Search

We propose a Quantum inspired Hash Function using controlled alternate quantum walks with Memory on cycles (QHFM), where the $j$th message bit decides whether to run quantum walk with one-step memory or to run quantum walk with two-step memory at the $j$th time step, and the hash value is calculated from the resulting probability distribution of the walker. Numerical simulation shows that the proposed hash function has near-ideal statistical performance and is at least on a par with the state-of-the-art hash functions based on quantum walks in terms of sensitivity of hash value to message, diffusion and confusion properties, uniform distribution property, and collision resistance property; and theoretical analysis indicates that the time and space complexity of the new scheme are not greater than those of its peers. The good performance of QHFM suggests that quantum walks that differ not only in coin operators but also in memory lengths can be combined to build good hash functions, which, in turn, enriches the construction of controlled alternate quantum walks.

Feature selection (FS) is a crucial step for effective data mining since it has largest effect on improving the performance of classifiers. This is achieved by removing the irrelevant features and using only the relevant features. Many metaheuristic approaches exist in the literature in attempt to address this problem. The performance of these approaches differ based on the settings of a number of factors including the use of chaotic maps, opposition-based learning (OBL) and the percentage of the population that OBL will be applied to, the metaheuristic (MH) algorithm adopted, the classifier utilized, and the threshold value used to convert real solutions to binary ones. However, it is not an easy task to identify the best settings for these different components in order to determine the relevant features for a specific dataset. Moreover, running extensive experiments to fine tune these settings for each and every dataset will consume considerable time. In order to mitigate this important issue, a hyper-heuristic based FS paradigm is proposed. In the proposed model, a two-stage approach is adopted to identify the best combination of these components. In the first stage, referred to as the training stage, the Differential Evolution (DE) algorithm is used as a controller for selecting the best combination of components to be used by the second stage. In the second stage, referred to as the testing stage, the received combination will be evaluated using a testing set. Empirical evaluation of the proposed framework is based on numerous experiments performed on the most popular 18 datasets from the UCI machine learning repository. Experimental results illustrates that the generated generic configuration provides a better performance than eight other metaheuristic algorithms over all performance measures when applied to the UCI dataset. Moreover, The overall paradigm ranks at number one when compared against state-of-the-art algorithms. Finally, the generic configuration provides a very competitive performance for high dimensional datasets.

LiNi0.5Co0.2Mn0.3O2 (NCM523) has become one of the most popular cathode materials for current lithium-ion batteries due to its high-energy density and cost performance. However, the rapid capacity fading of NCM severely hinders its development and applications. Here, the single crystal NCM523 materials under different degradation states are characterized using scanning transmission electron microscopy (STEM). Then we developed a neural network model with a two-sequential attention block to recognize the crystal structure and locate defects in STEM images. The number of point defects in NCM523 is observed to experience a trend of increasing first and then decreasing in the degradation process. The space between the transition metal columns shrinks obviously, inducing dramatic capacity decay. This analysis sheds light on the defect evolution and chemical transformation correlated with layered material degradation. It also provides interesting hints for researchers to regenerate the electrochemical capacity and design better battery materials with longer life.

Metal cutting using flood cooling has serious impacts on the environment and operator health. Therefore, the minimum quantity lubricant (MQL) technique has been proposed as a promising alternative to the conventional flood cooling technique. Replacing mineral oils by eco-friendly vegetable oils gives another environmental advantage to MQL metal cutting. In this study, two types of nanoparticles (Al2O3 and CuO) are added to rice bran vegetable oil to obtain eco-friendly nanofluid with enhanced thermophysical properties, which utilized as a cutting fluid in the so-called MQL-turning with nano-lubricants (MQL-TNL). The experimental plan was designed according to Taguchi L16 method. The effects of the main cutting parameters such as cutting speed, cutting depth, and feed on the cutting force, surface roughness, tool wear are investigated. Among all cutting parameters, cutting speed has the highest effect on all process responses. The use of CuO/oil nanofluid as a cutting fluid produces smooth machined surfaces with little tool wear compared with that of Al2O3/oil nanofluid. Moreover, an improved random vector functional link (RVFL) model trained using experimental results is utilized to predict the responses of the cutting process. The accuracy of the model is enhanced via incorporation with a metaheuristic optimization algorithm called political optimizer (PO), which used to obtain the optimal RVFL parameters. The predicted results obtained by the developed RVFL-PO model are compared with the experimental ones as well as those obtained by standalone RVFL and hybrid RVFL-PSO (particle swarm optimization). The accuracy of the three models is assessed using various statistical measures. The RVFL-PO shows the best accuracy among others. The lowest coefficient of determination of the predicted results for all investigated cases was 0.768, 0.844, and 0.961 for RVFL, RVFL-PSO, and RVFL-PO, respectively.

Highlights • Brazil and Russia become hotspots of the COVID-19, and they suffer from high increase of the daily confirmed cases. • Proposed an Artificial intelligence based forecasting model to forecast the number of confirmed cases of COVID-19 in Brazil and Russia. • Enhancing the performance of the ANFIS using an improved marine predators algorithm (MPA), called chaotic MPA (CMPA). • Compare the proposed CMPA-ANFIS model to three models, the original ANFIS, PSO-ANFIS, and the original MPA-ANFIS., COVID-19 is a new member of the Coronaviridae family that has serious effects on respiratory, gastrointestinal, and neurological systems. COVID-19 spreads quickly worldwide and affects more than 41.5 million persons (till 23 October 2020). It has a high hazard to the safety and health of people all over the world. COVID-19 has been declared as a global pandemic by the World Health Organization (WHO). Therefore, strict special policies and plans should be made to face this pandemic. Forecasting COVID-19 cases in hotspot regions is a critical issue, as it helps the policymakers to develop their future plans. In this paper, we propose a new short term forecasting model using an enhanced version of the adaptive neuro-fuzzy inference system (ANFIS). An improved marine predators algorithm (MPA), called chaotic MPA (CMPA), is applied to enhance the ANFIS and to avoid its shortcomings. More so, we compared the proposed CMPA with three artificial intelligence-based models include the original ANFIS, and two modified versions of ANFIS model using both of the original marine predators algorithm (MPA) and particle swarm optimization (PSO). The forecasting accuracy of the models was compared using different statistical assessment criteria. CMPA significantly outperformed all other investigated models.

The design of complex mechanical components is a time-consuming process which involves many design variables with multiple interacted objectives and constraints. Traditionally, the design process of mechanical components is performed manually depending on the intuition and experience of the designer. In recent decades, automatic methods have been proposed to effectively search diverse and large parameter spaces. There is a growing interest in design optimization of mechanical systems using metaheuristic algorithms to improve the product lifecycle and performance and minimize the cost. Nowadays, there is a growing interest in design optimization of mechanical systems using metaheuristic algorithms to improve the product lifecycle and performance and minimize the cost. This review article demonstrates the applications of different metaheuristic algorithms in enhancing the design process of different mechanical systems. First, the basic concepts of common used metaheuristic algorithms are introduced. Then the applications of theses algorithms in optimization of different mechanical systems are discussed.

Deep neural networks (DNNs) have evolved as a beneficial machine learning method that has been successfully used in various applications. Currently, DNN is a superior technique of extracting information from massive sets of data in a self-organized method. DNNs have different structures and parameters, which are usually produced for particular applications. Nevertheless, the training procedures of DNNs can be protracted depending on the given application and the size of the training set. Further, determining the most precise and practical structure of a deep learning method in a reasonable time is a possible problem related to this procedure. Meta-heuristics techniques, such as swarm intelligence (SI) and evolutionary computing (EC), represent optimization frames with specific theories and objective functions. These methods are adjustable and have been demonstrated their effectiveness in various applications; hence, they can optimize the DNNs models. This paper presents a comprehensive survey of the recent optimization methods (i.e., SI and EC) employed to enhance DNNs performance on various tasks. This paper also analyzes the importance of optimization methods in generating the optimal hyper-parameters and structures of DNNs in taking into consideration massive-scale data. Finally, several potential directions that still need improvements and open problems in evolutionary DNNs are identified.

Polymers have been widely used in energy storage, construction, medicine, aerospace, and so on. However, the complexity of chemical composition and morphology of polymers has brought challenges to their development. Thanks to the integration of machine learning algorithms and large data resources, the data‐driven methods have opened up a new road for the development of polymer science and engineering. The emerging polymer informatics attempts to accelerate the performance prediction and process optimization of new polymers by using machine learning models based on reliable data. With the gradual supplement of currently available databases, the emergence of new databases and the continuous improvement of machine learning algorithms, the research paradigm of polymer informatics will be more efficient and widely used. Based on these points, this paper reviews the development trends of machine learning assisted polymer informatics and provides a simple introduction for researchers in materials, artificial intelligence, and other fields.

Vehicles authentication, the integrity of messages exchanged, and privacy-preserving are essential features in vehicular ad hoc network (VANETs) security. Most of the previously proposed VANETs security solutions do not sufficiently satisfy the security and efficiency requirements. Besides, most of those solutions are heavily dependent on the system key and long-term sensitive data stored in an ideal tamper-proof device, which may not be practical or ideal for resource-constrained onboard units (OBUs), especially in the case of an unexpected cloning or physical attack. Therefore, a robust authentication solution should consider those security issues and the nature of resource-constrained nodes. To satisfy all these requirements, we propose a lightweight multi-factor authentication and privacy-preserving security solution for VANETs. It employs a combination of physically unclonable functions (PUF) and one-time dynamic pseudo-identities as authentication factors. Furthermore, it eliminates the heavy dependency on the system key by decentralising the wide precinct of the certificate authority (CA) into regional domains and achieves robust control of domains keys. A detailed analysis demonstrates that our scheme efficiently meets the VANETs security requirements, and offers more suitable communication and computation costs and features than existing schemes.

Cloud computing is a promising development in the field of information technology. However, it faces many issues, particularly in the area of security. Authentication and integrity is a critical issue in the field of data security and many interesting issues have been raised to realize or ensure any modification with data exchange between a pair of smart devices within a cloud system. Exciting methods in such a field can be powerless against well-known modification and malicious attacks. Therefore, a powerful method is needed to prevent tampering of data for E2E smart devices transmission in the cloud. In this work, we present a robust and one-time biometric message authentication code based on combining biometric feature extracted from the user’s iris and double steganography-based discrete wavelet transformation steganography. The result of the combination is that it preserves the user’s message integrity, ensures authentication, and prevents known attacks. This approach enjoys several security merits, such as phase key agreement, biometric key management, a one-time biometric key and message anonymity for each specific session, one-time message authentication code for each specific user’s session, double steganography, and mutual authentication. Moreover, the efficiency and security of this method have been proved through security analysis and experimental results.

Document Similarity Detection (DSD) is significant in our real life applications. However, the existing methods ignore the privacy of what is contained in the documents uploaded on remote servers, thus reducing the applicability of these methods. The proposed scheme allows documents to be compared without revealing to those remote servers. For each document, the fingerprint set is calculated. The inverted index is constructed on the basis of the whole fingerprint set. The inverted index is widely used for efficient retrieval. This index is under protection by Paillier cryptosystem before it gets uploaded to the server.

The viscosity of bitumen is extremely high at ambient temperature and in order to produce and transport the bitumen, it is necessary to reduce its viscosity. In this regard various n-alkane...

Image segmentation is one of the popular tasks in image processing that can be used in several applications. For that, there are several methods have been proposed for image segmentation; in which, these methods aim to minimize or maximize single objective (SO) function to find the optimal threshold to separate the image into the optimal number of regions. While a few of the image segmentation methods consider multi-objective functions which aim to find the optimal solutions that can reduce the conflict among the different objectives. However, these methods have some limitations as decreasing their performance when the number of objectives is increasing. Due to increasing the number of non-dominated solutions, so, there isn’t pressure towards the Pareto front. Therefore, this paper proposes an alternative image segmentation method using many-objective optimization (MaOP) algorithms considering seven objective functions. One of the most competitive MaOPs is called the Knee Evolutionary Algorithm (KnEA) which used to find the set of Pareto optimal solutions for seven objective functions to improve the image segmentation. The proposed KnEA is evaluated using a set of six images tested at six different levels of threshold, and its performance is compared with other MaOP methods. The experimental results show that the KnEA method has a better approximation to the optimal Pareto fronts (PFs) than the other MaOPs method in terms of the quality of the segmented image such as the peak signal-to-noise ratio (PSNR), the structural similarity index (SSIM), and the computational time. As well as, the quality of PFs is measuring using the hypervolume, coverage and spacing indicators.

Bioactive peptides from protein hydrolysates with antihypertensive properties have a great effect in health, which warrants their pharmaceutical use. Nevertheless, the process of their production may affect their efficacy. In this study, we investigate the inhibitory activities of various hydrolysates on angiotensin-converting enzyme (ACE) in relation to the chemical diversity of corresponding bioactive peptides. This depends on the enzyme specificity and process conditions used for the production of hydrolysates. In order to mitigate the uncontrolled chemical alteration in bioactive peptides, we propose a computational approach using the random vector functional link (RVFL) network based on the sine-cosine algorithm (SCA) to find optimal processing parameters, and to predict the ACE inhibition activity. The SCA is used to determine the optimal configuration of RVFL, improving the prediction performance. The experimental results show that the performance measures of the proposed model are better than the state-of-the-art methods.

In machine learning and data mining, feature selection (FS) is one of the most important tasks required to select the most relevant instances from a dataset. In other words, FS is used to reduce the amount of information, creating a subset that represents the entire pool of data. The accuracy of the FS is reflected in a good classification of the information. This article presents an improved version of the social spider optimization (SSO) algorithm. The SSO tends to fail in local optima during the iterative process and is not possible to avoid this situation in the standard form. The proposed version avoids selecting the irrelevant features that demerit the performance of the FS. To achieve this goal, the opposition-based learning is used, in which there is a rule used to increase the exploration of the search space and the prominent zones in a determined neighborhood. The proposed algorithm is called opposition-based social spider optimization (OBSSO), and it has been tested over different mathematical problems. Moreover, the OBSSO, also, has been tested and compared with similar approaches using different datasets with specific information selected from UCI repository. The experimental results provide the evidence of the capabilities of the OBSSO for solving complex optimization problems.

An accurate definition of the photovoltaic (PV) models is an essential task to emulate and understand the physical behavior of the PV cell/panels. The highly used PV models are the static equivalent circuits, including single and double diode models. However, the accurate definition of the static models is mainly based on their estimated parameters. Proposing a reliable Optimization-based approached is a challenging aim. So, this paper proposes a novel and efficient optimizer to identify PV single and double diode models’ parameters for several PV modules using different sets of experimentally measured data. The developed method depends on improving the gradient-based optimization algorithm (GBO) using a new crossover operator to enhances agents’ diversity. Furthermore, a modified local escaping operator is applied to improve exploitation of GBO. The performance of the improvement GBO (IGBO) is evaluated using different experimental datasets for numerous PV modules under several operating conditions of temperature and radiation. The efficiency of IGBO is validated through a massive comparison with a set of recent state-of-the-art techniques. Reported results, fitting curves, and convergence curves provide proof for the efficiency of IGBO in providing high qualifies results with remarkable convergence speed.

Recently, Okuyama and Ohzek [1] derived a speed limit for the imaginary-time Schrödinger equation, which is inspired by the prior work of Kieu, who had shown a new class of time–energy uncertainty relations suitable for actually evaluating the speed limit of quantum dynamics. In this paper, we apply the result of Okuyama and Ohzek to obtain a generalized speed limit for Grover’s search in imaginary-time quantum annealing. An estimate of the lower bound on the computational time is shown, from which the role of the coefficient function corresponding to the final Hamiltonian played in the quantum dynamics for the problem is sticking out. However, when trying to apply the speed limit to the analogue of Grover’s problem, we find that not only the coefficient of the target Hamiltonian is related to the time complexity of the algorithm, but also the coefficient of the initial Hamiltonian is crucial for determining the time complexity. This is new and generalizes one of the results in our previous work.

Superpixels have been widely used in computer vision tasks due to their representational and computational efficiency. Meanwhile, deep learning and end-to-end framework have made great progress in various fields including computer vision. However, existing superpixel algorithms cannot be integrated into subsequent tasks in an end-to-end way. Traditional algorithms and deep learning-based algorithms are two main streams in superpixel segmentation. The former is non-differentiable and the latter needs a non-differentiable post-processing step to enforce connectivity, which constraints the integration of superpixels and downstream tasks. In this paper, we propose a deep learning-based superpixel segmentation algorithm SIN which can be integrated with downstream tasks in an end-to-end way. Owing to some downstream tasks such as visual tracking require real-time speed, the speed of generating superpixels is also important. To remove the post-processing step, our algorithm enforces spatial connectivity from the start. Superpixels are initialized by sampled pixels and other pixels are assigned to superpixels through multiple updating steps. Each step consists of a horizontal and a vertical interpolation, which is the key to enforcing spatial connectivity. Multi-layer outputs of a fully convolutional network are utilized to predict association scores for interpolations. Experimental results show that our approach runs at about 80 fps and performs favorably against state-of-the-art methods. Furthermore, we design a simple but effective loss function which reduces much training time. The improvements of superpixel-based tasks demonstrate the effectiveness of our algorithm. We hope SIN will be integrated into downstream tasks in an end-to-end way and benefit the superpixel-based community. Code is available at: https://github.com/yuanqqq/SIN.

An innovative predictive model was employed to predict the key performance indicators of a full-scale wastewater treatment plant (WWTP) operated with an activated sludge treatment process. The data-driven model was obtained using data gathered from Cairo, Egypt. The proposed model consists of Random Vector Functional Link (RVFL) Networks incorporated with Manta Ray Foraging Optimizer (MRFO). RVFL is used as an advanced Artificial Neural Network (ANN) that avoids the common conventional ANN problems such as overfitting. MRFO is employed to determine the best RVFL parameters to maximize the prediction accuracy of the model. The developed MRFO-RVFL is compared with conventional RVFL to figure out the role of MRFO as an optimization tool to enhance model performance. Both models were trained and tested using experimental data measured during a long period of 222 days. This study aims to provide an accurate prediction of the most widely treated effluent indicators of BOD5 and TSS in the wastewater treatment plants. In this study, ten well-known influent wastewater parameters, BOD5, TSS, and VSS, influent flow rate, pH, ambient temperature, F/M ratio, SRT, WAS, and RAS, the output BOD5 and TSS were modeled and predicted using the integrated MRFO-RVFL algorithms and compared with the standalone RVFL model. The performance of the models was evaluated using different assessment measures such as R2, RMSE, and others. The obtained results of R2 and RMSE for the MRFO-RVFL model were 0.924 and 3.528 for BOD5 and 0.917 and 6.153 for TSS, which were much better than the results of conventional RVFL with 0.840 and 6.207 for BOD5 and 0.717 and 10.05 for TSS. Based on the obtained results, the selective model (MRFO-RVFL) exhibited a higher performance and validity to predict the TSS and optimal BOD5.

Document integrity and origin for E2E S2S in IoTcloud have recently received considerable attention because of their importance in the real-world fields. Maintaining integrity could protect decisions made based on these message/image documents. Authentication and integrity solutions have been conducted to recognise or protect any modification in the exchange of documents between E2E S2S (smart-to-smart). However, none of the proposed schemes appear to be sufficiently designed as a secure scheme to prevent known attacks or applicable to smart devices. We propose a robust scheme that aims to protect the integrity of documents for each users session by integrating HMAC-SHA-256, handwritten feature extraction using a local binary pattern, one-time random pixel sequence based on RC4 to randomly hide authentication codes using LSB. The proposed scheme can provide users with one-time bio-key, robust message anonymity and a disappearing authentication code that does not draw the attention of eavesdroppers. Thus, the scheme improves the data integrity for a users messages/image documents, phase key agreement, bio-key management and a one-time message/image document code for each users session. The concept of stego-anonymity is also introduced to provide additional security to cover a hashed value. Finally, security analysis and experimental results demonstrate and prove the invulnerability and efficiency of the proposed scheme.

An amendment to this paper has been published and can be accessed via a link at the top of the paper.

The proper use of evolutionary operators is crucial to find optimal solutions in a search space. Moreover, the diversity of the population affects the performance of Evolutionary Algorithms (EAs). This article introduces an EA called BWEAD which balances the influence of the operators. The proposal also performs a statistical analysis of the population when the diversity is low and decides which solutions might be replaced. Then BWEAD is able to explore the search space and exploit the prominent regions. The BWEAD has been tested over the CEC2014 set of benchmark functions. The experiments provide competitive results showing an improvement of 30% in 30-dimensional and 50-dimensional functions in comparison with state-of-the-art algorithms, overcoming some addressed instances and providing evidence of its capabilities on complex optimization problems.

In this paper, we focus on the task of generating reviews of diverse sentiment intensity via fine-grained text sentiment transfer. This task aims to revise an input review to satisfy a given sentiment intensity, while preserving the original semantic content. The challenge is to achieve fine-grained control of the sentiment intensity when generating reviews, different from conventional coarse-grained sentiment transfer task that only reverses the sentiment polarity of text. To tackle this problem, we propose a new text sentiment transfer model which encodes the original views by semantic attention and sentiment attention separately, then incorporates a numeric sentiment intensity value for decoding to finely control the sentiment diversity of the output reviews. Moreover, we formalize the optimization problem as to minimize the negative expected reward as well as the Kullback-Leibler divergence between the distributions of the original and generated reviews. Inspired by the existing Seq2SentiSeq approach, we train the model using a cycle reinforcement learning algorithm but introducing a more precise reward for sentiment transformation to include the sentiment difference of the backward generated reviews from the original ones. Experimental results show that our model outperforms existing methods by a large margin in both automatic evaluation and human evaluation.

Coronavirus pandemic (COVID-19) has infected more than ten million persons worldwide. Therefore, researchers are trying to address various aspects that may help in diagnosis this pneumonia. Image segmentation is a necessary pr-processing step that implemented in image analysis and classification applications. Therefore, in this study, our goal is to present an efficient image segmentation method for COVID-19 Computed Tomography (CT) images. The proposed image segmentation method depends on improving the density peaks clustering (DPC) using generalized extreme value (GEV) distribution. The DPC is faster than other clustering methods, and it provides more stable results. However, it is difficult to determine the optimal number of clustering centers automatically without visualization. So, GEV is used to determine the suitable threshold value to find the optimal number of clustering centers that lead to improving the segmentation process. The proposed model is applied for a set of twelve COVID-19 CT images. Also, it was compared with traditional k-means and DPC algorithms, and it has better performance using several measures, such as PSNR, SSIM, and Entropy.

In this paper, an improved version of the Grey Wolf Optimizer (GWO) is proposed to improve the exploration and the exploitation ability of the GWO algorithm. This improvement is performed through using the chaotic logistic map, the Opposition-Based Learning (OBL), the differential evolution(DE), and the disruption operator (DO). Where, the chaotic logistic map and the OBL are used to initialize the candidate solutions and these approaches avoid the drawbacks of the random population and increase the convergence of the algorithm. Then, the DE operators are combined with the GWO algorithm, in which, the DE operators work as a local search mechanism to improve the exploitation ability of the GWO through updating the population. Also, after updating the solutions by using a hybrid between the GWO and the DE, the DO is used to enhance the exploration ability, in which, the DO is used to maintain the diversity of the population. Therefore, the combinations with chaotic logistic map, OBL, DE, and DO, provide the GWO with tools to better balance between the exploration and the exploitation of the search space without affecting the computational time required for this task. The proposed algorithm, called COGWO2D, is compared with other seven algorithms through a set of experimental series that have been performed over two benchmark functions, the classical CEC2005, and the CEC2014. Also, the performance of the proposed algorithm to improve the classification of the galaxy images is evaluated, where it is used as a feature selection method. The aim of this experiment is to select the optimal subset of features from the extracted features of the galaxy images. The experimental results support the efficacy of the proposed approach to find the optimal solutions of the global optimization problem, as well as, increase the accuracy of the classification of the galaxy images.

Feature selection (FS) is a machine learning process commonly used to reduce the high dimensionality problems of datasets. This task permits to extract the most representative information of high sized pools of data, reducing the computational effort in other tasks as classification. This article presents a hybrid optimization method for the FS problem; it combines the slap swarm algorithm (SSA) with the particle swarm optimization. The hybridization between both approaches creates an algorithm called SSAPSO, in which the efficacy of the exploration and the exploitation steps is improved. To verify the performance of the proposed algorithm, it is tested over two experimental series, in the first one, it is compared with other similar approaches using benchmark functions. Meanwhile, in the second set of experiments, the SSAPSO is used to determine the best set of features using different UCI datasets. Where the redundant or the confusing features are removed from the original dataset while keeping or yielding a better accuracy. The experimental results provide the evidence of the enhancement in the SSAPSO regarding the performance and the accuracy without affecting the computational effort.

The similarity measures for molecules play an important role for research in chemistry, biology and drug design. In order to obtain similarity measures for giant molecules such as muscle protein titin, the existing classical algorithms possess high computational complexity and many other disadvantages. An effective quantum algorithm, Quantum Method for Similarity Measures for Molecules (QMSM), is introduced to obtain similarity measure for molecules based on the quantum phase estimation algorithm. Moreover, we discuss the feasibility of simulating the quantum algorithm QMSM with quantum circuits. Finally, the performance evaluation and comparison of the QMSM algorithm are presented, where the QMSM can obtain exponential speedups compared to its classical counterparts.

In this paper, a multilevel thresholding image segmentation method base on the enhancement of the performance of the whale optimization algorithm (WOA). The developed method, called the multi-leader whale optimization algorithm (MLWOA), aims to avoid the limitations of traditional WOA during the searching process, such as stagnation at the local optimum. This was achieved by integrating the different tools with WOA, such as memory mechanism, multi-leader method, self-learning strategy, and levy flight method. Each of these techniques has its own task, for example, the memory structure of traditional WOA and add a multi-leader mechanism to enhance the ability of exploration. The superiority of leaders will make more influence in MLWOA by adding a self-learning strategy. Also, it used levy flight trajectory to make the algorithm more robust and avoid premature convergence. To evaluate the performance of the developed MLWOA, a set of experiments are conducted using the CEC2017 benchmark. In addition, it is applied to determine the optimal threshold values to segment a set of images using the Otsu method, fuzzy entropy, and Kapur's entropy as a fitness function. The results of MLWOA are compared with well-known meta-heuristic algorithms inside the experiments. The comparison results indicated that MLWOA provides better performance in CEC2017 benchmark functions and shows high superiority in image segmentation in terms of performance measures. In addition, the MLWOA provides better results using Otsu, followed by the Fuzzy entropy and Kapur in terms of PSNR. In terms of SSIM, fuzzy entropy and Otsu have nearly the same SSIM value, but the fuzzy entropy provides better results.

This paper presents an alternative global optimization meta-heuristics (MHs) approach, inspired by the natural selection theory. The proposed approach depends on the competition among six MHs that allows generating an offspring, which can breed the high characteristics of parents since they are unique and competitive. Therefore, this leads to improve the convergence of the solutions towards an optimal solution and also, to avoid the limitations of other methods that aim to balance between exploitation and exploration. The six algorithms are differential evolution, whale optimization algorithm, grey wolf optimization, symbiotic organisms search algorithm, sine–cosine algorithm, and salp swarm algorithm. According to these algorithms, three variants of the proposed method are developed, in the first variant, one of the six algorithms will be used to update the current individual based on a predefined order and the probability of the fitness function for each individual. Whereas, the second variant updates each individual by permuting the six algorithms, then using the algorithms in the current permutation to update individuals. The third variant is considered as an extension of the second variant, which updates all individuals using only one algorithm from the six algorithms. Three different experiments are carried out using CEC 2014 and CEC 2017 benchmark functions to evaluate the efficiency of the proposed approach. Moreover, the proposed approach is compared with well known MH methods, including the six methods used to build it. Comparison results confirmed the efficiency of the proposed approach compared to other approaches according to different performance measures.

Artificial intelligence techniques have become powerful alternatives to conventional modeling techniques in different engineering disciplines. They have been applied for modeling, control, prediction, optimization, forecasting, and identification of complex systems. In this paper, a novel optimized artificial intelligence method is developed to predict the performance of Photovoltaic/Thermal Collector (PVTC) incorporated with Electrolytic Hydrogen Production (EHP) system in terms of power output of PV, PV surface cell temperature, output temperature of cooling fluid, thermal and electrical efficiency, and hydrogen production yield. A new metaheuristic algorithm called mayfly based optimization (MO) algorithm has been implemented with Random Vector Functional Link (RVFL) network to maximize the prediction accuracy. The proposed hybrid artificial intelligence model was trained and tested using experimental data. The experiments were conducted outdoors for the proposed PVTC-EHP system operating with two different cooling fluids, namely, air and water under Indian weather conditions, and their results were compared with the predicted RVFL-MO and conventional RVFL results. Moreover, five statistical criteria were used to evaluate the performance of the investigated algorithms. The experimental results showed the hybrid PVTC-EHP system can produce a daily accumulated PV output power and hydrogen production yield of 1.66 kW/day and 3.60 kg/day for water-based PVTC-EHP system and 1.22 kW/day and 4.41 kg/day for air-based PVTC-EHP system, respectively, at a mass flow rate of 0.66 kg/min. Moreover, the statistical measures showed a perfect fit between the experimental and the proposed prediction model results. The results revealed that the root mean square error for the training phase of the RVFL and RVFL-MO was 0.25 and 0.65, respectively, while it was 1.63 and 2.04 for the testing phase, which reveals the important role of MO in determining the best parameters of RVFL network that maximize its prediction performance.

In this paper, an improvement for the Moth-flame Optimization (MFO) algorithm is proposed based on Opposition-Based Learning (OBL), that enhances the exploration of the search space through computing the opposition values of solutions generated by MFO. Moreover, such an approach increases the efficiency of MFO as multiple regions in the search space are investigated at the same time. The proposed algorithm (referred to as OBMFO) avoids the limitations of MFO (and other swarm intelligence algorithms) that result from the moving in the direction of the best solution, especially if this direction does not lead to the global optimum. Experiments are run using classical six benchmark functions to compare the performance of OBMFO against MFO. Moreover, OBMFO is used to solve the feature selection problem, using eight UCI datasets, in order to improve the classification performance through removing irrelevant and redundant features. The comparison results show that the OBMFO superiors to MFO for the tested benchmark functions. It also outperforms another three swarm intelligence algorithms in terms of the classification performance.

People have proposed many data integrity techniques to secure data storage in cloud. The majority of these schemes assume that only the owner of the data can modify their storage in cloud. In recent years, researchers have allowed different cloud users to use integrity assurance for modifying data. As a result, schemes with stronger reality than before have been proposed. Nevertheless, these attempts are impractical due to the large computing costs for cloud users. Clients must also perform numerous computations to ensure the integrity of data storage. A robust and efficient scheme is put forward in this study to maintain data integrity in cases that involve public auditing. In this way, multiuser modification can be used to check the public integrity for cloud data and reduce the auditing cost. The proposed scheme uses public key cryptography equipped with a proxy re-encryption and a cryptographic hash function. We allow a third-party auditor (TPA) to conduct preprocessing of data for the sake of cloud users prior to uploading these data to the cloud service providers (CSPs) and then verify the integrity of data. We also allow the TPA to perform re-encryption of data for sharing data without losing privacy. The scheme is characterised by significant security features, such as management of key, privacy, low-cost computation, exchange of key, freeing clients from burdens, failure of CSPs in creating right verifier response in absence of data and one-time key requirement. Numerical analysis and extensive experimental results verify that the proposed scheme is efficient and scalable.

Multilevel thresholding image segmentation has received considerable attention in several image processing applications. However, the process of determining the optimal threshold values (as the preprocessing step) is time-consuming when traditional methods are used. Although these limitations can be addressed by applying metaheuristic methods, such approaches may be idle with a local solution. This study proposed an alternative multilevel thresholding image segmentation method called VPLWOA, which is an improved version of the volleyball premier league (VPL) algorithm using the whale optimization algorithm (WOA). In VPLWOA, the WOA is used as a local search system to improve the learning phase of the VPL algorithm. A set of experimental series is performed using two different image datasets to assess the performance of the VPLWOA in determining the values that may be optimal threshold, and the performance of this algorithm is compared with other approaches. Experimental results show that the proposed VPLWOA outperforms the other approaches in terms of several performance measures, such as signal-to-noise ratio and structural similarity index.

Automatic clustering based hybrid metaheuristic algorithms has attracted the center of interest of scientists and engineers which become a hot topic for different data analysis applications. For example, image clustering, bioinformatics, image segmentation, and natural language processing. Where the process of determining the number and position of centroids is an NP-hard problem. So, this paper presents an alternative automatic clustering algorithm based on the hybrid between the atom search optimization (ASO) and the sine-cosine algorithm (SCA). The main objective of the proposed clustering method, called ASOSCA, is to find automatically the optimal number of centroids and their positions in order to minimize the CS-index (which refers to Compact-separated index). To achieve this goal, the ASOSCA uses SCA as a local search operator to improve the quality of ASO. The performance of the proposed hybrid method is compared with other metaheuristic methods; in which all of them are tested on sixteen clustering datasets and using different cluster validity indexes as Dunn, Silihouette, Davies Bouldin, and Calinski Harabasz. The experimental results show that the ASOSCA depict high superiority in comparison with other types of hybrid metaheuristic in terms of clustering measures.

In this paper, we study the problem that when quantum adiabatic evolution with the most general form of system Hamiltonian will get failed. Here the most general form means that the initial and final Hamiltonians are just designed according to the adiabatic theorem in quantum mechanics. As we will see, even in this most general model of quantum adiabatic evolution, it still exists the possibility that the quantum adiabatic computation can fail totally if some condition is satisfied, which implies the time complexity of the quantum algorithm is infinity. That is, here we propose a rather general criterion for judging whether a quantum adiabatic evolution is successful. This result largely extends the authors’ previous research on this topic, and it may be seen as a further important clue for us when designing quantum algorithms in the framework of adiabatic evolution for some practical problems.

In data center companies, cloud computing can host multiple types of heterogeneous virtual machines (VMs) and provide many features, including flexibility, security, support, and even better maintenance than traditional centers. However, some issues need to be considered, such as the optimization of energy usage, utilization of resources, reduction of time consumption, and optimization of virtual machine placement. Therefore, this paper proposes an alternative multiobjective optimization (MOP) approach that combines the salp swarm and sine-cosine algorithms (MOSSASCA) to determine a suitable solution for virtual machine placement (VMP). The objectives of the proposed MOSSASCA are to maximize mean time before a host shutdown (MTBHS), to reduce power consumption, and to minimize service level agreement violations (SLAVs). The proposed method improves the salp swarm and the sine-cosine algorithms using an MOP technique. The SCA works by using a local search approach to improve the performance of traditional SSA by avoiding trapping in a local optimal solution and by increasing convergence speed. To evaluate the quality of MOSSASCA, we perform a series of experiments using different numbers of VMs and physical machines. The results of MOSSASCA are compared with well-known methods, including the nondominated sorting genetic algorithm (NSGA-II), multiobjective particle swarm optimization (MOPSO), a multiobjective evolutionary algorithm with decomposition (MOEAD), and a multiobjective sine-cosine algorithm (MOSCA). The results reveal that MOSSASCA outperforms the compared methods in terms of solving MOP problems and achieving the three objectives. Compared with the other methods, MOSSASCA exhibits a better ability to reduce power consumption and SLAVs while increasing MTBHS. The main differences in terms of power consumption between the MOSCA, MOPSO, MOEAD, and NSGA-II and the MOSSASCA are 0.53, 1.31, 1.36, and 1.44, respectively. Additionally, the MOSSASCA has higher MTBHS value than MOSCA, MOPSO, MOEAD, and NSGA-II by 362.49, 274.70, 585.73 and 672.94, respectively, and the proposed method has lower SLAV values than MOPSO, MOEAD, and NSGA-II by 0.41, 0.28, and 1.27, respectively.

Representation learning of knowledge bases aims to embed both entities and relations into a continuous vector space. Most existing models such as TransE, DistMult, ANALOGY and ProjE consider only binary relations involved in knowledge bases, while multi-fold relations are converted to triplets and treated as instances of binary relations, resulting in a loss of structural information. M-TransH is a recently proposed direct modeling framework for multi-fold relations but ignores the relation-level information that certain facts belong to the same relation. This paper proposes a Group-constrained Embedding method which embeds entity nodes and fact nodes from entity space into relation space, restricting the embedded fact nodes related to the same relation to groups with Zero Constraint, Radius Constraint or Cosine Constraint. Using this method, a new model is provided, i.e. Gm-TransH. We evaluate our model on link prediction and instance classification tasks, experimental results show that Gm-TransH outperforms the previous multi-fold relation embedding methods significantly and achieves excellent performance.

Vehicular ad-hoc networks (VANETs) have got a wide range of attention to improve traffic systems and smart cities. Security and privacy are critical issues and need to be addressed for effective VANET systems. In this paper, we propose an efficient conditional privacy-Preserving authentication scheme based on a group secret key. Many VANETs schemes have utilized a group signature to realize the identity-less authentication, but the existing schemes suffering from long and high computational procedures, especially in the Certificate Revocation List (CRL) verification process, and group management complexity issues. As a result, they cannot satisfy the VANETs efficiency requirements. On another hand, many pseudonym-ID based VANETs schemes utilized a system key signature, but those schemes suffering from communication overhead increasing which could be caused by the increasing of the revocation list, in addition to that, and any damage in the key will cause damage for the entire system. In our scheme, we use a permanent vehicle pseudonym-ID and temporary group secret key that offers a lightweight verification and authentication process for a secure and efficient VANET system.

Battery lifetime prediction is a promising direction for the development of next-generation smart energy storage systems. However, complicated degradation mechanisms, different assembly processes, and various operation conditions of the batteries bring tremendous challenges to battery life prediction. In this work, charge/discharge data of 12 solid-state lithium polymer batteries were collected with cycle lives ranging from 71 to 213 cycles. The remaining useful life of these batteries was predicted by using a machine learning algorithm, called symbolic regression. After populations of breed, mutation, and evolution training, the test accuracy of the quantitative prediction of cycle life reached 87.9%. This study shows the great prospect of a data-driven machine learning algorithm in the prediction of solid-state battery lifetimes, and it provides a new approach for the batch classification, echelon utilization, and recycling of batteries.

Providing an accurate and precise photovoltaic model is a vital stage prior to the system design, therefore, this paper proposes a novel algorithm, enhanced marine predators algorithm (EMPA), to identify the unknown parameters for different photovoltaic (PV) models including the static PV models (single-diode and double-diode) and dynamic PV model. In the proposed EMPA, the differential evolution operator (DE) is incorporated into the original marine predators algorithm (MPA) to achieve stable, and reliable performance while handling that nonlinear optimization problem of PV modeling. Three different real datasets are used to show the effectiveness of the proposed algorithm. In the first case study, the proposed algorithm is used to identify the unknown parameters of a single-diode and double-diode PV models. The root-mean-square error (RMSE) and standard deviation (STD) values for a single-diode are 7.7301 e - 04 and 5.9135 e - 07 . Similarly for double diode are 7.4396 e - 04 and 3.1849 e - 05 , respectively. In addition, the second case study is used to test the proposed model in identifying the unknown parameters of a double-diode PV model. Here, the proposed algorithm is compared with classical MPA in five scenarios at different operating conditions. In this case study, the RMSE and STD of the proposed algorithm are less than that obtained by the MPA algorithm. Moreover, the third case study is utilized to test the ability of the proposed model in identifying the parameters of a dynamic PV model. In this case study, the performance of the proposed algorithm is compared with the one obtained by MAP and heterogeneous comprehensive learning particle swarm optimization (HCLPSO) algorithms in terms of RMSE ± STD. The obtained value of RMSE ± STD by the proposed algorithm is 0.0084505 ± 1.0971 e - 17 , which is too small compared with that obtained by MPA and HCLPSO algorithms ( 0.0084505 ± 9.6235 e - 14 and 0.0084505 ± 2.5235 e - 9 ). The results show the proposed model’s superiority over the MPA and other recent proposed algorithms in data fitting, convergence rate, stability, and consistency. Therefore, the proposed algorithm can be considered as a fast, feasible, and a reliable optimization algorithm to identify the unknown parameters in static and dynamic PV models. The code of the dynamic PV models is available via this link: https://github.com/DAyousri/Identifying-the-parameters-of-the-integer-and-fractional-order-dynamic-PV-models?_ga=2.104793926.732834951.1616028563-1268395487.1616028563 .

Existing approaches for knowledge graph embedding usually represent knowledge as triples of binary relations between entities and embed them into continuous vector space, which are not practical for the complex facts in the form of multi-fold relations in real life. In this work, we address the problem of multi-fold relation embedding in knowledge graph and propose a new framework CoRelatE that learns correlations between entities, facts and relations from the instances. We first model the entity-relation correlation directly via combinational operator, then utilize a graph convolutional network to model the correlation between entities and their related facts. Finally, the facts are forced to embed close to their corresponding relations to learn the fact-relation correlation. We formulate the objective as a joint optimization problem and introduce an efficient algorithm to solve it. We compare the proposed CoRelatE model with several state-of-the-art models including HypE, NaLP and RAE, m-TransH. Experimental results on four datasets of multi-fold relations and two datasets of binary relations for link prediction and instance classification tasks validate the effectiveness and merits of our model.