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The service life of downstream dams, river hydraulics, waterworks construction, and reservoir management is significantly affected by the amount of sediment load (SL). This study combined models such as the artificial neural network (ANN) algorithm with the Firefly algorithm (FA) and Artificial Bee Colony (ABC) optimization techniques for the estimation of monthly SL values in the Çoruh River in Northeastern Turkey. The estimation of SL values was achieved using inputs of previous SL and streamflow values provided to the models. Various statistical metrics were used to evaluate the accuracy of the established hybrid and stand-alone models. The hybrid model is a novel approach for estimating sediment load based on various input variables. The results of the analysis determined that the ABC-ANN hybrid approach outperformed others in SL estimation. In this study, two combinations, M1 and M2, with different input variables, were used to assess the model's accuracy, and the best-performing model for monthly SL estimation was identified. Two scenarios, Q(t) and Q(t − 1), were coupled with the ABC-ANN algorithm, resulting in a highly effective hybrid approach with the best accuracy results (R2 = 0.90, RMSE = 1406.730, MAE = 769.545, MAPE = 5.861, MBE = − 251.090, Bias Factor = − 4.457, and KGE = 0.737) compared to other models. Furthermore, the utilization of FA and ABC optimization techniques facilitated the optimization of the ANN model parameters. The significant results demonstrated that the optimization and hybrid techniques provided the most effective outcomes in forecasting SL for both combination scenarios. As a result, the prediction outputs achieved higher accuracy than those of a stand-alone ANN model. The findings of this study can provide essential resources to various managers and policymakers for the management of water resources. [ABSTRACT FROM AUTHOR]

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This research aims to optimize the trajectory of a low-thrust spacecraft carrying biological cargo. The objective is to minimize the radiation exposure from the Van Allen belts, the optimal criterion for the control problem concerning orbital transfers from low orbits to high orbits. Since the minimum radiation stress criterion introduced in this article is unconventional, solving this optimal control problem is complex, necessitating using the honey bee optimization algorithm.The trajectory optimization in this study involves rewriting the motion equations based on the control variables and solving these newly defined equations using the honey bee optimization method. The primary advantage of this approach is its integration of optimal control theory with population-based optimization methods, employing a global approach. In the novel method presented, the optimal control problem is simplified by redefining the differential equation system, and the results demonstrate both accuracy and ease of solution.Based on the results obtained from the comparison between the optimal criterion of minimum time and minimum radiation stresses presented in this article, the minimum radiation stress criterion causes an increase of 8.89% in transfer time. However, this criterion significantly reduces exposure to magnetic radiation, which is crucial in the transfer to high orbits.

Cardiovascular disease (CVD) stands as the most widespread severe illness impacting human health on a global scale. Forecasting CVDs in advance becomes more and more crucial as CVDs increase exponentially every day. Deep Learning (DL) algorithms are selfadaptive to recognize patterns and analyze data more effectively in CVD prediction. Over the past few decades, many researchers and practitioners have examined different predictive algorithms, but most of those studies are based on small-sized datasets like less than 10,000 patient records. The major shortcomings of earlier research lie in its reliance on small-sized datasets, elevating the risk of overfitting. In contrast, our study addresses this limitation by utilizing Kaggle's cardiac dataset encompassing 70,000 patients and 11 features. The primary objective of this study is to minimize the risk of overfitting and accurately predict CVD by showcasing the effectiveness of using comprehensive datasets. This paper proposes a hybrid DL methodology by utilizing a Extra Tree Classifier with Artificial Bee Colony optimized Recurrent Neural Network (ETC-ABC-RNN) for accurate classification of CVDs with 96% accuracy. By measuring accuracy, precision, recall, and F1, the efficiency of the system is demonstrated. The outcomes demonstrated that the suggested methodology surpassed various methods in predicting heart disease. [ABSTRACT FROM AUTHOR]

In addressing the key issues of the data imbalance within ECG signals and modeling optimization, we employed the TimeGAN network and a local attention mechanism based on the artificial bee colony optimization algorithm to enhance the performance and accuracy of ECG modeling. Initially, the TimeGAN network was introduced to rectify data imbalance and create a balanced dataset. Furthermore, the artificial bee colony algorithm autonomously searched hyperparameter configurations by minimizing Wasserstein distance. Control experiments revealed that data augmentation significantly boosted classification accuracy to 99.51%, effectively addressing challenges with unbalanced datasets. Moreover, to overcome bottlenecks in the existing network, the introduction of the Efficient network was adopted to enhance the performance of modeling optimized with attention mechanisms. Experimental results demonstrated that this integrated approach achieved an impressive overall accuracy of 99.70% and an average positive prediction rate of 99.44%, successfully addressing challenges in ECG signal identification, classification, and diagnosis.

Preventing plunge pool scouring in hydraulic structures is crucial in hydraulic engineering. Although many studies have been conducted experimentally to determine relationship between the scour depth and water jets in several fields, available equations have deficiencies in calculating the exact scour due to complexity of scour process. This study investigated local scour depth in plunge pool using Metaheuristic Artificial Bee Colony-Optimized Feed Forward Neural Network (ABCFFNN), variational mode decomposition (VMD) and ensemble empirical mode decomposition (EEMD) techniques. To set modeling, the input parameters are impact angle, densimetric Froude number, impingement length, and nozzle diameter. The models' training and testing were conducted using data available in the literature. The models' performances were compared with experiments. The results demonstrate that scour depth, length, width, and ridge height can be calculated more accurately than available equations. A rank analysis was also applied to obtain the most critical parameter in predicting scour parameters in water jet scouring. ABC-FFNN, VMD-ABCFFNN and EEMD-VMD-FFNN hybrid models were performed to obtain scour parameters. As a result, ABC-FFNN algorithms produced the best solution to predict the scour due to circular water jets, with the values for training (R2: 0.331 to 0.778) and testing (R2: 0.495 to 0.863). HIGHLIGHTS This study analyzed the scour due to water jets using metaheuristic algorithms based on artificial bee colony ABC.; Metaheuristics optimized feed forward neural network (ABC-FFNN) and pre-processing techniques were used to predict the scour characteristics.;

BACKGROUND: Esophageal cancer (EC) is aggressive cancer with a high fatality rate and a rapid rise of the incidence globally. However, early diagnosis of EC remains a challenging task for clinicians. OBJECTIVE: To help address and overcome this challenge, this study aims to develop and test a new computer-aided diagnosis (CAD) network that combines several machine learning models and optimization methods to detect EC and classify cancer stages. METHODS: The study develops a new deep learning network for the classification of the various stages of EC and the premalignant stage, Barrett's Esophagus from endoscopic images. The proposed model uses a multi-convolution neural network (CNN) model combined with Xception, Mobilenetv2, GoogLeNet, and Darknet53 for feature extraction. The extracted features are blended and are then applied on to wrapper based Artificial Bee Colony (ABC) optimization technique to grade the most accurate and relevant attributes. A multi-class support vector machine (SVM) classifies the selected feature set into the various stages. A study dataset involving 523 Barrett's Esophagus images, 217 ESCC images and 288 EAC images is used to train the proposed network and test its classification performance. RESULTS: The proposed network combining Xception, mobilenetv2, GoogLeNet, and Darknet53 outperforms all the existing methods with an overall classification accuracy of 97.76% using a 3-fold cross-validation method. CONCLUSION: This study demonstrates that a new deep learning network that combines a multi-CNN model with ABC and a multi-SVM is more efficient than those with individual pre-trained networks for the EC analysis and stage classification. [ABSTRACT FROM AUTHOR]

As different artificial intelligence (AI) techniques continue to evolve, power systems are undergoing significant technological changes with the primary goal of reducing computational time, decreasing utility and consumer costs and ensuring the reliable operation of an electrical power system. AI techniques compute large amounts of data at a faster speed than numerical optimization methods with higher processing speeds. With these features, AI techniques can further automate and increase the performance of power systems. This paper presents a comprehensive overview of diverse AI techniques that can be applied in power system operation, control and planning, aiming to facilitate their various applications. We explained how AI can be used to resolve system frequency changes, maintain the voltage profile to minimize transmission losses, reduce the fault rate and minimize reactive current in distributed systems to increase the power factor and improve the voltage profile. [ABSTRACT FROM AUTHOR]

Abstract The behavior of a Vehicle Ad hoc Network (VANETs) is extremely unpredictable due to the high mobility and random network topology inherent to the nature of VANETs. Several problems, including frequent connection failures, scalability, multi-hop data transfer, and data loss, impact the performance of Transmission Control Protocols (TCP) in such wireless ad hoc networks. This study proposes using zone-based routing with consideration for mobility in VANETs as a means of avoiding this issue. A hybrid optimization approach is introduced and used to the routing process. Both Ant Colony Optimization (ACO) and Artificial Bee Colony Optimization (ABCO) are components of the hybrid algorithm (ABC). Link stability and Residual energy provide the basis of the fitness function. Several measures, including delivery ratio, time, and overhead, are used to evaluate the effectiveness of the suggested method. A comparison of the suggested method's efficiency with that of other algorithms.

The integration of battery energy storage systems (BESS) with microgrids (MG) is crucial to improve the reliability and flexibility of renewable energy sources (RES) integration. However, the reliability and regulatory policies are critical factors that affect the optimal operation of MGs in the market. This study aims to enhance the reliability of MGs integrated with RES and BESS by evaluating their performance under different regulatory frameworks, namely feed-in tariff (FiT), net metering (NM), and energy storage incentive (ESI). Also, a dynamic FiT (D-FiT) framework is utilized to improve the reliability of the MG. An artificial bee colony optimization algorithm is utilized to optimize the size of BESS for each regulatory policy to minimize the total cost of the MG. Each policy is formulated based on its specific constraints in the problem. Subsequently, the reliability indices of Loss of Load Expectation (LOLE) and Expected Energy not Supplied (EENS) are calculated for each optimized solution. Moreover, we have integrated the dynamic thermal rating (DTR) system into our proposed model, focusing on the safe augmentation of system component ratings. The study finds that the D-FiT and standard FiT frameworks provide the best reliability level, whereas the reliability improvement under the ESI policy is not significant, as most of the MG's demand is supplied by the main grid. Furthermore, the study shows that the improvements in EENS are higher than LOLE, indicating that installing BESS reduces the loss of energy rather than the number of interruption hours. D-FiT framework has a significant positive impact on both reliability indices, unlike the other frameworks that have a greater effect on EENS. Furthermore, we have noticed a substantial improvement in reliability indices when the DTR system is taken into account, as compared to the static thermal rating (STR) system.

Solar power changes according to irradiance and temperature in a day. A Maximum Power Point Tracking (MPPT) algorithm is actually necessary to obtain the maximum power from the photovoltaic (PV) arrangement. In this paper, in order to optimize power and improve the efficiency of PV module with regulated output voltage, soft computing MPPT techniques, flying squirrel search optimization and artificial bee colony methods are implemented on cascaded double voltage lift boost converter. The PV module is subjected to both with and without constraints to analyze the performance of the DC/DC converter, and the comparative outcomes are evaluated for resistive and different types of battery loads at various temperature conditions in MATLAB/Simulink platform. The optimized power is achieved by using artificial bee colony technique with less ripple in the output waveforms at constant 25 °C temperature irrespective of the changes in irradiation with the battery load and this can be used for charging of the battery system. [ABSTRACT FROM AUTHOR]

In order to actively control combustion reaction, this study proposes an adaptive neuro-fuzzy (ANFIS) control scheme of interaction between premixed combustion reaction and acoustic flame perturbation where the flame pressure movement will be considered as model perturbation. Using the Cantera database, it is possible to investigate the mechanisms by which the combustion process interacts with acoustic, vorticity, and entropy waves. A well-stirred reactor (WSR) has been extensively used to model combustion processes in three different reaction zone regimes. We designed the control architecture to achieve an intelligent representation of the system for various operating scenarios, which was motivated by the complexity of the mathematical model that was being used. This goal is accomplished by an artificial bee colony (ABC), which uses simulated data from a mathematical model to optimize a neuro-fuzzy with less computational expense. The optimized neuro-fuzzy identifier is converted to an adaptive neural-based (ANFIS) controller optimized to control the outputs of the system. In keeping with the combustion temperature set point, the results demonstrate a remarkable attenuation of flame perturbation and acceptable combustion reaction quality (NOx emission).

Data mining has become a popular process in recent times. However, with the increase in data, traditional data mining methods are not sufficient to solve many problems. Therefore, advanced techniques are needed to provide better results without consuming more time during execution. Soft computing algorithms are used for mathematical optimization to achieve better results in less time. The primary purpose of this work is to propose a framework for rule mining that shall generalize the currently applied methods in rule mining. In this respect, this paper represents the R-miner using a soft computing algorithm. The Grouped -Artificial Bee Colony Optimization (G-ABC) was used to select the relevant attribute set and further verify the features. Mean-Variance optimization is used to find whether the selected rule is valid for further classification. Furthermore, a neural-based deep learning method is applied to validate the outcome. The investigation outcome indicates that the proposed algorithm provides more optimized results in terms of the number of rules generated, the time required for calculation, and obtaining supplementary information for rule mining.

Streamflow estimation is important in hydrology, especially in drought and flood-prone areas. Accurate estimation of streamflow values is crucial for the sustainable management of water resources, the development of early warning systems for disasters, and for various applications such as irrigation, hydropower production, dam sizing, and siltation management. This study developed the ANN algorithm by optimizing with an artificial bee colony (ABC). Then, the ABC-ANN hybrid model, which was established, was combined with different signal decomposition techniques to evaluate its performance in streamflow estimation in the East Black Sea Region, Türkiye. For this purpose, the lagged streamflow values were divided into subcomponents using the local mean decomposition (LMD) with the empirical envelope and complete ensemble empirical mode decomposition with adaptive noise (CEEMDAN) signal decomposition techniques presented to the ABC-ANN algorithm. Thus, the success of the novel hybrid LMD-ABC-ANN and CEEMDAN-ABC-ANN approaches in streamflow prediction was evaluated. The outputs are reliable strategies and resources for water resource planners and policymakers. [ABSTRACT FROM AUTHOR]

In the phase of digital communication, social media sites have developed into a major centre for individuals to express their ideas, emotions, and views. Amidst this influx of user-generated content, mental health conditions, notably depression, have garnered increasing attention due to their pervasive impact on individuals and societies. Early detection and intervention are crucial in managing and preventing its adverse effects. As a consequence an innovative machine learning (ML) textual data classification framework is designed to detect depression through social media streams, employing a Firefly-Optimized Support Vector Machine (FFO-SVM) and Artificial Bee Colony (ABC) Optimized SVM classifiers. Initially, data collection and preprocessing are performed, followed by feature extraction using Time Frequency-Inverse Document Frequency (TF-IDF). After extraction of features classification is performed using FFO-SVM and ABC-SVM classifiers. To tune the parameters of SVM to work efficiently, FFO and ABC are employed. The proposed framework combines the power of ML with the optimization capabilities of the ABC and FFO Algorithm to enhance classification accuracy. Through extensive experimentation and analysis, the framework's performance is evaluated using relevant metrics. Results indicated that proposed classification techniques outperformed conventional methods, showcasing its effectiveness in handling the complexity of depression detection from social media data. [ABSTRACT FROM AUTHOR]

The behavior of a Vehicle Ad hoc Network (VANETs) is extremely unpredictable due to the high mobility and random network topology inherent to the nature of VANETs. Several problems, including frequent connection failures, scalability, multi-hop data transfer, and data loss, impact the performance of Transmission Control Protocols (TCP) in such wireless ad hoc networks. This study proposes using zone-based routing with consideration for mobility in VANETs as a means of avoiding this issue. A hybrid optimization approach is introduced and used to the routing process. Both Ant Colony Optimization (ACO) and Artificial Bee Colony Optimization (ABCO) are components of the hybrid algorithm (ABC). Link stability and Residual energy provide the basis of the fitness function. Several measures, including delivery ratio, time, and overhead, are used to evaluate the effectiveness of the suggested method. A comparison of the suggested method's efficiency with that of other algorithms. [ABSTRACT FROM AUTHOR]

In this manuscript, to control and operate an open loop unstable active magnetic bearing (AMB) system, first a closed loop active magnetic bearing system is proposed. This proposed close-loop contains an algorithm-driven intelligently optimized PID controller, a power amplifier, a position sensor and the fabricated AMB system. Development of a hardware model of proposed AMB system is carried out in laboratory. Using its constructional parameters and after performing some experimental observations, a linearized open loop transfer function is determined by implementing mathematical linearization technique. For a nominal point of operation, the gain variables of PID controller are calculated on the basis of four different evaluation indexes using, firefly algorithm, grasshopper optimization algorithm and artificial bee colony optimization algorithm. These evaluation indexes are: integral of absolute error, integral of squared error, integral of time multiplied absolute error and integral of time multiplied squared error. Further, a comparison among these optimization techniques is realized on three scale: transient state performance, observed statistical data and time taken in execution of algorithm. The efficacy and utility of the optimization techniques are demonstrated by computational calculations and a thorough study of the acquired data. [ABSTRACT FROM AUTHOR]

Vehicle Ad hoc Networks (VANETs) have high mobility and a randomized connection structure, resulting in extremely dynamic behavior. Several challenges, such as frequent connection failures, sustainability, multi-hop data transfer, and data loss, affect the effectiveness of Transmission Control Protocols (TCP) on such wireless ad hoc networks. To avoid the problem, in this paper, mobility-aware zone-based routing in VANET is proposed. To achieve this concept, in this paper hybrid optimization algorithm is presented. The hybrid algorithm is a combination of Ant colony optimization (ACO) and artificial bee colony optimization (ABC). The proposed hybrid algorithm is designed for the routing process which is transmitting the information from one place to another. The optimal routing process is used to avoid traffic and link failure. The fitness function is designed based on Link stability and Residual energy. The validation of the proposed algorithm takes solution encoding, fitness calculation, and updating functions. To perform simulation experiments, NS2 simulator software is used. The performance of the proposed approach is analyzed based on different metrics namely, delivery ratio, delay time, throughput, and overhead. The effectiveness of the proposed method compared with different algorithms. Compared to other existing VANET algorithms, the hybrid algorithm has proven to be very efficient in terms of packet delivery ratio and delay. [ABSTRACT FROM AUTHOR]

The security of multimedia information is a prime concern in the present digital world. As a remedy, a robust color image watermarking in the transform domain using artificial intelligence is reported in this article. A color host image is secured by embedding a color watermark by utilizing the singular value decomposition and discrete wavelet transform. The color watermark information is scrambled with a chaotic map to give an additional stage of protection to overcome the problem of illegal copying and alteration of digital data by unauthorized users. All three RGB color channels of host singular values are modified with principal components of respective RGB information of scrambled watermark image. Ownership of data is provided by extracting the watermark in the presence of a secret key used while embedding process. To overcome the trade-off between the imperceptibility and robustness of the proposed algorithm artificial bee colony is used to optimize the scaling factor. The implementation of the proposed algorithm is performed on the MATLAB tool to compute the performance against the intentional and non-intentional image processing attacks. Comparative analysis with other related watermarking algorithms proves the robust, secure, and invisible nature of the proposed watermarking scheme. [ABSTRACT FROM AUTHOR]

The spread rate of COVID-19 is expected to be high in the wake of the virus's mutated strain found recently in a few countries. Fast diagnosis of the disease and knowing its severity are the two significant concerns of all physicians. Even though positive or negative diagnosis can be obtained through the RT-PCR test, an automatic model that predicts severity and the diagnosis will help medical practitioners to a great extend for affirming medication. Machine learning is an efficient tool that can process vast volume of data deposited in various formats, including clinical symptoms. In this work, we have developed machine learning models for analysing a clinical data set comprising 65000 records of patients, consisting of 26 features. An optimum set of features was derived from this data set by the proposed variant of artificial bee colony optimization algorithm. By making use of these features, a binary classifier is modelled with support vector machine for the screening of COVID-19 patients. Different models were tested for this purpose and the support vector machine has showcased the highest accuracy of 96%. Successively, severity prediction in COVID positive patients was also performed successfully by the logistic regression model. The model managed to predict three severity status viz mild, moderate, and severe. The confusion matrix and the precision-recall values (0.96 and 0.97) of the binary classifier indicate the classifier's efficiency in predicting positive cases correctly. The receiver operating curve generated for the severity predicting model shows the highest accuracy, 96.0% for class 1 and 85.0% for class 2 patients. Doctors can infer these results to finalize the type of treatment/care/facilities that need to be given to the patients from time to time. [ABSTRACT FROM AUTHOR]

Edge detection techniques are the one of the best popular and significant implementation areas of the image processing. Moreover, image processing is very widely used in so many fields. Therefore, lots of methods are used in the development and the developed studies provide a variety of solutions to problems of computer vision systems. In many studies, metaheuristic algorithms have been used for obtaining better results. In this paper, aerial images are used for edge information extraction by using Artificial Bee Colony (ABC) Optimization Algorithm. Procedures were performed on gray scale aerial images which are taken from RADIUS/DARPA-IU Fort Hood database. Initially bee colony size was specified according to sizes of images. Then a threshold value was set for each image, which related with images’ standard deviation of gray scale values. After the bees were distributed, fitness values and probability values were computed according to gray scale value. While appropriate pixels were specified, the other ones were being abandoned and labeled as banned pixels therefore bees never located on these pixels again. So the edges were found without the need to examine all pixels in the image. Our improved method’s results are compared with other results found in the literature according to detection error and similarity calculations’. All the experimental results show that ABC can be used for obtaining edge information from images.

Swarm Intelligence simulates the collective behavior of decentralized and self-organized swarms. One of the main relevant methods is the Artificial Bee Colony (ABC) algorithm which simulates the foraging behavior of bee swarms in a colony to produce efficient solutions to various problems. The Urban Transit Routing Problem (UTRP) involves finding an efficient set of routes in a transit network to satisfy travel demand subject to operational and budget constraints. It is a complex, NP-Hard problem, in which otherwise correct solutions can be rejected because of impracticability. In this study, a hybrid algorithm consisting of a parallel ABC and Hill Climbing was used to find quality solutions to the UTRP. Thorough comparative results on Mandl's well-known instance and Mumford's large-scale instances demonstrate that the proposed algorithm outperforms existing techniques, achieving high levels of direct trip coverage in small computational times. Remarkably, when applied to the most extensive benchmark comprising over 6 million trips and 60 bus routes, the proposed algorithm demonstrates an impressive 11 % enhancement in direct coverage over the previously best-reported results, allowing the design of real-world bus networks in under 3 hours. • An optimization algorithm based on a Parallel Artificial Bee Colony-Hill Climbing Optimization methodology was developed. • The Artificial Bee Colony-Hill Climbing Optimization algorithm was adjusted to the discrete nature of the problemat hand. • It is the first time that this kind of algorithm is used to efficiently solve large-scale instances of the UTRP. • Algorithm's performance and computational cost were validated with corresponding literature results. [ABSTRACT FROM AUTHOR]

Heart disease is a noteworthy global health concern, profoundly affecting public health and individual well-being. Early prediction and diagnosis are crucial for reducing its negative impact. This work focuses on developing an effectual heart disease detection employing advanced optimization approaches and data processing. The proposed system incorporates a hybrid segmentation algorithm, Hybrid K-means-Fuzzy-C-means (HKMFCM) clustering, which assigns membership degrees to data points, enabling soft clustering and accommodating data uncertainty. Additionally, the Artificial Bee Colony (ABC) Optimization method is applied for feature selection, mimicking the foraging behavior of honeybees to identify the most discriminative attributes from the dataset. This optimization algorithm iteratively explores the feature space to select features that enhance the model's predictive accuracy. Furthermore, a novel classification architecture, termed the Attention-infused BiRecurrenTwin Network, is introduced to accurately predict heart disease based on segmented and extracted patient data profiles. This classifier leverages both Bidirectional Gated Recurrent Unit (BiGRU) and Bidirectional Long Short-Term Memory (BiLSTM) networks, with their bidirectional nature capturing both past and future contexts, thus increasing the classifier's capability to detect subtle temporal patterns in patient data. In addition, the proposed system addresses traditional approaches limitations through its advanced components like HKMFCM (effectively manages data uncertainty by enabling soft clustering), ABC Optimization technique (enabling an iterative, global search for the most discriminative attributes) Attention-infused BiRecurrenTwin Network (surpasses conventional classifiers by capturing both past and future temporal patterns in patient data). The simulation outcomes demonstrate that the developed system attains improved performance, with accuracy, ROC, F-measure, precision, recall, sensitivity, and specificity values of 96.09%, 97%, 95%, 96.6%, 94.3%, 95.14%, and 97.05%, respectively which is higher than the baseline models with an average of 7.75 % increase in accuracy. These results indicate that the developed predictive models show promise in accurately classifying individuals based on their extracted features, thereby facilitating the early recognition of heart disease. [ABSTRACT FROM AUTHOR]

Reliable and dynamic composition of web services is considered as essential for ensuring continuous services to the users since they are responsible for integrating varsity of applications in spite of their independency. The significant development in web services domain in the last two decades enable the option of devising novel service composition and service selection schemes for optimal performance and success rate of dynamic web service composition. Majority of the research works proposed for dynamic composition of web services was confirmed to be formulated using the characteristics of Quality of Service (QoS) or Transactional features of workflow. Improved Cosine Similarity-based Artificial Bee Colony Optimization Scheme for Web Service Composition (ICS-ABCO-WSC) is proposed for integrating the characteristics of QoS and transactional features for determining optimal candidate service solution from the workflow modeled graph generated during reactive service composition. ICS-ABCO-WSC is proved to enhance the rate of exploitation and exploration by incorporating opposition learning in employee bee phase and, combinatorial search strategical equations and enhance rate factor in the employee and onlooker bee phase respectively. The success rate and optimality index derived using experimental investigation of ICS-ABCO scheme is proved to be 26% and 32% excellent to compared baseline graph-modeled web service composition techniques. This improvement is realized in ICS-ABCO-WSC due to its potential of enhancing the precision and acceleration rate of converging solution achieved in Artificial Bee Colony Optimization technique.

Although IoT technology is advanced, wireless systems are prone to faults and attacks. The replaying information about routing in the case of multi-hop routing has led to the problem of identity deception among nodes. The devastating attacks against the routing protocols as well as harsh network conditions make the situation even worse. Although most of the research in the literature aim at making the IoT system more trustworthy and ensuring faultlessness, it is still a challenging task. Motivated by this, the present proposal introduces a trust-aware routing mechanism (TARM), which uses an edge node with mobility feature that can collect data from faultless nodes. The edge node works based on a trust evaluation method, which segregates the faulty and anomalous nodes from normal nodes. In TARM, a modified gray wolf optimization (GWO) is used for forming the clusters out of the deployed sensor nodes. Once the clusters are formed, each cluster's trust values are calculated, and the edge node starts collecting data only from trustworthy nodes via the respective cluster heads. The artificial bee colony optimization algorithm executes the optimal routing path from the trustworthy nodes to the mobile edge node. The simulations show that the proposed method exhibits around a 58% hike in trustworthiness, ensuring the high security offered by the proposed trust evaluation scheme when validated with other similar approaches. It also shows a detection rate of 96.7% in detecting untrustworthy nodes. Additionally, the accuracy of the proposed method reaches 91.96%, which is recorded to be the highest among the similar latest schemes. The performance of the proposed approach has proved that it has overcome many weaknesses of previous similar techniques with low cost and mitigated complexity. [ABSTRACT FROM AUTHOR]

This paper proposes an energy-efficient framework to provide a solution to the joint admission control, mode selection, and energy-efficient resource (channel and power) allocation (JACMSEERA) problem for D2D communication underlaying cellular networks. The JACMSEERA problem is a non-deterministic polynomial (NP) hard problem, whose computational complexity scales exponentially with the increase in the number of users. The allocation of channel and power in JACMSEERA problem depends on the mode selection. Such problems require two-step solution and are called bi-level optimization problems. Bi-level optimization increases the complexity and computation time. We propose a modified version of single-level artificial bee colony (ABC) algorithm to allocate the cellular, and reuse modes to the DUs with channel, and energy-efficient power allocation to solve the JACMSEERA problem. Majority of the existing literature decomposes such resource allocation problems into sub-problems by separating mode selection, and resource allocation. Consequently, such solutions are unable to satisfy the stringent constraints leading to inferior solutions. The success of nature-inspired optimization algorithms to solve resource allocation problems has motivated us to use the swarm intelligence based ABC algorithm to solve the JACMSEERA problem. The JACMSEERA problem's objective is to maximize the number of DUs admitted and energy-efficiency under power, interference, and rate constraints. A simple, scalable, low complexity solution is obtained for the JACMSEERA problem using a single variable, represented by the DUs, for joint admission control, mode selection, and energy-efficient resource allocation. The efficacy of the ABC aided approach is validated by numerical investigations under different simulation scenarios and provides an enhancement in energy efficiency to the extent of 20 % as compared to results reported in literature. [ABSTRACT FROM AUTHOR]

Edge detection techniques are the one of the best popular and significant implementation areas of the image processing. Moreover, image processing is very widely used in so many fields. Therefore, lots of methods are used in the development and the developed studies provide a variety of solutions to problems of computer vision systems. In many studies, metaheuristic algorithms have been used for obtaining better results. In this paper, aerial images are used for edge information extraction by using Artificial Bee Colony (ABC) Optimization Algorithm. Procedures were performed on gray scale aerial images which are taken from RADIUS/DARPA-IU Fort Hood database. Initially bee colony size was specified according to sizes of images. Then a threshold value was set for each image, which related with images' standard deviation of gray scale values. After the bees were distributed, fitness values and probability values were computed according to gray scale value. While appropriate pixels were specified, the other ones were being abandoned and labeled as banned pixels therefore bees never located on these pixels again. So the edges were found without the need to examine all pixels in the image. Our improved method's results are compared with other results found in the literature according to detection error and similarity calculations'. All the experimental results show that ABC can be used for obtaining edge information from images. [ABSTRACT FROM AUTHOR]

In video surveillance schemes, the motion object detection plays a significant role. To subtract the object background, a segmentation technique based on feature extraction is utilized in which the change in the training rate makes an alteration in the background. Thereafter, the extracted features are trained by using the self‐organizing map (SOM) network in which the weight parameters in the network is optimized with the help of artificial bee colony (ABC) optimization algorithm, so, the proposed methodology is named as HSOM‐ABC technique. This methodology is carried out to perform the classification process in this research. Initially, the whole dataset is preprocessed with the help of grayscale conversion method which converts the original image into grayscale color. After this, fuzzy c‐means clustering is applied to perform the segmentation process and this method divides the foreground and background parts efficiently. Then, feature extraction is done with the help of local binary pattern method which extract the relevant features from the segmented image. Finally, HSOM‐ABC method is proposed to accurate classification process. Hence, the moving objects are identified by categorizing the background and foreground images. MatLab platform is chosen for the proposed work simulation and the performance is evaluated by means of different parameters and it is compared with new existing approaches. Experimental outcomes show that the proposed strategy achieves higher precision value than any other existing methods. [ABSTRACT FROM AUTHOR]

Reliable and dynamic composition of web services is considered as essential for ensuring continuous services to the users since they are responsible for integrating varsity of applications in spite of their independency. The significant development in web services domain in the last two decades enable the option of devising novel service composition and service selection schemes for optimal performance and success rate of dynamic web service composition. Majority of the research works proposed for dynamic composition of web services was confirmed to be formulated using the characteristics of Quality of Service (QoS) or Transactional features of workflow. Improved Cosine Similarity-based Artificial Bee Colony Optimization Scheme for Web Service Composition (ICS-ABCO-WSC) is proposed for integrating the characteristics of QoS and transactional features for determining optimal candidate service solution from the workflow modeled graph generated during reactive service composition. ICS-ABCO-WSC is proved to enhance the rate of exploitation and exploration by incorporating opposition learning in employee bee phase and, combinatorial search strategical equations and enhance rate factor in the employee and onlooker bee phase respectively. The success rate and optimality index derived using experimental investigation of ICS-ABCO scheme is proved to be 26% and 32% excellent to compared baseline graph-modeled web service composition techniques. This improvement is realized in ICS-ABCO-WSC due to its potential of enhancing the precision and acceleration rate of converging solution achieved in Artificial Bee Colony Optimization technique. [ABSTRACT FROM AUTHOR]