Developing a Breast Cancer Resistance Protein Substrate Prediction System Using Deep Features and LDA

Breast cancer resistance protein(BCRP)is an important resistance protein that significantly impacts anticancer drug discovery,treatment,and rehabilitation.Early identification of BCRP substrates is quite a challenging task.This study aims to predict early substrate structure,which can help to optimize anticancer drug development and clinical diagnosis.For this study,a novel intelligent approach-based methodology is developed by modifying the ResNet101 model using transfer learning(TL)for automatic deep feature(DF)extraction followed by classification with linear discriminant analysis algorithm(TLRNDF-LDA).This study utilized structural fingerprints,which are exploited by DF contrary to conventional molecular descriptors.The proposed in silico model achieved an outstanding accuracy performance of 98.56%on test data compared to other state-of-the-art approaches using standard quality measures.Furthermore,the model’s efficacy is validated via a statistical analysisANOVAtest.It is demonstrated that the developedmodel can be used effectively for early prediction of the substrate structure.The pipeline of this study is flexible and can be extended for in vitro assessment efficacy of anticancer drug response,identification of BCRP functions in transport experiments,and prediction of prostate or lung cancer cell lines.

Prediction of Cloud Ranking in a Hyperconverged Cloud Ecosystem Using Machine Learning

Cloud computing is becoming popular technology due to its functional properties and variety of customer-oriented services over the Internet.The design of reliable and high-quality cloud applications requires a strong Quality of Service QoS parameter metric.In a hyperconverged cloud ecosystem environment,building high-reliability cloud applications is a challenging job.The selection of cloud services is based on the QoS parameters that play essential roles in optimizing and improving cloud rankings.The emergence of cloud computing is significantly reshaping the digital ecosystem,and the numerous services offered by cloud service providers are playing a vital role in this transformation.Hyperconverged software-based unified utilities combine storage virtualization,compute virtualization,and network virtualization.The availability of the latter has also raised the demand for QoS.Due to the diversity of services,the respective quality parameters are also in abundance and need a carefully designed mechanism to compare and identify the critical,common,and impactful parameters.It is also necessary to reconsider the market needs in terms of service requirements and the QoS provided by various CSPs.This research provides a machine learning-based mechanism to monitor the QoS in a hyperconverged environment with three core service parameters:service quality,downtime of servers,and outage of cloud services.

Intelligent Cloud Based Heart Disease Prediction System Empowered with Supervised Machine Learning

The innovation in technologies related to health facilities today is increasingly helping to manage patients with different diseases.The most fatal of these is the issue of heart disease that cannot be detected from a naked eye,and attacks as soon as the human exceeds the allowed range of vital signs like pulse rate,body temperature,and blood pressure.The real challenge is to diagnose patients with more diagnostic accuracy and in a timely manner,followed by prescribing appropriate treatments and keeping prescription errors to a minimum.In developing countries,the domain of healthcare is progressing day by day using different Smart healthcare:emerging technologies like cloud computing,fog computing,and mobile computing.Electronic health records(EHRs)are used to manage the huge volume of data using cloud computing.That reduces the storage,processing,and retrieval cost as well as ensuring the availability of data.Machine learning procedures are used to extract hidden patterns and data analytics.In this research,a combination of cloud computing and machine learning algorithm Support vector machine(SVM)is used to predict heart diseases.Simulation results have shown that the proposed intelligent cloud-based heart disease prediction system empowered with a Support vector machine(SVM)-based system model gives 93.33%accuracy,which is better than previously published approaches.

Fruits and Vegetables Freshness Categorization Using Deep Learning

The nutritional value of perishable food items,such as fruits and vegetables,depends on their freshness levels.The existing approaches solve a binary class problem by classifying a known fruit\vegetable class into fresh or rotten only.We propose an automated fruits and vegetables categorization approach that first recognizes the class of object in an image and then categorizes that fruit or vegetable into one of the three categories:purefresh,medium-fresh,and rotten.We gathered a dataset comprising of 60K images of 11 fruits and vegetables,each is further divided into three categories of freshness,using hand-held cameras.The recognition and categorization of fruits and vegetables are performed through two deep learning models:Visual Geometry Group(VGG-16)and You Only Look Once(YOLO),and their results are compared.VGG-16 classifies fruits and vegetables and categorizes their freshness,while YOLO also localizes them within the image.Furthermore,we have developed an android based application that takes the image of the fruit or vegetable as input and returns its class label and its freshness degree.A comprehensive experimental evaluation of proposed approach demonstrates that the proposed approach can achieve a high accuracy and F1score on gathered FruitVeg Freshness dataset.The dataset is publicly available for further evaluation by the research community.

IoMT-Based Smart Monitoring Hierarchical Fuzzy Inference System for Diagnosis of COVID-19

The prediction of human diseases,particularly COVID-19,is an extremely challenging task not only for medical experts but also for the technologists supporting them in diagnosis and treatment.To deal with the prediction and diagnosis of COVID-19,we propose an Internet of Medical Things-based Smart Monitoring Hierarchical Mamdani Fuzzy Inference System(IoMTSM-HMFIS).The proposed system determines the various factors like fever,cough,complete blood count,respiratory rate,Ct-chest,Erythrocyte sedimentation rate and C-reactive protein,family history,and antibody detection(lgG)that are directly involved in COVID-19.The expert system has two input variables in layer 1,and seven input variables in layer 2.In layer 1,the initial identification for COVID-19 is considered,whereas in layer 2,the different factors involved are studied.Finally,advanced lab tests are conducted to identify the actual current status of the disease.The major focus of this study is to build an IoMT-based smart monitoring system that can be used by anyone exposed to COVID-19;the system would evaluate the user’s health condition and inform them if they need consultation with a specialist for quarantining.MATLAB-2019a tool is used to conduct the simulation.The COVID-19 IoMTSM-HMFIS system has an overall accuracy of approximately 83%.Finally,to achieve improved performance,the analysis results of the system were shared with experts of the Lahore General Hospital,Lahore,Pakistan.

Intelligent Dynamic Gesture Recognition Using CNN Empowered by Edit Distance

Human activity detection and recognition is a challenging task.Video surveillance can benefit greatly by advances in Internet of Things(IoT)and cloud computing.Artificial intelligence IoT(AIoT)based devices form the basis of a smart city.The research presents Intelligent dynamic gesture recognition(IDGR)using a Convolutional neural network(CNN)empowered by edit distance for video recognition.The proposed system has been evaluated using AIoT enabled devices for static and dynamic gestures of Pakistani sign language(PSL).However,the proposed methodology can work efficiently for any type of video.The proposed research concludes that deep learning and convolutional neural networks give a most appropriate solution retaining discriminative and dynamic information of the input action.The research proposes recognition of dynamic gestures using image recognition of the keyframes based on CNN extracted from the human activity.Edit distance is used to find out the label of the word to which those sets of frames belong to.The simulation results have shown that at 400 videos per human action,100 epochs,234×234 image size,the accuracy of the system is 90.79%,which is a reasonable accuracy for a relatively small dataset as compared to the previously published techniques.

Drug Response Prediction of Liver Cancer Cell Line Using Deep Learning

Cancer is the second deadliest human disease worldwide with high mortality rate.Rehabilitation and treatment of this disease requires precise and automatic assessment of effective drug response and control system.Prediction of treated and untreated cancerous cell line is one of the most challenging problems for precise and targeted drug delivery and response.A novel approach is proposed for prediction of drug treated and untreated cancer cell line automatically by employing modified Deep neural networks.Human hepatocellular carcinoma(HepG2)cells are exposed to anticancer drug functionalized CFO@BTO nanoparticles developed by our lab.Prediction models are developed by modifying ResNet101 and exploiting the transfer learning concept.Last three layers of ResNet101 are re-trained for the identification of drug treated cancer cells.Transfer learning approach in an appropriate choice especially when there is limited amount of annotated data.The proposed technique is validated on acquired 203 fluorescentmicroscopy images of human HepG2 cells treated with drug functionalized cobalt ferrite@barium titanate(CFO@BTO)magnetoelectric nanoparticles in vitro.The developed approach achieved high prediction with accuracy of 97.5%and sensitivity of 100%and outperformed other approaches.The high performance reveals the effectiveness of the approach.It is scalable and fully automatic prediction approach which can be extended for other similar cell diseases such as lung,brain tumor and breast cancer.

Robust Length of Stay Prediction Model for Indoor Patients

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.

Flower Pollination Heuristics for Parameter Estimation of Electromagnetic Plane Waves

For the last few decades,the parameter estimation of electromagnetic plane waves i.e.,far field sources,impinging on antenna array geometries has attracted a lot of researchers due to their use in radar,sonar and under water acoustic environments.In this work,nature inspired heuristics based on the flower pollination algorithm(FPA)is designed for the estimation problem of amplitude and direction of arrival of far field sources impinging on uniform linear array(ULA).Using the approximation in mean squared error sense,a fitness function of the problem is developed and the strength of the FPA is utilized for optimization of the cost function representing scenarios for various number of sources non-coherent located in the far field.The worth of the proposed FPA based nature inspired computing heuristic is established through assessment studies on fitness,histograms,cumulative distribution function and box plots analysis.The other worthy perks of the proposed scheme include simplicity of concept,ease in the implementation,extendibility and wide range of applicability to solve complex optimization problems.These salient features make the proposed approach as an attractive alternative to be exploited for solving different parameter estimation problems arising in nonlinear systems,power signal modelling,image processing and fault diagnosis.