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In the rapidly increasing pharmaceutical sector, sales representatives are employed by pharmaceutical manufacturers and distributors to inform and educate physicians. To convince providers to prescribe the medications to their patients, these representatives rely on their product expertise and people's abilities to close deals. Instead of making direct sales, pharmaceutical sales representatives help medical professionals get the medications, treatments, and information they need to give their patients the best care possible. Furthermore, they inform the public about novel and occasionally life-saving treatments and share interesting medical developments. This study presents a hybrid methodology that integrates the benefits of local search and the firefly algorithm (FA) to determine the optimal planning for a sales representative. The objective is to maximize the rewards while adhering to certain constraints. The objective is to maximize the rewards while adhering to certain limits. Utilizing local search, the hybrid algorithm enhances firefly's global search behaviour and produces the best possible sales presentation planning. The experimental findings demonstrate the superior performance of the suggested algorithm compared to the FA and other literature methods in the sense of enhancing the convergence rate and preventing local minima. Furthermore, it can enhance the best-known solution for most benchmark instances. [ABSTRACT FROM AUTHOR]

Data are typically collected from sensors distributed across the network and transmitted for analysis and processing to a central base station (BS). However, a significant challenge in Internet of Things (IoT) sensor networks is the efficient aggregation of data from multiple sensors to increase network longevity and reduce the consumption of energy. During the aggregation of data, sensor nodes often transmit redundant data due to multiple factors, including overlapping distribution. The network should gather redundant packets and convert them into aggregated data. Aggregation is necessary to remove duplicate data and convert it into unified data, a task that requires large amounts of energy. In this research paper, we suggest a technique for aggregating data in IoT sensor networks, using clustering with an optimized firefly algorithm (FA), taking into consideration both energy consumed and distance. In this approach, a particular number of nodes are identified in each round. These nodes have a proximate node with a distance less than the threshold. After that cluster heads (CHs) are elected strategically based on brighter fireflies (nodes with higher fitness). The FA is employed for this purpose, where fireflies represent the sensor nodes, and their attractiveness is determined by their fitness, representing the quality of their solutions. The simulation outcomes, executed in MATLAB 2023b, indicated that the suggested method, the firefly optimization algorithm (FOA), outperformed the FA and LEACH in improving the quality‐of‐service parameters. Furthermore, the ANOVA testing of the simulation result demonstrated the superiority of the proposed approach as well. [ABSTRACT FROM AUTHOR]

Abstract Facial expression recognition (FER) has advanced applications in various disciplines, including computer vision, Internet of Things, and artificial intelligence, supporting diverse domains such as medical escort services, learning analysis, fatigue detection, and human-computer interaction. The accuracy of these systems is of utmost concern and depends on effective feature selection, which directly impacts their ability to accurately detect facial expressions across various poses. This research proposes a new hybrid approach called QIFABC (Hybrid Quantum-Inspired Firefly and Artificial Bee Colony Algorithm), which combines the Quantum-Inspired Firefly Algorithm (QIFA) with the Artificial Bee Colony (ABC) method to enhance feature selection for a multi-pose facial expression recognition system. The proposed algorithm uses the attributes of both the QIFA and ABC algorithms to enhance search space exploration, thereby improving the robustness of features in FER. The firefly agents initially move toward the brightest firefly until identified, then search transition to the ABC algorithm, targeting positions with the highest nectar quality. In order to evaluate the efficacy of the proposed QIFABC algorithm, feature selection is also conducted using QIFA, FA, and ABC algorithms. The evaluated features are utilized for classifying face expressions by utilizing the deep neural network model, ResNet-50. The presented FER system has been tested using multi-pose facial expression benchmark datasets, including RaF (Radboud Faces) and KDEF (Karolinska Directed Emotional Faces). Experimental results show that the proposed QIFABC with ResNet50 method achieves an accuracy of 98.93%, 94.11%, and 91.79% for front, diagonal, and profile poses on the RaF dataset, respectively, and 98.47%, 93.88%, and 91.58% on the KDEF dataset.

Abstract The Water Quality Index (WQI) is widely used as a classification indicator and essential parameter for water resources management projects. WQI combines several physical and chemical parameters into a single metric to measure the status of Water Quality. This study explores the application of five soft computing techniques, including Gene Expression Programming, Gaussian Process, Reduced Error Pruning Tree (REPt), Artificial Neural Network with FireFly (ANN-FFA), and combinations of Reduced Error Pruning Tree with bagging. These models aim to predict the WQI of Khorramabad, Biranshahr, and Alashtar sub-watersheds in Lorestan province, Iran. The dataset consists of 124 observations, with input variables being sulfate (SO4), total dissolved solids (TDS), the potential of Hydrogen (pH), chloride (Cl), electrical conductivity (EC), Potassium (K), bicarbonate (HCO), magnesium (Mg), sodium (Na), and calcium (Ca), and WQI as the output variable. For model creation (train subset) and model validation (test subset), the data were split into two subsets (train and test) in a ratio of 70:30. The performance evaluation parameters values of training and testing stages of various models indicate that the ANN-FFA based data-driven model performs better than the other modeling techniques applied with the values of coefficient of correlation 0.9990 & 0.9989; coefficient of determination 0.9612 & 0.9980; root mean square error 0.3036 & 0.3340; Nash–Sutcliffe error 0.9980 & 0.9979; and Mean average percentage error 0.7259% & 0.7969% for the train and test subsets, respectively. Taylor diagram results also suggest that ANN-FFA is the best-performing model, followed by the GEP model. This study introduces a novel model for predicting WQI using advanced soft computing models that have not been previously applied in this study area, highlighting its novelty and relevance. The proposed model significantly enhances predictive accuracy and efficiency, offering real-time, cost-effective WQI predictions that outperform traditional methods in handling complex, nonlinear environmental data.

The unbalanced load distribution in the electrical distribution network caused crucial power losses. This condition occurs in one of the electrical distribution networks, 20 kV Tarahan Substation, Province of Bandar Lampung, Indonesia. This condition can be maintained using optimal reconfiguration with the integration of Distributed Generation (DG) based on Renewable Energy (RE). This study demonstrates the optimal reconfiguration of the 20 kV Tarahan Substation with the integration of the Photovoltaic (PV) and Battery Energy Storage System (BESS). The reconfiguration process is optimized by using the Firefly Algorithm (FA). This process is conducted in the 24-hour simulation with various load profiles. The optimal reconfiguration is investigated in two scenarios based on without and with DG integration. The optimal configuration with more balanced load distribution conducted by FA reduces the power losses by up to 31.39% and 32.38% in without and with DG integration, respectively. Besides that, the DG integration improves the lowest voltage bus in the electrical distribution network from 0.95 p.u to 0.97 p.u.

The design and development of efficient microwave-absorbing and electromagnetic interference (EMI) shielding materials and structures to conceal electromagnetic (EM) waves remains a consistent and challenging task. Despite advancements in materials science and microwave engineering, there is a need for optimized materials that offer both effective microwave absorption and EMI shielding while minimizing material layer thickness. This research aims to address this gap by utilizing the firefly algorithm (FFA) to predict the optimal medium properties and thickness of microwave-absorbing and EMI shielding materials under specific constraints. In this context, a comprehensive investigation was carried out at the X-band involving numerical and experimental EM characterization of novel lightweight fiber-based samples. Additionally, the FFA has been applied to optimize these fiber-based microwave structures within the given constraints. Two separate objective functions (OBF) targeting minimum sample thickness, maximum microwave absorption, and shielding effectiveness (SE) bandwidth have been integrated into the FFA to address the thickness–bandwidth trade-off issue. Subsequently, resistive ink-coated glass fiber (IGF) and ink-coated mesh fiber (IMF) were developed and characterized based on the optimal solutions provided by the FFA. Consequently, an optimized IMF sample provides a minimum reflection coefficient (RC) of −19.0 dB at 10.7 GHz with a bandwidth of 2.8 GHz (9.6 to 12.4 GHz) below the −10 dB threshold. Besides, the optimal IGF sample achieves maximum SE of 11 dB at thickness of only 0.8 mm and covers the entire operating band. Furthermore, the response of the proposed structure was assessed for various oblique angles of incidence, revealing significant potential for various practical applications. A strong correlation between measured and theoretical findings underscores the potential of the proposed approach in realizing efficient microwave stealth and EMI shielding materials. [ABSTRACT FROM AUTHOR]

Engineering and economics both make extensive use of fractional programming. Because they are highly nonconvex and multimodal, they are regarded as challenging. This paper proposes an enhanced firefly algorithm (HFA) for solving fractional programming . The new population mean center is predicted by using the historical data of the population mean centers and added to the movement equation of fireflies to better guide their search. Numerical experiments are provided to demonstrate the efficiency and robustness of HFA. The results obtained by HFA show that it is always better than those produced by other methods. [ABSTRACT FROM AUTHOR]

The current research is based on the explanation of the optimal model for predicting the performance of companies using data mining techniques. The method of this research is of the applied type, in terms of the way of doing the work, it is of the descriptive-causal research type, and in terms of the time dimension, it is of the post-event research type. In the first step, by referring to databases such as thesis, articles and similar researches, the required literature was collected in order to write the theoretical foundations and background of the research. In the following, the information of the investigated companies selected as a statistical sample, whose information is available in the form of data banks on CDs and is under the supervision and review of the responsible institutions, was audited by referring to the financial statements and new implementation software was compiled. The mentioned information included the financial data of the companies admitted to the Tehran Stock Exchange for a period of 10 years from the beginning of 2011 to the end of 2014. Finally, the findings showed that the firefly algorithm, genetic algorithm and evolutionary algorithm were effective in predicting the ratio of QTobins, return on equity and return on equity, and the firefly algorithm had a higher power to predict the ratio of QTobins, return on equity and return. has shares. [ABSTRACT FROM AUTHOR]

This research aims to simultaneously use geochemical modeling and geological parameters for gold grade estimation to identify promising zones of epithermal gold mineralization in the Zailik region, northwest of Iran. For this purpose, the employed geological evidence includes lithology and alterations like silicification, iron oxides, phyllic, and propylitic. For geochemical modeling two methods were utulized: 1) artificial neural network (ANN), 2) integrating ANN with the Firefly algorithm. Geological evidence after quantification, along with the estimated amounts of gold in artificial intelligence methods, was entered into the hierarchical system in Expert Choice software for weighting. In this method, the weighting and determination of the degree of relative importance of geological parameters were attempted after consulting geological and exploration experts. Subsequently, artificial intelligence methods were also compared with each other using quantitative criteria such as the coefficient of determination and the root mean square error function. The results showed that the combined method of artificial neural networks with the Firefly algorithm provides better results due to the higher coefficient of determination (R2=0.643) and lower error function (RMSE=0.754). Therefore, it has a higher degree of importance to identify promising areas for mineralization. Finally, all the above parameters were combined with each other in the Arc GIS software using the fuzzy overlay method, and the optimal exploration targets were detected in the north and northeast of the region, enabling to continue the exploration targets along the root of gold mineralization in the neighboring areas according to the introduced model. [ABSTRACT FROM AUTHOR]

The unbalanced load distribution in the electrical distribution network caused crucial power losses. This condition occurs in one of the electrical distribution networks, 20 kV Tarahan Substation, Province of Bandar Lampung, Indonesia. This condition can be maintained using optimal reconfiguration with the integration of Distributed Generation (DG) based on Renewable Energy (RE). This study demonstrates the optimal reconfiguration of the 20 kV Tarahan Substation with the integration of the Photovoltaic (PV) and Battery Energy Storage System (BESS). The reconfiguration process is optimized by using the Firefly Algorithm (FA). This process is conducted in the 24-hour simulation with various load profiles. The optimal reconfiguration is investigated in two scenarios based on without and with DG integration. The optimal configuration with more balanced load distribution conducted by FA reduces the power losses by up to 31.39% and 32.38% in without and with DG integration, respectively. Besides that, the DG integration improves the lowest voltage bus in the electrical distribution network from 0.95 p.u to 0.97 p.u. [ABSTRACT FROM AUTHOR]

Microarray-based cancer biomarker detection is one of the popular trends for cancer classification. Though existing approaches have given competing performance in terms of classification accuracy and reduced feature subsets, the classification of different cancer microarray datasets still requires improvements. Recently, the swarm-based hybrid algorithms have given significant performance in cancer classification. However, the efficiency of a swarm algorithm is dominated by certain factors such as fitness value, convergence, exploration, and exploitation capabilities. Thus, a swarm-based hybrid approach is proposed for cancer classification with a new variant of the Firefly Algorithm (FA) and Correlation-based Feature Selection (CFS) filter. The slow convergence issue in the FA is resolved by non-fixed size solutions termed as mutable size solutions and a composite position update function is designed for the mutable solutions. In addition, the local optima issue is overcome by the population reinitialisation method. The proposed algorithm, named the CFS-Mutable Composite Firefly Algorithm (CFS-MCFA), is evaluated based on two metrics, namely classification accuracy and genes subset size, using a Support Vector Machine (SVM) classifier. Results show that CFS-MCFA-SVM achieved 100% accuracy with only a few biomarkers for all four cancer microarray datasets, indicating the efficiency and the competing performance of the proposed algorithm in biomarker detection for microarray-based cancer classification. Apart from that, the proposed algorithm would also contribute to cancer-related issues upon verifying the relevancy of particular genes via technical analysis from a medical perspective and would be utilised in feature selection applications.

This paper explores the use of the Firefly Algorithm (FA) and its binary variant (BFA) in optimizing analog circuit component sizing, specifically as a case study for a two-stage operational amplifier (op-amp) designed with a 65nm CMOS process. Recognizing the limitations of traditional optimization approaches in handling complex analog design requirements, this study implements both FA and BFA to enhance convergence speed and accuracy within multi-dimensional search spaces. The Python-Spectre framework in this paper facilitates automatic, iterative simulation and data collection, driving the optimization process. Through extensive benchmarking, the BFA outperformed traditional FA, balancing exploration and exploitation while achieving superior design outcomes across key parameters such as voltage gain, phase margin, and unity-gain bandwidth. Comparative analysis with existing optimization methods, including Particle Swarm Optimization (PSO) and Genetic Algorithm (GA), underscores the efficiency and accuracy of BFA in optimizing circuit metrics, particularly in power-constrained environments. This study demonstrates the potential of swarm intelligence in advancing automatic analog design and establishes a foundation for future enhancements in analog circuit automation.

Abstract Load frequency control (LFC) plays a critical role in ensuring the reliable and stable operation of power plants and maintaining a quality power supply to consumers. In control engineering, an oscillatory behavior exhibited by a system in response to control actions is referred to as “Porpoising”. This article focused on investigating the causes of the porpoising phenomenon in the context of LFC. This paper introduces a novel methodology for enhancing the performance of load frequency controllers in power systems by employing rat swarm optimization (RSO) for tuning and detecting the porpoising feature to ensure stability. The study focuses on a single-area thermal power generating station (TPGS) subjected to a 1% load demand change, employing MATLAB simulations for analysis. The proposed RSO-based PID controller is compared against traditional methods such as the firefly algorithm (FFA) and Ziegler-Nichols (ZN) technique. Results indicate that the RSO-based PID controller exhibits superior performance, achieving zero frequency error, reduced negative peak overshoot, and faster settling time compared to other methods. Furthermore, the paper investigates the porpoising phenomenon in PID controllers, analyzing the location of poles in the s-plane, damping ratio, and control actions. The RSO-based PID controller demonstrates enhanced stability and resistance to porpoising, making it a promising solution for power system control. Future research will focus on real-time implementation and broader applications across different control systems.

Abstract In the current century, heart-related sickness is one of the important causes of death for all humans. An estimated 17.5 million deaths occur due to heart disease worldwide. It is observed that more than 75% of peoples with average income level mostly suffer from heart diseases and its complications. So, there is need for predicting heart infection and its related complications. Data mining is the method of converting raw data into useful information. These tools allow given data to predict future trends. Data mining concepts were mainly adapted in heart disease data sets to interpret the intricate inferences out of it. In the modern world, many research are carried in health care engineering with the use of mining and prediction techniques. This investigation aims to identify significant features in heart disease dataset and to apply ensembling techniques for improving exactness of prediction. Prediction models are developed using different ensembling techniques like stacking and voting. For the experimental purpose, the Z-Alizadeh Sani dataset is used, which is available in the UCI machine learning data repository. Stacking and voting techniques are applied to the dataset. Stacking with substantial characteristics has the maximum accuracy of 86.79% in the Z-Alizadeh dataset. Test outcome proves that the prediction model implemented with the features selected using firefly algorithm and stacking-based classification model has the highest accuracy prediction than other technique. Furthermore, this study delineates a comparative analysis with prior works, showcasing the superior capabilities of the firefly algorithm in optimizing feature selection processes, which is crucial for advancing the accuracy of heart disease predictions.

Abstract The agriculture Internet of Things (IoT) has been widely applied in assisting pear farmers with pest and disease prediction, as well as precise crop management, by providing real-time monitoring and alerting capabilities. To enhance the effectiveness of agriculture IoT monitoring applications, clustering protocols are utilized in the data transmission of agricultural wireless sensor networks (AWSNs). However, the selection of cluster heads is a NP-hard problem, which cannot be solved effectively by conventional algorithms. Based on this, This paper proposes a novel AWSNs clustering model that comprehensively considers multiple factors, including node energy, node degree, average distance and delay. Furthermore, a novel high-performance cluster protocol based on Gaussian mutation and sine cosine firefly algorithm (GSHFA-HCP) is proposed to meet the practical requirements of different scenarios. The innovative Gaussian mutation strategy and sine–cosine hybrid strategy are introduced to optimize the clustering scheme effectively. Additionally, an efficient inter-cluster data transmission mechanism is designed based on distance between nodes, residual energy, and load. The experimental results show that compared with other four popular schemes, the proposed GSHFA-HCP protocol has significant performance improvement in reducing network energy consumption, extending network life and reducing transmission delay. In comparison with other protocols, GSHFA-HCP achieves optimization rates of 63.69%, 17.2%, 19.56%, and 35.78% for network lifespan, throughput, transmission delay, and packet loss rate, respectively.

Abstract: The logistic regression model is considered the most widely used in many applications, and it is one of the main models in the family of generalized linear models. Like other regression models, the model may contain many independent variables, which negatively affects the accuracy of the model and its simplicity in interpreting the results. This study aims to use the modified firefly algorithm and compare it with other methods for selecting variables in an exponential regression model using simulation and real data. The results showed that compared to other previously used methods, the proposed method performs better and helps reduce the mean square error of the model. .

Accurate and reliable short-term runoff prediction plays a pivotal role in water resource management, agriculture, and flood control, enabling decision-makers to implement timely and effective measures to enhance water use efficiency and minimize losses. To further enhance the accuracy of runoff prediction, this study proposes a FA-LSTM model that integrates the Firefly algorithm (FA) with the long short-term memory neural network (LSTM). The research focuses on historical daily runoff data from the Dahuangjiangkou and Wuzhou Hydrology Stations in the Xijiang River Basin. The FA-LSTM model is compared with RNN, LSTM, GRU, SVM, and RF models. The FA-LSTM model was used to carry out the generalization experiment in Qianjiang, Wuxuan, and Guigang hydrology stations. Additionally, the study analyzes the performance of the FA-LSTM model across different forecasting horizons (1–5 days). Four quantitative evaluation metrics—mean absolute error (MAE), root mean square error (RMSE), coefficient of determination (R2), and Kling–Gupta efficiency coefficient (KGE)—are utilized in the evaluation process. The results indicate that: (1) Compared to RNN, LSTM, GRU, SVM, and RF models, the FA-LSTM model exhibits the best prediction performance, with daily runoff prediction determination coefficients (R2) reaching as high as 0.966 and 0.971 at the Dahuangjiangkou and Wuzhou Stations, respectively, and the KGE is as high as 0.965 and 0.960, respectively. (2) FA-LSTM model was used to conduct generalization tests at Qianjiang, Wuxuan and Guigang hydrology stations, and its R2 and KGE are 0.96 or above, indicating that the model has good adaptability in different hydrology stations and strong robustness. (3) As the prediction period extends, the R2 and KGE of the FA-LSTM model show a decreasing trend, but the whole model still showed feasible forecasting ability. The FA-LSTM model introduced in this study presents an effective new approach for daily runoff prediction. [ABSTRACT FROM AUTHOR]

This study introduces an integrated power quality (PQ) conditioner, referred to as UPQC, that is linked with photovoltaic (PV) and battery energy systems (BSS) in order to address and solve PQ issues. It is proposed to employ the Levenberg–Marquardt (LM) backpropagation (LMBP) trained artificial neural network control (ANNC) technique for generating reference signal for converters in UPQC. This approach eliminates the need for traditional abc to dq0 to αβ conversions. Additionally, the hybrid algorithm (FFHSA) in combination of harmony search algorithm (HSA), and firefly algorithm (FFA) is also implemented for the optimal selection of adaptive neuro-fuzzy interface system (ANFIS) parameters to maintain direct current link capacitor voltage (DLCV) constant. The prime goal of the developed hybrid ANNC-FFHSA is to stabilize the DLCV with low settling time during load and solar irradiation (G), Temperature (T) changes, minimization of distortions in the source current signal to diminish total harmonic distortion (THD) in turn boosting the power factor (PF), suppression of fluctuations like disturbances, swell, sag and unbalances in the supply voltage. The suggested method is validated by four test cases with several combinations of variable irradiation (G), temperature and loads. On the other hand, to reveal the superiority of the developed method, the comparison is carried out with the genetic algorithm (GA) and Ant colony algorithm (ACA) along with instantaneous power (p–q) and Synchronous reference frame (SRF) conventional methods. The proposed approach significantly diminishes the total harmonic distortion to values of 3.61%, 3.48%, 3.48%, and 4.51%, which are notably lower compared to the values reported in the existing literature and also improves the power factor to almost unity. The design and implementation of this method were carried out using MATLAB/Simulink software. [ABSTRACT FROM AUTHOR]

Cable-driven parallel robots (CDPRs) have been gaining much attention due to their many advantages over traditional parallel robots or serial robots, such as their markedly large workspace and lightweight design. However, one of the main issues that needs to be urgently solved is the tension in the distribution of CDPRs due to two reasons. The first is that a cable can only be stretched but not compressed, and the other is the redundancy of the parallel robot. To address the problem, an optimization method for tension distribution is proposed in the paper. The structural design of the parallel robot is first discussed. The dynamics model of the parallel robot is established by the Newton–Euler method. Based on the minimum variance of cables' tension, an optimization method of tension distribution is presented for the parallel robot. Furthermore, the tension extreme average term is introduced in the optimization method, and the firefly algorithm is applied to obtain the optimal solution for tension distribution. Finally, the proposed approach is tested in the simulation case where the end-effector of the robot moves in a circular motion. Simulation results demonstrate that the uniformity and continuity of tension are both outstanding for the proposed method. In contrast with traditional solving methods, the efficiency of this method is largely improved. [ABSTRACT FROM AUTHOR]

Copyright of Journal of Frontiers of Computer Science & Technology is the property of Beijing Journal of Computer Engineering & Applications Journal Co Ltd. and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

The agriculture Internet of Things (IoT) has been widely applied in assisting pear farmers with pest and disease prediction, as well as precise crop management, by providing real-time monitoring and alerting capabilities. To enhance the effectiveness of agriculture IoT monitoring applications, clustering protocols are utilized in the data transmission of agricultural wireless sensor networks (AWSNs). However, the selection of cluster heads is a NP-hard problem, which cannot be solved effectively by conventional algorithms. Based on this, This paper proposes a novel AWSNs clustering model that comprehensively considers multiple factors, including node energy, node degree, average distance and delay. Furthermore, a novel high-performance cluster protocol based on Gaussian mutation and sine cosine firefly algorithm (GSHFA-HCP) is proposed to meet the practical requirements of different scenarios. The innovative Gaussian mutation strategy and sine–cosine hybrid strategy are introduced to optimize the clustering scheme effectively. Additionally, an efficient inter-cluster data transmission mechanism is designed based on distance between nodes, residual energy, and load. The experimental results show that compared with other four popular schemes, the proposed GSHFA-HCP protocol has significant performance improvement in reducing network energy consumption, extending network life and reducing transmission delay. In comparison with other protocols, GSHFA-HCP achieves optimization rates of 63.69%, 17.2%, 19.56%, and 35.78% for network lifespan, throughput, transmission delay, and packet loss rate, respectively. [ABSTRACT FROM AUTHOR]

In this paper, in order to detect 25 classes of malware, with the aim of increasing the detection accuracy, we used the pre-trained convolutional neural network of Alex Net and combined it with the perceptron neural network optimized with the Worm Shabbat algorithm. In fact, Alex Net's convolutional neural network automatically extracted 1000 feature vectors for each input image using the convolutional layer in its architecture. In the next step, we used the transfer learning method to classify the extracted features. In this thesis, we transferred the learning done by the Alex net convolutional neural network to a multi-layer perceptron neural network that was optimized using the firefly meta-heuristic algorithm for classification. In this work, we optimized the optimal weight and bias of the neural network by meta-heuristic algorithm. Finally, we were able to achieve 99.8% accuracy, which showed that the proposed method was superior in terms of accuracy compared to the compared methods. [ABSTRACT FROM AUTHOR]

Feature selection techniques play a vital role in the processes that deal with enormous amounts of data. These techniques have become extremely crucial and necessary for data mining and machine learning problems. Researchers have always been in a race to develop and provide libraries and frameworks to standardise this procedure. In this work, we propose a hybrid meta‐heuristic algorithm to facilitate the problem of feature selection for classification problems in machine learning. It is a python based, lucid and efficient algorithm geared towards optimising and striking a balance between the number of features selected and accuracy. The proposed work is a binary hybrid of existing meta‐heuristic algorithms, the particle swarm optimisation (PSO) algorithm, and the firefly algorithm (FA) such that it blends the best of each algorithm to provide an optimised and efficient way of solving the said problem. The suggested approach is assessed against six datasets from different domains that are publicly available at the UCI repository to demonstrate its validity. The datasets are Breast cancer, Iris, WBC, Mushroom, Glass ID, and Abalone. This approach has also been evaluated against similar, such evolutionary‐based approaches to prove its superiority. Various metrics such as accuracy, precision, recall, f1 score, number of selected features, and run time have been analysed, measured, and compared. The hybrid firefly particle swarm optimisation algorithm is found to be suitable for feature selection problems. [ABSTRACT FROM AUTHOR]

In the field of neural language processing, current research projects that apply meta-heuristic approaches to Arabic text are extremely limited given the complexity of this language in terms of structure, grammatical rules, morphology, syntactic analysis and derivation rules. In this paper, we propose a new approach for processing Arabic documents using the chaotic grey wolf optimization technique, which is employed as a supervised learning method by simulating the behavior of grey wolves in searching, circling and pursuing their prey. The proposed method is divided into four phases. The term weighting phase uses TF- IDFC-RF, which is based on TF-IDF and the relevance frequency of terms in documents and classes. The feature selection phase combines the chaotic method of the grey wolf optimization algorithm to both reduce and identify relevant features for model building. In the third phase, we use three classifiers: firefly algorithm, SVM and NB to classify documents into several classes. In the experimental process, we used two collections of reference documents to present a comparative study of the different term weighting systems. The experimental results show that the new architecture, based on the chaotic optimization of the grey wolf algorithm with the Firefly algorithm, obtained the best results in terms of accuracy. [ABSTRACT FROM AUTHOR]

The online collaborative learning platform for physical education is an interactive and open physical education teaching mode. To improve students' learning interest and efficiency, the online sports collaborative learning platform is designed. From the perspective of person-post matching, the role in the group is designed and the improved clustering algorithm is used to realize the grouping. The combination of the k-mean algorithm and the firefly algorithm is used to enhance the real-time and accuracy of learning resource recommendation. The outcomes demonstrated that the Firefly algorithm had obvious advantages in convergence speed and other aspects. Relative to the classical K-means algorithm and the Firefly algorithm, the average clustering accuracy of the presented algorithm was improved by 7.23 % as well as 2.18 %, and the average processing time was improved by 4.35 % and 2.26 %, respectively. In the dataset Iris, the average clustering accuracy and processing time were 91.29 and 8.65, respectively. The optimal, worst, and average values of the online collaborative learning platform on the ground of the firefly-optimized K-means algorithm were 0.3006, 3.2176, and 1.5234, respectively. The fusion algorithm proposed in this study can optimize the recommendation of teaching resources on sports online collaborative learning platforms, improve learners' learning passion, learning efficiency, and satisfaction, and relieve teachers' teaching pressure. [ABSTRACT FROM AUTHOR]

Security is the primary concern in the transmission of medical images, as it involves sensitive patient information. This study introduces an optimized watermarking approach, constructed using discrete wavelet transform and singular value decomposition. The Low- level frequency bands (LL3) sub-band singular values of the host image were embedded with the singular values of a binary watermark using multiple scaling factors. These MSFs were optimized using a recently proposed firefly Photinus algorithm to balance robustness and imperceptibility. The proposed method was applied to various images, including computed tomography images, where the visual quality of the signed and attacked images was evaluated by peak signal-to-noise ratio (PSNR) and normalized cross-correlation. The performance of the proposed algorithm demonstrates significant improvements in the embedding and extraction processes, showing an enhancement in the balance between robustness and imperceptibility, with a PSNR above 79.28 dB, compared to other related works.Article Highlights: Developing a medical image watermarking approach to secure sensitive patient information. Introducing new directions to enhance the robustness and imperceptibility of the watermark against various kinds of attacks. Achieving high performance in the embedding and extraction processes. [ABSTRACT FROM AUTHOR]

Copyright of Duzce University Journal of Science & Technology is the property of Duzce University Journal of Science & Technology and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Load frequency control (LFC) plays a critical role in ensuring the reliable and stable operation of power plants and maintaining a quality power supply to consumers. In control engineering, an oscillatory behavior exhibited by a system in response to control actions is referred to as "Porpoising". This article focused on investigating the causes of the porpoising phenomenon in the context of LFC. This paper introduces a novel methodology for enhancing the performance of load frequency controllers in power systems by employing rat swarm optimization (RSO) for tuning and detecting the porpoising feature to ensure stability. The study focuses on a single-area thermal power generating station (TPGS) subjected to a 1% load demand change, employing MATLAB simulations for analysis. The proposed RSO-based PID controller is compared against traditional methods such as the firefly algorithm (FFA) and Ziegler-Nichols (ZN) technique. Results indicate that the RSO-based PID controller exhibits superior performance, achieving zero frequency error, reduced negative peak overshoot, and faster settling time compared to other methods. Furthermore, the paper investigates the porpoising phenomenon in PID controllers, analyzing the location of poles in the s-plane, damping ratio, and control actions. The RSO-based PID controller demonstrates enhanced stability and resistance to porpoising, making it a promising solution for power system control. Future research will focus on real-time implementation and broader applications across different control systems. [ABSTRACT FROM AUTHOR]

Purposes Accurate prediction and evaluation of coal heat generation is an important foundation for coal quality analysis and thermal engineering calculation. The current model of neural network prediction of coal heat generation can effectively fit the nonlinear relationship, yet there are problems such as the ease to fall into the local minimum and slow convergence speed. Methods In order to accurately predict the heat generation of coal in the combustion process of industrial boilers, a coal heat generation prediction method by bionic algorithm FA-GA joint optimization BP neural network is proposed. The industrial analysis and elemental analysis data of 774 groups of coal commonly used in coal-fired boilers are preprocessed, and the characteristic variables of coal quality indexes are screened according to the average impact value, and finally the heat generation prediction model of FA-GA-BP is established, and the optimization algorithm optimization ability and model prediction accuracy are examined in terms of the error evaluation indexes and the number of iterations. Findings The prediction accuracy of the model is improved to 0.9561 after feature variable screening; the number of iterations of the joint FA-GA algorithm is significantly reduced compared with those of the single optimization algorithms FA, GA, and PSO, and the global search ability of the FA-GA algorithm is effectively improved; the FA-GA-BP model has a higher accuracy compared with single optimization models FA-BP, GA-BP, PSO-BP, as well as the currently commonly used heat generation models MLR and SVR, and the correlation coefficient can reach 0.9845. Conclusions The FA-GA algorithm optimizes the BP model with good results in predicting the heat generation from different regions and coal types in China for coal-fired boilers, which theoretically meets the industrial error requirements. The improved coal-fired heat generation prediction model can provide a new method for effective monitoring of real-time changes in coal quality in the furnace.

The identification of sweet corn seed vitality is an essential criterion for selecting high-quality varieties. In this research, a combination of hyperspectral imaging technique and diverse deep learning algorithms has been utilized to identify different vitality grades of sweet corn seeds. First, the hyperspectral data of 496 seeds, including four viability-grade seeds, are extracted and preprocessed. Then, support vector machine (SVM) and extreme learning machine (ELM) are used to construct the classification models. Finally, the one-dimensional convolutional neural networks (1DCNN), one-dimensional long short-term memory (1DLSTM), the CNN combined with the LSTM (CNN-LSTM), and the proposed firefly algorithm (FA) optimized CNN-LSTM (FA-CNN-LSTM) are utilized to distinguish spectral images of sweet corn seeds viability grade. The findings from the experimental analysis indicate that the deep learning models exhibit a significant advantage over traditional machine learning approaches in the discrimination of seed vitality levels, boasting a classification accuracy exceeding 94.26% in test datasets and achieving an accuracy improvement of at least 3% compared to the best-performing machine learning model. Moreover, the performance of the FA-CNN-LSTM model proposed in this study demonstrated a slight superiority over the other three models. Besides, the FA-CNN-LSTM achieved a classification accuracy of 97.23%, representing a significant improvement of 2.97% compared to the lowest-performing CNN and a 1.49% enhancement over the CNN-LSTM. In summary, this study reveals the potential of integrating deep learning with hyperspectral imaging as a promising alternative for discriminating sweet corn seed vitality grade, showcasing its value in agricultural research and cultivar breeding. [ABSTRACT FROM AUTHOR]

To address the problem of high false positive/negative rate and low accuracy in protein complex detection, we propose the Dual-population Firefly Algorithm Based on Gender Differences (DFAGD) based on the unique core-attachment structure of protein complexes and the biological properties of fireflies. This method divides the detection of protein complexes into two phases. Firstly, a global search of the male population is used to detect the core proteins, and then a local search of the female population is used to detect the attachment proteins, which improves the accuracy of detection. In the male population strategy, the population diversity is redefined, and when the diversity falls below the threshold, a spring model is introduced to bring the population into the repulsion phase so that it does not fall into a local optimum. The female population selects elite and excellent individuals from the detection results of the male population to perform guided neighborhood searches, which can effectively improve detection accuracy. Finally, the effectiveness of the protein complex detection method is tested by comparing it to eight classical detection methods using four datasets of Saccharomyces cerevisiae proteins. [ABSTRACT FROM AUTHOR]

Vector quantization (VQ) is a block coding method that is famous for its high compression ratio and simple encoder and decoder implementation. Linde–Buzo–Gray (LBG) is a renowned technique for VQ that uses a clustering-based approach for finding the optimum codebook. Numerous algorithms, such as Particle Swarm Optimization (PSO), the Cuckoo search algorithm (CS), bat algorithm, and firefly algorithm (FA), are used for codebook design. These algorithms are primarily focused on improving the image quality in terms of the PSNR and SSIM but use exhaustive searching to find the optimum codebook, which causes the computational time to be very high. In our study, our algorithm enhances LBG by minimizing the computational complexity by reducing the total number of comparisons among the codebook and training vectors using a match function. The input image is taken as a training vector at the encoder side, which is initialized with the random selection of the vectors from the input image. Rescaling using bilinear interpolation through the nearest neighborhood method is performed to reduce the comparison of the codebook with the training vector. The compressed image is first downsized by the encoder, which is then upscaled at the decoder side during decompression. Based on the results, it is demonstrated that the proposed method reduces the computational complexity by 50.2% compared to LBG and above 97% compared to the other LBG-based algorithms. Moreover, a 20% reduction in the memory size is also obtained, with no significant loss in the image quality compared to the LBG algorithm. [ABSTRACT FROM AUTHOR]

In the evolutionary algorithm, an intuitive phenomenon is that the eliminated bad particles are also beneficial to convergence in evolutionary algorithms by preventing the generated particles from being close to those eliminated bad particles. Most existing algorithms do not take full advantage of the historical information of these particles or use surrogate models without guaranteeing approximation accuracy. In this study, we propose a multi-granularity general framework to divide the feasible region into different granularities by utilizing completely random trees and computing the spatial distribution of individuals. Secondly, through the sampling and migration strategy, make full use of the sparsity of the calculated individual space distribution and the locality of the best individual in history to replace the poor individual in the current population to speed up the local convergence speed of the algorithm. The time complexity of the algorithm using this framework is equal to the maximum between the time complexity of the evolutionary algorithm using this framework and O(tMlogM), where M denotes the number of points and historical particles generated in an iteration and t denotes the number of iterations. Therefore, the additional computational cost incurred by this framework is very low. Experiments on 12 classical functions, including high-dimensional functions, show that the proposed framework can improve four respective evolutionary algorithms and achieve significantly better results in terms of convergence performance and optimization accuracy. [ABSTRACT FROM AUTHOR]

Load balancing in distributed computing systems is crucial for optimal resource utilization and performance enhancement. Swarm intelligence algorithms offer promising solutions due to their ability to mimic collective behavior observed in nature. This study proposes a novel approach for load balancing using the Firefly Algorithm, a bioinspired optimization technique based on the flashing behavior of fireflies. The algorithm is applied to dynamically distribute tasks among nodes in a distributed computing environment. The contribution lies in adapting the Firefly Algorithm specifically for load balancing purposes in distributed computing systems. The study explores the effectiveness of this approach in improving system performance and resource utilization. Experimental evaluations demonstrate the efficacy of the proposed approach in achieving load balancing objectives. The Firefly Algorithm effectively redistributes tasks among nodes, reducing processing delays and improving overall system efficiency. Comparative analysis against existing methods showcases the superiority of the proposed approach in various performance metrics. [ABSTRACT FROM AUTHOR]

The creation of frameworks for lowering natural hazards is a sustainable development goal specified by the United Nations. This study aims to predict drought occurrence in Tamil Nadu, India, using 26 years of data, with only 3 drought years. Since the drought-occurrence years are minimal, it is an imbalanced dataset, which gives a suboptimal classification performance. The accuracy metric has a tendency to produce misleadingly high results by focusing on the accuracy of forecasting the majority class while ignoring the minority class; hence, this work considers the metrics' precision and recall. A novel strategy uses attribute (or instance) weighting, which allots weights to attributes (or instances) based on their importance, to improve precision and recall. These weights are found using a bio-inspired optimization algorithm, by designing its fitness function to improve precision and recall of the minority (drought) class. Since increasing precision and recall is a tug-of-war, multi-objective optimization helps to identify optimal attribute (or instance) weight balancing precision and recall while maximizing both. The newly introduced Synergistic Optimization Algorithm (SOA) is utilized for multi-objective optimization in order to ascertain weights for attributes (or instances). In SOA, to solve multi-objective optimization, each objective's population was generated using three distinct algorithms, namely, the Genetic, Firefly, and Particle Swarm Optimization (PSO) algorithms. The experimental results demonstrated that the prediction performance for the minority drought class was superior when utilizing instance (or attribute) weighting compared to the approach not employing attribute/instance weighting. The Gradient Boosting classifier with an attribute-weighted dataset achieved precision and recall values of 0.92 and 0.79, whereas, with instance weighting, the values were 0.9 and 0.76 for the drought class. The attribute weighting shows that in addition to the default drought indices SPI and SPEI, pollution factors and mean sea level rise are valuable indicators in drought prediction. From instance weighting, it is inferred that the instances of the months of March, April, July, and August contribute most to drought prediction. [ABSTRACT FROM AUTHOR]

A workflow is sent from the internet of things (IoT) in the real world to a distributed edge micro data centre (EMDC), which is assigned to various resources for the necessary computation process. Since optimising energy consumption and other computing processes are crucial, a significant issue arises involving energy consumption and task execution time in an EMDC-integrated computing system. This study presented a task scheduling and allocation method with two phases in optimising the energy consumption and execution time of scientific workflow processing. The first phase assigned the workflow as a Directed Acyclic Graph (DAG) to an EMDC with the least latency. A selection algorithm then considered location routing problems (LRP), EMDC queue waiting time to initiate scheduling a DAG task and the EMDC capacity. The second phase demonstrated the allocation and schedule of DAG tasks to the resources of selected EMDCs. Subsequently, a critical path (CP) was proposed using the Multi-Objective Firefly Optimisation with Dynamic Voltage and Frequency Scaling (CP-MOFFO-DVFS) algorithm. This study demonstrated a 20 to 37% improvement in energy savings than the state-of-the-art method, thus substantially reducing execution time. [ABSTRACT FROM AUTHOR]

In reservoir operation rule curves, it is necessary to apply rule curves to guide long-term reservoir management. This study proposes an approach to optimizing reservoir operation rule curves (RORCs) using intelligent optimization techniques from the firefly algorithm (FA) and a unique combination method utilizing the artificial bee colony and cultural algorithm (ABC-CA). The aim is to establish a connection with the simulation model to determine the optimal RORCs for flood control. The proposed model was used to determine the optimal flood control RORC for the Nam-Oon Reservoir (NOR) in northeastern Thailand. A minimum frequency and minimum average of excess water were provided as an objective function for assessing the efficiency of the search process. The evaluation of the effectiveness of flood control RORCs involved expressing water scarcity and excess water situations in terms of frequency, magnitude, and duration using historical inflow data synthesized from 1000 events. The results demonstrated that when using the obtained RORC to simulate the NOR system for reducing flooding in long-term operations, excess water scenarios were smaller than those using the current RORC. The results showed that the excess water scenario using the RORC obtained from the proposed model can reduce the excess water better than the current RORC usage scenario. In decreasing flood situations, the newly acquired RORC from the suggested FA and ABC-CA models performed better than the current RORC. [ABSTRACT FROM AUTHOR]

Assessment of brain tumour using Three-Dimensional Magnetic Resonance Imaging (3D MRI) is computationally multifaceted task. Currently, hospitals employ 2D MRI scans, followed by manual evaluation by experienced doctors, aided by a Computerized Diagnostic Tool (CDT). This research aims to develop an advanced CDT to significantly enhance the accuracy of brain tumor assessment. The CDT presented in this study evaluates Axial-View (AV), Coronal-View (CV), and Sagittal-View (SV) MRI images. It encompasses a comprehensive pipeline, including pre-processing, post-processing, feature extraction, feature selection, and categorization phases. Various tumor segmentation techniques, including active contour, level-set, watershed, and region growing, are thoroughly explored. Additionally, a comparative analysis of classification methods such as SVM, ANFIS, k-NN, Random Forest, and Adaboost is conducted. Experimental validation using the BRATS 2016 dataset and real-time 2D MRI data demonstrates that the proposed CDT consistently achieves an average classification accuracy exceeding 95% in tumor-based categorization. This research represents a significant advancement in brain tumor assessment, leveraging machine learning and advanced MRI techniques to improve diagnostic precision. [ABSTRACT FROM AUTHOR]

The topic of regression analysis is receiving increasing and clear attention in most studies, especially economic and medical ones. The nonparametric regression model in general and the multiple nonparametric regression model in particular is one of the most important and prominent regression models used in recent years, which have witnessed great expansion, especially in the economic and environmental aspects. The Multivariate Nadaraya-Watson estimator is one of the most important estimators used in the multiple nonparametric regression model. In estimating the multiple nonparametric regression model, this estimator, in turn, relies on a matrix of parameters called smoothing parameters, the estimation of which is of great importance in achieving good fit of the estimated curve in the multiple nonparametric regression model. In this research, it was proposed to employ an algorithm inspired by nature, represented by the Fireflies algorithm, in the process of estimating the smoothing parameter matrix (Bandwidth matrix) in the Ndaria-Watson multiple estimator. The Monte Carlo simulation method was also used to generate data following a number of multiple nonparametric regression models. The simulation results showed the superiority of the proposed method compared to other estimation methods, using the mean square error as a standard for comparison. [ABSTRACT FROM AUTHOR]

Today, energy conservation and reduction of consumption are crucial concerns for manufacturing companies. Current research on integrated scheduling of processing and assembly typically focuses only on equipment resources and processing and assembly processes. A new method for energy-saving integrated scheduling in workshops has been proposed, which incorporates the recently introduced time-of-use tiered electricity prices into the scheduling optimization model. This method also introduces an operation strategy of turning equipment on and off during idle periods. A multi-objective mathematical model was developed to minimize energy consumption and assembly delay time in the processing and assembly processes. Due to the complexity of the model, the standard firefly algorithm was improved when used to solve the model. This involved designing a three-layer encoding method and two decoding methods, and providing detailed steps of the algorithm. Using a mixed flow production line as an example, the final scheduling solutions were obtained through model construction and algorithm solving, taking into account the tiered electricity price. The results of the example demonstrate that parallel processing and assembly effectively reduce assembly delay costs, and the implementation of the on/off strategy reduces power consumption during the machining process. [ABSTRACT FROM AUTHOR]

In this work, a firefly algorithm was implemented and used to optimise composite structures in the postbuckling regime. In the first case studied, the goal was to maximise the post-buckling load of a rectangular plate subjected, independently, to shear load and uniaxial compression. The orientations of the layers served as design variables, while a maximal transverse displacement was considered as a constraint. Next, a reinforced flat panel was studied, with the goal of maximising the shear load in the post-buckling regime while constrained by the Tsai-Wu criterion. The design variables were the positions of the stiffeners and the orientations of the layers of the laminate. The degree of improvement in the maximum post-buckling load depended on the specific problem and ranged from 2.5 to 36 % compared to baseline designs. The selection of the structures chosen for the analyses ensured that the firefly algorithm was tested with progressively more challenging optimisation problems. The results suggested that the firefly algorithm could be used in the design of laminated composite structures. [ABSTRACT FROM AUTHOR]