Alpha Fusion Adversarial Attack Analysis Using Deep Learning

The deep learning model encompasses a powerful learning ability that integrates the feature extraction,and classification method to improve accuracy.Convolutional Neural Networks(CNN)perform well in machine learning and image processing tasks like segmentation,classification,detection,identification,etc.The CNN models are still sensitive to noise and attack.The smallest change in training images as in an adversarial attack can greatly decrease the accuracy of the CNN model.This paper presents an alpha fusion attack analysis and generates defense against adversarial attacks.The proposed work is divided into three phases:firstly,an MLSTM-based CNN classification model is developed for classifying COVID-CT images.Secondly,an alpha fusion attack is generated to fool the classification model.The alpha fusion attack is tested in the last phase on a modified LSTM-based CNN(CNN-MLSTM)model and other pre-trained models.The results of CNN models show that the accuracy of these models dropped greatly after the alpha-fusion attack.The highest F1 score before the attack was achieved is 97.45 And after the attack lowest F1 score recorded is 22%.Results elucidate the performance in terms of accuracy,precision,F1 score and Recall.

Autonomous Eyewitness Identification by Employing Linguistic Rules forDisaster Events

Social networking platforms provide a vital source for disseminating information across the globe,particularly in case of disaster.These platforms are great mean to find out the real account of the disaster.Twitter is an example of such platform,which has been extensively utilized by scientific community due to its unidirectional model.It is considered a challenging task to identify eyewitness tweets about the incident from the millions of tweets shared by twitter users.Research community has proposed diverse sets of techniques to identify eyewitness account.A recent state-of-the-art approach has proposed a comprehensive set of features to identify eyewitness account.However,this approach suffers some limitation.Firstly,automatically extracting the feature-words remains a perplexing task against each feature identified by the approach.Secondly,all identified features were not incorporated in the implementation.This paper has utilized the language structure,linguistics,and word relation to achieve automatic extraction of feature-words by creating grammar rules.Additionally,all identified features were implemented which were left out by the state-of-the-art model.A generic approach is taken to cover different types of disaster such as earthquakes,floods,hurricanes,and wildfires.The proposed approach was then evaluated for all disaster-types,including earthquakes,floods,hurricanes,and fire.Based on the static dictionary,the Zahra et al.approach was able to produce an F-Score value of 0.92 for Eyewitness identification in the earthquake category.The proposed approach secured F-Score values of 0.81 in the same category.This score can be considered as a significant score without using a static dictionary.

CO oxidation on MXene(Mo_(2)CS_(2)) supported single-atom catalyst: A termolecular Eley-Rideal mechanism

Finding transition metal catalysts for effective catalytic conversion of CO to CO_(2)has attracted much attention.MXene as a new 2D layered material of early transition metal carbides,nitrides,and carbo-nitrides is a robust support for achoring metal atoms.In this study,the electronic structure,geometries,thermodynamic stability,and catalytic activity of MXene (Mo_(2)CS_(2)) supported single noble metal atoms (NM=Ru,Rh,Pd,Ir,Pt and Au) have been systematically examined using first-principles calculations and ab initio molecular dynamic (AIMD) simulations.First,AIMD simulations and phonon spectra demonstrate the dynamic and thermal stabilities of Mo_(2)CS_(2)monolayer.Three likely reaction pathways,LangmuirHinshelwood (LH),Eley-Rideal (ER),and Termolecular Eley–Rideal (TER) for CO oxidation on the Ru1-and Ir_(1)@Mo_(2)CS_(2)SACs,have been studied in detail.It is found that CO oxidation mainly proceeds via the TER mechanism under mild reaction conditions.The corresponding rate-determining steps are the dissociation of the intermediate (OCO-Ru_(1)-OCO) and formation of OCO-Ir_(1)-OCO intermediate.The downshift d-band center of Ru1-and Ir_(1)@Mo_(2)CS_(2)help to enhance activity and improve catalytst stability.Moreover,a microkinetic study predicts a maximum CO oxidation rate of 4.01×10^(2)s^(-1)and 4.15×10^(3)s^(-1)(298.15K) following the TER pathway for the Ru_(1)-and Ir_(1)@Mo_(2)CS_(2)catalysts,respectively.This work provides guideline for fabricating and designing highly efficient SACs with superb catalyts using MXene materials.

Property-performance relationship of core-shell structured black TiO_(2) photocatalyst for environmental remediation

Understanding the relationship between the properties and performance of black titanium dioxide with core-shell structure(CSBT)for environmental remediation is crucial for improving its prospects in practical applications.In this study,CSBT was synthesized using a glycerol-assisted sol-gel approach.The effect of different water-to-glycerol ratios(W:G=1:0,9:1,2:1,and 1:1)on the semiconducting and physicochemical properties of CSBT was investigated.The effectiveness of CSBT in removing phenolic compounds(PHCs)from real agro-industrial wastewater was studied.The CSBT synthesized with a W:G ratio of 9:1 has optimized properties for enhanced removal of PHCs.It has a distinct coreshell structure and an appropriate amount of Ti3+cations(11.18%),which play a crucial role in enhancing the performance of CSBT.When exposed to visible light,the CSBT performed better:48.30%of PHCs were removed after 180 min,compared to only 21.95%for TiO_(2) without core-shell structure.The CSBT consumed only 45.5235 kWh/m^(3) of electrical energy per order of magnitude and cost$2.4127 per unit volume of treated agro-industrial wastewater.Under the conditions tested,the CSBT demonstrated exceptional stability and reusability.The CSBT showed promising results in the treatment of phenols-containing agro-industrial wastewater.