Hybrid Intelligent System Research Articles

Hybrid intelligent intrusion detection system for multiple Wi‐Fi attacks in wireless networks using stacked restricted Boltzmann machine and deep belief networks

SummaryThe recent cutting edge mobile and internet advancements have increased Internet of Things based gadgets more pervasive and cohesive into our day to day lives. There emerges a need to beat the security challenges, prevalently came about because of the uncovered idea of a remote medium, for example a Wi‐Fi network setup. The linked gadgets are ubiquitous, creating high‐dimensional information on an expansive scale. It tends to be seen those illegitimate actions like illegal information access, information stealing, information alteration different other interruption exercises are quickly developing during a decade ago. Wireless network data transfers will be anticipated to surge strongly in the coming years. Feature learning, still, can evade the probable complications, possibly formed by means of large wireless network dimensions. Therefore, the proposed system focuses on developing a Hybrid Intelligent Intrusion Detection System (IDS) for detecting multiple assaults such as Impersonation, Injection, flooding along with other unknown malicious Wi‐Fi attacks using Deep Learning and Machine Learning algorithms. The proposed system is investigated on well referenced Aegean Wi‐Fi Intrusion Dataset (AWID) and the experimental results prove that the utility of deep learning algorithms in feature representation and classification has achieved higher detection accuracy along with lower false alarm rate. To get an optimum feature set and perform dimensionality reduction on Wi‐Fi network data, the proposed framework uses Deep Belief Network (DBN) with stacked layers of Restricted Boltzmann Machine (RBM). In order to identify and categories various Wi‐Fi threats, Deep Neural Networks, and algorithms like SVM, MLKNN, and RAkEL are employed. The anticipated DBN model was given an optimization strategy to enhance classification performance. The optimization strategy involved selecting the best possible feature subset using a feature selection technique, optimizing the DBN model using the best possible hyperparameter values, less reconstruction error values, and utilizing the regularization method to account for weight decay. The experimental findings demonstrate that the use of deep learning algorithms in feature representation and classification has produced improved detection accuracy coupled with a decreased false alarm rate when applied to the proposed hybrid scheme based Intelligent IDS using the well‐referenced AWID dataset.

Cervical Cancer Detection Techniques: A Chronological Review.

Cervical cancer is known as a major health problem globally, with high mortality as well as incidence rates. Over the years, there have been significant advancements in cervical cancer detection techniques, leading to improved accuracy, sensitivity, and specificity. This article provides a chronological review of cervical cancer detection techniques, from the traditional Pap smear test to the latest computer-aided detection (CAD) systems. The traditional method for cervical cancer screening is the Pap smear test. It consists of examining cervical cells under a microscope for abnormalities. However, this method is subjective and may miss precancerous lesions, leading to false negatives and a delayed diagnosis. Therefore, a growing interest has been in shown developing CAD methods to enhance cervical cancer screening. However, the effectiveness and reliability of CAD systems are still being evaluated. A systematic review of the literature was performed using the Scopus database to identify relevant studies on cervical cancer detection techniques published between 1996 and 2022. The search terms used included "(cervix OR cervical) AND (cancer OR tumor) AND (detect* OR diagnosis)". Studies were included if they reported on the development or evaluation of cervical cancer detection techniques, including traditional methods and CAD systems. The results of the review showed that CAD technology for cervical cancer detection has come a long way since it was introduced in the 1990s. Early CAD systems utilized image processing and pattern recognition techniques to analyze digital images of cervical cells, with limited success due to low sensitivity and specificity. In the early 2000s, machine learning (ML) algorithms were introduced to the CAD field for cervical cancer detection, allowing for more accurate and automated analysis of digital images of cervical cells. ML-based CAD systems have shown promise in several studies, with improved sensitivity and specificity reported compared to traditional screening methods. In summary, this chronological review of cervical cancer detection techniques highlights the significant advancements made in this field over the past few decades. ML-based CAD systems have shown promise for improving the accuracy and sensitivity of cervical cancer detection. The Hybrid Intelligent System for Cervical Cancer Diagnosis (HISCCD) and the Automated Cervical Screening System (ACSS) are two of the most promising CAD systems. Still, deeper validation and research are required before being broadly accepted. Continued innovation and collaboration in this field may help enhance cervical cancer detection as well as ultimately reduce the disease's burden on women worldwide.

Cloud–Edge Hybrid Computing Architecture for Large-Scale Scientific Facilities Augmented with an Intelligent Scheduling System

Synchrotron radiation sources are widely used in interdisciplinary research, generating an enormous amount of data while posing serious challenges to the storage, processing, and analysis capabilities of the large-scale scientific facilities worldwide. A flexible and scalable computing architecture, suitable for complex application scenarios, combined with efficient and intelligent scheduling strategies, plays a key role in addressing these issues. In this work, we present a novel cloud–edge hybrid intelligent system (CEHIS), which was architected, developed, and deployed by the Big Data Science Center (BDSC) at the Shanghai Synchrotron Radiation Facility (SSRF) and meets the computational needs of the large-scale scientific facilities. Our methodical simulations demonstrate that the CEHIS is more efficient and performs better than the cloud-based model. Here, we have applied a deep reinforcement learning approach to the task scheduling system, finding that it effectively reduces the total time required for the task completion. Our findings prove that the cloud–edge hybrid intelligent architectures are a viable solution to address the requirements and conditions of the modern synchrotron radiation facilities, further enhancing their data processing and analysis capabilities.

Implementation of a Hybrid Intelligence System Enabling the Effectiveness Assessment of Interaction Channels Use in HMI.

The article presents a novel idea of Interaction Quality Sensor (IQS), introduced in the complete solution of Hybrid INTelligence (HINT) architecture for intelligent control systems. The proposed system is designed to use and prioritize multiple information channels (speech, images, videos) in order to optimize the information flow efficiency of interaction in HMI systems. The proposed architecture is implemented and validated in a real-world application of training unskilled workers-new employees (with lower competencies and/or a language barrier). With the help of the HINT system, the man-machine communication information channels are deliberately chosen based on IQS readouts to enable an untrained, inexperienced, foreign employee candidate to become a good worker, while not requiring the presence of either an interpreter or an expert during training. The proposed implementation is in line with the labor market trend, which displays significant fluctuations. The HINT system is designed to activate human resources and support organizations/enterprises in the effective assimilation of employees to the tasks performed on the production assembly line. The market need of solving this noticeable problem was caused by a large migration of employees within (and between) enterprises. The research results presented in the work show significant benefits of the methods used, while supporting multilingualism and optimizing the preselection of information channels.

Knowledge Graph-Based Explainable Artificial Intelligence for Business Process Analysis

For critical operational decisions (e.g. consulting services), explanations and interpretable results of powerful Artificial Intelligence (AI) systems are becoming increasingly important. Knowledge graphs possess a semantic model that integrates heterogeneous information sources and represents knowledge elements in a machine-readable form. The integration of knowledge graphs and machine learning methods represents a new form of hybrid intelligent systems that benefit from each other’s strengths. Our research aims at an explainable system with a specific knowledge graph architecture that generates human-understandable results even when no suitable domain experts are available. Against this background, the interpretability of a knowledge graph-based explainable AI approach for business process analysis is focused. We design a framework of interpretation, show how interpretable models are generated by a single case study and evaluate the applicability of our approach by different expert interviews. Result paths on weaknesses and improvement measures related to a business process are used to produce stochastic decision trees, which improve the interpretability of results. This can lead to interesting consulting self-services for clients or be applied as a device for accelerating classical consulting projects.

Software Package to Support Students’ Research Activities in the Hybrid Intellectual Environment of Mathematics Teaching

The urgent need of society for creative and critical thinkers dictates the need to manage and develop the project-based and research activities of schoolchildren. Neural networks, as effective tools for solving complex, multi-component, multifunctional problems, have created an opportunity to develop tools for the content and quality assessment of project-based, research activities and personal achievements of each student with a dynamic and stratified sampling of complex knowledge, to identify individual educational paths, tools and instructions for research and creative activity reflected on the layers of an artificial neural network. The purpose of this study is to define and develop the concept of supporting a hybrid intelligent system of project-based and research activities in a comprehensive information and educational environment to create an applied intellectual technology that supports project-based and research activities and classifies the growth of schoolchildren’s scientific potential. The paper considers the development of technologies for the pedagogical, algorithmic and information organization of ontological engineering and support models for project-based and research activities, as well as the growth of scientific student training based on the construction of an artificial neural network with a teacher and an array of training samples using expert systems and decision theory. It also defines the selection criteria, hierarchies and content of the generalized constructs of complex knowledge (modern achievements in science) by modeling creative activities while further predicting the growth of a student’s scientific potential based on the layers of the neural network. The original technology of training samples used for constructing hybrid neural networks was determined using expert systems, the psychological method of parallel slices and clustering of the personal characteristics of schoolchildren. An innovative intellectual environment is being introduced into the practice of mathematical education in high schools in Russia. The study made it possible to create applied intelligent technology to support and display the dynamic profiles of the project-based and research activities of schoolchildren and act as a growth classifier of their scientific potential. In terms of concept implementation, the phased growth technology to analyze students’ scientific potential was developed during the study of the generalized construct of complex knowledge. Individual educational workshops for school students to master project-based and research activities and display their dynamic profiles during the implementation of a hybrid intellectual environment for support and decision-making will allow school students to develop their personal potential, increase their educational motivation and allow researchers and educators to realize the potential of adapting modern achievements in science to school mathematics and create conditions for the modernization of educational programs in a developing digital environment.

Preface

The 2022 International Conference on Intelligent Equipment and Data Processing (IEDP 2022) was successfully held on October 21st-23rd, 2022 in Guangzhou, China (virtual conference). IEDP 2022 encouraged exchange of information on frontiers of research in different areas, connected the most advanced academic resources in China and the world, and turned research results into industrial solutions.We had the honor of having invited Prof. Guixiong Liu from South China University of Technology, China to serve as our Conference General Chair. 90 individuals participated in the conference. Divided into three parts, the conference agenda covered keynote speeches, oral presentations, and online Q&A discussion. Firstly, keynote speakers were each allocated 30-45 minutes to hold their speeches. Then in the oral presentations, the excellent papers we had selected were presented by their authors one by one.During the conference, we were honored to invite four sophisticated keynote speakers to address their speeches. Among them, Prof. Wenbing Zhao from Cleveland State University, America delivered a speech on Video-Based Baseball Pitch Type Recognition. During the speech, he reported a preliminary study on video-based pitch type recognition using deep learning. A semi-automated way of building datasets based on publicly available video and pitch information for MLB games was proposed to facilitate the study and the two-stream inflated 3D convolutional neural network (I3D) was used for pitch type recognition. Next, Prof. Ming Jiang from Sun Yat-sen University, China addressed a presentation on Massive MIMO Beam Vector Assignment Based on Deep Learning and Location/Handover Information. In this paper, he proposed to apply the Gaussian anomaly detection (GAD) to improve the system performance, particularly in the handover zone, by exploiting the information of sea lane and locations of the user equipment (UE) and used convolutional deep belief networks (CDBN) and GAD in the further analysis. Their insightful speeches had triggered heated discussion in the third part of the conference. Besides, every participant praised this conference for disseminating useful and insightful knowledge.The proceedings of IEDP 2022 are a compilation of the accepted papers and represent an interesting outcome of the conference. These papers feature but are not limited to the following topics: Intelligent Inspection Technology, Hybrid Intelligent Systems, Integrated Data Terminals, Data Science and Big Data Technology, etc. All the papers have been checked through a rigorous review and process to meet the requirements of publication.We would like to acknowledge all of those who supported IEDP 2022 and made it a great success. Particularly, we would like to thank the Journal of Physics: Conference Series, for the efforts of all its colleagues in publishing this paper volume. All the help and contribution of each individual and institution was instrumental in the success of IEDP 2022.The Committee of IEDP 2022List of Committee member is available in this pdf.

Design and Conceptual Development of a Novel Hybrid Intelligent Decision Support System Applied towards the Prevention and Early Detection of Forest Fires

Forest fires have become a major problem that every year has devastating consequences at the environmental level, negatively impacting the social and economic spheres of the affected regions. Aiming to mitigate these terrible effects, intelligent prediction models focused on early fire detection are becoming common practice. Considering mainly a preventive approach, these models often use tools that indifferently apply statistical or symbolic inference techniques. However, exploring the potential for the hybrid use of both, as is already being done in other research areas, is a significant novelty with direct application to early fire detection. In this line, this work proposes the design, development, and proof of concept of a new intelligent hybrid system that aims to provide support to the decisions of the teams responsible for defining strategies for the prevention, detection, and extinction of forest fires. The system determines three risk levels: a general one called Objective Technical Fire Risk, based on machine learning algorithms, which determines the global danger of a fire in some area of the region under study, and two more specific others which indicate the risk over a limited area of the region. These last two risk levels, expressed in matrix form and called Technical Risk Matrix and Expert Risk Matrix, are calculated through a convolutional neural network and an expert system, respectively. After that, they are combined by means of another expert system to determine the Global Risk Matrix that quantifies the risk of fire in each of the study regions and generates a visual representation of these results through a color map of the region itself. The proof of concept of the system has been carried out on a set of historical data from fires that occurred in the Montesinho Natural Park (Portugal), demonstrating its potential utility as a tool for the prevention and early detection of forest fires. The intelligent hybrid system designed has demonstrated excellent predictive capabilities in such a complex environment as forest fires, which are conditioned by multiple factors. Future improvements associated with data integration and the formalization of knowledge bases will make it possible to obtain a standard tool that could be used and validated in real time in different forest areas.

Using ICT in Printing: Economic Transformation, Evolution and Prospects

The purpose of the study is to determine the peculiarities of the use of information and communication technologies in the printing industry; to substantiate the feasibility of technological transformation in terms of the prospects for economic development of the selected industry. Processing of primary data to provide the user with the necessary information is the main task of using information technology in the printing industry. Three main blocks – knowledge base, decision-making and intellectual interface – make up the structure of an intelligent printing system. In terms of decision-making, an intelligent printing system is defined as an information and computer system with artificial intelligence that solves problems without human intervention, in contrast to an intelligent system where the operator is involved in the decision-making process. The following types of intelligent systems are identified: intelligent information systems, expert printing systems, computational and logical systems, hybrid intelligent systems, reflex intelligent systems. It is noted that taking into account market trends, adapting to the environment and quickly adjusting their development strategy to occupy their niche in the market is important for the successful functioning of publishing and printing companies. According to the authors, this is possible based on using ICT in printing. Integrating modern technologies into the printing industry can positively influence efficiency and competitiveness. Adopting digital technologies will allow printing companies to be more flexible and respond quickly to changing market conditions, which is important for its development. In conclusion, it can be said that information and communication technologies have a great potential for the transformation of the printing industry in Ukraine. Companies will be able to automate and optimise production processes, reduce costs and improve product quality through the introduction of modern technologies. In addition, by introducing new print formats and creating specialised products for the digital market, the development of information technology will expand the range of services. According to the authors, this approach will help Ukrainian companies to compete internationally and take their products to new markets, helping to develop the whole industry. In addition, there will be a stimulus for innovation and the development of new services, which in turn will contribute to the growth of the industry as a whole

A textual and visual features-jointly driven hybrid intelligent system for digital physical education teaching quality evaluation.

The utilization of intelligent computing in digital teaching quality evaluation has been a practical demand in smart cities. Currently, related research works can be categorized into two types: textual data-based approaches and visual data-based approaches. Due to the gap between their different formats and modalities, it remains very challenging to integrate them together when conducting digital teaching quality evaluation. In fact, the two types of information can both reflect distinguished knowledge from their own perspectives. To bridge this gap, this paper proposes a textual and visual features-jointly driven hybrid intelligent system for digital teaching quality evaluation. Visual features are extracted with the use of a multiscale convolution neural network by introducing receptive fields with different sizes. Textual features serve as the auxiliary contents for major visual features, and are extracted using a recurrent neural network. At last, we implement the proposed method through some simulation experiments to evaluate its practical running performance, and a real-world dataset collected from teaching activities is employed for this purpose. We obtain some groups of experimental results, which reveal that the hybrid intelligent system developed by this paper can bring more than 10% improvement of efficiency towards digital teaching quality evaluation.

ПСИХОЛОГО-ПЕДАГОГИЧЕСКИЕ УСЛОВИЯ ФУНКЦИОНИРОВАНИЯ ГИБРИДНОЙ ИНТЕЛЛЕКТУАЛЬНОЙ СИСТЕМЫ ОБУЧЕНИЯ МАТЕМАТИКЕ В СТАРШЕЙ ШКОЛЕ

The article examines the characteristics of the psychological and pedagogical conditions required for the functioning of the hybrid intellectual system of teaching (HIST) mathematics in high school. The article describes such internal conditions related to the development and primary trial of the HIST as the methodological basis of the intellectual management of educational and cognitive activity of students in general and the research and project activities in particular, the identification of the personalized parameters of the high school students’ scientific potential which determines the teaching content, the development of such diagnostic tools that can take the students’ individual psychological characteristics into account and serve as a component of intellectual management in the mathematical modelling of research activities, the development of the procedures and the methods of creating individual educational routes that are based on the students’ types of thinking. These conditions determined the architecture and functions of the hybrid neural network. The trial experiment on the internal conditions of the functioning of HIST involved 711 high school students. The results of the trial study indicate a steady annual increase in the number of students in experimental groups who get high scores, which is connected with an in-depth study of mathematics within the framework of the student's educational and research activities aimed at acquiring complex mathematical knowledge with the help of a hybrid intellectual system. The obtained results confirmed the effectiveness of the intelligent management system of the students' research and project activities. It was found that the external conditions, i.e. the readiness of the participants of the research and project activities to use the HIST, is not unimportant for its successful implementation. Consequently, the effectiveness of the hybrid intelligent learning system is determined not only by the readiness of the teachers, but also by the motivational and technological readiness of high school students to carry out research and project activities in a hybrid learning environment.

Intelligent Cross-axis Solar Power Tracking System

The sun is an abundant and powerful renewable energy resource that plays a crucial role in sustainable energy production. With efficient power generation in mind, here is a system designed to make intelligent judgements based on a condition so that the solar panel is always tilted towards the maximum sunlight. The hybrid intelligent cross-axis solar power tracking system is built using the microcontroller, light sensors, solar panel, and motors. Here, there are two methods used namely, the Sun Position Algorithm and the other is using the sensors. The algorithm uses the azimuth angle and the sun’s altitude to identify the position of the sun depending on the geo location. For maximum power generation from the solar panel, we use the sensor-based technique tocome into picture whenever the output voltage is less than the fixed threshold voltage using the Sun Position Algorithm.

Hybrid Decision Fusion based Multimodal Ensemble Framework for Cervical Cancer Detection

Cervical cancer is fourth in the list of cancers that affect women. It has remained the main cause of death for women in developing nations. The cancer is spread through human papilloma virus (HPV), which is sexually transmitted. Pap smear and colposcopy image analysis remain prominent methods of diagnosis. These screening tests require skilled diagnostic experts, a scarce resource in developing countries thus restricting the effectiveness of the cancer detection process in large scale. Machine learning and deep learning are branches of artificial intelligence that are being used increasingly in cancer diagnosis. This study proposes a novel hybrid intelligent system for cervical cancer detection. A hybrid model of feature extraction and feature fusion is proposed for merging the two-state image and clinical data. Subsequently a machine learning ensemble learner is assembled to classify the features. The model performed with a satisfactory accuracy of 96.16%. Our results show that our method outperforms state of the art approaches and archives better, dependable accuracy.

A Hybrid Intelligent System for Wireless Mesh Networks: Assessment of Implemented System for Two Instances and Three Router Replacement Methods Using Vmax Parameter

A Hybrid Intelligent System for Wireless Mesh Networks: Assessment of Implemented System for Two Instances and Three Router Replacement Methods Using Vmax Parameter

Hybrid intelligent intrusion detection system for internet of things

The Internet of Things (IoT) has become one of the dynamic and fascinating fields of research with new technology emerging. This can be explained by the ever-rising development of data globally (due to mass deployment of IoT devices) which needs to be protected and secure to avoid misuse. The emergence of IoT and learning algorithms in an area Machine Learning (ML) and Deep Learning (DL) has given new scope to cyber security. The security attacks on IoT networks are possible to detect and prevent intelligently using ML and DL techniques. ML and DL can make traditional attack detection methods efficient, reliable, and robust. The aim of this article is to develop the novel hybrid intrusion (attack) detection model using DL techniques, Convolutional neural network (CNN) and Long short-term memory (LSTM) to achieve better attack detection accuracy. The model is trained and tested using two different datasets namely UNSW-NB1511https://research.unsw.edu.au/projects/unsw-nb15-dataset. and NSL-Botnet22https://research.unsw.edu.au/projects/bot-iot-dataset. to verify the adaptability of the model to different datasets. The developed model has a better detection rate and low false positive rate (FPR) when compared to a state-of-the-art work. The proposed model accuracy is 99.4% with NSL-Botnet dataset and 93% with UNSW-NB15 for binary classification and 92% with NSL-Botnet and 82% with UNSW-NB15.

Implementation of roulette wheel and random selection methods in a hybrid intelligent system: A comparison study for two Islands and Subway distributions considering different router replacement methods

The Internet of Things (IoT) and 5G wireless networks have made it possible to connect intelligent devices with each other and to the Internet. Different kind of networks and applications are appearing and they support us in everyday life. The Wireless Mesh Networks (WMNs) have emerged as a good approach for edge computing and last miles networks. They can be applied in different scenarios and areas with difficult terrains. In order to deploy reliable and highly connected WMNs at low cost, we optimize the connectivity of mesh routers and the coverage mesh clients. In our previous work, we designed and implemented a hybrid intelligent system called WMN-PSODGA, which integrates two intelligent algorithms: Particle Swarm Optimization (PSO) and Distributed Genetic Algorithm (DGA). In this work, we implement in our system Two Islands and Subway distributions of mesh clients and evaluate the performance of different router replacement methods considering two selection methods: roulette wheel and random. We compare the simulation results for each selection and router replacement method and find the best scenarios in terms of coverage, connectivity, and load balancing. For each combination, the achieved network connectivity and client coverage are 100%, but the best scenarios in terms of load balancing are when we combine the random selection method and Constriction Method (CM) for the Two Islands distribution, and the random selection method and Random Inertia Weight Method (RIWM) for the Subway distribution.

Demonstration of Intelligent Hybrid FSO/RF System Based on Enhanced GRU Prediction and Real-World Meteorological Dataset

Hybrid free space optical (FSO)/radio frequency (RF) system has emerged as a high-data-rate solution for the last mile access network. In this paper, we establish a hybrid FSO/RF testbed including an intensity modulation/direct detection (IM/DD) FSO subsystem with space diversity and a high-order quadrature amplitude modulation (QAM) RF subsystem with polarization diversity, respectively. Based on the testbed, we propose and experimentally validate an intelligent transmission system where the link selection and switching technology is implemented. In particular, we propose a gated recurrent unit (GRU) neural network enhanced by time attention mechanism for the hybrid system. The experimental results demonstrate that the proposed system can achieve the prediction of FSO channel fading with a high precision where absolute percentage error (APE) values lower than 3% account for up to 90% of the prediction results. The implemented intelligent hybrid system can significantly reduce the link switching frequency and link interruption duration. Then the BER is further verified on the testbed, and results show that the intelligent hybrid system can improve the BER from the order of 10 <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">−2</sup> to the order of 10 <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">−3</sup> during the time periods when link switching occurs.

Which Biases and Reasoning Pitfalls Do Explanations Trigger? Decomposing Communication Processes in Human-AI Interaction.

Collaborative human-AI problem-solving and decision making rely on effective communications between both agents. Such communication processes comprise explanations and interactions between a sender and a receiver. Investigating these dynamics is crucial to avoid miscommunication problems. Hence, in this article, we propose a communication dynamics model, examining the impact of the sender's explanation intention and strategy on the receiver's perception of explanation effects. We further present potential biases and reasoning pitfalls with the aim of contributing to the design of hybrid intelligence systems. Finally, we propose six desiderata for human-centered explainable AI and discuss future research opportunities.

Artificial Intelligence in Stroke – A Review

Artificial intelligence is a computer science discipline that can analyse complex medical information. Almost every field of medicine has looked at the capabilities of artificial intelligence systems. Although artificial neural network were the most widely employed logical tool, other artificial intelligent approaches such as transformational computation and hybrid intelligent systems were also applied in a variety of clinical settings. Stroke is a cerebrovascular condition distinguished by the sudden onset of symptoms and scientific findings. Stroke is a main reason of death and morbidity in the world. It can be either ischemic or haemorrhagic, with the former caused by an in-place thrombus or a far-off embolus. The influence of gender on acute ischemic stroke presentation, severity, aetiology, and outcome is becoming more widely recognised. Seizures, conversion illness, migraine headache, and are the most common state that can copy a stroke.

A Hybrid Intelligent Simulation System for Building IoT Networks: Performance Comparison of Different Router Replacement Methods for WMNs Considering Stadium Distribution of IoT Devices.

As the Internet of Things (IoT) devices and applications proliferate, it becomes increasingly important to design robust networks that can continue to meet user demands at a high level. Wireless local area networks (WLANs) can be a good choice as IoT infrastructure when high throughput is required. On the other hand, wireless mesh networks (WMNs), which are WLANs with mesh topology following the IEEE802.11s standard, have many advantages compared to conventional WLANs. Nevertheless, there are some problems that need solutions. One of them is the node placement problem. In this work, we propose and implement a hybrid intelligent system that solves this problem by determining the position of mesh nodes by maximizing the mesh connectivity and the coverage of IoT devices. The system is based on particle swarm optimization (PSO), simulated annealing (SA), and distributed genetic algorithm (DGA). We compare the performance of three router replacement methods: constriction method (CM), random inertia weight method (RIWM), and rational decrement of Vmax method (RDVM). The simulation results show that RIWM achieves better performance compared to CM and RDVM because it achieves the highest connectivity while covering more clients than the other two methods.