Journal of Intelligent & Fuzzy Systems - Volume 45, issue 3

Authors: Wajahat, Ahsan | He, Jingsha | Zhu, Nafei | Mahmood, Tariq | Nazir, Ahsan | Pathan, Muhammad Salman | Qureshi, Sirajuddin | Ullah, Faheem

Article Type: Research Article

Abstract: Positive developments in smartphone usage have led to an increase in malicious attacks, particularly targeting Android mobile devices. Android has been a primary target for malware exploiting security vulnerabilities due to the presence of critical applications, such as banking applications. Several machine learning-based models for mobile malware detection have been developed recently, but significant research is needed to achieve optimal efficiency and performance. The proliferation of Android devices and the increasing threat of mobile malware have made it imperative to develop effective methods for detecting malicious apps. This study proposes a robust hybrid deep learning-based approach for detecting and predicting …Android malware that integrates Convolutional Neural Networks (CNN) and Long Short-Term Memory (LSTM). It also presents a creative machine learning-based strategy for dealing with unbalanced datasets, which can mislead the training algorithm during classification. The proposed strategy helps to improve method performance and mitigate over- and under-fitting concerns. The proposed model effectively detects Android malware. It extracts both temporal and spatial features from the dataset. A well-known Drebin dataset was used to train and evaluate the efficacy of all creative frameworks regarding the accuracy, sensitivity, MAE, RMSE, and AUC. The empirical finding proclaims the projected hybrid ConvLSTM model achieved remarkable performance with an accuracy of 0.99, a sensitivity of 0.99, and an AUC of 0.99. The proposed model outperforms standard machine learning-based algorithms in detecting malicious apps and provides a promising framework for real-time Android malware detection. Show more

Keywords: Android malware detection, deep learning, CNN, LSTM, Drebin dataset

DOI: 10.3233/JIFS-231969

Citation: Journal of Intelligent & Fuzzy Systems, vol. 45, no. 3, pp. 5141-5157, 2023

Authors: Zhou, Qiaozhen | Wang, Fang | Zhao, Xuanyu | Hu, Kai | Zhang, Yujian | Shan, Xin | Lin, Xin | Zhang, Yupeng | Shan, Ke | Zhang, Kailiang

Article Type: Research Article

Abstract: Resistive random access memory (RRAM) has lots of advantages that make it a promising candidate for ultra-high-density memory applications and neuromorphic computing. However, challenges such as high forming voltage, low endurance, and poor uniformity have hampered the development and application of RRAM. To improve the uniformity of the resistive memory, this paper systematically investigates the HfOx -based RRAM by embedding nanoparticles. In this paper, the HfOx -Based RRAM with and without tungsten nanoparticles (W NPs) is fabricated by magnetron sputtering, UV lithography, and stripping. Comparing the various resistive switching behaviors of the two devices, it can be observed that the …W NPs device exhibits lower switching voltage (including a 69.87% reduction in Vforming and a reduction in Vset /Vreset from 1.4 V/-1.36 to 0.7 V/-1.0 V), more stable cycling endurance (>105 cycles), and higher uniformity. A potential switching mechanism is considered based on the XPS analysis and the research on the fitting of HRS and LRS: Embedding W NPs can improve the device performance by inducing and controlling the conductive filaments (CFs) size and paths. This thesis has implications for the performance enhancement and development of resistive memory. Show more

Keywords: Resistive random access memory (RRAM), HfOx, embedding W nanoparticles, uniformity, conduction mechanism

DOI: 10.3233/JIFS-232028

Citation: Journal of Intelligent & Fuzzy Systems, vol. 45, no. 3, pp. 5159-5167, 2023

Authors: Li, Zhenjiang | Zhang, Qianxue

Article Type: Research Article

Abstract: The writer identification task infers the writer by analyzing the texture, structure, and other representative features of the handwriting. Inspired by the attention mechanism, an end-to-end writer identification model is proposed in this paper, which combines both global features and local features. The Vision Transformer is used as the backbone network, and the Convolutional block attention module (CBAM) is introduced to enhance the ability of global feature awareness of the model. The proposed method is evaluated on two public data sets, IAM and CVL respectively. In the task of word-level writer identification, the accuracy rates in two data sets were …90.1% and 92.3% respectively. In the task of page-level writer identification, the accuracy rates were 98.6% and 99.5%, as a state-of-the-art performance. Show more

Keywords: Biometrics, writer identification, computer vision, neural network, vision transformer

DOI: 10.3233/JIFS-232134

Citation: Journal of Intelligent & Fuzzy Systems, vol. 45, no. 3, pp. 5169-5179, 2023

Authors: Wang, Chia-Hung | Cai, Jiongbiao | Ye, Qing | Suo, Yifan | Lin, Shengming | Yuan, Jinchen

Article Type: Research Article

Abstract: In recent years, it has been shown that deep learning methods have excellent performance in establishing spatio-temporal correlations for traffic speed prediction. However, due to the complexity of deep learning models, most of them use only short-term historical data in the time dimension, which limits their effectiveness in handling long-term information. We propose a new model, the Multi-feature Two-stage Attention Convolution Network (MTA-CN), to address this issue. The MTA-CN intercepts longer single-feature historical data, converts them into shorter multi-feature data with multiple time period features, and uses the most recent past point as the main feature. Furthermore, two-stage attention mechanisms …are introduced to capture the importance of different time period features and time steps, and a Temporal Graph Convolutional Network (T-GCN) is used instead of traditional recurrent neural networks. Experimental results on both the Los Angeles Expressway (Los-loop) and Shen-zhen Luohu District Taxi (Sz-taxi) datasets demonstrate that the proposed model outperforms several baseline models in terms of prediction accuracy. Show more

Keywords: Traffic speed prediction, attentional mechanisms, temporal dependence, spatial dependence, graph convolutional network

DOI: 10.3233/JIFS-231133

Citation: Journal of Intelligent & Fuzzy Systems, vol. 45, no. 3, pp. 5181-5196, 2023

Article Type: Retraction

DOI: 10.3233/JIFS-219329

Citation: Journal of Intelligent & Fuzzy Systems, vol. 45, no. 3, pp. 5197-5197, 2023