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This paper proposes a novel hybrid technique for enhancing power quality (PQ) in distributed generation (DG) systems by deploying a unified power quality conditioner (UPQC). Here, the proposed hybrid method is the joint execution of white shark optimizer (WSO) and recalling-enhanced recurrent neural network (RERNN), called the WSO-RERNN technique. The primary objective of this novel approach is to effectively mitigate voltage sag and reduce voltage harmonics under varying load conditions. It is important to investigate the voltage sag, swell and harmonic distortion of the system to obtain an enhanced PQ of the energy supply. Therefore, this paper shows the brief impact of PQ in DG utilizing the proposed unified PQ conditioner controller. The WSO-RERNN control technique enhances the performance of the UPQC controller by providing the optimal control signal. By then, the efficiency of the proposed approach is done in MATLAB, and the performance is compared with those of existing optimization techniques, including Ant Lion Optimizer (ALO), Grey wolf optimization (GWO) and Salp swarm algorithm (SSA) methods. [ABSTRACT FROM AUTHOR]

Due to the complex underground environment of coal mines, the unsafe behaviors of miners are likely to lead safety accidents. Therefore, research on underground abnormal behavior recognition methods based on video images is gradually gaining attention. This paper proposes an underground abnormal behavior recognition method based on an optimized Alphapose-ST-GCN. First, an image set captured in underground monitoring video is defogged and enhanced by the CycleGAN. Second, the Alphapose target detection is optimized using the LTWOA-Tiny-YOLOv3 model. Third, the ST-GCN is used for abnormal behavior recognition. The image quality of the dataset before and after a CycleGAN enhancement is compared, the convergence curves of LTWOA under four test functions are compared, and the mean average accuracy mAP of the LTWOA-Tiny-YOLOv3 model is evaluated. Finally, the performance of the proposed method is compared with other detection algorithms. The results show that CycleGAN significantly improves the quality of the dataset images. The whale optimization algorithm improved by the logistic-tent chaos mapping has a more significant convergence effect than the other optimization algorithms, and the LTWOA-Tiny-YOLOv3 model has a better recognition accuracy of 9.1% in mAP compared with the unoptimized model. The underground abnormal detection model proposed in this paper achieves an 82.3% accuracy on the coal mine underground behavior dataset. [ABSTRACT FROM AUTHOR]

Virtualization technology, as the technical support for cloud computing, frees users from the limitations of hardware resources. Dynamic migration technology enables virtual machines to be moved across hosts, and services running on the virtual machines are basically uninterrupted during the migration process, which greatly facilitates the maintenance work in the data center. However, dynamic migration needs to transmit a large amount of repeated data. In order to improve the migration efficiency, this paper conducts the following research on the original pre-copy algorithm. First, we introduce a high dirty page determination algorithm based on the locality principle to classify memory pages and propose an improved memory migration pre-copy algorithm on this basis. Second, in order to solve the problem that the pre-copy algorithm cannot converge under a high load, an improved VM downtime determination algorithm is proposed. Third, the paper also introduces the LZ4 compression algorithm to further optimize the memory migration process. Finally, this paper implements the above optimization scheme on a KVM platform and tests its performance. It is compared with the native pre-copy algorithm and Three-Phase Optimization algorithm (TPO). The result shows that the optimized scheme significantly reduces the total migration time and downtime. [ABSTRACT FROM AUTHOR]

Electrical Discharge Machining (EDM) is considered as the much traditional, adaptable and unconventional machining process that is proficient in generating complicated structures. Further, it is self-sufficient over the mechanical properties of workpiece materials only if it is conductive. The viability of the EDM process regarding the application types might get considerably improved with the constant enhancement in the personification of numerical control and the metal removal efficacy. This survey intends to make a review of 65 papers related to the optimization and modeling of EDM process machining. The analysis is thus made with regards to the following factors like Al alloy composite in Die Shrinking EDM, Optimization and Al metal matrix composites. Particularly, the hybrid optimization models in various papers are analyzed concerning their logic. Further, the algorithmic and parametric analysis is also made with the contributed papers. Finally, the research gaps and challenges in this EDM processing are discussed, which would pave the way for efficient research works in this area. [ABSTRACT FROM AUTHOR]

In data cleaning, the process of detecting and correcting corrupt, inaccurate or irrelevant records from the record set is a tedious task. Particularly, the process of "outlier detection" occupies a significant role in data cleaning that removes or eliminates the outlier's that exist in data. Traditionally, more efforts have been taken to remove the outliers, and one of the promising ways is customizing clustering models. In this manner, this paper intends to propose a new outlier detection model via enhanced k-means with outlier removal (E-KMOR), which assigns all outliers into a group naturally during the clustering process. For assigning the point to be outliers, a new intra-cluster based distance evaluation is employed. The main contribution of this paper is to select cluster centroid optimally through a newly proposed hybrid optimization algorithm termed particle updated lion algorithm (PU-LA), which hybrids the concepts of LA and particle swarm optimization (PSO), respectively. Thereby, the proposed work is named as E-KMOR-PU-LA. Finally, the efficacy of the proposed E-KMOR-PU-LA model is proved through a comparative analysis over conventional models by concerning runtime and accuracy. [ABSTRACT FROM AUTHOR]

Power system often experiences the problem of low-frequency electromechanical oscillations which leads the system to unstable condition. The problem can be corrected by implementing power system stabilizers (PSSs) in the excitation control system of alternator. This paper provides a novel and efficient approach to design an Improved Grasshopper Optimization Algorithm (IGOA)-based dual-input controller to damp the inter-area-mode power system oscillations. A three-fold optimization criterion has been formulated to calculate the optimum values of the controllers required for power system stability. The damping performance of the proposed controller is compared with conventional PSS and genetic algorithm-based controllers to validate the better performance of the proposed IGOA-based controller under various system loading conditions and disturbances. [ABSTRACT FROM AUTHOR]

To solve the problem of low accuracy in the traditional FunkSVD recommendation algorithm, an improved FunkSVD algorithm (RM-FS) is proposed. RM-FS is an improvement of the traditional FunkSVD algorithm, using RMSProp, a deep learning optimization algorithm. The RM-FS algorithm can not only solve the problem of reduced accuracy of the traditional FunkSVD algorithm because of iterative oscillations but also alleviate the impact of data sparseness on the accuracy of the algorithm, achieving the effect of improving the accuracy of the traditional algorithm. The experimental results show that the RM-FS algorithm proposed in this paper effectively improves the accuracy of the recommendation algorithm, which is better than the traditional FunkSVD recommendation algorithm and other improved FunkSVD algorithms. [ABSTRACT FROM AUTHOR]

Numerous research based on offline Tamil recognition deals only with few Tamil characters since it becomes extremely complicated in distinguishing small variations in large handwritten document. The writer's complexity affects the overall formation of the characters. Such types of complexities are due to discontinuation of structures, unnecessary over loops, variation in shapes as well as irregular curves. This complex issue results in enhanced error value rate. Therefore, to conquer such issues, this paper proposes a novel approach to enhance the offline Tamil handwritten character recognition by utilizing four principal steps: pre-processing, segmentation, feature extraction and classification. For optimal segmentation of Tamil characters, this paper utilizes the Tsallis entropy approach-based atom search (TEAS) optimization algorithm. Then a Newton algorithm based deep convolution extreme learning (DELM) approach is utilized for the extraction and classification of input images. Finally, experiments are carried out for numerous Tamil handwritten recognition-based approaches. The proposed Tamil character recognition utilizes the datasets of isolated Tamil handwritten characters established by HP lab India to evaluate the efficiency of the system. [ABSTRACT FROM AUTHOR]

This paper proposes an innovative image compression scheme by utilizing the Adaptive Discrete Wavelet Transform-based Lifting Scheme (ADWT-LS). The most important feature of the proposed DWT lifting method is splitting the low-pass and high-pass filters into upper and lower triangular matrices. It also converts the filter execution into banded matrix multiplications with an innovative lifting factorization presented with fine-tuned parameters. Further, optimal tuning is the most important contribution that is achieved via a new hybrid algorithm known as Lioness-Integrated Whale Optimization Algorithm (LI-WOA). The proposed algorithm hybridizes the concepts of both the Lion Algorithm (LA) and Whale Optimization Algorithm (WOA). In addition, innovative cosine evaluation is initiated in this work under the CORDIC algorithm. Also, this paper defines a single objective function that relates multi-constraints like the Peak Signal-to-Noise Ratio (PSNR) as well as Compression Ratio (CR). Finally, the performance of the proposed work is compared over other conventional models regarding certain performance measures. [ABSTRACT FROM AUTHOR]

The original minimum exposure path (MEP) problem in WSNs (wireless sensor networks) requires the starting point and ending point of a moving target to be fixed, thus limiting the evaluation of coverage quality relative to the locations of these points. To resolve this issue, a minimum exposure path problem with various boundaries (VB-MEP) is proposed in this paper. Because the corresponding graph model cannot be established, the original classical methods (grid method and Voronoi diagram method) used to solve the minimum exposure path problem are no longer effective for the VB-MEP problem. This paper first transforms the problem into a hybrid optimization problem with constraint conditions and then considers the characteristics of the transformed mathematical optimization model with high dimensionality, unfixed dimensionality and high nonlinearity so that the deterministic optimization methods are no longer applicable. A hybrid artificial colony solution algorithm that incorporates the actual characteristics of the problem is designed, and a convergence analysis and a proof of the designed algorithm are given. Through simulation experiments based on large-scale node distribution scenarios, it is found that the designed hybrid optimization model with constraints and hybrid artificial bee colony solution algorithm can effectively solve the proposed VB-MEP problem. [ABSTRACT FROM AUTHOR]

In this paper, we consider a class of optimal control problems involving continuous control and state inequality constraints where the control is almost smooth. We first employ the control parametrization technique via approximating the control signal by a piecewise linear function. Then, we develop a time scaling transformation procedure for transforming the approximate problem into an equivalent problem that can be solved readily using conventional methods. On this basis, a novel exact penalty function method is constructed by appending penalized constraint violations to the cost function. The gradient formulas and convergent properties ensure that the transformed unconstrained optimal parameter selection problems can be solved by existing optimization algorithms or software packages. Finally, an example is solved showing the effectiveness and applicability of the approach proposed. [ABSTRACT FROM AUTHOR]

In this paper, a multi-agent system (MAS) based on the A-Team concept is proposed to solve the Distributed Resource-Constrained Multi-Project Scheduling Problem (DRCMPSP). In the DRCMPSP, multiple distributed projects are considered. Hence, the local task schedule for each project and a coordination of the shared decisions are considered. The DRCMPSP belongs to the class of the strongly NP-hard optimization problems. Multi-agent system seems the natural way of solving such problems. The A-Team MAS, proposed in this paper, has been built using the JABAT environment where two types of the optimization agents are used: local and global. Local optimization agents are used to find solutions for the local projects, and global optimization agents are responsible for the coordination of the local projects and for finding the global solutions. The approach has been tested experimentally using 140 benchmark problem instances from MPSPLIB library with minimizing the Average Project Delay (APD) as global optimization criterion. [ABSTRACT FROM AUTHOR]

Aquila optimizer (AO) is an efficient meta-heuristic optimization method, which mimics the hunting style of Aquila in nature. However, the AO algorithm may suffer from immature convergence during the exploitation stage. In this paper, two strategies are elegantly employed into conventional AO, such as random opposition-based learning and nonlinear flexible jumping factor, which can efficiently enhance the performance of conventional AO. Experiments on 1 7 benchmark functions and image segmentation demonstrate the effectiveness of the proposed algorithm. Comparison with several state-of-the-art meta-heuristic optimization techniques indicates the efficacy of the developed method. [ABSTRACT FROM AUTHOR]

The smooth operation of the power system is of significant importance for ensuring societal stability. Therefore, regular inspections of the power system are necessary. The most efficient method for power inspection is to utilize Unmanned Aerial Vehicles (UAVs) to assist inspection. However, the UAV inspection still needs human resources to operate and manage the UAVs effectively. This paper proposes a UAV-airport power inspection system, where the airport can operate and manage UAVs instead of humans. The key contribution of the system is the computation offloading technology and the Wireless Power Transfer (WPT) capability. In other words, the airport can help UAVs execute electrical image fault detection and the UAVs can be charged by the airport. We propose a joint optimization strategy for UAV flight speed, local execution frequency for fault detection, and WPT time to maximize the number of images in the electrical dataset captured by the UAV that have undergone power fault detection within the inspection time. Comprehensive experiments are conducted to verify the performance of the UAV-airport power inspection system and our optimization strategy. [ABSTRACT FROM AUTHOR]

Nowadays, imbalanced data classification is a hot topic in data mining and recently, several valuable researches have been conducted to overcome certain difficulties in the field. Moreover, those approaches, which are based on ensemble classifiers, have achieved reasonable results. Despite the success of these works, there are still many unsolved issues such as disregarding the importance of samples in balancing, determination of proper number of classifiers and optimizing weights of base classifiers in voting stage of ensemble methods. This paper intends to find an admissible solution for these challenges. The solution suggested in this paper applies the support vector data descriptor (SVDD) for sampling both minority and majority classes. After determining the optimal number of base classifiers, the selected samples are utilized to adjust base classifiers. Finally, genetic algorithm optimization is used in order to find the optimum weights of each base classifier in the voting stage. The proposed method is compared with some existing algorithms. The results of experiments confirm its effectiveness. [ABSTRACT FROM AUTHOR]

In this paper, the problem we study is how to maximize a monotone non-submodular function with cardinality constraint. Different from the previous streaming algorithms, this paper mainly considers the sliding window model. Based on the concept of diminishing-return ratio γ , we propose a (1 3 γ 2 −) -approximation algorithm with the memory O (k log 2 (k Φ 1 γ) 2 ) , where Φ is the ratio between maximum and minimum values of any singleton element of function f. Then, we improve the approximation ratio to (1 2 γ −) through the sub-windows at the expense of losing some memory. Our results generalize the corresponding results for the submodular case. [ABSTRACT FROM AUTHOR]

Data clustering is the task of separating data samples into a set of clusters. K -means is a popular partitional clustering algorithm. However, it has a lot of weaknesses, including sensitivity to initialization and the ability to become stuck in local optima. Hence, nature-inspired optimization algorithms were applied to the clustering problem to overcome the limitations of the K -means algorithm. However, due to the high-dimensionality of a search space, the nature-inspired optimization algorithm suffers from local optima and poor convergence rates. To address the mentioned issues, this paper presents a simplex method-based bacterial colony optimization (SMBCO) algorithm. The simplex method is a stochastic variant approach that improves population diversity while increasing the algorithm's local searching ability. The potential and effectiveness of the proposed SMBCO clustering algorithm are assessed using a variety of benchmark machine learning datasets and the generated groups were evaluated using different performance measures. When compared to several well-known nature-inspired algorithms, the experimental results reveal that the SMBCO model produces superior clustering efficiency and a faster convergence rate. [ABSTRACT FROM AUTHOR]

In the heterogeneous distributed computing systems, efficient task scheduling is necessary for high performance. System reliability and completion time are generally the two important metrics. In this paper, we proposed an algorithm named Merging and Duplication for Makespan and Reliability (MDMR) which combines task merging and task duplication to achieve single-objective constrained optimization of workflow reliability and completion time. The MDMR algorithm is divided into three phases: task merging, entry task duplication and reliability detection with nonentry task duplication. In the task merging phase, two tasks with a sole parent–child relationship are merged into a single task, then this process is iterated until no tasks satisfy the merging condition, thus simplifying the directed acyclic graph. In the entry task duplication phase, the entry task with the lowest reliability is duplicated on the processors, which are available at that moment to increase the reliability while reducing the workflow completion time. In the reliability detection and nonentry task duplication phase, the current workflow scheduling reliability is first calculated, then the result is directly output if the reliability is higher than the target value, otherwise the nonentry task duplication is executed with the aim of increasing the reliability until the value surpasses the target. According to the experimental results, MDMR dominates the compared algorithms and ensures that the entire workflow achieves the target reliability and shortens the workflow makespan as much as possible. [ABSTRACT FROM AUTHOR]

Due to the high ability and flexibility of meta-heuristic algorithms (MAs), they can widely be used in many applications to solve different problems. Recently, real-world engineering applications of these optimization algorithms have attracted researchers' attention. This paper applies particle swarm optimization (PSO) as an effective population-based MAs to design the baghouse (BH). BH filters are among the most commonly used devices in air pollution control systems in mining and food manufacturers and power plants. Designing the BH depends on several parameters such as its capacity or airflow (Nm3/h), air-to-cloth ratio (a ∕ c), cam velocity, and installation limitations. Generally, industrial designers select the number and length of bags and their arrangement based on the experimental observations to meet the parameters mentioned above. The minimum cost or total weight of equipment is utilized for proposing a competitive price for suppliers. In this paper, a PSO algorithm is used to minimize the total cost by finding the best possible design (the number, length, and arrangement of bags). In addition, a real example of installed BH in a pelletizing plant is given and compared with PSO results to investigate the efficiency of the proposed algorithm. The results suggest that PSO can find a better design with minimum total cost than an installed BH filter, and therefore, PSO is applicable to industrial designers. [ABSTRACT FROM AUTHOR]

Distributed optimization algorithms are widely used for solving science and engineering problems in recent years. However, these algorithms become vulnerable in the face of external attacks, so it is urgent to develop novel resilient distributed optimization algorithms against attacks. In this paper, we design a novel median-based resilient distributed optimization algorithm to defend the Byzantine attack. The existence of transition matrix is proved, and the convergence and optimality of the proposed algorithm are analyzed. Simulation experiments are performed to verify the effectiveness of the proposed algorithm and the results show that it is more effective than the compared one and can get the best possible results against the attack. [ABSTRACT FROM AUTHOR]

It is very important for the application of artificial intelligence to accurately and quickly help the electric vehicles to find matching charging facilities. The site selection for electric vehicle charging station (EVCS) is a new field of artificial intelligence application, using artificial intelligence to analyze the current complex urban electric vehicle driving path, and then determining the location of charging stations. This paper proposes a novel hybrid model to decide the location of EVCS. First of all, this paper carries out the flow-refueling location model (FRLM) based on path requirement to determine the site selection of EVCS. Secondly, robust optimization algorithm is used to resolve the location model considering the uncertainty of charging demand. Then, queuing theory, which takes the charging load as a constraint in the location model, is integrated into the model. Last, but not the least, a case is conducted to verify the validity of the proposed model when dealing with location problem. As a result of the above analysis, it is effective to apply robust optimization algorithm and to determine the location of EVCSs effectively when charging demand generated on the path is uncertain. At the same time, queuing theory can help to determine the optimal number of EVCSs effectively, and reduce the cost of building EVCSs. [ABSTRACT FROM AUTHOR]

This paper presents a systematic literature review on optimizing feature extraction for palm and wrist multimodal biometrics. Identifying informative features across different modalities can be computationally expensive and time-consuming in such complex systems. Optimization techniques can streamline this process, making it more efficient thereby improving accuracy and reliability. The paper frames four research questions on input traits, approaches for feature extraction, classification approaches, and performance metrics of image data. The search query is generated based on the research questions that help retrieve the information on the above parameters. The focus of this paper is to provide the comprehensive and exhaustive gestalt of the appropriate input traits for image data from the information retrieved as well as optimal feature extraction and selection. However, the paper also intends to highlight the various classification approaches taken as well as the performance indicators against those classifiers. Further, the paper aims to analyze the effectiveness of various filtering techniques in eliminating image noise and improving overall system performance using MATLAB 2018. The paper concludes that a combination of palm and wrist biometrics could be a good input-trait combination. This work is novel as it covers multi-faceted processing, addressing various aspects of optimizing feature extraction and selection for palm and wrist multimodal biometrics. [ABSTRACT FROM AUTHOR]

Handover modifies the user equipment using mobility in which base station provides the best one. The repeated handovers may corrupt mobility reliability due to high signaling load and therefore, network capability enhancement is affected. Here, a network management system in a network is difficult one owing to the rising number of complexity issues and base stations. In this paper, Crow Sun Flower Optimization (CSFO)-based handover method is developed for enabling efficient handover in Fifth Generation (5G) network. This handover method mainly consists of four parts, such as User Preference (UP) section, Network Quality of Service (NQ) module, power section, and Decision System (DS) module. The Quality of service (QoS) is controlled by UP section and NQ module, whereas the power module is concentrated on power. Thus, the handover is decided based on three segments and DS module is used to enable the network. The DS module is effectively decided whether to offer handover in 5G network or not. Moreover, the decision is optimally selected based on an optimization technique, named as CSFO algorithm. The developed CSFO technique is newly designed by integrating Crow Search Algorithm (CSA) and Sun Flower Optimization (SFO) technique. Additionally, three performance indicators, including received power, throughput, and user-served ratio, are used to assess how well the created CSFO-based handover model performs. High received power, throughput, and user served ratio of − 9 0. 2 6 dBm, 2 2 1. 4 kbps and 0.071, respectively, are achieved by the developed handover strategy. [ABSTRACT FROM AUTHOR]

In video Super Resolution (SR), the problem of cost expense concerning the attainment of enhanced spatial resolution, computational complexity and difficulties in motion blur makes video SR a complex task. Moreover, maintaining temporal consistency is crucial to achieving an efficient and robust video SR model. This paper plans to develop an intelligent SR model for video frames. Initially, the video frames in RGB format will be transformed into HSV. In general, the improvement in video frames is done in V-channel to achieve High-Resolution (HR) videos. In order to enhance the RGB pixels, the current window size is enhanced to high-dimensional window size. As a novelty, this paper intends to formulate a high-dimensional matrix with enriched pixel intensity in V-channel to produce enhanced HR video frames. Estimating the enriched pixels in the high-dimensional matrix is complex, however in this paper, it is dealt in a significant way by means of a certain process: (i) motion estimation (ii) cubic spline interpolation and deblurring or sharpening. As the main contribution, the cubic spline interpolation process is enhanced via optimization in terms of selecting the optimal resolution factor and different cubic spline parameters. For optimal tuning, this paper introduces a new modified algorithm, which is the modification of the Rider Optimization Algorithm (ROA) named Mean Fitness-ROA (MF-ROA). Once the HR image is attained, it combines the HSV and converts to RGB, which obtains the enhanced output RGB video frame. Finally, the performance of the proposed work is compared over other state-of-the-art models with respect to BRISQUE, SDME and ESSIM measures, and proves its superiority over other models. [ABSTRACT FROM AUTHOR]

In view of the two shortcomings of the AODV routing protocol, they do not consider the bandwidth, delay and cost in the actual network, and the routing table has only one path from the basic node to the target node. This paper attempts to improve the AODV protocol by using particle swarm optimization. Through simulation experiments, this paper compares four improved particle swarm optimization algorithms, inertia weight, linear decline, shrinkage factor and chaos, and finds that ACPSO can find the optimal path faster and transmit data quickly. So, this paper uses chaotic particle swarm optimization (CACPSO) to improve AODV protocol. Finally, based on NS2 simulation platform, the improved AODV protocol is simulated and experimented. Different network environments are set up to test packet delivery rate, network delay and routing discovery frequency. The experimental results show that in the process of data transmission, the improved protocol has higher routing performance than AODV protocol, and can transmit data faster and more stably. [ABSTRACT FROM AUTHOR]

An introduction is presented in which the editor discusses various reports within the issue on topics including identification of the nonlinear continuous dynamic systems, the usage of random projection, and the combination of evolution strategy (ES) with ordinal optimization (OO).

An introduction is presented in which the editor discusses various papers within the issue including sampling-based online bagging, ensemble classifier and joint accuracy and diversity optimization.

Threshold segmentation has been widely used in recent years due to its simplicity and efficiency. The method of segmenting images by the two-dimensional maximum entropy is a species of the useful technique of threshold segmentation. However, the efficiency and stability of this technique are still not ideal and the traditional search algorithm cannot meet the needs of engineering problems. To mitigate the above problem, swarm intelligent optimization algorithms have been employed in this field for searching the optimal threshold vector. An effective technique of lightning attachment procedure optimization (LAPO) algorithm based on a two-dimensional maximum entropy criterion is offered in this paper, and besides, a chaotic strategy is embedded into LAPO to develop a new algorithm named CLAPO. In order to confirm the benefits of the method proposed in this paper, the other seven kinds of competitive algorithms, such as Ant–lion Optimizer (ALO) and Grasshopper Optimization Algorithm (GOA), are compared. Experiments are conducted on four different kinds of images and the simulation results are presented in several indexes (such as computational time, maximum fitness, average fitness, variance of fitness and other indexes) at different threshold levels for each test image. By scrutinizing the results of the experiment, the superiority of the introduced method is demonstrated, which can meet the needs of image segmentation excellently. [ABSTRACT FROM AUTHOR]

As the only nonlinear operation, masked S-box is the core to resist differential power attack (DPA) for advanced encryption standard (AES) cipher chips. In order to suit for the resource-constrained applications, a compact masked S-box based on composite field is proposed in this paper. Firstly, the architecture of masked S-box is designed with composite field masking method. Secondly, four masked S-boxes based on GF ((24)2), which are based on four basis methods with the optimal coefficient and the corresponding optimal root, are implemented and optimized by the delay-aware common subexpression elimination (DACSE) algorithm. Finally, experimental results show that, while maintaining the DPA-resistance performance, our best masked S-box achieves better area performance with the fastest speed compared with the existing works. Therefore, our masked S-box is suitable for resource-constrained applications with fast speed requirements. [ABSTRACT FROM AUTHOR]

"Image Captioning is the process of generating a textual description of an image". It deploys both computer vision and natural language processing for caption generation. However, the majority of the image captioning systems offer unclear depictions regarding the objects like "man", "woman", "group of people", "building", etc. Hence, this paper intends to develop an intelligent-based image captioning model. The adopted model comprises of few steps like word generation, sentence formation, and caption generation. Initially, the input image is subjected to the Deep learning classifier called Convolutional Neural Network (CNN). Since the classifier is already trained in the relevant words that are related to all images, it can easily classify the associated words of the given image. Further, a set of sentences is formed with the generated words using Long-Short Term Memory (LSTM) model. The likelihood of the formed sentences is computed using the Maximum Likelihood (ML) function, and the sentences with higher probability are taken, which is further used for generating the visual representation of the scene in terms of image caption. As a major novelty, this paper aims to enhance the performance of CNN by optimally tuning its weight and activation function. This paper introduces a new enhanced optimization algorithm Rider with Randomized Bypass and Over-taker update (RR-BOU) for this optimal selection. In the proposed RR-BOU is the enhanced version of the Rider Optimization Algorithm (ROA). Finally, the performance of the proposed captioning model is compared over other conventional models with respect to statistical analysis. [ABSTRACT FROM AUTHOR]

In this paper, a strategy has been set for minimizing the corner error in the modification of pulse and non-pulse parameters. Taguchi's philosophy has been used to design the experiment by varying process parameters (i.e. Spark-on Time (STon), Wire Tension (WT), Servo-Voltage (Sv), Discharge Current (DC) and Wire-speed (Sw)), to explore the machining outcomes. The response characteristics have been measured in terms of cutting speed (Cs), Corner Error (CD) and surface Roughness (RA) using Topas plus X wire of ϕ 0.25 mm diameter. The machining performance characteristics were analyzed using main effect plots and analysis of variance (ANOVA). Furthermore, a soft computing-based hybrid optimization technique (Artifical Neural Network (ANN)-based Multi-Objective Grey Wolf Optimizer (MOGWO)) has been utilized to search the multi-optimum parameter setting for superior machining results. The most significant parameter observed is DC for Cs, which is determined to be 34.81%. Moreover, WT found 26.29% and 34.10% for CD and RA, respectively. The confirmation test shows that the maximum absolute percentage errors are observed as 3.89%, 6.3% and 9.7% for Cs, CD and RA, respectively. The proposed hybrid technique can generate superior solutions compared to the existing algorithms. Notably, the outcomes obtained on new instances exhibit potential, purposeful, and efficacy. [ABSTRACT FROM AUTHOR]

This paper discusses how to efficiently recognize flowers based on a convolutional neural network (CNN) using multiple features. Our proposed work consists of three phases including segmentation by Otsu thresholding with particle swarm optimization algorithms, feature extraction of color, shape, texture and recognition with the LeNet-5 neural network. In the feature extraction, an improved H component with the definition of WGB value is applied to extract the color feature, and a new algorithm based on local binary pattern (LBP) is proposed to enhance the accuracy of texture extraction. Besides this, we replace ReLU with Mish as activation function in the network design, and therefore increase the accuracy by 8% accuracy according to our comparison. The Oxford-102 and Oxford-17 datasets are adopted for benchmarking. The experimental results show that the combination of color features and texture features generates the highest recognition accuracy as 92.56% on Oxford-102 and 93% on Oxford-17. [ABSTRACT FROM AUTHOR]

In this paper, we investigated the day-ahead scheduling of network consisting of several distributed generation sources and connected loads. This scheduling is an optimization problem that has two objective functions, namely, operating costs and the cost caused by the emission of pollutants. In this scheduling, while introducing a new structure for micro-grid utilization, the possibility of exchanging power among networks and exchanging power between networks and the upstream distribution network is provided. The uncertainty of distributed wind generation sources and solar units has been investigated using appropriate probabilistic distributions. Incent-driven load-responsiveness management programs have been used as to encourage subscribers to reduce their power consumption during high-load hours. Whale optimization algorithm has been used for scheduling of network. [ABSTRACT FROM AUTHOR]

In this paper, we propose a sparse tensor regression model for multi-view feature selection. Apart from the most of existing methods, our model adopts a tensor structure to represent multi-view data, which aims to explore their underlying high-order correlations. Based on this tensor structure, our model can effectively select the meaningful feature set for each view. We also develop an iterative optimization algorithm to solve our model, together with analysis about the convergence and computational complexity. Experimental results on several popular multi-view data sets confirm the effectiveness of our model. [ABSTRACT FROM AUTHOR]

In this paper, 11-level multilevel inverter with reduced power switch configuration is developed and investigated. This proposed configuration can be extended for N levels with the required number of power switches. Comparative analysis between the proposed multilevel inverter topology and well-known existing multilevel inverter topologies is carried out. Fundamental switching angle calculation for the power switches of the proposed topology has been optimized using black widow optimization algorithm technique and also the proposed topology has been tested with the classical multicarrier alternative phase opposition disposition PWM technique. To test the effectiveness of the proposed topology, different tests have been conducted such as testing with different modulation indexes, DC input voltages, load settings and voltage ride-through capabilities. This paper presents various simulation and experimental results under various operating conditions to prove the performance of the proposed multilevel inverter topology. [ABSTRACT FROM AUTHOR]

In this paper, the design of nearly linear-phase recursive digital filters using a constrained optimization method is investigated. The method is based on existing constrained optimization techniques for nearly linear-phase IIR digital filters, and it is expected to be useful in applications where both magnitude and phase response specifications are required to be satisfied. Starting from an initial filter, the proposed method minimizes the group delay deviation under a set of linear constraints in terms of the magnitude response and filter stability. Improved sampling functions are introduced to the optimization problem, which are used to control the sampling points that are used for approximating the group delay and the rest of the constraints in various frequency bands. By using the proposed sampling functions we get an improved IIR filter response. [ABSTRACT FROM AUTHOR]

In this paper, we investigate system reliability optimization of multi-source multi-sink flow networks subject to transmission budget constraints. More specifically, we present a mathematical model of the optimization problem and a genetic algorithm (GA) to solve it. The GA is based on determining the optimal set of lower boundary points that maximize system reliability such that transmission cost does not exceed a specified upper bound. Finally, to ensure the efficiency of our approach, we apply our proposed algorithm to various network examples. [ABSTRACT FROM AUTHOR]

This paper discusses a nonlinear optimization problem with the system of max-Archimedean bipolar fuzzy relation equations as constraints. Some results related to the structure of the solution set of max-Archimedean bipolar fuzzy relation equations are proved. Using these results, a genetic algorithm is proposed to solve the problem for obtaining optimal or converging solutions. The effectiveness of the algorithm is also compared with other methods found in the literature. The previous methods require conversion of the problem into 0-1 mixed integer optimization problem solvable by some nonlinear optimization solvers and thereby, the computational work may increase with the size of the problem. Some test problems are developed to evaluate the performance of the proposed algorithm. [ABSTRACT FROM AUTHOR]

Electrocardiogram (ECG) is considered as the important diagnostic tests in medical field for detecting the cardiac anomalies. But, the ECG signals are polluted with numerous noise from power line intrusion, muscle noise, baseline wander, motion artifacts, low frequency noise signals, high frequency noise signals and T-wave, which automatically affects the QRS profile. The existing method provides the result in lesser accuracy with higher rate of error detection. To overcome these issues, QRS detector using modified maximum mean minimum (MoMaMeMi) filter optimized with mayfly optimization algorithm (QRS-MoMaMeMi-MOA) is proposed in this paper for less computational cost along with resource requirements. The proposed filter design consists of two phases for detecting QRS detector, such as filtering process associated to the enhancement and detection phase. Initially, the ECG data are taken from MIT/BIH arrhythmia dataset (MIT-AD). For eradicating the baseline wander in ECG data, MaMeMi filter is used. For expanding the performance of the modified MaMeMi filter, filter parameters, such as σ and Δ are optimized by MOA to accomplish the best values and measure the performance of the whole QRS detector. For high frequency noise suppression in ECG data, the range function, noise subtractors, modified triangular detector are used. Then, heart beat detection can be done with the help of adaptive thresholding technique. The proposed filter design is carried out in MATLAB and implemented on field programmable gate arrays (FPGAs). The proposed QRS-MoMaMeMi-MOA filter design had 0.93%, 0.12% and 0.19% higher accuracy and 89.32%, 50% and 62% low detection error rate, compared to the existing filters, like Kalman filtering based adaptive threshold algorithm for QRS complex detection (QRS-KF-ATA), QRS detection of ECG signal utilizing hybrid derivative with MaMeMi filter by efficiently removing the baseline wander (QRS-HD-MaMeMi), and knowledge-based QRS detection operated by cascade of moving average filters (QRS-CAF). Then, the device utilization of the proposed FPGA implementation of the QRS-MoMaMeMi-MOA filter provides 95.556% and 71.428% lower power usage compared with the existing algorithms, like Kalman filtering based adaptive threshold algorithm for QRS complex detection in FPGA (FPGA-QRS-KF-ATA), and efficient architecture for QRS detection in FPGA utilizing integer Haar wavelet transform (FPGA-QRS-IHWT). [ABSTRACT FROM AUTHOR]

Image contrast enhancement (CE) is a frequent image enhancement requirement in diverse applications. Histogram equalization (HE), in its conventional and different further improved ways, is a popular technique to enhance the image contrast. The conventional as well as many of the later versions of HE algorithms often cause loss of original image characteristics particularly brightness distribution of original image that results artificial appearance and feature loss in the enhanced image. Discrete Cosine Transform (DCT) coefficient mapping is one of the recent methods to minimize such problems while enhancing the image contrast. Tuning of DCT parameters plays a crucial role towards avoiding the saturations of pixel values. Optimization can be a possible solution to address this problem and generate contrast enhanced image preserving the desired original image characteristics. Biological behavior-inspired optimization techniques have shown remarkable betterment over conventional optimization techniques in different complex engineering problems. Gray wolf optimization (GWO) is a comparatively new algorithm in this domain that has shown promising potential. The objective function has been formulated using different parameters to retain original image characteristics. The objective evaluation against CEF, PCQI, FSIM, BRISQUE and NIQE with test images from three standard databases, namely, SIPI, TID and CSIQ shows that the presented method can result in values up to 1.4, 1.4, 0.94, 19 and 4.18, respectively, for the stated metrics which are competitive to the reported conventional and improved techniques. This paper can be considered a first-time application of GWO towards DCT-based image CE. [ABSTRACT FROM AUTHOR]

Internet of Things (IoT) significantly gained attraction nowadays, as it assists in numerous purposes. Several computerized technologies are employed for monitoring the plant disease in the IoT paradigm, and the major challenging lies in detecting the intrusions while monitoring the plant disease. This paper proposes an Exponential Sun Flower Rider Optimization Algorithm-driven Deep Residual Network (ExpSFROA-based DRN) for achieving effective intrusion detection results in the IoT. The proposed ExpSFROA is devised by incorporating Exponential Sun Flower Optimization (Exponential-SFO) and Rider Optimization Algorithm (ROA). Meanwhile, Exponential-SFO is designed by combining the Exponential Weighted Moving Average (EWMA) and Sunflower Optimization (SFO) algorithm. Here, the information is collected from the simulated IoT nodes based on the Cluster Head (CH), and performs the process of routing in order to predict the leaf disease more effectively. Based on the disease prediction process, the intrusion detection process is achieved by the devised ExpSFROA-based DRN. The performance of the newly developed ExpSFROA-based DRN is evaluated using four metrics such as accuracy, throughput, energy, and True Positive Rate (TPR). The developed method attained better results than the existing methods, such as SecTrust-RPL+DRN, OSEAP+IBFO+DRN, LASeR++DRN with a maximum accuracy of 0.950, higher throughput of 7533350 bps, minimal energy of 49.74 J, and higher TPR of 0.956. The proposed method is used in the agricultural land, which helps the farmers for monitoring the conditions of the fields anywhere and anytime, thereby minimizing the manpower and time. [ABSTRACT FROM AUTHOR]

In this work, an intelligent multi-objective optimization technique is proposed to optimize various parameters such as power consumption ( P c ), computation time ( C t ) and area (A) for a health monitoring system designed using the Secured Dynamic Partial Reconfiguration (SDPR) architecture. The cost-efficient Dynamic Partial Reconfiguration (DPR)-based field-programmable gate array (FPGA) is very useful for analyzing many applications such as automation and data processing. The novelty of this paper is to design a new intelligent SDPR (i-SDPR) module in order to achieve better performance in a health monitoring system by considering various performance parameters. The SDPR architecture is incorporated with two reconfigurable modules such as Encrypt and Authenticate, which results in an increase in the core power consumption ( P c ), computation time ( C t ) and area (A). So, to improve the performance of the SDPR-based health monitoring system, there is a necessity to optimize the performance parameters and it is achieved through intelligent multi-objective evolutionary (MOEA) techniques. The intelligent multi-objective evolutionary techniques such as Niched-Pareto Genetic Algorithm (NPGA), Pareto Archived Evolution Strategy (PAES) and Pareto Envelope-based Selection Algorithm (PESA) have been considered for better optimization in the performance parameters. For the study, the free-to-use MIMIC-III dataset is taken, which contains critically admitted various intensive care unit patient data. The dataset is processed through any one of the multi-objective evolutionary operators till satisfying the conditions and then forwarded to implementation. The proposed architecture has been implemented and tested using Cyclone Ⓡ V SX SoC Development Kit. The comparative analysis of various performance parameters was done for the proposed i-SDPR with the existing techniques such as the DPR and SDPR approach to show the improvement. The results declare that the proposed techniques obtain better performance compared to the existing techniques. [ABSTRACT FROM AUTHOR]

Numerous surrogate-assisted expensive multi-objective optimization algorithms were proposed to deal with expensive multi-objective optimization problems in the past few years. The accuracy of the surrogate models degrades as the number of decision variables increases. In this paper, we propose a surrogate-assisted expensive multi-objective optimization algorithm based on decision space compression. Several surrogate models are built in the lower dimensional compressed space. The promising points are generated and selected in the lower compressed decision space and decoded to the original decision space for evaluation. Experimental studies show that the proposed algorithm achieves a good performance in handling expensive multi-objective optimization problems with high-dimensional decision space. [ABSTRACT FROM AUTHOR]

Van Genuchten (VG) equation is the most commonly used equation of soil moisture characteristic curve, and the accuracy of its parameters directly affects the calculation accuracy of soil moisture motion equation. In order to obtain the parameters of the equation more accurately, this paper establishes an optimization model of the VG equation parameters. This optimization model uses the advantages of the cuckoo search algorithm and the differential evolution algorithm to combine the two into a new hybrid cuckoo search algorithm, namely DECS algorithm, and uses this algorithm to solve the parameter optimization problem of VG equation of soil moisture characteristic curve. By collecting and analyzing the relevant experimental data of various soil qualities, the dehumidification and moisture absorption curves of three different soil qualities were selected for simulation calculation. The results show that in the experiment of solving the parameter estimation problem of the VG equation, the DECS hybrid algorithm has better exploration and development capabilities than the cuckoo search algorithm. The hybrid algorithm has relatively significant performance in terms of calculation accuracy and convergence. [ABSTRACT FROM AUTHOR]

Dynamic binary translation (DBT) is a core technology that enables the migration of legacy software to different instruction set architectures while maintaining the original semantics. However, the development and maintenance of an efficient cross-DBT system are challenging. Key challenges include memory access overhead, inefficient instruction simulation, and frequent context switches. In this paper, we propose three novel optimization techniques. First, we formalize a register mapping cost model and investigate a hierarchical register mapping approach to bridge the memory access overhead. Second, we accelerate floating point (FP) emulation by surrounding the use of hardware FP unit with high-efficiency non-FP code. Third, we present a function inlining approach to alleviating the overhead associated with indirect control lookup. On the system side, we implement our approach on ARM64 and SW64 architectures based on QEMU and extensively evaluate the effectiveness with the SPEC2006 benchmark suite. The experimental results show that an average of 1.28× performance speedup and 13.41% code size reduction can be achieved on SW64. Similarly, on ARM64, we achieve an average of 1.15× performance speedup and 11.48% code size reduction. [ABSTRACT FROM AUTHOR]

Multi-view feature selection is an important research direction in the field of multi-view learning. To remove the negative effect of irrelevant and redundant information and select the important features from the multi-view data, in this paper, we propose a novel hierarchical unsupervised multi-view feature selection method, which integrates matrix decomposition and hierarchical regularization as a joint model. Specifically, to exploit the consistency information of multiple views for projection matrix, we project the multiple views into a latent basis space based on a matrix decomposition model. The hierarchical regularization containing the l 2 , 1 -norm, dependence and Frobenius-norm is imposed on projection matrix to, respectively, exploit the row-level, dependency-level and view-level feature selection. We also develop an efficient optimization algorithm to optimize our method. Extensive experimental results on six popular multi-view datasets show the effectiveness and superiority of our method by comparing with the state-of-the-art methods. [ABSTRACT FROM AUTHOR]

In this paper, we investigate the problem of personalized ranking from implicit feedback (PRIF). It is a more common scenario (e.g. purchase history, click log and page visitation) in recommender systems. The training data are only binary in these problems, reflecting the users' actions or inactions. One shortcoming of previous PRIF algorithms is noise sensitivity: outliers in training data might bring significant fluctuations in the training process and lead to inaccuracy of the algorithm. In this paper, we propose two robust PRIF algorithms to solve the noise sensitivity problem of existing PRIF algorithms by using the pairwise sigmoid and pairwise fidelity loss functions. These two pairwise loss functions are flexible and can easily be adopted by popular collaborative filtering models such as the matrix factorization (MF) model and the K-nearest-neighbor (KNN) model. A learning process based on stochastic gradient descent with bootstrap sampling is utilized for the optimization. Experiments are conducted on practical datasets containing noisy data points or outliers. Results demonstrate that the proposed algorithms outperform several state-of-the-art one class collaborative filtering (OCCF) algorithms on both the MF and KNN models over different evaluation metrics. [ABSTRACT FROM AUTHOR]

In recent times, many MAC protocols were implemented for boosting and improving energy efficiency (EE) in WSNs. Moreover, the cooperative MIMO method is found to be much capable of enhancing the EE of WSNs if configured properly. This paper intends to propose cross-layer design for multihop virtual MIMO system to enhance the end-to-end (ETE) reliability, EE, and QoS of the adopted WSN. The protocol is set here to focus on the energy utilization for transmission of data packets by optimal selection of transmission constraints for each node of the network. Moreover, the protocol's ETE latency and throughput are also modeled as the dependent variables of BER performance of every link. To discover the improved BER criteria of each link that meets the ETE QoS requirement in reduced energy utilization, this paper employs a new hybrid optimization algorithm named Lion Mutated Dragonfly Algorithm (LM–DA) that is a hybrid variant of both Lion Algorithm (LA) and Dragonfly Algorithm (DA). Finally, the performance of the adopted scheme is validated over other state-of-the-art models. The results state that the energy consumed by the adopted LM–DA approach is about 2.65%, 1.77%, and 1.77% reduced over LA, PSO, and DA schemes, respectively. [ABSTRACT FROM AUTHOR]

In cloud computing, a lot of challenges like the server failures, loss of confidentiality, improper workloads, etc. are still bounding the efficiency of cloud systems in real-world scenarios. For this reason, many research works are being performed to overcome the shortcoming of existing systems. Among them, load balancing seems to be the most critical issue that worsen the performance of the cloud sector, and hence there necessitates the optimal load balancing with optimal task scheduling. With the intention of accomplishing optimal load balancing by effectual task deployment, this paper plans to develop an advanced load balancing model with the assistance acquired from the metaheuristic algorithms. Usually, handling of tasks in cloud system is an NP-hard problem and moreover, nonpreemptive independent tasks are crucial in cloud computing. This paper goes with the introduction of a new optimal load balancing model by considering three major objectives: minimum makespan, priority, and load balancing, respectively. Moreover, a new single-objective function is also defined that incorporates all the three objectives mentioned above. Furthermore, the deployment of tasks must be optimal and for this a new hybrid optimization algorithm referred as Firefly Movement insisted WOA (FM-WOA) is introduced. This FM-WOA is the conceptual amalgamation of standard Whale Optimization Algorithm (WOA) and Firefly (FF) algorithm. Finally, the performances of the proposed FM-WOA model is compared over the conventional models with the intention of proving its efficiency in terms of makespan, task completion (priority), and degree of imbalance as well. [ABSTRACT FROM AUTHOR]