Computer Research Articles

COLLECTIVE INTELLIGENCE PHENOMENON STUDY AS A PRIORITY FOR ENSURING INNOVATIVE DEVELOPMENT OF ENTERPRISES

Objective. The purpose of the article is a critical analysis of the main approaches to collective intelligence as a priority for ensuring the innovative development of enterprises and the proposals development in the context of its effective use. Methodology. The development of a theoretical and methodological approach to the study of collective intelligence should be based on the business structures practical activity, the theory of collective intelligence phenomenon of individuals and their development in innovative activities; theories of organizational and competitive advantages; motivation, creativity and innovation. General and special methods are used: dialectical cognition; deductions and inductions; generalization and scientific abstraction; synergies; morphological analysis; competence, dialectical and synergistic approaches. Results. A scientific position has been created regarding the collective intelligence effective use as a priority for ensuring the innovative development of enterprises. An analysis of collective intelligence status essence and subject, the causes, mechanisms and consequences of its formation was carried out. It is shown that in contrast to the technocratic approach, which mixes collective intelligence with concepts of artificial intelligence, neural networks, big data, etc., it can be understood from a humanistic perspective as an integral set of human consciousnesses. A new understanding of the essence of the category “collective intelligence” is proposed. The issue of collective intelligence integration with computer systems and technologies framing is considered. The author emphasizes people-centeredness, which plays an important role in the intellectual and innovative activity of economic entities. The scientific novelty. The methodological foundation of collective intelligence nature is developed. The conceptual approach to the application of collective intelligence in order to ensure the innovative development of enterprises is substantiated. Measures for the development of collective intelligence are proposed, which gives the research practical value. Practical significance of the obtained results. The results of theoretical developments and practical recommendations of the author can be used to ensure the innovative development of enterprises due to the development of enterprises collective intelligence and their network associations.

Design and implementation in power-efficient and thermal conductivity of vedic processor for embedded applications

An embedded system that handles critical applications requires power economy, performance and thermal management, and an efficient Vedic processor with highly efficienct thermal conductivity. The proposed contemporary processor architecture has been designed to use Vedic mathematics and elaborate power and thermal optimization approaches to formulate an Arithmetic Logic Unit (ALU). This ALU is based on Vedic Sutras to enable quick computation, high precision floating point operations, and low power consumption. Since thermal management leads to energy loss in the form of heat, the embedded processor’s thermal conductivity is enhanced using novel materials and unique ways of heat dissipation. This improves performance as well as power efficiency. The proposed approach uses extensive simulation thermal analysis by EDA tools to build an optimised embedded processor that consumes 35% less power, has 40% high thermal conductivity, is 25% faster, and utilizes 15% less area when compared to conventional embedded processors. This paper helps in furthering embedded systems research by showing how old mathematical ideas can be used in a new engineering approach to solve current technology issues. The suggested novel embedded system is more efficient and thermally resistant for IOT, mobile computing, and industrial control systems.

Network Traffic Feature Behaviour Augmentation Fusion based Attacks Classification for Intrusion Detection System in SDN Framework

The implementation of Software-Defined Network(SDN) across multiple data centers aims to simplify thecontrol and management of networks. However, the increasingpopularity of SDN has also attracted the attention of attackers.To tackle this problem, it is essential to have an intrusiondetection system (IDS) in place, which plays a crucial role incybersecurity by addressing external threats. The advantageof SDN’s centralized nature is that it facilitates the training ofan IDS based on machine learning. However, there is a scarcityof research specifically focused on intrusion detection in SDN.Existing literature often treats SDN intrusion detection assimilar to traditional computer systems and relies on intrusiondatasets generated for those systems. We explore the issueof intrusion detection in SDN using the most recent publicdataset (InSDN). However, InSDN is an imbalanced data set.In this paper, we have recommended a method to balance thedata as well as a method to find the best features to improvethe quality of IDS using Machine Learning. In addition, wealso suggest a method of classifying SDN network traffic andnormal network traffic. At the same time, we also evaluate theefficiency of the SDN system with the load balancing systemand without the load balancing system.

Application of high-performance computing in sustainable energy campus building design

In high-performance computing (HPC) systems, energy and power considerations play an increasingly important role. This work aims to ensure the implementation of China’s green and ecological concepts, respond to China’s strategy of building an environment-friendly and resource-saving society, and actively promote the construction of sustainable development campuses. First, the theoretical basis of sustainable energy campus architecture is described. Next, the teaching feedback model under HPC is constructed. Finally, with the evaluation results of students’ task completion processes and students’ task works as measurement indexes, the corresponding data is collected and comprehensively evaluated and analyzed to verify the effectiveness of the model. The analysis results indicate that most students are able to complete tasks within two hours; however, their proficiency with multimedia technology is inadequate, and they lack initiative in the learning process. There is a correlation between the overall evaluation of task performance and the students’ level of understanding of the tasks. By implementing a teaching feedback model, students’ learning enthusiasm and the quality of their work improved, providing effective educational support for promoting sustainable development on campus. Overall, the awareness of using computers and other multimedia technologies among students is not strong, and their learning process is relatively closed, with insufficient enthusiasm and initiative. This model can achieve the acquisition, integration, and statistical analysis of teacher feedback information. The model can realize the acquisition, integration, and statistical analysis of teachers’ feedback information. This work hopes to use this learning and feedback mode in practical teaching to address specific problems in computer multimedia courses.

Artificial intelligence and machine learning algorithms in diagnosis and therapy of the ankle joint.

The recent advancement of computational systems provides fast information exchange and the collection of large amounts of data. Growing number of those systems allow for effective processing of huge amounts of information, utilizing advanced algorithms that are called artificial intelligence (AI). AI has been used for many years, and the number of its applications is growing in various areas. Such solutions are also being developed increasingly in medicine, including orthopedics and radiology, to support the diagnostic and therapeutic processes. Progress in this area is particularly targeted at the skeletal sites that most often require intervention, such as the hip or knee area, with modest interest in the ankle joint. The ankle is one of the most complicated human joints, and therapeutic procedures for its treatment are relatively common. One of the solutions used in the event of serious ankle joint damage is arthroplasty. This review summarizes the current state of AI applications for the diagnosis and therapy of the ankle joint, focusing on trends and achievements in ankle joint arthroplasty and contemporary orthopedic AI solutions. Ideas from other fields of medical diagnostics or orthopedic surgery that may be utilized in the diagnosis and treatment of ankle joint are also discussed.

Design and Evaluation of Real-Time Data Storage and Signal Processing in a Long-Range Distributed Acoustic Sensing (DAS) Using Cloud-Based Services.

In cloud-based Distributed Acoustic Sensing (DAS) sensor data management, we are confronted with two primary challenges. First, the development of efficient storage mechanisms capable of handling the enormous volume of data generated by these sensors poses a challenge. To solve this issue, we propose a method to address the issue of handling the large amount of data involved in DAS by designing and implementing a pipeline system to efficiently send the big data to DynamoDB in order to fully use the low latency of the DynamoDB data storage system for a benchmark DAS scheme for performing continuous monitoring over a 100 km range at a meter-scale spatial resolution. We employ the DynamoDB functionality of Amazon Web Services (AWS), which allows highly expandable storage capacity with latency of access of a few tens of milliseconds. The different stages of DAS data handling are performed in a pipeline, and the scheme is optimized for high overall throughput with reduced latency suitable for concurrent, real-time event extraction as well as the minimal storage of raw and intermediate data. In addition, the scalability of the DynamoDB-based data storage scheme is evaluated for linear and nonlinear variations of number of batches of access and a wide range of data sample sizes corresponding to sensing ranges of 1-110 km. The results show latencies of 40 ms per batch of access with low standard deviations of a few milliseconds, and latency per sample decreases for increasing the sample size, paving the way toward the development of scalable, cloud-based data storage services integrating additional post-processing for more precise feature extraction. The technique greatly simplifies DAS data handling in key application areas requiring continuous, large-scale measurement schemes. In addition, the processing of raw traces in a long-distance DAS for real-time monitoring requires the careful design of computational resources to guarantee requisite dynamic performance. Now, we will focus on the design of a system for the performance evaluation of cloud computing systems for diverse computations on DAS data. This system is aimed at unveiling valuable insights into performance metrics and operational efficiencies of computations on the data in the cloud, which will provide a deeper understanding of the system's performance, identify potential bottlenecks, and suggest areas for improvement. To achieve this, we employ the CloudSim framework. The analysis reveals that the virtual machine (VM) performance decreases significantly the processing time with more capable VMs, influenced by Processing Elements (PEs) and Million Instructions Per Second (MIPS). The results also reflect that, although a larger number of computations is required as the fiber length increases, with the subsequent increase in processing time, the overall speed of computation is still suitable for continuous real-time monitoring. We also see that VMs with lower performance in terms of processing speed and number of CPUs have more inconsistent processing times compared to those with higher performance, while not incurring significantly higher prices. Additionally, the impact of VM parameters on computation time is explored, highlighting the importance of resource optimization in the DAS system design for efficient performance. The study also observes a notable trend in processing time, showing a significant decrease for every additional 50,000 columns processed as the length of the fiber increases. This finding underscores the efficiency gains achieved with larger computational loads, indicating improved system performance and capacity utilization as the DAS system processes more extensive datasets.

Review of Knowledge on the Graphs of Cyber-Attacks by 2024 for IBIMA Publishing Journals

An attack graph is a visual tool used to represent potential attack paths, that a cybercriminal can use to gain access to a computer system or network. It consists of nodes and edges, where Nodes represent resources such as computers, servers, applications or data. Edges represent dependencies between nodes, such as network connections or data flows. Attack graphs are visual tools used to analyse and understand different types of attacks, including cyber-attacks, physical attacks and terrorist attacks. These visualisations help security professionals, analysts and decision makers to identify trends, assess risks and develop defence strategies. Attack graphs are a valuable tool that can help organisations identify, assess and mitigate cyber security risks. They are easy to use and can be customised to suit different needs.

Optimal resource management for multi-access edge computing without using cross-layer communication

We consider a Multi-access Edge Computing (MEC) system with a set of users, a base station (BS) with an attached MEC server, and a cloud server. The users can serve the service requests locally or can offload them to the BS which in turn can serve a subset of the offloaded requests at the MEC and can forward the requests to the cloud server. The user devices and the MEC server can be dynamically configured to serve different classes of services. The service requests offloaded to the BS incur offloading costs and those forwarded to the cloud incur additional costs; the costs could represent service charges or delays. Aggregate cost minimization subject to stability warrants optimal service scheduling and offloading at the users and the MEC server, at their application layers, and optimal uplink packet scheduling at the users’ MAC layers. Classical back-pressure (BP) based solutions entail cross-layer message exchange, and hence are not viable. We propose virtual queue-based drift-plus-penalty algorithms that are throughput optimal, and achieve the optimal delay arbitrarily closely but do not require cross-layer communication. We first consider an MEC system without local computation, and subsequently, extend our framework to incorporate local computation also. We demonstrate that the proposed algorithms offer almost the same performance as BP based algorithms. These algorithms contain tuneable parameters that offer a trade off between queue lengths at the users and the BS and the offloading costs.

Laboratory-based surveillance of non-tuberculous mycobacterial pulmonary disease in Japan

BackgroundIntractable pulmonary diseases with non-tuberculous mycobacteria (NTM-PDs) and antimicrobial resistance have become increasingly concerning worldwide. Nevertheless, a surveillance system for NTM has not been established in most countries, thus requiring repeated, intermittent, and time-consuming cross-sectional nationwide surveys.MethodsTo establish a nationwide surveillance system for NTM-associated diseases, we aimed to develop a prototype computer system primarily designed to continuously estimate NTM-PD incidence using a bacteriological case-defining algorithm through an automatic process of integrating bacteriological data collected from microbiology laboratories across Japan. To validate the accuracy of our study results, we compared the distribution of pulmonary TB cases between our laboratory data and the national surveillance data, which is representative of the Japanese population.ResultsOur estimates implied a 17.7% increase in NTM-PD incidence from 15.8 [14.9–16.8] in 2013 to 19.2 [18.2–20.4] in 2017, per 100 000 population. Moreover, 93.0% of the identified NTM-PD cases were dominated by theMycobacterium avium-intracellularecomplex, and the proportion ofM. abscessusspecies exceeded that ofM. kansasiifor the first time in Japan. We also revealed significant age and sex differences in NTM-PDs. Notably, we found similar characteristics between our laboratory data and national surveillance data covering almost the entire Japanese population.ConclusionThese findings, despite being laboratory-based, are extrapolatable to the general population in Japan and provide evidence that supports our system as a viable alternative to the nationwide NTM surveillance system.

Volatile data acquisition and analysis by using Memory Forensics techniques

Memory forensics is a vital component of digital investigations, involving the analysis of volatile memory (RAM) in computer systems to gather evidence, identify malicious activities, and reconstruct cybercrime incidents. This paper provides an overview of memory forensics, highlighting its definition, importance, purpose, and scope. It explores the evolution and significance of memory forensics in response to increasingly complex cyber threats. The memory forensics process is discussed, covering memory acquisition and analysis. Legal and ethical considerations related to the admissibility of memory evidence and privacy protection are examined. The paper also discusses the types of memory, including physical and virtual memory, and their characteristics and significance in memory forensics. Furthermore, it explores the memory acquisition process, different methods, tools, and techniques used, as well as the importance of preserving evidence integrity. Finally, the paper introduces various tools for memory analysis, such as Volatility, Volatility Workbench, FTK Imager, Encase, Hibernation Recon, and Xplico, and highlights their role in extracting valuable evidence from memory dumps.

Efficient Malware Investigation and Recognition Using Machine Learning Algorithms

Malware is unique of the biggest problems that modern internet users have to deal with. Polymorphic malware is a new type of harmful software that is extra pliable than prior peers of bugs. Polymorphic malware continuously alters its signature characteristics in order to evade detection by conventional malware detection techniques. We applied various machine learning algorithms to detect malware or dangerous threats. A high detection ratio meant that the most accurate algorithm had been chosen to be used within the system. One advantage of the confusion matrix is its ability to track false positives and false negatives, providing deeper insights into the system’s performance. In particular, it revealed that machine learning algorithms like Naïve Bayes, Support Vector Machine (SVLM), Random Forest (RF), and K-Nearest Neighbor (kNN) can be used to detect harmful traffic on computer systems by calculation changes in correlation patterns. This approach enhances the effectiveness of malware detection and overall security in computer networks. The findings demonstrated that NB (87%), kNN (91.76%), SVM (92.41%), and RF (98.07%) performed well in terms of detection accuracy when compared to other classifiers. These findings are important as malicious software is growing more prevalent and sophisticated.

Rewireable Building Blocks for Enzyme-Powered DNA Computing Networks.

Neural networks enable the processing of large, complex data sets with applications in disease diagnosis, cell profiling, and drug discovery. Beyond electronic computers, neural networks have been implemented using programmable biomolecules such as DNA; this confers unique advantages, such as greater portability, electricity-free operation, and direct analysis of patterns of biomolecules in solution. Analogous to bottlenecks in electronic computers, the computing power of DNA-based neural networks is limited by the ability to add more computing units, i.e., neurons. This limitation exists because current architectures require many nucleic acids to model a single neuron. Each additional neuron compounds existing problems such as long assembly times, high background signal, and cross-talk between components. Here, we test three strategies to solve this limitation and improve the scalability of DNA-based neural networks: (i) enzymatic synthesis for high-purity neurons, (ii) spatial patterning of neuron clusters based on their network position, and (iii) encoding neuron connectivity on a universal single-stranded DNA backbone. We show that neurons implemented via these strategies activate quickly, with a high signal-to-background ratio and process-weighted inputs. We rewired our modular neurons to demonstrate basic neural network motifs such as cascading, fan-in, and fan-out circuits. Finally, we designed a prototype two-layer microfluidic device to automate the operation of our circuits. We envision that our proposed design will help scale DNA-based neural networks due to its modularity, simplicity of synthesis, and compatibility with various neural network architectures. This will enable portable computing power for applications in portable diagnostics, compact data storage, and autonomous decision making for lab-on-a-chips.

Interpretable Spatial–Temporal Graph Convolutional Network for System Log Anomaly Detection

To ensure seamless information flow and operational integrity, computer systems need effectively to manage their system logs, but the expansion in their scale and complexity makes it hard to detect anomalies. Current methodologies exhibit deficiencies, including inefficiencies in handling abnormal sequences, lack of interpretability, and limited consideration of both temporal and spatial information. To improve, this paper develops a semi-supervised graph neural network model termed the Interpretable Spatial–Temporal Graph Convolutional Network (IST-GCN). By integrating temporal and event similarity perspectives, the IST-GCN harnesses directed and undirected graphs to capture the temporal and spatial aspects of system log events. Hence, the IST-GCN offers temporal and spatial interpretability. Further, a lightweight feature regularization technique is developed to enhance interpretability in both time and space domains, and thus facilitates anomaly detection efficiently. Comprehensive testing verifies that the IST-GCN approach surpasses nearly all state-of-the-art methods across five public log anomaly datasets. On average, IST-GCN improves Average Precision (AP) by approximately 3% and ROC AUC (RC) by about 4% compared to the best-performing baseline methods, underscoring its effectiveness and robustness.

Simulating cyberattacks with extended Petri nets

Cybersecurity is an urgent concern. Cybersecurity simulation is an important part of the response to it. This article describes a research program consisting of several interconnected cybersecurity simulation research projects. Cyberattacks are modeled using Petri nets extended with features designed for modeling cyberattacks, including representations of the attacker’s and defender’s strategies, their actions, and their actions’ cost. A database of known attack patterns is automatically processed to generate cyberattack component models, one for each attack pattern. The models are verified and validated using multiple application-relevant methods that consider both Petri nets’ theoretical properties and cyberattacks’ practical characteristics. Because the source attack pattern database is attacker-centric, the cyberattack component models are enhanced to include defender actions and responses, as well as representations of normal user activities on the computer system being attacked. Cyberattack component models stored in a repository are selected and composed into complete models of target computer systems. Metadata associated with each model guides the selection and composition. The cyberattack models are executed to simulate cyberattacks. Multiple simulation iterations are used to train reinforcement learning algorithms that automatically learn improved attacker or defender strategies.

Clinical decision support system using hierarchical fuzzy diagnosis model for migraine and tension-type headache based on International Classification of Headache Disorders, 3rd edition.

To determine whether the diagnostic ability of the newly designed hierarchical fuzzy diagnosis method is consistent with that of headache experts for probable migraine (PM) and probable tension-type headache (PTTH). Clinical decision support systems (CDSS) are computer systems designed to help doctors to make clinician decisions by information technology, and have proven to be effective in improving headache diagnosis by making medical knowledge readily available to users in some studies. However, one serious drawback is that the CDSS lacks the ability to deal with some fuzzy boundaries of the headache features utilized in diagnostic criteria, which might be caused by patients' recall bias and subjective bias. A hybrid mechanism of rule-based reasoning and hierarchical fuzzy diagnosis method based on International Classification of Headache Disorders, 3rd edition (ICHD-3) was designed and then validated by a retrospective study with 325 consecutive patients and a prospective study with 380 patients who were clinically diagnosed with migraine and TTH at the headache clinic of Chinese PLA General Hospital. The results of the diagnostic test in the retrospective study indicated that the fuzzy-based CDSS can be used in the diagnosis of migraine without aura (MO) (sensitivity 97.71%, specificity 100%), TTH (sensitivity 98.57%, specificity 100%), PM (sensitivity 91.25%, specificity 98.75%) and PTTH (sensitivity 90.91%, specificity 99.63%). While in the prospective study, the diagnostic performances were MO (sensitivity 91.62%, specificity 96.52%), TTH (sensitivity 92.17%, specificity 95.47%), PM (sensitivity 85.48%, specificity 98.11%) and PTTH (sensitivity 87.50%, specificity 98.60%). Cohen's kappa values for the consistency test were 0.984 ± 0.018 (MO), 0.991 ± 0.018 (TTH), 0.916 ± 0.051 (PM), 0.932 ± 0.059 (PTTH) in the retrospective study and 0.884 ± 0.047 (MO), 0.870 ± 0.055 (TTH), 0.853 ± 0.073 (PM), 0.827 ± 0.118 (PTTH) in the prospective study, which indicated good consistency with the fuzzy-based CDSS and the gold standard (p < 0.001). We developed a fuzzy-based CDSS performs much more similarly to expert diagnosis and performs better than the routine CDSS method in the diagnosis of migraine and TTH, and it could promote the application of artificial intelligence in the area of headache diagnosis.

An Operational Quality Model of Embedded Software Aligned with ISO 25000

Embedded systems omnipresent in everyday life and industry are mainly composed of hardware and software that must comply with a number of standards and regulations. However, there is no consensus on the quality characteristics and sub-characteristics of embedded software. This paper presents the steps for modeling an operational quality model for embedded software aligned with the ISO 25000 series of quality models for traditional computer systems. From a literature review composed of 40 studies on quality modeling for embedded systems and software, 85 of the most frequent quality characteristics and sub-characteristics were first identified, including a subset of 16 referenced or cited in at least 25% of the literature. Next, the design of a quality model for embedded software aligned with the ISO 25000 series was proposed with 13 characteristics and 27 sub-characteristics. The operational aspect of this quality model for embedded software is addressed next through a set of measures and measurement functions from ISO 25000 to aggregate the results of the quantification of the characteristics and sub-characteristics. A survey involving 25 embedded software specialists is presented next to gauge, using Fleiss’ Kappa criteria, their agreement with the proposed quality model. Furthermore, the computed importance weights derived from the survey participants’ individual opinions were compared with those derived from an analysis of 40 embedded software studies, bolstering the credibility of the model. The results of this study suggest that the proposed quality model can serve as a framework for evaluating and understanding the quality characteristics across diverse expertise levels. Furthermore, the convergence between the survey and the literature strengthens the model's credibility by anchoring it in both established literature and practitioners’ agreements.

A Novel Method for Optimising Energy Efficiency in High-Performance Computing Systems

This paper introduces a new method to promote energy efficiency in computing systems. The rapidly growing demand for computational power in high-performance computing (HPC) systems is accompanied by a significant increase in energy consumption. This research investigates the many ways to minimise energy consumption in HPC systems without sacrificing computational performance. Building upon previous research we combined and refined ideas to develop an optimised approach. Our method utilises a tri-modular framework incorporating an Energy-Efficient Hardware Design, Resource Management, and Optimisation, and Server Virtualisation. The first module employs a method of designing smart energy-efficient hardware. This method incorporates leakage reduction techniques such as clock gating and sleep states. The second module uses a technique focused on optimising server software. This method is based on Dynamic Voltage and frequency scaling (DVFS) for power management in data centres. The third module is based on server virtualisation and employs software to create multiple virtual machines on a single physical server allowing for a significant reduction in energy and hardware costs. The methods used in these three modules are systematically integrated to produce a more efficient consumption of electricity. This will allow for the computing system to minimise energy consumption without compromising computational power.

An Intelligent Image Caption Generator Model using Deep Learning

Replicating the innate human capability to understand image content and create descriptive text is a formidable challenge for machines. The application of image captioning is highly versatile and holds great importance. It involves the creation of concise and descriptive captions for images using a variety of advanced techniques such as Natural Language Processing (NLP), Computer Vision (CV), and Deep Learning (DL). These techniques enable the automated generation of accurate and meaningful captions, which have numerous practical applications across industries such as healthcare, entertainment, marketing to mention but few. This study employed the machine learning algorithms of Convolutional Neural Network (CNN) and Long Short-Term Memory (LSTM) to train computer vision using the Hibs dataset. The model leveraged input images with associated captions to identify and analyze predicted captions within the computer system. Model evaluation is a step in assessing the model's performance. This involves metrics such as BLEU score and METEOR score to measure the quality of the model's output in comparison to reference translation. These metrics help in quantifying the accuracy and fluency of the generated text, and the results are then presented to provide insights into the model's performance.

Experiment and industrial application of dust concentration measurement method using image transmittance characteristic

Abstract To prevent the harm of respirable dust and dust explosions, it is necessary to detect the concentration of dust generated in the process of bulk materials to help dust suppression. In this investigation, a dust concentration measurement method is proposed based on the eigenvalue calculation of image transmittance. The method is combined with the dark channel prior theory into the computer system and calibrated in the laboratory to calculate the transmittance of dust images. Furthermore, a dust concentration online measurement experimental platform is established to verify the correctness and reliability. Moreover, the industrial application is also carried out. The average relative error of dust concentration is 4.53%, and the relative error is less than 10% of the dust concentration measured by online measurement experimental platforms and industrial applications, which satisfied the measurement requirements. It is concluded that the measurement method has high applicability and can be practically applied to dust concentration measurements.

Cost‐Effective and Fully Hardware‐Oriented Reservoir Computing Based on IGZO/HZO Ferroelectric Thin‐Film Transistor with Electrically and Optically Distinguishable States

AbstractHardware‐based reservoir computing (RC) systems provide benefits like energy efficiency and effective predictability. The implementation of different switching characteristics for the reservoir and readout layers requires the use of different types of devices or additional processing. However, an RC system with distinguishable switching characteristics obtained by changing stimulation on a single device is not identified yet, but it is appealing in terms of process simplicity and efficient processing costs. This study develops an RC system that uses ferroelectric thin‐film transistor (FeTFT) devices with an indium gallium zinc oxide channel and Hf0.5Zr0.5O2 ferroelectric layer for both networks. The nonvolatile FeTFT utilizes the remnant polarization properties of the ferroelectric layer through electrical stimulation, showing stable retention characteristics (104 s) and long‐term potentiation/depression. By using optical stimulation, the volatile FeTFT demonstrates short‐term characteristics, such as paired‐pulse facilitation, and a 4‐bit RC system. This proves that it is possible to meet the functional requirements of both the reservoir and readout networks by simply varying the type of stimulation applied to a single FeTFT. Finally, the fully FeTFT‐based RC system can recognize digit patterns from the Modified National Institute of Standards and Technology database with a high accuracy of 90.5%.