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Intrusion detection in metaverse environment internet of things systems by metaheuristics tuned two level framework

SCIENTIFIC REPORTS 2025年第1期15卷 1-31页

作者： Antonijevic, Milos Zivkovic, Miodrag Jovicic, Milica Djuric Nikolic, Bosko Perisic, Jasmina Milovanovic, Marina Jovanovic, Luka Abdel-Salam, Mahmoud Bacanin, Nebojsa Singidunum Univ Belgrade 11000 Serbia Univ Belgrade Innovat Ctr Sch Elect Engn Belgrade 11000 Serbia Univ Belgrade Sch Elect Engn Belgrade 11000 Serbia Mansoura Univ Fac Comp & Informat Sci Mansoura 35516 Egypt SIMATS Saveetha Sch Engn Chennai 602105 Tamil Nadu India Sinergija Univ Bijeljina 76300 Bosnia & Herceg

Internet of Things (IoT) is one of the most important emerging technologies that supports Metaverse integrating process, by enabling smooth data transfer among physical and virtual domains. Integrating sensor devices, wearables, and smart gadgets into Metaverse environment enables IoT to deepen interactions and enhance immersion, both crucial for a completely integrated, data-driven Metaverse. Nevertheless, because IoT devices are often built with minimal hardware and are connected to the Internet, they are highly susceptible to different types of cyberattacks, presenting a significant security problem for maintaining a secure infrastructure. Conventional security techniques have difficulty countering these evolving threats, highlighting the need for adaptive solutions powered by artificial intelligence (AI). This work seeks to improve trust and security in IoT edge devices integrated in to the Metaverse. This study revolves around hybrid framework that combines convolutional neural networks (CNN) and machine learning (ML) classifying models, like categorical boosting (CatBoost) and light gradient-boosting machine (LightGBM), further optimized through metaheuristics optimizers for leveraged performance. A two-leveled architecture was designed to manage intricate data, enabling the detection and classification of attacks within IoT networks. A thorough analysis utilizing a real-world IoT network attacks dataset validates the proposed architecture's efficacy in identification of the specific variants of malevolent assaults, that is a classic multi-class classification challenge. Three experiments were executed utilizing data open to public, where the top models attained a supreme accuracy of 99.83% for multi-class classification. Additionally, explainable AI methods offered valuable supplementary insights into the model's decision-making process, supporting future data collection efforts and enhancing security of these systems.

关键词： Metaverse CatBoost LightGBM optimization Metaheuristics algorithms chimp optimization algorithm

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Predicting the settlement of pile based on a hybrid form of the model by considering Least Square Support Vector Regression

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MULTISCALE AND MULTIDISCIPLINARY MODELING EXPERIMENTS AND DESIGN 2024年第1期7卷 529-542页

作者： Chen, Qiang Chongqing Vocat Coll Transportat Sch Marxism Chongqing 402247 Peoples R China

Understanding the vertical settlement is crucial when designing the pile and foundation type used in real estate, specifically regarding the pile settlement (Sp). This issue is of utmost importance due to the numerous variables involved in designing piles that penetrate rock. Despite multiple efforts, clear and precise theoretical explanations regarding the interactions between soil and piles are currently unclear. As a result, many studies have opted to employ artificial intelligence techniques for determining the subsidence rate of piles over time under different loading conditions. This study presents a machine learning (ML) that effectively predicts the values of Sp, namely Least Square Support Vector Regression (LSSVR). In addition, the proposed model coupled with three meta-heuristic algorithms, including the Flow Direction algorithm (FDA), chimp optimization algorithm (ChOA), and Rider optimization algorithm (ROA), to improve the performance and obtain the optimal results as a framework of hybrid. As a result, LSFD determined the most suitable effects with R2 and RMSE values equal to 0.2503 and 0.9952, respectively. Overall, using LSSVR with FDA, ChOA, and ROA can improve the accuracy and robustness of the model in predicting pile settlement, making it a valuable tool for geotechnical engineers designing foundation systems.

关键词： Pile settlement Least Square support vector regression Flow direction algorithm chimp optimization algorithm Rider optimization algorithm

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A novel fuzzy hybrid red fox chimp for optimal power flow in FACTS devices

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ELECTRICAL ENGINEERING 2023年第6期105卷 4481页

作者： Mahadevan, J. Rengaraj, R. Anna Univ Fac Elect Engn Chennai India Sri Sivasubramaniya Nadar Coll Engn Dept Elect & Elect Engn Chennai India

Due to the large-scale, multimodal, non-convex, and nonlinear characteristics of OPF, mitigation of OPF problems in power transmission systems becomes a more complicated task. Though there already exist numerous optimization approaches to manage OPF issues, they remained unconstrained (i.e., fall into local optima issues). Therefore, in this paper, we proposed a novel fuzzy hybrid red fox chimp (FHRC) algorithm for handling a high level of uncertainties that occurs in power systems on equipping FACTS devices and renewable energy sources. Achieving optimal system variables using the proposed FHRC algorithm optimizes the chosen objective function rather than fulfilling operational inequality constraints and power flow equations. The integrated functioning of the red fox algorithm and chimp optimization algorithm with the fuzzy logic system efficiently optimizes the OPF problems. The efficiency of the proposed FHRC algorithm is evaluated using the IEEE 30 bus system in terms of measures, namely power generation cost, power loss, cost function value, and voltage deviations, under the application of diverse load conditions. The experimentation results reveal that the FHRC algorithm optimizes the objective function of OPF problems more than other compared methods. In future, our proposed approach will be implemented in various other energy storage systems and real power network.

关键词： Optimal power flow FACTS devices Red fox algorithm chimp optimization algorithm Fuzzy logic system Power loss Voltage deviation Power generation cost

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Deep learning-based hybrid sentiment analysis with feature selection using optimization algorithm

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MULTIMEDIA TOOLS AND APPLICATIONS 2023年第28期82卷 43273-43296页

作者： Daniel, D. Anand Joseph Meena, M. Janaki VIT Chennai Sch Comp Sci & Engn Chennai 600127 Tamil Nadu India

In the past few years, sentiment analysis (SA) of online content has gained more attention in the research area due to the enormous increase of online content from various sources like websites, social blogs, etc. Many organizations use SA techniques to determine the opinion of users and to ensure their satisfaction. Numerous techniques are suggested by many researchers to identify the sentiments of online content. Among them, hybrid of deep learning and lexicon-based SA techniques are gaining more attention due to their outstanding performance than other approaches. Though the lexicon-based SA approaches integrated with deep learning SA approaches possess more advantages they suffer from lack of accuracy and scalability issues due to the high-dimensional features. To eliminate this issue, a hybrid SA approach is proposed in this paper with a bio-inspired feature selection technique. The Valence Aware Dictionary for Sentiment Reasoning (VADER) approach is integrated with the hybrid deep learning approach of attention-based bidirectional long short-term memory and variable pooling convolutional neural network (VPCNN-ABiLSTM) for SA. The optimal features are selected to minimize the scalability issue by integrating the chimp optimization algorithm with the opposition-based learning technique. The performance of the proposed approach is evaluated for four types of benchmark datasets in terms of precision, accuracy, recall, and F1 score. The proposed approach with OBL-CHOA based feature selection technique achieved higher accuracy of 97.1% with the reduction of 13.6% features. The accuracy of the proposed approach with the feature selection technique is 6.9% higher than the existing BiLSTM-CNN based SA approach.

关键词： Sentiment analysis VADER Convolutional neural network Deep learning Long short-term memory chimp optimization algorithm

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Micro-Calcification Classification Analysis in Mammogram Images with Aid of Hybrid Technique Analysis

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WIRELESS PERSONAL COMMUNICATIONS 2023年第2期128卷 1287-1307页

作者： Shanker, M. C. Vadivel, M. Sathyabama Inst Sci & Technol Dept Elect Engn Chennai Tamil Nadu India Vidya Jyothi Inst Technol Dept Elect & Commun Engn Hydrabad Andra Pradesh India

Breast cancer is the leading cause of death in women. Early identification can contribute significantly to improving the survival rate. For diagnosis and accurate therapy automatic detection of micro-calcification is therefore essential. In the paper, an automated technique is utilized in the mammogram images according to their micro-calcification classification. The automated technique is working with the combination of Deep Belief Neural Network (DBNN) and chimp optimization algorithm (COA). The proposed method is working with three phases such as pre-processing phase, feature extraction, and classification phase. In the pre-processing phase, a median filter is utilized to remove unwanted information from the images. In the feature extraction phase, Gray Level Co-Occurrence Matrix (GLCM), Scale-Invariant Feature Transform (SIFT), and Hu moments are utilized to extract essential features from the mammogram images. After that, the detection and classification are performed on the mammogram images according to their micro-calcifications with the utilization of the proposed advanced deep learning method. From the classification stage, the normal and abnormal images are identified from the images. The proposed method is implemented in the MATLAB platform and analyzed their statistical performances like accuracy, sensitivity, specificity, precision, recall, and F-measure. To evaluate the effectiveness of the proposed method this is compared with the existing method such as Support Vector Machine (SVM), Random Forest (RF), and Artificial Neural Network (ANN).

关键词： Breast cancer classification Deep belief neural network Pre-processing Feature extraction Deep learning method chimp optimization algorithm

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An improved H infinity controller using hybrid optimization for PV integrated MMC-HVDC system

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MULTIMEDIA TOOLS AND APPLICATIONS 2023年第2期83卷 3917-3943页

作者： Ali, Qazi Waqar Inst Appl Technol Abu Dhabi U Arab Emirates

Generally, circulating current is generated in a Modular Multilevel Converter (MMC) by fluctuations in the capacitor voltage of sub-modules. Therefore, this research work seeks to build the Photo Voltaic (PV) integrated MMC-High-Voltage Direct Current (HVDC) systems with optimal H-infinity controller which is appropriate for 3-phase system. To verify the unstable voltages of sub-modules, the reference current is initially set to zero, as well as the unstable currents in each phase are monitored with a current sensor as well as contrasted to the reference current, resulting in the generation of an incorrect circulating current. The main contribution is to reduce the error among the reference current as well as the actual circulating current. In the H-infinity controller, the gains are optimally tuned via a new hybrid algorithm referred as Jaya optimization Insisted Explored chimp (JOI-EC) algorithm, which is the hybridized version of the Jaya optimization algorithm (JOA) and the chimp optimization algorithm (ChOA). Moreover, the performance of the proposed model is examined through control analysis, converter performance analysis, as well as capacitor voltage analysis in addition to the circulating current. The result of simulation shows the robust performance of the proposed controller with respect to minimum circulating current, voltage, and stability analysis.

关键词： MMC H-infinity controller Jaya optimization insisted explored chimp Circulating current Jaya optimization algorithm chimp optimization algorithm

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An effective optimized deep learning for emotion classification from EEG signals

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SIGNAL IMAGE AND VIDEO PROCESSING 2023年第4期17卷 1631-1642页

作者： Lokesh, Sittiahgari Reddy, Tadipathri Sreenivasulu Sri Venkatewara Univ SV Univ Coll Engn Dept Elect & Commun Engn Tirupati 517502 Andhra Pradesh India Sri Venkatewara Univ Dept Elect & Commun Engn Tirupati 517502 Andhra Pradesh India

Electroencephalography (EEG) signals can be used for emotion recognition (ER), which is an effective method for determining someone's mental state. However, because an EEG signal is non-stationary, the ER is a fascinating challenge. Additionally, the categorization of the mood patterns in the EEG signal makes use of signal processing techniques to extract pertinent data from EEG signals. As a result, the fractional chimp optimization algorithm (FrChOA), which was developed for this work, is introduced as an improved deep learning technique for choosing the best channel and classifying emotions from EEG signals. By merging the chimp optimization algorithm (CA) with fractional calculus, the created FrChOA is modeled (ChOA). Pre-processing, optimal channel selection, feature extraction, and human emotion categorization are the processing stages carried out in this instance by the ER. First, the low pass filtering technique is used to pre-process the incoming EEG signal. The best channel is then chosen using a developed algorithm called FrChOA, which bases its choice on classification accuracy. In order to increase classification performance, the essential features are extracted at the end of the feature extraction procedure. Additionally, the deep neuro-fuzzy network, whose training process is created FrChOA, is used for emotion classification. The developed algorithm also produced the best results, as evidenced by its testing accuracy, sensitivity, and specificity of 0.8848, 0.8763, and 0.8946, respectively.

关键词： chimp optimization algorithm Fractional calculus Low pass filtering Deep neuro-fuzzy network Channel selection

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An efficient vector-borne disease prediction using SS optimization-based hybrid support vector random forest model

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SIGNAL IMAGE AND VIDEO PROCESSING 2023年第8期17卷 3943-3952页

作者： Munirathinam, Babu Aroulanandam, Vijay Vasanth Saravanan, Prabakeran Rajalakshmi Engn Coll Dept Informat Technol Chennai India SRM Inst Sci & Technol Fac Engn & Technol Dept Networking & Commun Kattankulathur 603203 Tamil Nadu India

The transmission of infections caused by infected species or arthropoda, such as ticks, blackflies, sandflies, mosquitoes, and triatomine bugs, is known as vector-borne diseases. Arthropod vector is responsible in transmitting the most harmful illnesses that affect people as well as animals. This results in a significant impact on human health, leading to increased mortality rates and various side effects, ultimately reducing the life expectancy of people. This article proposes a novel approach to predict vector-borne diseases using medical data. The approach combines the Sine Cosine as well as Spotted Hyena-based chimp optimization algorithm (SSC) as well as hybrid Support Vector Machine-based Random Forest (SVM-RF) approach. The SSC algorithm is designed by incorporating three different algorithms, namely the chimp optimization algorithm, the Spotted Hyena Optimizer algorithm, and the Sine Cosine algorithm (SCA). The proposed hybrid SVM-RF classifier approach accurately detects vector-borne diseases. Using the vector-borne dataset, the proposed SSC-optimized hybrid SVM-RF approach outperformed other approaches with values of 92, 93.25, 92.53, and 91.52%, respectively. Overall, the proposed approach has significant potential in predicting and diagnosing vector-borne diseases, which can ultimately lead to improved public health outcomes.

关键词： Vector-borne disease prediction Hyena optimization algorithm chimp optimization algorithm Random Forest Approach and Support Vector Machine

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Detection and Isolation of Selfish Nodes in MANET Using Collaborative Contact-Based Watchdog with chimp-AODV

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WIRELESS PERSONAL COMMUNICATIONS 2023年第2期128卷 1373-1390页

作者： Sherif, Bismin, V Salini, P. Puducherry Technol Univ Dept Comp Sci & Engn Pondicherry India

Mobile Ad-hoc Network (MANET) is one of the most important self-configuring and independent wireless network. Numerous intermediate nodes are used among MANET to interchange the information without the requirement of any centralized infrastructure. But some nodes act selfishly and utilize the resources only for their own purposes and do not share with the neighbors. This selfish nodes might delay or drop the packet and do not perform routing. Though watchdog is a well-known selfish node detection technique, it causes false negatives and false positives that can affect the performance in terms of precision and speed. To eliminate the drawbacks of existing approaches in selfish node detection, this paper integrates both Ad-hoc On-Demand Distance Vector (AODV) protocol incorporated with chimp optimization algorithm and Collaborative Contact based Watchdog to propose a novel technique called chimp-CoCoWa-AODV in order to improve the performance of MANET. The main role of chimp optimization algorithm in AODV is to undergo optimal route selection process. The performance of the proposed chimp-CoCoWa-AODV approach is compared with existing approaches in terms of average routing load, Average Packet Delivery Fraction (PDF), Average End-to-end Delay (EED), Average Throughput, Total packet drop in the application layer, and maliciously dropped packet in the routing layer. The simulation results shows that the proposed approach is effective with 82% PDF and 7.4 ms EED at 50 nodes in detection and isolation of selfish nodes in MANET even in the presence of malicious node.

关键词： Ad-hoc on-demand distance vector Mobile ad-hoc network chimp optimization algorithm Collaborative contact based watchdog Selfish node

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A three-tier BERT based transformer framework for detecting and classifying skin cancer with HSCGS algorithm

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MULTIMEDIA TOOLS AND APPLICATIONS 2023年第17期83卷 51441-51467页

作者： George, Joseph Rao, Anne Koteswara Kalasalingam Acad Res & Educ Dept Comp Sci & Engn Srivilliputhur 626126 Tamil Nadu India

Skin cancer is the process of identifying and diagnosing, a disease in which abnormal skin cells grow and spread uncontrollably. An innovative deep learning-based skin cancer detection model is introduced in this research work. The proposed model is divided into five main phases: (a) Pre-Processing (b) Segmentation (c) Feature Extraction (d) 3-Tier Classification (e) post-processing. Initially, the collected raw image is pre-processed via contrast enhancement, decimal scaling, and augmentation methods. From the pre-processed image, the important feature is extracted by using the statistical features like mean, variance, and information gain. Then, from the extracted image, the region of interest ROI is identified via fuzzy assisted Kapur's multi-level thresholding. The optimal features are selected using the hybrid Self-Improved chimp optimization algorithm with Glow Swarm optimization algorithm (HSCGS). The three-tier classification using the BERT based Transformer with HSCGS based Gated Recurred Unit (GRU), BiLSTM, and Graph Neural Network is projected for classification. The proposed model is implemented using the PYTHON platform. The findings are evaluated in terms of accuracy, sensitivity, precision, FPR, FNR, etc. using the present models. The proposed model has recorded the highest detection accuracy as 97% and highest MCC and NPV values. Proposed model has shown the best performance and has outperformed other models.

关键词： Skin cancer detection chimp optimization algorithm Glow swarm optimization Gated recurrent unit BiLSTM

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