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Oversampling-Enhanced Feature Fusion-Based Hybrid ViT-1DCNN Model for Ransomware Cyber Attack Detection

computer Modeling in engineering & Sciences 2025年第2期142卷 1667-1695页

作者： Muhammad Armghan Latif Zohaib Mushtaq Saifur Rahman Saad Arif Salim Nasar Faraj Mursal Muhammad Irfan Haris Aziz Department of Computer and Information System Cleveland State UniversityOhio44115USA Department of Electrical Electronics and Computer SystemsCollege of Engineering and TechnologyUniversity of SargodhaSargodha40100Pakistan Electrical Engineering Department College of EngineeringNajran UniversityNajran61441Saudi Arabia Department of Mechanical Engineering College of EngineeringKing Faisal UniversityAl Ahsa31982Saudi Arabia Department of Mechanical Industrial and Energy System EngineeringUniversity of SargodhaSargodha40100Pakistan

Ransomware attacks pose a significant threat to critical infrastructures,demanding robust detection *** study introduces a hybrid model that combines vision transformer(ViT)and one-dimensional convolutional neural network(1DCNN)architectures to enhance ransomware detection *** common challenges in ransomware detection,particularly dataset class imbalance,the synthetic minority oversampling technique(SMOTE)is employed to generate synthetic samples for minority class,thereby improving detection *** integration of ViT and 1DCNN through feature fusion enables the model to capture both global contextual and local sequential features,resulting in comprehensive ransomware *** on the UNSW-NB15 dataset,the proposed ViT-1DCNN model achieved 98%detection accuracy with precision,recall,and F1-score metrics surpassing conventional *** approach not only reduces false positives and negatives but also offers scalability and robustness for real-world cybersecurity *** results demonstrate the model’s potential as an effective tool for proactive ransomware detection,especially in environments where evolving threats require adaptable and high-accuracy solutions.

关键词： Ransomware attacks cybersecurity vision transformer convolutional neural network feature fusion encryption threat detection

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Advancement in piezoelectric nanogenerators for acoustic energy harvesting

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Microsystems & Nanoengineering 2024年第6期10卷 21-54页

作者： Fandi Jean Muhammad Umair Khan Anas Alazzam Baker Mohammad Department of Computer and Information Engineering Khalifa UniversityAbu Dhabi 12778UAE System on Chip Lab Khalifa UniversityAbu Dhabi 12778UAE Department of Mechanical Engineering Khalifa UniversityAbu Dhabi 12778UAE

The demand for sustainable energy sources to power small electronics like IoT devices has led to exploring innovative solutions like acoustic energy harvesting using piezoelectric nanogenerators(PENGs).Acoustic energy harvesting leverages ambient noise,converting it into electrical energy through the piezoelectric effect,where certain materials generate an electric charge in response to mechanical stress or *** review paper provides a comprehensive analysis of the advancements in PENG technology,emphasizing their role in acoustic energy *** begin by discussing the essential principles of piezoelectricity and the design considerations for nanogenerators to optimize energy capture from sound *** discussion includes a detailed examination of various piezoelectric materials,such as polyvinylidene fluoride(PVDF),lead zirconate titanate(PZT),and zinc oxide(ZnO)nanowires,which are known for their superior piezoelectric properties.A critical aspect of this review is the exploration of innovative structural designs and resonance devices that enhance the efficiency of *** delve into the mechanisms and benefits of using Helmholtz resonators,quarter-wavelength tubes,and cantilever beams,which are instrumental in amplifying acoustic signals and improving energy conversion *** device’s design parameters and operational principles are scrutinized to highlight their contributions to the *** review addresses practical applications of PENGs in various *** monitoring systems,wearable electronics,and medical devices stand to benefit significantly from the continuous and sustainable power supplied by *** applications can reduce reliance on batteries and minimize maintenance by harnessing ambient acoustic energy,leading to more efficient and longer-lasting *** the promising potential of PENGs,several challenges remain,including material degradation,efficiency limitations,and integrating these devices i

关键词： piezoelectric ambient integrating

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Deep Learning Based Autonomous Robotic Grasping and Sorting system for Industry 4.0 6

Deep Learning Based Autonomous Robotic Grasping and Sorting ...

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6th International Conference on Robotics and Automation in Industry, ICRAI 2024

作者： Arsalan, Muhammad Imran, Abid Shah, Syed Salman Malaika, Sahar Iqbal, Zar Afshan GIK Institute Faculty of Mechanical Engineering Pakistan GIKI Institute Faculty of Mechanical Engineering Pakistan UET Department of Computer system Engineering Peshawar Pakistan UET Department of Electrical Engineering Peshawar Pakistan

ISBN: (纸本)9798350355703

This paper introduces a deep learning (DL)-based vision system for real-time object detection, grasping, and sorting using a UR5e robotic manipulator, aimed to address the challenges faced in industrial automation while grasping and sorting randomly placed objects in the workspace. The system utilizes a fine-tuned detection model, YOLOv8s, trained on a custom dataset of three distinct objects for testing a real-time grasping and sorting system using DL. Once the objects are detected in the workspace, image processing techniques are applied to each detected object to extract the accurate grasping information like the centroid and the orientation. This information is then converted to the robotic arm-based coordinate system to ensure the accurate manipulation of the objects in the workspace. The system is tested in the cluttered environment of the tabletop workspace to grasp and sort the objects, significantly enhancing both speed and accuracy in real-time industrial tasks. Experimental results show grasping accuracy of 93.33%, demonstrating the system's potential to improve automation efficiency in Industry 4.0 applications. © 2024 IEEE.

关键词： Deep learning

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Production Scheduling Heuristic for Mass Customization with Partial Rescheduling 7

Production Scheduling Heuristic for Mass Customization with ...

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7th International Conference on mechanical Manufacturing and Industrial engineering, MMIE 2024

作者： Tsui, Zhe-Luen Chou, Ywh-Leh Yuan, Cadmus Feng Chia University Department of Industrial Engineering and System Management Taiwan Feng Chia University Department of Mechanical and Computer-Aided Engineering Taiwan

ISBN: (数字)9781510683297

ISBN: (纸本)9781510683280

This research responds to the complex challenges of mass customization and multi-objective parameter optimization. Leveraging the Call-Auction Matching proposed by Cadmus Yuan, Zhe-Luen Tsui, and Ywh-Leh Chou[1], we improved a Production Date Prediction Call-Auction Matching (PDP-CAM) scheduling algorithm. Our numerical analysis validated the ability of algorithm to implement a partial rescheduling mechanism and generate scheduling results in the face of mass customization scenarios. Importantly, the effectiveness of the PDP-CAM scheduling algorithm in minimizing instance rescheduling was demonstrated, thereby enhancing the computational efficiency of the algorithm. © 2024 SPIE.

关键词： Cleaning

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Indentation behavior of a semi-infinite piezoelectric semiconductor under a rigid flat-ended cylindrical indenter

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Applied Mathematics and Mechanics(English Edition) 2024年第4期45卷 649-662页

作者： Shijing GAO Lele ZHANG Jinxi LIU Guoquan NIE Weiqiu CHEN State Key Laboratory of Mechanical Behavior and System Safety of Traffic Engineering Structures Shijiazhuang Tiedao UniversityShijiazhuang 050043China School of Mechanical Engineering Shijiazhuang Tiedao UniversityShijiazhuang 050043China Hebei Key Laboratory for Mechanics of Intelligent Materials and Structures Department of Engineering MechanicsShijiazhuang Tiedao UniversityShijiazhuang 050043China State Key Laboratory of Computer Aided Design and Computer Graphics Key Laboratory of Soft Machines and Smart Devices of Zhejiang ProvinceDepartment of Engineering MechanicsZhejiang UniversityHangzhou 310027China

This paper theoretically studies the axisymmetric frictionless indentation of a transversely isotropic piezoelectric semiconductor(PSC)half-space subject to a rigid flatended cylindrical *** contact area and other surface of the PSC half-space are assumed to be electrically *** the Hankel integral transformation,the problem is reduced to the Fredholm integral equation of the second *** equation is solved numerically to obtain the indentation behaviors of the PSC half-space,mainly including the indentation force-depth relation and the electric potential-depth *** results show that the effect of the semiconductor property on the indentation responses is limited within a certain range of variation of the steady carrier *** dependence of indentation behavior on material properties is also analyzed by two different kinds of *** element simulations are conducted to verify the results calculated by the integral equation technique,and good agreement is demonstrated.

关键词： piezoelectric semiconductor(PSC) insulating indenter electromechanical response singular integral equation finite element simulation

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Brain inspired iontronic fluidic memristive and memcapacitive device for self-powered electronics

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Microsystems & Nanoengineering 2025年第1期11卷 465-476页

作者： Muhammad Umair Khan Bilal Hassan Anas Alazzam Shimaa Eissa Baker Mohammad Center for Cyber-Physical Systems-System on Chip Lab Khalifa UniversityAbu Dhabi 127788UAE Department of Computer and Information Engineering Khalifa UniversityAbu Dhabi 127788UAE Department of Electrical Engineering Khalifa UniversityAbu Dhabi 127788UAE Division of Engineering New York UniversityAbu Dhabi 129188UAE Department of Mechanical and Nuclear Engineering Khalifa UniversityAbu Dhabi 127788UAE Department of Chemistry Khalifa UniversityAbu Dhabi 127788UAE

Ionic fluidic devices are gaining interest due to their role in enabling self-powered neuromorphic computing *** this study,we present an approach that integrates an iontronic fluidic memristive(IFM)device with low input impedance and a triboelectric nanogenerator(TENG)based on ferrofluid(FF),which has high input *** incorporating contact separation electromagnetic(EMG)signals with low input impedance into our FF TENG device,we enhance the FF TENG’s performance by increasing energy harvesting,thereby enabling the autonomous powering of IFM devices for self-powered ***,replicating neuronal activities using artificial iontronic fluidic systems is key to advancing neuromorphic *** fluidic devices,composed of soft-matter materials,dynamically adjust their conductance by altering the solution *** developed voltage-controlled memristor and memcapacitor memory in polydimethylsiloxane(PDMS)structures,utilising a fluidic interface of FF and polyacrylic acid partial sodium salt(PAA Na^(+)).The confined ion interactions in this system induce hysteresis in ion transport across various frequencies,resulting in significant ion memory *** IFM successfully replicates diverse electric pulse patterns,making it highly suitable for neuromorphic ***,our system demonstrates synapse-like learning functions,storing and retrieving short-term(STM)and long-term memory(LTM).The fluidic memristor exhibits dynamic synapse-like features,making it a promising candidate for the hardware implementation of neural *** TENG/EMG device adaptability and seamless integration with biological systems enable the development of advanced neuromorphic devices using iontronic fluidic materials,further enhanced by intricate chemical designs for self-powered electronics.

关键词： iontronic memristive ionic fluidic devices memcapacitive ff teng s ff teng fluidic triboelectric nanogenerator teng based

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Android Based Arduino Learning Model by Implementing Multi Marker Augmented Reality

Android Based Arduino Learning Model by Implementing Multi M...

引用

2023 International Conference on Informatics engineering, Science and Technology, INCITEST 2023

作者： Adler, John Setiyadi, Dianto Br Ginting, Selvia Lorena Rinaldy Ginting, Yogie Wicaksono, Mochamad Fajar Universitas Komputer Indonesia Department Computer Engineering Bandung Indonesia Universitas Komputer Indonesia Department of Computer System Bandung Indonesia Universitas Komputer Indonesia Department Computer System Bandung Indonesia Universitas Riau Pekanbaru Faculty of Engineering Department of Mechanical Engineering Indonesia

ISBN: (纸本)9798350317862

This research aims to develop an interactive learning system based on Arduino Uno basic material. To realize this system, Augmented Reality technology is used which will explain each Arduino Uno pin function along with an explanation of each example of the basic circuit provided. This started with the Arduino Uno learning process which often caused damage to the Arduino or the components that were connected to the Arduino itself during the process of making the circuit. This problem can occur due to a lack of basic understanding of theory. The use of Augmented Reality technology is able to display objects in the form of Arduinos in virtual 3D, so that 3D objects can appear, markers are used to mark each object, the introduction of the Arduino Uno itself can be simulated by providing a real picture using the multi marker method where there is a main marker containing 3D objects in the Arduino series and supporting markers containing explanations of the series. Thus, learning is not limited to conventional learning and minimizes errors in trials. The method used to develop the system is the waterfall method. The waterfall method itself is part of implementing a design with a good and reasonable target. Based on the tests obtained, the Arduino Uno learning application is able to run well using multiple markers, 3D or 2D objects can be run on a smartphone, and black box testing and marker reading testing are carried out with different lighting levels and reading from different sides. © 2023 IEEE.

关键词： Smartphones

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Incorporating Lasso Regression to Physics-Informed Neural Network for Inverse PDE Problem

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computer Modeling in engineering & Sciences 2024年第10期141卷 385-399页

作者： Meng Ma Liu Fu Xu Guo Zhi Zhai National Key Lab of Aerospace Power System and Plasma Technology Xi’an Jiaotong UniversityXi’an710049China School of Mechanical Engineering Xi’an Jiaotong UniversityXi’an710049China Department of Electrical and Computer Engineering University of Massachusetts LowellLowellMA 01854USA

Partial Differential Equation(PDE)is among the most fundamental tools employed to model dynamic *** PDE modeling methods are typically derived from established knowledge and known phenomena,which are time-consuming and ***,discovering governing PDEs from collected actual data via Physics Informed Neural Networks(PINNs)provides a more efficient way to analyze fresh dynamic systems and establish *** study proposes Sequentially Threshold Least Squares-Lasso(STLasso),a module constructed by incorporating Lasso regression into the Sequentially Threshold Least Squares(STLS)algorithm,which can complete sparse regression of PDE coefficients with the constraints of l0 *** further introduces PINN-STLasso,a physics informed neural network combined with Lasso sparse regression,able to find underlying PDEs from data with reduced data requirements and better *** addition,this research conducts experiments on canonical inverse PDE problems and compares the results to several recent *** results demonstrated that the proposed PINN-STLasso outperforms other methods,achieving lower error rates even with less data.

关键词： Physics-informed neural network inverse partial differential equation Lasso regression scientific machine learning

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Interference Suppression and Jitter Elimination Ability-Based Adaption Tracking Guidance for Robotic Fishes

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IEEE/CAA Journal of Automatica Sinica 2025年第1期12卷 126-137页

作者： Dongfang Li Jie Huang Rob Law Xin Xu Limin Zhu Edmond Q.Wu IEEE the School of Electrical Engineering and Automation Fuzhou University the Key Laboratory of System Control and Information Processing Ministry of Education the School of Electrical Engineering and Automation Fuzhou University 5G+Industrial Internet Institute Fuzhou University the University of Macau the College of Mechatronics and Automation National University of Defense Technology the School of Mechanical Engineering Shanghai Jiao Tong University the Department of Computer Science and Engineering Shanghai Jiao Tong University

This work presents an adaptive tracking guidance method for robotic fishes. The scheme enables robots to suppress external interference and eliminate motion jitter. An adaptive integral surge line-of-sight guidance rule is designed to eliminate dynamics interference and sideslip issues. Limited-time yaw and surge speed observers are reported to fit disturbance variables in the model. The approximation values can compensate for the system's control input and improve the robots' tracking ***, this work develops a terminal sliding mode controller and third-order differential processor to determine the rotational torque and reduce the robots' run jitter. Then, Lyapunov's theory proves the uniform ultimate boundedness of the proposed method. Simulation and physical experiments confirm that the technology improves the tracking error convergence speed and stability of robotic fishes.

关键词： Adaptive integral surge line-of-sight approximation robotic fish speed observer

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Improving Thyroid Disorder Diagnosis via Ensemble Stacking and Bidirectional Feature Selection

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computers, Materials & Continua 2024年第3期78卷 4225-4241页

作者： Muhammad Armghan Latif Zohaib Mushtaq Saad Arif Sara Rehman Muhammad Farrukh Qureshi Nagwan Abdel Samee Maali Alabdulhafith Yeong Hyeon Gu Mohammed A.Al-masni Department of Computer and Information System Cleveland State UniversityOhio44115USA Department of Electrical Electronics and Computer SystemsCollege of Engineering and TechnologyUniversity of SargodhaSargodha40100Pakistan Department of Mechanical Engineering College of EngineeringKing Faisal UniversityAl-Ahsa31982Saudi Arabia Department of Biomedical Engineering Riphah International UniversityIslamabad44000Pakistan Department of Electrical Engineering Riphah International UniversityIslamabad44000Pakistan Department of Information Technology College of Computer and Information SciencesPrincess Nourah bint Abdulrahman UniversityP.O.Box 84428Riyadh11671Saudi Arabia Department of Artificial Intelligence College of Software and Convergence TechnologySejong UniversitySeoul05006Korea

Thyroid disorders represent a significant global health challenge with hypothyroidism and hyperthyroidism as two common conditions arising from dysfunction in the thyroid *** and timely diagnosis of these disorders is crucial for effective treatment and patient *** research introduces a comprehensive approach to improve the accuracy of thyroid disorder diagnosis through the integration of ensemble stacking and advanced feature selection *** forward feature selection,sequential backward feature elimination,and bidirectional feature elimination are investigated in this *** ensemble learning,random forest,adaptive boosting,and bagging classifiers are *** effectiveness of these techniques is evaluated using two different datasets obtained from the University of California Irvine-Machine Learning Repository,both of which undergo preprocessing steps,including outlier removal,addressing missing data,data cleansing,and feature *** experimentation demonstrates the remarkable success of proposed ensemble stacking and bidirectional feature elimination achieving 100%and 99.86%accuracy in identifying hyperthyroidism and hypothyroidism,*** enhancing detection accuracy,the ensemble stacking model also demonstrated a streamlined computational complexity which is pivotal for practical medical *** significantly outperformed existing studies with similar objectives underscoring the viability and effectiveness of the proposed *** research offers an innovative perspective and sets the platform for improved thyroid disorder diagnosis with broader implications for healthcare and patient well-being.

关键词： Ensemble learning random forests boosting dimensionality reduction machine learning smart healthcare computer aided diagnosis

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