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Backhaul-Aware Intelligent Positioning of UAVs and Association of Terrestrial Base Stations for Fronthaul Connectivity

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IEEE TRANSACTIONS ON NETWORK SCIENCE AND ENGINEERING 2021年第4期8卷 2742-2755页

作者： Shehzad, Muhammad K. Ahmad, Arsalan Hassan, Syed Ali Jung, Haejoon Natl Univ Sci & Technol NUST Sch Elect Engn & Comp Sci SEECS Islamabad 44000 Pakistan Incheon Natl Univ Dept Informat & Telecommun Engn Incheon 22012 South Korea

The mushroom growth of cellular users requires novel advancements in the existing cellular infrastructure. One way to handle such a tremendous increase is to densely deploy terrestrial small-cell base stations (TSBSs) with careful management of smart backhaul/fronthaul networks. Nevertheless, terrestrial backhaul hubs significantly suffer from the dense fading environment and are difficult to install in a typical urban environment. Therefore, this paper considers the idea of replacing terrestrial backhaul network with an aerial network consisting of unmanned aerial vehicles (UAVs) to provide the fronthaul connectivity between the TSBSs and the ground core-network (GCN). To this end, we focus on the joint positioning of UAVs and the association of TSBSs such that the sum-rate of the overall system is maximized. In particular, the association problem of TSBSs with UAVs is formulated under communication-related constraints, i.e., bandwidth, number of connections to a UAV, power limit, interference threshold, UAV heights, and backhaul data rate. To meet this joint objective, we take advantage of the genetic algorithm (GA) due to the offline nature of our optimization problem. The performance of the proposed approach is evaluated using the unsupervised learning-based k-means clustering algorithm. We observe that the proposed approach is highly effective to satisfy the requirements of smart fronthaul networks.

关键词： Base stations Bandwidth Wireless communication Optimization Genetic algorithms Unmanned aerial vehicles Interference Unsupervised learning Backhaul capacity bandwidth allocation drones evolutionary computing fronthaul backhaul network genetic algorithm (GA) optimal deployment small cell base stations unsupervised learning unmanned aerial vehicles (UAVs) 5G

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Optimizing weighted lazy learning and Naive Bayes classification using differential evolution algorithm

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JOURNAL OF AMBIENT INTELLIGENCE AND HUMANIZED computing 2021年第6期13卷 3005-3024页

作者： Bai, Yu Bain, Michael Univ New South Wales Sydney NSW Australia

Naive Bayes is a well-known classification algorithm in machine learning area. Since it is based on combinations of efficiently computable probability estimates, NB is often a good choice for high-dimensional problems such as text classification. However, NB is based on a conditional independence assumption between attributes which is often violated in real-world applications. Accordingly, lots of work has been done to improve the performance of NB, such as structure extension, attribute selection, attribute weighting, instance weighting and instance selection. An alternative strategy to address the limitations of NB is to apply NB only over the neighbors of the instance that needs to be classified, where the independence assumption may be more justified. However, this introduces another practical problem - the high variance resulting from insufficient training data when building the NB model only over the neighbors rather than the original training dataset. In this paper, a new learning algorithm named Weighted Lazy Naive Bayes (WLNB) is presented. WLNB is designed to address the variance issue by augmenting the nearest neighbors of a test instance. Then, a self-adaptive evolutionary process is applied to automatically learn two key parameters in WLNB. In this way, a method named evolutionary Weighted Lazy Naive Bayes (EWLNB) is formed. EWLNB uses Differential Evolution to search optimal values for parameters, which makes the method quite effective. Experimental evaluations on 56 UCI machine learning benchmark datasets demonstrate that EWLNB significantly outperforms NB and several other improved NB algorithms in terms of classification accuracy and class probability estimation.

关键词： Instance weighting Self-adaptive Naive Bayes evolutionary computing

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Optimal subset selection for causal inference using machine learning ensembles and particle swarm optimization

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COMPLEX & INTELLIGENT SYSTEMS 2021年第1期7卷 41-59页

作者： Sharma, Dhruv Willy, Christopher Bischoff, John George Washington Univ 2023 N Cleveland St Arlington VA 22201 USA George Washington Univ 21610 South Essex Dr Lexington Pk MD 20653 USA George Washington Univ 203 Greenhow Ct Se Leesburg VA USA

We suggest and evaluate a method for optimal construction of synthetic treatment and control samples for the purpose of drawing causal inference. The balance optimization subset selection problem, which formulates minimization of aggregate imbalance in covariate distributions to reduce bias in data, is a new area of study in operations research. We investigate a novel metric, cross-validated area under the receiver operating characteristic curve (AUC) as a measure of balance between treatment and control groups. The proposed approach provides direct and automatic balancing of covariate distributions. In addition, the AUC-based approach is able to detect subtler distributional differences than existing measures, such as simple empirical mean/variance and count-based metrics. Thus, optimizing AUCs achieves a greater balance than the existing methods. Using 5 widely used real data sets and 7 synthetic data sets, we show that optimization of samples using existing methods (Chi-square, mean variance differences, Kolmogorov-Smirnov, and Mahalanobis) results in samples containing imbalance that is detectable using machine learning ensembles. We minimize covariate imbalance by minimizing the absolute value of the distance of the maximum cross-validated AUC on M folds from 0.50, using evolutionary optimization. We demonstrate that particle swarm optimization (PSO) outperforms modified cuckoo swarm (MCS) for a gradient-free, non-linear noisy cost function. To compute AUCs, we use supervised binary classification approaches from the machine learning and credit scoring literature. Using superscore ensembles adds to the classifier-based two-sample testing literature. If the mean cross-validated AUC based on machine learning is 0.50, the two groups are indistinguishable and suitable for causal inference.

关键词： Analytics evolutionary computing Swarm optimization Machine learning

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Novel approach to content based image retrieval using evolutionary computing

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Research Journal of Applied Sciences, Engineering and Technology 2014年第6期8卷 691-701页

作者： Imran, Muhammad Hashim, Rathiah Khalid, Noor Elaiza Abd University Tun Hussein Onn Malaysia Parit Raja Batu Pahat Johor 86400 Malaysia Universiti Teknologi MARA Malaysia

Content Based Image Retrieval (CBIR) is an active research area in multimedia domain in this era of information technology. One of the challenges of CBIR is to bridge the gap between low level features and high level semantic. In this study we investigate the Particle Swarm Optimization (PSO), a stochastic algorithm and Genetic Algorithm (GA) for CBIR to overcome this drawback. We proposed a new CBIR system based on the PSO and GA coupled with Support Vector Machine (SVM). GA and PSO both are evolutionary algorithms and in this study are used to increase the number of relevant images. SVM is used to perform final classification. To check the performance of the proposed technique, rich experiments are performed using coral dataset. The proposed technique achieves higher accuracy compared to the previously introduced techniques (FEI, FIRM, simplicity, simple HIST and WH). © Maxwell Scientific Organization, 2014.

关键词： CBIR evolutionary computing Genetic algorithm PSO SVM

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Beef cattle mating recommendation based on bioeconomic models

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LIVESTOCK SCIENCE 2024年 289卷

作者： Gomes, A. P. Camargo, S. S. Yokoo, M. J. -I. Campus St Ana Livramento Inst Fed Sul Rio Grandens 410 Paul Harris Ave BR-97574360 St'Ana Do Livramento RS Brazil Univ Fed Pampa Programa Posgrad Comp Aplicada 1650 Maria Anunciacao Gomes Godoy Ave BR-96413172 Bage RS Brazil Embrapa Pecuaria Sul Empresa Brasileira Pesquisa A BR-153 RdKm 632-9 BR-96401970 Bage RS Brazil

Recognizing the pivotal role of mating in animal breeding, this study strives to establish a robust strategy for recommending optimal matings among bovines. This strategy is built to maximize a single value derived from the economic selection index of full-cycle system in Brangus cattle. The study endeavors to apply computational methodologies to explore economically significant traits comprehensively, thereby leading to amplified financial gains for Brangus cattle breeders. Anchored within this overarching objective, a strategic deployment of a genetic algorithm is employed to formulate mating recommendations that precisely align with the priority traits designated by the genetic evaluation program of the Brazilian Brangus Association (BBA). The data set of the BBA for the simulations in this study encompass a range of selection criteria, including: i) birth weight;ii) mature cow weight;iii) ribeye area;iv) subcutaneous fat thickness;v) subcutaneous fat thickness at the rump;vi) escape speed;vii) nematode egg count per gram of feces;and viii) tick count. The research findings underscore that the recommendations provided by the computational strategy converge to increase the bioeconomic index while controlling the trade-off between this index and progeny inbreeding.

关键词： Artificial Intelligence Combinatorial optimization evolutionary computing Animal breeding

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ECML driven geographical location of utility poles in smart grid: Data analysis and high-definition recognition

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APPLIED SOFT computing 2023年 134卷

作者： Chen, Tao Othman, Muhammad Murtadha Wang, Xianju Zhu, Yong Zhu, Zelei Xiao, Jiakai Univ Teknol MARA Sch Elect Engn Coll Engn Selangor 40450 Malaysia Fuyang Normal Univ Sch Phys & Elect Engn Fuyang 236000 Peoples R China China Elect Power Planning & Engn Inst Beijing 100120 Peoples R China State Grid Anhui Elect Power Hefei 230022 Peoples R China

The construction of smart grid is inseparable from the appropriate deployment of utility poles. China has many poles;if they are fully utilized, can they bring more power to the smart grid? Fortunately, thanks to advances in technologies such as artificial intelligence and the Internet of Things, can we use Google Earth to recognize utility poles and optimize the deployment of power facilities? Fundamentally, Google Earth generates static images. Faced with many static images, we need further image processing for high-definition recognition of utility poles. In addition to the current chaotic distribution of poles, we also need to optimize the deployment of power facilities. However, due to the resolution of Google Earth and the aerial photography angle, many backlight phenomena are not conducive to the recognition of utility poles. Therefore, this paper proposes a backlight image enhancement algorithm based on convolutional neural networks (CNN) and constructs a novel network architecture that integrates decomposition, restoration, and adjustment to recognize poles in high-definition under backlight. Furthermore, to solve the problems of the overflow of CNN parameters and unclear training effect, particle swarm optimization (PSO), the evolutionary computing-based machine learning (ECML) is used to search CNN parameters automatically and seek the optimal solution to achieve the optimization of the overall model. The experiments prove that the CNN-based image enhancement algorithm effectively recognizes the utility poles under different illumination and backlight. At the same time, the experimental results show that the PSO-based optimization method can optimize CNN parameters obviously, and the classification accuracy is increased. (c) 2022 Elsevier B.V. All rights reserved.

关键词： ECML Utility poles Image enhancement Smart grid CNN evolutionary computing PSO

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Plant and Salamander Inspired Network Attack Detection and Data Recovery Model

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SENSORS 2023年第12期23卷 5562-5562页

作者： Sharma, Rupam Kumar Issac, Biju Xin, Qin Gadekallu, Thippa Reddy Nath, Keshab Rajiv Gandhi Univ Dept Comp Sci & Engn Itanagar 791112 India Northumbria Univ Dept Comp & Informat Sci Newcastle Upon Tyne NE1 8ST England Univ Faroe Isl Fac Sci & Technol Vestara Bryggja 15 FO-100 Torshavn Faroe Islands Vellore Inst Technol & Engn Sch Informat Technol & Engn Vellore 632014 India Lebanese Amer Univ Dept Elect & Comp Engn POB 36 Byblos Lebanon Zhongda Grp Jiaxing 314312 Haiyan County Peoples R China Jiaxing Univ Coll Informat Sci & Engn Jiaxing 314001 Peoples R China Lovely Profess Univ Div Res & Dev Phagwara 144401 India Indian Inst Informat Technol Dept Comp Sci & Engn Kottayam 686635 India

The number of users of the Internet has been continuously rising, with an estimated 5.1 billion users in 2023, which comprises around 64.7% of the total world population. This indicates the rise of more connected devices to the network. On average, 30,000 websites are hacked daily, and nearly 64% of companies worldwide experience at least one type of cyberattack. As per IDC's 2022 Ransomware study, two-thirds of global organizations were hit by a ransomware attack that year. This creates the desire for a more robust and evolutionary attack detection and recovery model. One aspect of the study is the bio-inspiration models. This is because of the natural ability of living organisms to withstand various odd circumstances and overcome them with an optimization strategy. In contrast to the limitations of machine learning models with the need for quality datasets and computational availability, bio-inspired models can perform in low computational environments, and their performances are designed to evolve naturally with time. This study concentrates on exploring the evolutionary defence mechanism in plants and understanding how plants react to any known external attacks and how the response mechanism changes to unknown attacks. This study also explores how regenerative models, such as salamander limb regeneration, could build a network recovery system where services could be automatically activated after a network attack, and data could be recovered automatically by the network after a ransomware-like attack. The performance of the proposed model is compared to open-source IDS Snort and data recovery systems such as Burp and Casandra.

关键词： intrusion detection network security bio-inspired algorithm evolutionary computing ransomware

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Review of artificial intelligence for enhancing intrusion detection in the internet of things

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ENGINEERING APPLICATIONS OF ARTIFICIAL INTELLIGENCE 2024年第PartA期127卷

作者： Saied, Mohamed Guirguis, Shawkat Madbouly, Magda Inst Grad Studies & Res Alexandria Egypt

Internet of Things is shaping the quality of living standard. With the rapid growth and expansion of adopting IoTbased approaches, their security represents a growing challenge for both manufacturers and consumers. There is a recent rising trend towards employing artificial intelligence approaches to enhance the security of IoT infrastructure. This survey paper focuses on reviewing recent developments in applying artificial intelligence to intrusion detection in the IoT domain. Selected articles are classified according to the applied AI algorithm. This study provides an in-depth survey highlighting the recent advances in artificial intelligence for improving the security of IoT. It summarizes and organizes the recent related research, then presents a comprehensive discussion on research challenges, open issues, and needed future research.

关键词： Internet of things Intrusion detection system Security Artificial intelligence Machine learning Deep learning evolutionary computing

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Where Computation and Dynamics Meet: Heteroclinic Network-Based Controllers in evolutionary Robotics

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IEEE TRANSACTIONS ON NEURAL NETWORKS AND LEARNING SYSTEMS 2020年第4期31卷 1084-1097页

作者： Egbert, Matthew D. Jeong, Valerie Postlethwaite, Claire M. Univ Auckland Sch Comp Sci Auckland 1142 New Zealand Univ Auckland Te Ao Marama Ctr Fundamental Inquiry Auckland 1142 New Zealand Univ Auckland Dept Math Auckland 1142 New Zealand

In the fields of artificial neural networks and robotics, complicated, often high-dimensional systems can be designed using evolutionary/other algorithms to successfully solve very complex tasks. However, dynamical analysis of the underlying controller can often be near impossible, due to the high dimension and nonlinearities in the system. In this paper, we propose a more restricted form of controller, such that the underlying dynamical systems are forced to contain a dynamical object called a heteroclinic network. Systems containing heteroclinic networks share some properties with finite-state machines (FSMs) but are not discrete: both space and time are still described with continuous variables. Thus, we suggest that the heteroclinic networks can provide a hybrid between continuous and discrete systems. We investigate this innovated architecture in a minimal categorical perception task. The similarity of the controller to an FSM allows us to describe some of the system's behaviors as transition between states. However, other, essential behavior involves subtle ongoing interaction between the controller and the environment that eludes description at this level.

关键词： Cognition Computer architecture Artificial neural networks Task analysis Robot sensing systems Stability analysis Erbium Artificial neural networks evolutionary computing heteroclinic networks nonlinear dynamical systems

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Short-Term Wind Power Prediction Using Hybrid Auto Regressive Integrated Moving Average Model and Dynamic Particle Swarm Optimization

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INTERNATIONAL JOURNAL OF COGNITIVE INFORMATICS AND NATURAL INTELLIGENCE 2021年第2期15卷 124-151页

作者： Singh, Pavan Kumar Singh, Nitin Negi, Richa MNNIT Allahabad Uttar Pradesh India MNNIT Dept Elect Engn Allahabad Prayagraj India

With the upsurge in restructuring of the power markets, wind power has become one of the key factors in power generation in the smart grids and gained momentum in the recent years. The accurate wind power forecasting is highly desirable for reduction of the reserve capability, enhancement in penetration of the wind power, stability and economic operation of the power system. The time series models are extensively used for the wind power forecasting. The model estimation in the ARIMA model is usually accomplished by maximizing the log likelihood function and it requires to be re-estimated for any change in input value. This degrades the performance of the ARIMA model. In the proposed work, the model estimation of the ARIMA model is done using latest evolutionary algorithm (i.e., dynamic particle swarm optimization [DPSO]). The use of DPSO algorithm eliminates the need for re-estimation of the model coefficients for any change in input value and moreover, it improves the performance of ARIMA model. The performance of proposed DPSO-ARIMA model has been compared to the existing models.

关键词： ARIMA Box-Jenkins Method Dynamic Particle Swami Optimization evolutionary computing Linear Time Series Model Smart Grids Swami Intelligence Wind Power Prediction

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