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Feature selection plays a key role in reducing the dimensionality of a feature vector by discarding redundant and irrelevant ones. In this paper, a Genetic Algorithm-based hierarchical feature selection (HFS) model has been designed to optimize the local and global features extracted from each of the handwritten word images under consideration. In this context, two recently developed feature descriptors based on shape and texture of the word images have been taken into account. Experimentation is conducted on an in-house dataset of 12,000 handwritten word samples written in Bangla script. This database comprises names of 80 popular cities of West Bengal, a state of India. Proposed model not only reduces the feature dimension by nearly 28%, but also enhances the performance of the handwritten word recognition (HWR) technique by 1.28% over the recognition performance obtained with unreduced feature set. Moreover, the proposed HFS-based HWR system performs better in comparison with some recently developed methods on the present dataset.

Extensive development in Generative Artificial Intelligence and the growth of Online Social Networks have facilitated the creation and sharing of synthetic images like never before. This has led to an overwhelming increase in the dissemination of fake content on OSNs. Maintaining the integrity of OSNs is paramount, and detecting synthetic images plays a crucial role in preserving social balance. Existing solutions, while achieving perfect detection performance on test datasets, often experience significant degradation when applied to OSN images. In our work, we propose a robust fake image detector that relies on features minimally affected by common OSN perturbations. Specifically, our solution leverages gradient features in color channels, including chrominance and luminance channels, accompanied by a residual-based CNN. Our low-parameterized solution is characterized by low complexity, making it particularly resource-efficient and suitable for edge devices. Thorough experiments demonstrate that our method achieves 100% accuracy in identifying fake images on our test dataset. We further evaluate the approach on images generated by contemporary generative adversarial networks and diffusion models, where it consistently exhibits strong detection performance. In addition, when applied to images that undergo post-processing operations designed to mimic OSN circulation, the proposed detector maintains high accuracy and robustness. Overall, results indicate that our proposed gradient-based color-channel features, coupled with a low-complexity residual network, provide an effective and OSN-resilient solution for synthetic image detection across both generic and post-processed/compressed scenarios. •AI-generated hyper-realistic synthetic images pose cyber-social threats.•OSN-specific transformations on synthetic images further complicate detection.•Introduced GoC for detection, leveraging gradient magnitude/direction in chroma-luma.•Proposed RNet, with GoC, achieves up to 100% accuracy with minimal parameters.•Robust OSN detection with SOTA results against post-processing and compression.

Car theft has become a significant issue in modern societies, with far-reaching individual and social consequences. This criminal act causes substantial financial losses for vehicle owners, undermines public trust in security systems, and increases social and governmental costs. Therefore, research on developing innovative and efficient methods for detecting and preventing car theft holds particular importance. In this study, advanced methods for detecting car theft have been evaluated and compared through two main approaches: deep learning and machine learning. First, pre-trained deep neural networks were examined. In the second phase, various image features were extracted using feature extraction methods, such as Edge Direction Histogram (EDH), Edge Oriented Histogram (EOH), and Histogram Oriented Gradient (HOG), followed by the assessment of machine learning approaches. Finally, a hybrid model based on Hybrid Edge and Gradient-Based Features (HFEM) combined with an XGBoost classifier was proposed, achieving an accuracy of 98.6% in predicting car theft.

The interpretability of neural networks has garnered significant attention. In the domain of computer vision, gradient-based feature attribution techniques like RectGrad have been proposed to utilize saliency maps to demonstrate feature contributions to predictions. Despite advancements, RectGrad falls short in category discrimination, producing similar saliency maps across categories. This paper pinpoints the ineffectiveness of threshold-based strategies in RectGrad for distinguishing feature gradients and introduces Class activation map Guided BackPropagation (CGBP) to tackle the issue. CGBP leverages class activation maps during backpropagation to enhance gradient selection, achieving consistent improvements across four models (VGG16, VGG19, ResNet50, and ResNet101) on ImageNet’s validation set. Notably, on VGG16, CGBP improves SIC, AIC, and IS scores by 10.3%, 11.5%, and 4.5%, respectively, compared to RectGrad while maintaining competitive DS performance. Moreover, CGBP demonstrates greater sensitivity to model parameter changes than RectGrad, as confirmed by a sanity check. The proposed method has broad applicability in scenarios like model debugging, where it identifies causes of misclassification, and medical image diagnosis, where it enhances user trust by aligning visual explanations with clinical insights.

Far-infrared pedestrian detection approaches for advanced driver-assistance systems based on high-dimensional features fail to simultaneously achieve robust and real-time detection. We propose a robust and real-time pedestrian detection system characterized by novel candidate filters, novel pedestrian features and multi-frame approval matching in a coarse-to-fine fashion. Firstly, we design two filters based on the pedestrians’ head and the road to select the candidates after applying a pedestrian segmentation algorithm to reduce false alarms. Secondly, we propose a novel feature encapsulating both the relationship of oriented gradient distribution and the code of oriented gradient to deal with the enormous variance in pedestrians’ size and appearance. Thirdly, we introduce a multi-frame approval matching approach utilizing the spatiotemporal continuity of pedestrians to increase the detection rate. Large-scale experiments indicate that the system works in real time and the accuracy has improved about 9% compared with approaches based on high-dimensional features only.

Feature selection (FS) is a classic and challenging optimization task in the field of machine learning and data mining. Gradient-based optimizer (GBO) is a recently developed metaheuristic with population-based characteristics inspired by gradient-based Newton's method that uses two main operators: the gradient search rule (GSR), the local escape operator (LEO) and a set of vectors to explore the search space for solving continuous problems. This article presents a binary GBO (BGBO) algorithm and for feature selecting problems. The eight independent GBO variants are proposed, and eight transfer functions divided into two families of S-shaped and V-shaped are evaluated to map the search space to a discrete space of research. To verify the performance of the proposed binary GBO algorithm, 18 well-known UCI datasets and 10 high-dimensional datasets are tested and compared with other advanced FS methods. The experimental results show that among the proposed binary GBO algorithms has the best comprehensive performance and has better performance than other well known metaheuristic algorithms in terms of the performance measures.

Human activity recognition (HAR) plays a vital role in different real-world applications such as in tracking elderly activities for elderly care services, in assisted living environments, smart home interactions, healthcare monitoring applications, electronic games, and various human–computer interaction (HCI) applications, and is an essential part of the Internet of Healthcare Things (IoHT) services. However, the high dimensionality of the collected data from these applications has the largest influence on the quality of the HAR model. Therefore, in this paper, we propose an efficient HAR system using a lightweight feature selection (FS) method to enhance the HAR classification process. The developed FS method, called GBOGWO, aims to improve the performance of the Gradient-based optimizer (GBO) algorithm by using the operators of the grey wolf optimizer (GWO). First, GBOGWO is used to select the appropriate features; then, the support vector machine (SVM) is used to classify the activities. To assess the performance of GBOGWO, extensive experiments using well-known UCI-HAR and WISDM datasets were conducted. Overall outcomes show that GBOGWO improved the classification accuracy with an average accuracy of 98%.

The rapid digitalization of modern energy systems—including smart grids, advanced metering infrastructures (AMI), and supervisory control and data acquisition (SCADA) networks—has increased their vulnerability to cyberattacks. Although encryption secures energy data, it also conceals malicious traffic, complicating intrusion detection. This vulnerability exposes critical systems to threats, such as false data injection, command tampering, and advanced persistent attacks. Consequently, distinguishing benign from malicious activity becomes increasingly challenging, especially when attackers exfiltrate sensitive data through encrypted channels. This study proposes an adaptive feature selection (AFS) method to enhance cybersecurity in energy systems. In contrast to conventional models that focus solely on statistical relevance, AFS incorporates gradient-based relevance to capture context-sensitive traffic patterns, thereby revealing malicious activities within encrypted, noisy environments. Experimental results, conducted on the CIRA-CIC-DoHBrw-2020 dataset, show that AFS improves detection accuracy by 24.74% and reduces training time by 35% compared to conventional PCA-based methods. This approach strengthens cybersecurity in energy systems by improving the detection performance of intrusion detection frameworks, thereby enhancing operational reliability, data integrity, and overall network security.

Machine learning-based network intrusion detection systems (NIDSs) remain vulnerable to adversarial manipulation, but the robustness literature for tabular NIDS data is still dominated by single-model, single-dataset, and non-adaptive evaluations. In this paper, we reposition the manuscript as a comparative robustness study of a four-component defense pipeline rather than as a claim of a universal defense primitive. We evaluate XGBoost, LightGBM, TabNet, and Residual MLP on RT_IOT2022 and Web_IDS23 under standard attacks, representative constrained/adaptive attacks, component-wise ablations, sample-fraction sensitivity, repeated-run significance tests, per-class F1 analysis, and computational-overhead measurements. The results show strong dataset and architecture dependence. On RT_IOT2022, tree-based models close most of the robustness gap under strong attacks but often only after large clean-accuracy reductions; Residual MLP achieves a more favorable balance, while the full defense stack over-regularizes TabNet. On Web_IDS23, aggregate robustness-gap reduction remains positive, yet simpler baselines such as adversarial-training-only or ensemble-only configurations frequently outperform the full four-stage pipeline in absolute clean/attack accuracy. Across both datasets, median filtering is the most fragile component: larger filter windows substantially degrade both clean and attacked accuracy, whereas contamination rate, anomaly-mixing weight, and ensemble size are comparatively stable. Representative constrained/adaptive evaluations reduce performance only modestly relative to standard FGSM/PGD, but per-class and overhead analyses show that minority-class collapse and training cost remain important deployment limitations. These findings support a more cautious conclusion: adversarial defense for tabular NIDS is validation driven and dataset specific, and the full defense stack should not be treated as a universal default.

Short-Term Load Forecasting (STLF) is essential for the reliable and economic operation of modern power systems. Deep Residual Networks (DRNs) have emerged as an effective framework for STLF due to their ability to model nonlinear and multi-scale load patterns. Although numerous DRN-based extensions have been proposed through architectural refinement and feature enhancement, the role of gradient-based optimization algorithms in DRN-based STLF has received limited systematic investigation. Most existing studies rely on the Adaptive Moment Estimation (Adam) algorithm as the default optimization strategy, without comprehensively examining alternative gradient-based optimizers. To address this gap, this study conducts a hypothesis-driven comparative evaluation of representative gradient-based optimization algorithms within a unified DRN-based STLF framework across both temperate (ISO-NE) and tropical (MyPJ) climatic conditions. Both the original DRN, which primarily incorporates temperature as the meteorological input, and its enhanced variant, the Principal Component Analysis–Deep Residual Network (PCA-DRN), which integrates multiple weather variables through PCA, are investigated using real-world electricity load datasets. Forecasting performance is evaluated using multiple metrics, with Mean Absolute Percentage Error (MAPE) as the primary criterion, and statistical significance is assessed through a nonparametric bootstrap resampling procedure. The results demonstrate that optimizer selection significantly influences training stability and forecasting accuracy. AMSGrad achieves the most consistent performance within the original DRN across climatic conditions, whereas under PCA-based feature representation the relative advantage shifts, indicating that meteorological feature compression reshapes the optimization landscape. Overall, the findings highlight the importance of systematic optimizer evaluation and feature-representation strategies for enhancing the reliability and stability of DRN-based STLF.