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Utilization of prediction and optimization techniques in machining operations assists with the decision-making required for machining parameter selection, directly impacting process outcomes including the surface roughness of the workpiece. Often these methods exclude the consideration of tool wear and critical information contained within sensor data collected during the cutting process. This study enhances the application of a hybrid physics-based and machine learning predictive framework for Ra that incorporates tool wear information via a focused investigation of computer numerical control (CNC) turning AISI 316, followed by a comparative analysis of metaheuristic optimizers to determine the optimal machining parameters. The experimental results include an analysis on the influence of the machining parameters, flank wear, and total cutting distance of the tool on the surface roughness. The proposed prediction model was improved for AISI 316 from a previous study conducted by the authors. The model achieved a root-mean-square error (RMSE) of 0.108 μm and testing results indicated that 87% of predictions fell within limits set by the ASME B46.1-2019 standard. Genetic algorithm (GA), particle swarm optimization (PSO), and simulated annealing (SA) are compared using the modified prediction model as the objective function. Despite GA producing the lowest minimum Ra for constrained and unconstrained testing cases, SA generated the highest accuracy during validation testing, achieving an error of 4.54% for a constrained scenario. The outcomes of this study strengthen the hybrid prediction framework proposed by the authors and reinforce the value the optimization process provides to machining operators in the manufacturing industry. [ABSTRACT FROM AUTHOR]

Considering the extensive applications of turning and facing operations in mechanical engineering manufacturing, the energy consumption of machining equipment has emerged as a significant concern in the manufacturing industries, such as aerospace and automotive. This article focuses on establishing a predictive model and optimizing energy consumption for facing and turning operations on two types of steel, namely, AISI 1038 and AISI 4142, using two different cutting tools. The experiments were planned using Taguchi's L16 orthogonal array, and the coefficients of the predictive model were determined through a linear regression approach. Additionally, a genetic algorithm (GA) with two distinct selection techniques was employed to optimize the three main variables: cutting depth, cutting speed, and feed rate, all aimed at reducing energy consumption. The results of this study show that the selection method used in GA significantly affects convergence toward the optimal solution, while the choice of cutting tool has a considerable impact on energy usage. Moreover, the variation in the effect of cutting parameters on machining energy consumption is analyzed to determine which parameters contribute most to energy savings. The results underscore the importance for manufacturers of using advanced predictive and optimization tools, offering them a competitive edge by achieving economic, environmental, and performance-related benefits. [ABSTRACT FROM AUTHOR]

In this paper, a multi-objective prediction and optimization integration method combining response surface method (RSM) and non-dominated sorting genetic algorithm-II (NSGA-II) was proposed to improve the overhead low-power pulsed laser-MAG (metal active gas) hybrid welding quality. A four-factor with a five-level experiment matrix considering the weld current(I), weld speed(V), laser power(P), and assembly clearance(C) was established based on te central composite design method. The relationship between weld parameters and back weld width (BW) and weld reinforcement (WR) was approximated by RSM. The significance and adequacies were validated by the analysis of variance and validation experiments, and the average error of validation experiments between predict and actual value is less than 5%, indicating that the model exhibits high predictive accuracy. The individual effect of a single parameter and the interaction of multiple parameters on BW and WR were studied, and the weld current and assembly clearance are the most significant parameters influencing BW and WR, respectively. NSGA-II was used for multi-objective optimization taking the constructed RSM models as objective functions and generating the Pareto-optimal front composed of optimal solutions. Finally, the verification experiments show that the optimal solutions of different fronts can obtain the desired welding morphology without any defect. [ABSTRACT FROM AUTHOR]

The edge-cloud computing continuum effectively uses fog and cloud servers to meet the quality of service (QoS) requirements of tasks when edge devices cannot meet those requirements. This paper focuses on the workflow offloading problem in edge-cloud computing and formulates this problem as a nonlinear mathematical programming model. The objective function is to minimize the monetary cost of executing a workflow while satisfying constraints related to data dependency among tasks and QoS requirements, including security and deadlines. Additionally, it presents a genetic algorithm for the workflow offloading problem to find near-optimal solutions with the cost minimization objective. The performance of the proposed mathematical model and genetic algorithm is evaluated on several real-world workflows. Experimental results demonstrate that the proposed genetic algorithm can find admissible solutions comparable to the mathematical model and outperforms particle swarm optimization, bee life algorithm, and a hybrid heuristic-genetic algorithm in terms of workflow execution costs. [ABSTRACT FROM AUTHOR]

Tensile properties of multifilament polyester woven fabrics are of great importance for their end uses such as parachutes, sails, tents, sleeping bags, filters and surgical textiles. The filament fineness, weave type and weave density have a great influence on the tensile properties of these fabrics. In this study, artificial intelligence (AI) models such as artificial neural networks (ANN), fuzzy logic (FL) and genetic algorithms (GA) were developed to forecast breaking strength and breaking elongation values of multifilament polyester woven fabrics. The fabric samples used in the study have three different microfilament finenesses and two different conventional filament finenesses with plain, twill and satin weave types. By applying four different weft density values, totally 60 woven fabric samples were obtained in the experimental design. The regression coefficient values ( R 2 ) between actual and predicted results were obtained as 0.80, 0.90 and 0.92 with ANN, FL and ANN–GA hybrid methods, respectively. The mean absolute percentage error (MAPE) was lower than 6% for all AI techniques used in this study. As a conclusion, it was proved that the breaking strength and breaking elongation properties of multifilament polyester woven fabrics can be forecasted with high accuracy rates by AI techniques. [ABSTRACT FROM AUTHOR]

Low-impact development (LID) practices are critical for mitigating urban stormwater runoff and alleviating flood risks. The strategic placement of LID facilities is paramount to optimizing their efficacy within urban landscapes. This study conducts a comprehensive bibliometric analysis of LID-related literature over the past decade, utilizing data visualization tools to elucidate key disciplines, publication trends, and the prevalence of various optimization algorithms. We delve into the application of multi-objective optimization (MOO) algorithms in LID facility layout, examining their practical applications, theoretical underpinnings, and case studies. The paper also scrutinizes the strengths and limitations of these algorithms, proposing future research trajectories that leverage MOO to enhance LID's role in urban stormwater management. [ABSTRACT FROM AUTHOR]

The vibration of machine tool spindle is very important for machining. Firstly, in order to explore the relationship between spindle vibration and process parameters, the spindle vibration acceleration when polishing aero-engine blade was measured, and the quadratic polynomial models between the spindle vibration acceleration in X, Y and Z direction and the process parameters were established. Secondly, a quadratic polynomial model for the influence of process parameters on polished surface roughness was also determined. Thirdly, through the Analysis of Variance (ANOVA) and main effect analysis, it can be seen that the spindle speed has the greatest impact on the vibration. Finally, a multi-objective optimization model was established with the optimization objective of minimizing spindle vibration acceleration and polished surface roughness, and the optimal process parameters were solved using genetic algorithm. The optimal process parameters were verified and the results show that the polished surface roughness with the optimal process parameters are all less than 0.4 μm, and the deviation rates between the theoretical optimization results of spindle vibration acceleration and the experimental results are all less than 10%, indicating that the optimization results are good. [ABSTRACT FROM AUTHOR]

With the increasing demand for fresh food markets, refrigerated transportation has become an essential component of logistics operations. Currently, fresh food transportation frequently faces issues of high energy consumption and high costs, which are inconsistent with the development needs of the modern logistics industry. This paper addresses the optimization problem of multi-vehicle type fresh food distribution under time-varying conditions. It comprehensively considers the changes in road congestion at different times and the quality degradation characteristics of fresh goods during distribution. The objectives include transportation cost, dual carbon cost, and damage cost, subject to constraints such as delivery time windows and vehicle capacity. A piecewise function is used to depict vehicle speeds, proposing a dynamic urban fresh food logistics vehicle routing optimization method. Given the NP-hard nature of the problem, a hybrid Tabu Search (TS) and Genetic Algorithm (GA) approach is designed to compute an optimal solution. Comparison with TS and GA algorithm results shows that the TS-GA algorithm provides the best optimization efficiency and effectiveness for solving large-scale distribution problems. The results indicate that using the TS-GA algorithm to optimize a distribution network with one distribution center and 30 delivery points resulted in a total cost of CNY 12,934.02 and a convergence time of 16.3 s. For problems involving multiple vehicle types and multiple delivery points, the TS-GA algorithm reduces the overall cost by 2.94–7.68% compared to traditional genetic algorithms, demonstrating superior performance in addressing multi-vehicle, multi-point delivery challenges. [ABSTRACT FROM AUTHOR]

Air conditioning, as an essential appliance in daily life, has the function of ensuring comfortable room temperature, but it is also accompanied by a large amount of power consumption. Consequently, the study suggests an energy consumption prediction model based on improved genetic algorithm—least squares support vector machine—to accurately predict the energy consumption of building heating, ventilation, and air conditioning. This model uses the improved genetic algorithm for regularization parameter and kernel parameter optimization to prevent overfitting and underfitting issues. According to the testing results, the least squares support vector machine, an upgraded genetic algorithm, may accomplish convergence faster than other algorithms, taking only 0.2 milliseconds to finish. In addition, the average relative error of the improved genetic algorithm- least squares support vector machine did not exceed 0.6%. In the energy consumption prediction for the whole year of 2022, the average error of the improved genetic algorithm-least squares support vector machine was only 2.0 × 106 kWh, and the prediction accuracy could reach up to 97.2%. The above outcomes revealed that the energy consumption prediction model can accurately predict the air conditioning energy consumption, which provides a strong support for the control and optimization of the air conditioning system. [ABSTRACT FROM AUTHOR]

As wireless communication systems increasingly require compact and efficient antennas, conventional antenna design methods are proving difficult to meet the rigorous demands of modern applications. For this purpose, this study introduces a methodology which uses pixelated Inverted-F Antenna (IFA) inspired designs optimised through genetic algorithms to enhance performance in constrained spatial environments. As pixels the antenna features the exemplary use of Einstein Hat-shaped tiles, enabling the antenna to efficiently utilize space. A with the proposed method optimised antenna is compared to traditional IFA designs and shows improved properties like enhanced antenna gain and efficiency as well as smaller reflection coefficient offering a promising solution for future compact antenna systems in the Internet of Things and beyond. Finally, a prototype was manufactured and the scattering parameters and antenna gain were measured within an anechoic chamber. [ABSTRACT FROM AUTHOR]

The Johnson-Cook model, widely used in numerical analysis, is typically derived from split Hopkinson pressure bar tests, which are costly, time-consuming, and challenging to perform consistently. To address this, research aims to develop more efficient and consistent dynamic material models using optimization algorithms. This study employs genetic algorithm to predict key variables of the Johnson-Cook failure model for aluminum 6061, using consistent quasi-static test results as inputs. Validation was conducted through impact tests on 6 mm and 8 mm thick aluminum plates, comparing the predicted variables with impact analyses based on penetration hole diameters. The predicted variables yielded errors of 0.12 % and 2.31 % for the 6 mm and 8 mm plates, respectively, whereas reference variables showed errors of 0.40 % and 5.08 %. These findings confirm that analyses using the predicted variables more accurately simulate impact tests, demonstrating the effectiveness of combining genetic algorithms with quasi-static tests for predicting material model variables. [ABSTRACT FROM AUTHOR]

To mitigate the existing challenges with piles driven in intermediate geomaterial (IGM), this study presents an economic impact assessment for steel piles in IGMs based on the newly developed and existing static analysis (SA) methods using 149 test pile data from seven US states. The assessment determines the differences in the number of piles and the equivalent steel pile weight. The proposed SA methods yield, on average, a smaller difference in steel weight based on states, pile types, and bearing IGM layers. Three machine learning (ML) algorithms: random forest, support vector machine (SVM) and neural network are applied to predict the difference in steel weight. Three percentage-based variables are employed in the ML algorithms as inputs: total pile penetration, total shaft resistance and end bearing in IGM. Based upon 31 testing data, SVM with the lowest RMSE, MAD and highest pseudo-R2 is identified as the best algorithm. The predicted difference in steel weight from SVM is optimized to zero using a novel application of the genetic algorithm, and various contour maps are generated. These contour maps can be used to predict the difference in steel weight graphically based on the three percentage-based variables for future driven steel piles in IGMs. [ABSTRACT FROM AUTHOR]

The internet and technology have developed so fast that the entire world is experiencing the fourth industrial revolution. However, organizations' traditional digital capabilities are not enough to respond to the growing market needs. With the use of Internet technologies, virtualization can be used dynamically in the operational management of supply chains. As a result, supply chains can be controlled, planned, and optimized remotely and through the internet based on virtual objects instead of direct observation. This research seeks to optimize the supply chain by considering the dimensions of supply chain virtualization in the form of two objective functions, profit and processing rate. The presented model has been optimized under conditions of demand uncertainty and in a triangular Neutrosophic environment using the Non-Dominated Sorting Genetic Algorithm II. Finally, by solving a numerical example, the sensitivity analysis of the algorithm performance and its application in the model have been investigated. The results of this research showed that the profit of the virtual supply chain increases compared to the traditional supply chain due to tracking defective parts and identifying returnable products. [ABSTRACT FROM AUTHOR]

This paper focuses on the difficulties in high-quality and high-efficiency micro-milling nickel-based superalloy micro thin-walled parts. The second-generation Non-dominated Sorting Genetic Algorithm (NSGA-II) is improved. A central composite experiment is designed, and a surface roughness prediction model is developed for micro-milling thin-walled parts. A prediction model for surface residual stress on thin-walled parts is developed using an L9(34) orthogonal simulation experiment. Using the NSGA-II algorithm, the four cutting parameters (spindle speed, feed per tooth, axial cutting depth, and radial cutting depth) are optimized to achieve low surface roughness and high material removal rate, while stable cutting and surface compressive residual stress are considered constraints. Finally, the high-quality and high-efficiency micro-milling of the Inconel 718 cross-shaped thin-walled parts is realized. [ABSTRACT FROM AUTHOR]

Predicting seismic events has long been a huge challenge due to intricate nature of the underlying occurrence mechanisms. The traditional approach to estimating recurrence intervals is highly reliant on prior knowledge and expert experience, which often leads to considerable error margins. Recent advances in high-performance monitoring systems and artificial intelligence (AI) achieving great successes in various areas have renewed interest in identifying failure precursors and predicting seismic events. In this paper, we propose a genetic algorithm wrapped stochastic configuration networks (GAWSCN) approach to explore seismic physics for time-to-failure prediction. Specifically, stochastic configuration networks (SCN), a novel randomized learning paradigm, is utilized to learn essential seismic information from acoustic emission signals. To further enhance the generalization capability and alleviate overfitting, a regularization technique is imposed during the SCN training to establish a robust seismic prediction model. In particular, a genetic algorithm is integrated with SCN to optimize the selection of pertinent seismic features and further improve prediction performance. The experimental results demonstrate that our GAWSCN method outperforms other machine learning methods across evaluation metrics. This study offers promising insights for geophysical hazards assessment. [ABSTRACT FROM AUTHOR]

We formulate the generalized group technology concept in cellular manufacturing under two new assumptions. Each process routing is indexed by a level of quality and each pair of machines is indexed by a level of compatibility. The quality of parts depends on their processing. The compatibility between machines corresponds to their ability to work together. Three objective functions are designed. They are minimizing exceptional elements, maximizing total quality of parts, and maximizing total compatibility between machines. A genetic algorithm (GA) embedded simple augmented ε-constraint (SAEC) is proposed. Computational experiments are performed on the GA-embedded SAEC, the SAEC, and a nondominated sorting GA. We compare the trade-off solutions produced by the three techniques. Due to the similarity in structure, we also make a direct comparison between the GA-embedded SAEC and the SAEC in terms of the quality of the most preferred solutions obtained. The results indicate that the hybrid approach performs satisfactorily within reasonable computation time. [ABSTRACT FROM AUTHOR]

A lead-free Ni–B–W (ENB-W) coating deposited by electroless method was investigated in present work. Heat-treated ENB-W coating (450 °C for 3 h) were exposed to tribological tests within a range of parameters, including load (10–50 N), speed (0.5–1.5 m/s), and distance (400–1000 m). Prior to tests, coated specimens before and after heat-treated condition were characterized. The typical globular morphology and crystalline nature was seen. The hardness, scratch hardness and first critical load of failure improved in heat treated condition. To optimize wear rate and coefficient of friction (COF), an approach has been introduced, i.e., integration of an artificial neural network (ANN) with a genetic algorithm (GA). The ANN model yielded a mean absolute percentage error of 8.4421% for predicting wear rate and 2.4138% for predicting COF. Pareto front analysis identified the optimal operating conditions as load of 34.2099 N, speed of 0.5006 m/s, and distance of 726.7243 m, resulting in both a minimum wear rate of 0.1003 × 10−8 g N−1 m−1 and a COF of 0.2099. In addition to optimization, the study also involved the characterization of the wear mechanisms of the coatings to gain a deeper understanding of their tribological behaviour. Different wear mechanisms were seen at different range of the parameters. The wear rate and COF did not vary significantly with sliding distance except at 10 N load. In fact, the wear rate was lower at 30 N and 50 N compared to 10 N. [ABSTRACT FROM AUTHOR]

The energy efficient electric motors save energy thus reduce operating costs. Since there are several losses affecting the motor efficiency, fan design plays an important role to minimize these losses. This study examines the effects of the design parameters of a radial bladed fan on the motor efficiency in a 132 frame with a power of 7.5 kW. The parametric analysis was carried out with the computational fluid dynamics method, and the results were used for the multiobjective genetic algorithm (MOGA) optimization study with the highest efficiency and the lowest motor body temperature objectives. The hub height, hub radius, distance to body cover, blade rising angle, fan cover entrance distance, blade edge angle, blade center radius, blade edge radius, blade end radius, and center edge distance were selected as optimization parameters. 151 simulations were performed. The results showed that the most important parameter for fan efficiency is the hub height which is the parameter that determines the height of the fan impeller diameter. According to the results, the optimum fan design increased the efficiency by 8% compared to the original fan and reduced the winding temperature by 8 °C. The optimized fan design was manufactured and tested against imulation data. This study contributes to sustainable development goals by improving motor efficiency that reduces the cost, designing of new components, and cooling the fan effectively that reduces the amount of copper used. [ABSTRACT FROM AUTHOR]

The increasing demands for precision and efficiency in machining processes, driven by advancements in aerospace, biomedical, and automotive industries, have led to a growing need for highly accurate machined parts. Stainless steel 316, a commonly used material in these sectors, presents specific challenges due to its intended applications, particularly in biomedical and aerospace fields. Electric discharge machining (EDM) is a favored method for working with this alloy. However, EDM has inherent challenges, such as dimensional overcuts, which have limited its applicability. To address these issues, the potential of three different electrode materials, namely, copper (Cu), brass, and aluminum (Al), has been extensively explored. Additionally, the choice of the optimal dielectric is crucial as it directly affects the heat input to the electrode, influencing the melting and vaporization of the tool wear. In this context, the inclusion of graphene as an additive has been explored to minimize radial overcuts. It is worth noting that these concerns have not been comprehensively addressed before. The experimental design by Taguchi has been employed for the research, and the results indicate that the performance of the Cu electrode surpasses that of other dielectrics. The artificial intelligence-based artificial neural network (ANN) was constructed to predict the values of OC. It was found that in the given dataset, the R2 has a value greater than 0.99 for all possible training and validation. Based on the ANN, a multi-objective optimization through genetic algorithm (MOGA) was performed. It was found that the dimensional accuracy achieved by Cu electrode was 70.51% better than the highest OC value given by same electrode. Moreover, the OC suggested by the MOGA for brass and Al electrodes was 40.21% and 34.37% better compared to the highest values of radial overcut obtained during the actual experimentation. [ABSTRACT FROM AUTHOR]

Silicon wafers find applications in manufacturing large-scale micro-components widely available in consumer-based electronic devices. The milling-electrochemical spark micromachining (Milling-ECSMM) process is popular nowadays as an advanced micromanufacturing process for creating microchannels and several other geometric features. This paper presents a parametric effect of voltage, tool rotation, electrolyte concentration, and duty cycle on MRR, Ra, and TWR, using a tungsten carbide (WC) two-fluted flat-end milling cutter. The experimental model was developed using response surface methodology (RSM), and parametric multi-objective optimization was carried out using desirability function analysis (DFA) and genetic algorithm (GA). SEM and optical images were taken to identify the surface quality of the machined microchannel. The optimal combination of parameters obtained from GA as 90 V, 13 rpm, 64 g/l, and 40% DC gives better convergence and multi-objective response for MRR, TWR, and Ra as 0.409 mg/min, 0.0428 mg/min, and 0.572 µm, respectively. [ABSTRACT FROM AUTHOR]

The need for intelligence in today's era has tremendously increased the demand for Internet of Things (IoT) devices implanted to collect and process diverse data. Cloud computing offers a plethora of services to computationally constrained internet of things devices, but latency degrades the performance of real-time applications. A few other computing paradigms have been developed through the years to overcome this limitation of IoT devices. Fog computing, one of these paradigms, comes into picture as a backbone and offers services to applications which are required to be processed within a deadline. However, addressing challenges such as heterogeneity, offloading mechanisms, resource allocation, and complexity is crucial. This paper presents a framework for intelligent offloading mechanisms and an efficient resource allocation that results in improving the quality of service (QoS) parameters in an integrated cloud-fog-IoT environment. The proposed Fuzzy based Harris Hawks -Genetic Algorithm (HHGA) applies fuzzy-based logic to offload tasks to respective paradigms (cloud or fog), where the upcoming IoT request will be executed. In addition, the Fuzzy-based HHGA algorithm is developed by combining conventional Harris Hawks Optimization (HHO) and Genetic Algorithm (GA) to improve the exploration and exploitation. The proposed algorithm is eventually integrated with the present framework to search for the optimal resources for upcoming requests and reduce the service cost, time, and energy consumption. The experiments are conducted and consecutively the performance of the proposed framework is evaluated. The results demonstrate that the proposed algorithm outperforms Harris Hawks Optimization by 16.95%, Genetic Algorithm by 38.23% and Particle Swarm Optimization (PSO) by 23.09%. [ABSTRACT FROM AUTHOR]

First-Person Shooter (FPS) games are among the most popular latency-sensitive multiplayer online games. A separate game server manages each multiplayer group hosted appropriately to meet the given Quality of Service (QoS) and Quality of Experience (QoE) requirements of the game and the players. Cloud computing is typically used to host these game servers to cope with changing workload requirements, given its elasticity, scalability, and on-demand provisioning characteristics [1]. However, the Cloud still suffers from known latency limitations, especially when dealing with latency-sensitive applications such as FPS games. Hence, Fog computing can be a good alternative for hosting FPS game servers, given that Fog nodes hosting the servers can be placed close to the players, thereby reducing latency. However, a careful resource management is necessary given the Fog's capacity unpredictability. This paper introduces an optimization-based game server placement approach that minimizes server costs while optimizing latency delays and, eventually, QoE for the players. A static model for the server placement problem is formulated as an Integer Linear Program (ILP) with valid inequalities. Given the complexity of the problem, a resolution strategy based on a non-linear, double penalty relaxation technique in conjunction with a proven Genetic Algorithm (GA) is developed. A dynamic version of the placement problem is also formulated to cope with the realistic scenarios dealing with changing multiplayer groups and Fog servers. A two-step dynamic scheme, based on the developed GA, is applied. Extensive simulations are conducted showing a good performance of the proposed approach both in terms of solution quality and execution time. [ABSTRACT FROM AUTHOR]

Virtual machine placement (VMP) has a significant importance with respect to resource utilization in cloud data centers. Indeed, the optimized management of machine placement usually results in a significant reduction in energy consumption. VMP is a bin packing problem generalization, which is a well known hard combinatorial optimization problem. Besides being NP-hard, VMP is characterized by conflicting objectives and a noisy search space. Meta-heuristics, such as genetic algorithms, particle swarm optimization (PSO), cuckoo search (CS), tabu search and simulated annealing (SA) have been shown to be effective for this category of problems. This paper reports a performance comparison between SA, CS and PSO meta-heuristics to solve the VMP problem. In contrast to reported research work in this area, we study the performance behavior of these three meta-heuristics with respect to, not only the quality of solutions, but also the quality of the explored solution sub-space, in addition to the convergence speed towards reported solutions and the speed with which each meta-heuristic evolves towards the best reported optimized solution. Extensive simulations on randomly generated tests with sizes varying between 200 and 1000 virtual machine demands show that PSO achieves the best performance behavior with respect to all criteria. Moreover, for all tests, PSO produces a reduction of as much as 17% of the number of physical machines, 15% of the energy cost and 21% of the resource utilization of physical machines. [ABSTRACT FROM AUTHOR]

In this research paper, we propose a two-stage hybrid approach that uses machine learning techniques and meta-heuristic algorithms. The first step, known as data preparation, involves converting string values to numeric format and subsequently normalizing the data. To increase the performance beyond the limitations of traditional methods, we use population-based meta-heuristic algorithms, namely Atom Search Optimization (ASO) and Equilibrium Optimization (EO), for feature selection, aiming to achieve global optimization. The second step, called attack detection, focuses on distinguishing normal traffic from malicious traffic. To improve the performance of this step, we use K-means clustering and firefly algorithm (FA). In addition, an elitism method is randomly integrated. The resulting approach is called ASO-EO-FA-K-means. We evaluate the performance of our proposed method using two datasets, namely NSL-KDD, UNSW_NB15, and KDD_CUP99. To establish a benchmark, we compare our method with other approaches including Particles Swarm Optimization (PSO), Genetic, Grey Wolf Optimization (GWO), Ant colony optimization (ACO), Harris Hawk Optimization (HHO), NSGA-2, Multi-objective PSO, Multi-objective GWO, learning vector quantization (LVQ), XGBoost, particle swarm optimization based on C4.5 (PSO-C4.5) and genetic algorithm based on multilayer perceptron (GA-MLP)) we compare. The evaluation results show that the proposed method achieves the highest accuracy and the lowest error rate in three datasets NSL-KDD and UNSW_NB15 KDD-CUP99 with accuracy values of 0.998, 0.995 and 0.995, respectively. In addition, our method shows superior efficiency in terms of computation time. In general, our research shows the effectiveness of the ASO-EO-FA-K-means method in intrusion detection and provides better accuracy and efficiency compared to alternative approaches. In all three data sets, the results have shown that NSL-KDD data set with MSE 0.012, accuracy value 0.998 has obtained better results than other data sets. [ABSTRACT FROM AUTHOR]

Many businesses utilize cost-efficient cloud services to execute their industrial and scientific workflow applications. Business continuity is a very important issue for both cloud users and providers. To have reliable workflow execution, the engagement of reliable resources is a challenging job that can supply business continuity. In addition, the lowest execution time and monetary cost are two cost features that adhere users to providers. In this regard, the task scheduling algorithm is very prominent in reducing costs in favor of users and providers. To address the issue, a system framework and different cost-type models are suggested. Then, the task scheduling issue is formulated into an optimization problem with an overall cost reduction viewpoint. To solve this NP-Hard problem, a hybrid genetic algorithm (HGA) is presented for reliable and cost-efficient task scheduling of workflow execution in a heterogeneous cloud computing environment. The proposed HGA has different phases chief amongst them is to apply new crossover and mutation operators for global search, and a Walking around procedure to enhance the quality of local search solutions. It makes a good balance between local and global searches in a huge search space that leads to efficient results. To verify the proposed hybrid algorithm, it has been tested in different twelve scenarios with variable communication to computation ratios datasets. The results of extensive simulations in twelve datasets scenarios prove that HGA significantly dominates other state-of-the-art in terms of three prominent cost metrics, namely, makespan, monetary cost, and failure cost in the amount of 14.10%, 18.70%, and 42.30% cost reduction respectively. [ABSTRACT FROM AUTHOR]

To ensure that ecotourism development remains sustainable, the best place for such activities should be chosen based on the ecological potential. This study attempts to identify suitable ecotourism sites by developing a quantitative geographic model using multi-criteria evaluation (MCE), optimized by a non-dominated sorting genetic algorithm (NSGA-II). Three criteria (physical, biological, and socio-economic features), 13 sub-criteria, and 33 indices were first collected from primary and secondary data sources. Then, MCE method was applied to find ecotourism suitable areas, in which two methods of fuzzy overlay and weighted linear combination (WLC) were used to overlay criteria maps. Finally, NSGA-II was used to optimize ecotourism zoning through defining three objectives, including minimizing the distance from the sub-criteria of natural attractions, vegetation, and historical-cultural sites. Results show the WLC method is better than the fuzzy method at combining different layers to determine suitable zones for ecotourism, through which more than 50% of the study area, about 28,000 hectares, was classified as suitable for ecotourism. Matching 85% of suitable areas obtained by NSGA-II with high and very high suitable classes obtained by WLC shows that combining the MCE method with NSGA-II provided a more suitable hybrid method for ecotourism site suitability evaluation. This study creates a valuable tool for those responsible for planning and carrying out ecotourism initiatives, allowing them to further assess and conduct ecotourism projects. [ABSTRACT FROM AUTHOR]

Due to the significant participation of the users, it is highly challenging to handle enormous datasets using machine learning algorithms. Deep learning methods are therefore designed with efficient hyperparameter sets to enhance the processing of the vast corpus. Different hyperparameter tuning models have been used previously in various studies. Still, tuning the deep learning models with the greatest possible number of hyperparameters has not yet been possible. This study developed a modified optimization methodology for effective hyperparameter identification, addressing the shortcomings of the previous studies. To get the optimum outcome, an enhanced genetic algorithm is used with modified crossover and mutation. The method has the ability to tune several hyperparameters simultaneously. The benchmark datasets for online reviews show outstanding results from the proposed methodology. The outcome demonstrates that the presented enhanced genetic algorithm-based hyperparameter tuning model performs better than other standard approaches with 88.73% classification accuracy, 87.31% sensitivity, 90.15% specificity, and 88.58% F-score value for the IMDB dataset and 92.17% classification accuracy, 91.89% sensitivity, 92.47% specificity, and 92.50% F-score value for the Yelp dataset while requiring less processing effort. To further enhance the performance, attention mechanism is applied to the designed model, achieving 89.62% accuracy, 88.59% sensitivity, 91.89% specificity, and 89.35% F-score with the IMDB dataset and 93.29% accuracy, 92.04% sensitivity, 93.22% specificity, and 92.98% F-score with the Yelp dataset. [ABSTRACT FROM AUTHOR]

By analyzing the influence of stochastic perturbation matters on vehicle path optimization, a perturbation scheduling model for logistics and distribution with a carbon tax mechanism is established under the premise of time window variation and load capacity constraints. Herein, we propose an enhanced Genetic Algorithm (GA) based on a Gaussian matrix mutation (GMM) operator, which maintains the diversity of the population while speeding up the algorithm's convergence. The model builds a Gaussian probability matrix using the site positional order distribution characteristics implied in the original site data information, and applies the Gaussian probability matrix to individual gene mutations using a roulette-wheel-selection method; thus, the study guarantees the genetic diversity of the population while guiding it to evolve in the high-fitness direction. Finally, an experimental simulation is performed using data obtained from a commercial supermarket, thereby verifying the effectiveness of the proposed algorithm and comparing it with other algorithms. The results reveal that compared with the classical GA, the average convergence speed of the improved GA can be increased by 50–60% and the consumed algorithm time can be reduced by 48% while maintaining the difference in solution accuracy within 1%. [ABSTRACT FROM AUTHOR]

White tea is valuable for having a high antioxidant content, which is considered to possess numerous beneficial effects on health. This study investigated the application of microwave-assisted extraction (MAE) for the extraction of total phenolic compounds from white tea. The experimental setup included four independent variables: microwave power (ranging from 100 to 300 W), extraction time (ranging from 10 to 40 min), temperature (ranging from 35 to 50 °C), and the ratio of food to solvent (ranging from 0.25 to 0.5 g/10 mL). The responses that were evaluated were IC50 (ppm) and total phenolic content (mg/g). The experimental design consisted of thirty runs conducted within the MAE system. The group method of data handling (GMDH) models were used to predict important efficiency measures (IC50 and total phenol content) in the extraction process. The models were assessed based on their ability to capture the relationships between input conditions and efficiency outputs. Three GMDH variants were compared: baseline GMDH, GMDH optimized with a genetic algorithm (GMDH-GA), and GMDH optimized with a harmony search algorithm (GMDH-HS). While all models achieved high predictive ability on a test set, GMDH-HS emerged as the superior performer. It achieved near-perfect agreement with observations (d-index > 0.998), minimal errors (NRMSE < 0.02), and effectively captured data variance (NSE > 0.99) for both outputs. Correlation diagrams and Taylor diagrams confirmed the superior performance of GMDH-HS in terms of linearity, correlation, and error minimization. This study demonstrates the effectiveness of hybridizing GMDH with a harmony search algorithm for complex modeling tasks, paving the way for improved efficiency and yield optimization in extraction processes. [ABSTRACT FROM AUTHOR]

The severe damage observed in the Kathmandu Basin, Nepal, during past earthquakes necessitates a thorough study of the seismic behavior of the basin sediments. As the shear-wave velocity is directly related to the elastic shear modulus of the material, it is essential to determine it to incorporate the behavior of the soil in the design of the structure. Hence, we determined average shear-wave velocity in upper 30 m (Vs30) of soil in Bhaktapur district in the eastern part of the Kathmandu Basin at 73 observation points, employing two methods involving the use of non-invasive microtremor array measurements (MAMs). These MAMs are widely used for determining subsurface soil characteristics by analyzing the ambient vibrations of the ground. The first method involves inversion using a genetic algorithm, and the second is a method for obtaining Vs30 directly from the dispersion curve. We found that Vs30 in the southeastern part of the study area was higher than that in other parts. Conversely, Vs30 in the western region was lower. The calculated Vs30 values were used to classify the sites. The elevated eastern and southeastern areas with high Vs30 were categorized as dense soil or soft rock, whereas the areas with low Vs30 that had suffered significant damage during the 2015 Gorkha earthquake were classified as soft soil sites. [ABSTRACT FROM AUTHOR]

The novel coronavirus disease has caused severe threats to the daily life and health of people all over the world. Hence, early detection and timely treatment of this disease are significant to prevent the coronavirus's spread and ensure more effective patient care. This work adopted an integrated framework comprising deep learning and attention mechanism to provide a more effective and reliable diagnosis. This framework consists of two convolution neural network (CNN), a bidirectional LSTM, two fully-connected layers (FCL), and an attention mechanism. The main aim of the proposed framework is to reveal a promising approach based on deep learning for early and timely detection of coronavirus disease. For greater accuracy, the framework's hyperparameters are tuned by means of a genetic algorithm. The effectiveness of the proposed framework has been examined utilizing a public dataset including 18 different blood findings from Albert Einstein Israelita Hospital in Sao Paulo, Brazil. Additionally, within the experimental studies, the proposed framework is subjected to comparison with the state-of-the-art techniques, evaluated across various metrics. Based on the derived consequences, the proposed framework has yielded enhancements in accuracy, recall, precision, and F1-score, registering approximate improvements of 1.27%, 4.07%, 3.20%, and 2.88%, respectively, as measured against the second-best rates. [ABSTRACT FROM AUTHOR]

This paper proposes a solution to enhance and compare different neural network (NN)-based side-slip angle estimators. The feed-forward neural networks (FFNNs), recurrent neural networks, long short-term memory units (LSTMs), and gated recurrent units are investigated. However, there is a lack in the selection criteria of the architectures' hyper-parameters. Therefore, the genetic algorithm is integrated with the NN-based estimators to find the optimal hyper-parameters for the studied architectures. The tuned hyper-parameters in this work include the number of neurons, number of layers, activation function, optimizer type, and learning rate. The objective function of the optimization problem is minimizing the root-mean-square error (RMSE) on multiple testing data. The optimal models are further included in the design of a hybrid NN estimator with Kalman filter. In the hybrid estimators, the optimal NN estimators are used as virtual sensors to correct the prediction of the side-slip angle resulting from the mathematical lateral vehicle model. Eventually, the performance of the best selected model is evaluated in terms of different metrics; mean RMSE, mean error variance, mean training time, and mean estimation time. LSTMs are found to achieve the lowest mean RMSE while being tested on highly generalized data yielding the highest training and estimation time. However, FFNNs achieve the lowest RMSE while being tested on low generalized data and the lowest training and estimation time. Meanwhile, it is observed that the hybrid estimators achieved lower RMSE with great enhancement compared to the non-hybridized ones proving the effectiveness of the proposed approach and increasing the side-slip estimation generalization ability in unknown environments with high uncertainties, which are not covered by the training dataset for the NNs estimators. [ABSTRACT FROM AUTHOR]

Cloud environment is used for its high efficient utilization of bandwidth and its high processing speed. In 5G mobile communication environment, most of users sends and receives more number of highly occupied multimedia information. Hence, It is important to handle such cloud environment with minimal bandwidth and maximize speed. Therefore, it is mandatory to have proper schedule for processing of multimedia videos over the cloud environment. In this paper, Workflow Scheduling and Optimization Algorithm (WSOA) using deep learning model is proposed for the processing of multimedia video. The frames in each multimedia video are separated and processed individually. The Gabor transform is applied on each spatial frame to convert them into time–frequency frame. From this time–frequency frame, various frame parameters and features such as Local Binary Pattern (LBP), Local Ternary Pattern (LTP) and statistical features are computed to form the feature set. This feature set is scrutinized using Evolutionary Approach as Genetic Algorithm (GA) and the final optimized feature set is classified by the proposed Convolutional Neural Networks (CNN) architecture which produces the priority results. [ABSTRACT FROM AUTHOR]

With the high-speed development of the automobile industry, the number of domestic vehicles has increased significantly, which has posed a great challenge to the efficiency of road access. To fully utilize the intersection scheduling function of the intelligent transportation system, an optimization of the stranded traffic system based on LoRa is studied. Then, the greedy strategy and adaptive coefficient are used for optimization to obtain an improved genetic algorithm (GA), which is used to verify the intersection scheduling function. In addition, comparative experiments are conducted with standard GA, adaptive genetic algorithm (AGA), and hybrid genetic algorithm (HGA). The AGA optimizes the GA by adapting genetic parameters, while the HGA combines GA with a simulated annealing algorithm. The results showed that comparing the improved GA with GA, AGA, and HGA, the improved GA reduced the average value of individual extremum by 50.36%, 47.51%, and 37.16%, respectively. Compared with other mainstream algorithms, the improved GA reduced the number of stranded vehicles at intersections to 0 during traffic light timing. This indicates that the improved GA has higher and better performance and scheduling advantages in the intersection scheduling of intelligent transportation road service systems. The research aims to provide new ideas for improving traffic efficiency in intelligent transportation systems and promoting the informatization development of modern traffic management. [ABSTRACT FROM AUTHOR]

An effective health status assessment (HSA) for pump station units (PSUs) is crucial for accurately determining their real status and providing technical support for safe operational decisions. Due to the limitations of existing data-driven HSA methods, which primarily focus on the temporal dependencies of monitoring signals and fail to explore the complex interconnections among signals comprehensively. Moreover, when constructing performance degradation indices based on linear differences, these methods do not effectively integrate heterogeneous signals, resulting in an incomplete and inaccurate assessment of the overall system degradation. This paper proposes a real-time comprehensive HSA method for PSUs based on multi-source heterogeneous uncertainty information. Initially, a health benchmark model (HBM) is built using CrossGNN, which possesses cross-scale and cross-variable interaction capabilities, to precisely capture the temporal dependencies and dynamic relationships among variables in monitoring signals. Subsequently, key measurement points that reflect the operational status of the PSUs are identified through correlation analysis to establish multi-source evaluation indices. Then, considering the uncertainty in signal changes, a novel health degradation index (HDI) is developed using Mahalanobis distance (MD) and the Gaussian Cloud Model (GCM) to analyze changes in unit status. Furthermore, a weighting calculation method based on the non-dominated sorting genetic algorithm (NSGA-II) is proposed to establish a real-time comprehensive health index (RCHDI) for a thorough assessment of PSUs status. Finally, the effectiveness of the proposed method is validated through a case study using data from a pump station in the South-to-North Water Diversion Project in China. The results show that, compared to other studies, the proposed method significantly improves the stability and smoothness of the state assessment curve, with increases of 21.5% and 47.1% respectively, providing a new perspective for comprehensively assessing the health status of PSUs.V [ABSTRACT FROM AUTHOR]

This work addresses the Knapsack Problem with Forfeit Sets, a recently introduced variant of the 0/1 Knapsack Problem considering subsets of items associated with contrasting choices. Some penalty costs need to be paid whenever the number of items in the solution belonging to a forfeit set exceeds a predefined allowance threshold. We propose an effective metaheuristic to solve the problem, based on the Biased Random-Key Genetic Algorithm paradigm. An appropriately designed decoder function assigns a feasible solution to each chromosome, and improves it using some additional heuristic procedures. We show experimentally that the algorithm outperforms significantly a previously introduced metaheuristic for the problem. [ABSTRACT FROM AUTHOR]

In this study, four Artificial intelligence (AI) - based machine learning models were developed to estimate the Residual compressive strength (RCS) value of concrete supported with nano additives of Nanocarbon tubes (NCTs) and Nano alumina (NAl), after exposure to elevated temperatures ranging from 200 to 800 degrees. These models were developed via adapting meta- heuristic models including the Water cycle algorithm (WCA), Genetic algorithm (GA), and classical AI models of Artificial neural networks (ANNs), Fuzzy logic models (FLM), in addition to the statistical method of Multiple linear regression (MLR). 156 post heating experimental results available as a literature data (represents four input parameters of temperature change, heat exposure duration, nanomaterial type, and replacement proportion) are used to achieve the study's objective. Results of the developed models demonstrated that ANN and FLM have strong potential in predicting RCS. However, it is often infeasible to generate practical equations that relate input and output variables from these models. Upon analysing the results of the WCA and GA, it was found that WCA yielded the most accurate predictions based on all performance indicators. Furthermore, RCS prediction equations with superior accuracy were derived utilizing the meta-heuristic AI models of WCA and GA, with Mean absolute errors (MAEs) of 3.09 kg/cm² and 3.53 kg/cm² for the training, 1.91 kg/cm² and 2.72 kg/cm² for the validation, and 1.91 kg/cm² and 2.72 kg/cm² for the testing data sets, respectively. Additionally, sensitivity analysis via neural networks weights and SHAP investigation were performed to reveals the impact and relationship of the input variables with the output variables. Both techniques reveal that temperature degree and time of exposure had the highest positive impact on RCS value, followed by NAl and NCTs, in order. [ABSTRACT FROM AUTHOR]

Tool design is very important in the modern manufacturing industry. However, the traditional tool design process often has a heavy workload, which reduces the production efficiency of tool manufacturing enterprises. To deal with these problems, this paper proposes an integrated tool design platform to solve the problems in tool design. The tool-integrated design platform uses parametric modeling technology to allow design engineers to quickly create a geometric model of the tool by setting key parameters. The platform also introduces cutting simulation technology. Design engineers can perform simulation analysis on the platform and evaluate its performance and feasibility by simulating different tool design schemes. At the same time, this paper also uses cloud database technology to realize the function of multi-person remote collaborative design. In addition, the platform also uses genetic algorithm to optimize the cutting depth, cutting width, feed rate, and spindle speed of the tool with the minimum three-way milling force as the goal to guide the actual processing and production. In summary, by introducing technologies such as parametric modeling, parametric simulation, and cloud databases, enterprises can effectively solve the problems in the tool design process. The design time cycle is greatly shortened, and multi-person remote collaborative design becomes more convenient. This will significantly improve the enterprise's production efficiency and help the tool's precise design. [ABSTRACT FROM AUTHOR]

Cloud computing is the paradigm for delivering streaming content, office applications, software functions, computing power, storage, and more as services over the Internet. It offers elasticity and scalability to the service consumer and profit to the provider. The success of such a paradigm has resulted in a constant increase in the providers' infrastructure, most notably data centers. Data centers are energy-intensive installations that require power for the operation of the hardware and networking devices and their cooling. To serve cloud computing needs, the data center organizes work as virtual machines placed on physical servers. The policy chosen for the placement of virtual machines over servers is critical for managing the data center resources, and the variability of workloads needs to be considered. Inefficient placement leads to resource waste, excessive power consumption, and increased communication costs. In the present work, we address the virtual machine placement problem and propose an Imitation-Based Optimization (IBO) method inspired by human imitation for dynamic placement. To understand the implications of the proposed approach, we present a comparative analysis with state-of-the-art methods. The results show that, with the proposed IBO, the energy consumption decreases at an average of 7%, 10%, 11%, 28%, 17%, and 35% compared to Hybrid meta-heuristic, Extended particle swarm optimization, particle swarm optimization, Genetic Algorithm, Integer Linear Programming, and Hybrid Best-Fit, respectively. With growing workloads, the proposed approach can achieve monthly cost savings of €201.4 euro and CO 2 Savings of 460.92 lbs CO 2 /month. [ABSTRACT FROM AUTHOR]

A deep-neural-network-based multi-sensor data and defect prediction algorithm for gas metal arc additive manufacturing (GMA-AM) is proposed. The core idea is to collect current, voltage and sound signals during GMA-AM using multiple sensors and to combine with a one-dimensional convolutional neural network (1D-CNN) model to identify different defect states. First, the current, voltage and sound signals are adaptively decomposed by optimizing the variational modal decomposition (VMD) parameters through the whale optimization algorithm (WOA), and the energy entropy of the decomposition components is used as the feature vector to detect different defect states. Then, a defect prediction model is established using 1D-CNN to classify and discriminate five types of typical defects: trajectory deviation, pore, slagging, thermal deformation, and surface unevenness. Experimental verification shows WOA-VMD can adaptively decompose the signals of arc additive manufacturing and detect different defect states with 92.22% accuracy. [ABSTRACT FROM AUTHOR]

Roll force prediction plays a significant role in rolling schedule and optimization. For a specific steel grade, the roll force can be determined by the aid of several factors: rolling speed, initial thickness, ratio of thickness reduction, the starting temperature of the strip, and the friction coefficient in the contact region. Roll force prediction mathematical models are sometimes rare and inaccurate. This paper presents a new approach to predict roll separating force using semi-supervised support vector regression (SSSVR). The parameters affecting the sensitivity of the SSSVR were optimized using the genetic algorithm to maximize the r-squared accuracy score. The intelligent system is evaluated using two quality metrics: the root mean square error (RMSE) and the mean absolute error calculated between the measured force from the industrial rolling field and the predicted force using the proposed system from one side, and the measured force and the calculated force from another side. Obtained results show the improvement while using the intelligent predictive system. The reduction in RMSE was achieved by the proposed system by 66.9% and 32.1% for oval and round shape passes, respectively in comparison to the conventional calculation method. [ABSTRACT FROM AUTHOR]

The atmosphere is one of the game elements that can significantly influence player's emotions. However, creating an immersive atmosphere that effectively influences player emotions poses several challenges, necessitating the utilization of various elements, such as audio-visual coordination and gameplay design. This paper introduces a general framework for procedurally generating dungeons with joyful and horror atmospheres in games, providing an abstract perspective to address these challenges. The proposed framework introduces a categorization system for game elements based on their role within the game. Leveraging this categorization, the Comprehensive Arrangement of Game Elements (CAGE) pattern is introduced, which facilitates the appropriate placement of elements within the dungeon environment. Subsequently, the General Framework for Generating Dungeons with Atmosphere (GFGDA) is employed to procedurally create the dungeon using the Feasible–Infeasible Two-Population (FI-2Pop) algorithm. To enhance gameplay experience, similar elements in the dungeon environment that impact gameplay are grouped and their coordination is evaluated by creating a graph based on the CAGE pattern. The transition and coordination of audio-visual elements along the path between these impactful elements are assessed in order to generate an immersive atmosphere within the dungeon. To ensure diversity, examining the variety of dungeons generated over 100 runs demonstrates that our method consistently produces distinct results in each iteration. Moreover, two comparative studies were conducted, one with 51 volunteers and another with 10 volunteers. In the first study, the Game Experience Questionnaire (GEQ) was utilized to assess the emotional impact of dungeons generated by our method. These were compared to dungeons created using a uniform random approach, alongside relevant research. The results suggest that our method significantly influences player emotions across the four components of the GEQ—sensory and imaginary immersion, flow, negative effects, and challenge—when compared to dungeons generated by the uniform random approach and another researched method. In another study, the emotional impact of two dungeons, one generated with joyful elements and the other with eerie elements, was evaluated using the GEQ. The findings indicate significant differences between the two components of the GEQ—tension and positive effects—when players interacted with the level containing joyful elements compared to the one with eerie elements. [ABSTRACT FROM AUTHOR]

The proliferation of data sharing over the Internet has given rise to pressing concerns surrounding data security. Addressing these concerns, steganography emerges as a viable mechanism to safeguard data during transmission. It involves concealing messages within other media, such as images, exchanged over networks. In this research, we propose an innovative image steganography approach by harnessing the capabilities of bio-inspired algorithms. A central challenge in steganography revolves around the inherent pixel correlations within cover images, which may inadvertently leak sensitive information to potential intruders. To tackle this challenge head-on, we harness the potential of bio-inspired algorithms, which have exhibited promise in efficiently mitigating these vulnerabilities. This paper introduces a steganography strategy rooted in a fusion model that seamlessly integrates diverse bio-inspired algorithms. Our novel embedding approach ensures the production of robust and high-quality cover images and disrupts bit sequences effectively, thereby enhancing resistance against potential attacks. We meticulously evaluate the performance of our method using a comprehensive dataset encompassing grayscale and JPEG color images. Our particular emphasis on color images arises from their superior capacity to conceal a greater volume of information. The results vividly demonstrate our approach's effectiveness in achieving secure and efficient data concealment within images. [ABSTRACT FROM AUTHOR]

Distributed Denial of Service (DDoS) attacks continue to pose a significant threat to network infrastructures, exploiting vulnerabilities within existing security protocols and disrupting the seamless availability of online services. The intricate interconnections of nodes within computer networks contribute to the dynamic structure of this environment, complicating efforts to establish a secure and productive user experience. Effectively mitigating DDoS attacks in this complex networked setting remains a challenge. While current strategies primarily rely on anomaly detection and signature-based techniques, utilizing statistical analysis and predefined patterns to identify and thwart attacks, none have consistently demonstrated efficacy or reliability. Consequently, there is a compelling need for advancements in security mechanisms to address DDoS threats more effectively. This research introduces an innovative and highly efficient approach that incorporates various classification algorithms, including Random Forest, Decision Tree, Gradient Boosting, Linear SVM, Logistics, K-nearest neighbors (KNN), and AdaBoost, for DDoS attack detection. The performance of these machine learning classifiers is evaluated using key metrics such as accuracy, recall, F1-score, and precision. Remarkably, experimental results reveal outstanding accuracy rates, with Random Forest achieving the highest accuracy in detecting attacks. Additionally, a genetic algorithm is employed to select optimal features from the dataset, further enhancing the performance of the classifiers. This results in a notable 25% increase in accuracy, surpassing AdaBoost and Logistics, with K-nearest neighbors emerging as the top performer in terms of accuracy. [ABSTRACT FROM AUTHOR]

Preconception and prenatal genetic counseling is a well-established means of risk assessment in many parts of the world, and in recent years, an emerging concept in India. Likelihood of an offspring having autosomal recessive disorder increases based on the degree of consanguinity. Hence, genetic testing of the couple for the identification of carrier status for disease-causing variants is crucial. The purpose of this study is to understand the frequency of genetic abnormalities in consanguineous marriages by using a comprehensive genetic testing algorithm where in karyotyping, FISH, exome sequencing and microarray are used sequentially to determine the genetic etiology based on the clinical presentation and to evaluate the need and benefits of preconceptional and prenatal genetic counseling. This retrospective study includes 66 couples having consanguinity referred for genetic counseling and testing. Of the 66 couples, 58 underwent comprehensive genetic testing which included Karyotyping, Fluorescence in Situ Hybridization (FISH), Microarray and Exome sequencing based on their clinical presentation. The analyses revealed a genetic abnormality in approximately 31% and chromosomal polymorphic variations & variants of uncertain significance in 17% of the couples. Counseling in these couples helped in identifying the carrier status and enabled them to take an informed decision in subsequent pregnancies. These findings reiterate the acute need for preconception and prenatal genetic counseling services in India. [ABSTRACT FROM AUTHOR]

Conventional supply chains face challenges in reducing overall logistics costs and optimizing the consumption of logistics resources. To address these challenges, we propose a two-phase path planning method for handling periodic logistics tasks. The novelty of our approach lies in harnessing the collective power of vehicles with periodic commuting paths to minimize logistics costs, offering a practical and convenient solution for achieving smart supply chains. In the first phase, we develop an optimization model to determine the optimal delivery paths for periodic logistics tasks, with the objective of minimizing the overall cost. Leveraging the A-star algorithm, we search for suitable sequence sets of commuting vehicles within the commuting path network for each logistics task. In the second phase, we select vehicle sequences from the available sets for each task, forming a combination that can effectively accomplish all logistics tasks. To obtain the optimal solution, we employ an adaptive genetic algorithm to refine and optimize this combination. Experimental results demonstrate that our approach effectively solves the problem of periodic logistics delivery planning in smart supply chains. It achieves cost savings of 15.6% and 13.8% in two cases compared to traditional methods, showcasing its practicality and efficiency in reducing logistics costs. [ABSTRACT FROM AUTHOR]

Compared with the traditional AC grid, the flexible DC grid has the advantages of low wire loss and large transmission capacity, but it is difficult to extract fault signals and diagnose various faults. Therefore, a fault detection method based on complete ensemble empirical mode decomposition with adaptive noise analysis (CEEMDAN) multiscale entropy (MSE) and genetic algorithm optimization support vector machine (GA-SVM) is proposed. Firstly, CEEMDAN is used to decompose the extracted fault line mode voltage signal into several intrinsic mode function (IMF). The IMF containing more fault information is selected to reconstruct the denoising signal. The MSE of the reconstructed signal is calculated and input into the GA-SVM classifier as the fault feature, and the fault line mode voltage signals of different fault types under different operating conditions are classified and recognized. A large number of simulation results prove that the proposed method has strong anti-interference ability and high reliability, and has high classification accuracy in the case of small sample data. Compared with Linear-SVM, PSO-SVM, KNN and Fine Tree intelligent algorithms, the proposed method shows a significantly improved accuracy, 93.8888% on average. [ABSTRACT FROM AUTHOR]

The classical methods for optimizing wireless power transfer (WPT) systems using mathematical equations or finite element methods can be time-consuming and may only sometimes yield optimal designs. In order to overcome this challenge, this paper introduces a novel approach integrating machine learning techniques with meta-heuristic methods to design and optimize a miniaturized, high-efficiency WPT receiving coil for biomedical applications. The objective is to achieve dimensions below 20 mm, a depth of 30 mm within the tissue, and a frequency of 13.56 MHz. Our approach leverages a neural network (NN) model to predict efficiency based on geometric coil parameters, eliminating the need for complex equations. The NN was trained on a dataset generated via finite element method simulations. We employ two meta-heuristic algorithms, the genetic algorithm and the coyote optimization method, to find optimal parameters that maximize efficiency. Our NN model demonstrates exceptional accuracy, exceeding 97%. Furthermore, the proposed WPT coil design approach enhances transfer efficiency by up to 76%, significantly reducing computation time compared to classical methods. Finally, we validate our results using finite element simulation with Ansys Maxwell 3D. [ABSTRACT FROM AUTHOR]

The fault-induced delayed voltage recovery (FIDVR) and short-term voltage instability are increasing, especially due to the widespread implementation of residential air conditioners (RACs) in modern power systems. Single-phase induction motors in RACs have a high potential to stall in less than two to three cycles following a voltage dip in transmission or distribution systems. Using Shunt-FACTS devices, such as SVC and STATCOM, is a suitable solution for mitigating FIDVR events. In this paper, the Bayesian regularized artificial neural networks technique is employed to solve multidimensional mapping problems, taking into account the reactive powers injected into Busses. Following this, a multi-objective dynamic VAR programming is proposed to identify the optimal size of STATCOM for short-term voltage instability using trajectory sensitivities and heuristic optimization. This method is subject to complying with the criteria for dynamic and transient performance during FIDVR events. Dynamic VAR planning is carried out with assistance of the non-dominated sorting genetic algorithm II (NSGA-ӀӀ). The proposed multi-objective approach has been tested on the IEEE 39-bus system, taking into account time-varying practical load models. The results illustrate the effectiveness of the proposed approach in solving reactive power optimization problems while moderating the consequences of FIDVR. [ABSTRACT FROM AUTHOR]

Machine learning and deep learning-based techniques are now widely used to identify and diagnose various diseases. However, getting a sufficient data for these machine learning models is difficult, and usually collected data is unbalanced i.e. less number of instances with disease class and a large number of classes without the disease. These imbalanced data cause poor performance of the classifier in the detection of minority or disease classes. To address this class imbalance problem for medical data we have applied 17 different class imbalance handling techniques on four publically available datasets with Random forest as a base classifier. The comprehensive review covering the time frame of 1990 to 2023 is also done on "Class imbalance handling techniques" to gather insights using VOSviewer software. Performances of different class imbalance handling techniques are statistically evaluated and impact of different disease datasets on the prediction performance is also statistically assessed. Two novel techniques Genetic Algorithm-Cost sensitive-Deep neural network(GA-CS-DNN) and Class imbalance handling technique-Genetic Algorithm-Deep neural Network(CIH-GA-DNN)are proposed for handling class imbalance problems. Performances of proposed techniques are compared with other state of art class imbalance handling techniques and obtained results showed that OnesidedSelection outperformed all other techniques. A statistical test further demonstrated that OnesidedSelection performs differently than SMOTENN. Significant statistical differences in illness prediction can be seen between the kidney and diabetes, prostate and kidney, and kidney and heart datasets when compared pair-wise. [ABSTRACT FROM AUTHOR]