Deep Consensus Network for Recycling Waste Detection in Smart Cities

Recently,urbanization becomes a major concern for developing as well as developed countries.Owing to the increased urbanization,one of the important challenging issues in smart cities is waste management.So,automated waste detection and classification model becomes necessary for the smart city and to accomplish better recyclable waste management.Effective recycling of waste offers the chance of reducing the quantity of waste disposed to the land fill by minimizing the requirement of collecting raw materials.This study develops a novel Deep Consensus Network with Whale Optimization Algorithm for Recycling Waste Object Detection(DCNWORWOD)in Smart Cities.The goal of the DCNWO-RWOD technique intends to properly identify and classify the objects into recyclable and non-recyclable ones.The proposed DCNWO-RWOD technique involves the design of deep consensus network(DCN)to detect waste objects in the input image.For improving the overall object detection performance of the DCN model,the whale optimization algorithm(WOA)is exploited.Finally,Na飗e Bayes(NB)classifier is used for the classification of detected waste objects into recyclable and non-recyclable ones.The performance validation of theDCNWO-RWOD technique takes place using the open access dataset.The extensive comparative study reported the enhanced performance of the DCNWO-RWOD technique interms of several measures.

Integration of Fog Computing for Health Record Management Using Blockchain Technology

Internet of Medical Things (IoMT) is a breakthrough technologyin the transfer of medical data via a communication system. Wearable sensordevices collect patient data and transfer them through mobile internet, thatis, the IoMT. Recently, the shift in paradigm from manual data storage toelectronic health recording on fog, edge, and cloud computing has been noted.These advanced computing technologies have facilitated medical services withminimum cost and available conditions. However, the IoMT raises a highconcern on network security and patient data privacy in the health caresystem. The main issue is the transmission of health data with high security inthe fog computing model. In today’s market, the best solution is blockchaintechnology. This technology provides high-end security and authenticationin storing and transferring data. In this research, a blockchain-based fogcomputing model is proposed for the IoMT. The proposed technique embedsa block chain with the yet another consensus (YAC) protocol building securityinfrastructure into fog computing for storing and transferring IoMT data inthe network. YAC is a consensus protocol that authenticates the input datain the block chain. In this scenario, the patients and their family membersare allowed to access the data. The empirical outcome of the proposedtechnique indicates high reliability and security against dangerous threats.The major advantages of using the blockchain model are high transparency,good traceability, and high processing speed. The technique also exhibitshigh reliability and efficiency in accessing data with secure transmission. Theproposed technique achieves 95% reliability in transferring a large number offiles up to 10,000.

Resource Assessment of Wind Energy Potential of Mokha in Yemen with Weibull Speed

The increasing use of fossil fuels has a significant impact on the environment and ecosystem,which increases the rate of pollution.Given the high potential of renewable energy sources in Yemen and other Arabic countries,and the absence of similar studies in the region.This study aims to examine the potential of wind energy in Mokha region.This was done by analyzing and evaluating wind properties,determining available energy density,calculating wind energy extracted at different altitudes,and then computing the capacity factor for a few wind turbines and determining the best.Weibull speed was verified as the closest to the average actual wind speed using the cube root,as this was verified using 3 criteria for performance analysis methods(R^(2)=0.9984,RMSE=0.0632,COE=1.028).The wind rose scheme was used to determine the appropriate direction for directing the wind turbines,the southerly direction was appropriate,as the winds blow from this direction for 227 days per year,and the average southerly wind velocity is 5.27 m/s at an altitude of 3 m.The turbine selected in this study has a tower height of 100m and a rated power of 3.45 MW.The capacitance factor was calculated for the three classes of wind turbines classified by the International Electrotechnical Commission(IEC)and compared,and the turbine of the first class was approved,and it is suitable for the study site,as it resists storms more than others.The daily and annual capacity of a single,first-class turbine has been assessed to meet the needs of 1,447 housing units in Mokha region.The amount of energy that could be supplied to each dwelling was around 19 kWh per day,which was adequate to power the basic loads in the home.

Weather Forecasting Prediction Using Ensemble Machine Learning for Big Data Applications

The agricultural sector’s day-to-day operations,such as irrigation and sowing,are impacted by the weather.Therefore,weather constitutes a key role in all regular human activities.Weather forecasting must be accurate and precise to plan our activities and safeguard ourselves as well as our property from disasters.Rainfall,wind speed,humidity,wind direction,cloud,temperature,and other weather forecasting variables are used in this work for weather prediction.Many research works have been conducted on weather forecasting.The drawbacks of existing approaches are that they are less effective,inaccurate,and time-consuming.To overcome these issues,this paper proposes an enhanced and reliable weather forecasting technique.As well as developing weather forecasting in remote areas.Weather data analysis and machine learning techniques,such as Gradient Boosting Decision Tree,Random Forest,Naive Bayes Bernoulli,and KNN Algorithm are deployed to anticipate weather conditions.A comparative analysis of result outcome said in determining the number of ensemble methods that may be utilized to improve the accuracy of prediction in weather forecasting.The aim of this study is to demonstrate its ability to predict weather forecasts as soon as possible.Experimental evaluation shows our ensemble technique achieves 95%prediction accuracy.Also,for 1000 nodes it is less than 10 s for prediction,and for 5000 nodes it takes less than 40 s for prediction.

Evolutionary Algorithsm with Machine Learning Based Epileptic Seizure Detection Model

Machine learning (ML) becomes a familiar topic among decisionmakers in several domains, particularly healthcare. Effective design of MLmodels assists to detect and classify the occurrence of diseases using healthcaredata. Besides, the parameter tuning of the ML models is also essentialto accomplish effective classification results. This article develops a novelred colobuses monkey optimization with kernel extreme learning machine(RCMO-KELM) technique for epileptic seizure detection and classification.The proposed RCMO-KELM technique initially extracts the chaotic, time,and frequency domain features in the actual EEG signals. In addition, the minmax normalization approach is employed for the pre-processing of the EEGsignals. Moreover, KELM model is used for the detection and classificationof epileptic seizures utilizing EEG signal. Furthermore, the RCMO techniquewas utilized for the optimal parameter tuning of the KELM technique insuch a way that the overall detection outcomes can be considerably enhanced.The experimental result analysis of the RCMO-KELM technique has beenexamined using benchmark dataset and the results are inspected under severalaspects. The comparative result analysis reported the better outcomes of theRCMO-KELM technique over the recent approaches with the accuy of 0.956.

Machine Learning Based Depression,Anxiety,and Stress Predictive Model During COVID-19 Crisis

Corona Virus Disease-2019(COVID-19)was reported at first in Wuhan city,China by December 2019.World Health Organization(WHO)declared COVID-19 as a pandemic i.e.,global health crisis onMarch 11,2020.The outbreak of COVID-19 pandemic and subsequent lockdowns to curb the spread,not only affected the economic status of a number of countries,but it also resulted in increased levels of Depression,Anxiety,and Stress(DAS)among people.Therefore,there is a need exists to comprehend the relationship among psycho-social factors in a country that is hypothetically affected by high levels of stress and fear;with tremendously-limitingmeasures of social distancing and lockdown in force;and with high rates of new cases and mortalities.With this motivation,the current study aims at investigating theDAS levels among college students during COVID-19 lockdown since they are identified as a highly-susceptible population.The current study proposes to develop Intelligent Feature Subset Selection withMachine Learning-based DAS predictive(IFSSML-DAS)model.The presented IFSSML-DAS model involves data preprocessing,Feature Subset Selection(FSS),classification,and parameter tuning.Besides,IFSSML-DAS model uses Group Gray Wolf Optimization based FSS(GGWO-FSS)technique to reduce the curse of dimensionality.In addition,Beetle Swarm Optimization based Least Square Support Vector Machine(BSO-LSSVM)model is also employed for classification in which the weight and bias parameters of the LSSVM model are optimally adjusted using BSO algorithm.The performance of the proposed IFSSML-DAS model was tested using a benchmark DASS-21 dataset and the results were investigated under different measures.The outcome of the study suggests the development of specialized programs to handleDAS among population so as to overcome COVID-19 crisis.

Deep Learning Enabled Microarray Gene Expression Classification for Data Science Applications

In bioinformatics applications,examination of microarray data has received significant interest to diagnose diseases.Microarray gene expression data can be defined by a massive searching space that poses a primary challenge in the appropriate selection of genes.Microarray data classification incorporates multiple disciplines such as bioinformatics,machine learning(ML),data science,and pattern classification.This paper designs an optimal deep neural network based microarray gene expression classification(ODNN-MGEC)model for bioinformatics applications.The proposed ODNN-MGEC technique performs data normalization process to normalize the data into a uniform scale.Besides,improved fruit fly optimization(IFFO)based feature selection technique is used to reduce the high dimensionality in the biomedical data.Moreover,deep neural network(DNN)model is applied for the classification of microarray gene expression data and the hyperparameter tuning of the DNN model is carried out using the Symbiotic Organisms Search(SOS)algorithm.The utilization of IFFO and SOS algorithms pave the way for accomplishing maximum gene expression classification outcomes.For examining the improved outcomes of the ODNN-MGEC technique,a wide ranging experimental analysis is made against benchmark datasets.The extensive comparison study with recent approaches demonstrates the enhanced outcomes of the ODNN-MGEC technique in terms of different measures.

Hybrid Deep Learning Enabled Intrusion Detection in Clustered IIoT Environment

Industrial Internet of Things(IIoT)is an emerging field which connects digital equipment as well as services to physical systems.Intrusion detection systems(IDS)can be designed to protect the system from intrusions or attacks.In this view,this paper presents a novel hybrid deep learning with metaheuristics enabled intrusion detection(HDL-MEID)technique for clustered IIoT environments.The HDL-MEID model mainly intends to organize the IIoT devices into clusters and enabled secure communication.Primarily,the HDL-MEID technique designs a new chaotic mayfly optimization(CMFO)based clustering approach for the effective choice of the Cluster Heads(CH)and organize clusters.Moreover,equilibrium optimizer with hybrid convolutional neural network long short-term memory(HCNNLSTM)based classification model is derived to identify the existence of the intrusions in the IIoT environment.Extensive experimental analysis is performed to highlight the enhanced outcomes of the HDL-MEID technique and the results were investigated under different aspects.The experimental results highlight the supremacy of the proposed HDL-MEID technique over recent state-of-the-art techniques.

Analysis and Assessment of Wind Energy Potential of Socotra Archipelago in Yemen

The increasing use of fossil fuels has a significant impact on the environment and ecosystem,which increases the rate of pollution.Given the high potential of renewable energy sources inYemen and the absence of similar studies in the region,this study aims to examine the potential of wind energy in Socotra Island.This was done by analyzing and evaluating wind properties,determining available energy density,calculating wind energy extracted at different altitudes,and then computing the capacity factor for a number of wind turbines and determining the best.The average wind speed in Socotra Island was obtained from the Civil Aviation and Meteorology Authority data,only for the five-year data currently available.The results showed high wind speeds from June to September(9.85-14.88 m/s)while the wind speed decreased for the rest of the year.The average wind speed in the five years was 7.95 m/s.The average annual wind speed,wind energy density,and annual energy density were calculated at different altitudes(10,30,and 50 m).According to the International Wind Energy Rating criteria,the region of Socotra Island falls under Category 7 and is classified as‘Superb’for most of the year.This study provides useful information for developing wind energy and an efficient wind approach.

Biomedical Osteosarcoma Image Classification Using Elephant Herd Optimization and Deep Learning

Osteosarcoma is a type of malignant bone tumor that is reported across the globe.Recent advancements in Machine Learning(ML)and Deep Learning(DL)models enable the detection and classification of malignancies in biomedical images.In this regard,the current study introduces a new Biomedical Osteosarcoma Image Classification using Elephant Herd Optimization and Deep Transfer Learning(BOIC-EHODTL)model.The presented BOIC-EHODTL model examines the biomedical images to diagnose distinct kinds of osteosarcoma.At the initial stage,Gabor Filter(GF)is applied as a pre-processing technique to get rid of the noise from images.In addition,Adam optimizer with MixNet model is also employed as a feature extraction technique to generate feature vectors.Then,EHOalgorithm is utilized along with Adaptive Neuro-Fuzzy Classifier(ANFC)model for recognition and categorization of osteosarcoma.EHO algorithm is utilized to fine-tune the parameters involved in ANFC model which in turn helps in accomplishing improved classification results.The design of EHO with ANFC model for classification of osteosarcoma is the novelty of current study.In order to demonstrate the improved performance of BOIC-EHODTL model,a comprehensive comparison was conducted between the proposed and existing models upon benchmark dataset and the results confirmed the better performance of BOIC-EHODTL model over recent methodologies.