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Abstract Retinopathy of prematurity (ROP) represents a vasoproliferative disease, especially in newborns and infants, which can potentially affect and damage the vision. Despite recent advances in neonatal care and medical guidelines, ROP still remains one of the leading causes of worldwide childhood blindness. The paper presents a unique dataset of 6,004 retinal images of 188 newborns, most of whom are premature infants. The dataset is accompanied by the anonymized patients’ information from the ROP screening acquired at the University Hospital Ostrava, Czech Republic. Three digital retinal imaging camera systems are used in the study: Clarity RetCam 3, Natus RetCam Envision, and Phoenix ICON. The study is enriched by the software tool ReLeSeT which is aimed at automatic retinal lesion segmentation and extraction from retinal images. Consequently, this tool enables computing geometric and intensity features of retinal lesions. Also, we publish a set of pre-processing tools for feature boosting of retinal lesions and retinal blood vessels for building classification and segmentation models in ROP analysis.

Wireless sensor networks (WSNs) are a family of wireless networks that usually operate with irreplaceable batteries. The energy sources limitation raises the need for designing specific protocols to prolong the operational lifetime of such networks. These protocols are responsible for messages exchanging through the wireless communications medium from the sensors to the base station (sink node). Therefore, the determination of the optimal location of the sink node becomes crucial to assure both the prolongation of the network's operation and the quality of the provided services. This paper proposes a novel algorithm based on a Particle Swarm Optimization (PSO) approach for designing an energy-aware topology control protocol. The deliverable of the algorithm is the optimal sink node location within a deployment area. The proposed objective function is based on a number of topology control protocol's characteristics such as numbers of neighbors per node, the nodes’ residual energy, and how they are far from the center of the deployment area. The simulation results show that the proposed algorithm reveals significant effectiveness to both topology construction and maintenance phases of a topology control protocol in terms of the number of active nodes, the topology construction time, the number of topology reconstructions, and the operational network's lifetime.

Maintaining stable voltage levels is essential for power systems' efficiency and reliability. Voltage fluctuations during load changes can lead to equipment damage and costly disruptions. Automatic voltage regulators (AVRs) are traditionally used to address this issue, regulating generator terminal voltage. Despite progress in control methodologies, challenges persist, including robustness and response time limitations. Therefore, this study introduces a novel approach to AVR control, aiming to enhance robustness and efficiency. A custom optimizer, the quadratic wavelet-enhanced gradient-based optimization (QWGBO) algorithm, is developed. QWGBO refines the gradient-based optimization (GBO) by introducing exploration and exploitation improvements. The algorithm integrates quadratic interpolation mutation and wavelet mutation strategy to enhance search efficiency. Extensive tests using benchmark functions demonstrate the QWGBO's effectiveness in optimization. Comparative assessments against existing optimization algorithms and recent techniques confirm QWGBO's superior performance. In AVR control, QWGBO is coupled with a cascaded real proportional-integral-derivative with second order derivative (RPIDD2) and fractional-order proportional-integral (FOPI) controller, aiming for precision, stability, and quick response. The algorithm's performance is verified through rigorous simulations, emphasizing its effectiveness in optimizing complex engineering problems. Comparative analyses highlight QWGBO's superiority over existing algorithms, positioning it as a promising solution for optimizing power system control and contributing to the advancement of robust and efficient power systems.

The decision-making process is characterized by some doubt or hesitation due to the existence of uncertainty among some objectives or criteria. In this sense, it is quite difficult for decision maker(s) to reach the precise/exact solutions for these objectives. In this study, a novel approach based on integrating the technique for order preference by similarity to ideal solution (TOPSIS) with the intuitionistic fuzzy set (IFS), named TOPSIS-IFS, for solving a multi-criterion optimization problem (MCOP) is proposed. In this context, the TOPSIS-IFS operates with two phases to reach the best compromise solution (BCS). First, the TOPSIS approach aims to characterize the conflicting natures among objectives by reducing these objectives into only two objectives. Second, IFS is incorporated to obtain the solution model under the concept of indeterminacy degree by defining two membership functions for each objective (i.e., satisfaction degree, dissatisfaction degree). The IFS can provide an effective framework that reflects the reality contained in any decision-making process. The proposed TOPSIS-IFS approach is validated by carrying out an illustrative example. The obtained solution by the approach is superior to those existing in the literature. Also, the TOPSIS-IFS approach has been investigated through solving the multi-objective transportation problem (MOTP) as a practical problem. Furthermore, impacts of IFS parameters are analyzed based on Taguchi method to demonstrate their effects on the BCS. Finally, this integration depicts a new philosophy in the mathematical programming field due to its interesting principles.

Automatic voltage regulators (AVRs) are essential components in electrical systems to maintain stable voltage output, ensuring optimal performance and equipment protection. The effectiveness of AVRs rely on key parameters such as voltage regulation, response time, stability, and efficiency. Integrating controllers with AVRs offers centralized monitoring and regulation, enhancing voltage output efficiency. This study therefore first discusses various controller types which showcase distinctive capabilities based on their tuning strategies and then introduces an intelligent optimization approach using fractional-order proportional-integral-derivative with double derivative (FOPIDD2) controller tailored for AVRs. The study adopts the artificial rabbits optimization (ARO) algorithm, fortified with an adaptive local search (ALS) mechanism and experience-based perturbed learning (EPL) strategy. The modified version (m-ARO) enhances solution diversity and navigational efficacy, resulting in improved optimization quality. The study's core objective is to demonstrate the superior performance of the m-ARO-based FOPIDD2 controller in addressing the multifaceted challenges of AVR control, outperforming conventional techniques in stability, speed of response, robustness, and efficiency. To validate the method's efficacy, a comparative analysis is conducted using existing controllers with different tuning algorithms. Results indicate that the proposed m-ARO-based FOPIDD2 controller achieves superior performance metrics, demonstrating its capability. The study also includes a wider perspective by considering nineteen different controllers, reported in the literature, for comprehensive comparison. Notably, the proposed method exhibits the best stability, further affirming its effectiveness.

Abstract In this paper, we propose a hybrid meta-heuristic algorithm called MRFO-PSO that hybridizes the Manta ray foraging optimization (MRFO) and particle swarm optimization (PSO) with the aim to balance the exploration and exploitation abilities. In the MRFO-PSO, the concept of velocity of the PSO is incorporated to guide the searching process of the MRFO, where the velocity is updated by the first best and the second-best solutions. By this integration, the balancing issue between the exploration phase and exploitation ability has been further improved. To illustrate the robustness and effectiveness of the MRFO-PSO, it is tested on 23 benchmark equations and it is applied to estimate the parameters of Tremblay's model with three different commercial lithium-ion batteries including the Samsung Cylindrical ICR18650-22 lithium-ion rechargeable battery, Tenergy 30209 prismatic cell, Ultralife UBBL03 (type LI-7) rechargeable battery. The study contribution exclusively utilizes hybrid machine learning-based tuning for Tremblay's model parameters to overcome the disadvantages of human-based tuning. In addition, the comparisons of the MRFO-PSO with six recent meta-heuristic methods are performed in terms of some statistical metrics and Wilcoxon’s test-based non-parametric test. As a result, the conducted performance measures have confirmed the competitive results as well as the superiority of the proposed MRFO-PSO.

Several optimization problems can be abstracted into many-objective optimization problems (MaOPs). The key to solving MaOPs is designing an effective algorithm to balance the exploration and exploitation issues. This paper proposes a novel many-objective African vulture optimization algorithm (MaAVOA) that simulating the African vultures' foraging and navigation behaviours to solve the MaOPs. MaAVOA is an updated version of the African Vulture Optimization Algorithm (AVOA), which was recently proposed to solve the MaOPs. A new social leader vulture for the selection process is introduced and integrated into the proposed model. In addition, an environmental selection mechanism based on the alternative pool is adapted to improve the selection process to maintain diversity for approximating different parts of the whole Pareto Front (PF). The best-nondominated solutions are saved in an external Archive based on the Fitness Assignment Method (FAM) during the population evolution. FAM is based on a convergence measure that promotes convergence and a density measure that promotes variety. Also, a Reproduction of Archive Solutions (RAS) procedure is developed to improve the quality of archiving solutions. RAS has been designed to help reach out to the missing areas of the PF that the vultures easily miss. Two experiments are conducted to verify and validate the suggested MaAVOA's performance efficacy. First, MaAVOA was applied to the DTLZ functions, and its performance was compared to that of several popular many-objective algorithms and according to the results, MaAVOA outperforms the competitor algorithms in terms of inverted generational distance and hypervolume performance measures and has a beneficial adaptation ability in terms of both convergence and diversity performance measures. Also, statistical tests are implemented to demonstrate the suggested algorithm's statistical relevance. Second, MaAVOA has been applied to solve two real-life constrained engineering MaOPs applications, namely, the series-parallel system and overspeed protection for gas turbine problems. The experiments show that the suggested algorithm can tackle many-objective real-world applications and provide promising choices for decision-makers.

Cryptosystem cryptanalysis is regarded as an NP-Hard task in modern cryptography. Due to block ciphers that are part of a modern cipher and have nonlinearity and low autocorrelation in their structure, traditional techniques and brute-force attacks suffer from breaking the key presented in traditional techniques, and brute-force attacks against modern cipher S-AES (simplified-advanced encryption standard) are complex. Thus, developing robust and reliable optimization with high searching capability is essential. Motivated by this, this paper attempts to present a novel binary hybridization algorithm based on the mathematical procedures of the grey wolf optimizer (GWO) and particle swarm optimization (PSO), named BPSOGWO, to deal with the cryptanalysis of (S-AES). The proposed BPSOGWO employs a known plaintext attack that requires only one pair of plaintext–ciphertext pairs instead of other strategies that require more pairs (i.e., it reduces the number of messages needed in an attack, and secret information such as plaintext-ciphertext pairs cannot be obtained easily). The comprehensive and statistical results indicate that the BPSOGWO is more accurate and provides superior results compared to other peers, where it improved the cryptanalysis accurateness of S-AES by 82.5%, 84.79%, and 79.6% compared to PSO, GA, and ACO, respectively. Furthermore, the proposed BPSOGWO retrieves the optimal key with a significant reduction in search space compared to a brute-force attack. Experiments show that combining the suggested fitness function with HPSOGWO resulted in a 109-fold reduction in the search space. In cryptanalysis, this is a significant factor. The results prove that BPSOGWO is a promising and effective alternative to attack the key employed in the S-AES cipher.

This paper proposes a hybrid optimization method to optimize the topological structure of an offshore-wind-farm power collection system, in which the cable connection, cable selection and substation location are optimally designed. Firstly, the optimization model was formulated, which integrates cable investment, energy loss and line construction. Then, the Prim algorithm was used to initialize the population. A novel hybrid optimization, named PSAO, based on the merits of the particle swarm optimization (PSO) and aquila optimization (AO) algorithms, was presented for topological structure optimization, in which the searching characteristics between PSO and AO are exploited to intensify the searching capability. Lastly, the proposed PSAO method was validated with a real case. The results showed that compared with GA, AO and PSO algorithms, the PSAO algorithm reduced the total cost by 4.8%, 3.3% and 2.6%, respectively, while achieving better optimization efficiency.

In social influence analysis, viral marketing, and other fields, the influence maximization problem is a fundamental one with critical applications and has attracted many researchers in the last decades. This problem asks to find a k-size seed set with the largest expected influence spread size. Our paper studies the problem of fairness budget distribution in influence maximization, aiming to find a seed set of size k fairly disseminated in target communities. Each community has certain lower and upper bounded budgets, and the number of each community’s elements is selected into a seed set holding these bounds. Nevertheless, resolving this problem encounters two main challenges: strongly influential seed sets might not adhere to the fairness constraint, and it is an NP-hard problem. To address these shortcomings, we propose three algorithms (FBIM1, FBIM2, and FBIM3). These algorithms combine an improved greedy strategy for selecting seeds to ensure maximum coverage with the fairness constraints by generating sampling through a Reverse Influence Sampling framework. Our algorithms provide a (1/2−ϵ)-approximation of the optimal solution, and require OkTlog(8+2ϵ)nln2δ+ln(kn)ϵ2, OkTlognϵ2k, and OTϵlogkϵlognϵ2k complexity, respectively. We conducted experiments on real social networks. The result shows that our proposed algorithms are highly scalable while satisfying theoretical assurances, and that the coverage ratios with respect to the target communities are larger than those of the state-of-the-art alternatives; there are even cases in which our algorithms reaches 100% coverage with respect to target communities. In addition, our algorithms are feasible and effective even in cases involving big data; in particular, the results of the algorithms guarantee fairness constraints.

In recent years, the issue of maximizing submodular functions has attracted much interest from research communities. However, most submodular functions are specified in a set function. Meanwhile, recent advancements have been studied for maximizing a diminishing return submodular (DR-submodular) function on the integer lattice. Because plenty of publications show that the DR-submodular function has wide applications in optimization problems such as sensor placement impose problems, optimal budget allocation, social network, and especially machine learning. In this research, we propose two main streaming algorithms for the problem of maximizing a monotone DR-submodular function under cardinality constraints. Our two algorithms, which are called StrDRS1 and StrDRS2, have (1/2−ϵ), (1−1/e−ϵ) of approximation ratios and O(nϵlog(logBϵ)logk), O(nϵlogB), respectively. We conducted several experiments to investigate the performance of our algorithms based on the budget allocation problem over the bipartite influence model, an instance of the monotone submodular function maximization problem over the integer lattice. The experimental results indicate that our proposed algorithms not only provide solutions with a high value of the objective function, but also outperform the state-of-the-art algorithms in terms of both the number of queries and the running time.

Unmanned aerial vehicles (UAVs) have emerged as a powerful technology for introducing untraditional solutions to many challenges in non-military fields and industrial applications in the next few years. However, the limitations of a drone’s battery and the available optimal charging techniques represent a significant challenge in using UAVs on a large scale. This problem means UAVs are unable to fly for a long time; hence, drones’ services fail dramatically. Due to this challenge, optimizing the scheduling of drone charging may be an unusual solution to drones’ battery problems. Moreover, authenticating drones and verifying their charging transactions with charging stations is an essential associated problem. This paper proposes a scheduling and secure drone charging system in response to these challenges. The proposed system was simulated on a generated dataset consisting of 300 drones and 50 charging station points to evaluate its performance. The optimization of the proposed scheduling methodology was based on the particle swarm optimization (PSO) algorithm and game theory-based auction model. In addition, authenticating and verifying drone charging transactions were executed using a proposed blockchain protocol. The optimization and scheduling results showed the PSO algorithm’s efficiency in optimizing drone routes and preventing drone collisions during charging flights with low error rates with an MAE = 0.0017 and an MSE = 0.0159. Moreover, the investigation to authenticate and verify the drone charging transactions showed the efficiency of the proposed blockchain protocol while simulating the proposed system on the Ethereum platform. The obtained results clarified the efficiency of the proposed blockchain protocol in executing drone charging transactions within a short time and low latency within an average of 0.34 s based on blockchain performance metrics. Moreover, the proposed scheduling methodology achieved a 96.8% success rate of drone charging cases, while only 3.2% of drones failed to charge after three scheduling rounds.

The unmanned aerial vehicle (UAV) path planning problem is primarily concerned with avoiding collision with obstacles while determining the best flight path to the target position. This paper first establishes a cost function to transform the UAV route planning issue into an optimization issue that meets the UAV’s feasible path requirements and path safety constraints. Then, this paper introduces a modified Mayfly Algorithm (modMA), which employs an exponent decreasing inertia weight (EDIW) strategy, adaptive Cauchy mutation, and an enhanced crossover operator to effectively search the UAV configuration space and discover the path with the lowest overall cost. Finally, the proposed modMA is evaluated on 26 benchmark functions as well as the UAV route planning problem, and the results demonstrate that it outperforms the other compared algorithms.

An important problem in the context of viral marketing in social networks is the Influence Threshold (IT) problem, which aims at finding some users (referred to as a seed set) to begin the process of disseminating their product’s information so that the benefit gained exceeds a predetermined threshold. Even though, marketing strategies exhibit different in several realistic scenarios due to market dependence or budget constraints. As a consequence, picking a seed set for a specific threshold is not enough to come up with an effective solution. To address the disadvantages of previous works with a new approach, we study the Multiple Benefit Thresholds (MBT), a generalized version of the IT problem, as a result of this phenomenon. Given a social network that is subjected to information distribution and a set of thresholds, T={T1,T2,…,Tk},Ti>0, the issue aims to seek the seed sets S1,S2,…,Sk with the lowest possible cost so that the benefit achieved from the influence process is at the very least T1,T2,…,Tk, respectively. The main challenges of this problem are a #NP-hard problem and the estimation of the objective function #P-Hard under traditional information propagation models. In addition, adapting the exist algorithms many times to different thresholds can lead to large computational costs. To address the abovementioned challenges, we introduced Efficient Sampling for Selecting Multiple Seed Sets, an efficient technique with theoretical guarantees (ESSM). At the core of our algorithm, we developed a novel algorithmic framework that (1) can use the solution to a smaller threshold to find that of larger ones and (2) can leverage existing samples with the current solution to find that of larger ones. The extensive experiments on several real social networks were conducted in order to show the effectiveness and performance of our algorithm compared with current ones. The results indicated that our algorithm outperformed other state-of-the-art ones in terms of both the total cost and running time.

Information hiding can be seen everywhere in our daily life, and this technology improves the security of information. The requirements for information security are becoming higher and higher. The coverless information hiding with the help of mapping relationship has high capacity, but there is still a problem in which the secret message cannot find the mapping relationship and the process requires extra storage burden during the transmission. Therefore, on the basis of symmetric reversible watermarking, the paper introduces the two-level mechanism and novel arrangements to solve the problem of sufficient diversity of features and has better capacity and image quality as a whole. Besides, for the security of secret message, this paper designs a new encryption model based on Logistic mapping. This method only employs coverless information hiding of one carrier image to transmit secret message with the help of the two-level mechanism and look-up table. Reversible information hiding is applied to embed the generated location table on the original image so that ensures storage and security. The experiment certifies that the diversity of hash code is increased by using the two-level image mechanism and the quality of the image is excellent, which proves the advantages of the proposed symmetric method over the previous algorithm.

Communication between the primary and secondary sides is pivotal to the wireless power transfer (WPT) system. The system control commands and feedback information need simultaneous wireless information and power transfer (SWIPT). In this paper, a FSK-based SWIPT system with full-duplex communication via a shared channel is provided. Considering the complexity of the coupling relationship in this kind of full-duplex SWIPT system, this paper proposes an analysis method based on the transmission channel, studies the crosstalk between the power channel and the information channel, and between the forward and reverse transfer of information. A design method of full-duplex communication SWIPT system based on shared coupling channels is provided. A 60 W SWIPT prototype with a full-duplex communication rate of 20 kbps is built to verify the proposed method.

Many real networks in biology, chemistry, industry, ecological systems, or social networks have an inherent structure of simplicial complexes reflecting many-body interactions. Over the past few decades, a variety of complex systems have been successfully described as networks whose links connect interacting pairs of nodes. Simplicial complexes capture the many-body interactions between two or more nodes and generalized network structures to allow us to go beyond the framework of pairwise interactions. Therefore, to analyze the topological and dynamic properties of simplicial complex networks, the closed trail metric is proposed here. In this article, we focus on the evolution of simplicial complex networks from clicks and k-CT graphs. This approach is used to describe the evolution of real simplicial complex networks. We conclude with a summary of composition k-CT graphs (glued graphs); their closed trail distances are in a specified range.

Improved energy usage efficiency is a common goal for economic and environmental reasons. In this manuscript, we present a new approach for the execution of a point-to-point robot motion. The energy efficiency of an industrial or collaborative robot is increased by the reduction of the energy consumption during nontechnological, path-independent movements. The novel trajectory generation method relies on particle swarm optimization with a Bezier curve interpolator. We present the effectiveness of the algorithm on several chosen trajectories, where the best result yields up to 40% energy saving, while the worst is still at least 10%. We verified the results of our method by real-world tests on a UR3 robotic arm.

In wireless charging devices, a transmitter that applies a single inverter to output dual-frequency can effectively solve the charging incompatibility problem caused by different wireless charging standards and reduce the equipment volume. However, it is very difficult to solve the switching angle of the modulated dual-frequency waveform, which involves non-linear high-dimensional multi-objective optimization with multiple constraints. In this paper, an improved differential evolution (DE) algorithm is proposed to solve the transcendental equations of switching angle trains of dual-frequency programmed harmonic modulation (PHM) waveform. The proposed algorithm maintains diversity while preserving the elites and improves the convergence speed of the solution. The advantage of the proposed algorithm was verified by comparing with non-dominated sorting genetic algorithm II (NSGA II) and multi-objective particle swarm optimization (MOPSO). The simulation and experimental results validate that the proposed method can output dual-frequency with a single inverter for wireless power transfer (WPT).

Space-filling curves (SFCs) represent an efficient and straightforward method for sparse-space indexing to transform an n-dimensional space into a one-dimensional representation. This is often applied for multidimensional point indexing which brings a better perspective for data analysis, visualization and queries. SFCs are involved in many areas such as big data analysis and visualization, image decomposition, computer graphics and geographic information systems (GISs). The indexing methods subdivide the space into logic clusters of close points and they differ in various parameters including the cluster order, the distance metrics, and the pattern shape. Beside the simple and highly preferred triangular and square uniform grids, the hexagonal uniform grids have gained high interest especially in areas such as GISs, image processing and data visualization for the uniform distance between cells and high effectiveness of circle coverage. While the linearization of hexagons is an obvious approach for memory representation, it seems there is no hexagonal SFC indexing method generally used in practice. The main limitation of hexagons lies in lacking infinite decomposition into sub-hexagons and similarity of tiles on different levels of hierarchy. Our research aims at defining a fast and robust hexagonal SFC method. The Gosper fractal is utilized to preserve the benefits of hexagonal grids and to efficiently and hierarchically linearize points in a hexagonal grid while solving the non-convex shape and recursive transformation issues of the fractal. A comparison to other SFCs and grids is conducted to verify the robustness and effectiveness of our hexagonal method.

Scheduling is a key problem in distributed heterogeneous computing systems in order to benefit from the large computing capacity of such systems and is an NP-complete problem. In this paper, we present a metaheuristic technique, namely the Particle Swarm Optimization (PSO) algorithm, for this problem. PSO is a population-based search algorithm based on the simulation of the social behavior of bird flocking and fish schooling. Particles fly in problem search space to find optimal or near-optimal solutions. The scheduler aims at minimizing makespan, which is the time when finishes the latest task. Experimental studies show that the proposed method is more efficient and surpasses those of reported PSO and GA approaches for this problem.

Mobile embedded systems belong among the typical applications of distributed systems control in real-time. An example of a mobile control system is a robotic system. The proposal and realization of such a distributed control system represents a demanding and complex task for real-time control. In the process of robot soccer game applications, extensive data is accumulated. The reduction of such data is a possible win in a game strategy. The main topic of this article is a description of an efficient method for rule selection from a strategy. The proposed algorithm is based on the geometric representation of rules. A described problem and a proposed solution can be applied to other areas dealing with effective searching of rules in structures that also represent coordinates of the real world. Because this construed strategy describes a real space and the stores physical coordinates of real objects, our method can be used in strategic planning in the real world where we know the geographical positions of objects.

The world has changed a lot in the past years. The rapid advances in technology and the changing of the communication channels have changed the way people work and, for many, where do they work from. The Internet and mobile technology, the two most dynamic technological forces in modern information and communications technology (ICT) are converging into one ubiquitous mobile Internet service, which will change our way of both doing business and dealing with our daily routine activities. As the use of ICT expands globally, there is need for further research into cultural aspects and implications of ICT. The acceptance of Information Technology (IT) has become a fundamental part of the research plan for most organizations (Igbaria 1993). In IT research, numerous theories are used to understand users’ adoption of new technologies. Various models were developed including the Technology Acceptance Model, Theory of Reasoned Action, Theory of Planned Behavior, and recently, the Unified Theory of Acceptance and Use of Technology. Each of these models has sought to identify the factors which influence a citizen’s intention or actual use of information technology. Drawing on the UTAUT model and Flow Theory, this research composes a new hybrid theoretical framework to identify the factors affecting the acceptance and use of Mobile Internet -as an ICT application- in a consumer context. The proposed model incorporates eight constructs: Performance Expectancy, Effort Expectancy, Facilitating Conditions, Social Influences, Perceived Value, Perceived Playfulness, Attention Focus, and Behavioral intention. Data collected online from 238 respondents in Saudi Arabia were tested against the research model, using the structural equation modeling approach. The proposed model was mostly supported by the empirical data. The findings of this study provide several crucial implications for ICT and, in particular, mobile Internet service practitioners and researchers