Artificial Bee Colony Technique Research Articles

Aspect-based sentiment analysis: A dual-task learning architecture using imbalanced maximized-area under the curve proximate support vector machine and reinforcement learning

Aspect-based sentiment analysis (ABSA) deals with understanding sentiments about specific attributes. Aspect term extraction (ATE) plays an essential role in aspect sentiment classification (ASC) by specifying the main aspects for sentiment accuracy. This paper introduces a dual-task framework for ATE and ASC models in which, the ATE model uses the imbalanced maximized-area under the curve proximate support vector machine (ImAUC-PSVM) method to mitigate the imbalance problem and the output of ATE serves as the reward in the training process of ASC supported by RL (reinforcement learning). The ImAUC-PSVM uses the conventional PSVM (proximate support vector machine) strength for sentiment tasks and the AUC (area under the curve) optimization for imbalance addressing. The ASC model employs multiple kernels for word embedding, with a transductive long short-term memory (TLSTM) for sentence representation that considers samples near the test point for better model refinement. The policy of the RL-based training strategy of the ASC model is represented by a multilayer perceptron (MLP) that categorizes sentences into positive, negative and neutral sentiments. In this RL context, aspect terms serve as rewards in the training process. The scope loss function (SLF) is also integrated within RL to rectify dataset imbalances. To optimize ATE and ASC models, an artificial bee colony (ABC) technique is incorporated for hyperparameter optimization. Experiments and evaluations conducted on English (Restaurant and Laptop) and Hindi datasets show the superiority of the proposed model over existing models of the literature. Moreover, transfer learning (TL) is applied to a Twitter dataset to assess its effect on ATE. The F-measures of the ATE model are obtained as 90.12 and 84.10 and the accuracies of the ASC model are calculated as 84.10 and 82.02 on Restaurant and Laptop datasets, respectively.

Optimized Forest Framework with A Binary Multineighborhood Artificial Bee Colony for Enhanced Diabetes Mellitus Detection

Diabetes mellitus (DM) is a common chronic condition that mainly affects older adults. It's important to identify it early to prevent complications. Machine learning is essential for early detection of DM. This article introduces a new method for detecting DM using a random forest ensemble within an optimized framework. The optimized forest framework depends on finding the best DM features, which are identified using the binary multineighborhood artificial bee colony (BMNABC) technique. During preprocessing, the BMNABC algorithm efficiently identifies important features and then inputs them into the random forest within the optimized forest framework for accurate classification. Five modern DM datasets were used to validate the suggested model. The comparison of the proposed model with other leading models revealed significant insights. The BMNABC + ODF(RFE) model demonstrated exceptional proficiency in detecting diabetes mellitus (DM) across various datasets. It achieved an accuracy of 96.36% and a sensitivity of 99.95% on the merged dataset (130 US and PIMA images). The Iranian Ministry of Health dataset showed an accuracy of 97.28% and a sensitivity of 97.12%. In the Sylhet Diabetes Hospital dataset, the accuracy and sensitivity were 96.81% and 98.07% respectively. However, on the PIMA dataset, the model displayed a nuanced performance, with an accuracy of 77.21% and a sensitivity of 68.83%. Lastly, on the questionnaire dataset, the BMNABC + ODF(RFE) model achieved an accuracy of 96.43% and a sensitivity of 97.15%. These findings emphasize the model's ability to adapt and perform effectively in different clinical environments, outperforming other models in terms of accuracy and sensitivity in detecting DM.

Proposed algorithm base optimisation plan for feature selection-based intrusion detection in cloud computing

<span>A crucial element in detecting unusual network system behavior is the network intrusion detection system (NIDS), which also helps to stop network attacks from happening. Despite the fact that a great deal of machine learning techniques has been used in intrusion detection, current solutions still struggle to provide accurate classification results. Furthermore, when dealing with imbalanced multi-category traffic data, a single classifier may not be able to produce a superior. Particularly, internet of things (IoT) gadgets is now a commonplace aspect of life. On the other hand, some problems are becoming worse and lack clear remedies. Convergence, communication speed, and security between various IoT devices are among the primary concerns. In order to achieve this goal, an enhanced artificial bee colony technique utilizing binary search equations and neural networks—known as the (BABCN) algorithm for intrusion detection in terms of convergence and communication speed—is presented in this study. The artificial bee is improved by the depth-first search framework and binary search equations upon which the BABCN method is based. The suggested approach has a good ability to detect intrusions in the network and enhances categorization, according to the findings obtained by using the NSL-KDD dataset.</span>

Secure Steganography Model over Cloud Environment using Adaptive ABC and Optimum Pixel Adjustment Algorithm

An effective security model with low computational complexity, minimal quality-compromised, and improved security robustness is essential due to the rapid expansion of multimedia data communication through different cloud services. An image steganography model has been proposed for the security of multimedia data in an unreliable cloud environment. This study's main goal is to provide efficient steganography with barely hidden information. A secure data communication model has been developed over a cloud environment using the Adaptive Artificial Bee Colony Algorithm and the Optimum Pixel Adjustment Algorithm Based Image Steganography Method. An adaptive ABC (artificial bee colony) technique is applied to select the optimal pixel positions in the cover image because the goal of this investigation is to increase PSNR. The Optimal Pixel Adjustment approach is used to minimise embedding errors while maintaining the stego image's appearance identical to the cover image after embedding. The MATLAB platform is used to implement the proposed method. The results show that the proposed AABC-based OPA is more efficient across all measures investigated during the embedding and extraction processes.

Ant with Artificial Bee Colony Techniques in Vehicular Ad-hoc Networks

A VANET faces many problems due to dynamic changing of networks with certain requirements such as low delay, high (PDR) packet delivery ratio, low routing overhead and throughput. However, numerous routing protocols have been suggested to meet the demands of Quality of Service (QoS), but none of them can consistently maintain the highest level of QoS simultaneously. The proposed method Ant with Artificial Bee Colony Techniques provides better performance when compared to the existing techniques. This work is compared with latest developed Techniques in VANET to find the best path and different performance metrics are used to check the performance. This work premeditated the comparative analysis of Quality of services made by the performance of latest emerging techniques in VANET and will provide the best solution for the recognition problem in finding the best path based on the evaluation of performance of Quality of Service. Simulation results imply the benefits of the proposed Ant with Artificial Bee Colony Techniques (AABC) produces better result when compare to the other conventional method and Ant Colony Techniques(ACT)in terms of high packet delivery ratio, less end-to-end delay and less energy consumption level. The performance is evaluated by using Ns2 simulator and results shows that the AABC successfully achieve the optimal routes.

Investigation of the impact of PSO, ABC, BFO and Cuckoo search optimization techniques on UPFC device for sustainable voltage stability margin improvement

This paper investigates the enhancement of voltage stability margin using Unified Power Flow Control (UPFC) device tuned with Particle Swarm Optimization (PSO), Artificial Bee Colony (ABC), Bacterial Foraging Optimization (BFO) and Cuckoo Search techniques (CS) on Nigerian 330 kV 56-bus practical network. In order to have a voltage stable power system, it is good to keep voltages within the acceptable limits. This is achieved using continuation power flow embedded in Power System Analysis Toolbox (PSAT). The optimal location and tuning of the UPFC device is determined using line stability index and the met-heuristics techniques. The effects of UPFC and the proposed optimizing techniques on voltage stability margin are examined. The results so obtained for tuning PSO, ABC, BFO and Cuckoo Search with UPFC device are compared to know the technique that yields the best loading parameter for Nigerian 56- bus power system for voltage stability margin enhancement. It is found that UPFC tuned with Cuckoo Search outperforms the other techniques in terms of the increased loading parameter of the Nigerian power system with margin improvement of 86.6%, 90% and 88.6% for Adiabor (Bus 45), Jalingo (Bus 34) and Jos (Bus 13). The tuned UPFC device has proved beyond reasonable doubt that it can improve voltage stability margin of the entire Nigerian 330 kV Network as envisaged from results before and after optimization.

LQR controller design for stabilization of non-linear DIP system based on ABC algorithm

Inverted pendulum systems, such as double or single, rotational or translational inverted pendulums are non-linear and unstable, which have been the most dominant approaches for control systems. The double inverted pendulum is one kind of a non-linear, unstable system, multivariable, and strong coupling with a wide range of control methods. To model these types of systems, many techniques have been proposed so that motivating researchers to come up with new innovative solutions. The Linear Quadratic Regulator (LQR) controller has been a common controller used in this field. Meanwhile, the Artificial Bee Colony (ABC) technique has become an alternative solution for employing Bee Swarm Intelligence algorithms. The research solutions of the artificial bee colony algorithm in the literature can be beneficial, however, the utilization of discovered sources of food is ineffective. Thus, in this paper, we aim to provide a double inverted pendulum system for stabilization by selecting linear quadratic regulator parameters using a bio-inspired optimization methodology of artificial bee colony and weight matrices Q and R. The results show that when the artificial bee colony algorithm is applied to a linear quadratic regulator controller, it gains the capacity to autonomously tune itself in an online process. To further demonstrate the efficiency and viability of the suggested methodology, simulations have been performed and compared to conventional linear quadratic regulator controllers. The obtained results demonstrate that employing artificial intelligence (AI) together with the proposed controller outperforms the conventional linear quadratic regulator controllers by more than 50 % in transient response and improved time response and stability performance

Using the artificial bee colony technique to optimize machine learning algorithms in estimating the mature weight of camels

This paper aims to predict male and female camels' mature weight (MW) through various morphological traits using hybrid machine learning (ML) algorithms. For this aim, biometrical measurements such as birth weight (BW), length of face (FL), length of the neck (NL), a girth of the heart (HG), body length (BL), withers height (WH), and hind leg length (HLL) were used to estimate the mature weight for eight camel breeds of Pakistan. In this study, multivariate adaptive regression splines (MARS), random forest (RF), and support vector machine (SVM) were applied to develop prediction models. Furthermore, the artificial bee colony (ABC) algorithm is employed to optimize ML models' internal parameters and improve prediction accuracy. The predictive performance of ML and hybrid models was evaluated on a testing dataset using goodness-of-fit measures such as mean absolute deviation (MAD), mean absolute percentage error (MAPE), coefficient of determination (R2), and root mean square error (RMSE). The results of the study revealed the ABC-SVM model was the best predictive model. The experimental results of this study showed that the proposed ABC-SVM method could effectively improve the accuracy for MW prediction of camels, thus having a research and practical value.

Utilizing Artificial Bee Colony Algorithm as Feature Selection Method in Arabic Text Classification

A huge amount of crucial information is contained in documents. The vast increase in the number of E-documents available for user access makes the utilization of automated text classification essential. Classifying or arranging documents into predefined groups is called Text classification. Feature selection (FS) is needed for minimizing the dimensionality of high-dimensional data and extracting only the features that are most pertinent to a particular task. One of the widely used algorithms for feature selection in text classification is the Evolutionary algorithm. In this paper, the filter method chi-square and the Artificial Bee Colony (ABC) algorithm were both used as FS methods. The chi-square method is a useful technique for reducing the number of features and removing those that are superfluous or redundant. The ABC technique considers the chi-square method's chosen features as viable solutions (food sources). The ABC algorithm searches for the most efficient selection of features that increase classification performance. Support Vector Machine and Naïve Bayes classifiers were used as a fitness function for the ABC algorithm. The experiment results demonstrated that the proposed feature selection method was able of decreasing the number of features by approximately 89.5%, and 94%, respectively when NB and SVM were used as fitness functions in comparison to the original dataset, while also enhancing classification performance

Design and Optimization of Ultradeep Submicron CMOS Inverter Using a Unified All Regional MOSFET Model

Complementary Metal Oxide Semiconductor (CMOS) has always remained the dominant integrated circuit technology specifically for designing digital circuits. This paper examines the modelling (utilizing $\alpha$-power based MOSFET model), simulation (utilizing HSPICE simulation) and optimization (utilizing Particle Swarm Optimization (PSO) and Artificial Bee Colony (ABC) techniques) of ultradeep submicron CMOS based digital inverter, performs insightful analysis and extracts formulas for the optimal transistor sizing. Additionally, the study serves as an implementation forum for the thermal analysis of transient characteristics of CMOS inverters at the ultradeep submicron technology node (at 300K and 400K). The results lie within the acceptable range of 1-10\%.

Economic Load Dispatch by Improved Drone Optimization Technique

In this paper, the ELD problem is resolved via ABC (Artificial BEE Colony) technique. The major goal of this study is to use the IDO method to present very efficient & reliable approach for solving ED problem in Power system. The suggested approach is used to solve a variety of non-convex ED issues, including banned operating zones with ramp rate constraints. This problem is described as an optimization of the objective function and minimization of the overall operating cost while gratifying all allied constraints, accompanied by the lowest down & up time limitations, startup cost, and spinning reserve. A six generators scheduling problem is discussed, along with its formulation, representation, and simulation result.

Deep-Learning-Based Cancer Profiles Classification Using Gene Expression Data Profile.

The quantity of data required to give a valid analysis grows exponentially as machine learning dimensionality increases. In a single experiment, microarrays or gene expression profiling assesses and determines gene expression levels and patterns in various cell types or tissues. The advent of DNA microarray technology has enabled simultaneous intensive care of hundreds of gene expressions on a single chip, advancing cancer categorization. The most challenging aspect of categorization is working out many information points from many sources. The proposed approach uses microarray data to train deep learning algorithms on extracted features and then uses the Latent Feature Selection Technique to reduce classification time and increase accuracy. The feature-selection-based techniques will pick the important genes before classifying microarray data for cancer prediction and diagnosis. These methods improve classification accuracy by removing duplicate and superfluous information. The Artificial Bee Colony (ABC) technique of feature selection was proposed in this research using bone marrow PC gene expression data. The ABC algorithm, based on swarm intelligence, has been proposed for gene identification. The ABC has been used here for feature selection that generates a subset of features and every feature produced by the spectators, making this a wrapper-based feature selection system. This method's main goal is to choose the fewest genes that are critical to PC performance while also increasing prediction accuracy. Convolutional Neural Networks were used to classify tumors without labelling them. Lung, kidney, and brain cancer datasets were used in the procedure's training and testing stages. Using the cross-validation technique of k-fold methodology, the Convolutional Neural Network has an accuracy rate of 96.43%. The suggested research includes techniques for preprocessing and modifying gene expression data to enhance future cancer detection accuracy.

Algorithm for optimum design of space trusses

Optimization technique helps to minimize the weight of steel structure by identifying the economical sections in structural design. Meta-heuristic algorithms are used for searching minimization of weight design. The main objective of this investigation is to identify the optimum design of truss structures in such a way the cost of the truss structures can be reduced. In the present method, latest Improved Cuckoo Search (ICS) optimization algorithm is used for identifying the economical sections which are used in truss structures such as 4-, 9-, 24-, and 32– bars in order to minimize the weight and cost. The same truss configuration is analysed by using other algorithms and the results of the ICS are compared with other techniques such as Genetic and Artificial bee colony techniques.

Optimization Techniques for Improving the Performance of Solar Photovoltaic System in Conjunction with Health Informatics and the Hospital Energy Management System

Energy savings, clean energy, savings in utility and energy governance tools are buzzwords in the healthcare industry. Healthcare sectors become largest consumer of energy in the modern world. Based on data of American Society of Heating, Refrigerating, and Air-Conditioning Engineers (ASHRAE), a moderate hospital consume 2.5 times higher energy than commercial buildings. As a result, increased worries about energy costs and environmental issues, as well as the anticipation of rising energy prices in the future and the need to enhance the dependability of healthcare facilities, have led to a focus on in-house power generation systems and the importance of energy management in hospitals and their health care facilities. Solar power systems that are clean and ecologically friendly have grown in popularity as distributed power generation (DPG) systems in recent years. In this work, a Grid-tied Solar PV system incorporated with Battery energy storage technology is considered in conjunction with health informatics and the hospital Energy Management System reduces energy consumption cost and improves the reliability of the power supply to run all clinical equipment available in the hospital’s Intensive Care Unit (ICU) and other premises. In this context, the Energy management controller utilised in the hospital Energy Management System will effectively use the electricity supplied by the Solar PV system while minimising grid demand and stabilising the voltage in the DC bus, which must be inverted into AC using an inverter to feed clinical loads. Furthermore the maximum power point tracking method is adopted, which enhances the quality of DC voltage generated by solar PV panels and feed to the DC bus. Sliding mode controller (SMC) is adopted in the inverter side and the quality of the inverted voltage is optimized using artificial bee colony (ABC) method. The proposed solar PV system in conjunction with health informatics and the hospital Energy Management System is developed and simulated in the MATLAB Simulink. The response of the suggested SMC and ABC techniques are compared and their outcomes are shown to confirm the performance of hospital energy management system.

Artificial Neural Networks Based Optimization Techniques: A Review

In the last few years, intensive research has been done to enhance artificial intelligence (AI) using optimization techniques. In this paper, we present an extensive review of artificial neural networks (ANNs) based optimization algorithm techniques with some of the famous optimization techniques, e.g., genetic algorithm (GA), particle swarm optimization (PSO), artificial bee colony (ABC), and backtracking search algorithm (BSA) and some modern developed techniques, e.g., the lightning search algorithm (LSA) and whale optimization algorithm (WOA), and many more. The entire set of such techniques is classified as algorithms based on a population where the initial population is randomly created. Input parameters are initialized within the specified range, and they can provide optimal solutions. This paper emphasizes enhancing the neural network via optimization algorithms by manipulating its tuned parameters or training parameters to obtain the best structure network pattern to dissolve the problems in the best way. This paper includes some results for improving the ANN performance by PSO, GA, ABC, and BSA optimization techniques, respectively, to search for optimal parameters, e.g., the number of neurons in the hidden layers and learning rate. The obtained neural net is used for solving energy management problems in the virtual power plant system.

A precise neuro-fuzzy model enhanced by artificial bee colony techniques for assessment of rock brittleness index

A precise neuro-fuzzy model enhanced by artificial bee colony techniques for assessment of rock brittleness index

A rapidly converging artificial bee colony algorithm for portfolio optimization

A survey of the relevant literature shows that there have been many studies of the portfolio optimization problem, and that the number of these studies that have been based on heuristic techniques is quite high. We present a heuristic approach to the portfolio optimization problem using the artificial bee colony technique. As a test dataset, we use weekly prices from March 1992 to September 1997 from the following indices: Hang Seng in Hong Kong, DAX 100 in Germany, FTSE 100 in the UK, S&P 100 in the USA and Nikkei in Japan. This test dataset also includes daily prices from May 2013 to April 2016 from the XU030 and XU100 indices in Turkey. In this study, the cardinality-constrained mean–variance portfolio optimization model is treated as a mixed quadratic and integer programming problem, for which heuristic approaches are appropriate. The results of this study are compared with those of genetic algorithms, tabu search, simulated annealing, particle swarm optimization, a differential evaluation algorithm, a greedy randomized adaptive search procedure, an artificial bee colony, ant colony optimization, and a variable neighborhood search algorithm. The purpose of this paper is to present a relatively efficient and effective heuristic method to the portfolio optimization problem. The results show that the proposed artificial bee colony approach achieves these aims.

Artificial Bee Colony Optimized Deep Neural Network Model for Handling Imbalanced Stroke Data

The healthcare domain gets wide attention among the research community due to incremental data growth, advanced diagnostic tools, medical imaging processes, and many more. Enormous healthcare data is generated through diagnostic tool and medical imaging process, but handling of these data is a tough task due to its nature. A large number of machine learning techniques are presented for handling the healthcare data and right diagnosis of disease. However, the accuracy is one of primary concerns regarding the disease diagnosis. Hence, this study explores the applicability of deep neural network (DNN) technique for handling the imbalance of healthcare data. An artificial bee colony technique is adopted to determine the relevant features of stroke disease called ABC-FS-optimized DNN. The performance of proposed ABC-FS-optimized DNN model is evaluated using accuracy, precision, and recall parameters and compared with state of art existing techniques. The simulation results showed that proposed model obtains 87.09%, 84.28%, and 85.72% accuracy, precision, and recall rates, respectively.

A New Chaotic Artificial Bee Colony for the Risk-Constrained Economic Emission Dispatch Problem Incorporating Wind Power

Due to the rapid increase in the consumption of electrical energy and the instability of fossil fuel prices, renewable energy, such as wind power (WP), has become increasingly economically competitive compared to other conventional energy production methods. However, the intermittent nature of wind energy creates certain challenges to the power network operation. The combined economic environmental dispatch (CEED) including WP is one of the most fundamental challenges in power system operation. Within this context, this paper presents a new attempt to solve the probabilistic CEED problem with WP penetration. The optimal WP to be incorporated in the grid is determined in such a way that the system security is within acceptable limits. The system security is described by various fuzzy membership functions in terms of the probability that power balance cannot be met. These membership functions are formulated based on the dispatcher’s attitude. This probabilistic and non-convex CEED problem is solved using a new technique combining chaos theory and artificial bee colony (ABC) technique. In this improved version of ABC (IABC), chaotic maps are used to generate initial solutions, and the random numbers involved in the standard ABC are substituted by chaotic sequences. The effectiveness of IABC is tested on two groups of benchmark functions and practical cases. The impacts of dispatcher’s attitude and risk level are investigated in the simulation section.

Flower pollination algorithm-based I/Q phase imbalance compensation strategy

For wideband receiver systems, it is challenging to compensate the in-phase/quadrature (I/Q) phase mismatch by traditional methods, especially with a time delay deviation (TDD) between the I/Q channels. Considering the above situation, this paper proposes a full-scale I/Q phase imbalance model concerning TDD. The model divides phase mismatch into two parts, i.e., the linear phase (LP) part and the nonlinear phase part, and compensates each part with the corresponding compensation module separately. The design strategy of the compensation module is innovatively transformed into a constrained nonlinear optimization problem, and a metaheuristic algorithm, the flower pollination algorithm (FPA), is utilized to be the optimizer. The results of the contrast simulation with the LP elimination method show the efficiency of the proposed method. In addition, the superiority of the FPA-based structure is verified by comparing with other metaheuristic algorithms, the artificial bee colony technique, the bat algorithm, and the differential evolution algorithm, in terms of the compensation accuracy, algorithm stability, runtime consumption, and convergence performance. Ultimately, the image rejection ratio improvement on the actual platform after compensation is measured, which validates the proposed compensation structure and the corresponding optimization method practically, and the FPA is still the best choice among the competent optimizers.