Pattern Recognition Tasks Research Articles

Bicriteria Shapes: Hierarchical Grouping and Aggregation of Polygons with an Efficient Graph-Cut Approach

An important task of pattern recognition and map generalization is to partition a set of disjoint polygons into groups and to aggregate the polygons within each group to a representative output polygon. We introduce a new method for this task called bicriteria shapes. Following a classical approach, we define the output polygons by merging the input polygons with a set of triangles that we select from a conforming Delaunay triangulation of the input polygons’ exterior. The innovation is that we control the selection of triangles with a bicriteria optimization model that is efficiently solved via graph cuts. In particular, we minimize a weighted sum that combines the total area of the output polygons and their total perimeter. In a basic problem, we ask for a single solution that is optimal for a preset parameter value. In a second problem, we ask for a set containing an optimal solution for every possible value of the parameter. We discuss how this set can be approximated with few solutions and show that it is hierarchically nested. Hence, the output is a hierarchical clustering that corresponds to multiple levels of detail. An evaluation with building footprints as input and a comparison with α -shapes that are based on the same underlying triangulation conclude the article. An advantage of bicriteria shapes compared to α -shapes is that the sequence of solutions for decreasing values of the parameter is monotone with respect to the total perimeter of the output polygons, resulting in a monotonically decreasing visual complexity.

Opposite synaptic plasticity in oxidation-layer-controlled 2D materials-based memristors for mimicking heterosynaptic plasticity

Memristors with nonstoichiometric tungsten oxide (WOx) as an active layer, derived from the oxidation of atomically thin two-dimensional tungsten diselenide (WSe2), enable the creation of the monolithic layered structure of WOx/WSe2. These devices are promising candidates for emulating various biological synaptic functions in the human brain. In this study, we fabricate monolithic few-layer WOx/WSe2 memristors with precisely controlled WOx thickness by UV-ozone treatment from 1 L to 9 L, depending on chuck temperature. The postsynaptic responses of the topmost single-layer (1 L) oxidized WSe2 and fully (9 L) oxidized WSe2 memristors exhibit sharply contrasting behaviors, which can be applied to mimic the heterosynaptic plasticity in the CA1 region of the hippocampus. Beyond the significance of emulating the biological synaptic characteristics, we explore the feasibility of using each oxidation-layer-controlled memristor as a hardware accelerator. Their performances are assessed through application in a CIFAR-10 pattern recognition task using a convolutional neural network. Pattern recognition rates of 84 % and 71 % are obtained for the 1 L and 9 L WOx-based devices, respectively. We also examine the applicability of a synaptic cell composed of devices with oppositely switched characteristics. Consequently, the synaptic weight—defined as the difference in conductance between two synaptic devices—can be either increased (potentiated) or decreased (depressed) by simultaneously updating both devices with the same voltage signal. This weight update concept achieves a moderate recognition rate of 85.94 % when using an MNIST pattern-based recognition task, simplifying the complex weight-adjustment process.

Comparison of machine learning methods for Predicting 3-Year survival in elderly esophageal squamous cancer patients based on oxidative stress

SummaryBackgroundOxidative stress process plays a key role in aging and cancer; however, currently, there is paucity of machine-learning model studies investigating the relationship between oxidative stress and prognosis of elderly patients with esophageal squamous cancer (ESCC).MethodsThis study included elderly patients with ESCC who underwent curative ESCC resection surgery continuously from January 2013 to December 2020 and were stratified into the training and external validation cohorts. Using Cox stepwise regression analysis based on Akaike information criterion, the relationship between oxidative stress biomarkers and prognosis was explored, and a geriatric ESCC-related oxidative stress score (OSS) was constructed. To construct a predictive model for 3-year overall survival (OS), machine-learning strategies including decision tree (DT), random forest (RF), and support vector machine (SVM) were employed. These machine-learning strategies play a key role in data mining and pattern recognition tasks. Each model was tested in the external validation cohort through 1000 resampling iterations. Validation was conducted using receiver operating characteristic area under the curve (AUC) and calibration plots.ResultsThe training cohort and validation cohort consisted of 340 and 145 patients, respectively. In the training cohort, the 3-year OS rate for patients was 59.2%. We constructed the OSS based on systemic oxidative stress biomarkers using the training cohort. The study found that pathological N stage, pathological T stage, tumor histological type, lymphovascular invasion, CEA, OSS, CA 19 − 9, and the amount of bleeding were the most important factors influencing the 3-year OS. These eight important features were included in training the RF, DT, and SVM and trained on the training cohort and validated cohort, respectively. In the training cohort, the RF model demonstrated the highest predictive performance with an AUC of 0.975 (0.962–0.987), while the DT model is 0.784 (0.739–0.830) and the SVM is 0.879 (0.843–0.916). In the external validation cohort, the RF model again exhibited the highest performance with an AUC of 0.791 (0.717–0.864), compared to the DT model with an AUC of 0.717 (0.640–0.794) and 0.779 (0.702–0.856) in SVM.ConclusionsThe random forest clinical prediction model constructed based on OSS can effectively predict the prognosis of elderly patients with ESCC after curative surgery.

Enhancing decision-making with linear diophantine multi-fuzzy set: application of novel information measures in medical and engineering fields

This study offers a comprehensive analysis of novel information for linear diophantine multi-fuzzy sets and illustrates its applications in practical scenarios. We introduce innovative similarity metrics tailored for linear diophantine multi-fuzzy sets, including Cosine similarity, Jaccard similarity, and Exponential similarity. Additionally, we propose Entropy, Inclusion, and Distance measures, providing a robust theoretical foundation supported by developed theorems that explain the interactions between these metrics. The practical implications of these theoretical advancements are demonstrated through various case studies. Specifically, we apply the similarity measures to predict preeclampsia, a severe condition affecting pregnant women, showcasing their potential in medical diagnostics. The entropy measure is used to identify the optimal materials manufacturing method for medical surgical robots, underscoring its importance in ensuring patient safety and the effectiveness of medical procedures. Furthermore, the inclusion measure is employed in pattern recognition tasks, highlighting its utility in complex data analysis. The comparative and superiority analysis shows the effectiveness of our research. The novel aspect of this study is the implementation of information metrics for LDMFS. These efforts aim to enhance the impact and practical applicability of linear diophantine multi-fuzzy sets, fostering innovation and improving outcomes across multiple fields.

Machine Learning-Potato Leaf Disease Detection App (MR-PoLoD)

Potato production in Indonesia has grown very rapidly, making Indonesia the largest potato producer in Southeast Asia. However, there are challenges for farmers in growing potatoes. Such as treating potatoes for various diseases. 2 diseases will occur in potato plants if not treated quickly, namely early blight disease caused by the fungus Alternaria solani and late blight disease caused by the microorganism Phytophthora infestans. The project "Potato Plant Leaf Disease Detector (MR-PoLod)" aims to design an android application that can classify leaves on potato plants into 3 classifications, namely healthy, early, and late blight disease. This application uses the CNN (Convolutional Neural Network) Machine Learning Algorithm because currently, CNN is recognized as the most efficient and effective model in pattern and image recognition tasks. This application uses the Python programming language which is rich in library and framework availability so that it can meet the needs of machine learning and image classification tasks. The total data used for training data, data validation and data testing is 3165 images. With each division of the data process on the training data of 70%, validation of 15% testing of 15% to test the effectiveness of the model that has been created. The performance of MR-PoLod for each class, obtained a precision value, recall, and f1-score of 0.99. Likewise, the accuracy value achieved by the model is 0.99 or 99%. Thus, the expected application can facilitate farmers in classifying diseases on potato plant leaves.

Hybrid Computational Intelligence Models for Robust Pattern Recognition and Data Analysis

In the era of big data, robust pattern recognition and accurate data analysis have become critical in various fields, including healthcare, finance, and industrial automation. This study presents a novel hybrid computational intelligence model that integrates deep learning techniques and evolutionary algorithms to enhance the precision and resilience of pattern recognition tasks. Our proposed model combines Convolutional Neural Networks (CNN) for high-dimensional feature extraction with a Genetic Algorithm (GA) for feature optimization and selection, providing a more efficient approach to processing complex datasets. The hybrid model achieved an accuracy of 98.7% on the MNIST dataset and outperformed conventional methods in terms of recall (95.5%) and precision (97.2%) on large-scale image classification tasks. Additionally, it demonstrated substantial improvements in computation time, reducing processing duration by 35% over traditional deep learning approaches. Experimental results on diverse datasets, including time-series and unstructured data, confirmed the model's versatility and adaptability, achieving F1-scores of 0.92 in healthcare data analysis and 0.89 in financial anomaly detection. By incorporating a Particle Swarm Optimization (PSO) algorithm, the model further optimized hyperparameters, leading to a 25% reduction in memory consumption without compromising model performance. This approach not only enhances computational efficiency but also enables the model to perform reliably in resource-constrained environments. Our results suggest that hybrid computational intelligence models offer a promising solution for robust, scalable pattern recognition and data analysis, addressing the evolving demands of real-world applications.

Versatility Evaluation of Landslide Risk with Window Sizes and Sampling Techniques Based on Deep Learning

Across the globe, landslides cause significant loss of life, injuries, and widespread damage to homes and infrastructure. Therefore, assessing and analyzing landslide hazards is crucial to human, environmental, cultural, economic, and social sustainability. This study utilizes ArcGIS 10.8 and Python 3.9 to create landslide databases for Niigata Prefecture (NIG), Iwate and Miyagi Prefectures (IWT-MYG), and Hokkaido (HKD), drawing on data obtained from the National Research Institute for Earth Science and Disaster Resilience, Japan. A distinguishing feature of this study is the application of a Convolutional Neural Network (CNN), which significantly outperforms traditional machine learning models in image-based pattern recognition by extracting contextual information from surrounding areas, a distinct advantage in image and pattern recognition tasks. Unlike conventional methods that often require manual feature selection and engineering, CNNs automate feature extraction, enabling a more nuanced understanding of complex patterns. By experimenting with CNN input window sizes ranging from 3 × 3 to 27 × 27 pixels and employing diverse sampling techniques, we demonstrate that larger windows enhance the model’s predictive accuracy by capturing a wider range of environmental interactions critical for effective landslide modeling. CNN models with 19 × 19 pixel windows typically yield the best overall performance, with CNN-19 achieving an AUC of 0.950, 0.982 and 0.969 for NIG, HKD, and IWT-MYG, respectively. Furthermore, we improve prediction reliability using oversampling and a random window-moving method. For instance, in the NIG region, the AUC of the oversampling CNN-19 is 0.983, while the downsampling AUC is 0.950). These techniques, less commonly applied in traditional machine learning approaches to landslide detection, help address the issue of data imbalance often seen in landslide datasets, where instances of landslides are far outnumbered by non-landslide occurrences. While challenges remain in enhancing the model’s generalization, this research makes significant progress in developing more robust and adaptable tools for landslide prediction, which are vital for ensuring environmental and societal resilience.

Rectified activity-dependent population plasticity implicates cortical adaptation for memory and cognitive functions

Cortical network undergoes rewiring everyday due to learning and memory events. To investigate the trends of population adaptation in neocortex overtime, we record cellular activity of large-scale cortical populations in response to neutral environments and conditioned contexts and identify a general intrinsic cortical adaptation mechanism, naming rectified activity-dependent population plasticity (RAPP). Comparing each adjacent day, the previously activated neurons reduce activity, but remain with residual potentiation, and increase population variability in proportion to their activity during previous recall trials. RAPP predicts both the decay of context-induced activity patterns and the emergence of sparse memory traces. Simulation analysis reveal that the local inhibitory connections might account for the residual potentiation in RAPP. Intriguingly, introducing the RAPP phenomenon in the artificial neural network show promising improvement in small sample size pattern recognition tasks. Thus, RAPP represents a phenomenon of cortical adaptation, contributing to the emergence of long-lasting memory and high cognitive functions.

PIDNODEs: Neural ordinary differential equations inspired by a proportional–integral–derivative controller

Neural Ordinary Differential Equations (NODEs) are a novel family of infinite-depth neural-net models through solving ODEs and their adjoint equations. In this paper, we present a strategy to enhance the training and inference of NODEs by integrating a Proportional–Integral–Derivative (PID) controller into the framework of Heavy Ball NODE, resulting in the proposed PIDNODEs and its generalized version, GPIDNODEs. By leveraging the advantages of control, PIDNODEs and GPIDNODEs can address the stiff ODE challenges by adjusting the parameters (i.e., Kp, Ki and Kd) in the PID module. The experiments confirm the superiority of PIDNODEs/GPIDNODEs over other NODE baselines on different computer vision and pattern recognition tasks, including image classification, point cloud separation and learning long-term dependencies from irregular time-series data for a physical dynamic system. These experiments demonstrate that the proposed models have higher accuracy and fewer function evaluations while alleviating the dilemma of exploding and vanishing gradients, particularly when learning long-term dependencies from a large amount of data.

Enhanced salience of edge frequencies in auditory pattern recognition.

Within musical scenes or textures, sounds from certain instruments capture attention more prominently than others, hinting at biases in the perception of multisource mixtures. Besides musical factors, these effects might be related to frequency biases in auditory perception. Using an auditory pattern-recognition task, we studied the existence of such frequency biases. Mixtures of pure tone melodies were presented in six frequency bands. Listeners were instructed to assess whether the target melody was part of the mixture or not, with the target melody presented either before or after the mixture. In Experiment 1, the mixture always contained melodies in five out of the six bands. In Experiment 2, the mixture contained three bands that stemmed from the lower or the higher part of the range. As expected, Experiments 1 and 2 both highlighted strong effects of presentation order, with higher accuracies for the target presented before the mixture. Notably, Experiment 1 showed that edge frequencies yielded superior accuracies compared with center frequencies. Experiment 2 corroborated this finding by yielding enhanced accuracies for edge frequencies irrespective of the absolute frequency region. Our results highlight the salience of sound elements located at spectral edges within complex musical scenes. Overall, this implies that neither the high voice superiority effect nor the insensitivity to bass instruments observed by previous research can be explained by absolute frequency biases in auditory perception.

Enhancing Hopfield network performance for pattern retrieval using sparse recovery algorithm and Parzen estimator

An improved pattern recovery approach that integrates the Hopfield neural network (HNN) with the iterative signal reconstruction and Parzen window-based classification is proposed. The HNN is observed as a form of associative memory network, used for various pattern recognition and optimization tasks. However, when the input pattern is highly damaged with a very limited set of available samples, the Hopfield network fails to perform the retrieval. The convex optimization-based gradient descent algorithm is considered for pattern recovery of damaged inputs in order to provide an improved pattern approximation for further processing within the HNN, enabling successful network performance. Additionally, in the case of grayscale images, the Parzen window approach is used to classify the probability density functions (pdfs) of the training set and to choose those being comparable to the pdf of the input pattern, therefore refining the selection of patterns and providing better convergence to the exact retrieval. The theoretical considerations are verified experimentally, showing the high performance of the proposed approach when only 10 % of the pixels are available for binary patterns and 40 % of pixels for grayscale patterns.

A FRAMEWORK TO ANALYZE OS SYSTEMS ARTIFACTS FROM LINUX MACHINES

Memory forensics plays a pivotal role in digital investigations, providing crucial insights into the activities and artifacts of an operating system. This paper explores the application of deep learning techniques in the domain of memory forensics within the Linux environment. Linux-based systems are widely used in various contexts, including servers, embedded devices, and desktops, making memory analysis in this ecosystem of paramount importance. Traditional memory forensics techniques have relied on manual analysis, which is often time-consuming and error prone. Deep learning, a subfield of machine learning, has demonstrated remarkable capabilities in pattern recognition and feature extraction tasks. In response, this paper presents a novel framework that automates and improves memory analysis through deep learning. Key components of this framework include data collection, preprocessing, feature extraction, and model selection. We introduce a unique dataset specifically curated for Linux memory forensics, facilitating the development and evaluation of deep learning models. Our experimental results demonstrate the efficacy of using a ResNet-50 model for detecting and classifying malware from memory dumps, achieving a detection rate of 98.75% and an accuracy rate of 89% in classifying malware types. Additionally, we acknowledged the challenges and limitations of applying deep learning in memory forensics, such as model interpretability and data privacy concerns. Future research directions are discussed, including real-time memory analysis integration and techniques for handling encrypted and compressed memory data.

Enhancing the Recognition of Handwritten Arabic Characters through Hybrid Convolutional and Bidirectional Recurrent Neural Network Models

Handwritten Arabic character recognition remains a challenging task in pattern recognition due to the inherent complexities of the cursive script and visual similarities between characters. While deep learning techniques have demonstrated promising results in this domain, further enhancements to the model architecture can drive even greater performance improvements. This study introduces a hybrid deep learning approach that combines Convolutional Neural Networks (CNNs) with Bidirectional Recurrent Neural Networks, specifically Bidirectional Long Short-Term Memory (Bi-LSTM) and Bidirectional Gated Recurrent Units (Bi-GRU). By leveraging the strengths of both convolutional and recurrent neural network components, the proposed models are able to effectively capture spatial features as well as model the temporal dynamics and contextual relationships present in handwritten Arabic text. Experiments conducted on the AHCD and Hijjaa benchmark datasets show that the CNN-Bi-GRU framework achieved state-of-the-art accuracy rates of 97.05% and 91.78% respectively, outperforming previous deep learning-based methods. These results demonstrate the significant performance gains that can be achieved by integrating specialized temporal modeling and contextual representation capabilities into the handwriting recognition pipeline, without the need for explicit segmentation. The findings of this research represent a crucial advancement in the continued development of sophisticated and precise deep learning systems for Arabic handwriting recognition, with broad applications across domains that rely on efficient text extraction from handwritten documents.

Listening to two-talker conversations in quiet settings: the role of listeners’ cognitive processing capabilities for memory and listening effort

Listening to conversing talkers in quiet environments and remembering the content is a common activity. However, research on the cognitive demands involved is limited. This study investigates the relevance of individuals’ cognitive functions for listeners’ memory of two-talker conversations and their listening effort in quiet listening settings. A dual-task paradigm was employed to explore memory of conversational content and listening effort while analyzing the role of participants’ (n = 29) working memory capacity (measured through the operation span task), attention (Frankfurt attention inventory 2), and information-processing speed (trail making test). In the primary task, participants listened to a conversation between a male and female talker and answered content-related questions. The two talkers’ audio signals were presented through headphones, either spatially separated (+ /– 60°) or co-located (0°). Participants concurrently performed a vibrotactile pattern recognition task as a secondary task to measure listening effort. Results indicated that attention and processing speed were related to memory of conversational content and that all three cognitive functions were related to listening effort. Memory performance and listening effort were similar for spatially separated and co-located talkers when considering the psychometric measures. This research offers valuable insights into cognitive processes during two-talker conversations in quiet settings.

NEUROBIOLOGICAL PROPERTIES OF THE STRUCTURE OF THE PARALLEL-HIERARCHICAL NETWORK AND ITS USAGE FOR PATTERN RECOGNITION

The paper presents the analysis of neurobiological data on the existence of the structure of a parallel-hierarchical network. Discussed method of parallel-hierarchical transformation based on population coding and its application for the pattern recognition task. Based on the analysis, we can conclude that using the methods proposed, it is possible to measure the geometric parameters and properties of images, which can significantly increase the efficiency of processing, in particular estimating the center of mass based on moment characteristics. Experimental results demonstrate that due to various destabilizing factors, accurately measuring the energy center coordinates of laser beam spot images is challenging. However, training the PI network and classifying the fragments into "good" and "bad" can considerably enhance the accuracy of these measurements.

AUDIOSIGNAL BASED EVENT DETECTION USING DEEP LEARNING TECHNIQUES

Deep learning has garnered significant interest from researchers for performing pattern recognition tasks. In particular, the detection of events based on audio signals and the recognition of natural sounds in the environment stand out. The DCASE challenge – Detection and Classification of Acoustic Scenes and Events – has further highlighted the efficiency of deep learning in accomplishing these tasks. This paper reviews the works of other researchers that applied various deep learning techniques to detect emergency events based on audio signals. It focuses on the complexity and specific challenges of recognizing polyphonic sound-based events. The use and structures of neural networks are presented, with an emphasis on the application of CNN and RNN for event detection based on audio signals. Evaluation metrics and an overview of datasets are also provided.

Self‐Rectifying Short‐Term Memory Phenomena Through Integration of TiOx Oxygen Reservoir and Al2O3 Barrier Layers for Neuromorphic System

AbstractIn this study, a tri‐layer Pt/Al/TiOx/HfOx/Al2O3/Pt memristor device is fabricated and analyze its electrical characteristics for reservoir computing and neuromorphic systems applications. This device incorporates an oxygen reservoir of a TiOx and a barrier layer of an Al2O3, enabling stable bipolar switching characteristics without the need for an electroforming process over 103 cycles. It also exhibits self‐rectifying properties under a negative bias. Based on these characteristics, it is investigated essential synaptic functions such as long‐term potentiation (LTP), long‐term depression (LTD), paired‐pulse facilitation (PPF), spike‐rate‐dependent plasticity (SRDP), spike‐duration‐dependent plasticity (SDDP), spike‐number‐dependent plasticity (SNDP), and spike‐amplitude‐dependent plasticity (SADP), to assess their suitability for neuromorphic applications that mimic biological synapses. Furthermore, utilizing the short‐term memory characteristics of the device, reservoir computing (RC) measurement from [0000] to [1111] in 4‐bit representation is conducted. This capability enables us to achieve a high accuracy of 95.5% in MNIST pattern recognition tasks. Lastly, the natural decay characteristics caused by oxygen ion migration in the device, examining the transition from short‐term to long‐term memory in image memorization tasks is explored. The potential for deployment in high‐density crossbar arrays by calculating the read margin based on the device I–V curve and programming scheme is also evaluated.

Neuromorphic learning and recognition in WO3−x thin film-based forming-free flexible electronic synapses

In pursuing advanced neuromorphic applications, this study introduces the successful engineering of a flexible electronic synapse based on WO3-x, structured as W/WO3-x/Pt/Muscovite-Mica. This artificial synapse is designed to emulate crucial learning behaviors fundamental to in-memory computing. We systematically explore synaptic plasticity dynamics by implementing pulse measurements capturing potentiation and depression traits akin to biological synapses under flat and different bending conditions, thereby highlighting its potential suitability for flexible electronic applications. The findings demonstrate that the memristor accurately replicates essential properties of biological synapses, including short-term plasticity (STP), long-term plasticity (LTP), and the intriguing transition from STP to LTP. Furthermore, other variables are investigated, such as paired-pulse facilitation, spike rate-dependent plasticity, spike time-dependent plasticity, pulse duration-dependent plasticity, and pulse amplitude-dependent plasticity. Utilizing data from flat and differently bent synapses, neural network simulations for pattern recognition tasks using the Modified National Institute of Standards and Technology dataset reveal a high recognition accuracy of ∼95% with a fast learning speed that requires only 15 epochs to reach saturation.

(Invited) Bio-Inspired Time Varying Networks for Novel Computing Primitives

As a result of a hundred million years of evolution, living animals have adapted extremely well to their ecological niche. Such adaptation implies species-specific interactions with their immediate environment by processing sensory cues and responding with appropriate behavior. Understanding how living creatures perform pattern recognition and cognitive tasks is of particular importance for computing architectures: by studying these information pathways refined over eons of evolution, researchers may be able to streamline the process of developing more highly advanced, energy efficient autonomous systems.With the advent of novel electronic and ionic components along with a deeper understanding of information pathways in living species, a plethora of opportunities to develop completely novel information processing avenues are within reach.Basal biological principles are highlighted, including phylogenies, ontogenesis, and homeostasis, with particular emphasis on network topology and dynamics. While in machine learning, system training is performed on virgin networks without any a priori knowledge, the approach proposed here distinguishes itself unambiguously by employing growth mechanisms as a guideline to design novel computing architectures. Within this framework, experiments on low-frequency relaxation type oscillators coupled via complex time-varying networks will be presents. The spatio-temporal development of the network results from a mutual interaction between the oscillator ensemble and the network structure. The blooming and pruning of suddenly appearing conductive bridges and their relation to the synchrony state of the oscillator ensemble will be discussed.

Convolutional Neural Networks Based Detection System for Cyber-attacks in Industrial Control Systems

Convolutional Neural Networks (CNNs) have emerged as a powerful tool for various pattern recognition tasks, including the detection of cyber-attacks in Industrial Control Systems (ICS). This paper presents a CNN-based detection system specifically designed to safeguard ICS from sophisticated cyber threats. The proposed system leverages the capability of CNNs to automatically learn and extract high-level features from raw data, enabling it to identify anomalies indicative of cyber-attacks with high accuracy. Utilizing a comprehensive cyber-attack dataset containing 59 different types of attacks, the system is trained to distinguish between normal and malicious traffic effectively. Based on the extensive dataset, this study demonstrates that the CNN-based detection system achieves a detection rate of 98.5% and a false-positive rate of 1.2%, significantly outperforming traditional methods. The high detection rate indicates the system's ability to accurately identify a wide range of attack types, while the low false-positive rate ensures minimal disruption to normal operations due to incorrect alerts. These results underscore the system's robustness and reliability in identifying subtle and complex attack patterns. Moreover, the CNN-based system is designed to adapt to new types of attacks as it continues to learn from updated datasets, making it a scalable and future-proof solution. Implementing this system can significantly enhance the cybersecurity posture of industrial environments by providing real-time monitoring and rapid response capabilities. The CNN-based detection system offers a significant advancement in ICS cybersecurity. Effectively identifying and mitigating cyber-attacks contributes to the resilience and reliability of critical infrastructure. The proposed approach improves security measures and ensures industrial processes' continuous and safe operation, which is vital for economic stability and public safety.