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Enhancing Human Action Recognition with Adaptive Hybrid Deep Attentive Networks and Archerfish Optimization

Computers, Materials & Continua 2024年第9期80卷 4791-4812页

作者： ahmad Yahiya ahmad Bani ahmad Jafar Alzubi Sophers James Vincent Omollo Nyangaresi Chanthirasekaran Kutralakani Anguraju Krishnan Department of Accounting and Finance Faculty of BusinessMiddle East UniversityAmman11831Jordan Faculty of Engineering Al-Balqa Applied UniversitySalt19117Jordan Department of Mathematics Kongunadu College of Engineering and Technology(Autonomous)TholurpattiTrichy621215India Department of Computer Science and Software Engineering Jaramogi Oginga Odinga University of Science and TechnologyBondo210-40601Kenya Department of Electronics and Communication Engineering Saveetha School of EngineeringSaveetha Institute of Medical and Technical SciencesChennai602105India Department of Electronics and Communication Engineering Saveetha Engineering College(Autonomous)Chennai602105India Department of Computer Science and Engineering Kongunadu College of Engineering and Technology(Autonomous)TholurpattiTrichy621215India

In recent years,wearable devices-based Human Activity Recognition(HAR)models have received significant *** developed HAR models use hand-crafted features to recognize human activities,leading to the extraction of basic *** images captured by wearable sensors contain advanced features,allowing them to be analyzed by deep learning algorithms to enhance the detection and recognition of human *** lighting and limited sensor capabilities can impact data quality,making the recognition of human actions a challenging *** unimodal-based HAR approaches are not suitable in a real-time ***,an updated HAR model is developed using multiple types of data and an advanced deep-learning ***,the required signals and sensor data are accumulated from the standard *** these signals,the wave features are *** the extracted wave features and sensor data are given as the input to recognize the human *** Adaptive Hybrid Deep Attentive Network(AHDAN)is developed by incorporating a“1D Convolutional Neural Network(1DCNN)”with a“Gated Recurrent Unit(GRU)”for the human activity recognition ***,the Enhanced Archerfish Hunting Optimizer(EAHO)is suggested to fine-tune the network parameters for enhancing the recognition *** experimental evaluation is performed on various deep learning networks and heuristic algorithms to confirm the effectiveness of the proposed HAR *** EAHO-based HAR model outperforms traditional deep learning networks with an accuracy of 95.36,95.25 for recall,95.48 for specificity,and 95.47 for precision,*** result proved that the developed model is effective in recognizing human action by taking less ***,it reduces the computation complexity and overfitting issue through using an optimization approach.

关键词： Human action recognition multi-modal sensor data and signals adaptive hybrid deep attentive network enhanced archerfish hunting optimizer 1D convolutional neural network gated recurrent units

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A convolutional neural network to detect possible hidden data in spatial domain images

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Cybersecurity 2024年第1期7卷 37-52页

作者： Jean De La Croix Ntivuguruzwa Tohari ahmad Department of Informatics Institut Teknologi Sepuluh Nopember(ITS)Kampus ITS Keputih SukoliloSurabaya60111Indonesia African Center of Excellence in the Internet of Things College of Science and TechnologyUniversity of Rwanda3900KigaliRwanda

Hiding secret data in digital multimedia has been essential to protect the ***,attackers with a steganalysis technique may break *** steganalysis methods have good results with conventional Machine Learning(ML)techniques;however,the introduction of Convolutional Neural Network(CNN),a deep learning paradigm,achieved better performance over the previously proposed ML-based *** the existing CNN-based approaches yield good results,they present performance issues in classification accuracy and stability in the network training *** research proposes a new method with a CNN architecture to improve the hidden data detection accuracy and the training phase stability in spatial domain *** proposed method comprises three phases:pre-processing,feature extraction,and ***,in the pre-processing phase,we use spatial rich model filters to enhance the noise within images altered by data hiding;secondly,in the feature extraction phase,we use two-dimensional depthwise separable convolutions to improve the signal-to-noise and regular convolutions to model local features;and finally,in the classification,we use multi-scale average pooling for local features aggregation and representability enhancement regardless of the input size variation,followed by three fully connected layers to form the final feature maps that we transform into class probabilities using the softmax *** results identify an improvement in the accuracy of the considered recent scheme ranging between 4.6 and 10.2%with reduced training time up to 30.81%.

关键词： Information security Spatial domain steganalysis Deep learning Convolutional neural network Infrastructure

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Novel Framework for an Intrusion Detection System Using Multiple Feature Selection Methods Based on Deep Learning

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Tsinghua Science and Technology 2024年第4期29卷 948-958页

作者： A.E.M.Eljialy Mohammed Yousuf Uddin Sultan ahmad Department of Information Systems College of Computer Engineering and SciencesPrince Sattam Bin Abdulaziz UniversityAlkharjSaudi Arabia Department of Computer Science College of Computer Engineering and SciencesPrince Sattam Bin Abdulaziz UniversityAlkharjSaudi Arabiaand also with University Center for Research and Development(UCRD)Department of Computer Science and EngineeringChandigarh UniversityPunjabIndia

Intrusion detection systems (IDSs) are deployed to detect anomalies in real time. They classify a network’s incoming traffic as benign or anomalous (attack). An efficient and robust IDS in software-defined networks is an inevitable component of network security. The main challenges of such an IDS are achieving zero or extremely low false positive rates and high detection rates. Internet of Things (IoT) networks run by using devices with minimal resources. This situation makes deploying traditional IDSs in IoT networks unfeasible. Machine learning (ML) techniques are extensively applied to build robust IDSs. Many researchers have utilized different ML methods and techniques to address the above challenges. The development of an efficient IDS starts with a good feature selection process to avoid overfitting the ML model. This work proposes a multiple feature selection process followed by classification. In this study, the Software-defined networking (SDN) dataset is used to train and test the proposed model. This model applies multiple feature selection techniques to select high-scoring features from a set of features. Highly relevant features for anomaly detection are selected on the basis of their scores to generate the candidate dataset. Multiple classification algorithms are applied to the candidate dataset to build models. The proposed model exhibits considerable improvement in the detection of attacks with high accuracy and low false positive rates, even with a few features selected.

关键词： random forest feature selection decision tree software-defined network logistic regression intrusion detection system XGB classifier AdaBoost

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Solution approximations for a mathematical model of relativistic electrons with beta derivative

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Applied Mathematics(A Journal of Chinese Universities) 2024年第3期39卷 469-485页

作者： Ibrahim Yalcinkaya Orkun Tasbozan Ali Kurt Hijaz ahmad Department of Mathematics and Computer Sciences Faculty of ScienceNecmettin Erbakan UniversityKonyaTurkey Department of Mathematics Faculty of Science and LiteratureHatay Mustafa Kemal UniversityHatayTurkey Department of Mathematics Faculty of SciencePamukkale UniversityDenizliTurkey Near East University Operational Research Center in HealthcareTRNC Mersin 10Nicosia99138Turkey Department of Mathematics Faculty of ScienceIslamic University of MadinahMadinahSaudi Arabia

The main aim of this paper is to obtain the exact and semi-analytical solutions of the nonlinear Klein-Fock-Gordon(KFG)equation which is a model of relativistic electrons arising in the laser thermonuclear fusion with beta *** this purpose,both the modified extended tanh-function(mETF)method and the homotopy analysis method(HAM)are *** applying the mETF the chain rule for beta derivative and complex wave transform are used for obtaining the exact *** advantage of this procedure is that discretization or normalization is not *** applying the mETF,the exact solutions are ***,by applying the HAM semi-analytical results for the considered equation are *** HAM?curve gives us a chance to find the suitable value of the for the convergence of the solution ***,comparative graphical representations are given to show the effectiveness,reliability of the *** results show that the m ETF and HAM are reliable and applicable tools for obtaining the solutions of non-linear fractional partial differential equations that involve beta *** study can bring a new perspective for studies on fractional differential *** the other hand,it can be said that scientists can apply the considered methods for different mathematical models arising in physics,chemistry,engineering,social sciences and *** involves fractional *** the results may cause a new insight who studies on relativistic electron modelling.

关键词： Klein-Fock-Gordon(KFG)equation beta derivative semi-analytical solution exact solution

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Assessing Multidimensional Vulnerability of Rural Areas to Flooding:An Index‑Based Approach

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International Journal of Disaster Risk Science 2024年第1期15卷 88-106页

作者： Samavia Rasool Irfan ahmad Rana Hassam Bin Waseem Department of Urban and Regional Planning School of Civil and Environmental Engineering(SCEE)National University of Sciences and Technology(NUST)Islamabad 44000Pakistan

Vulnerability assessment is essential for understanding and launching effective flood risk reduction *** study aimed to examine the vulnerability of flood-prone rural communities in southern Punjab,Pakistan to external *** concept of vulnerability encompasses a range of dimensions,including physical,social,institutional,environmental,economic,and *** a composite index method,indices were developed for each dimension and combined to create a multidimensional measure of vulnerability.A sample of 365 communities was selected using the Yamane sampling technique,and data were collected through a questionnaire containing 65 indicators across all *** statistics and ANOVA tests were used to analyze the *** results show that communities near the Chenab River had higher attitudinal and institutional vulnerability compared to other *** attitudinal vulnerabilities were as sociated with poorly perceived flood risks and low preparedness measures,whereas institutional vulnerabilities were driven by conventional flood protection strategies,lack of institutional trust,and lack of flood risk *** research provides insights into the various components of vulnerability in flood-prone rural communities in Pakistan and demonstrates a useful methodology that can be applied to other disasters at different spatial scales.

关键词： Chenab River Flood hazard Indus River Pakistan-Rural areas:Vulnerability to flooding

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Machine learning-enhanced Monte Carlo and subset simulations for advanced risk assessment in transportation infrastructure

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Journal of Mountain Science 2024年第2期21卷 690-717页

作者： Furquan ahmad Pijush SAMUI S.S.MISHRA Department of Civil Engineering National Institute of TechnologyPatna 800006India

The maintenance of safety and dependability in rail and road embankments is of utmost importance in order to facilitate the smooth operation of transportation *** study introduces a comprehensive methodology for soil slope stability evaluation,employing Monte Carlo Simulation(MCS)and Subset Simulation(SS)with the"UPSS 3.0 Add-in"in *** on an 11.693-meter embankment with a soil slope(inclination ratio of 2H:1V),the investigation considers earthquake coefficients(kh)and pore water pressure ratios(ru)following Indian zoning *** chance of slope failure showed a considerable increase as the Coefficient of Variation(COV),seismic coefficients(kh),and pore water pressure ratios(ru)experienced an *** SS approach showed exceptional efficacy in calculating odds of failure that are notably *** computational modeling,the study optimized the worst-case scenario using ANFIS-GA,ANFIS-GWO,ANFIS-PSO,and ANFIS-BBO *** ANFIS-PSO model exhibits exceptional accuracy(training R2=0.9011,RMSE=0.0549;testing R2=0.8968,RMSE=0.0615),emerging as the most *** study highlights the significance of conducting thorough risk assessments and offers practical insights into evaluating and improving the stability of soil slopes in transportation *** findings contribute to the enhancement of safety and reliability in real-world situations.

关键词： Monte Carlo Simulation Subset Simulation Machine Learning Seismic coefficient

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An Improved Lung Cancer Segmentation Based on Nature-Inspired Optimization Approaches

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Computer Modeling in Engineering & Sciences 2024年第2期138卷 1051-1075页

作者： Shazia Shamas Surya Narayan Panda Ishu Sharma Kalpna Guleria Aman Singh ahmad Ali AlZubi Mallak ahmad AlZubi Chitkara University Institute of Engineering and Technology Chitkara UniversityRajpuraPunjab140401India Higher Polytechnic School Universidad Europea del AtlánticoC/Isabel Torres 21Santander39011Spain Department of Engineering Universidad Internacional IberoamericanaAreciboPR00613USA Uttaranchal Institute of Technology Uttaranchal UniversityDehradunUttarakhand248007India Computer Science Department King Saud UniversityRiyadhSaudi Arabia Faculty of Medicine Jordan University of Science and TechnologyIrbidJordan

The distinction and precise identification of tumor nodules are crucial for timely lung cancer diagnosis andplanning intervention. This research work addresses the major issues pertaining to the field of medical imageprocessing while focusing on lung cancer Computed Tomography (CT) images. In this context, the paper proposesan improved lung cancer segmentation technique based on the strengths of nature-inspired approaches. Thebetter resolution of CT is exploited to distinguish healthy subjects from those who have lung cancer. In thisprocess, the visual challenges of the K-means are addressed with the integration of four nature-inspired swarmintelligent techniques. The techniques experimented in this paper are K-means with Artificial Bee Colony (ABC),K-means with Cuckoo Search Algorithm (CSA), K-means with Particle Swarm Optimization (PSO), and Kmeanswith Firefly Algorithm (FFA). The testing and evaluation are performed on Early Lung Cancer ActionProgram (ELCAP) database. The simulation analysis is performed using lung cancer images set against metrics:precision, sensitivity, specificity, f-measure, accuracy,Matthews Correlation Coefficient (MCC), Jaccard, and *** detailed evaluation shows that the K-means with Cuckoo Search Algorithm (CSA) significantly improved thequality of lung cancer segmentation in comparison to the other optimization approaches utilized for lung cancerimages. The results exhibit that the proposed approach (K-means with CSA) achieves precision, sensitivity, and Fmeasureof 0.942, 0.964, and 0.953, respectively, and an average accuracy of 93%. The experimental results prove thatK-meanswithABC,K-meanswith PSO,K-meanswith FFA, andK-meanswithCSAhave achieved an improvementof 10.8%, 13.38%, 13.93%, and 15.7%, respectively, for accuracy measure in comparison to K-means segmentationfor lung cancer images. Further, it is highlighted that the proposed K-means with CSA have achieved a significantimprovement in accuracy, hence can be utilized by resea

关键词： Lesion lung cancer segmentation medical imaging meta-heuristic Artificial Bee Colony(ABC) Cuckoo Search Algorithm(CSA) Particle Swarm Optimization(PSO) Firefly Algorithm(FFA) segmentation

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(Sm/Eu/Tm)^(3+) doped tantalum semiconductor system for photovoltaic and electrochemical functionality amplification

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Journal of Rare Earths 2024年第8期42卷 1586-1594,I0007页

作者： Shaan Bibi Jaffri Khuram Shahzad ahmad Isaac Abrahams Ibrahim A.A Materials and Environmental Chemistry Lab Lab-E21Department of Environmental SciencesFatima Jinnah Women UniversityRawalpindiPakistan School of Physical and Chemical Sciences Queen Mary University of LondonLondonUnited Kingdom Department of Botany and Microbiology College of ScienceKing Saud UniversityP.O.Box 2455Riyadh11451Saudi Arabia

This work reports the synthesis,characterization,and energy focused applications of the novel lanthanides co-doped tantalum pentoxide hetero-system(Sm^(3+)-Eu^(3+)-Tm^(3+):Ta_(2)O_(5)).Ln^(3+)-doped Ta_(2)O_(5) express excellent opto-electronic features reflected by the narrow band gap energy of 3.87 *** vibrations confirm the presence of Ta-O-Ta and Ta-O *** synthesized system possesses orthorhombic geometry with 59.46 nm particle *** the smoother and compact morphology,the synthesized material succeeds in augmenting the performance of different systems aimed at energy *** ambient perovskite solar cell device fabricated with the Ln^(3+)-doped Ta_(2)O_(5) as an electron transport layer excels in achieving an efficiency and fill factor of 14.17% and 76% under artificial *** device was marked by the negligible hysteresis behavior showing profound photovoltaic *** electrochemical activity of the Ln^(3+)-doped Ta_(2)O_(5) decorated electrode was evaluated for electrical charge storage potential with pseudocapacitive behavior,With the highest specific capacitance of 355.39 F/g and quicker ionic diffusion rate,the designed electrode excels conventionally used ***-catalysis of water with Ln^(3+)-doped Ta_(2)O_(5) material indicates its capacity for H_(2) production with the lowest overpotential and Tafel slope values of 148 and 121.2 mV/dec,while the O_(2) generation is comparatively *** the stable electrochemical output,this rare earth modified material has the potential to replace conventionally used environmentally perilous and costly materials.

关键词： Lanthanide doping Rare earth materials Perovskite solar cells Supercapacitors Water splitting

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Elevating haptic interfaces:Dual-rate sampling and field programmable gate array implementation for multi-degree-of-freedom performance enhancement

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International Journal of Mechanical System Dynamics 2024年第2期4卷 171-187页

作者： Majid Koul Suhail Khosa Babar ahmad Mechanical Engineering Islamic University of Science and TechnologyKashmirJammu and KashmirIndia Mechanical Engineering National Institute of Technology SrinagarJammu and KashmirIndia

In this work,our primary focus centered on exploring the adaptability of the dualrate sampling scheme proposed earlier to enhance the performance of multi-degreeof-freedom(multi-DOF)impedance-based haptic *** scheme employed independent sampling rates in a haptics controller,effectively mitigating the issue of reduced Z-width at higher sampling rates.A key aspect of our investigation was the intricate implementation of the dual-rate sampling scheme on a field programmable gate array(FPGA).This implementation on a logic hardware FPGA was challenging and led to the effective comparison of the uniform-rate and dual-rate sampling schemes of the multi-DOF haptic *** used an in-house developed two-DOF pantograph as the haptic interface and an FPGA for implementing the controller ***-based implementation presented challenges that were vital in testing controller performances at higher sampling *** wall experiments were conducted to determine the stable and unstable interactions with the virtual *** complement the experimental results,we simulated the haptics force law for multi-DOF system on Simulink/***,the dual-rate sampling approach maintained the Z-width of the two-DOF haptic interface,even at higher controller sampling rates,distinguishing it from the conventional two-DOF uniform-rate control *** example,employing a dual-rate sampling combination of 20–2 kHz consistently ensured the stable rendering of a maximum virtual stiffness of approximately 700 N/mm and maintained a reliable virtual damping range spanning from 0 to 5 Ns/*** contrast,the 20 kHz uniform-rate sampling approach failed to ensure interface stability in the presence of virtual damping,ultimately resulting in the unsuccessful implementation of any virtual stiffness at higher sampling *** work,therefore,establishes the potential of dual-rate sampling in the realm of haptic technology,with practical applications in multi-DOF systems.

关键词： control FPGA haptic device simulation virtual reality Z-width

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Massless Dirac perturbations of black holes in f(Q)gravity:quasinormal modes and a weak deflection angle

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Communications in Theoretical Physics 2024年第9期76卷 73-82页

作者： ahmad Al-Badawi Sohan Kumar Jha Department of Physics Al-Hussein Bin Talal UniversityPO Box 2071111Ma'anJordan Department of Physics Chandernagore CollegeChandernagoreHooghlyWest BengalIndia

This article considers a static and spherical black hole(BH)in f(Q)gravity.f(Q)gravity is the extension of symmetric teleparallel general relativity,where both curvature and torsion are vanishing and gravity is described by *** this study,we investigate the possible implications of quasinormal mode(QNM)modified Hawking spectra and deflection angles generated by the *** Wentzel–Kramers–Brillouin method is used to solve the equations of motion for massless Dirac perturbation fields and explore the impact of the nonmetricity parameter(Q_(0)).Based on the QNM computation,we can ensure that the BH is stable against massless Dirac perturbations and as Q_(0)increases the oscillatory frequency of the mode *** then discuss the weak deflection angle in the weak field limit *** compute the deflection angle up to the fourth order of approximation and show how the nonmetricity parameter affects *** find that the Q_(0)parameter reduces the deflection angle.

关键词： black holes in f(Q)gravity massless Dirac perturbations quasinormal modes deflection angle

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