Exploring the Cation Regulation Mechanism for Interfacial Water Involved in the Hydrogen Evolution Reaction by In Situ Raman Spectroscopy

Interfacial water molecules are the most important participants in the hydrogen evolution reaction(HER).Hence,understanding the behavior and role that interfacial water plays will ultimately reveal the HER mechanism.Unfortunately,investigating interfacial water is extremely challenging owing to the interference caused by bulk water molecules and complexity of the interfacial environment.Here,the behaviors of interfacial water in different cationic electrolytes on Pd surfaces were investigated by the electrochemistry,in situ core-shell nanostructure enhanced Raman spectroscopy and theoretical simulation techniques.Direct spectral evidence reveals a red shift in the frequency and a decrease in the intensity of interfacial water as the potential is shifted in the positively direction.When comparing the different cation electrolyte systems at a given potential,the frequency of the interfacial water peak increases in the specified order:Li+<Na^(+)<K^(+)<Ca^(2+)<Sr^(2+).The structure of interfacial water was optimized by adjusting the radius,valence,and concentration of cation to form the two-H down structure.This unique interfacial water structure will improve the charge transfer efficiency between the water and electrode further enhancing the HER performance.Therefore,local cation tuning strategies can be used to improve the HER performance by optimizing the interfacial water structure.

Cationic ordering transition in oxygen-redox layered oxide cathodes

Understanding the structural origin of the competition between oxygen 2p and transition-metal 3d orbitals in oxygen-redox(OR)layered oxides is eminently desirable for exploring reversible and high-energy-density Li/Na-ion cathodes.Here,we reveal the correlation between cationic ordering transition and OR degradation in ribbon-ordered P3-Na_(0.6)Li_(0.2)Mn_(0.8)O_(2) via in situ structural analysis.Comparing two different voltage windows,the OR capacity can be improved approximately twofold when suppressing the in-plane cationic ordering transition.We find that the intralayer cationic migration is promoted by electrochemical reduction from Mn^(4+)to Jahn–Teller Mn^(3+)and the concomitant NaO_(6) stacking transformation from triangular prisms to octahedra,resulting in the loss of ribbon ordering and electrochemical decay.First-principles calculations reveal that Mn^(4+)/Mn^(3+)charge ordering and alignment of the degenerate eg orbital induce lattice-level collective Jahn–Teller distortion,which favors intralayer Mn-ion migration and thereby accelerates OR degradation.These findings unravel the relationship between in-plane cationic ordering and OR reversibility and highlight the importance of superstructure protection for the rational design of reversible OR-active layered oxide cathodes.

Enabling an Inorganic-Rich Interface via Cationic Surfactant for High-Performance Lithium Metal Batteries

An anion-rich electric double layer(EDL)region is favorable for fabricating an inorganic-rich solid-electrolyte interphase(SEI)towards stable lithium metal anode in ester electrolyte.Herein,cetyltrimethylammonium bromide(CTAB),a cationic surfactant,is adopted to draw more anions into EDL by ionic interactions that shield the repelling force on anions during lithium plating.In situ electrochemical surface-enhanced Raman spectroscopy results combined with molecular dynamics simulations validate the enrichment of NO_(3)^(−)/FSI−anions in the EDL region due to the positively charged CTA^(+).In-depth analysis of SEI structure by X-ray photoelectron spectroscopy and time-of-flight secondary ion mass spectrometry results confirmed the formation of the inorganic-rich SEI,which helps improve the kinetics of Li^(+)transfer,lower the charge transfer activation energy,and homogenize Li deposition.As a result,the Li||Li symmetric cell in the designed electrolyte displays a prolongated cycling time from 500 to 1300 h compared to that in the blank electrolyte at 0.5 mA cm^(-2) with a capacity of 1 mAh cm^(-2).Moreover,Li||LiFePO_(4) and Li||LiCoO_(2) with a high cathode mass loading of>10 mg cm^(-2) can be stably cycled over 180 cycles.

Person-Dependent Handwriting Verification for Special Education Using DeepLearning

Individuals with special needs learn more slowly than their peers and they need repetitions to be permanent.However,in crowded classrooms,it is dif-ficult for a teacher to deal with each student individually.This problem can be overcome by using supportive education applications.However,the majority of such applications are not designed for special education and therefore they are not efficient as expected.Special education students differ from their peers in terms of their development,characteristics,and educational qualifications.The handwriting skills of individuals with special needs are lower than their peers.This makes the task of Handwriting Recognition(HWR)more difficult.To over-come this problem,we propose a new personalized handwriting verification sys-tem that validates digits from the handwriting of special education students.The system uses a Convolutional Neural Network(CNN)created and trained from scratch.The data set used is obtained by collecting the handwriting of the students with the help of a tablet.A special education center is visited and the handwrittenfigures of the students are collected under the supervision of special education tea-chers.The system is designed as a person-dependent system as every student has their writing style.Overall,the system achieves promising results,reaching a recognition accuracy of about 94%.Overall,the system can verify special educa-tion students’handwriting digits with high accuracy and is ready to integrate with a mobile application that is designed to teach digits to special education students.

Third-Order Nonlinear Optical Responses of Bis(15-crown-5)-stilbenes Binding to One-or Two-Alkali Metal Cation(Li^(+),Na^(+)and K^(+))

Bis(15-crown-5)-stilbenes containing crown ether parts have been widely used in a variety of chemical applications,such as cation detectors,because of their ability to selectively bind to alkali metal cations,Bis(15-crown-5)-stilbenes and its derivatives with complexation of one-or two-alkali metal cation(Li^(+),Na^(+)and K^(+))have been theoretically investigat-ed by quantum chemistry methods.The coordination of alkali cations results in partial shrinkage of crown ethers,which directly affected natural distribution analysis charges and molecular orbital energy levels.The number of alkali metal ions has significant effects on absorption spectra and mean second hyperpolarizability.When one alkali metal ion was added to the anticonformer of bis(15-crown-5)-stilbene,the absorption spectra were obvious-ly redshifted and the mean second hyperpolarizability values were slightly increased;while two alkali metal ions were added to bis(15-crown-5)-stilbene,the absorption spectra were ob-viously blue shifted and the mean second hyperpolarizability values decreased.On the other hand,as the radius of the alkali ions increased,the mean second hyperpolarizability values of the compounds increased gradually.It is indicated that the mean second hyperpolarizability value is sensitive to the number and radius of the alkali metal cations,thus the third order nonlinear optical response can be used as a signal to detect the number and type of alkali met-al ions.

Misclassification analysis of discriminant model

This paper extends the criterion of the misclassification ratio of discriminant model and presents a new selection method of discriminant model.For selecting the discriminant model,this method establishes the rule of misclassification degree ratio through misclassification ratio of the discriminant model and misclassification degree of the samples.To test the effect of this method,this work uses seven UCI data sets.Numerical experiments on these examples indicate that this method has certain rationality and has a better effect to select a discriminant model.

An Ordinal Multi-Dimensional Classification(OMDC)for Predictive Maintenance

Predictive Maintenance is a type of condition-based maintenance that assesses the equipment's states and estimates its failure probability and when maintenance should be performed.Although machine learning techniques have been frequently implemented in this area,the existing studies disregard to the nat-ural order between the target attribute values of the historical sensor data.Thus,these methods cause losing the inherent order of the data that positively affects the prediction performances.To deal with this problem,a novel approach,named Ordinal Multi-dimensional Classification(OMDC),is proposed for estimating the conditions of a hydraulic system's four components by taking into the natural order of class values.To demonstrate the prediction ability of the proposed approach,eleven different multi-dimensional classification algorithms(traditional Binary Relevance(BR),Classifier Chain(CC),Bayesian Classifier Chain(BCC),Monte Carlo Classifier Chain(MCC),Probabilistic Classifier Chain(PCC),Clas-sifier Dependency Network(CDN),Classifier Trellis(CT),Classifier Dependency Trellis(CDT),Label Powerset(LP),Pruned Sets(PS),and Random k-Labelsets(RAKEL))were implemented using the Ordinal Class Classifier(OCC)algorithm.Besides,seven different classification algorithms(Multilayer Perceptron(MLP),Support Vector Machine(SVM),k-Nearest Neighbour(kNN),Decision Tree(C4.5),Bagging,Random Forest(RF),and Adaptive Boosting(AdaBoost))were chosen as base learners for the OCC algorithm.The experimental results present that the proposed OMDC approach using binary relevance multi-dimensional classification methods predicts the conditions of a hydraulic system's multiple components with high accuracy.Also,it is clearly seen from the results that the OMDC models that utilize ensemble-based classification algorithms give more reliable prediction performances with an average Hamming score of 0.853 than the others that use traditional algorithms as base learners.

A novel cationic collector for silicon removal from collophane using reverse flotation under acidic conditions

We analyzed a novel cationic collector using chemical plant byproducts,such as cetyltrimethylammonium bromide(CTAB)and dibutyl phthalate(DBP).Our aim is to establish a highly effective and economical process for the removal of quartz from collophane.A microflotation test with a 25 mg·L^(−1)collector at pH value of 6-10 demonstrates a considerable difference in the floatability of pure quartz and fluorapatite.Flotation tests for a collophane sample subjected to the first reverse flotation for magnesium removal demonstrates that a rough flotation process(using a 0.4 kg·t−1 new collector at pH=6)results in a collophane concentrate with 29.33wt%P_(2)O_(5)grade and 12.66wt%SiO2 at a 79.69wt%P_(2)O_(5)recovery,providing desirable results.Mechanism studies using Fourier transform infrared spectroscopy,zeta potential,and contact angle measurements show that the adsorption capacity of the new collector for quartz is higher than that for fluorapatite.The synergistic effect of DBP increases the difference in hydrophobicity between quartz and fluorapatite.The maximum defoaming rate of the novel cationic collector reaches 142.8 mL·min−1.This is considerably higher than that of a conventional cationic collector.

One-step crosslinking preparation of tannic acid particles for the adsorption and separation of cationic dyes

In this study,a new tannic acid adsorbent(ethylene glycol diglycidyl ether crosslinked tannic acid,TAEGDE)for adsorptive removal of dyes from water was prepared using EGDE as a cross-linking agent.The resultant TA-EGDE was in particulate form with rough surface morphology and a diameter ranging from 10 to 30μm.The adsorption performance of the TA-EGDE was evaluated in a flow-through mode using water samples contaminated with methylene blue(MB)and two-component mixed dyes,respectively.The TA-EGDE provided adsorption capacity up to 721.8 mg·g^(-1)at 65°C for MB.It showed a high removal efficiency(99%)of MB(50 mg·L^(-1))from the water sample and could recovery 90%of the adsorbed MB by eluting with acidic ethanol aqueous solution.The excellent adsorption of MB and neutral red on the TA-EGDE may be the result of the synergy of electrostatic interaction andπ-πinteraction.Furthermore,the TA-EGDE could separate dyes from water samples contaminated with twocomponent mixed dyes with a separation coefficient ranging from 1.8 to 36.5.The anionic TA-EGDE would be an effective adsorbent to remove and recycle dyes from the contaminated water.

Divalent nitrogen-rich cationic salts with great gas production capacities

Monocyclic nitrogen-rich 3-(aminomethyl)-4,5-diamine-1,2,4-triazole(1)and fused cyclic 3,7-diamine-6-(aminomethyl)-[1,2,4]triazolo[4,3-b][1,2,4]triazole(9)were synthesized through the convenient cyclization reaction from the readily available reactant.Their energetic salts with high nitrogen content were proved to be rare examples of divalent monocyclic/fused cyclic cationic salts according to the single crystal analyses.The structure of intermediate B was also identified and verified by its trivalent cation crystal 17.5H_2O indirectly.Energetic compounds 2-8 and 10-17 were fully characterized by NMR spectroscopy,infrared spectroscopy,differential scanning calorimetry,elemental analysis.These energetic salts exhibit good thermal stability with decomposition temperatures ranged from 182℃to 245℃.The sensitivity of compounds 2,6,10 and 14 is similar or superior to that of RDX while the others were much more insensitive to mechanical stimulate.Furthermore,detonation velocity of 10(8843 m/s)surpass that of RDX(D=8795 m/s).Considering the high gas production volume(≥808 L/kg)of 2,4,10and 12,constant-volume combustion experiments were conduct to evaluate their gas production capacities specifically.These compounds possess much higher maximum gas-production pressures(P_(max):7.88-10.08 MPa)than the commonly used reagent guanidine nitrate(GN:P_(max)=4.20 MPa),which indicate their strong gas production capacity.

Styrene epoxidation catalyzed by polyoxometalate/quaternary ammonium phase transfer catalysts: The effect of cation size and catalyst deactivation mechanism

Catalytic epoxidation of alkenes is an important type of organic reaction in chemical industry,and the deep insight into catalyst deactivation will help to develop new epoxidation process.In this work,series of quaternary ammoniums bearing different cationic sizes,i.e.MTOA+(methyltrioctylammonium,[(C_(8)H_(17))_(3)CH_(3)N]+),HTMA+(hexadecyltrimethylammonium,[(C_(16)H_(33))(CH_(3))_(3)N]+) and DMDOA+(dimethyldioctadecylammonium,[(C_(18)H_(37))_(2)(CH_(3))_(2)N]+) were incorporated with polyoxometalate (POM) anions to prepare phase transfer catalysts (PTCs),which were used in the styrene epoxidations.Among them,(MTOA)_(3)PW_(4)O_(24)exhibits the best catalytic performance judged from the highest styrene conversion rate(52%) and styrene oxide selectivity (93%),during which the styrene epoxidation conditions were optimized.Meanwhile,the deactivation mechanism of this kind of PTCs was proposed firstly,i.e.in the case of low H_(2)O_(2) content,the oxidant can only be used in the styrene epoxidation,in which the catalyst can transform into stable Keggin-type POM.But when the content of H_(2)O_(2) is higher,the excess H_(2)O_(2) can reactivate the Keggin-type POM into active (PW_(4)O_(24))_(3)-anions,which can trigger the ring-opening polymerization of styrene oxide.Consequently,the catalyst is deactivated by adhered poly(styrene oxide)irreversibly,which was determined by NMR spectra.In this situation,the active moiety{PO_(4)[WO(O_(2))_(2)]_(4)}_(3)-in phase-transfer catalytic system can break into some unidentified species with low W/P ratio with the presence of epoxides.This work will be beneficial for the design of new PTCs in alkene epoxidation in fine chemical industry.

Addressing cation mixing in layered structured cathodes for lithium-ion batteries:A critical review

High-performance lithium-ion batteries(LIB)are important in powering emerging technologies.Cathodes are regarded as the bottleneck of increasing battery energy density,among which layered oxides are the most promising candidates for LIB.However,a limitation with layered oxides cathodes is the transition metal and Li site mixing,which significantly impacts battery capacity and cycling stability.Despite recent research on Li/Ni mixing,there is a lack of comprehensive understanding of the origin of cation mixing between the transition metal and Li;therefore,practical means to address it.Here,a critical review of cation mixing in layered cathodes has been provided,emphasising the understanding of cation mixing mechanisms and their impact on cathode material design.We list and compare advanced characterisation techniques to detect cation mixing in the material structure;examine methods to regulate the degree of cation mixing in layered oxides to boost battery capacity and cycling performance,and critically assess how these can be applied practically.An appraisal of future research directions,including superexchange interaction to stabilise structures and boost capacity retention has also been concluded.Findings will be of immediate benefit in the design of layered cathodes for high-performance rechargeable LIB and,therefore,of interest to researchers and manufacturers.

Optimal Deep Belief Network Enabled Malware Detection and Classification Model

Cybercrime has increased considerably in recent times by creating new methods of stealing,changing,and destroying data in daily lives.Portable Docu-ment Format(PDF)has been traditionally utilized as a popular way of spreading malware.The recent advances of machine learning(ML)and deep learning(DL)models are utilized to detect and classify malware.With this motivation,this study focuses on the design of mayfly optimization with a deep belief network for PDF malware detection and classification(MFODBN-MDC)technique.The major intention of the MFODBN-MDC technique is for identifying and classify-ing the presence of malware exist in the PDFs.The proposed MFODBN-MDC method derives a new MFO algorithm for the optimal selection of feature subsets.In addition,Adamax optimizer with the DBN model is used for PDF malware detection and classification.The design of the MFO algorithm to select features and Adamax based hyperparameter tuning for PDF malware detection and classi-fication demonstrates the novelty of the work.For demonstrating the improved outcomes of the MFODBN-MDC model,a wide range of simulations are exe-cuted,and the results are assessed in various aspects.The comparison study high-lighted the enhanced outcomes of the MFODBN-MDC model over the existing techniques with maximum precision,recall,and F1 score of 97.42%,97.33%,and 97.33%,respectively.

Optimal Sparse Autoencoder Based Sleep Stage Classification Using Biomedical Signals

The recently developed machine learning(ML)models have the ability to obtain high detection rate using biomedical signals.Therefore,this article develops an Optimal Sparse Autoencoder based Sleep Stage Classification Model on Electroencephalography(EEG)Biomedical Signals,named OSAE-SSCEEG technique.The major intention of the OSAE-SSCEEG technique is tofind the sleep stage disorders using the EEG biomedical signals.The OSAE-SSCEEG technique primarily undergoes preprocessing using min-max data normalization approach.Moreover,the classification of sleep stages takes place using the Sparse Autoencoder with Smoothed Regularization(SAE-SR)with softmax(SM)approach.Finally,the parameter optimization of the SAE-SR technique is carried out by the use of Coyote Optimization Algorithm(COA)and it leads to boosted classification efficiency.In order to ensure the enhanced performance of the OSAE-SSCEEG technique,a wide ranging simulation analysis is performed and the obtained results demonstrate the betterment of the OSAE-SSCEEG tech-nique over the recent methods.

Cephalopods Classification Using Fine Tuned Lightweight Transfer Learning Models

Cephalopods identification is a formidable task that involves hand inspection and close observation by a malacologist.Manual observation and iden-tification take time and are always contingent on the involvement of experts.A system is proposed to alleviate this challenge that uses transfer learning techni-ques to classify the cephalopods automatically.In the proposed method,only the Lightweight pre-trained networks are chosen to enable IoT in the task of cephalopod recognition.First,the efficiency of the chosen models is determined by evaluating their performance and comparing thefindings.Second,the models arefine-tuned by adding dense layers and tweaking hyperparameters to improve the classification of accuracy.The models also employ a well-tuned Rectified Adam optimizer to increase the accuracy rates.Third,Adam with Gradient Cen-tralisation(RAdamGC)is proposed and used infine-tuned models to reduce the training time.The framework enables an Internet of Things(IoT)or embedded device to perform the classification tasks by embedding a suitable lightweight pre-trained network.Thefine-tuned models,MobileNetV2,InceptionV3,and NASNet Mobile have achieved a classification accuracy of 89.74%,87.12%,and 89.74%,respectively.Thefindings have indicated that thefine-tuned models can classify different kinds of cephalopods.The results have also demonstrated that there is a significant reduction in the training time with RAdamGC.

A Cross-Domain Trust Model of Smart City IoT Based on Self-Certification

Smart city refers to the information system with Intemet of things and cloud computing as the core tec hnology and government management and industrial development as the core content,forming a large scale,heterogeneous and dynamic distributed Internet of things environment between different Internet of things.There is a wide demand for cooperation between equipment and management institutions in the smart city.Therefore,it is necessary to establish a trust mechanism to promote cooperation,and based on this,prevent data disorder caused by the interaction between honest terminals and malicious temminals.However,most of the existing research on trust mechanism is divorced from the Internet of things environment,and does not consider the characteristics of limited computing and storage capacity and large differences of Internet of hings devices,resuling in the fact that the research on abstract trust trust mechanism cannot be directly applied to the Internet of things;On the other hand,various threats to the Internet of things caused by security vulnerabilities such as collision attacks are not considered.Aiming at the security problems of cross domain trusted authentication of Intelligent City Internet of things terminals,a cross domain trust model(CDTM)based on self-authentication is proposed.Unlike most trust models,this model uses self-certified trust.The cross-domain process of internet of things(IoT)terminal can quickly establish a trust relationship with the current domain by providing its trust certificate stored in the previous domain interaction.At the same time,in order to alleviate the collision attack and improve the accuracy of trust evaluation,the overall trust value is calculated by comprehensively considering the quantity weight,time attenuation weight and similarity weight.Finally,the simulation results show that CDTM has good anti collusion attack ability.The success rate of malicious interaction will not increase significantly.Compared with other models,the resource consumption of our proposed model is significantly reduced.

Big Data Analytics with Optimal Deep Learning Model for Medical Image Classification

In recent years,huge volumes of healthcare data are getting generated in various forms.The advancements made in medical imaging are tremendous owing to which biomedical image acquisition has become easier and quicker.Due to such massive generation of big data,the utilization of new methods based on Big Data Analytics(BDA),Machine Learning(ML),and Artificial Intelligence(AI)have become essential.In this aspect,the current research work develops a new Big Data Analytics with Cat Swarm Optimization based deep Learning(BDA-CSODL)technique for medical image classification on Apache Spark environment.The aim of the proposed BDA-CSODL technique is to classify the medical images and diagnose the disease accurately.BDA-CSODL technique involves different stages of operations such as preprocessing,segmentation,fea-ture extraction,and classification.In addition,BDA-CSODL technique also fol-lows multi-level thresholding-based image segmentation approach for the detection of infected regions in medical image.Moreover,a deep convolutional neural network-based Inception v3 method is utilized in this study as feature extractor.Stochastic Gradient Descent(SGD)model is used for parameter tuning process.Furthermore,CSO with Long Short-Term Memory(CSO-LSTM)model is employed as a classification model to determine the appropriate class labels to it.Both SGD and CSO design approaches help in improving the overall image classification performance of the proposed BDA-CSODL technique.A wide range of simulations was conducted on benchmark medical image datasets and the com-prehensive comparative results demonstrate the supremacy of the proposed BDA-CSODL technique under different measures.

An Intelligent Deep Neural Sentiment Classification Network

A Deep Neural Sentiment Classification Network(DNSCN)is devel-oped in this work to classify the Twitter data unambiguously.It attempts to extract the negative and positive sentiments in the Twitter database.The main goal of the system is tofind the sentiment behavior of tweets with minimum ambiguity.A well-defined neural network extracts deep features from the tweets automatically.Before extracting features deeper and deeper,the text in each tweet is represented by Bag-of-Words(BoW)and Word Embeddings(WE)models.The effectiveness of DNSCN architecture is analyzed using Twitter-Sanders-Apple2(TSA2),Twit-ter-Sanders-Apple3(TSA3),and Twitter-DataSet(TDS).TSA2 and TDS consist of positive and negative tweets,whereas TSA3 has neutral tweets also.Thus,the proposed DNSCN acts as a binary classifier for TSA2 and TDS databases and a multiclass classifier for TSA3.The performances of DNSCN architecture are evaluated by F1 score,precision,and recall rates using 5-fold and 10-fold cross-validation.Results show that the DNSCN-WE model provides more accuracy than the DNSCN-BoW model for representing the tweets in the feature encoding.The F1 score of the DNSCN-BW based system on the TSA2 database is 0.98(binary classification)and 0.97(three-class classification)for the TSA3 database.This system provides better a F1 score of 0.99 for the TDS database.

SF-CNN: Deep Text Classification and Retrieval for Text Documents

Researchers and scientists need rapid access to text documents such as research papers,source code and dissertations.Many research documents are available on the Internet and need more time to retrieve exact documents based on keywords.An efficient classification algorithm for retrieving documents based on keyword words is required.The traditional algorithm performs less because it never considers words’polysemy and the relationship between bag-of-words in keywords.To solve the above problem,Semantic Featured Convolution Neural Networks(SF-CNN)is proposed to obtain the key relationships among the searching keywords and build a structure for matching the words for retrieving correct text documents.The proposed SF-CNN is based on deep semantic-based bag-of-word representation for document retrieval.Traditional deep learning methods such as Convolutional Neural Network and Recurrent Neural Network never use semantic representation for bag-of-words.The experiment is performed with different document datasets for evaluating the performance of the proposed SF-CNN method.SF-CNN classifies the documents with an accuracy of 94%than the traditional algorithms.

Disaster Monitoring of Satellite Image Processing Using Progressive Image Classification

The analysis of remote sensing image areas is needed for climate detec-tion and management,especially for monitoringflood disasters in critical environ-ments and applications.Satellites are mostly used to detect disasters on Earth,and they have advantages in capturing Earth images.Using the control technique,Earth images can be used to obtain detailed terrain information.Since the acquisi-tion of satellite and aerial imagery,this system has been able to detectfloods,and with increasing convenience,flood detection has become more desirable in the last few years.In this paper,a Big Data Set-based Progressive Image Classification Algorithm(PICA)system is introduced to implement an image processing tech-nique,detect disasters,and determine results with the help of the PICA,which allows disaster analysis to be extracted more effectively.The PICA is essential to overcoming strong shadows,for proper access to disaster characteristics to false positives by operators,and to false predictions that affect the impact of the disas-ter.The PICA creates tailoring and adjustments obtained from satellite images before training and post-disaster aerial image data patches.Two types of proposed PICA systems detect disasters faster and more accurately(95.6%).