Efficient and Secure IoT Based Smart Home Automation Using Multi-Model Learning and Blockchain Technology

The concept of smart houses has grown in prominence in recent years.Major challenges linked to smart homes are identification theft,data safety,automated decision-making for IoT-based devices,and the security of the device itself.Current home automation systems try to address these issues but there is still an urgent need for a dependable and secure smart home solution that includes automatic decision-making systems and methodical features.This paper proposes a smart home system based on ensemble learning of random forest(RF)and convolutional neural networks(CNN)for programmed decision-making tasks,such as categorizing gadgets as“OFF”or“ON”based on their normal routine in homes.We have integrated emerging blockchain technology to provide secure,decentralized,and trustworthy authentication and recognition of IoT devices.Our system consists of a 5V relay circuit,various sensors,and a Raspberry Pi server and database for managing devices.We have also developed an Android app that communicates with the server interface through an HTTP web interface and an Apache server.The feasibility and efficacy of the proposed smart home automation system have been evaluated in both laboratory and real-time settings.It is essential to use inexpensive,scalable,and readily available components and technologies in smart home automation systems.Additionally,we must incorporate a comprehensive security and privacy-centric design that emphasizes risk assessments,such as cyberattacks,hardware security,and other cyber threats.The trial results support the proposed system and demonstrate its potential for use in everyday life.

Improving Prediction of Chronic Kidney Disease Using KNN Imputed SMOTE Features and TrioNet Model

Chronic kidney disease(CKD)is a major health concern today,requiring early and accurate diagnosis.Machine learning has emerged as a powerful tool for disease detection,and medical professionals are increasingly using ML classifier algorithms to identify CKD early.This study explores the application of advanced machine learning techniques on a CKD dataset obtained from the University of California,UC Irvine Machine Learning repository.The research introduces TrioNet,an ensemble model combining extreme gradient boosting,random forest,and extra tree classifier,which excels in providing highly accurate predictions for CKD.Furthermore,K nearest neighbor(KNN)imputer is utilized to deal withmissing values while synthetic minority oversampling(SMOTE)is used for class-imbalance problems.To ascertain the efficacy of the proposed model,a comprehensive comparative analysis is conducted with various machine learning models.The proposed TrioNet using KNN imputer and SMOTE outperformed other models with 98.97%accuracy for detectingCKD.This in-depth analysis demonstrates the model’s capabilities and underscores its potential as a valuable tool in the diagnosis of CKD.

Hepatitis C virus prevalence and genotype distribution inPakistan:Comprehensive review of recent data

Hepatitis C virus(HCV) is endemic in Pakistan and its burden is expected to increase in coming decades owing mainly to widespread use of unsafe medical procedures. The prevalence of HCV in Pakistan has previously been reviewed. However, the literature search conducted here revealed that at least 86 relevant studies have been produced since the publication of these systematic reviews. A revised updated analysis was therefore needed in order to integrate the fresh data. A systematic review of data published between 2010 and 2015 showed that HCV seroprevalence among the general adult Pakistani population is 6.8%, while active HCV infection was found in approximately 6% of the population. Studies included in this review have also shown extremely high HCV prevalence in rural and underdeveloped peri-urban areas(up to 25%), highlighting the need for an increased focus on this previously neglected socioeconomic stratum of the population. While a 2.45% seroprevalence among blood donors demands immediate measures to curtail the risk of transfusion transmitted HCV, a very high prevalence in patients attending hospitals with various non-liver disease related complaints(up to 30%) suggests a rise in the incidence of nosocomial HCV spread. HCV genotype 3a continues to be the most prevalent subtype infecting people in Pakistan(61.3%). However, recent years have witnessed an increase in the frequency of subtype 2a in certain geographical sub-regions within Pakistan. In Khyber Pakhtunkhwa and Sindh provinces, 2a was the second most prevalent genotype(17.3% and 11.3% respectively). While the changing frequency distribution of various genotypes demands an increased emphasis on research for novel therapeutic regimens, evidence of high nosocomial transmission calls for immediate measures aimed at ensuring safe medical practices.

Recent progress on MOF/MXene nanoarchitectures:A new era in coordination chemistry for energy storage and conversion

The development of urbanization and industrialization leads to rapid depletion of fossil fuels.Therefore,the production of fuel from renewable resources is highly desired.Electrotechnical energy conversion and storage is a benign technique with reliable output and is eco-friendly.Developing an exceptional electrochemical catalyst with tunable properties like a huge specific surface area,porous channels,and abundant active sites is critical points.Recently,Metal-organic frameworks(MOFs)and two-dimensional(2D)transition-metal carbides/nitrides(MXenes)have been extensively investigated in the field of electrochemical energy conversion and storage.However,advances in the research on MOFs are hampered by their limited structural stability and conventionally low electrical conductivity,whereas the practical electrochemical performance of MXenes is impeded by their low porosity,inadequate redox sites,and agglomeration.Consequently,researchers have been designing MOF/MXene nanoarchitectures to overcome the limitations in electrochemical energy conversion and storage.This review explores the recent advances in MOF/MXene nanoarchitectures design strategies,tailoring their properties based on the morphologies(0D,1D,2D,and 3D),and broadening their future opportunities in electrochemical energy storage(batteries,supercapacitors)and catalytic energy conversion(HER,OER,and ORR).The intercalation of MOF in between the MXene layers in the nanoarchitectures functions synergistically to address the issues associated with bare MXene and MOF in the electrochemical energy storage and conversion.This review gives a clear emphasis on the general aspects of MOF/MXene nanoarchitectures,and the future research perspectives,challenges of MOF/MXene design strategies and electrochemical applications are highlighted.

Customer Prioritization for Medical Supply Chain During COVID-19 Pandemic

During COVID-19,the escalated demand for various pharmaceutical products with the existing production capacity of pharmaceutical companies has stirred the need to prioritize its customers in order to fulfill their demand.This study considers a two-echelon pharmaceutical supply chain considering various pharma-distributors as its suppliers and hospitals,pharmacies,and retail stores as its customers.Previous studies have generally considered a balanced situation in terms of supply and demand whereas this study considers a special situation of COVID-19 pandemic where demand exceeds supply Various criteria have been identified from the literature that influences the selection of customers.A questionnaire has been developed to collect primary data from pharmaceutical suppliers pertaining to customerselection criteria.These criteria have been prioritized with respect to eigenvalues obtained from Principal Component Analysis and also validated with the experts’domain-related knowledge using Analytical Hierarchy Process.Profit potential appeared to be the most important criteria of customer selection followed by trust and service convenience brand loyalty,commitment,brand awareness,brand image,sustainable behavior,and risk.Subsequently,Multi Criteria Decision Analysis has been performed to prioritize the customerselection criteria and customers with respect to selection criteria.Three experts with seven and three and ten years of experience have participated in the study.Findings of the study suggest large hospitals,large pharmacies,and small retail stores are the highly preferred customers.Moreover,findings of prioritization of customer-selection criteria fromboth Principal Component Analysis and Analytical Hierarchy Process are consistent.Furthermore,this study considers the experience of three experts to calculate an aggregate score of priorities to reach an effective decision.Unlike traditional supply chain problems of supplier selection,this study considers a selection of customers and is useful for procurement and supply chain managers to prioritize customers while considering multiple selection criteria.

Papaya Ring Spot Virus:An Understanding of a Severe Positive-Sense Single Stranded RNA Viral Disease and Its Management

Viral diseases have been studied in-depth for reducing quality,yield,health and longevity of the fruit,to highlight the economic losses.Positive-sense single-stranded RNA viruses are more devastating among all viruses that infect fruit trees.One of the best examples is papaya ringspot virus(PRSV).It belongs to the genus Potyvirus and it is limited to cause diseases on the family Chenopodiaceae,Cucurbitaceae and Caricaceae.This virus has a serious threat to the production of papaya,which is famous for its high nutritional and pharmaceutical values.The plant parts such as leaves,latex,seeds,fruits,bark,peel and roots may contain the biological compound that can be isolated and used in pharmaceutical industries as a disease control.Viral disease symptoms consist of vein clearing and yellowing of young leaves.Distinctive ring spot patterns with concentric rings and spots on fruit reduce its quality and taste.The virus has two major strains P and W.The former cause disease in papaya and cucurbits while the later one in papaya.Virion comprises 94.4%protein,including a 36 kDa coat protein which is a component responsible for a non-persistent transmission through aphids,and 5.5%nucleic acid.Cross protection,development of transgenic crops,exploring the resistant sources and induction of pathogen derived resistance have been recorded as effective management of PRSV.Along with these practices reduced aphid population through insecticides and plant extracts have been found ecofriendly approaches to minimize the disease incidence.Adoption of transgenic crops is a big challenge for the success of disease resistant papaya crops.The aim of this review is to understand the genomic nature of PRSV,detection methods and the different advanced control methods.This review article will be helpful in developing the best management strategies for controlling PRSV.

Hydrological response under CMIP6 climate projection in Astore River Basin,Pakistan

Climate change strongly influences the available water resources in a watershed due to direct linkage of atmospheric driving forces and changes in watershed hydrological processes.Understanding how these climatic changes affect watershed hydrology is essential for human society and environmental processes.Coupled Model Intercomparison Project phase 6(CMIP6)dataset of three GCM's(BCC-CSM2-MR,INM-CM5-0,and MPIESM1-2-HR)with resolution of 100 km has been analyzed to examine the projected changes in temperature and precipitation over the Astore catchment during 2020-2070.Bias correction method was used to reduce errors.In this study,statistical significance of trends was performed by using the Man-Kendall test.Sen's estimator determined the magnitude of the trend on both seasonal and annual scales at Rama Rattu and Astore stations.MPI-ESM1-2-HR showed better results with coefficient of determination(COD)ranging from 0.70-0.74 for precipitation and 0.90-0.92 for maximum and minimum temperature at Astore,Rama,and Rattu followed by INM-CM5-0 and BCC-CSM2-MR.University of British Columbia Watershed model was used to attain the future hydrological series and to analyze the hydrological response of Astore River Basin to climate change.Results revealed that by the end of the 2070s,average annual precipitation is projected to increase up to 26.55%under the SSP1-2.6,6.91%under SSP2-4.5,and decrease up to 21.62%under the SSP5-8.5.Precipitation also showed considerable variability during summer and winter.The projected temperature showed an increasing trend that may cause melting of glaciers.The projected increase in temperature ranges from-0.66℃ to 0.50℃,0.9℃ to 1.5℃ and 1.18℃ to 2℃ under the scenarios of SSP1-2.6,SSP2-4.5 and SSP5-8.5,respectively.Simulated streamflows presented a slight increase by all scenarios.Maximum streamflow was generated under SSP5-8.5 followed by SSP2-4.5 and SSP1-2.6.The snowmelt and groundwater contributions to streamflow have decreased whereas rainfall and glacier melt components have increased on the other hand.The projected streamflows(2020-2070)compared to the control period(1990-2014)showed a reduction of 3%-11%,2%-9%,and 1%-7%by SSP1-2.6,SSP2-4.5,and SSP5-8.5,respectively.The results revealed detailed insights into the performance of three GCMs,which can serve as a blueprint for regional policymaking and be expanded upon to establish adaption measures.

Genome-wide association analysis for stripe rust resistance in spring wheat(Triticum aestivum L.) germplasm

Stripe rust is a continuous threat to wheat crop all over the world.It causes considerable yield losses in wheat crop every year.Continuous deployment of adult plant resistance(APR)genes in newly developing wheat cultivars is the most judicious strategy to combat this disease.Herein,we dissected the genetics underpinning stripe rust resistance in Pakistani wheat germplasm.An association panel of 94 spring wheat genotypes was phenotyped for two years to score the infestation of stripe rust on each accession and was scanned with 203 polymorphic SSRs.Based on D’measure,linkage disequilibrium(LD)exhibited between loci distant up to 45 c M.Marker-trait associations(MTAs)were determined using mixed linear model(MLM).Total 31 quantitative trait loci(QTLs)were observed on all 21 wheat chromosomes.Twelve QTLs were newly discovered as well as 19 QTLs and 35 previously reported Yr genes were validated in Pakistani wheat germplasm.The major QTLs were QYr.uaf.2 AL and QYr.uaf.3 BS(PVE,11.9%).Dissection of genes from the newly observed QTLs can provide new APR genes to improve genetic resources for APR resistance in wheat crop.

On Riemann-Type Weighted Fractional Operators and Solutions to Cauchy Problems

In this paper,we establish the new forms of Riemann-type fractional integral and derivative operators.The novel fractional integral operator is proved to be bounded in Lebesgue space and some classical fractional integral and differential operators are obtained as special cases.The properties of new operators like semi-group,inverse and certain others are discussed and its weighted Laplace transform is evaluated.Fractional integro-differential freeelectron laser(FEL)and kinetic equations are established.The solutions to these new equations are obtained by using the modified weighted Laplace transform.The Cauchy problem and a growth model are designed as applications along with graphical representation.Finally,the conclusion section indicates future directions to the readers.

Deep Learning Approach for Automatic Cardiovascular Disease Prediction Employing ECG Signals

Cardiovascular problems have become the predominant cause of death worldwide and a rise in the number of patients has been observed lately.Currently,electrocardiogram(ECG)data is analyzed by medical experts to determine the cardiac abnormality,which is time-consuming.In addition,the diagnosis requires experienced medical experts and is error-prone.However,automated identification of cardiovascular disease using ECGs is a challenging problem and state-of-the-art performance has been attained by complex deep learning architectures.This study proposes a simple multilayer perceptron(MLP)model for heart disease prediction to reduce computational complexity.ECG dataset containing averaged signals with window size 10 is used as an input.Several competing deep learning and machine learning models are used for comparison.K-fold cross-validation is used to validate the results.Experimental outcomes reveal that the MLP-based architecture can produce better outcomes than existing approaches with a 94.40%accuracy score.The findings of this study show that the proposed system achieves high performance indicating that it has the potential for deployment in a real-world,practical medical environment.

Ethnoveterinary medicines used against various livestock disorders in the flora of Shamozai Valley, Swat,KP Pakistan

Background:The Shamozai Valley of Swat District is remarkable with various plant treasures.Ethnoveterinary information offers folk remedies for livestock,which are cheaper than standard treatment methods and are readily available.Methods:Different trips were organized for gathering(harvesting)and recording medicinal plants in the area during 2018.A total of 140 local residents were interviewed.Then,the obtained data were evaluated using used value,relative frequency citations,fidelity level,consensus index,and informant consensus factor.Results:In this study,41 plants were presented,and these plants were used commonly as medication for treating various livestock ailments.The therapeutic plants with most used value were Artemisia scoparia(0.607),Berberis lyceum Royle(0.610),Bromus japonicus(0.491),Avena sativa(0.482),Cannabis sativa L.(0.473),Capsicum annum L.(0.471),Cedrus deodara(0.462),and Chenopodium murale(0.453).On the basis of relative frequency citations values,the most quoted plant species by the indigenous people are Artemisia scoparia(0.760),Berberis lyceum(0.742),Bromus japonicus(0.731),Avena sativa(0.721),and Cannabis sativa L.(0.711).Consensus index percentage showed the highest for Artemisia scoparia(83.109%),followed by Berberis lyceum Royle(80.454%),whereas the ethnomedicinal plant species with most fidelity level were Artemisia scoparia(76.320%),Berberis lyceum Royle(73.403%),Bromus japonicus(72.013%),Avena sativa(71.024%),Cannabis sativa L.(69.322%),Capsicum annum L(68.344%),Cedrus deodara(67.215%),and Chenopodium murale(66.060%)for various disorders.Informant consensus factor ranged from 0.947 to 1.000,whereas different ailments viz.appetite-causing agent,carminative treatment,eye diseases,mouth ulcers,myiasis,pediculosis,septicemia,and tick infestation had maximum informant consensus factor value.Conclusion:The publics of Shamozai are deeply reliant on ethomedicinal plants for treating numerous livestock ailments.Folk information always offers a baseline for further phytochemical and pharmacologic study.

Electrocardiogram Feature Extraction and Pattern Recognition Using a Novel Windowing Algorithm

This paper presents a Novel Windowing Algorithm for Electrocardiogram Feature Extraction and Pattern Recognition. The work presented here deals with a simple and efficient way of detecting ECG features that are P, Q, R, S and T waves. Windowing method is used to select these waves. Windows are based on varying R-R intervals. It has been tested on ECG simulator data and also on different records of the MIT-BIH arrhythmia database, producing satisfactory results. ECG timing intervals are also required for monitoring the cardiac condition of patients. Hence after feature detections ECG timing intervals like the PR interval, QRS duration, the QT interval, the QT corrected interval and Vent Rate are efficiently calculated using proposed Formulae.

Risk factor profiles for gastric cancer prediction with respect to Helicobacter pylori:A study of a tertiary care hospital in Pakistan

BACKGROUND Gastric cancer(GC)is the fourth leading cause of cancer-related deaths worldwide.Diagnosis relies on histopathology and the number of endoscopies is increasing.Helicobacter pylori(H.pylori)infection is a major risk factor.AIM To develop an in-silico GC prediction model to reduce the number of diagnostic surgical procedures.The meta-data of patients with gastroduodenal symptoms,risk factors associated with GC,and H.pylori infection status from Holy Family Hospital Rawalpindi,Pakistan,were used with machine learning.METHODS A cohort of 341 patients was divided into three groups[normal gastric mucosa(NGM),gastroduodenal diseases(GDD),and GC].Information associated with socioeconomic and demographic conditions and GC risk factors was collected using a questionnaire.H.pylori infection status was determined based on urea breath test.The association of these factors and histopathological grades was assessed statistically.K-Nearest Neighbors and Random Forest(RF)machine learning models were tested.RESULTS This study reported an overall frequency of 64.2%(219/341)of H.pylori infection among enrolled subjects.It was higher in GC(74.2%,23/31)as compared to NGM and GDD and higher in males(54.3%,119/219)as compared to females.More abdominal pain(72.4%,247/341)was observed than other clinical symptoms including vomiting,bloating,acid reflux and heartburn.The majority of the GC patients experienced symptoms of vomiting(91%,20/22)with abdominal pain(100%,22/22).The multinomial logistic regression model was statistically significant and correctly classified 80%of the GDD/GC cases.Age,income level,vomiting,bloating and medication had significant association with GDD and GC.A dynamic RF GC-predictive model was developed,which achieved>80%test accuracy.CONCLUSION GC risk factors were incorporated into a computer model to predict the likelihood of developing GC with high sensitivity and specificity.The model is dynamic and will be further improved and validated by including new data in future research studies.Its use may reduce unnecessary endoscopic procedures.It is freely available.