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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 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]

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]

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]

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]

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]

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 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]

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]

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]

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]

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]

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]

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]

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]

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 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]

Portfolio optimization has been studied extensively by researchers in computer science and finance, with new and novel work frequently published. Traditional methods, such as quadratic programming, are not computationally effective for solving complex portfolio models. For example, portfolio models with constraints that introduce nonlinearity and non-convexity (such as boundary constraints and cardinality constraints) are NP-Hard. As a result, researchers often use meta-heuristic approaches to approximate optimal solutions in an efficient manner. This paper conducts a comprehensive review of over 140 papers that have applied evolutionary and swarm intelligence algorithms to the portfolio optimization problem. These papers are categorized by the type of portfolio optimization problem considered, i.e., unconstrained or constrained, and are further categorized by single-objective and multi-objective approaches. Furthermore, the various portfolio models used, as well as the constraints, objectives, and properties in which they differ, are also discussed in a detailed analysis. Based on the findings of the reviewed work, guidance for future research in portfolio optimization is given. Possible areas for future work include dynamic portfolio optimization, predictive pricing, the further investigation of multi-objective approaches. [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]

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]

These days, exploring information retrieval models is one of the most essential aspects of English sentence retrieval research. These models are driven by diverse retrieval mechanisms that offer varying similarity calculations and directly influence the final result ranking. However, despite decades of work due to technical constraints, deep semantic analysis has been challenging. This gap emphasizes the importance of a precise semantic understanding of information acquisition through learning approaches. Based on the above opening, this paper establishes a fast retrieval model of English sentences based on the statistical language model (SLM). First, the proposed method utilizes SLM to extract significant feature words from the corpus. These feature words are identified by analyzing co-occurrence patterns and frequency distributions within the standard. Second, it employs the N-gram model to calculate the probabilities of word occurrences based on their contextual dependencies. This framework represents feature words and their associated probabilities in a structured manner by capturing the intricate nuances of language semantics. Third, the model integrates ontology to bridge the gap between human language and machine understanding by enabling the mapping natural language expressions to conceptual entities. Finally, the suggested model retrieves English sentences through semantic matching by leveraging the comprehensive semantic framework and ontology-based search. The experimental study revealed that the proposed model demonstrated an impressive retrieval ratio of 98.5% by outperforming existing models in the comparison. Moreover, these results show that the proposed algorithm performs better than the Term Frequency-Inverse Document Frequency (TF-IDF) algorithm, and the accuracy of this algorithm is improved by 7.52% compared with TF-IDF. When the labelled corpus is very small and the unlabeled corpus is relatively large, the algorithm enhances the classifier's performance by 12.6%. This shows that the algorithm used in this paper reduces the influence of the synonym processing stage on the overall performance while retaining the advantages of high precision and accuracy of calculation results. [ABSTRACT FROM AUTHOR]

Sustainable development requires social development and economic construction to be coordinated with the carrying capacity of the ecological environment. It necessitates adhering to the concept of sustainable development while pursuing economic development and social progress, improving the effective utilization rate of natural resources, and protecting and improving the ecological environment in which people live. This paper takes the e-commerce trade ecological industrial park in Dalian Economic Development Zone as an example to comprehensively evaluate its sustainable development ability using the state-of-the-art techniques of soft computing. Soft computing has recently gained appeal over traditional hard computing because it can cope with uncertainty just as easily as a human can. The basic aim of the study is to avoid excessive resource and environmental pressure hindering the coordinated development of various subsystems of the park. First, an evaluation index system containing four subsystem elements of the environment, society, economy, and resources was constructed, and the importance of each index in the evaluation index system was calculated. Second, the distance model and fuzzy logic of soft computing were used to comprehensively evaluate the sustainability of the selected industrial park. Six criteria were established to describe the sustainability of the industrial park based on the opinions of industry professionals, with references to the research findings of local and international forerunners. The finding of the paper shows that the development level index of each subsystem of the industrial park has continued to rise, except for a few cases. The result also demonstrates that the overall coordinated development degree of the park has increased year by year from 2010 to 2019. This means that the coordinated development level has improved from the initial barely coordinated level in 2010 to the high-quality coordinated development level in 2019; however, the overall coordinated development degree is still less than 0.9. Through this study, it is possible to judge the sustainable development ability of the four subsystems of the industrial park and whether it meets the relevant standards of resource consumption and environmental protection stipulated by the state. It is of great practical significance for promoting the formation of a good ecological chain between enterprises in the park, realizing resource recycling, and reducing waste discharge. [ABSTRACT FROM AUTHOR]

In recent years, the rapid advancement of artificial intelligence (AI) has revolutionized various industries, with image processing playing an important role in the revolution. AI-powered image analysis has opened up new possibilities in healthcare diagnostics, autonomous navigation, and security surveillance, fuelling demand for sophisticated image-processing solutions. This paper describes an innovative approach to AI image processing that combines multi-sensor data fusion with convolutional neural networks (CNNs) within a fuzzy neural network framework. This integration uses data from various sensors, including cameras, lidar, and radar, to improve the robustness and precision of image analysis and interpretation. The T–S model serves as the foundation for the information fusion strategy. A comprehensive investigation of deep learning algorithms reveals inherent strengths such as robustness and parallelism. However, it also identifies limitations, particularly in image segmentation tasks, characterized by challenges like premature convergence and prolonged computation times. The paper proposes a quantization technique for deep learning algorithms to address these issues and introduces chaotic optimization to expedite convergence rates. It also presents a novel three-dimensional Otsu threshold segmentation method based on CNNs, which overcomes noise susceptibility in traditional two-dimensional approaches. Integrating Gray morphology and this three-dimensional Otsu threshold segmentation method results in the development of a three-dimensional Gray Otsu model. This model is the basis for designing a fitness function for the CNN algorithm, optimizing its efficacy. Experimental validation demonstrates the proposed algorithm's effectiveness, achieving an impressive 91% accuracy rate while displaying robust noise resistance and versatility. Comparative assessments against other leading AI architectures, including multilayer perceptron, radial basis function network, recurrent neural network, and long short-term memory network, affirm the superior performance achieved by the proposed approach. [ABSTRACT FROM AUTHOR]

Statistical inference is the process of drawing conclusions about underlying population(s) using sample data to either confirm or falsify hypotheses. However, the complexity of real-life problems often makes the underlying statistical models inadequate, as information is often imprecise in many respects. To address this common problem, some papers have been published on modifications and extensions of test concepts by employing tools of fuzzy statistics. In this paper, we present a non-parametric test for the difference between quantiles of two independent populations based on fuzzy random variables. For this purpose, we consider the fuzzy quantile function and its estimation based on α -values of fuzzy random variables. We then provide a fuzzy test based on the fuzzy empirical distribution function for the difference of fuzzy order statistics from these independent populations. We also suggest a specific degree-based criterion to compare the fuzzy test statistics at a specific significance level to decide whether the underlying fuzzy null hypothesis can be rejected or not. The effectiveness of the proposed two-sample test on quantiles is investigated via numerical examples. [ABSTRACT FROM AUTHOR]

Due to the multimodal nature of real-world optimization problems, in recent years, there has been a great interest in multi-modal optimization algorithms. Multimodal optimization problems involve identifying multiple local/global optima. Niching techniques have been widely used to tackle multi-modal optimization problems. Most of the existing niching methods either require predefined niching parameters or extra information about the problem space. This paper presents a novel multimodal algorithm based on Butterfly Optimization Algorithm, which is constructed using the Fitness-Distance Balance (FDB) selection method. The purpose of applying the FDB selection method is to discover local/global optima with high potential as a fitness value along with the appropriate distance from solution candidates. Also, a local search scheme is used to enhance the convergence speed of the algorithm. Niching is a technique used in multimodal optimization to maintain diversity among multiple solutions in the population. The minimum distance between the solutions is called a "niche". The proper niching radius is the main challenge for existing approaches. Knowing the problem space helps determine the niche radius. This paper proposes a new multimodal optimization scheme that does not require prior knowledge of the problem space or the niching parameter. Seven state-of-the-art multi-modal optimization algorithms are compared to the multi-modal butterfly optimization algorithm (MBOA) on 16 benchmarks from the CEC 2013 and CEC 2015 competitions to evaluate its performance. Success rate, Number of function evaluations, Success performance, average number of optima found, Success accuracy, Maximum peak ratio, and Run-time performance criteria were measured over 25 runs to assess the efficiency of the proposed method. The experimental results demonstrate that MBOA outperforms other algorithms according to most of the performance criteria. [ABSTRACT FROM AUTHOR]

In order to solve the problems of insufficient optimization accuracy, slow convergence speed and easy to fall into local optimum in the whale optimization algorithm, this paper proposes a whale optimization algorithm with improved neighborhood search strategy. First, the algorithm generates a more evenly distributed and higher-quality initial population through an initialization strategy based on the opposite-based learning of pinhole imaging to expand the early search space of the algorithm. Secondly, it adopts improved neighborhood search strategy based on similarity and uses Mahalanobis distance and the law of universal gravitation to calculate and rank the similarity of solutions. At the same time, the algorithm counts the times of convergence oscillation. According to the algorithm iteration process, it selects the corresponding similarity ranking solution as the object to update the position and performs the second position update for the solution with most times of oscillation, so as to implement the space exploration of the target population to speed up the convergence of the algorithm and enhance the ability to jump out of the local optimum. Finally, an adaptive step size adjustment strategy is introduced, and the population convergence is adjusted using adaptive step size parameters according to the algorithm optimization process to improve the algorithm's global search performance and avoid premature convergence of the algorithm. The improved algorithm proposed in this paper is analyzed and compared with the sine–cosine optimization algorithm, artificial bee colony algorithm and three improved whale algorithms on a set of 20 test functions in low-dimensional and high-dimensional, respectively, and perform ANOVA and T-test on the simulation results. The results show that the improved algorithm proposed in this paper effectively improves the convergence accuracy and convergence speed. In addition, the improved optimization algorithm proposed in this paper is applied to the engineering optimization design. The solutions show that the improved algorithm can obtain the optimal value with higher accuracy and more stability than other algorithms, and can effectively solve the engineering design problem. [ABSTRACT FROM AUTHOR]

Cold-chain logistics presents significant challenges to sustainable development in terms of economics, society, and the environment. To address this issue, this paper focuses on solving a real-world problem—the multipath location-routing problem-based cold-chain logistics (MPLRPCCL), which considers soft time windows and heterogeneous fleets. The mathematical model aims to optimize the total logistics costs, including fuel consumption, carbon emissions, cargo deterioration, penalties, operating depots, and renting fleets. To effectively solve this problem, the paper proposes three versions of a memetic algorithm (MA) that incorporate control modules for monitoring the MA's performance and improving its global optimization performance. Computational evaluations were conducted, and the results show that the proposed algorithms are effective and outperform the classic MA framework. Furthermore, the proposed model and algorithms were applied to real-world cases in Hangzhou city, China, demonstrating their practical applicability. Overall, this paper offers a promising approach to tackling the complex challenges posed by cold-chain logistics and contributes to the sustainable development of this important field. [ABSTRACT FROM AUTHOR]

The main objective of the study is to review different types of multi-criteria decision-making (MCDM) problems that are analysed by using the fuzzy TOPSIS method and its different variants. An effort has been made to review different studies in which either the TOPSIS method is used or its extensions have been developed to analyse any real-life MCDM problem. A total of 184 research papers were carefully selected and reviewed from 83 reputed journals published by Elsevier, Springer, Wiley, Taylor and Francis, and others from 1981 to the first quarter of 2023. These papers are selected on the basis of different kinds of applications in the fields of mathematics, engineering, science, environment, technology, management, business, etc. The selected papers are further categorised based on the application areas, publication year, contributions of the countries, authors, journals, and type of research. This study will give an idea of different types of TOPSIS methods, their extensions, and applications, along with recent trends in diverse research fields. Based on the findings, it is observed that in recent years, the number of papers in which TOPSIS and its extensions have been used has increased exponentially. [ABSTRACT FROM AUTHOR]

Music expresses thoughts and emotions in artistic form and is made up of different components including harmony, rhythm, and melody. Several musical elements are tied together during the songwriting process in order to generate melodies that are harmonic. A music melody is the essential element of any music to generate strong feelings from listeners and capture their interest. In the process of music appreciation, melody controls the emotional changes of music. It is an efficiently perceived part and the tone of a song. In recent years, Sichuan unvoiced music has developed rapidly and attracted much attention. This paper selects Sichuan unvoiced music as the main research theme and constructs a melody generation algorithm by utilizing the state-of-the-art techniques of deep learning (DL) and evolutionary algorithms (EAs) such as recurrent neural network-long short-term memory (RNN-LSTM) and genetic algorithm (GA). Firstly, this paper briefly describes the concept of DL algorithms, the deep generation model, and sequence to sequence model, as they constitute the technological foundation for this research. Secondly, this paper proposes a melody generation algorithm that utilizes RNN-LSTM for melody generation and GA for melody optimization. More specifically, the melody is generated by preprocessing data, creating, and training the RNN-LSTM model. A GA was used to determine the melodic fitness function for eight songs as the fitness function directly affects the selected termination condition. The fitness function can be thought of as either a person or an evolutionary rule. Finally, the average score of these songs, before and after evolution, is calculated, which demonstrates that the analysis and rotation creation methods are more precise and that the song's average melody score is higher. The method proposed in this study has been thoroughly compared to the existing approaches proposed in earlier studies, and it was found that the approach we propose is more effective in terms of accuracy and the average melody score. [ABSTRACT FROM AUTHOR]

The emergence of Artificial Intelligence (AI) provides brand-new technical support and a broader resource platform for college oral English teaching and evaluation. The advancement of science, technology, and networks has resulted in significant educational changes. Because of these, the demand for English instruction is rising quickly, along with the level of cultural integration. However, due to the limits of conventional teaching techniques and the slow expansion of information technology, oral English classes have been discarded and discontinued for a long time. Based on the preceding, this paper first addresses the shortcomings of traditional college-level spoken English instruction by exposing content and timing deficiencies. It then describes how spoken English data are collected, preprocessed, and framed before being standardized for consistency. After that, this paper uses mapping recognition results using a DBN speech model and correlation coefficients in the evaluation. In addition, it uses the Pairwise Variability Index in rhythm evaluation to assess stress distribution differences between test and standard pronunciation. Besides the above, the proposed approach uses Support Vector Machine (SVM) optimization inside statistical learning to improve phoneme recognition and reliability, especially when dealing with difficult-to-distinguish phoneme sets. Further analysis is carried out using neural networks, which include excitation functions and error computations. Finally, the complete design emphasizes user-centric functional characteristics such as practicability, user needs, and robust system management subsystems. Experiments suggest that the natural language processing-based oral English teaching mode can increase students' overall oral English skills. These results reveal that students' excitement increased by 33.3%, their verbal fluency increased by 86%, and their vocabulary learning increased by 16.1%. The results show that this method can effectively assist students in learning oral English. [ABSTRACT FROM AUTHOR]

Many ordinary approaches in optimization are mathematical-based. Due to the limitations of such methods, optimal problems have been commonly defined in single-objective and quadratic forms, leading to non-optimal solutions based on the main design requirements. In order to address these issues, this paper presents a new type of genetic programming (GP) called "multi-objective archived-based genetic programming (MAGP)". The absolute forms of cost functions, which are unsolvable using the conventional methods, could be assessed herein through the suggested algorithm. It is shown that in addition to the possibility of obtaining optimal single-objective solutions, It is shown that in addition to the possibility of obtaining optimal single-objective solutions, some very fruitful non-dominant solutions on the Pareto fronts would be acquired, providing the designer with a set of optimal analytical solutions which could be selected depending on the design requirements. For example, in the case studies examined in this paper, around 30,000 and 14,000 non-dominant solutions were obtained for linear and nonlinear problems, respectively, indicating the high performance of the novel proposed MAGP algorithm in acquiring non-dominant solutions in optimization problems. Generally, it is observed that obtaining and comparing optimal solutions in the quadratic form are basically unreliable and as a consequence, an optimal control problem must be analyzed in its absolute form of indices. [ABSTRACT FROM AUTHOR]

A novel online critical neural network weight adjustment algorithm is proposed in this paper by combining policy iteration and recursive least squares (RLSs) to address the problem of optimal control of players in nonlinear systems with nonzero-sum games. The interaction between players and the nonlinearity of the system make it difficult to solve the Hamiltonian function directly. From a linear regression perspective, this paper regards any admissible control and its corresponding value function as the input and output affected by perturbations. By using RLS to process the current data and store the historical data's covariance matrix to adjust the weights, the calculation is greatly simplified by avoiding the space waste caused by a lot of historical data storage and the time waste caused by data collection. When calculating the cumulative error, a discount factor is introduced to avoid the total error value tending to infinity and the effect of historical policy evaluation, where the covariance matrix tends to zero and loses its adjustment effect. When the error of the Hamiltonian equation is zero, the proposed adjustment law will also tend to zero. After that, the stability of the covariance matrix was analysed as well as the convergence of the weight errors was proved. Finally, two simulations are conducted to verify the effectiveness of the proposed algorithm based on the RLS method for solving the Nash equilibrium solution online. [ABSTRACT FROM AUTHOR]