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Background Purpose Methods Results Conclusion In magnetic resonance imaging (MRI), maintaining a highly uniform main magnetic field (B0) is essential for producing detailed images of human anatomy. Passive shimming (PS) is a technique used to enhance B0 uniformity by strategically arranging shimming iron pieces inside the magnet bore. Traditionally, PS optimization has been implemented using linear programming (LP), posing challenges in balancing field quality with the quantity of iron used for shimming.In this work, we aimed to improve the efficacy of passive shimming that has the advantages of balancing field quality, iron usage, and harmonics in an optimal manner and leads to a smoother field profile.This study introduces a hybrid algorithm that combines particle swarm optimization with sequential quadratic programming (PSO‐SQP) to enhance shimming performance. Additionally, a regularization method is employed to reduce the iron pieces' weight effectively.The simulation study demonstrated that the magnetic field was improved from 462  to 3.6 ppm, utilizing merely 1.2 kg of iron in a 40 cm diameter spherical volume (DSV) of a 7T MRI magnet. Compared to traditional optimization techniques, this method notably enhanced magnetic field uniformity by 96.7% and reduced the iron weight requirement by 81.8%.The results indicated that the proposed method is expected to be effective for passive shimming. [ABSTRACT FROM AUTHOR]

For making the grid to be more intelligent, a unique design is required in this changing sphere. A better-automated grid is more important for controlling the variations in the entire system. This is possible by introducing secondary controllers where both power and voltage are shifted at necessary stages. Therefore, in this article, a Neural Fizzy Identification System with Partial Integral Controller (NFISPID) is introduced. Also, a secondary controller named Selective Voltage Pulse Width Modulation with Hybrid Artificial Bee Colony-Particle Swarm Optimization technique is incorporated for handling all false situations in the grid. The efficiency of the proposed system is checked with MATLAB environment where the results prove to be much efficient in terms of all parametric values, and the grid becomes stable within a short span of time than other existing controllers. [ABSTRACT FROM AUTHOR]

Fuel prices are rising, bringing attention to the utilization of alternative energy sources (RES). Even though load forecasting is more accurate at making predictions than wind power forecasting is. To address the operational challenges with the supply of electricity, wind energy forecasts remain essential. A certain kind of technology has recently been applied to forecast wind energy. On wind farms, a variety of wind power forecasting methods have been developed and used. The main idea underlying recurrent networks is parameter sharing across the multiple layers and neurons, which results in cycles in the network's graph sequence. Recurrent networks are designed to process sequential input. A novel hybrid optimization‐based RNN model for wind power forecasting is proposed in this research. Using the SpCro algorithm, a proposed optimization method, the RNN's weights are adjusted. The Crow Search Optimization (CSA) algorithm and the Sparrow search algorithm are combined to form the SpCro Algorithm (SSA). The suggested Algorithm was developed using the crow's memory traits and the sparrow's detecting traits. The proposed system is simulated in MATLAB, and the usefulness of the suggested approach is verified by comparison with other widely used approaches, such as CNN and DNN, in terms of error metrics. Accordingly, the MAE of the proposed method is 45%, 10.02%, 10.04%, 33.58%, 94.81%, and 10.01% higher than RNN, SOA+RNN, CSO+RNN, SSA+DELM, CFU‐COA, and GWO+RNN method. [ABSTRACT FROM AUTHOR]

In a highly automated Flexible Manufacturing System (FMS), optimal utilization and scheduling of resources and equipment are paramount. This necessity underpins the full utilization of automation capabilities, leading to increased productivity and minimized downtime. Efficient resource allocation and scheduling also contribute to better overall performance, allowing the FMS to meet production demands effectively while maintaining a high level of operational efficiency. In this paper, the job-shop production scheduling problem is studied which involve with the concurrent scheduling of jobs processing and mobile robot assignment for job transportation within FMS environment. The hybrid Genetic Algorithm and Tabu Search algorithm is proposed to solve the combinatorial NP-hard job-shop and mobile robot scheduling problem. The primary objective is to search for the best scheduling plan that includes job allocation and mobile robot assignment, aiming to minimize the overall time necessary to complete all tasks, also known as makespan, to the minimum as possible. The developed algorithm has been evaluated and compared with the classical (or standard) genetic algorithm and other hybrid GA (with Simulated Annealing (SA) algorithm) using two job datasets spanning from small to large-scale problems adopted from renowned benchmark job instances. The results of computer experiments substantiate the effectiveness of the proposed hybrid algorithm, showcasing superior-quality solutions with an approximate up to 2.86% and 3.55% improvements compared to the hybrid GA – SA and standard algorithm, respectively. The developed algorithm has been run and tested in the Matlab software environment.

With the growing need for renewable energy, wind farms are playing an important role in generating clean power from wind resources. The best wind turbine architecture in a wind farm has a major influence on the energy extraction efficiency. This paper describes a unique strategy for optimizing wind turbine locations on a wind farm that combines the capabilities of particle swarm optimization (PSO) and artificial neural networks (ANNs). The PSO method was used to explore the solution space and develop preliminary turbine layouts, and the ANN model was used to fine- tune the placements based on the predicted energy generation. The proposed hybrid technique seeks to increase energy output while considering site-specific wind patterns and topographical limits. The efficacy and superiority of the hybrid PSO-ANN methodology are proved through comprehensive simulations and comparisons with existing approaches, giving exciting prospects for developing more efficient and sustainable wind farms. The integration of ANNs and PSO in our methodology is of paramount importance because it leverages the complementary strengths of both techniques. Furthermore, this novel methodology harnesses historical data through ANNs to identify optimal turbine positions that align with the wind speed and direction and enhance energy extraction efficiency. A notable increase in power generation is observed across various scenarios. The percentage increase in the power generation ranged from approximately 7.7% to 11.1%. Owing to its versatility and adaptability to site-specific conditions, the hybrid model offers promising prospects for advancing the field of wind farm layout optimization and contributing to a greener and more sustainable energy future.

Abstract Due to growing network data dissemination in cloud, the elasticity, pay as you go options, globally accessible facilities, and security of networks have become increasingly important in today's world. Cloud service providers, including AWS, Azure, GCP, and others, facilitate worldwide expansion within minutes by offering decentralized communication network functions, hence providing security to cloud is still remains a challenging task. This paper aims to introduce and evaluate the Biz-SCOP model, a novel intrusion detection system developed for cloud security. The research addresses the pressing need for effective intrusion detection in cloud environments by combining hybrid optimization techniques and advanced deep learning methodologies. The study employs prominent intrusion datasets, including CSE-CIC-IDS 2018, CIC-IDS 2017, and a cloud intrusion dataset, to assess the proposed model's performance. The study's design involves implementing the Biz-SCOP model using Matlab 2019 software on a Windows 10 OS platform, utilizing 8 GB RAM and an Intel core i3 processor. The hybrid optimization approach, termed HyPSM, is employed for feature selection, enhancing the model's efficiency. Additionally, an intelligent deep learning model, C2AE, is introduced to discern friendly and hostile communication, contributing to accurate intrusion detection. Key findings indicate that the Biz-SCOP model outperforms existing intrusion detection systems, achieving notable accuracy (99.8%), precision (99.7%), F1-score (99.8%), and GEO (99.9%). The model excels in identifying various attack types, as demonstrated by robust ROC analysis. Interpretations and conclusions emphasize the significance of hybrid optimization and advanced deep learning techniques in enhancing intrusion detection system performance. The proposed model exhibits lower computational load, reduced false positives, ease of implementation, and improved accuracy, positioning it as a promising solution for cloud security.

Accurate cotton images are significant component for surveiling cotton development and its precise control. A suitable technique for charting the distribution of cotton at the county or field level must be available to researchers and production managers. The classification of cotton remote sensing models at the county level has significant implications for precision farming, land management, and government decision-making. This work aims to develop a novel cotton crop classification model using satellite images based on soil behaviour. It includes phases like preprocessing, segmentation, feature extraction, and classification. Here, preprocessing is carried out by Gaussian filtering to improve the quality of the input image. Then Modified Deep Joint Segmentation method is employed for the segmentation process. The features such as wide dynamic range vegetation index, simple ratio, Green Chlorophyll index, Transformed vegetation index, and Green leaf area index are extracted for classifying the input. The hybrid Improved CNN (ICNN) and Bidirectional Gated recurrent Unit (Bi-GRU) have used for classification purposes, which is computed by the improved score level fusion. The suggested new hybrid optimization model known as the Battle Royale assisted Butterfly optimization algorithm (BRABOA) is used for adjusting the hidden neuron count of both the ICNN and Bi-GRU classifiers for improving the accuracy. At last, the efficiency of the suggested model is then evaluated to other schemes using a variety of metrics. The suggested HC + BRABOA method obtains a maximum accuracy of (0.95) over conventional methods at a learning percentage of 90% for classifying cotton crops using satellite images. [ABSTRACT FROM AUTHOR]

With the growing need for renewable energy, wind farms are playing an important role in generating clean power from wind resources. The best wind turbine architecture in a wind farm has a major influence on the energy extraction efficiency. This paper describes a unique strategy for optimizing wind turbine locations on a wind farm that combines the capabilities of particle swarm optimization (PSO) and artificial neural networks (ANNs). The PSO method was used to explore the solution space and develop preliminary turbine layouts, and the ANN model was used to finetune the placements based on the predicted energy generation. The proposed hybrid technique seeks to increase energy output while considering site-specific wind patterns and topographical limits. The efficacy and superiority of the hybrid PSO-ANN methodology are proved through comprehensive simulations and comparisons with existing approaches, giving exciting prospects for developing more efficient and sustainable wind farms. The integration of ANNs and PSO in our methodology is of paramount importance because it leverages the complementary strengths of both techniques. Furthermore, this novel methodology harnesses historical data through ANNs to identify optimal turbine positions that align with the wind speed and direction and enhance energy extraction efficiency. A notable increase in power generation is observed across various scenarios. The percentage increase in the power generation ranged from approximately 7.7% to 11.1%. Owing to its versatility and adaptability to site-specific conditions, the hybrid model offers promising prospects for advancing the field of wind farm layout optimization and contributing to a greener and more sustainable energy future. [ABSTRACT FROM AUTHOR]

The present study focused on the machinability of Ti-3Al-2.5 V for wire-electrical discharge machining (WEDM) using "BroncoCut-X wire" (zinc-coated copper wire). The machining characteristics have been evaluated by varying wire-tension (Tw), wire-speed (Sw), flushing-pressure (Pf), discharge current (Id), and spark-on-time (Son). The response characteristics associated with cutting-speed (Cs), kerf-width (KW), and surface roughness (RA) have been collected and analyzed using main-effect plots, scanning electron microscope (SEM), and analysis of variance (ANOVA). The maximum Cs and minimum KW and RA are obtained upto 8.90 mm/min, 3.34 µm and 0.2218 mm, respectively. Additionally, the novelty lies in the smart hybrid prediction tool considering the conflicting nature of responses are converted into single responses using Grey Relation Analysis (GRA) and Fuzzy Interference System (FIS) (Namely: Gray-Fuzzy Reasoning Grade (GFRG)). Furthermore, the optimal performance is calculated using Rao-algorithms (i.e., Rao1, Rao2, and Rao3). The obtained ideal machining condition is 16N wire-tension, 3 m/min wire-speed, 8 kg/mm2 flushing-pressure, 21A discharge current, and 14 µs spark-on-time. The result has also been compared with the JAYA-algorithm and improved-grey wolf optimizer (I-GWO) to demonstrate the efficacy of the intended approach. The confirmation test has been conducted and obtained that the GFRG-based results are further improved by using a hybrid GFRG-based Rao-algorithm of 9.55%, 2.36%, and 7.99% as Cs, KW and RA, respectively. Furthermore, this study shows that the proposed multi-objective optimization method not only leads to more stable solutions but also to shorter run times and enhanced quality to support engineers in reducing the cost of item failures. [ABSTRACT FROM AUTHOR]

One of the most widely used 3D printing techniques is fused deposition modeling (FDM) which builds things layer by layer by dispensing molten materials through a heated nozzle. To showcase the flexibility of 3D printing, test samples were made using polylactic acid (PLA). Trial runs with Taguchi’s (L27) orthogonal array were performed. Layer thickness, printing speed, and carbon deposition (C-deposition) were the three input parameters that were improved. This was accomplished by combining principal component analysis (PCA) with multi-objective optimization based on ratio analysis (MOORA) in an integrated approach to multi-criteria optimization. The main goal of this study is to maximize the input parameters for the Industry 4.0 technological production process of embossing components. The MOORA-PCA technique finds the best combinations of process variables; for example, printing at a speed of 75mm/s, layer thickness of 0.1mm, and carbon content of 15mg yield the required results. The results of this study will help production managers and researchers choose the best FDM 3D printing methods for improving mechanical qualities and surface roughness. The economic feasibility of the 3D printing businesses may be strengthened by these research findings, which will benefit customers looking for ecologically friendly items. [ABSTRACT FROM AUTHOR]

Due to growing network data dissemination in cloud, the elasticity, pay as you go options, globally accessible facilities, and security of networks have become increasingly important in today's world. Cloud service providers, including AWS, Azure, GCP, and others, facilitate worldwide expansion within minutes by offering decentralized communication network functions, hence providing security to cloud is still remains a challenging task. This paper aims to introduce and evaluate the Biz-SCOP model, a novel intrusion detection system developed for cloud security. The research addresses the pressing need for effective intrusion detection in cloud environments by combining hybrid optimization techniques and advanced deep learning methodologies. The study employs prominent intrusion datasets, including CSE-CIC-IDS 2018, CIC-IDS 2017, and a cloud intrusion dataset, to assess the proposed model's performance. The study's design involves implementing the Biz-SCOP model using Matlab 2019 software on a Windows 10 OS platform, utilizing 8 GB RAM and an Intel core i3 processor. The hybrid optimization approach, termed HyPSM, is employed for feature selection, enhancing the model's efficiency. Additionally, an intelligent deep learning model, C2AE, is introduced to discern friendly and hostile communication, contributing to accurate intrusion detection. Key findings indicate that the Biz-SCOP model outperforms existing intrusion detection systems, achieving notable accuracy (99.8%), precision (99.7%), F1-score (99.8%), and GEO (99.9%). The model excels in identifying various attack types, as demonstrated by robust ROC analysis. Interpretations and conclusions emphasize the significance of hybrid optimization and advanced deep learning techniques in enhancing intrusion detection system performance. The proposed model exhibits lower computational load, reduced false positives, ease of implementation, and improved accuracy, positioning it as a promising solution for cloud security. [ABSTRACT FROM AUTHOR]

Given the pressing demand and ongoing necessity for fossil fuels, there is an imperative to actively seek alternative resources to replace petroleum-based fuels. The presents study considers a problem of experimentally investigating the effect of varying levels of important input parameters of a diesel engine fuelled with a novel blend of biodiesel-diesel and cerium oxide nano-additive on the sustainable performance characteristics of a diesel engine. Four input parameters, i.e., blend percentage (B in %), nanoparticle concentration (NPC in ppm), engine load (LD in %) and ignition pressure (IP in bar) each at four levels are considered. Experiments are conducted as per the Taguchi's L16 standard orthogonal array and for each experiment, performance parameters (such as Brake thermal efficiency (BTE) and brake specific fuel consumption (BSFC)), emission measures (Carbon monoxide (CO), oxides of nitrogen (NOx), unburnt hydrocarbons (UBHC) and Vibration level (VL)) of the diesel engine are collected. A hybrid multi-criteria decision-making (MCDM) approach, i.e., integrated MEREC-MARCOS method along with signal-to-noise (S/N) ratio and analysis of mean (ANOM) is employed to determine optimal setting of the input parameters that yield optimal multiple performance characteristics. The results reveal that B at 40%, NPC at 80 ppm, LD at 50% and IP at 200 bar is the optimal setting of the input parameters that produce optimum values of the output responses considered simultaneously. Further, results of the analysis of variance (ANOVA) show that Nanoparticle concentration percentage contribution is the maximum (79.63%) followed by engine load (8.40%), ignition pressure (6.28%), and blend percentage (2.11%). The optimization results are: BTE is 32.87%, BSEC is 0.285, CO is 0.018%, NOx is 559.6 ppm, UBHC is 28.1 ppm and VL= 19.57m2/sec which were validated with a confirmation test. Henceforth, such hybrid fuels provide sustainable energy solutions and environmental conservation simultaneously addressing the current and future demands. [ABSTRACT FROM AUTHOR]

Solutions to nonlinear optimal control problems (OCPs) exhibit dispersions under model uncertainties and it is desirable to generate optimal solutions that exhibit less sensitivity to model uncertainties. We propose a novel solution desensitization method dubbed “Reduced Desensitization Formulation (RDF)” by leveraging non-uniqueness of the solution of the costate differential equations when a hybrid indirect-direct optimization method is used. A key property of the RDF method is a significant reduction in the number of differential equations needed for generating desensitized solutions. This feature facilitates the generation of open-loop desensitized trajectories and makes the methodology applicable to OCPs with a larger number of uncertain parameters. To demonstrate the utility of the RDF method, three important classes of trajectory optimization problems are considered with uncertainty in the thrust magnitude of the propulsion system: (1) minimum-fuel low-thrust interplanetary rendezvous maneuvers, (2) low-thrust orbit-raising maneuvers, and (3) minimum-fuel high-thrust rocket-landing problems. For the considered problems with bang-bang control profiles, an analysis is presented on the change in the number of control switches between sensitive and desensitized optimal solutions. Numerical results demonstrate desensitization of the considered performance indices with respect to the thrust magnitude of the propulsion system. [ABSTRACT FROM AUTHOR]

The Internet of Things (IoT) enabled Underwater Wireless Sensor Networks (UWSNs) to emerge for applications like underwater imagining, underwater equipment monitoring, marine data collection, etc. These all applications relied on the routing method for data transmissions. However, a designing routing protocol for UWSNs using acoustic should address lower throughput, longer delay, high energy consumption, and collisions. We propose the novel Hybrid Optimization-based Cooperative Opportunistic Routing (HOCOR) for improving the performance of IoT-enabled UWSNs. As the Swarm Intelligence (SI) methods have already proven effective for routing and clustering problems of various wireless networks, we aim to design a hybrid SI technique using popular Genetic Algorithm (GA) and Particle Swarm Optimization (PSO) methods. In this hybrid SI technique, we exploit the benefits of both GA and PSO for optimal forwarding relay selection. In HOCOR, hybrid optimization aims at forming a reliable opportunistic data transmission route from any underwater sensor node to the surface sink. The hybrid optimization solves the problem of maximizing the Quality of Service (QoS) and network lifetime using the novel fitness function while evaluating each sensor node. The fitness function of the hybrid optimization algorithm computes the four parameters of each underwater sensor node such as underwater distance, node connectivity, packet transmission ability, and residual energy. We select these parameters to avoid packet collisions and void communications in UWSNs. Simulation results show that the HOCOR protocol outperforms the state-of-art methods in terms of the average throughput, Packet Delivery Ratio (PDR), average energy consumption, and average delay. [ABSTRACT FROM AUTHOR]

There are many algorithms that have been designed for the purpose of applying them to the subject of object detection, including Particle Swarm Optimization (PSO), as well as Gray Wolf Optimizer (GWO), and many other algorithms. In this article, I will address the problem of object detection and take advantage of some characteristics of the PSO and GWO algorithms to build an algorithm that is distinguished by the accuracy and browsing of the PSO algorithm, as well as the search speed characteristic provided by the GWO algorithm. The proposed algorithm was called Proposed-Hybrid Optimization (PHO), and a set of functions were defined and three of them were selected to solve the problem of object detection. The accuracy and speed of the results were acceptable and good compared to previous studies that were discussed in a special section. [ABSTRACT FROM AUTHOR]

Vehicle suspension systems have become increasingly crucial for both driving safety and comfort. Active suspension systems can dynamically adjust suspension characteristics in real-time by introducing force into the system. Designing a controller for the real-time adjustment of the control force in active suspension systems is essential to meet challenging control objectives, including body acceleration, suspension deflection, and tire deflection. This article proposes a hybrid optimization approach named Coati–Grey Wolf Optimization (COAGWO), which combines the strengths of the coati optimization algorithm and grey wolf optimization to tune the gains of linear quadratic control applied to vehicle suspension systems. The COAGWO algorithm incorporates a unique strategy inspired by the Coati Optimization Algorithm, allowing wolves to climb trees. This enhancement significantly improves the wolves’ ability to explore the global search space and reduces the likelihood of being trapped in local optima. Initially, we conduct extensive experiments using a suite of challenging optimization problems from the CEC2019 benchmark to evaluate the effectiveness of the COAGWO algorithm. The effectiveness of COAGWO is compared against several state-of-the-art algorithms, including grey wolf, coati, aquila-grey wolf, whale, reptile search, tunicate swarm, and seagull optimization algorithms. The experimental results demonstrate that COAGWO consistently outperforms these algorithms in terms of solution quality and convergence speed. For the optimal weight selection problem of linear quadratic control applied to the control of vehicle suspension systems, the excellent performance of the proposed method is illustrated through comparative simulation studies under various road disturbance conditions. The results indicate that the COAGWO algorithm achieves a more efficient active suspension system compared to competitor algorithms by reducing the overall acceleration of the driver’s body, thereby enhancing ride comfort.

Swarm-inspired computing techniques are the best candidates for solving various nonlinear problems. The current study aims to exploit the swarm intelligence technique known as Particle Swarm Optimization (PSO) for the numerical investigation of a nonlinear system of latently infected CD4+T cells. The strength of the Mexican Hat Wavelet (MHW) based unsupervised Feed Forward Artificial Neural Network (FFANN) is used to solve the nonlinear system of latently infected CD4+T cells. The function approximation of unsupervised ANN is used to construct the mathematical model of the latently infected CD4+T cells by defining the error function in the mean square manner. The adjustable parameters called the unknowns of the network are optimized by using the Particle Swarm Optimization (PSO), Nedler Mead Simplex Method (NMSM), and their hybrid PSO-NMSM. The PSO applied for the global optimization of weights aided by the NMSM algorithm for rapid local search. Finally, a Comprehensive Monte Carlo simulation and statistical analysis of the analytical method, numerical Range Kutta (RK) method, ANN optimized with Genetic Algorithm (GA) aided with Sequential Quadratic Programming (SQP) known as GA-SQP, ANN-PSO-SQP and the proposed MHW-HIVFFANN-PSO-NMSM are performed to validate the effectiveness, stability, convergence, and computational complexity of each scheme. It is observed that the proposed MHW-FFANN-HIVPSO-NMSM scheme has converged in all classes at $10~^{-6}$ , $10^{-7}$ , and $10~^{-8}$ and solved the nonlinear system of latently infected CD4+ T cells more accurately and effectively. The absolute error lies in $10^{-3}$ , $10^{-4}$ , $10^{-4}$ , and $10^{-5}$ for numerical, ANN-GA-SQP, ANN-PSO-SQP, and proposed MHW-ANN-PSO-NMSM respectively. Moreover, the proposed scheme is stable for the large number of independent runs. The values for global statistical indicators’ global mean squared error are lies 8.15E-09, 3.25E-10, 4.15E-09, and 3.15E-10 for class X(t), W(t), Y(t), and V(t) respectively whereas the global mean absolute deviation lies in range 7.35E-09, 8.50E-10, 2.10E-10 and 7.10E-09.

In this paper, we present a novel and robust watermarking method that combines the Discrete Wavelet Transform (DWT), Discrete Cosine Transform (DCT), and Set Partitioning in Hierarchical Trees (SPIHT) algorithm. The proposed method is designed to embed two different types of watermarks, Watermark 1 and Watermark 2, in different sub-bands of the decomposed host image. Initially cover image and watermark1 is optimized using hybrid optimization techniques (combination of whale and Cookoo optimization techniques). To ensure the security and robustness of the embedded watermarks, Watermark 1 is encrypted using the Arnold transform, while Watermark 2 is encoded using a Hamming encoder. Both encrypted watermarks are then embedded in their respective sub-bands. Finally, to compress the watermarked image without losing the embedded watermarks, we apply the SPIHT compression algorithm. Further, performance of the proposed method is evaluated against various watermarking attacks, including JPEG compression, Gaussian noise, and salt and pepper noise. Our experimental results demonstrate the high robustness of the proposed method against various watermarking attacks, including JPEG compression, Gaussian noise, and salt and pepper noise. The peak signal-to-noise ratio (PSNR), normalized correlation (NC), bit error rate (BER), and structured similarity index (SSIM) values were used as performance metrics. The proposed method achieves a maximum PSNR value of 42.52 dB and a maximum NC value of 0.9999, indicating the high fidelity and similarity between the original and watermarked images. The BER values were found to be consistently low, indicating the accuracy of watermark retrieval. Additionally, the proposed method maintains the quality of the cover image with minimal distortion. Overall, the proposed method offers a secure and robust solution for digital image watermarking, which can be applied in various applications, such as copyright protection and authentication. [ABSTRACT FROM AUTHOR]

The calibration of cameras plays a critical role in close-range photogrammetry because the precision of calibration has a direct effect on the quality of results. When handling image capture using a camera, traditional swarm intelligence algorithms such as genetic algorithms and particle swarm optimization, in conjunction with Zhang's calibration method, frequently face difficulties regarding local optima and sluggish convergence. This study presents an enhanced hybrid optimization approach utilizing both the principles of differential evolution and particle swarm optimization, which is then employed in the context of camera calibration. Initially, we establish a measurement model specific to the camera in close-range photogrammetry and determine its interior orientation parameters. Subsequently, employing these parameters as initial values, we perform global optimization and iteration using the improved hybrid optimization algorithm. The effectiveness of the proposed approach is subsequently validated through simulation and comparative experiments. Compared to alternative approaches, the proposed algorithm enhances both the accuracy of camera calibration and the convergence speed. It effectively addresses the issue of other algorithms getting trapped in local optima due to image distortion. These research findings provide theoretical support for practical engineering applications in the field of control theory and optimization to a certain extent. [ABSTRACT FROM AUTHOR]

The evolution of critical Internet of Things (IoT) wings research on Vehicular Ad-hoc Networks (VANETs) added transmission as the primary pre-requisite for the successful deployment of Intelligent Transportation System. The major challenge, however, with VANETs, lies in ensuring network security, particularly in the face of complex attacks. For secure communication, the focus has recently been shifted to trust-based models as simpler and cost-effective alternative to cryptography based solutions, especially in handling delay-sensitive situations. However, the trust-based models suffer from poor diversity and lack in adapting with ever-changing dynamic VANET scenarios. Some recent works reveal that Artificial Intelligence (AI) models can help with the rigidity of the trust based models and provide the near-optimal solution with adaptive decision-making. However, there is a huge gap to fill from the research perspective. Working on these gaps, in this work, an AI-based novel hybrid optimization algorithm combining Rider Optimization and Remora Optimization has been proposed that finds the optimal secure routing path using fitness parameters considering trust, energy, delay, and distance between the nodes. The performance of the proposed algorithm is further enhanced by implementing the concept of fractional calculus to improve its convergence capability for faster decisions. The results prove the proposed trust based algorithm's adaptive efficiency in terms of energy, trust, throughput, and packet delivery ratio in varying scenarios of sparse and dense traffic (with and without attacks). Moreover, simulation results show considerable improvement with the proposed hybrid approach compared to the individual algorithms. [ABSTRACT FROM AUTHOR]

Sales forecasting, situated at the intersection of art and science, is critical for inspiring managers toward achieving profitable outcomes. Its precision sustains production levels and capital and plays a pivotal role in the company's and its leaders' overall success and career progression. In the context of Mahram Food Industries, the challenge arises from diverse investor perspectives and the impactful nature of numerous variables. To address this, a new sales forecasting algorithm has been introduced to enhance accuracy. The aim is to predict future sales through a comprehensive approach, leveraging technical analysis, time series modeling, machine learning, neural networks, and random forest techniques. The research methodology integrates various advanced techniques to improve sales forecasting for Mahram Food Industries. Technical analysis, time series modeling, machine learning, neural networks, and random forest methods are combined to create a robust framework. The focus is on predicting sales for a future period within the artificial intelligence-based machine learning domain. The study employs metrics such as Mean Absolute Deviation (MAD), MAD Percentage (MADP), and Mean Squared Error (MSE) to evaluate and compare the performance of the proposed neural network against traditional methods like multiple variable regression and time series modeling. The study's results highlight the superior performance of the neural network in sales forecasting for Mahram Food Industries. The Mean Absolute Deviation (MAD) for the neural network is 28.33, outperforming multiple variable regression (28.54) and time series modeling (29.45). Additionally, the neural network demonstrates a better MAD Percentage (MADP) with a value of 10.2%, surpassing the values associated with multiple variable regression (10.35%) and time series modeling (10.30%). The Mean Squared Error (MSE) further confirms the neural network's superiority with a value of 6452 compared to 6472 and 7865 for multiple variable regression and time series modeling, respectively. In conclusion, the study showcases the effectiveness of integrating advanced techniques, particularly the neural network, in enhancing the accuracy of sales forecasting for Mahram Food Industries. The comprehensive approach, combining technical analysis, time series modeling, machine learning, neural networks, and random forest, is a valuable strategy for predicting future sales. The superior performance of the neural network, as evidenced by lower MAD, MADP, and MSE values, suggests its potential for guiding informed decision-making in goal setting, hiring, budgeting, and other critical aspects of business management. [ABSTRACT FROM AUTHOR]

Malware transmission is a significant security issue in WSN, however, the influence of the attack and defensive processes on malware propagation is rarely taken into account in traditional malware propagation prevention methods. Advanced methods are in need to stop the propagation of malware of sensor nodes. With the formulation of representing dynamics among states, a new decision-making problem as the optimal control problem via hybrid optimization algorithm. The proposing model is termed as Butterfly Updated Bald Eagle Optimization based Prevention of Malware Propagation in Wireless Sensor Network (BUBEO-PMPWSN). In the proposed controlling system, optimal system parameters are analyzed via the BUBEO for preventing malware propagation in WSN. Particularly, the sensor node states considered are Susceptible, Infectious, Infectious and sleeping, recovered, Recovered and sleeping, and finally Dead. The system parameter tuning will be under the evaluation of fitness calculation under probability of infectious sensor node becoming recovered and the probability of infectious sensor node entering sleeping state. This optimal tuning strategy ensures the preventing of malware propagation. Finally, the performance of proposed BUBEO-PMPWSN model is evaluated and validated successfully by comparing other state-of-the-art models. The BUBEO-PMPWSN achieved 250 recovered nodes for time 500, while the HGS, BOA, HBA, COOT, and HHO scored 123, 115, 236, 172, and 180, respectively, for recovered nodes. [ABSTRACT FROM AUTHOR]

With the quick growth of digital technology, image transferring is the most common process over the internet. Now, location registration on mobiles has become trendier in social media. At the same time, the criminals can modify the geo-position location as per their needs. Hence, there is a need to detect the genuineness of geo-position. Currently available techniques for spoofing detection fail to detect prior information and they are effective for small databases. Hence, the efficient deep learning-based geo-position spoofing detection technique is developed for the IM platform. The residual noise that exists in an image is mined from the input image by a DRN, which is trained by developed HBSMO. Then, the camera footprints are extracted from the noise-free images by the fuzzy filter. Based on the extracted camera footprints, the spoofed image is detected using the RV coefficient, and finally, DCT and correlation are utilized to determine which image is the spoofed image. Finally, the developed HBSMO-based DRN approach is assessed for its effectiveness using several measures, such as testing accuracy, FPR, and TPR, and is obtained that the proposed method has attained values of 0.92, 0.89, and 0.91, correspondingly. [ABSTRACT FROM AUTHOR]

This manuscript addresses the critical need for precise paint application to ensure product durability and aesthetics. While manual work carries risks, robotic systems promise accuracy, yet programming diverse product trajectories remains a challenge. This study aims to develop an autonomous system capable of generating paint trajectories based on object geometries for user-defined spraying processes. By emphasizing energy efficiency, process time, and coating thickness on complex surfaces, a hybrid optimization technique enhances overall efficiency. Extensive hardware and software development results in a robust robotic system leveraging the Robot Operating System (ROS). Integrating a low-cost 3D scanner, calibrator, and trajectory optimizer creates an autonomous painting system. Hardware components, including sensors, motors, and actuators, are seamlessly integrated with a Python and ROS-based software framework, enabling the desired automation. A web-based GUI, powered by JavaScript, allows user control over two robots, facilitating trajectory dispatch, 3D scanning, and optimization. Specific nodes manage calibration, validation, process settings, and real-time video feeds. The use of open-source software and an ROS ecosystem makes it a good choice for industrial-scale implementation. The results indicate that the proposed system can achieve the desired automation, contingent upon surface geometries, spraying processes, and robot dynamics. [ABSTRACT FROM AUTHOR]

Deep drawing is one of the primary sheet metal forming processes that is used all over the world. The current study focused on using Analytical Hierarchy Process-Elimination and Choice Translating the Reality (AHP-ELECTRE I), Response Surface Methodology-Genetic Algorithm (RSM-GA), and Artificial Neural Network-Genetic Algorithm (ANN–GA) for determining deep–drawing performance parameters. A hybrid FEA–MCDA–RSM–ANN–GA was built using an experimental design obtained from RSM to develop better quality products. It is integrated with finite element-based numerical deep drawing simulation to understand the intended responses and the impact of design factors without the need for costly trial tests. To improve the quality of drawn cups characterization, three process parameters-clearance, punch radius, and coefficient of friction-have been tuned to their optimum values like resultant tool force (N), spring back (µm), max forming limit curve (%), and max thinning rate. The optimization results showed the efficacy of the technique for process design, resulting in the reduction of both cost and time. The desirability index was calculated and compared with all the predictions. The hybrid models that have been developed may be suggested for accurate prediction and optimization of various process parameters and outcomes for any industrial application issues that could arise. [ABSTRACT FROM AUTHOR]

An efficient hybrid optimization algorithm is introduced in this paper to optimize the lightweight design of high-aspect-ratio wings, tackling the complexities associated with the mixed optimization design of layout and size variables within these wing structures. A hybrid binary unified coding description facilitates the optimization process for layout and size variables. The study influences one-dimensional convolutional neural networks to establish an aeroelastic surrogate model, primarily chosen for their exceptional performance in handling multi-parameter aeroelastic regression problems. Additionally, the squirrel optimization algorithm is chosen over the genetic algorithm for the mixed optimization problem, leading to notable savings in computational costs. The research demonstrates that the proposed hybrid optimization method, integrating the one-dimensional convolutional neural network and the squirrel optimization algorithm, offers superior performance in optimizing high aspect ratio wings. Specifically, it results in a reduction of 4.1% in the weight of the wing structure. Moreover, the study highlights the necessity of this hybrid approach due to the observed coupling between the layout variables of the wing ribs and the size variables of the wing beams. [ABSTRACT FROM AUTHOR]

Rubber solid tires are mostly used in mine heavy-duty vehicles, and their mechanical behavior its quite different from that of traditional pneumatic tires. The actual movement status of mine heavy vehicles cannot be accurately described, which severely restricts the study of mine vehicle dynamics and stability due to the lack of accurate tire model parameters. In order to establish an accurate parameter identification model for heavy traction solid tire in mine vehicles, its longitudinal force calculation formula is modified by PAC2002 magic formula model and the parameters to be identified are determined. A hybrid optimization parameter identification algorithm for heavy rubber solid tire classical model is proposed by using a combination of 3 iterative means: Gaussian Newton iteration, genetic iteration and simulated annealing. The basic experimental data of filled rubber tire fitted to a underground coal mine 25 t Heavy-Duty vehicle are acquired through a six component testing equipment, and the parameters of the tire model under two operating conditions of pure longitudinal slip and laterality longitudinal slip compound are discriminated by a hybrid optimization algorithm and a genetic algorithm, and the relative root mean square error and determination coefficient are introduced as evaluation indexes of identification accuracy. The results show that: For the parameter identification model under two working conditions, the maximum root mean square error of the objective function value is 0.08135 and 0.07965 respectively, and the determination coefficients are 0.98815 and 0.98765 respectively. Moreover, the hybrid optimization process has better global control and fast convergence ability than simple genetic iteration. the average algebra convergence to global optimum is reduced by 36% and the average time to global optimum is reduced by 31%; Finally, the vehicle traction characteristics under different load conditions are tested, the test results show that the deviation between the longitudinal traction of a single tire calculated by the identification parameters and the vehicle test results is not more than 4%, and the validity of the identification model is verified.

Effectively managing complex logistics data is essential for development sustainability and growth, especially in optimizing distribution routes. This article addresses the limitations of current logistics path optimizationmethods, such as inefficiencies and high operational costs. To overcome these drawbacks, we introduce the Hybrid Firefly- Spotted Hyena Optimization (HFSHO) algorithm, a novel approach that combines the rapid exploration and global search abilities of the Firefly Algorithm (FO) with the localized search and region-exploitation skills of the Spotted Hyena Optimization Algorithm (SHO). HFSHO aims to improve logistics path optimization and reduce operational costs. The algorithm’s effectiveness is systematically assessed through rigorous comparative analyses with established algorithms like theAnt ColonyAlgorithm(ACO), Cuckoo SearchAlgorithm(CSA) and JayaAlgorithm (JA). The evaluation also employs benchmarking methodologies using standardized function sets covering diverse objective functions, including Schwefel’s, Rastrigin, Ackley, Sphere and the ZDT and DTLZ Function suite. HFSHO outperforms these algorithms, achieving a minimum path distance of 546 units, highlighting its prowess in logistics path optimization. This comprehensive evaluation authenticates HFSHO’s exceptional performance across various logistic optimization scenarios. These findings emphasize the critical significance of selecting an appropriate algorithm for logistics path navigation, with HFSHO emerging as an efficient choice. Through the synergistic use of FO and SHO, HFSHO achieves a 15% improvement in convergence, heightened operational efficiency and substantial cost reductions in logistics operations. It presents a promising solution for optimizing logistics paths, offering logistics planners and decision-makers valuable insights and contributing substantively to sustainable sectoral growth. [ABSTRACT FROM AUTHOR]

Having one's life threatened by a disease like ovarian cancer is the single most crucial thing in the whole world. It is difficult to achieve high performance without sacrificing computational efficiency; the results of the denoising process are not as good as they could be; the proposed models are nonconvex and involve several manually chosen parameters, which provides some leeway to boost denoising performance; the methods generally involve a complex optimisation problem in the testing stage; Here at DnCNN, we've developed our own version of the deep ii learning model, a discriminative learning technique. The goal was to eliminate the need for the iterative optimisation technique at the time it was being evaluated. The goal was to avoid having to go through testing altogether, thus this was done. It is highly advised to use a Deep CNN model, the efficacy of which can be evaluated by comparing it to that of more traditional filters and pre-trained DnCNN. The Deep CNN strategy has been shown to be the best solution to minimise noise when an image is destroyed by Gaussian or speckle noise with known or unknown noise levels. This is because Deep CNN uses convolutional neural networks, which are trained using data. This is because convolutional neural networks, which are the foundation of Deep CNN, are designed to learn from data and then use that learning to make predictions. Deep CNN achieves a 98.45% accuracy rate during testing, with an error rate of just 0.002%. [ABSTRACT FROM AUTHOR]

This article proposes a solution to the Profit-Based Unit Commitment (PBUC) problem to maximize the profit of a power generation company that owns thermal units and compressed air energy storage (CAES) systems, considering the Day-Ahead market. The proposed methodology is more realistic as it considers a mixed-integer nonlinear formulation of the PBUC. The problem is solved through two stages, with Stage 1 dedicated to obtaining the operational state of the generating units (On or Off) and the operation mode of the storage system (energy exchange: charging, discharging, idle). Stage 2 determines the dispatch of power from the thermoelectric units and the energy exchange in the storage system. The analysis of the system consisting of 20 thermoelectric units and three storage systems shows the efficiency of the proposed method in making decisions for the power generation company and is therefore promising for real-world applications. [ABSTRACT FROM AUTHOR]

Recently, when handling high-dimensional data, it has become extremely difficult to search this optimal subset of selected features due to the restriction of reducing the exponential increase of the search procedure, and most of those feature selection models neglect the interactions of features or feature and decision class. This paper develops a novel feature selection approach using symmetric uncertainty and hybrid optimization for high-dimensional data (FSUHO) for high-dimensional data. First, to fully reflect the interaction relationship of features or feature and decision class, the F-relevance between features and the C-correlation between feature and decision class based on the symmetric uncertainty are constructed to remove those redundant features. Then, a strong correlation threshold is improved based on the C-correlation and random coefficient to prevent the removal of the effective features in this first stage. Second, to decrease this expensive computational consumption, one criterion for judging a weakly correlated feature is designed to sort all features, and another criterion is developed to select the class center. The similarity between features and class centers is calculated, and similar features are clustered into one class. Then, the symmetric uncertainty correlation-based feature clustering model can be constructed in this second stage. In the third stage, a hybrid optimization approach of particle swarm optimizer (PSO) and wild horse optimizer (WHO) for feature selection is proposed, where the association-guided group initialization probability with a multiobjective optimized particle selection scheme is defined as a criterion for the PSO in selecting stallion particles for the WHO, and the improved WHO is developed by integrating the nonlinear inertial weight factor and the Brownian motion operator to obtain the optimal subset of selected features. Finally, a novel three-stage feature selection algorithm is developed. Experimental results apply to 16 datasets prove the efficiency of FSUHO in tackling high-dimensional feature selection problems in metrics of classification accuracy and running time. [ABSTRACT FROM AUTHOR]

Advancements in cloud technologies have increased the infrastructural needs of data centers due to storage needs and processing of extensive dimensional data. Many service providers envisage anomaly detection criteria to guarantee availability to avoid breakdowns and complexities caused due to large-scale operations. The streaming log data generated is associated with multi-dimensional complexity and thus poses a considerable challenge to detect the anomalies or unusual occurrences in the data. In this research, a hybrid model is proposed that is motivated by deep belief criteria and meta-heuristics. Using Search-and-Rescue—BrainStorm Optimization (SAR-BSO), a hybrid feature selection (FS) and deep belief network classifier is used to localize and detect anomalies for streaming data logs. The significant contribution of the research lies in FS, which is carried out using SAR-BSO which increases the detection power of the model as it selects the most significant variables by minimizing redundant features. The evaluation of accuracy is efficiently improved when compared with the predictable methods, such as Extract Local Outlier Factor (ELOF), Track-plus, Hybrid Distributed Batch Stream (HDBS), IForestASD, DBN, BSO-based Feature Selection with DBN, Genetic Algorithm-Deep Belief Network (GA-DBN), Mutual Information-Deep Belief Network (MI-DBN), information entropy-Deep Belief Network(I + DBN), Flat Field-Deep Belief Network (FF + DBN), African Vulture Optimization Algorithm-Deep Belief Network(AVOA + DBN), Gorilla Troop Optimizer-Deep Belief Network(GTO-DBN), and SARO-based Feature Selection with DBN. Further, the accurate detection of the anomalies in the data stream is established by the Deep Belief Neural Network (DBN) classifier. The model's efficacy is determined using Apache, Hadoop, HDFS, Spark, and Linux datasets and evaluated against existing similar models. The model efficiency is provided using multiple evaluation metrics and is found effective. From the experimentation, the accuracy of the proposed model is found to be 93.3, 95.4, 93.6, 94.2, and 93.5% respectively for the dataset such as Apache, Hadoop, HDFS, spark, and Linux. This enhancement in accuracy is due to the selection of optimal features by the proposed SAR-BSO algorithm. [ABSTRACT FROM AUTHOR]

Global organisations are concentrating on lowering greenhouse emissions as a result of the enormous environmental pollution caused by the usage of conventional energy sources. The expensive price of an electric vehicle and the absence its charging stations, however, prevent their mass adoption. This study suggests a data-driven demand side management strategy can be used to manage the electricity demand for an electric vehicle charging station of electric vehicles connected to a microgrid powered by photovoltaics. The suggested approach lessens dependency on traditional energy sources and addresses the lack of electric vehicle charging facilities by utilising solar-powered charging stations to meet energy demands during peak times. Real-time data from photovoltaic power plants, industrial and residential demands, and electric vehicle charging stations was gathered so that it could mimic the system. The electric vehicles are charged during the off-peak times in order to regulate the energy that is supplied to the microgrid and also determine the energy storage system’s level of charge, a hybrid genetic algorithm sperm swarm optimisation approach was designed. The outcomes show that the electric vehicle charging station effectively offset peak demand during those hours. [ABSTRACT FROM AUTHOR]

The reference crop evapotranspiration (ET0) statistic is useful for estimating agricultural system water requirements and managing irrigation. In dry areas, the accurate calculation of ET0 is crucial for optimal agricultural water resource utilization. By investigating the relationship between meteorological information and ET0 in Shihezi City, four prediction models were developed: a BP neural network prediction model, a BP neural network prediction model improved by genetic algorithm (GA-BP), a BP neural network prediction model improved by particle swarm algorithm (PSO-BP), as well as an improved hybrid BP neural network prediction model (GA-PSO-BP). The Pearson correlation analysis found that the key parameters influencing ET0 were temperature (Tmax, Tave, Tmin), hours of sunshine (N), relative humidity (RH), wind speed (U), as well as average pressure (AP). Based on the analysis results, different combinations of meteorological input factors were established for modeling, and the results showed that when the input factors were temperature (Tmax, Tave, Tmin), hours of sunshine (N), as well as relative humidity (RH), the overall effect of the ET0 prediction model was better than the other input combinations, and the GA-PSO-BP prediction model was the best, which could provide some guidance for the deployment and use of water resources. This may assist in the allocation and utilization of agricultural water resources in Shihezi. [ABSTRACT FROM AUTHOR]

Multilateral wells (MLWs) equipped with multiple flow control devices (FCDs) are becoming increasingly favored within the oil sector due to their ability to enhance well-to-reservoir exposure and effectively handle unwanted fluid breakthrough. However, combining various types of FCDs in multilateral wells poses a complex optimization problem with a large number of highly correlated control variables and a computationally expensive objective function. Consequently, standard optimization algorithms, including metaheuristic and gradient-based approaches, may struggle to identify an optimal solution within a limited computational resource. This paper introduces a novel hybrid optimization (HO) framework combining particle swarm optimization (PSO) and Simultaneous Perturbation Stochastic Approximation (SPSA). It is developed to efficiently optimize the completion design of MLWs with various FCDs while overcoming the individual limitations of each optimization algorithm. The proposed framework is further enhanced by employing surrogate modelling and global sensitivity analysis to identify critical parameters (i.e., highly sensitive) that greatly affect the objective function. This allows for a focused optimization effort on these key parameters, ultimately enhancing global optimization performance. The performance of the novel optimization framework is evaluated using the Olympus benchmark reservoir model. The model is developed by three intelligent dual-lateral wells, with inflow control devices (ICDs) installed within the laterals and interval control valves (ICVs) positioned at the lateral junctions. The results show that the proposed hybrid optimization framework outperforms all industry-standard optimization techniques, achieving a Net Present Value of approximately USD 1.94 billion within a limited simulation budget of 2500 simulation runs. This represents a substantial 26% NPV improvement compared to the open-hole case (USD 1.54 billion NPV). This improvement is attributed to more efficient water breakthrough management, leading to a notable 24% reduction in cumulative water production and, consequently, a 26% increase in cumulative oil production. [ABSTRACT FROM AUTHOR]