Lightweight Hybrid Research Articles

An Improved and Effective Approach for User Attention in the IoT Through the Hybrid Lightweight Security Framework (HLSF) with Data Mining

An Improved and Effective Approach for User Attention in the IoT Through the Hybrid Lightweight Security Framework (HLSF) with Data Mining

XSShield: A Novel Dataset and Lightweight Hybrid Deep Learning Model for XSS Attack Detection

With the proliferation of web applications, cross-site scripting (XSS) attacks have increased significantly and now pose a significant threat to users' information security and privacy. To enhance the efficiency of XSS attack detection, the adoption of machine learning (ML) and deep learning (DL) techniques offers promising solutions, but their effectiveness is limited by the lack of comprehensive and diverse datasets. Moreover, existing approaches often prioritize detection accuracy over real-time processing capabilities, which are essential for effective defense. To address these challenges, in this paper, we propose a novel framework that automatically collects web resources, efficiently extracts informative features, and constructs an up-to-date XSS attack dataset, which is then used to train a machine learning-based XSS detection model. Using this framework, we created and published a well-structured dataset over 100,000 samples for the research community. Furthermore, we present a hybrid detection model that leverages the strengths of both Convolutional Neural Networks (CNNs) and Long Short-Term Memory (LSTM) networks. Extensive evaluations of our dataset demonstrate that the proposed model outperforms other baseline ML models across various metrics, including processing rate. Notably, our model achieves an accuracy of 99.27% while maintaining a low false positive rate of 0.06% and high processing rate of exceeding 1000 samples per second. These results highlight its high accuracy and robustness in detecting XSS, and suitability for real-time applications. Our work presents a comprehensive solution for enhancing web application security by providing a diverse dataset and a high-accuracy detection model with low latency.

A Dual Detection Head YOLO Model With Its Application in Wheat Ear Recognition

The spike number(SN) per unit area is an important indicator for measuring wheat yield. The use of AI to detect and count wheat ears is increasingly valued by researchers. In order to improve the accuracy of wheat ear recognition, the model is deployed on limited mobile devices to achieve accurate estimation of wheat yield. This paper proposes a lightweight hybrid design network Wheat-YoloNet based on improved YOLOv8 for wheat ear recognition. On the basis of the YOLOv8 network structure, we only retain small and large object detection heads, reducing the number of network parameters while ensuring detection performance. We added CBAM attention mechanism module to Backbone to improve the feature extraction performance of the model and introduce Wise-IoU to balance the samples. Compared with the YOLOv8n baseline model, the improved model has reduced parameter count by 33.3%, outperforming other comparison object detection algorithms, and can be deployed and run on devices with limited computing power.

Integration of blockchain with hybrid lightweight cryptosystem

Integration of blockchain with hybrid lightweight cryptosystem

Comparative tribological study of the material intended for a lightweight HAMNC brake rotor sliding against NAO brake pad material

Abstract The present work studies the lightweight Hybrid Aluminium Metal Matrix Nanocomposite (HAMNC) for brake rotor application. The novel HAMNC brake rotor material is fabricated by reinforcing 1 wt. % nano Boron carbide (nB4C) and 0.75 wt.% nano Titanium dioxide (nTiO2) employing ultrasonic-squeeze-assisted stir-casting process. The developed HAMNC and a commercial Gray Cast Iron (GCI) brake rotor material was subjected to density, hardness, thermal, corrosion, and tribological studies. The results indicated that the HAMNC brake rotor material is 60% lighter and extremely corrosive resistant compared with GCI material. Also, the dry sliding wear study done using Non Asbestos Organic (NAO) commercial brake pad as the pin material exhibited that the HAMNC brake rotor material possessed a higher wear-resistant behavior compared to GCI.

Fast Hyperspectral Image Classification with Strong Noise Robustness Based on Minimum Noise Fraction

A fast hyperspectral image classification algorithm with strong noise robustness is proposed in this paper, aiming at the hyperspectral image classification problems under noise interference. Based on the Fast 3D Convolutional Neural Network (Fast-3DCNN), this algorithm enables the classification model to have good tolerance for various types of noise by using a Minimum Noise Fraction (MNF) as dimensionality reduction module for hyperspectral image input data. In addition, by introducing lightweight hybrid attention modules with the spatial and the channel information, the deep features extracted by the Convolutional Neural Network are further refined, ensuring that the model has high classification accuracy. Public dataset experiments have shown that compared to traditional methods, the MNF in this algorithm reduces the dimensionality of input spectral data, preserves information with higher signal-to-noise ratio(SNR) in the spectral bands, and aggregates spectral features into class feature vectors, greatly improving the noise robustness of the model. At the same time, based on a lightweight spectral–spatial hybrid attention mechanism, combined with fewer spectral dimensions, the model effectively avoids overfitting. With less loss in model training speed, it achieved better classification accuracy in small-scale training sample experiments, fully demonstrating the good generalization ability of this algorithm.

PILEA, an Advanced Hybrid Lightweight Algorithm utilizing Logical Mathematical Functions and Chaotic Systems

PILEA, an Advanced Hybrid Lightweight Algorithm utilizing Logical Mathematical Functions and Chaotic Systems

Optimizing Plant Disease Classification with Hybrid Convolutional Neural Network–Recurrent Neural Network and Liquid Time-Constant Network

This paper addresses the practical challenge of detecting tomato plant diseases using a hybrid lightweight model that combines a Convolutional Neural Network (CNN) and Recurrent Neural Network (RNN). Traditional image classification models demand substantial computational resources, limiting their practicality. This study aimed to develop a model that can be easily implemented on low-cost IoT devices while maintaining high accuracy with real-world images. The methodology leverages a CNN for extracting high-level image features and an RNN for capturing temporal relationships, thereby enhancing model performance. The proposed model incorporates a Closed-form Continuous-time Neural Network, a lightweight variant of liquid time-constant networks, and integrates Neural Circuit Policy to capture long-term dependencies in image patterns, reducing overfitting. Augmentation techniques such as random rotation and brightness adjustments were applied to the training data to improve generalization. The results demonstrate that the hybrid models outperform their single pre-trained CNN counterparts in both accuracy and computational cost, achieving a 97.15% accuracy on the test set with the proposed model, compared to around 94% for state-of-the-art pre-trained models. This study provides evidence of the effectiveness of hybrid CNN-RNN models in improving accuracy without increasing computational cost and highlights the potential of liquid neural networks in such applications.

Ultrasonic joining of carbon-reinforced polyamide with oak wood for manufacturing sustainable hybrid structures

The feasibility of manufacturing strong and lightweight hybrid joints of carbon fiber-reinforced polyamide (PA6-15CF) and oak wood using ultrasonic energy was successfully demonstrated. Before joining, a groove pattern was generated on the surface of the wood platelets by laser texturing. Wood platelets with and without texturing were joined with the polymer using the ultrasonic joining (U-Joining) process. Mechanical interlocking at the polymer-wood interface was improved, as the grooves and open vessels on the surface of the textured wood platelets were filled with the polymer composite. Therefore, the lap-shear strength of joints with textured wood was 9.2 ± 0.7 MPa, whereas the shear strength of joints with untextured wood was just 4.1 ± 0.8 MPa.

Enhancing Image Classification Accuracy With A Lightweight Hybrid Densenet And Machine Learning Model

Enhancing Image Classification Accuracy With A Lightweight Hybrid Densenet And Machine Learning Model

Blockchain-Machine Learning Fusion for Enhanced Malicious Node Detection in Wireless Sensor Networks

In wireless sensor networks (WSNs), the presence of malicious nodes (MNs) poses significant challenges to data integrity, network stability, and system reliability. These issues are intensified by energy resource constraints and limitations within centralized authentication systems, necessitating an energy-efficient solution to ensure real-time responsiveness. Although artificial intelligence-driven approaches enhance detection capabilities, they overcome challenges related to data volume, coordination overhead, and latency in centralized control. This study introduces blockchain-machine learning (BC-ML), a novel hybrid model that seamlessly integrates blockchain and machine learning (ML) techniques to effectively identify MNs in WSNs. The model establishes an energy-efficient blockchain among cluster heads (CHs) for robust node authentication, incorporating a Schnorr-like zero-knowledge-proof technique to validate node data during communication initiation. Utilizing a hybrid lightweight approach with both symmetric and asymmetric ciphers enhances the security of node data transmission. A new proof-of-authority method is introduced, which leverages node digital certificates instead of conventional data transactions. This consensus mechanism reduces the processing overhead associated with larger data sizes in traditional proof-of-work methods, thereby improving both energy efficiency and scalability. To address dataset imbalances, the model employs a hybrid unsupervised ML technique, combining adaptive synthetic sampling with a convolutional neural network for efficient analysis of nodes and network features. The ML model, hosted on a robust data server, ensures ongoing oversight by updating CHs with security levels for detected MNs, thereby reducing storage and mitigating coordination challenges. Comprehensive analyses validate the effectiveness of the BC-ML model for detecting MNs, optimizing resource utilization, minimizing delays, and prolonging node and network lifetimes. Security analysis further confirms the ability of the model to mitigate diverse attacks and meet the stringent WSN security requirement.

Automated echocardiographic diastolic function grading: A hybrid multi-task deep learning and machine learning approach

BackgroundAssessing left ventricular diastolic function (LVDF) with echocardiography as per ASE guidelines is tedious and time-consuming. The study aims to develop a fully automatic approach of this procedure by a lightweight hybrid algorithm combining deep learning (DL) and machine learning (ML). MethodsThe model features multi-modality input and multi-task output, measuring LV ejection fraction (LVEF), left atrial end-systolic volume (LAESV), and Doppler parameters: mitral E wave velocity (E), A wave velocity (A), mitral annulus e’ velocity (e’), and tricuspid regurgitation velocity (TRmax). The algorithm was trained and tested on two internal datasets (862 and 239 echocardiograms) and validated using three external datasets, including EchoNet-Dynamic and CAMUS. The ASE diastolic function decision tree and total probability theory were used to provide diastolic grading probabilities. ResultsThe algorithm, named MMnet, demonstrated high accuracy in both test and validation datasets, with Dice coefficients for segmentation between 0.922 and 0.932 and classification accuracies between 0.9977 and 1.0. The mean absolute errors (MAEs) for LVEF and LAESV were 3.7 % and 5.8 ml, respectively, and for LVEF in external validation, MAEs ranged from 4.9 % to 5.6 %. The diastolic function grading accuracy was 0.88 with hard criteria and up to 0.98 with soft criteria which account for the top two probability in total probability theory. ConclusionsMMnet can automatically grade ASE diastolic function with high accuracy and efficiency by annotating 2D videos and Doppler images.

DM-GAN: CNN hybrid vits for training GANs under limited data

Generative adversarial network (GAN) training demands substantial data and computational resources. This paper aims to explore an economical approach for generating novel images with limited image data, addressing the challenge of data scarcity. Our contributions involve resolving the few-shot image generation challenge through the development of an unsupervised hybrid generative adversarial network named DM-GAN. We introduce a lightweight hybrid module (DC-Vit) comprising convolution and visual transformation, merging local and global features to enhance image perception, expressiveness, and ensure stable image generation. Additionally, a multi-scale adaptive skip connection module is incorporated to effectively mitigate the feature loss problem arising from inter-layer jumps, thereby producing more complete and regular images. To enhance the texture learning process and improve the quality and realism of synthesized images, we integrate the gray conjugate matrix into the loss function. Empirical evaluations are conducted on small sample datasets at various resolutions, including publicly accessible collections of art paintings, real-life photographs, and proprietary artifact image datasets. The experimental results unequivocally demonstrate the qualitative and quantitative superiority of our model over existing methods, underscoring its efficacy and robustness.

Lightweight Hybrid Entropy Source True Random Number Generator Based on Jitter and Metastability

Lightweight Hybrid Entropy Source True Random Number Generator Based on Jitter and Metastability

Advancements in intrusion detection: A lightweight hybrid RNN-RF model.

Computer networks face vulnerability to numerous attacks, which pose significant threats to our data security and the freedom of communication. This paper introduces a novel intrusion detection technique that diverges from traditional methods by leveraging Recurrent Neural Networks (RNNs) for both data preprocessing and feature extraction. The proposed process is based on the following steps: (1) training the data using RNNs, (2) extracting features from their hidden layers, and (3) applying various classification algorithms. This methodology offers significant advantages and greatly differs from existing intrusion detection practices. The effectiveness of our method is demonstrated through trials on the Network Security Laboratory (NSL) and Canadian Institute for Cybersecurity (CIC) 2017 datasets, where the application of RNNs for intrusion detection shows substantial practical implications. Specifically, we achieved accuracy scores of 99.6% with Decision Tree, Random Forest, and CatBoost classifiers on the NSL dataset, and 99.8% and 99.9%, respectively, on the CIC 2017 dataset. By reversing the conventional sequence of training data with RNNs and then extracting features before applying classification algorithms, our approach provides a major shift in intrusion detection methodologies. This modification in the pipeline underscores the benefits of utilizing RNNs for feature extraction and data preprocessing, meeting the critical need to safeguard data security and communication freedom against ever-evolving network threats.

Design and implementation of a lightweight hybrid optical system for aircraft head-up display

Design and implementation of a lightweight hybrid optical system for aircraft head-up display

Lightweight hybrid fiber-reinforced sheet molding compound with enhanced mechanical properties and excellent acid resistance

Lightweight hybrid fiber-reinforced sheet molding compound with enhanced mechanical properties and excellent acid resistance

Experimental and Numerical Analysis of Lightweight Hybrid Composites Under Low Velocity Impact

Experimental and Numerical Analysis of Lightweight Hybrid Composites Under Low Velocity Impact

COVID-19 detection from Chest X-ray images using a novel lightweight hybrid CNN architecture

COVID-19 detection from Chest X-ray images using a novel lightweight hybrid CNN architecture

Investigation on the torsional property of hybrid composite/metal shafts at different service temperatures: Experimental and analytical study

AbstractTorsional tests were conducted to investigate the influence of service temperature on torsional property of composite/steel hybrid structures. In the experiment, two types of lightweight hybrid composite‐metal shafts were tested at both room temperature and various elevated temperatures up to 150°C, including hybrid shafts with and without a metal core. Consequently, the torsional stiffness of the two hybrid shafts at different temperatures was calculated based on the recorded angular deformation‐torque curves. The experimental results revealed a significant decrease in the torsional stiffness of the hybrid shaft without metal core as the increase of service temperature, while the torsional stiffness of the one with metal core remained almost unchanged, especially at high temperatures. In addition, a finite element model was proposed to predict the torsional behavior of the hybrid shafts at different service temperatures. In the model, an empirical equation was introduced, and micromechanical method was adopted to determine the temperature‐dependant elastic properties of composite material and adhesive layer in the hybrid structure. Then, the predicted torsional property was compared to the experimental results for model verification.Highlights Two types of hybrid composite‐metal shafts are designed and manufactured. Torsional tests are conducted at both room and elevated temperatures up to 150°C. A finite element model applicable to shaft at various temperatures is proposed. The temperature‐dependant property is characterized by an efficient equation.