Hyperspectral Remote Sensing Image Classification Using Improved Metaheuristic with Deep Learning

Remote sensing image(RSI)classifier roles a vital play in earth observation technology utilizing Remote sensing(RS)data are extremely exploited from both military and civil fields.More recently,as novel DL approaches develop,techniques for RSI classifiers with DL have attained important breakthroughs,providing a new opportunity for the research and development of RSI classifiers.This study introduces an Improved Slime Mould Optimization with a graph convolutional network for the hyperspectral remote sensing image classification(ISMOGCN-HRSC)model.The ISMOGCN-HRSC model majorly concentrates on identifying and classifying distinct kinds of RSIs.In the presented ISMOGCN-HRSC model,the synergic deep learning(SDL)model is exploited to produce feature vectors.The GCN model is utilized for image classification purposes to identify the proper class labels of the RSIs.The ISMO algorithm is used to enhance the classification efficiency of the GCN method,which is derived by integrating chaotic concepts into the SMO algorithm.The experimental assessment of the ISMOGCN-HRSC method is tested using a benchmark dataset.

Modified Buffalo Optimization with Big Data Analytics Assisted Intrusion Detection Model

Lately,the Internet of Things(IoT)application requires millions of structured and unstructured data since it has numerous problems,such as data organization,production,and capturing.To address these shortcomings,big data analytics is the most superior technology that has to be adapted.Even though big data and IoT could make human life more convenient,those benefits come at the expense of security.To manage these kinds of threats,the intrusion detection system has been extensively applied to identify malicious network traffic,particularly once the preventive technique fails at the level of endpoint IoT devices.As cyberattacks targeting IoT have gradually become stealthy and more sophisticated,intrusion detection systems(IDS)must continually emerge to manage evolving security threats.This study devises Big Data Analytics with the Internet of Things Assisted Intrusion Detection using Modified Buffalo Optimization Algorithm with Deep Learning(IDMBOA-DL)algorithm.In the presented IDMBOA-DL model,the Hadoop MapReduce tool is exploited for managing big data.The MBOA algorithm is applied to derive an optimal subset of features from picking an optimum set of feature subsets.Finally,the sine cosine algorithm(SCA)with convolutional autoencoder(CAE)mechanism is utilized to recognize and classify the intrusions in the IoT network.A wide range of simulations was conducted to demonstrate the enhanced results of the IDMBOA-DL algorithm.The comparison outcomes emphasized the better performance of the IDMBOA-DL model over other approaches.

Aquila Optimization with Machine Learning-Based Anomaly Detection Technique in Cyber-Physical Systems

Cyber-physical system(CPS)is a concept that integrates every computer-driven system interacting closely with its physical environment.Internet-of-things(IoT)is a union of devices and technologies that provide universal interconnection mechanisms between the physical and digital worlds.Since the complexity level of the CPS increases,an adversary attack becomes possible in several ways.Assuring security is a vital aspect of the CPS environment.Due to the massive surge in the data size,the design of anomaly detection techniques becomes a challenging issue,and domain-specific knowledge can be applied to resolve it.This article develops an Aquila Optimizer with Parameter Tuned Machine Learning Based Anomaly Detection(AOPTML-AD)technique in the CPS environment.The presented AOPTML-AD model intends to recognize and detect abnormal behaviour in the CPS environment.The presented AOPTML-AD framework initially pre-processes the network data by converting them into a compatible format.Besides,the improved Aquila optimization algorithm-based feature selection(IAOA-FS)algorithm is designed to choose an optimal feature subset.Along with that,the chimp optimization algorithm(ChOA)with an adaptive neuro-fuzzy inference system(ANFIS)model can be employed to recognise anomalies in the CPS environment.The ChOA is applied for optimal adjusting of the membership function(MF)indulged in the ANFIS method.The performance validation of the AOPTML-AD algorithm is carried out using the benchmark dataset.The extensive comparative study reported the better performance of the AOPTML-AD technique compared to recent models,with an accuracy of 99.37%.

Chaotic Flower Pollination with Deep Learning Based COVID-19 Classification Model

The Coronavirus Disease(COVID-19)pandemic has exposed the vulnerabilities of medical services across the globe,especially in underdeveloped nations.In the aftermath of the COVID-19 outbreak,a strong demand exists for developing novel computer-assisted diagnostic tools to execute rapid and cost-effective screenings in locations where many screenings cannot be executed using conventional methods.Medical imaging has become a crucial component in the disease diagnosis process,whereas X-rays and Computed Tomography(CT)scan imaging are employed in a deep network to diagnose the diseases.In general,four steps are followed in image-based diagnostics and disease classification processes by making use of the neural networks,such as network training,feature extraction,model performance testing and optimal feature selection.The current research article devises a Chaotic Flower Pollination Algorithm with a Deep Learning-Driven Fusion(CFPADLDF)approach for detecting and classifying COVID-19.The presented CFPA-DLDF model is developed by integrating two DL models to recognize COVID-19 in medical images.Initially,the proposed CFPA-DLDF technique employs the Gabor Filtering(GF)approach to pre-process the input images.In addition,a weighted voting-based ensemble model is employed for feature extraction,in which both VGG-19 and the MixNet models are included.Finally,the CFPA with Recurrent Neural Network(RNN)model is utilized for classification,showing the work’s novelty.A comparative analysis was conducted to demonstrate the enhanced performance of the proposed CFPADLDF model,and the results established the supremacy of the proposed CFPA-DLDF model over recent approaches.

Design of Miniature UWB-Based Antenna by Employing a Tri-Sectional SIR Feeder

A novel ultra-wideband(UWB)-based microstrip antenna is presented in this work by using a slotted patch resonator,a tri-sectional stepped impedance resonator(SIR)feeder,as well as a reduced ground plane.The whole structure was realized on an FR4 substrate.The impact of incorporating several cases of ground planes on the input reflection has been thoroughly investigated under the same tri-sectional SIR feeder and by employing a slotted patch radiator.Since the complete ground plane presents an inadequate frequency response,by reducing the ground plane,the induced UWB responses are apparent while the antenna exhibits higher impedance bandwidth.The impact of both the uniform impedance resonator(UIR)as well as the SIR feeder on the input reflection has also been examined by following the same adopted reduced ground technique and using a slotted patch radiator.As a result,the UIR feeder exhibits a dual-band frequency response,when a wide notched band is incorporated in the range from 4.5–6.5 GHz.The dual-band response of the bi-sectional SIR feeder is still apparent with a narrower notched band in the frequency range from 4–5 GHz.As far as the tri-sectional SIR feeder is concerned,the UWB response is discernible without recording the existence of a notched band.Additionally,the antenna displays a higher impedance bandwidth compared with the previously reported steps.Our proposed antenna configuration is designed with highly compact dimensions and an overall size of 14×27.2 mm2.Moreover,it operates under the impedance bandwidth of 2.86–10.31 GHz that can be leveraged for numerous applications where wireless systems are used.Our approach presents several advantages compared with the other reported UWB-based antennas in the literature,whereas the measured S11 pattern is in good agreement with the simulated one.

Improvement method for cervical cancer detection: A comparative analysis

Cervical cancer is a prevalent and deadly cancer that affects women all over the world.It affects about 0.5 million women anually and results in over 0.3 million fatalities.Diagnosis of this cancer was previously done manually,which could result in false positives or negatives.The researchers are still contemplating how to detect cervical cancer automatically and how to evaluate Pap smear images.Hence,this paper has reviewed several detection methods from the previous researches that has been done before.This paper reviews pre-processing,detection method framework for nucleus detection,and analysis performance of the method selected.There are four methods based on a reviewed technique from previous studies that have been running through the experimental procedure using Matlab,and the dataset used is established Herlev Dataset.The results show that the highest performance assessment metric values obtain from Method 1:Thresholding and Trace region boundaries in a binary image with the values of precision 1.0,sensitivity 98.77%,specificity 98.76%,accuracy 98.77%and PSNR 25.74%for a single type of cell.Meanwhile,the average values of precision were 0.99,sensitivity 90.71%,specificity 96.55%,accuracy 92.91%and PSNR 16.22%.The experimental results are then compared to the existing methods from previous studies.They show that the improvement method is able to detect the nucleus of the cell with higher performance assessment values.On the other hand,the majority of current approaches can be used with either a single or a large number of cervical cancer smear images.This study might persuade other researchers to recognize the value of some of the existing detection techniques and offer a strong approach for developing and implementing new solutions.

8-HQCdCl_(2)H_(2)O as an organic Q-switcher in erbium laser cavity

This paper demonstrated a Q-switched erbium-doped fiber laser(EDFL)using an organic saturable absorber(SA)based on 8-HQCdCl_(2)H_(2)O material.The organic thin film was prepared using the casting process.The proposed Q-switched EDFL has a maximum repetition rate of 143 kHz,minimum pulse duration of 1.85μs and the highest pulse energy of 167 nJ.The Q-switched peak laser was at a central wavelength of 1531 nm with a 3 dB bandwidth of 3.52 nm and power intensity of 2.64 dBm.

Preparation and Antibacterial Activity of Superhydrophobic Modified ZnO/PVC Nanocomposite

We fabricated a superhydrophobic modified ZnO/PVC nanocomposite cluster with antibacterial properties using the chemical precipitation method and selected solvent/non-solvent(THF/ethanol)to PVC.The effects of ethanol content(47%,50%,53%,and 56%)on nanocomposite morphology and Water Contact Angles(WCAs)were investigated.XRD measurements confirmed the polycrystalline structure of ZnO with a wurtzite hexagonal phase,and EDX results indicated the presence of all element peaks.FESEM analysis of specimens revealed a rough surface structure resembling a cluster of NPs,and that structure was dominant when the ethanol content increased to 56%.The WCA increased on the superhydrophobic nanocomposite as ethanol content increased,and an optimum WCA(160°±2°)was obtained at an ethanol content of 56%.Antibacterial activity was tested on the superhydrophobic and hydrophobic states,and the superhydrophobic specimens showed good inhibition against Klebsiella spp.and Staphylococcus epidermidis.However,the hydrophobic specimens demonstrated no antibacterial activity against S.epidermidis.These promising results can inform the development of nanocomposites for many environmental applications.

Polymethyl Methacrylate-Collagen-Magnesium Hydroxyapatite Bone Cement Preparation for Orthopedic Application

In this research,bone cement was prepared by mixing 2 g of magnesium hydroxyapatite(laboratory synthesized),12 g of polymethyl methacrylate,4 g of methyl methacrylate,and collagen(1,3,and 6 g).The samples were molded in a circular shape.They were inspected by visual microscopy,FTIR,XRD,and FESEM.They were engrossed in synthesized simulated body fluid for 1 month and then inspected by visual microscopy,FTIR,XRD,and FESEM.The samples prepared from 6 g of collagen showed the highest hydroxyapatite formation(high osseointegration)than the other samples.