materials & Continua2021，，9： | 0 |
| 15 | Intelligent Ammunition Detection and Classification System Using Convolutional Neural Network显示文摘Security is a significant issue for everyone due to new and creative ways to commit cybercrime.The Closed-Circuit Television(CCTV)systems are being installed in offices,houses,shopping malls,and on streets to protect lives.Operators monitor CCTV;however,it is difficult for a single person to monitor the actions of multiple people at one time.Consequently,there is a dire need for an automated monitoring system that detects a person with ammunition or any other harmful material Based on our research and findings of this study,we have designed a new Intelligent Ammunition Detection and Classification(IADC)system using Convolutional Neural Network(CNN).The proposed system is designed to identify persons carrying weapons and ammunition using CCTV cameras.When weapons are identified,the cameras sound an alarm.In the proposed IADC system,CNN was used to detect firearms and ammunition.The CNN model which is a Deep Learning technique consists of neural networks,most commonly applied to analyzing visual imagery has gained popularity for unstructured(images,videos)data classification.Additionally,this system generates an early warning through detection of ammunition before conditions become critical.Hence the faster and earlier the prediction,the lower the response time,loses and potential victims.The proposed IADC system provides better results than earlier published models like VGGNet,OverFeat-1,OverFeat-2,and OverFeat-3. | Gulzar Ahmad Saad Alanazi Madallah Alruwaili Fahad Ahmad Muhammad Adnan Khan Sagheer Abbas Nadia Tabassum | 2021 | Computers, Materials & Continua2021，，5： | 0 |
| 16 | A Neuro-Fuzzy Approach to Road Traffic Congestion Prediction显示文摘The fast-paced growth of artificial intelligence applications provides unparalleled opportunities to improve the efficiency of various systems.Such as the transportation sector faces many obstacles following the implementation and integration of different vehicular and environmental aspects worldwide.Traffic congestion is among the major issues in this regard which demands serious attention due to the rapid growth in the number of vehicles on the road.To address this overwhelming problem,in this article,a cloudbased intelligent road traffic congestion prediction model is proposed that is empowered with a hybrid Neuro-Fuzzy approach.The aim of the study is to reduce the delay in the queues,the vehicles experience at different road junctions across the city.The proposed model also intended to help the automated traffic control systems by minimizing the congestion particularly in a smart city environment where observational data is obtained from various implanted Internet of Things(IoT)sensors across the road.After due preprocessing over the cloud server,the proposed approach makes use of this data by incorporating the neuro-fuzzy engine.Consequently,it possesses a high level of accuracy by means of intelligent decision making with minimum error rate.Simulation results reveal the accuracy of the proposed model as 98.72%during the validation phase in contrast to the highest accuracies achieved by state-of-the-art techniques in the literature such as 90.6%,95.84%,97.56%and 98.03%,respectively.As far as the training phase analysis is concerned,the proposed scheme exhibits 99.214% accuracy. The proposed prediction modelis a potential contribution towards smart cities environment. | Mohammed Gollapalli Atta-ur-Rahman Dhiaa Musleh Nehad Ibrahim Muhammad Adnan Khan Sagheer Abbas Ayesha Atta Muhammad Aftab Khan Mehwash Farooqui Tahir Iqbal Mohammed Salih Ahmed Mohammed Imran BAhmed Dakheel Almoqbil Majd Nabeel Abdullah Omer | 2022 | Computers, Materials & Continua2022，，10： | 0 |
| 17 | Modelling and Verification of Context-Aware Intelligent Assistive Formalism显示文摘Recent years have witnessed the expeditious evolution of intelligentsmart devices and autonomous software technologies with the expandeddomains of computing from workplaces to smart computing in everydayroutine life activities. This trend has been rapidly advancing towards the newgeneration of systems where smart devices play vital roles in acting intelligently on behalf of the users. Context-awareness has emerged from the pervasive computing paradigm. Context-aware systems have the ability to acquirecontextual information from the surrounding environment autonomously,perform reasoning on it, and then adapt their behaviors accordingly. With theproliferation of context-aware systems and smart sensors, real-time monitoring of environmental situations (context) has become quite trivial. However,it is often challenging because the imperfect nature of context can cause theinconsistent behavior of the system. In this paper, we propose a contextaware intelligent decision support formalism to assist cognitively impairedpeople in managing their routine life activities. For this, we present a semanticknowledge-based framework to contextualize the information from the environment using the protégé ontology editor and Semantic Web Rule Language(SWRL) rules. The set of contextualized information and the set of rulesacquired from the ontology can be used to model Context-aware Multi-AgentSystems (CMAS) in order to autonomously plan all activities of the users andnotify users to act accordingly. To illustrate the use of the proposed formalism,we model a case study of Mild Cognitive Impaired (MCI) patients usingColored Petri Nets (CPN) to show the reasoning process on how the contextaware agents collaboratively plan activities on the user’s behalf and validatethe correctness properties of the system. | Shahid Yousaf Hafiz Mahfooz Ul Haque Abbas Khalid Muhammad Adnan Hashmi Eraj Khan | 2022 | Computers, Materials & Continua2022，，5： | 0 |
| 18 | Automated File Labeling for Heterogeneous Files Organization Using Machine Learning显示文摘File labeling techniques have a long history in analyzing the anthological trends in computational linguistics.The situation becomes worse in the case of files downloaded into systems from the Internet.Currently,most users either have to change file names manually or leave a meaningless name of the files,which increases the time to search required files and results in redundancy and duplications of user files.Currently,no significant work is done on automated file labeling during the organization of heterogeneous user files.A few attempts have been made in topic modeling.However,one major drawback of current topic modeling approaches is better results.They rely on specific language types and domain similarity of the data.In this research,machine learning approaches have been employed to analyze and extract the information from heterogeneous corpus.A different file labeling technique has also been used to get the meaningful and`cohesive topic of the files.The results show that the proposed methodology can generate relevant and context-sensitive names for heterogeneous data files and provide additional insight into automated file labeling in operating systems. | Sagheer Abbas Syed Ali Raza MAKhan Muhammad Adnan Khan Atta-ur-Rahman Kiran Sultan Amir Mosavi | 2023 | Computers, Materials & Continua2023，，2： | 0 |
| 19 | Robust Length of Stay Prediction Model for Indoor Patients显示文摘Due to unforeseen climate change,complicated chronic diseases,and mutation of viruses’hospital administration’s top challenge is to know about the Length of stay(LOS)of different diseased patients in the hospitals.Hospital management does not exactly know when the existing patient leaves the hospital;this information could be crucial for hospital management.It could allow them to take more patients for admission.As a result,hospitals face many problems managing available resources and new patients in getting entries for their prompt treatment.Therefore,a robust model needs to be designed to help hospital administration predict patients’LOS to resolve these issues.For this purpose,a very large-sized data(more than 2.3 million patients’data)related to New-York Hospitals patients and containing information about a wide range of diseases including Bone-Marrow,Tuberculosis,Intestinal Transplant,Mental illness,Leukaemia,Spinal cord injury,Trauma,Rehabilitation,Kidney and Alcoholic Patients,HIV Patients,Malignant Breast disorder,Asthma,Respiratory distress syndrome,etc.have been analyzed to predict the LOS.We selected six Machine learning(ML)models named:Multiple linear regression(MLR),Lasso regression(LR),Ridge regression(RR),Decision tree regression(DTR),Extreme gradient boosting regression(XGBR),and Random Forest regression(RFR).The selected models’predictive performance was checked using R square andMean square error(MSE)as the performance evaluation criteria.Our results revealed the superior predictive performance of the RFRmodel,both in terms of RS score(92%)and MSE score(5),among all selected models.By Exploratory data analysis(EDA),we conclude that maximumstay was between 0 to 5 days with the meantime of each patient 5.3 days and more than 50 years old patients spent more days in the hospital.Based on the average LOS,results revealed that the patients with diagnoses related to birth complications spent more days in the hospital than other diseases.This finding could help predict the future length of hospital stay of new patients,which will help the hospital administration estimate and manage their resources efficiently. | Ayesha Siddiqa Syed Abbas Zilqurnain Naqvi Muhammad Ahsan Allah Ditta Hani Alquhayz M.A.Khan Muhammad Adnan Khan | 2022 | Computers, Materials & Continua2022，，3： | 0 |
| 20 | Intelligent Model for Predicting the Quality of Services Violation显示文摘Cloud computing is providing IT services to its customer based on Service level agreements(SLAs).It is important for cloud service providers to provide reliable Quality of service(QoS)and to maintain SLAs accountability.Cloud service providers need to predict possible service violations before the emergence of an issue to perform remedial actions for it.Cloud users’major concerns;the factors for service reliability are based on response time,accessibility,availability,and speed.In this paper,we,therefore,experiment with the parallel mutant-Particle swarm optimization(PSO)for the detection and predictions of QoS violations in terms of response time,speed,accessibility,and availability.This paper also compares Simple-PSO and Parallel MutantPSO.In simulation results,it is observed that the proposed Parallel MutantPSO solution for cloud QoS violation prediction achieves 94%accuracy which is many accurate results and is computationally the fastest technique in comparison of conventional PSO technique. | Muhammad Adnan Khan Asma Kanwal Sagheer Abbas Faheem Khan T.Whangbo | 2022 | Computers, Materials & Continua2022，，5： | 0 |
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