Deep Learning-Based ECG Classification for Arterial Fibrillation Detection

The application of deep learning techniques in the medical field,specifically for Atrial Fibrillation(AFib)detection through Electrocardiogram(ECG)signals,has witnessed significant interest.Accurate and timely diagnosis increases the patient’s chances of recovery.However,issues like overfitting and inconsistent accuracy across datasets remain challenges.In a quest to address these challenges,a study presents two prominent deep learning architectures,ResNet-50 and DenseNet-121,to evaluate their effectiveness in AFib detection.The aim was to create a robust detection mechanism that consistently performs well.Metrics such as loss,accuracy,precision,sensitivity,and Area Under the Curve(AUC)were utilized for evaluation.The findings revealed that ResNet-50 surpassed DenseNet-121 in all evaluated categories.It demonstrated lower loss rate 0.0315 and 0.0305 superior accuracy of 98.77%and 98.88%,precision of 98.78%and 98.89%and sensitivity of 98.76%and 98.86%for training and validation,hinting at its advanced capability for AFib detection.These insights offer a substantial contribution to the existing literature on deep learning applications for AFib detection from ECG signals.The comparative performance data assists future researchers in selecting suitable deep-learning architectures for AFib detection.Moreover,the outcomes of this study are anticipated to stimulate the development of more advanced and efficient ECG-based AFib detection methodologies,for more accurate and early detection of AFib,thereby fostering improved patient care and outcomes.

Nodule Detection Using Local Binary Pattern Features to Enhance Diagnostic Decisions

Pulmonary nodules are small, round, or oval-shaped growths on the lungs. They can be benign (noncancerous) or malignant (cancerous). The size of a nodule can range from a few millimeters to a few centimeters in diameter. Nodules may be found during a chest X-ray or other imaging test for an unrelated health problem. In the proposed methodology pulmonary nodules can be classified into three stages. Firstly, a 2D histogram thresholding technique is used to identify volume segmentation. An ant colony optimization algorithm is used to determine the optimal threshold value. Secondly, geometrical features such as lines, arcs, extended arcs, and ellipses are used to detect oval shapes. Thirdly, Histogram Oriented Surface Normal Vector (HOSNV) feature descriptors can be used to identify nodules of different sizes and shapes by using a scaled and rotation-invariant texture description. Smart nodule classification was performed with the XGBoost classifier. The results are tested and validated using the Lung Image Consortium Database (LICD). The proposed method has a sensitivity of 98.49% for nodules sized 3–30 mm.

Dynamic characteristics of nanoindentation in Ni:A molecular dynamics simulation study

In this work, three-dimensional molecular dynamics simulation is carried out to elucidate the nanoindentation behaviour of single crystal Ni. The substrate indenter system is modelled using hybrid interatomic potentials including the manybody potential （embedded atom method） and two-body Morse potential. The spherical indenter is chosen, and the simulation is performed for different loading rates from 10 m/s to 200 m/s. Results show that the maximum indentation load and hardness of the system increase with the increase of velocity. The effect of indenter size on the nanoindentation response is also analysed. It is found that the maximum indentation load is higher for the large indenter whereas the hardness is higher for the smaller indenter. Dynamic nanoindentation is carried out to investigate the behaviour of Ni substrate to multiple loading-unloading cycles. It is observed from the results that the increase in the number of loading unloading cycles reduces the maximum load and hardness of the Ni substrate. This is attributed to the decrease in recovery force due to defects and dislocations produced after each indentation cycle.

Molecular dynamics study of the mechanical characteristics of Ni/Cu bilayer using nanoindentation

In the present work, a three-dimensional molecular dynamics simulation is carried out to perform the nanoindentation experiment on Ni single crystal. The substrate indenter system is modeled using hybrid interatomic potentials including the many-body potential embedded atom method （EAM）, and two-body morse potential. To simulate the in- dentation process, a spherical indenter （diameter = 80A, 1A=0.1 nm） is chosen. The results show that the mechanical behaviour of a monolithic Ni is not affected by crystalline orientation. To elucidate the effect of a heterogeneous interface, three bilayer interface systems are constructed, namely Ni（100）/Cu（111）, Ni（110）/Cu（111）, and Ni（111）/Cu（111）. The simulations along these systems clearly describe that mechanical behaviour directly depends on the lattice mismatch. The interface with the smaller mismatch between the specified crystal planes is proved to be harder and vice versa. To describe the relationship between film thickness and interface effect, we choose various values of film thickness ranging from 20 A to 50 A to perform the nanoindentation experiment. It is observed that the interface is significant only for the relatively small thickness of film and the separation between interface and the indenter tip. It is shown that with the increase in film thickness, the mechanical behaviour of the film shifts more toward that of monolithic material.

CNN-based segmentation frameworks for structural component and earthquake damage determinations using UAV images

Buildings undergo various kinds of structural damage during earthquakes,and damage detection and functional assessment of these structures in the aftermath of the events have been challenging issues.Under these circumstances,computer vision techniques offer a promising solution by automating the inspection process.This study presents an effective methodology for automatic structural components and damage detection using unmanned aerial vehicle(UAV)images of damaged buildings.Two types of neural network architectures are considered for appropriate feature extractions in different task detections.The feature pyramid network(FPN)is employed for crack,spall,rebar,and component damage segmentation,while the UNet++network is utilized for the damage state.For network training and validation,a total of 3805 original images of size 1920×1080 pixels are processed by the proposed method and reduced the image pixels.From the FPN,the achieved highest intersection over unions(IoUs)were 0.59,0.93,0.42,and 0.99 for crack,spall,rebar,and components,respectively.These predicted labels were found in close agreement with the labels.Similarly,the UNet++recognized the semantic information and damage state with an IoU of 0.72.This demonstrated the applicability of the proposed method for automated post-earthquake building inspection process accurately without information loss from the original images.

Structural, electronic, and optical properties of ZnO_(1-x)Se_x alloys using first-principles calculations

The structural, electronic, and optical properties of binary ZnO, ZnSe compounds, and their ternary ZnOl_xSex alloys are computed using the accurate full potential linearized augmented plane wave plus local orbital （FP-LAPW ＋ lo） method in the rocksalt （B 1） and zincblende （B3） crystallographic phases. The electronic band structures, fundamental energy band gaps, and densities of states for ZnO1_xSex are evaluated in the range 0 〈 x 〈 1 using Wu-Cohen （WC） generalized gradient approximation （GGA） for the exchange-correlation potential. Our calculated results of lattice parameters and bulk modulus reveal a nonlinear variation for pseudo-binary and their ternary alloys in both phases and show a considerable deviation from Vegard＇s law. It is observed that the predicted lattice parameter and bulk modulus are in good agreement with the available experimental and theoretical data. We establish that the composition dependence of band gap is semi-metallic in B1 phase, while a direct band gap is observed in B3 phase. The calculated density of states is described by taking into account the contribution of Zn 3d, O 2p, and Se 4s, and the optical properties are studied in terms of dielectric functions, refractive index, reflectivity, and energy loss function for the B3 phase and are compared with the available experimental data.

Lung Cancer Detection Using Modified AlexNet Architecture and Support Vector Machine

Lung cancer is the most dangerous and death-causing disease indicated by the presence of pulmonary nodules in the lung.It is mostly caused by the instinctive growth of cells in the lung.Lung nodule detection has a significant role in detecting and screening lung cancer in Computed tomography(CT)scan images.Early detection plays an important role in the survival rate and treatment of lung cancer patients.Moreover,pulmonary nodule classification techniques based on the convolutional neural network can be used for the accurate and efficient detection of lung cancer.This work proposed an automatic nodule detection method in CT images based on modified AlexNet architecture and Support vector machine(SVM)algorithm namely LungNet-SVM.The proposed model consists of seven convolutional layers,three pooling layers,and two fully connected layers used to extract features.Support vector machine classifier is applied for the binary classification of nodules into benign andmalignant.The experimental analysis is performed by using the publicly available benchmark dataset Lung nodule analysis 2016(LUNA16).The proposed model has achieved 97.64%of accuracy,96.37%of sensitivity,and 99.08%of specificity.A comparative analysis has been carried out between the proposed LungNet-SVM model and existing stateof-the-art approaches for the classification of lung cancer.The experimental results indicate that the proposed LungNet-SVM model achieved remarkable performance on a LUNA16 dataset in terms of accuracy.

Hand dysfunction after transradial artery catheterization for coronary procedures

AIM To sythesize the available literature on hand dysfunction after transradial catheterization.METHODS We searched MEDLINE and EMBASE. The search results were reviewed by two independent judicators for studies that met the inclusion criteria and relevant reviews. We included studies that evaluated any transradial procedure and evaluated hand function outcomes post transradial procedure. There were no restrictions based on sample size. There was no restriction on method of assessing hand function which included disability, nerve damage, motor or sensory loss. There was no restriction based on language of study. Data was extracted, these results were narratively synthesized.RESULTS Out of 555 total studies 13 studies were finally included in review. A total of 3815 participants with mean age of 62.5 years were included in this review. A variety of methods were used to assess sensory and motor dysfunction of hand. Out of 13 studies included, only 3 studies reported nerve damage with a combined incidence of 0.16%, 5 studies reported sensory loss, tingling and numbness with a pooled incidence of 1.52%. Pain after transradial access was the most common form of hand dysfunction(6.67%) reported in 3 studies. The incidence of hand dysfunction defined as disability, grip strength change, power loss or any other hand complication was incredibly low at 0.26%. Although radial artery occlusion was not our primary end point for this review, it was observed in 2.41% of the participants in total of five studies included.CONCLUSION Hand dysfunction may occur post transradial catheterisation and majority of symptoms resolve without any clinical sequel.

Theoretical investigations of half-metallic ferromagnetism in new Half-Heusler YCrSb and YMnSb alloys using first-principle calculations

Structural,electronic,and magnetic properties of new predicted half-Heusler YCrSb and YMnSb compounds within the ordered MgAgAs Clb-type structure are investigated by employing first-principal calculations based on density functional theory.Through the calculated total energies of three possible atomic placements,we find the most stable structures regarding YCrSb and YMnSb materials,where Y,Cr(Mn),and Sb atoms occupy the(0.5,0.5,0.5),(0.25,0.25,0.25),and(0,0,0) positions,respectively.Furthermore,structural properties are explored for the non-magnetic and ferromagnetic and anti-ferromagnetic states and it is found that both materials prefer ferromagnetic states.The electronic band structure shows that YCrSb has a direct band gap of 0.78 eV while YMnSb has an indirect band gap of 0.40 eV in the majority spin channel.Our findings show that YCrSb and YMnSb materials exhibit half-metallic characteristics at their optimized lattice constants of 6.67 and 6.56 ,respectively.The half-metallicities associated with YCrSb and YMnSb are found to be robust under large in-plane strains which make them potential contenders for spintronic applications.

Profiling Casualty Severity Levels of Road Accident Using Weighted Majority Voting

To determine the individual circumstances that account for a road traffic accident,it is crucial to consider the unplanned connections amongst various factors related to a crash that results in high casualty levels.Analysis of the road accident data concentrated mainly on categorizing accidents into different types using individually built classification methods which limit the prediction accuracy and fitness of the model.In this article,we proposed a multi-model hybrid framework of the weighted majority voting(WMV)scheme with parallel structure,which is designed by integrating individually implemented multinomial logistic regression(MLR)and multilayer perceptron(MLP)classifiers using three different accident datasets i.e.,IRTAD,NCDB,and FARS.The proposed WMV hybrid scheme overtook individual classifiers in terms of modern evaluation measures like ROC,RMSE,Kappa rate,classification accuracy,and performs better than state-of-theart approaches for the prediction of casualty severity level.Moreover,the proposed WMV hybrid scheme adds up to accident severity analysis through knowledge representation by revealing the role of different accident-related factors which expand the risk of casualty in a road crash.Critical aspects related to casualty severity recognized by the proposed WMV hybrid approach can surely support the traffic enforcement agencies to develop better road safety plans and ultimately save lives.

A Coprocessor Architecture for 80/112-bit Security Related Applications

We have proposed a flexible coprocessor key-authentication architecture for 80/112-bit security-related applications over GF(2m)field by employing Elliptic-curve Diffie Hellman(ECDH)protocol.Towards flexibility,a serial input/output interface is used to load/produce secret,public,and shared keys sequentially.Moreover,to reduce the hardware resources and to achieve a reasonable time for cryptographic computations,we have proposed a finite field digit-serial multiplier architecture using combined shift and accumulate techniques.Furthermore,two finite-statemachine controllers are used to perform efficient control functionalities.The proposed coprocessor architecture over GF(2^(163))and GF(2^(233))is programmed using Verilog and then implemented on Xilinx Virtex-7 FPGA(field-programmable-gate-array)device.For GF(2^(163))and GF(2^(233)),the proposed flexible coprocessor use 1351 and 1789 slices,the achieved clock frequency is 250 and 235MHz,time for one public key computation is 40.50 and 79.20μs and time for one shared key generation is 81.00 and 158.40μs.Similarly,the consumed power over GF(2^(163))and GF(2^(233))is 0.91 and 1.37mW,respectively.The proposed coprocessor architecture outperforms state-of-the-art ECDH designs in terms of hardware resources.

FPGA Implementation of Elliptic-Curve Diffie Hellman Protocol

This paper presents an efficient crypto processor architecture for key agreement using ECDH(Elliptic-curve Diffie Hellman)protocol over GF2163.The composition of our key-agreement architecture is expressed in consisting of the following:(i)Elliptic-curve Point Multiplication architecture for public key generation(DESIGN-I)and(ii)integration of DESIGN-I with two additional routing multiplexers and a controller for shared key generation(DESIGN-II).The arithmetic operators used in DESIGN-I and DESIGNII contain an adder,squarer,a multiplier and inversion.A simple shift and add multiplication method is employed to retain lower hardware resources.Moreover,an essential inversion operation is operated using the Itoh-Tsujii algorithm with similar hardware resources of used squarer and multiplier units.The proposed architecture is implemented in a Verilog HDL.The implementation results are given on a Xilinx Virtex-7 FPGA(field-programmable gate array)device.For DESIGN-I and DESIGN-II over GF2163,(i)the utilized Slices are 3983 and 4037,(ii)the time to compute one public key and a shared secret is 553.7μs and 1170.7μs and(iii)the consumed power is 29μW and 57μW.Consequently,the achieved area optimized and power reduced results show that the proposed ECDH architecture is a suitable alternative(to generate a shared secret)for the applications that require low hardware resources and power consumption.

Mechanical behavior of Cu-Zr bulk metallic glasses (BMGs): A molecular dynamics approach

In the present work, three-dimensional molecular dynamics simulation is carried out to elucidate the nanoinden- tation behaviors of CuZr Bulk metallic glasses （BMGs）. The substrate indenter system is modeled using hybrid interatomic potentials including both many-body Finnis Sinclair （FS） and two-body Morse potentials. A spherical rigid indenter （diameter= 60 A（1 A = 10 ^-10 m）） is employed to simulate the indentation process. Three samples of BMGs including Cu25Zr75, CusoZr50, and Cu75Zr25 are designed and the metallic glasses are formed by rapid cooling from the melt state at about 2000 K. The radial distribution functions are analyzed to reveal the dynamical evolution of the structure of the atoms with different compositions and different cooling rates. The mechanical behavior can be well understood in terms of load-depth curves and Hardness-depth curves during the nanoindentation process. Our results indicate a positive linear relationship between the hardness and the Cu concentration of the BMG sample. To reveal the importance of cooling rate provided during the processing of BMGs, we investigate the indentation behaviors of CusoZr50 at three different quenching rates. Nanoindentation results and radial distribution function （RDF） curves at room temperature indicate that a sample can be made harder and more stable by slowing down the quenching rate.

Preventing Cloud Network from Spamming Attacks Using Cloudflare and KNN

Cloud computing is one of the most attractive and cost-saving models,which provides online services to end-users.Cloud computing allows the user to access data directly from any node.But nowadays,cloud security is one of the biggest issues that arise.Different types of malware are wreaking havoc on the clouds.Attacks on the cloud server are happening from both internal and external sides.This paper has developed a tool to prevent the cloud server from spamming attacks.When an attacker attempts to use different spamming techniques on a cloud server,the attacker will be intercepted through two effective techniques:Cloudflare and K-nearest neighbors(KNN)classification.Cloudflare will block those IP addresses that the attacker will use and prevent spamming attacks.However,the KNN classifiers will determine which area the spammer belongs to.At the end of the article,various prevention techniques for securing cloud servers will be discussed,a comparison will be made with different papers,a conclusion will be drawn based on different results.

A Flexible Architecture for Cryptographic Applications: ECC and PRESENT

This work presents a flexible/unified hardware architecture of Elliptic-curve Cryptography(ECC)and PRESENT for cryptographic applications.The features of the proposed work are(i)computation of only the point multiplication operation of ECC over GF(2163)for a 163-bit key generation,(ii)execution of only the variant of an 80-bit PRESENT block cipher for data encryption&decryption and(iii)execution of point multiplication operation(ECC algorithm)along with the data encryption and decryption(PRESENT algorithm).To establish an area overhead for the flexible design,dedicated hardware architectures of ECC and PRESENT are implemented in the first step,and a sum of their hardware area is computed.Then,the implementation of the proposed flexible architecture for ECC and PRESENT algorithms is presented.Implementation results regarding the area,clock cycles,latency,clock frequency,and power after the place-and-route level on Xilinx Virtex-5,Virtex-6,and Virtex-7 FPGA devices are presented.Hence,the implementation results and comparisons show that the proposed architecture suits applications demanding flexible implementation of cryptographic applications.

Ab-initio calculations of bandgap tuning of In1-xGaxY(Y=N,P)alloys for optoelectronic applications

The III–V alloys and doping to tune the bandgap for solar cells and other optoelectronic devices has remained a hot topic of research for the last few decades.In the present article,the bandgap tuning and its influence on optical properties of In1-xGaxN/P,where(x=0.0,0.25,0.50,0.75,and 1.0)alloys are comprehensively analyzed by density functional theory based on full-potential linearized augmented plane wave method(FP-LAPW)and modified Becke and Johnson potentials(TB-mBJ).The direct bandgaps turn from 0.7 eV to 3.44 eV,and 1.41 eV to 2.32 eV for In1-xGaxN/P alloys,which increases their potentials for optoelectronic devices.The optical properties are discussed such as dielectric constants,refraction,absorption,optical conductivity,and reflection.The light is polarized in the low energy region with minimum reflection.The absorption and optical conduction are maxima in the visible region,and they are shifted into the ultraviolet region by Ga doping.Moreover,static dielectric constant e1(0)is in line with the bandgap from Penn’s model.

Image Authenticity Detection Using DWT and Circular Block-Based LTrP Features

Copy-move forgery is the most common type of digital image manipulation,in which the content from the same image is used to forge it.Such manipulations are performed to hide the desired information.Therefore,forgery detection methods are required to identify forged areas.We have introduced a novel method for features computation by employing a circular block-based method through local tetra pattern(LTrP)features to detect the single and multiple copy-move attacks from the images.The proposed method is applied over the circular blocks to efficiently and effectively deal with the post-processing operations.It also uses discrete wavelet transform(DWT)for dimension reduction.The obtained approximate image is distributed into circular blocks on which the LTrP algorithm is employed to calculate the feature vector as the LTrP provides detailed information about the image content by utilizing the direction-based relation of central pixel to its neighborhoods.Finally,Jeffreys and Matusita distance is used for similarity measurement.For the evaluation of the results,three datasets are used,namely MICC-F220,MICC-F2000,and CoMoFoD.Both the qualitative and quantitative analysis shows that the proposed method exhibits state-of-the-art performance under the presence of post-processing operations and can accurately locate single and multiple copy-move forgery attacks on the images.

Directional mechanical and thermal properties of single-layer black phosphorus by classical molecular dynamics

Black phosphorus （BP） has received attention due to its own higher carrier mobility and layer dependent electronic properties, such as direct band gap. Interestingly, the single layer black phosphorus （SLBP） has had large popularity in applications related to thermoelectric, optoelectronic, and electronic devices. Here, we investigate the phonon spectrum, thermal conductivities, and stress strain effects. Robust anisotropy was mainly observed in the thermal conductivities together with the alongside zigzag （ZZ） direction value, compared to the armchair （AC） directions. We also investigated the attitude of stress that was anisotropic in both directions, and the stress effects were two times greater across the ZZ path than those in the AC direction at a low temperature. We obtained a ~oung＇s modulus of 63.77 and 20.74 GPa in the AC and ZZ directions, respectively, for a strain range of 0.01. These results had good agreement with first principle calculations. Our study here is useful and significant for the thermal tuning of phosphorus-based nanoelectronics and thermalelectric applications of phosphorus.

Intelligent Sound-Based Early Fault Detection System for Vehicles

An intelligent sound-based early fault detection system has been proposed for vehicles using machine learning.The system is designed to detect faults in vehicles at an early stage by analyzing the sound emitted by the car.Early detection and correction of defects can improve the efficiency and life of the engine and other mechanical parts.The system uses a microphone to capture the sound emitted by the vehicle and a machine-learning algorithm to analyze the sound and detect faults.A possible fault is determined in the vehicle based on this processed sound.Binary classification is done at the first stage to differentiate between faulty and healthy cars.We collected noisy and normal sound samples of the car engine under normal and different abnormal conditions from multiple workshops and verified the data from experts.We used the time domain,frequency domain,and time-frequency domain features to detect the normal and abnormal conditions of the vehicle correctly.We used abnormal car data to classify it into fifteen other classical vehicle problems.We experimented with various signal processing techniques and presented the comparison results.In the detection and further problem classification,random forest showed the highest results of 97%and 92%with time-frequency features.

A Multi-Modal Deep Learning Approach for Emotion Recognition

In recent years,research on facial expression recognition(FER)under mask is trending.Wearing a mask for protection from Covid 19 has become a compulsion and it hides the facial expressions that is why FER under the mask is a difficult task.The prevailing unimodal techniques for facial recognition are not up to the mark in terms of good results for the masked face,however,a multi-modal technique can be employed to generate better results.We proposed a multi-modal methodology based on deep learning for facial recognition under a masked face using facial and vocal expressions.The multimodal has been trained on a facial and vocal dataset.We have used two standard datasets,M-LFW for the masked dataset and CREMA-D and TESS dataset for vocal expressions.The vocal expressions are in the form of audio while the faces data is in image form that is why the data is heterogenous.In order to make the data homogeneous,the voice data is converted into images by taking spectrogram.A spectrogram embeds important features of the voice and it converts the audio format into the images.Later,the dataset is passed to the multimodal for training.neural network and the experimental results demonstrate that the proposed multimodal algorithm outsets unimodal methods and other state-of-the-art deep neural network models.