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Quantitative Analysis of Worm Transmission and Insider Risks in Air-Gapped Networking Using a Novel Machine Learning Approach

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IEEE ACCESS 2023年 11卷 111034-111052页

作者： Sulaiman, Muhammad Khan, Awais Ali, Addisu Negash Laouini, Ghaylen Alshammari, Fahad Sameer Abdul Wali Khan Univ Dept Math Mardan 23200 Pakistan Bahir Dar Univ Bahir Dar Inst Technol Fac Mech & Ind Engn Bahir Dar 6000 Ethiopia Amer Univ Middle East Coll Engn & Technol Egaila 54200 Kuwait Prince Sattam Bin Abdulaziz Univ Coll Sci & Humanities Alkharj Dept Math Al Kharj 11942 Saudi Arabia

Researchers and practitioners in the fields of science and engineering encounter significant challenges when it comes to mitigating the proliferation of computer worms, owing to their rapid spread within computer and communication networks. This study delves into a comprehensive analysis of the mathematical model governing the hazard of worm propagation in such networks. Specifically, the mathematical framework employed herein encompasses a system of ordinary differential equations. In numerous instances, mathematical models have been employed to quantitatively investigate the propagation patterns of worms across computer networks. In this scholarly article, we present an enhanced Susceptible-Exposed-Infected-Quarantined-Vaccinated (SEIQV) model, denoted as Susceptible-Exposed-Infected-Quarantined-Patched (SEIQP), which effectively captures the dissemination dynamics of an insider threat within a network featuring air gaps. To facilitate the study, we leverage the power of feedforward neural networks that are trained using the backpropagated Levenberg-Marquardt optimization algorithm. These neural networks serve as surrogate tools, providing solutions to the SEIQP model. To evaluate the efficacy of our approach, we meticulously assess their performance across three distinct scenarios. Additionally, the stability of the mathematical model is examined by manipulating the probability of an insider threat removing a patch from the host, denoted as $\eta $ . Our empirical findings conclusively establish the effectiveness of the proposed approach in addressing the intricate challenges associated with insider threats within network environments.

关键词： SEIQV model insider threat artificial neural networks machine learning system ofdifferential equations surrogate solutions optimization algorithm anti-virus numerical solutions patching

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Classification of Liver Disease Using Conventional Tree-Based Machine Learning Approaches with Feature Prioritization Using a Heuristic algorithm

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INTERNATIONAL JOURNAL OF ADVANCED COMPUTER SCIENCE AND APPLICATIONS 2024年第11期15卷 357-363页

作者： Mondal, Proloy Kumar Byeon, Haewon Khulna Univ Engn & Technol Dept Elect & Commun Engn Khulna Bangladesh Inje Univ Dept Digital Antiaging Healthcare BK21 Gimhae 50834 South Korea

Liver disease ranks as one of the leading causes of mortality globally, often going undetected until advanced stages. This study aims to enhance early detection of liver disease by employing machine learning models that utilize key health indicators. Utilizing the Indian Liver Patient Dataset (ILPD) from the UCI repository, we developed a predictive model using the CatBoost algorithm, achieving an initial accuracy of 74%. To improve this, feature selection was performed using the Whale optimization algorithm (WOA) and Harris Hawk optimization (HHO), which increased accuracy to 82% and 85% respectively. The methodology involved preprocessing to correct data imbalances and outlier removal through univariate and bivariate analyses. These optimizations highlight the critical features enhancing the model's predictive capability. The results indicate that integrating metaheuristic algorithms in feature selection significantly improves the accuracy of liver disease prediction models. Future research could explore the integration of additional datasets and machine learning models to further refine predictive capabilities and understand the underlying pathophysiology of liver diseases.

关键词： Liver disease classification prediction CatBoost algorithm machine learning optimization algorithm

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Optimizing LSTM-Based Model with Ant-Lion algorithm for Improving Thyroid Prognosis

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INTERNATIONAL JOURNAL OF ADVANCED COMPUTER SCIENCE AND APPLICATIONS 2024年第10期15卷 894-902页

作者： Yousef, Maria Univ Petra Dept Comp Sci Amman Jordan

the healthcare sector, early and accurate disease detection is essential for providing appropriate care on time. This is especially crucial in thyroid problems, which can be difficult to diagnose because of their many symptoms. This study aims to propose a new thyroid disease prediction model by utilizing the Ant Lion optimization (ALO) approach to enhance the hyperparameters of the Long Short-Term Memory (LSTM) deep learning algorithm. To achieve this, after the preprocessing step, we utilize the entropy technique for feature selection, which selects the most important features as an optimal subset of features. The ALO is then employed to optimize the LSTM, identifying the optimal hyperparameters that can influence the model and enhance its efficiency. To assess the suggested methodology, we chose the widely used thyroid disease data. This dataset contains 9,172 samples and 31 features. A set of criteria was used to evaluate the model's performance, including accuracy, precision, recall, and F1 score. The experimental results showed that: 1) the entropy technique in the feature selection step can reduce the total number of features from 31 to 10;2) the recommended strategy, which selected the optimal hyperparameter for the LSTM using the Alo algorithm, improved the classifier overall by 7.2% and produced the highest accuracy of 98.6%.

关键词： Thyroid disease LSTM ALO prediction model optimization algorithm

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Optimal regression analysis for estimating the settlement of the deep foundations socketed into rock

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MULTISCALE AND MULTIDISCIPLINARY MODELING EXPERIMENTS AND DESIGN 2024年第6期7卷 5171-5186页

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

Piles, which are classified as deep foundations, are used in civil engineering applications to provide stable support for structures by being driven into the earth. Given the substantial load-bearing capacity of such foundations, it is essential to consider their settling throughout the design process meticulously. Hence, the management and assessment of settlement pose a noteworthy concern in the realm of piling design and construction. The main goal of the current research is to assess the appropriateness of using a neural network model utilizing optimal support vector regression (SVR) analysis to forecast the settling of piles (SP) in rock formations. Both the Aquila optimizer (AO) and the Henry gas solubility optimizer (HGSO) were merged with SVR to get the best possible values for SVR's crucial factors. The purpose of this research is to show that the predetermined methods have not been used to forecast the SP in rocks. The results indicate that both SVR-AO and SVR-HGSO show considerable potential in accurately predicting the SP. Specifically, the SVR-AO model achieved coefficient of determination (R2) values of 0.9957 and 0.994 during its learning and testing phases, while the SVR-HGSO model achieved R2 values of 0.9888 and 0.9893, respectively. The results of OBJ presented that the SVR-AO model got the minimum OBJ at 0.2187 in contrast to SVR-HGSO at 0.3436. The results of this study are more powerful and accurate than the literature by gaining the higher values of R2 and lower values of error-based metrics. Our research establishes a robust framework for accurately estimating SP in rock formations, offering practical implications for enhancing the design and construction of pile foundations in civil engineering applications. Ultimately, the goal is to leverage machine learning to solve complex problem, make accurate predictions, automate tasks, and gain insights from data.

关键词： Deep foundation Pile socketed to rock Settlement optimization algorithm Regression analysis

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A Two-Stage Method for Damage Identification in Concrete Gravity Dams by Using a Modal Curvature-Based Index and Pathfinder algorithm

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ARABIAN JOURNAL FOR SCIENCE AND ENGINEERING 2024年第4期49卷 5927-5947页

作者： Mohebian, Pooya Motahari, Mohammad Reza Aval, Seyed Bahram Beheshti Arak Univ Fac Engn Dept Civil Engn Arak Iran KN Toosi Univ Technol Fac Civil Engn Tehran Iran

Dams are among the most important civil engineering structures, but they are also susceptible to damage that can endanger their stability and functionality. It is therefore crucial to detect any damage in dams to ensure their long-term integrity and overall safety. In light of this, the present study proposes a new two-stage method for identifying damage in concrete gravity dams. In the first stage, a modal curvature-based damage index (MCBDI) is presented to detect the location of suspected damaged zones along the height of the dam. The mode shape data collected from some measurement points on the downstream side of the dam is used to accomplish this task. In the second stage, the pathfinder algorithm (PFA) as a powerful meta-heuristic optimization technique is applied to determine the severity of potentially damaged zones by minimizing an objective function specified in terms of natural frequencies and mode shapes. The capability and effectiveness of the proposed method are evaluated by implementing two numerical simulation examples of concrete gravity dams under both noise-free and noisy conditions. The results obtained suggest that the proposed two-stage method, comprising the MCBDI indicator and the PFA algorithm, represents an accurate and efficient approach for localizing and quantifying damage in concrete gravity dams.

关键词： Damage identification Gravity dams Vibration method Modal curvature optimization algorithm

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The Obnoxious Competitive Facility Location Model

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NETWORKS & SPATIAL ECONOMICS 2023年第4期23卷 885-903页

作者： Drezner, Tammy Drezner, Zvi Zerom, Dawit Calif State Univ Fullerton Coll Business & Econ Fullerton CA 92834 USA

In this paper we propose a new competitive location model that considers the possible negative impact generated by competing facilities (such as cannabis dispensaries) on surrounding communities. The facilities cannot be located too close to the communities. Therefore, when distances are Euclidean, the facilities must be located at a point outside a set of circles centered at the communities. After formulating the model, a specially designed efficient algorithm that solves the single facility location problem within a given relative accuracy of optimality is constructed. A total of 128 instances are solved in a relatively short time. The largest instance of 10 existing competing facilities and 20,000 demand points was solved in less than 15 min of computer time. This new model opens avenues for future research by designing similar new models. Also, the algorithm designed in this paper can be applied to solving other location problems with outside of a set of circles constraints.

关键词： Facility location Obnoxious facility Competitive facility optimization algorithm

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Estimation of fracture size and azimuth in the universal elliptical disc model based on trace information

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Journal of Rock Mechanics and Geotechnical Engineering 2023年第6期15卷 1391-1405页

作者： Jichao Guo Jun Zheng Qing Lü Jianhui Deng Department of Civil Engineering Zhejiang UniversityHangzhou310058China State Key Laboratory of Hydraulics and Mountain River Engineering College of Water Resource and HydropowerSichuan UniversityChengdu610065China

The geometric characteristics of fractures within a rock mass can be inferred by the data sampling from boreholes or exposed ***,the universal elliptical disc(UED)model was developed to represent natural fractures,where the fracture is assumed to be an elliptical disc and the fracture orientation,rotation angle,length of the long axis and ratio of short-long axis lengths are considered as *** paper aims to estimate the fracture size-and azimuth-related parameters in the UED model based on the trace information from sampling *** stereological relationship between the trace length,size-and azimuth-related parameters of the UED model was established,and the formulae of the mean value and standard deviation of trace length were *** proposed formulae were validated via the Monte Carlo simulations with less than 5%of error rate between the calculated and true *** respect to the estimation of the size-and azimuth-related parameters using the trace length,an optimization method was developed based on the pre-assumed size and azimuth distribution forms.A hypothetical case study was designed to illustrate and verify the parameter estimation method,where three combinations of the sampling windows were used to estimate the parameters,and the results showed that the estimated values could agree well with the true ***,a hypothetical three-dimensional(3D)elliptical fracture network was constructed,and the circular disc,non-UED and UED models were used to represent *** simulated trace information from different models was compared,and the results clearly illustrated the superiority of the proposed UED model over the existing circular disc and non-UED models。

关键词： Universal elliptical disc(UED)model Rock mass Discrete fracture network(DFN) optimization algorithm Inverse problem

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Geomagnetic Interpolation Method Incorporating Bidirectional Variability

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JOURNAL OF GUIDANCE CONTROL AND DYNAMICS 2024年第6期47卷 1222-1230页

作者： Ji, Caijuan Song, Chengying Li, Sheng Chen, Qingwei Nanjing Univ Sci & Technol Dept Control Sci & Engn Nanjing 210094 Jiangsu Peoples R China

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Automatic Fiducial Points Detection for Multi-facial Expressions via Invariant Features and Multi-layer Kernel Sliding Perceptron

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JOURNAL OF ELECTRICAL ENGINEERING & TECHNOLOGY 2023年第1期18卷 651-661页

作者： Rizwan, Syeda Amna Alsufyani, Nawal Shorfuzzaman, Mohammad Alarfaj, Mohammed Jalal, Ahmad Kim, Kibum Air Univ Dept Comp Sci & Engn Islamabad Pakistan Taif Univ Coll Comp & Informat Technol Dept Comp Sci Taif 21944 Saudi Arabia King Faisal Univ Coll Engn Dept Elect Engn Al Hasa 31982 Saudi Arabia Hanyang Univ Dept Human Comp Interact Seoul South Korea

In recent years, automatic facial expression recognition (FER) is a primary processing method of non-verbal communication and conveys their intention states among human-machine interaction. In this paper, we have proposed a novel FER system that wisely detects automatic fiducial points, generates robust multi-perspective views facial masks and recognizes expressions via kernel sliding perceptron. Initially, we detect multiple faces in a scene via saliency factor and detect 38 fiducial points by connecting maximum interest points in each face. These points are used for generating a face mask by measuring triangles formation and B-spline curve fitting. Then, we extract invariant features, such as fused HOG-LBP, advance 0 degrees-180 degrees intensity and fast marching features, and seek the best points' junction optimizer with an artificial bee colony algorithm. Finally, we propose a novel multi-layer kernel sliding perceptron method to classify six basic facial expressions. The proposed system outperforms the existing well-known statistical state-of-the-art FER methods in terms of recognition accuracy of 91.05% over Chicago Faces and 88.50% over Fam2a datasets, respectively. The proposed system has a possible broader impact and potential applications of FER for multimodal intelligent systems.

关键词： Fiducial points detection Facial expression recognition Kernel sliding perceptron Mask generation optimization algorithm

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A voting-based machine learning approach for classifying biological and clinical datasets

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BMC BIOINFORMATICS 2023年第1期24卷 1-17页

作者： Daneshvar, Negar Hossein-Nezhad Masoudi-Sobhanzadeh, Yosef Omidi, Yadollah Univ Coll Nabi Akram Dept Comp Engn Tabriz Iran Tabriz Univ Med Sci Biomed Inst Res Ctr Pharmaceut Nanotechnol Tabriz Iran Tabriz Univ Med Sci Fac Adv Med Sci Tabriz Iran Nova Southeastern Univ Coll Pharm Dept Pharmaceut Sci Ft Lauderdale FL 33328 USA

Background: Different machine learning techniques have been proposed to classify a wide range of biological/clinical data. Given the practicability of these approaches accordingly, various software packages have been also designed and developed. However, the existing methods suffer from several limitations such as overfitting on a specific dataset, ignoring the feature selection concept in the preprocessing step, and losing their performance on large-size datasets. To tackle the mentioned restrictions, in this study, we introduced a machine learning framework consisting of two main steps. First, our previously suggested optimization algorithm (Trader) was extended to select a near-optimal subset of features/genes. Second, a voting-based framework was proposed to classify the biological/clinical data with high accuracy. To evaluate the efficiency of the proposed method, it was applied to 13 biological/clinical datasets, and the outcomes were comprehensively compared with the prior methods. Results: The results demonstrated that the Trader algorithm could select a near-optimal subset of features with a significant level of p-value < 0.01 relative to the compared algorithms. Additionally, on the large-sie datasets, the proposed machine learning framework improved prior studies by similar to 10% in terms of the mean values associated with fivefold cross-validation of accuracy, precision, recall, specificity, and F-measure. Conclusion: Based on the obtained results, it can be concluded that a proper configuration of efficient algorithms and methods can increase the prediction power of machine learning approaches and help researchers in designing practical diagnosis health care systems and offering effective treatment plans.

关键词： Clinical datasets Feature selection Gene selection Machine learning optimization algorithm Voting-based approach

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