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Avionics systems are a crucial part of aircraft, and the heterogeneity of resources also leads to load differentials within these systems. Inefficient load balancing technology faces the dual challenge of over-utilization and under-utilization of resources, which results in the decline of service performance (in the case of over-utilization) or the waste of resources (in the case of under-utilization). However, it is necessary to control avionics systems via an efficient load balancing method under time constraints and resource states. Therefore, this paper proposes a load balancing method for avionics systems using both artificial bee colony and simulated annealing algorithms. First, the load balancing model of avionics systems is established; this model can reflect the demands of the tasks for the resources in detail. Then, the load balancing of avionics systems is realized by artificial bee colony and simulated annealing algorithms, the hybrid algorithm not only retains the advantages of simple and easy implementation of ABC algorithm, but also utilizes the probability jump of SA algorithm to jump out of the local extreme and achieve the effect of global optimization. Finally, compared with the existing algorithms, the experimental results show that the algorithm proposed in this paper produces a good and stable load balance performance. [ABSTRACT FROM AUTHOR]

Wireless sensor location is a challenging task issue in the Internet of Things (IoT). Distance vector-hop (DV-hop) algorithm provides a range-free positioning scheme, but its position prediction method based on least square method brings a large positioning error. To overcome this issue, this paper constructs a three-dimensional (3D) many-objective positioning model. Specifically, we consider many factors, such as the error characteristics of the estimated distance, the distribution characteristics of nodes and the computational cost. Based on these factors, we propose a many-objective 3D-DV-hop positioning model, and propose a data preprocessing strategy and outlier removal strategy. Finally, a fashionable many-objective optimization algorithm is employed to solve the model. The experimental results show that the model proposed in this paper has great advantages in accuracy and robustness, and is superior to the current single and multi-objective positioning model. [ABSTRACT FROM AUTHOR]

Fuzzing is an important technique in software and security testing that involves continuously generating a large number of test cases against target programs to discover unexpected behaviors such as bugs, crashes, and vulnerabilities. Recently, fuzzing has advanced considerably owing to the emergence of new methods and corresponding tools. However, it still suffers from low coverage, ineffective detection of specific vulnerabilities, and difficulty in deploying complex applications. Therefore, to comprehensively survey the development of fuzzing techniques and analyze their advantages and existing challenges, this paper provides a comprehensive survey of the development of fuzzing techniques, summarizes the main research issues, and provides a categorized overview of the latest research advances and applications. The paper first introduces the background and related work on fuzzing. Research issues are subsequently addressed and summarized, along with the latest research developments. Furthermore, various customized fuzzing techniques in different applications are presented. Finally, the paper discusses future research directions. [ABSTRACT FROM AUTHOR]

Mobile manipulator robots that combine mobile platforms and robotic arms have been attracting considerable attention in recent years. In this paper, the motion control problem of the mobile manipulator system is considered with the following proposed control strategy. Firstly, a decoupled dynamic model is created to increase operation safety and to reduce complexity such that the independent controllers can be designed for the mobile system and the manipulator system. Subsequently, an effective reference trajectory generator is proposed to guide the mobile manipulator systems to the proper position for the manipulation being able to grip the target. Thereafter, the fuzzy controllers are designed for the mobile system to eliminate the tracking error and for the manipulator system to accomplish the gripping mission. The stability of the mobile manipulator control system can be guaranteed by the Lyapunov theory. Finally, the numerical simulations are given to demonstrate the effectiveness of the proposed approach. From the simulation results, it can be seen that this paper as well as other compared methods have good control response in case of small controller gain, in which the convergence time are about 4–5 s. However, by increasing the controller gain to improve the control response, the other methods will make the system unstable or the controller output will produce a large amount of chattering. The proposed controller in this paper can not only decrease the convergence time, from 5 to 3 s, but also provide a smooth control response. [ABSTRACT FROM AUTHOR]

In this paper, an Economic Order Quantity (EOQ) model of a fractional order with time-varying holding cost is discussed in detail with the help of numerical computations. As is well known, financial variables possess memories, which makes it appropriate for developing fractional modeling to describe the effect of memory in the financial system. This paper is concerned with the issues of deterioration of the items of the inventory system where the importance of time-varying holding cost on the memory-dependent inventory model has been studied. For the inclusion of the memory effect, fractional order derivative and fractional order integration have been used. Illustrative numerical examples and graphical presentations are exploited to verify the efficiency of the effect of fractional rate of change of inventory model and memory-affected costs. The numerical result shows that the output of memory-affected systems is different from the output of memory-less and low-memory-affected systems. Sensitivity analysis has been carried out in long memory effect as well as low memory effect to highlight the critical parameters of the two different systems. Sensitivity analysis clears that in the long memory effect, a small change in deterioration rate can disturb the system but in the low memory effect, such type of disturbances does not arises. At last, some conclusions are recommended. [ABSTRACT FROM AUTHOR]

In a competitive online retail market, orders for assembled products such as refrigerators, air conditioners, smart televisions, etc., attract significant attention due to their high gross merchandise value. Unlike other regular products, product delivery has a two-stage process—delivery of product components and assembly and installation of the final product—involving multiple parties that may be internal or external to the organization. Coordination of the above activities is essential to reduce customer dissatisfaction and to curb the various waiting or demurrage costs due to delayed arrivals of goods vehicles and traveling salesman. This paper attempts to model and solve such a realistic synchronized goods delivery and service problem against the online booking. In this model, one goods vehicle starts from the company's storehouse with all the goods to be delivered and moves continually, dropping the goods at the specified locations. For service, a traveling salesman separately moves and uses the appropriate conveyances among the available ones at each node to reach the customers. This paper poses some interesting research questions to understand the requirements of separate tour paths for goods vehicles and traveling salesman along with appropriate conveyance for traveling salesman's arrival. This is an NP-hard traveling salesman problem. For solving, a varied offspring memetic algorithm (VOMA) with modified probabilistic selection, varied offspring three-parent (i.e., surro-embryos) crossover and Fibo-generation-dependent mutation is developed and tested on some standard test functions to establish its superiority over the standard ones. VOMA implementation on the above proposed problem reveals the influence of unloading and service times, halt time and third-party outsourcing charges on the final optimum route design. Finally, the paper provides a structured decision-making framework for practitioners and showcases a case study by implementing VOMA in a similar problem context. [ABSTRACT FROM AUTHOR]

This paper focuses on the problem of motorcyclist helmet detection in single images. Although some previous works have been developed to deal with this problem, yet most of them are designed for videos and not suitable for single-image helmet detection. In view of this, in this paper, we propose a novel dual-detection framework for single-image motorcyclist helmet detection with multi-head self-attention. Particularly, two types of detectors are first trained, namely the rider detector and the head-shoulder detector, which are jointly leveraged in the dual-detection scheme. To take advantage of the contextual relevance information, the multi-head self-attention mechanism is incorporated, where multiple self-attention layers are integrated to capture the complex relationships among the input features so as to further enhance the detection accuracy. A new benchmark dataset, termed the SCAU helmet detection on motorcyclists (SCAU-HDM) dataset, is presented, which consists of 8000 training images and 2000 test images. Extensive experiments on the benchmark dataset demonstrate the effectiveness of the proposed framework. The code is available at https://github.com/LiChunHong2020/SCAUHDM. [ABSTRACT FROM AUTHOR]

In recent years, the automotive industry has witnessed the increasing adoption of mixed-model assembly lines (MMALs) to meet the demands of mass customization. However, the diverse components of various end products are posing significant challenges to the material feeding processes in MMALs. To address this issue and enhance the material feeding efficiency, this paper proposes an innovative automatic monorail shuttle system (AMSS). The AMSS is designed based on the layout of line-integrated supermarkets and incorporates the load-exchangeable shuttles and crossovers. Considering the importance of cost control in manufacturing enterprises, the objective of this paper is to minimize the total costs associated with the material feeding system, which includes the installation costs of crossovers, as well as the input and operation costs of load-exchangeable shuttles. A mathematical model is thereby established to describe the problem, and its accuracy is validated through the Gurobi solver. Recognizing the NP-hard nature of the proposed problem, a shuffled frog leaping-based hyper-heuristic (SBHH) algorithm is developed to allocate and schedule the shuttles and crossovers, which adopts the shuffled frog leaping algorithm as the high-level heuristic for selecting low-level heuristics. To further improve the performance, dynamic decision unit is introduced to raise the solution accuracy and the convergence speed. Simulation results verify the superiority of the proposed SBHH algorithm both in solution quality and convergence speed by comparing with other benchmark optimization algorithms. Managerial applications indicate the effectiveness and practicality of the proposed approach, offering implications for practitioners in the automotive industry. [ABSTRACT FROM AUTHOR]

The rapid advancement of IoT technology presents transformative opportunities across various sectors, with education being a prominent beneficiary. Smart classrooms, a product of IoT integration, are being widely adopted to create technology-enhanced, student-centric learning environments that cater to students' information literacy needs, particularly during events like pandemics. This widespread adoption generates substantial amounts of educational data, commonly known as big data, necessitating innovative solutions for analysis and utilization. To solve these challenges, this paper proposes utilizing the Apriori algorithm—a data mining technique renowned for uncovering valuable patterns and associations within extensive datasets. This paper evaluates the impact of various information resources with differing quality, considering individuals' information literacy skills. Utilizing data mining techniques, it delves into university students' information literacy data, integrating it with the university library resources to establish a data-driven information literacy education model. It then focuses on criteria, components, and effective methods for instructing college students in information literacy. Finally, a diverse group of students, from first-year undergraduates to doctoral candidates at a specific university, is studied for their engagement in information literacy instruction. Based on the experimental findings, sophomore students exhibited the highest level of participation at 75.9% accuracy, while postgraduate students received more information literacy training than undergraduates and Ph.D. students. When comparing this method to others, such as SVM, KNN, LR, RF, and DT, it achieved superior performance. Additionally, the quality of information literacy training in university libraries was assessed through three dimensions: student learning, behavior, and achievements. Only junior, senior, and first-year graduate students scored above 4, with scores of 4.18, 4.15, and 4.26, respectively. [ABSTRACT FROM AUTHOR]

This paper addresses the optimal control problems with an interval-valued objective function. We consider a type of total order relationships ≤ adm between two intervals. For each total order relationship, Kuhn–Tucker sufficient conditions for the optimal control problems with an interval-valued objective function are obtained. A numerical example is considered and solved. Kuhn–Tucker sufficient conditions provided under the total order relationship ≤ adm are sufficient conditions under the partial order relationship ⪯ LU for interval-valued optimal control problems. The results of this paper could help construct evolutionary algorithms to solve interval-valued optimal control problems. [ABSTRACT FROM AUTHOR]

This review aims to exploit a study on different benchmark test functions used to evaluate the performance of Meta-Heuristic (MH) optimization techniques. The performance of the MH optimization techniques is evaluated with the different sets of mathematical benchmark test functions and various real-world engineering design problems. These benchmark test functions can help to identify the strengths and weaknesses of newly proposed MH optimization techniques. This review paper presents 215 mathematical test functions, including mathematical equations, characteristics, search space and global minima of the objective function and 57 real-world engineering design problems, including mathematical equations, constraints, and boundary conditions of the objective functions carried out from the literature. The MATLAB code references for mathematical benchmark test functions and real-world design problems, including the Congress of Evolutionary Computation (CEC) and Genetic and Evolutionary Computation Conference (GECCO) test suite, are presented in this paper. Also, the winners of CEC are highlighted with their reference papers. This paper also comprehensively reviews the literature related to benchmark test functions and real-world engineering design challenges using a bibliometric approach. This bibliometric analysis aims to analyze the number of publications, prolific authors, academic institutions, and country contributions to assess the field's growth and development. This paper will inspire researchers to innovate effective approaches for handling inequality and equality constraints. [ABSTRACT FROM AUTHOR]

In this paper, Protist algorithm (PA) and Otocyon megalotis optimization algorithm (OOA) are applied to solve the power loss lessening problem. Protist Algorithm (PA) is modelled based on the Protist's natural activities. Protist exists in moist places. The leading nutritious phase is Plasmodium, the energetic and vibrant phase of Protist. In this segment, the organic substance in Protist search for food in surroundings and conceals enzymes for digestion. Natural actions of Otocyon megalotis are emulated to design the OOA approach. In the projected OOA searching of regions in exploration, for foodstuff the Otocyon megalotis mark the prey in the space is indicated as a global exploration. Real power loss reduction and Voltage stability enhancement are the key objectives of the paper. To solve the problem, Protist algorithm (PA) and Otocyon megalotis optimization algorithm. In the course of the migration procedure, the anterior end outspreads and interconnected arterial system that authorize cytoplasm to stream inside. Then, mutation and cross-over probability are employed to augment the performance of the Protist algorithm (PA). With this integration engendering of the population is done. Mutation classes the population exploration agents (PN) in uphill order conferring to the agents appropriateness (fitness) cost. Consequently, the technique splits the organized agents into three fragments rendering to their fitness value. In which PN/3 denotes to the population possess pre-eminent (aptness) fitness values, subsequently with second pre-eminent and poorest aptness (fitness) values. Then, in this paper, Otocyon megalotis optimization algorithm (OOA) is applied for solving the Power loss lessening problem. In the subsequent segment, navigate during the haunt to seal prey previous to the hit was replicated as a local search. In exploration, the data obtained is shared to all the associates of the family unit for continued existence and growth. Examination of the nearby terrain is modelled with reference to the fitness of all entities. Most excellent entity has investigated the majority fascinating terrain and it will be shared with family unit of Otocyon megalotis. Primarily, Otocyon megalotis show that it not involved in hunting. Conversely, as soon as moving near to prey Otocyon megalotis will perform the attack in quick mode. This approach imitated and designed in the local search segment. Authenticity of the Protist algorithm (PA) and Otocyon megalotis optimization algorithm (OOA) is corroborated in 23 benchmark functions, IEEE 30, 57, 300 and 354 test systems. Power Loss reduction achieved with voltage stability enhancement. Real power loss reduction attained. Both the Protist algorithm (PA) and Otocyon megalotis optimization algorithm (OOA) performed well in solving the Power loss reduction problem. [ABSTRACT FROM AUTHOR]

The paper aims to provide a theoretical basis for the application of revenue-sharing contract under bounded rationality and capital constraints. We consider an uncooperative ordering model in a supplier-Stackelberg game and coordination strategy with revenue-sharing contract for a loss-averse and capital-constrained retailer. We drive the existence and uniqueness conditions of the optimal solutions under bank financing and revenue-sharing contract. We also develop a series of propositions and corollaries to determine the optimal solutions and offer some managerial insights. The key contribution of the paper is to deepen and expand the revenue-sharing contract under the risk-neutral assumption, and to provide a theoretical basis for the application of revenue-sharing contract under bounded rationality and capital constraints. We find that the revenue-sharing ratio of loss-averse and capital-constrained retailer is larger than that of neutral retailer and the expected utility of loss-averse and capital-constrained retailer is larger than that of neutral retailer under coordination strategy with revenue-sharing contract. [ABSTRACT FROM AUTHOR]

A maximum cut (Max-Cut) problem in graph theory is NP-hard. This paper proposes a new randomized algorithm for finding local Max-Cut in graphs by using fuzzy logic. This paper proves that: (1) the computational complexity of computing local Max-Cut in graphs is in the class of randomized polynomial time (RP); (2) the real number solution of the new algorithm satisfies ϵ - δ condition; (3) local Max-Cut solutions are maintained after defuzzification that converts real number vectors to integer vectors. Numerical experiments show that the new algorithm outperforms IBM CPLEX solvers. The new algorithm is nine times faster than the CPLEX Convex solver and more than thirty times faster than the CPLEX Global solver. The new algorithm could find local Max-Cut in signed graphs whereas CPLEX Convex solver failed to find Max-Cut in signed graphs when Laplacian matrix was not positive semidefinite. [ABSTRACT FROM AUTHOR]

In today's global landscape, efficient logistics management is crucial for fostering sustainable development across industries. Integrating big data analytics into logistics operations has emerged as a transformative approach to boost efficiency, minimize waste, and mitigate environmental impact. This paper aims to explore the significance of utilizing big data analytics to optimize logistics management for sustainable development. The current challenges in logistics management primarily revolve around four key aspects: the efficiency of goods delivery, raw material costs, one-stop solutions, and service evaluation. These challenges persist in traditional logistics management due to their strong independence. Therefore, this paper is devoted to the optimization of logistics flow management through the lens of sustainable development and ecological cycles. Initially, by considering the Sustainable Development logistics management mode and survey data from 302 logistics enterprises, improvements are made in the four aspects. Subsequently, a four-element analysis function is designed, comprising factors for efficiency improvement, cost reduction, comprehensive optimization, and service enhancement. This function, which aims to assess logistics operations thoroughly, is a crucial component of the research. It includes components for enhancing services, cutting costs, improving efficiency, and conducting comprehensive optimization. MATLAB 21.0 b software is utilized for model analysis, primarily focusing on Volar model area analysis. The aim is to identify and categorize new optimization directions for SD logistics management mode based on the collected data and analyzed samples. The effectiveness of the SD logistics management mode is verified by observing model results and trends. Ultimately, the paper demonstrates the relevance of optimizing logistics management mode by significantly improving the efficiency of goods delivery, reducing raw material costs, enhancing one-stop solutions, and evaluating service-sharing information. [ABSTRACT FROM AUTHOR]

Internet of things establishes communication among heterogeneous devices. IoT network is low power and lossy network known as LLN. The components in LLN use low power for its operations. The Internet Engineering Task Force (IETF) has defined routing protocol for standardized LLN, i.e., routing protocol for low-power and lossy networks (RPL). One of the major challenges in RPL is efficient conservation of node energy to improve the life of the LLN network. In the RPL network, most of the energy is consumed while regulating and controlling the packets rather than transmission. The algorithm used for regulating and controlling packet in RPL is called trickle timer algorithm. Hence to improve the lifetime of network it is essential to modify the existing trickle timer algorithm. In this paper, we have proposed a new algorithm called EE-trickle. The performance of EE-trickle is compared with existing trickle using the simulator Cooja and using open test bed of future Internet of things lab. From the experiments, it is identified that EE-trickle provides better PDR along with less energy consumption than the existing trickle. Hence, the paper helps the future researchers who work on energy consumption in RPL to make use of EE-trickle in their experiment rather than existing trickle. [ABSTRACT FROM AUTHOR]

The number of papers on machine learning and deep neural networks applied to intrusion detection systems (IDS) is ever-increasing. Differently from existing work on the topic, this paper explores the effect of training-time randomness of deep neural networks, which is overlooked by the related literature. Training-time randomness is regulated by the seed of the pseudorandom number generator, and affects the performance of IDS models. The seed selection is studied in conjunction with other critical learning parameters: to the best of our knowledge, there are no similar studies in IDS. The experiments are done with a recent and widely consolidated intrusion detection benchmark, which is used to train and test a neural network under different combinations of seeds and parameters both in supervised and semi-supervised learning modes. The results are inferred by a mixture of explorative analysis, design of experiments, and analysis of variance. According to the results, the choice of the seed yields either excellent or scarce detection metrics; more importantly, the seed selection might be as relevant as the other major learning parameters assessed. [ABSTRACT FROM AUTHOR]

In this paper, we first point out that the proof of the assertions (Theorems 3.12 and 3.25) in a previous paper by Jose and Mathew [Merin Jose and Sunil C. Mathew, Generalization of LM-filters: sum, subspace, product, quotient and stratification, Soft Computing 27(2023) 809-819] is incorrect. Then, we give the new constructs of the supremum and product of a family of LM-G-filters. Moreover, we give the definition of LM-G-fuzzy congruence relations on X. And, we prove that the category of LM-G-fuzzy congruence relational spaces and the category of LM-G-filter spaces are categorically isomorphic. [ABSTRACT FROM AUTHOR]

In the grey prediction, the GM(1, 1) model is an important type, but it sometimes shows big prediction errors. To improve the prediction precision of GM(1, 1) model, the paper makes improvements from the following three aspects: (1) to improve the data's adaptability to the model, the paper transforms the accumulated generating sequence of original time sequence; (2) to make the model meet the variation characteristics of data, the paper extends the grey action of traditional GM(1, 1) model; (3) to avoid big average simulation or prediction relative error of model, the paper considers the minimum of the maximum of the two errors as the optimization objective function. The new extended grey model is called the GM(1, 1, exp × sin, exp × cos) model. The paper uses an improved particle swarm optimization (PSO) algorithm for the parameter optimization of GM(1, 1, exp × sin, exp × cos) model and thus improves the model's convergence rate and precision. According to the model and method proposed, the paper builds a GM(1, 1, exp × sin, exp × cos) model for China's GDP per capita. Results show that the model has high precision. [ABSTRACT FROM AUTHOR]

Aiming at the shortcomings of Chimp optimization algorithm (ChOA), which is easy to fall into local optimal value and imbalance between global exploration ability and local exploitation ability. To improve ChOA from the perspective of multi-strategy mixing, MSChimp was proposed, and the algorithm was applied to global optimization and minimum spanning tree problems. The main research work of this paper is as follows: (1) In the initialization stage of ChOA, an opposition-based learning strategy was introduced to improve the population diversity; Sine Cosine Algorithm (SCA) was introduced in the exploitation process to improve the convergence speed and accuracy of the algorithm in the later stage, so as to balance the exploration and exploitation capabilities of the algorithm. (2) The improved algorithm was compared with different types of meta-heuristic algorithms in 20 benchmark functions and CEC 2019 test sets, and was used to solve the minimum spanning tree. The experimental results show that the improved ChOA has significantly improved the ability to find the optimal value, which verifies the effectiveness and feasibility of MSChimp. Compared with other algorithms, the algorithm proposed in this paper has strong competitiveness. [ABSTRACT FROM AUTHOR]

In recent years, the focus on modernization and advanced communication technologies has shifted to smart cities, where society and families, particularly, are engaged with various means of entertainment, e.g., cinema. In the context of cinema, movies have a pivotal role, where cultural heritage, along with urban and rural images, have gradually been marginalized from the commercial field to an elite position. The real picture of rural life has been narrowed down to explore the local cultural heritage. In China, rural communities have existed for centuries, where the rural images of farmers depict their standard of living, civilization and the way in which the ecological environment is protected. This paper studies the factors relevant to the transmission of rural images for a better understanding of cultural heritage conducive to rural culture's inheritance and development. This paper first constructs water yield and soil conservation models for rural eco-environmental protection. Next, the paper analyzes the main factors that influence the communication of rural images in these models, followed by an analysis of the communication mode used by each factor. Moreover, the proposed study disseminates rural images by creating an imaging action framework. Wuzhen, a famous historic town in China, is taken as a case study for this paper. The study analyzed the representation of Wuzhen's rural pictures over time, with 77% of the content dominated by bridges and rivers. The highest number of shootings, involving 82 households, was recorded in 2018. Therefore, 2019 showed a slight decline in shootings, with 67 households involved, indicating potential fluctuations in interest or external factors affecting video production that year. However, in 2019, bridges and rivers still constituted 75% of the content, underscoring their enduring significance in portraying the rural beauty of Wuzhen. These findings highlight the appeal and importance of these natural features in the region's visual narrative. [ABSTRACT FROM AUTHOR]

Automated software transformations and the process of automated program repair and improvement are an important aspect of modern software engineering practices. In this paper, we describe a system which uses a graph-based deep learning model that can be trained to automatically transform and improve computer programs. By operating on language-agnostic, universal graph-like structures easily extractable from source code files (abstract syntax trees), the deep learning agent learns which transformations should be effectively applied to various structures recognized in the source code in order to improve it. By defining a metric which we want to improve and introducing an optimization task—a reinforcement learning setting, an agent learns to automatically apply a chain of transformations to the program, drastically improving it. While similar program improvement processes exist, they exclusively use exhaustive search algorithms to try all the possible code transformations which is a long process susceptible to local optimum issues. Our solution aims to model and embed structural knowledge about the programs being transformed which greatly helps the agent to choose best possible code transformations to apply. Elements of the approach we present in this paper are further applicable not just to automatic software improvement tasks, but also to other code-related tasks. [ABSTRACT FROM AUTHOR]

To safeguard the content from malicious devices and users and to provide authentication and security to the transmitted content, it becomes mandatory to devise a combined watermarking and encryption algorithm. This paper addresses this concern by developing an algorithm to authenticate and encrypt the image before transmitting it safely. The algorithm was established using a watermarking technique. The watermarking was done by transforming the image into coefficients and embedding it in high-frequency components. The watermarked image was encrypted using a key generated using a combined logistical tent map and converted to DNA sequences. The sequences are stored and transmitted as such. At the receiver end, the image was recovered and de-watermarked. The label used for the watermark was compared with the obtained watermark at the receiver end. The proposed algorithm conforms to the required CIA triad (confidentiality, integrity, availability). The algorithm was tested for images in the Standard Test images database. On average, for an encrypted image, the peak signal to noise ratio (PSNR) was 9.1934 dB and had an entropy of 7.999 bits. The correlation coefficient of 0.0001, 0.0019, and 0.0003 for horizontal, vertical and diagonal directions was obtained. The watermark could be satisfactorily recovered from the cover, even with the addition of noise. The extracted watermark had a PSNR above 30 dB, with normalized cross-correlation (NCC) above 0.9, bit error rate (BER) below 0.1 and structural similarity index metric (SSIM) above 0.9. These results make the proposed algorithm suitable for authentication and encryption. [ABSTRACT FROM AUTHOR]

Wireless capsule endoscopy (WCE) is a technology that uses a pill-sized camera to visualize images of the digestive tract. It presents several advantages, since it is far less invasive, does not require sedation and has less potential complications compared to standard endoscopy. Hence, it might be exploited as alternative to the standard procedure. WCE is used to diagnosis a variety of gastro-intestinal diseases such as polyps, ulcers, Crohn's disease and hemorrhages. Nevertheless, WCE videos can contain thousands of images per patient that must be screened by medical specialists, besides, the capsule free mobility and technological limits cause production of a low quality images. In this paper, a Nouvel method based on Dense-UNet deep learning segmentation model is presented. This approach aims at red lesion, ulcer and polyp detection from WCE images. Then, we propose a modified residual attention network for images classification. The proposed methods training and validation accuracies are 97.57% and 92.70%, with training and validation intersection over union and dice coefficients of 75.31%, 71.29%, 83.50% and 80.66%, respectively, in the red lesion dataset. In the polyp dataset, the method achieved a training and validation accuracies of 98.26% and 92.33%, with training and validation intersection over union and dice coefficients of 92.09%, 95.62%, 80.13% and 87.14%, respectively. Besides, the proposed architecture reached an average accuracy of 99.30%, sensitivity, specificity and F1 score of 98.61%, 100% and 99.30%, respectively. These results demonstrate that the proposed approach is satisfactory in polyp and red lesion segmentation and ulcer detection. [ABSTRACT FROM AUTHOR]

Association rule mining (ARM) is a widely used technique in data mining for pattern discovery. However, association rule mining in numerical data poses a considerable challenge. In recent years, researchers have turned to optimization-based approaches as a potential solution. One particular area of interest in numerical association rules mining (NARM) is controlling the length of itemset intervals. In this paper, we propose a novel evolutionary algorithm based on the multi-objective firefly algorithm for efficiently mining numerical association rules (MOFNAR). MOFNAR utilizes Balance, square of cosine (SOC) and comprehensibility as objectives of evolutionary algorithm to assess rules and achieve a rule set that is both simple and accurate. We introduce the Balance measure to effectively control the intervals of numerical itemsets and eliminate misleading rules. Furthermore, we suggest a penalty approach, and the crowding-distance method is employed to maintain high diversity. Experimental results on five well-known datasets show the effectiveness of our method in discovering a simple rule set with high confidence that covers a significant percentage of the data. [ABSTRACT FROM AUTHOR]

With the expansion of IOTs and Wireless devices, data transmission has increased over the internet. Maintaining security during data transmission using invisible watermarks has become a focus and has captured the attention of numerous researchers. The three security necessities, privacy, integrity, and authenticity, call for close attention and remedial actions. In this respect, watermarking and cryptographic techniques are a probable way to handle the above-mentioned issues adequately. The paper presents a novel approach to watermarking using a combination of NSST, HD, and SVD transforms, which improves the robustness and security of the watermarked image. The method involves using a grayscale image as the watermark and embedding it in multiple cover images. The resulting images are transformed sequentially using NSST, HD, and SVD to obtain the final watermarked images. The authors also apply AES encryption to enhance security, which generates a hash subkey and authentication tag. The proposed methodology performs better than other watermarking techniques, with a maximum improvement of 47.81%. Overall, the proposed approach offers a more secure and robust solution for watermarking. [ABSTRACT FROM AUTHOR]

Type II fuzzy sets consider the uncertainties involved in the membership function of classical fuzzy set theory. The membership function of a Type II fuzzy set is obtained by blurring the boundaries of the original fuzzy set membership function. The interval-based modified Type II fuzzy set method is presented in this paper to measure the fuzziness present in medical images. Using Hamacher T-conorm as the aggregation operator, the membership functions of the upper and lower intervals have been combined to obtain the contrast-enhanced image. For experimental analysis, quantitative and qualitative metrics have been evaluated for different kinds of medical data sets. To test the efficiency of the proposed technique, the computed results are compared with state-of-the-art techniques. The qualitative and quantitative results clearly demonstrate that the performance of the proposed techniques is much better than the existing techniques for almost all the image data sets. The results evaluated for average values with standard deviation for all the datasets bear witness to the performance of the proposed technique. The mean opinion score and the processing time also support the efficacy of the proposed technique, which is much better than most state-of-the-art techniques except at some of the cases. [ABSTRACT FROM AUTHOR]

This paper proposes a thermal image-based diagnostic process using explainable artificial intelligence that can interpret 3D printer states. Through this process, it is possible to classify the status of the 3D printer, check the classified active area, and help determine the status of the equipment. First, the proposed methodology stores the thermal image through an acquisition system that visualizes the thermal image of the 3D printer. Second, training a convolution neural network (CNN) model is carried out by defining the state data for the thermal image. Finally, based on the CNN model, the proposed process classifies the thermal images of the 3D printer and visualizes the area that is the basis for the inference classification result using an explanatory artificial intelligence algorithm. Through the proposed process, the operator can check the 3D printers' condition through the thermal image. By presenting the basis for inference the state, even unskilled operators can quickly check the state of the 3D printer. [ABSTRACT FROM AUTHOR]

Particle swarm optimization approach (PSO) has illustrated promising performance on a variety of optimization problems. The critical issues that must be addressed in PSO research are how to balance conflicts around exploration, exploitation, and reasonably distribute computational resources. In this paper, a neighborhood-aware N states Markovian Switching PSO algorithm (NS-MJPSOloc) is proposed, where we have employed a local search based approach. The neighborhood of PSO algorithm is a single particle itself in the early stages of optimization. The neighborhood will eventually expand to include all particles as the amount of generations increases. The search space is segmented into N potential states according to the size of problem. The value of N is dependent on the dimension of problem space. Every iteration serves a transition probability-based Markovian switching technique to estimate the acceleration coefficients in an adaptable way. The transition probability determines the next state and adequate values for acceleration coefficients. Through adopting a local search strategy while interacting with all other particles in the group, every particle possesses a chance of improving itself. Moreover, new features have been incorporated to accomplish accuracy and stability. The primary contributions of the proposed work are to enhance the topological structure of a recently developed PSO variant by using a local search (loc) mechanism rather than a global (gbest) approach. The purpose of this idea is to develop a more adaptable and less sensitive novel version of PSO that can handle multi-modal and highly challenging problems. The proposed algorithm was then tested using a variety of uni-modal, multi-modal and rotated benchmark functions. The experimental findings show that the proposed NS-MJPSOloc outperformed most of the current versions in terms of accuracy and convergence time, notably for multi-modal problems. [ABSTRACT FROM AUTHOR]

Given the need for ship emission reduction and the energy constraints of unmanned surface vehicles, it is imperative to develop an efficient ship path planning method that incorporates energy-saving considerations. In this paper, considering the challenges posed by the complexity of the marine environment, an adaptive ant colony system is proposed for ship energy-saving path planning. Firstly, a ship energy consumption estimation model is presented by analyzing the influence of wind, wave, and current on energy consumption. Secondly, two process evaluation indexes are designed for the quality and diversity of the paths in the optimization process. Based on these indexes, three improvements are presented to enhance the performance of the ant colony system. These improvements include (a) an adaptive state transition rule adjusted dynamically based on the information entropy, (b) the number of ants contributing to the global updating rule and the corresponding update value are controlled dynamically based on the quality of the path, and (c) an improved recombination mechanism. Besides, an improved turning point optimization method and B-spline curve are introduced for path post-processing. Finally, the effectiveness of the proposed method is verified by simulation experiments. [ABSTRACT FROM AUTHOR]

Maintaining power balance between generation and demand, as well as frequency regulation, is more difficult in a microgrid (MG) power system, especially when the MG is operating in island mode with the integration of renewable energy (RE) sources and a varying load profile. In this instance, an optimized automatic load frequency control (ALFC) is more successful in lowering frequency deviations and enhancing the stability and resilience of the MG network. Hence, this paper proposes a modified sperm swarm optimization (MSSO) technique for ALFC of a bio and renewable energy (RE) integrated MG system. A chaotic search based on a one-dimensional (1D) chaotic map is adopted to intensify the exploitation and exploration characteristics of the sperm swarm optimization algorithm. The proposed MSSO technique is used to tune the gains of the proportional integral derivative (PID) controller to regulate the frequency of the MG system through the minimization of the integral time absolute error (ITAE) of frequency deviation. The effectiveness of the technique is evaluated in terms of steady-state and transient performance indices for the response of frequency deviation. According to the evaluated results of frequency deviation obtained under different dynamic conditions, the suggested MSSO-tuned PID controller significantly improved transient indices such as settling time, control error, and peak overshoot by a maximum of 39.8%, 11.7%, and 68.9%, respectively, over PID controllers tuned with other techniques (Salp swarm algorithm (SSA), particle swarm optimization (PSO), and sperm swarm optimization (SSO)). In addition, the steady-state indices such as IAE, ITAE, ISE, and ITSE were improved by a maximum of 86.6%, 93.9%, 96.2%, and 98.8%, respectively, using the suggested MSSO technique, as compared to other techniques. Additionally, a sensitivity analysis is performed under varying load conditions, changes in system parameters, and real-time variation in RE sources to validate the resilience. The proposed MSSO technique outperforms other techniques in terms of steady-state and transient indices for the response to frequency deviations, as determined by sensitivity analysis results. The real-time implementation of the proposed controller for the MG system is validated in hardware-in-loop analysis with its stability analysis in the frequency domain. [ABSTRACT FROM AUTHOR]

In this study, the goal is to obtain optimal solutions for fractional interval linear quadratic regulator problems. The major contribution made in this paper is to apply an indirect approach based on the interval calculus of variations with a joint application of constrained interval arithmetic to transcribe the fractional problem under study into a system of Volterra integral equations. To do so, we present the fractional interval Pontryagin's minimum principle for the extraction of necessary optimality conditions. The obtained conditions are first transformed into a system of Volterra integral equations and then using the Laplace transform method, we solve these equations and obtain the optimal solutions. One of the key points of this method is to keep the continuous form of the problems, which we can change to an equivalent form without discretizing it and proving the existence of its solution. Some numerical examples are carried out to confirm the standard criteria with theoretically accurate results. [ABSTRACT FROM AUTHOR]

Evaluating large-scale multi-objective problems is usually time-consuming due to the vast number of decision variables. However, most of the existing algorithms for large-scale multi-objective optimization require a significant number of problem evaluations to achieve satisfactory results, which makes the optimization process very inefficient. To address this issue, a fast interpolation-based multi-objective evolutionary algorithm is proposed in this paper for solving large-scale multi-objective optimization problems with high convergence speed and accuracy. In the proposed algorithm, decision variables are generated based on a small number of variables using an interpolation function. With this approach, only a small number of variables need to be optimized, so that the convergence speed can be greatly improved to make it possible to obtain satisfactory results with relatively low computation cost. The experimental results verified the superiority of our proposed algorithm over other state-of-the-art algorithms in terms of convergence speed and convergence accuracy on 108 test instances with up to 1000 decision variables. [ABSTRACT FROM AUTHOR]

In recent years, the rapid advancement of artificial intelligence (AI) technology has made remarkable strides in the music industry, profoundly impacting various aspects of our lives. This progress has extended to revolutionizing industries, streamlining processes, and enhancing personalization in music. AI's expanding capabilities have driven computer vision, natural language processing, and machine learning breakthroughs. As music enthusiasts increasingly turn to online platforms, the need for effective recommendation systems has become more pressing. The exponential growth of musical content poses a challenge in discovering music tailored to individual tastes. This paper proposed a personalized music recommendation system powered by deep learning, explicitly utilizing convolutional neural networks (CNNs) to address the challenges posed by expanding music libraries and diverse song resources. In today's era of ever-growing music databases and increasing content diversity, more than traditional retrieval methods are needed to efficiently connect users with their preferred track. Traditional music platforms rely solely on retrieval mechanisms, necessitating manual inputs such as artist names and titles to locate specific songs. However, this approach needs in-depth music data analysis and delivers personalized recommendations generated by various deep learning-based algorithms. This paper bridges this gap by harnessing deep learning algorithms for comprehensive music data analysis and personalized song suggestions. The proposed CNN-based recommendation system explores the domains of deep learning and content-based approaches, empowering users to discover their favorite tunes swiftly. This innovative mechanism leverages CNNs and deep learning algorithms to offer efficient, personalized, and data-driven music recommendations, transforming the user experience with their music collections. In experimental results, the proposed CNN model is compared with various deep learning algorithms, including DNN, RNN, and LSTM, as well as traditional algorithms like SVM, KNN, RF, and DT. Impressively, the CNN model achieved the highest accuracy at 95%, surpassing all other algorithms. [ABSTRACT FROM AUTHOR]

The linear induction motor based on local public transport has turned out to be more cost-effective in respect of energy cost as well as maintenance. This is due to the fact that linear induction motors constitute a strong durability rate, simple geometrical structure, large force application capability, and independent adhesiveness that make the motor more advanced than conventional motors based on propulsion systems. The single linear induction motor for urban transit modeled by T-type equivalent circuit model enhancement is proposed in this paper. For the proposed system, a double-sided and single-sided linear induction motor is described. For the linear induction motor, the converter control strategies are implemented which is responsive to the velocity and force/current control system, and their integrated performance propulsion drives is described in this paper. Finally, the efficiency and other intrinsic factors needed for the structure's performance assessment are derived. The relationship between penetration depth and frequency is performed and the penetration rapidly drops at low frequencies and tends to stabilize beyond 10 Hz, with the Al having a relatively larger penetration depth. Finally, the performance analysis such as variation in the number of poles, mechanical air gap, penetration depth and frequency variations are calculated on the basis of various features. [ABSTRACT FROM AUTHOR]

This paper addresses the problem of dynamic multi- objective optimization problems (DMOPs), by demonstrating new approaches to change detection and change prediction in an evolutionary algorithm framework. Because the objectives of such problems change over time, the Pareto optimal set (PS) and Pareto optimal front (PF) are also dynamic. First, we propose a new change detection method which achieves greater sensitivity by considering changes in both the PS and the PF, unlike most previous approaches. Second, when changes occur, a second-order (acceleration-based) prediction strategy is proposed to predictively reinitialize the population close to the new set of optima. We compare the performance of the proposed algorithm against two other state-of-the-art algorithms from the literature, using ten different dynamic benchmark problems. Experimental results show that the proposed change detection strategy in this paper can not only consider the effect of the optimal individuals but also can consider the effect of their corresponding objective values. Compared with the other two methods, the DMOPs achieved both the ability of precisely predicting the direction of changes and the ability of predicting the future trend of change direction. So, the DMOPs can also converge to the true PF in much less iterations compared with other methods. After multiple experiments, the proposed method outperforms the other algorithms on most of the test problems. [ABSTRACT FROM AUTHOR]

This paper presents an improved arithmetic optimization algorithm that incorporates hybrid elite pool strategies to address the limitations of the arithmetic optimization algorithm (AOA). In AOA, the linear mathematical optimization acceleration (MOA) function cannot balance global exploitation and local exploration well. Therefore, the accuracy and convergence speed of the algorithm cannot be guaranteed. To improve the performance of AOA, this paper reconstructed a nonlinear MOA function, which is expected to balance the exploitation and the exploration of AOA. Furthermore, four hybrid elite pool strategies are integrated to enhance the ability to escape local optima. The proposed algorithm inherits the fast convergence of AOA and develops the performance of escaping local optima. Numerical experiment results on benchmark functions and engineering problems show that the proposed algorithm outperforms other compared meta-heuristic algorithms in terms of convergence speed and accuracy. [ABSTRACT FROM AUTHOR]

Software quality is becoming a momentous challenge in software engineering processes, and software testing has a pivotal role in its measurements. Nature-inspired metaheuristic methods play an essential role in software testing, in which various studies have been conducted in this field; however, due to a lack of wide-ranging papers reviewing these methods, conducting a comprehensive systematic review to examine an array of crucial mechanisms in this field has become a necessity. This study aims to present a detailed analysis and taxonomically classifies the metaheuristic methods inspired by nature. This paper compromises a systematic literature review of 65 chosen studies published between 2015 and 2022. Genetic algorithm-based, hybrid, ant colony optimization-based, cuckoo search-based, firefly algorithm-based, artificial bee colony-based, and other metaheuristic methods constitute this systematic study's stratification. Evaluation methods, applied tools, merits, and demerits of each reviewed article are investigated. Additionally, future directions and open issues are addressed. This conducted paper not only expounds on software testing strengths, open issues, and future works, but also recognizes the quest for optimizing the insufficient metrics in software testing, such as mutation score, complexity, and scalability, which would be the propulsion of the testing process if consummated. [ABSTRACT FROM AUTHOR]

In many real-world problems, each sample has more than one label, which may also exist semantic correlations among labels. However, in multi-label datasets, there are many irrelevant features that impact the accuracy of multi-label learning. A solution to this problem is feature selection. Although many papers have presented new methods to select features, more effort needs to be put into improving their accuracy. This paper presents a novel feature selection method based on correlation labels using a belief function called FSCLBF. Our proposed method increases the accuracy of multi-label learning by considering the correlation labels and the data uncertainty. The proposed method's first step is to select the essential features for each label using a belief function based on a rough set theory (B-R belief function). The next step is to identify equivalent labels based on overlapping their essential features. Then, the tag of samples is determined in the set of equivalent labels. In the final step, our proposed method selects the essential features for each equivalent label set, using the B-R belief function by considering the sample differentiation to label. The experimental results show the superiority of FSCLBF to state-of-the-art methods in terms of Hamming Loss, One error, and Accuracy measures. [ABSTRACT FROM AUTHOR]

Due to the fast-growing Internet speed, processing power, and the use of sophisticated algorithms, information is generated at a very fast speed. This information is broad in scope and covers a variety of fields, including the medical field, transportation sector, business firms, and education institutes. Due to the abundance of information, it is challenging to identify useful materials in general, but finding the right materials for students is particularly challenging. To address this issue, this paper aims to study the design of a personalized sports teaching resource recommendation system using a fuzzy clustering technique. To do so, we collected relevant data from entities such as students and teachers, which includes a range of attributes related to physical education, including curricular materials, student profiles, past performance records, and resource metadata. The collected data were then preprocessed to prepare it for further analysis. The features, preferences, and learning styles of each student are examined to develop student profiles based on the data that have been collected. A database schema was created that stored all the information related to physical education teaching resources, students, and teachers. The fuzzy C-means clustering algorithm is used to improve the collaborative filtering recommendation algorithm and reduce the data sparsity of the teaching resources recommendation algorithm. Through a series of experiments, it has been proven that the system designed in this paper can recommend suitable learning resources for different learners and has good performance. At the same time, the recommended method has higher recommendation accuracy and can effectively improve the quality of physical education teaching. [ABSTRACT FROM AUTHOR]

The emergence of data-driven analytics and intelligent monitoring systems is radically transforming the world of competitive sports. However, robust real-time activity recognition remains elusive for rapid-paced sports like table tennis due to the complexity of strokes and quick maneuvering. This paper tries to overcome this challenge through an innovative cloud-supported system integrating the Internet of Things (IoT), machine learning, and wearable sensors for automated analysis of table tennis gameplay. We strategically set up a multi-camera IoT system around a table tennis court to compile a rich labeled image dataset encapsulating over 15,000 frames. To improve model generalization, the images captured various playing styles, lighting conditions, and camera angles. We developed tailored SVM and CNN architectures optimized for table tennis activity classification. The models were trained on GPU-accelerated platforms using the curated dataset. After hyperparameter tuning and cross-validation, the CNN model achieved 96.2% accuracy, a precision value of 96.3%, a recall value of 95.2%, and an F1-score of 96.3% in classifying standard strokes and serves. Additionally, the models exhibited impressive processing speeds of 22–34 fps, enabling real-time utilization. Our proposed model outperformed the latest models in this field with higher accuracy, recall values, F1-score, and precision. While expanding the dataset diversity and testing variations of deep network architectures could further enhance performance, this paper demonstrates a crucial leap toward helping real-time analytics in table tennis using an AI-powered computer vision approach. The hybrid system combining IoT, wearables, and machine learning establishes a framework to transform data into actionable and timely insights for table tennis players and coaches. [ABSTRACT FROM AUTHOR]

Parameter estimation is one of key issues in the study of uncertain differential equations. In this paper, one novel estimation method named the estimating function technique of uncertain differential equations based on uncertain integrals is proposed, which is the first method based on uncertain integrals appeared in the literature. Compared to existing methods, our approach fully utilizes information from both the parameters and observed data. In certain settings, previous methods can be viewed as special cases of our technique. One numerical example with different transform functions is provided to illustrate our method and the efficiency is shown by residual analysis. Additionally, a case study on Alibaba stock prices is included to demonstrate the application of our method. The paper concludes with final remarks and recommendations for future research. [ABSTRACT FROM AUTHOR]

With the rapid development of information technology, third-party platforms have emerged and become the important channels to promote collaborations among manufacturing enterprises. Under this background, this paper focuses on a third-party platform whose multilateral users are manufacturers, suppliers, and logistics providers. There exist various cross-side network effects (i.e., CNEs) and same-side network effects (i.e., SNEs) among users (including manufacturers, suppliers, and logistics providers) at different development stages of the platform, which may affect the multi-value chain collaboration of users positively or negatively. By building a system dynamics model, this paper examines the interactions of the network effects and analyzes their impacts on the multi-value chain collaboration and platform's revenues. Then, the key factors that affect the network effect interactions are explored. The results show that the network effect interactions are significant in the early development stage of the platform. The growth of the SNEs of suppliers and logistics providers has significant impacts on their own CNEs and profits. Interestingly, the increase of manufacturers' SNEs can affect the CNEs of their own and suppliers simultaneously, which may decrease the profits of other participants but does not change their own profits. Affected by multiple network effects, the platform needs to implement a differentiated pricing strategy for its users. That is, the platform should charge suppliers and logistics providers access fee while giving manufacturers a certain subsidy. These findings can provide some valuable insights to the operation of the third-party platform. [ABSTRACT FROM AUTHOR]

The symmetric triangular approximation under a general condition was first proposed by Ban and Coroianu (Ban and Coroianu Soft Comput 20:1249–1261, 2016). Ban and Coroianu completed the calculation and provided a computational formula. However, symmetric trapezoidal approximation cannot be deduced from their conclusions. The aim of this paper is to continue and foster the development of this topic. The calculation for the symmetric trapezoidal approximations of a fuzzy number under a general condition will be completed in this paper. Some applications related with value, expected value and ambiguity, which are important parameters, are given too. Numerical examples are given and the quantitative improvement in approximation is also addressed. [ABSTRACT FROM AUTHOR]

This paper describes a new randomized algorithm for calculating local optima in standard quadratic optimization problems (StQPs) over the standard simplex. The new algorithm transforms StQPs into a new form by using a fuzzification technique. This paper proves that: (1) the computational complexity of the new algorithm for computing local optima in StQPs is in the class of randomized polynomial time; (2) the solution of the new algorithm satisfies ϵ - δ condition. Examples are given that demonstrate that the new algorithm outperforms IBM ILOG CPLEX (CPLEX). Numerical experiments indicate that for calculating the first local optima of StQPs, the average computational time of the new algorithm is one hundred time faster than that of CPLEX when the number of variables is 1000. [ABSTRACT FROM AUTHOR]

The important part of mechanical equipment is rotating machinery, used mostly in industrial machinery. Rolling element bearings are the utmost dominant part in rotating machinery, so even small defects in these components could result in catastrophic system failure and enormous financial losses. Hence, it is crucial to create consistent and affordable condition monitoring and fault diagnosis systems that estimate severity level and failure modes and to create an appropriate maintenance strategy. The studies reveal that the fault diagnostic system focuses on single fault diagnosis of the shaft-bearing system. However, in real scenarios, the occurrence of a single fault is very unlikely. Thus, multifault diagnosis of the shaft-bearing system is of greater significance. This paper aims at steadily and broadly summarizing the development of the intelligent multifault diagnostic and condition monitoring systems. In addition, there is a rapid development of application of Internet of things, cloud computing and artificial intelligence techniques for fault diagnosis. In this paper, we summarize the study of various fault diagnostic system built on the architecture and application of these cutting-edge technologies for predictive maintenance of mechanical equipment. [ABSTRACT FROM AUTHOR]

The fast progress of artificial intelligence (AI) technology has significantly impacted the field of interpretation. Nonetheless, interpretation education at many Chinese schools and universities is poor quality. It strongly emphasizes theory while providing basic content, resulting in minimal pupil growth. Furthermore, existing interpretation assessment procedures must be revaluated to improve comprehensive instructional growth. Aiming at this problem, this paper systematically constructs the Automatic Interpretation Assessment System by integrating key components. Firstly, this paper explains the significance of interpretation as a cognitive bridge between AI and human cognition by emphasizing the critical relevance of interpretability in AI systems. Second, the research explores the intricacies of the internal workings of backpropagation (BP) neural networks. It explains their structural composition, signal propagation processes, and weight adjustment procedures, all of which are strategically employed to enable optimal learning within the context of the model. Third, it integrates genetic algorithms (GA) with BP neural networks in a unique way, combining the optimization capabilities of GA. This collaborative strategy employs selection, crossover, and mutation strategies to improve system performance and create a strong issue-detection feedback mechanism. Finally, utilizing interpretable AI theory, the study methodically creates the Automatic Interpretation Assessment System. This approach has a three-tier paradigm, with careful evaluation criteria including interpreter ability and skill. The requirements include bilingual competence, extra-lingual knowledge, interpreting abilities, professional excellence, and psychological quality. We comprehensively evaluated using essential parameters such as accuracy, precision, recall, F1 score, and execution time. These results demonstrate that our proposed system excels with an accuracy of 95%, a precision of 94%, a recall of 96%, and an F1-score of 0.95, showcasing its superiority over the other systems. These findings highlight our proposed system effectiveness in reliably assessing interpretation quality and efficient processing capabilities. [ABSTRACT FROM AUTHOR]

Dual-resource-constrained scheduling problems (DRCSP) have been hotly debated during the last two decades. DRCSPs focus on the causes and consequences of problems arising from improper simultaneous planning of human resources and machines in various industrial sectors, yielding decreasing productivity of industrial factories. This research focuses on time-oriented problems (including makespan, setup, transportation), cost-oriented problems (including execution cost, workforce salary, raw material, maintenance, and repair), workforce-oriented problems (salary, homogeneous workers, overload workers, aged workers, mobile workers) and performance and energy problems in DRCSPs. To this end, special attention is given to the root and reasons for emerging significant issues, such as the lack of machines, system imbalance, material transferring, bottleneck machines, voids, and human resource over-allocation that usually happens during the scheduling of dual-resource-constrained systems. For this purpose, an in-depth analysis has been conducted by reviewing 122 dominant research papers available in the literature from 2006 to 2022. The advantages, limitations, and significant drawbacks of scheduling DRC problems have been discussed for each studied case. This study's results clarified gaps to be addressed in studied areas of DRCSP, showing that various aspects of workforce, energy, and performance should receive more attention in future research. A list of directions for future research is provided at the end of the paper. [ABSTRACT FROM AUTHOR]

The high-speed train (HST) system is one of the most critical components of national and international passenger transportation networks. Selecting the appropriate train sets is a critical task for railway operators to build an efficient, productive, safe, inexpensive and environmentally friendly passenger transport network system. On the other hand, selecting the proper HST set is a highly complex process since many conflicting criteria, and decision alternatives make it difficult for decision-makers. This paper suggests the fuzzy Grey Relational Analysis technique. In addition, the fuzzy technique proposed in the current paper has been implemented in two ways: using both the experts' linguistic evaluations and crisp numbers to compare real numerical values and fuzzy evaluations. A comprehensive sensitivity analysis was then conducted to assess the validation of the proposed fuzzy technique and its results in applying this method. The decision alternative of A8 Siemens is the best option for all scenarios, and it has been observed that there are slight differences, which cannot change the overall result in the ranking positions of the options. The analysis results prove that the fuzzy method can be applied to solve these complicated decision-making problems and that the obtained results are robust, accurate, applicable, and realistic. [ABSTRACT FROM AUTHOR]

This paper investigates and analyzes the dynamics of the two-dimensional Duffing map. Multistability behavior has been observed from the system numerically. Such behavior, especially the coexistence of chaotic and periodic attractors, is undesirable in the applications of chaos-based cryptography. Therefore, we design and implement a Sine–Cosine chaotification technique to enhance chaos in the multi-stable regions. Furthermore, this paper proposes a new image encryption algorithm to examine the performance of the generalized Duffing map in cryptography applications. Simulation results and security analysis reveal that the proposed algorithm can effectively encrypt and decrypt several image types with a high level of security. [ABSTRACT FROM AUTHOR]