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Today, artificial intelligence (AI) and machine learning (ML) have dramatically advanced in various industries, especially medicine. AI describes computational programs that mimic and simulate human intelligence, for example, a person’s behavior in solving problems or his ability for learning. Furthermore, ML is a subset of artificial intelligence. It extracts patterns from raw data automatically. The purpose of this paper is to help researchers gain a proper understanding of machine learning and its applications in healthcare. In this paper, we first present a classification of machine learning-based schemes in healthcare. According to our proposed taxonomy, machine learning-based schemes in healthcare are categorized based on data pre-processing methods (data cleaning methods, data reduction methods), learning methods (unsupervised learning, supervised learning, semi-supervised learning, and reinforcement learning), evaluation methods (simulation-based evaluation and practical implementation-based evaluation in real environment) and applications (diagnosis, treatment). According to our proposed classification, we review some studies presented in machine learning applications for healthcare. We believe that this review paper helps researchers to familiarize themselves with the newest research on ML applications in medicine, recognize their challenges and limitations in this area, and identify future research directions.

Since the first fully homomorphic encryption scheme was published in 2009, many papers have been published on fully homomorphic encryption and its applications. Machine learning is one of the most interesting applications and has drawn a lot of attention from researchers. To better represent and understand the field of Homomorphic Encryption in Machine Learning (HEML), this paper utilizes automated citation and topic analysis to characterize the HEML research literature over the years and provide the bibliometrics assessments for this burgeoning field. This is conducted by using a bibliometric statistical analysis approach. We make use of web-based literature databases and automated tools to present the development of HEML. This allows us to target several popular topics for in-depth discussion. To achieve these goals, we have chosen the well-established Scopus literature database and analyzed them through keyword counts and Scopus relevance searches. The results show a relative increase in the number of papers published each year that involve both homomorphic cryptography and machine learning. Using text mining of articles titles, we have found that cloud computing is a popular topic in this field, which also includes neural networks, big data, and the Internet of Things. The analysis results show that China, the US, and India have generated almost half of all the research contributions in HEML. The citation statistics, keyword statistics, and topic analyses give us a quick overview of the development of the field, which can be of great help to new researchers. It is also possible to apply our methodology to other research areas, and we see great value in this approach.

Feature selection is a well-known prepossessing procedure, and it is considered a challenging problem in many domains, such as data mining, text mining, medicine, biology, public health, image processing, data clustering, and others. This paper proposes a novel feature selection method, called AOAGA, using an improved metaheuristic optimization method that combines the conventional Arithmetic Optimization Algorithm (AOA) with the Genetic Algorithm (GA) operators. The AOA is a recently proposed optimizer, it has been employed to solve several benchmark and engineering problems and has shown a promising performance. The main aim behind the modification of the AOA is to enhance its search strategies. The conventional version suffers from weaknesses, the local search strategy, and the trade-off between the search strategies. Therefore, the operators of the GA can overcome the shortcomings of the conventional AOA. The proposed AOAGA was evaluated with several well-known benchmark datasets, using several standard evaluation criteria, namely accuracy, number of selected features, and fitness function. Finally, the results were compared with the state-of-the-art techniques to prove the performance of the proposed AOAGA method. Moreover, to further assess the performance of the proposed AOAGA method, two real-world problems containing gene datasets were used. The findings of this paper illustrated that the proposed AOAGA method finds new best solutions for several test cases, and it got promising results compared to other comparative methods published in the literature.

In the supervised classification area, the algorithm selection problem (ASP) refers to determining the a priori performance of a given classifier in some specific problem, as well as the finding of which is the most suitable classifier for some tasks. Recently, this topic has attracted the attention of international research groups because a very promising vein of research has emerged: the application of some measures of data complexity in the pattern classification algorithms. This paper aims to analyze the response of the Customized Naïve Associative Classifier (CNAC) in data taken from the business area when some measures of data complexity are introduced. To perform this analysis, we used classification datasets from real-world related to business, 22 in total; then, we computed the value of nine measures of data complexity to compare the performance of the CNAC against other algorithms of the state of the art. A very important aspect of performing this task is the creation of an artificial dataset for meta-learning purposes, in which we considered the performance of CNAC, and then we trained a decision tree as meta learner. As shown, the CNAC classifier obtained the best results for 10 out of 22 datasets of the experimental study.

In the literature of marriage, divorce choices are usually assumed to not affect the distribution of types in the pool of singles. The scope of the present paper is to overcome this assumption. We analyse divorce choices when separation decision influences the distribution of singles and, thus, their expected quality. We consider a three-period model where heterogeneous individuals may unilaterally experience divorce and return to the marriage market. The choices of individuals are based on the change in the distribution of singles and the cost of waiting and divorcing, taking into consideration the individual’s eligibility in the marriage market. There are two main findings: Firstly, positive assortative matching dissolves with divorce for some intermediate types. Therefore, the endogenous positive assortative matching that usually emerges in models with nontransferable utility is weakened when matches can dissolve. Secondly, the existence of ranges where divorce emerges among individuals with positive assortative matching implies the existence of two disconnected classes of types. If matchings in the first period were to occur between individuals of different classes, such matches would be dissolved later.

Recently, unmanned aerial vehicles (UAVs) have been used in several applications of environmental modeling and land use inventories. At the same time, the computer vision-based remote sensing image classification models are needed to monitor the modifications over time such as vegetation, inland water, bare soil or human infrastructure regardless of spectral, spatial, temporal, and radiometric resolutions. In this aspect, this paper proposes an ensemble of DL-based multimodal land cover classification (EDL-MMLCC) models using remote sensing images. The EDL-MMLCC technique aims to classify remote sensing images into the different cloud, shades, and land cover classes. Primarily, median filtering-based preprocessing and data augmentation techniques take place. In addition, an ensemble of DL models, namely VGG-19, Capsule Network (CapsNet), and MobileNet, is used for feature extraction. In addition, the training process of the DL models can be enhanced by the use of hosted cuckoo optimization (HCO) algorithm. Finally, the salp swarm algorithm (SSA) with regularized extreme learning machine (RELM) classifier is applied for land cover classification. The design of the HCO algorithm for hyperparameter optimization and SSA for parameter tuning of the RELM model helps to increase the classification outcome to a maximum level considerably. The proposed EDL-MMLCC technique is tested using an Amazon dataset from the Kaggle repository. The experimental results pointed out the promising performance of the EDL-MMLCC technique over the recent state of art approaches.

The negative impact of false information on social networks is rapidly growing. Current research on the topic focused on the detection of fake news in a particular context or event (such as elections) or using data from a short period of time. Therefore, an evaluation of the current proposals in a long-term scenario where the topics discussed may change is lacking. In this work, we deviate from current approaches to the problem and instead focus on a longitudinal evaluation using social network publications spanning an 18-month period. We evaluate different combinations of features and supervised models in a long-term scenario where the training and testing data are ordered chronologically, and thus the robustness and stability of the models can be evaluated through time. We experimented with 3 different scenarios where the models are trained with 15-, 30-, and 60-day data periods. The results show that detection models trained with word-embedding features are the ones that perform better and are less likely to be affected by the change of topics (for example, the rise of COVID-19 conspiracy theories). Furthermore, the additional days of training data also increase the performance of the best feature/model combinations, although not very significantly (around 2%). The results presented in this paper build the foundations towards a more pragmatic approach to the evaluation of fake news detection models in social networks.

Multimodal identification, which exploits biometric information from more than one biometric modality, is more secure and reliable than unimodal identification. Face recognition and fingerprint recognition have received a lot of attention in recent years for their unique advantages. However, how to integrate these two modalities and develop an effective multimodal identification system are still challenging problems. Hetero-associative memory (HAM) models store some patterns that can be reliably retrieved from other patterns in a robust way. Therefore, in this paper, face and fingerprint biometric features are integrated by the use of a hetero-associative memory method for multimodal identification. The proposed multimodal identification system can integrate face and fingerprint biometric features at feature level when the system converges to the state of asymptotic stability. In experiment 1, the predicted fingerprint by inputting an authorized user’s face is compared with the real fingerprint, and the matching rate of each group is higher than the given threshold. In experiment 2 and experiment 3, the predicted fingerprint by inputting the face of an unauthorized user and the stealing authorized user’s face is compared with its real fingerprint input, respectively, and the matching rate of each group is lower than the given threshold. The experimental results prove the feasibility of the proposed multimodal identification system.

Due to complex background interference and weak space–time connection, traditional driver fatigue detection methods perform poorly for open-pit truck drivers. For these issues, this paper presents a driver fatigue detection method based on Libfacedetection and an LRCN. The method consists of three stages: (1) using a face detection module with a tracking method to quickly extract the ROI of the face, (2) extracting and coding the features, (3) combining the coding model to build a spatiotemporal classification network. The innovation of the method is to utilize the spatiotemporal features of the image sequence to build a spatiotemporal classification model suitable for this task. Meanwhile, a tracking method is added to the face detection stage to reduce time expenditure. As a result, the average speed with the tracking method for face detection on video is increased by 74% in comparison with the one without the tracking method. Our best model adopts a DHLSTM and feature-level frame aggregation, which achieves high accuracy of 99.30% on the self-built dataset.

Implementing an Emerging Technology (ET) is a difficult task due to people lacking ET-related knowledge and skills or having skeptical and negative attitudes towards the ET. As learners construct their understanding about an ET and develop related skills by actually passing through the ET Innovation Process (IP) stages (Awareness, Acceptance and Adoption), it could be useful to provide them with training that imitates certain IP stages. Using Artificial Intelligence Enhanced Robotics (AIER) as the example ET, we designed a two-day workshop to lead learners (n = 16) through the AIER IP Awareness stage, and a six-week training course with eight contact days to simulate the AIER IP Acceptance stage to learners (n = 10). Using online surveys and quantitative content analysis methods we confirmed that the workshop format increased the AIER-related self-confidence and general knowledge in 78% of participants, while the training course helped more than half of the participants to construct usable knowledge about a specific AIER and to see its possibilities in their specific work-place contexts. This paper is the pilot of using the Technology-Enhanced Learning Innovation Process (TELIP) model, first tested on a STEAM innovation, outside the educational context, for developing appropriate training approaches for specific ET IP stages.

Flood is one of the deadliest natural hazards worldwide, with the population affected being more than 2 billion between 1998–2017 with a lack of warning systems according to WHO. Especially, flash floods have the potential to generate fatal damages due to their rapid evolution and the limited warning and response time. An effective Early Warning Systems (EWS) could support detection and recognition of flash floods. Information about a flash flood can be mainly provided from observations of hydrology and from satellite images taken before the flash flood happens. Then, predictions from satellite images can be integrated with predictions based on sensors’ information to improve the accuracy of a forecasting system and subsequently trigger warning systems. The existing Deep Learning models such as UNET has been effectively used to segment the flash flood with high performance, but there are no ways to determine the most suitable model architecture with the proper number of layers showing the best performance in the task. In this paper, we propose a novel Deep Learning architecture, namely PSO-UNET, which combines Particle Swarm Optimization (PSO) with UNET to seek the best number of layers and the parameters of layers in the UNET based architecture, thereby improving the performance of flash flood segmentation from satellite images. Since the original UNET has a symmetrical architecture, the evolutionary computation is performed by paying attention to the contracting path and the expanding path is synchronized with the following layers in the contracting path. The UNET convolutional process is performed four times. Indeed, we consider each process as a block of the convolution having two convolutional layers in the original architecture. Training of inputs and hyper-parameters is performed by executing the PSO algorithm. In practice, the value of Dice Coefficient of our proposed model exceeds 79.75% (8.59% higher than that of the original UNET model). Experimental results on various satellite images prove the advantages and superiority of the PSO-UNET approach.

At present, iris recognition has been widely used as a biometrics-based security enhancement technology. However, in some application scenarios where a long-distance camera is used, due to the limitations of equipment and environment, the collected iris images cannot achieve the ideal image quality for recognition. To solve this problem, we proposed a modified sparrow search algorithm (SSA) called chaotic pareto sparrow search algorithm (CPSSA) in this paper. First, fractional-order chaos is introduced to enhance the diversity of the population of sparrows. Second, we introduce the Pareto distribution to modify the positions of finders and scroungers in the SSA. These can not only ensure global convergence, but also effectively avoid the local optimum issue. Third, based on the traditional contrast limited adaptive histogram equalization (CLAHE) method, CPSSA is used to find the best clipping limit value to limit the contrast. The standard deviation, edge content, and entropy are introduced into the fitness function to evaluate the enhancement effect of the iris image. The clipping values vary with the pictures, which can produce a better enhancement effect. The simulation results based on the 12 benchmark functions show that the proposed CPSSA is superior to the traditional SSA, particle swarm optimization algorithm (PSO), and artificial bee colony algorithm (ABC). Finally, CPSSA is applied to enhance the long-distance iris images to demonstrate its robustness. Experiment results show that CPSSA is more efficient for practical engineering applications. It can significantly improve the image contrast, enrich the image details, and improve the accuracy of iris recognition.

This paper focuses on a numerical approach to finding the p–y curves for laterally loaded piles. The Drucker–Prager plastic model is employed and implemented within a finite element MATLAB code. The pre- and post-processing code for Gmsh and related numerical tools are established as well. The p–y curve results from this new approach have been validated and compared to the typical design equations of API (American Petroleum Institute) and Matlock. The validation reveals that the code leads to lower p–y curves than the API and Matlock equations when the horizontal displacement is less than 0.35 times the diameter of the pile (B). A sensitivity analysis of the number of elements and the interface thickness is presented. The results indicate that the obtained p–y curves are independent of the two factors. Finally, the influence of clay content on the p–y behavior is investigated by the implemented MATLAB code. When y &lt, 0.15B, the same lateral capacity values are resulted at clay contents of 27.5% and 55%, and they are higher than the ones for 0% clay content. The p–y curves show a decreasing trend with increasing clay content after y &gt, 0.15B.

In this paper, we develop an economic order quantity (EOQ) model for fixed shelf-life items and a non-increasing demand. The objective of this model is to maximize the total profit. We find the criterion to decide (i) the interior maximum solution or (ii) the boundary maximum solution. Eight numerical examples are given to illustrate all possible scenarios of this generalized model. Our results identify a scenario for which the maximum profit is always negative. This is highly relevant for firms in the public sector operating at a financial loss.

Document-level Sentiment Analysis is a complex task that implies the analysis of large textual content that can incorporate multiple contradictory polarities at the phrase and word levels. Most of the current approaches either represent textual data using pre-trained word embeddings without considering the local context that can be extracted from the dataset, or they detect the overall topic polarity without considering both the local and global context. In this paper, we propose a novel document-topic embedding model, DocTopic2Vec, for document-level polarity detection in large texts by employing general and specific contextual cues obtained through the use of document embeddings (Doc2Vec) and Topic Modeling. In our approach, (1) we use a large dataset with game reviews to create different word embeddings by applying Word2Vec, FastText, and GloVe, (2) we create Doc2Vecs enriched with the local context given by the word embeddings for each review, (3) we construct topic embeddings Topic2Vec using three Topic Modeling algorithms, i.e., LDA, NMF, and LSI, to enhance the global context of the Sentiment Analysis task, (4) for each document and its dominant topic, we build the new DocTopic2Vec by concatenating the Doc2Vec with the Topic2Vec created with the same word embedding. We also design six new Convolutional-based (Bidirectional) Recurrent Deep Neural Network Architectures that show promising results for this task. The proposed DocTopic2Vecs are used to benchmark multiple Machine and Deep Learning models, i.e., a Logistic Regression model, used as a baseline, and 18 Deep Neural Networks Architectures. The experimental results show that the new embedding and the new Deep Neural Network Architectures achieve better results than the baseline, i.e., Logistic Regression and Doc2Vec.

Swarm intelligence techniques have been created to respond to theoretical and practical global optimization problems. This paper puts forward an enhanced version of the firefly algorithm that corrects the acknowledged drawbacks of the original method, by an explicit exploration mechanism and a chaotic local search strategy. The resulting augmented approach was theoretically tested on two sets of bound-constrained benchmark functions from the CEC suites and practically validated for automatically selecting the optimal dropout rate for the regularization of deep neural networks. Despite their successful applications in a wide spectrum of different fields, one important problem that deep learning algorithms face is overfitting. The traditional way of preventing overfitting is to apply regularization, the first option in this sense is the choice of an adequate value for the dropout parameter. In order to demonstrate its ability in finding an optimal dropout rate, the boosted version of the firefly algorithm has been validated for the deep learning subfield of convolutional neural networks, with respect to five standard benchmark datasets for image processing: MNIST, Fashion-MNIST, Semeion, USPS and CIFAR-10. The performance of the proposed approach in both types of experiments was compared with other recent state-of-the-art methods. To prove that there are significant improvements in results, statistical tests were conducted. Based on the experimental data, it can be concluded that the proposed algorithm clearly outperforms other approaches.

Digital storytelling platforms have proven to be a great way of bringing cultural heritage closer to people. What lacks is a deeper understanding of the user experience of such systems, especially in multi-platform digital storytelling. For the last three years, we have been developing a project called Spotlight Heritage Timisoara, which is at its core a digital storytelling platform for the city of Timisoara (Romania), soon to be European Capital of Culture in 2023. The project consists of a website, mobile applications, and interactive museographic and street exhibitions. This paper presents a multi-platform usability evaluation study which employed semi-structured interviews, observations, think-aloud protocol, SUS questionnaire, Net Promoter Score and Product Reaction Cards to gather insights from 105 participants and reveal usability problems in the Spotlight Heritage context. We found out that the four platforms, i.e., interactive touchscreen table, desktop/laptop, mobile and Augmented Reality, have very good usability scores, are considered accessible and useful, work seamlessly together, and create user satisfaction and loyalty, across demographic groups, having the potential to bring people closer to cultural heritage.

Stock index prediction plays an important role in the creation of better investment strategies. However, prediction can be difficult due to the random fluctuation of financial time series. In pursuit of improved stock index prediction, a hybrid prediction model is proposed in this paper, which contains two-step data pretreatment, double prediction models, and smart optimization. In the data pretreatment stage, in order to carry more information about the prediction target, multidimensional explanatory variables are selected by the Granger causality test, and to eliminate data redundancy, feature extraction is inserted with the help of principal component analysis, both of these can provide a higher-quality dataset. Bi-directional long short-term memory and bi-directional gated recurrent unit network, as the concurrent prediction models, can improve not only the precision, but also the robustness of results. In the last stage, the proposed model integrates the weight optimization of the cuckoo search of the two prediction results to take advantage of both. For the model performance test, four main global stock indices are used. The experimental results show that our model performs better than other benchmark models, which indicates the potential of the proposed model for wide application.

With the evolution of modern digital pathology, examining cancer cell tissues has paved the way to quantify subtle symptoms, for example, by means of image staining procedures using Eosin and Hematoxylin. Cancer tissues in the case of breast and lung cancer are quite challenging to examine by manual expert analysis of patients suffering from cancer. Merely relying on the observable characteristics by histopathologists for cell profiling may under-constrain the scale and diagnostic quality due to tedious repetition with constant concentration. Thus, automatic analysis of cancer cells has been proposed with algorithmic and soft-computing techniques to leverage speed and reliability. The paper’s novelty lies in the utility of Zernike image moments to extract complex features from cancer cell images and using simple neural networks for classification, followed by explainability on the test results using the Local Interpretable Model-Agnostic Explanations (LIME) technique and Explainable Artificial Intelligence (XAI). The general workflow of the proposed high throughput strategy involves acquiring the BreakHis public dataset, which consists of microscopic images, followed by the application of image processing and machine learning techniques. The recommended technique has been mathematically substantiated and compared with the state-of-the-art to justify the empirical basis in the pursuit of our algorithmic discovery. The proposed system is able to classify malignant and benign cancer cell images of 40× resolution with 100% recognition rate. XAI interprets and reasons the test results obtained from the machine learning model, making it reliable and transparent for analysis and parameter tuning.

Conventional pipeline corrosion assessment methods for failure pressure prediction do not account for interacting defects subjected to internal pressure and axial compressive stress. In any case, the failure pressure predictions are conservative. As such, numerical methods are required. This paper proposes an alternative to the computationally expensive numerical methods, specifically an empirical equation based on Finite Element Analysis (FEA). FEA was conducted to generate training data for an ANN after validating the method against full scale burst test results from past research. An ANN with four inputs and one output was developed. The equation was developed based on the weights and biases of an ANN model trained with failure pressure from the FEA of a high toughness pipeline for various defect spacings, defect depths, defect lengths, and axial compressive stresses. The proposed model was validated against actual burst test results for high toughness materials, with a R2 value of 0.99. Extensive parametric study was subsequently conducted to determine the effects of defect spacing, defect length, defect depth, and axial compressive stress on the failure pressure of the pipe. The results of the empirical equation are comparable to the results from numerical methods for the pipes and loadings considered in this study.

In this study, we consider the hybrid nonlinear features of the Exponential Smooth Transition Autoregressive-Fractional Fourier Function (ESTAR-FFF) form unit root test. As is well known, when developing a unit root test for the ESTAR model, linearization is performed by the Taylor approximation, and thereby the nuisance parameter problem is eliminated. Although this linearization process leads to a certain amount of information loss in the unit root testing equation, it also causes the resulting test to be more accessible and consistent. The method that we propose here contributes to the literature in three important ways. First, it reduces the information loss that arises due to the Taylor expansion. Second, the research to date has tended to misinterpret the Fourier function used with the Kapetanios, Shin and Snell (2003) (KSS) unit root test and considers it to capture multiple smooth transition structural breaks. The simulation studies that we carry out in this study clearly show that the Fourier function only restores the Taylor residuals of the ESTAR type function rather than accounting forthe smooth structural break. Third, the new nonlinear unit root test developed in this paper has very strong power in the highly persistent near unit root environment that the financial data exhibit. The application of the Kapetanios Shin Snell- Fractional Fourier (KSS-FF) test to ex-post real interest rates data of 11 OECD countries for country-specific sample periods shows that the new test catches nonlinear stationarity in many more countries than the KSS test itself.

COVID-19 has led people to question numerous aspects of life, including family budgetary arrangements and wealth management. The COVID-19 pandemic has thrown many of us a financial curveball. Managing personal finances is important, particularly during a crisis, such as the COVID-19 pandemic. Although the economic consequences are evident, financially induced stress caused by uncertainty is less visible. Individual wealth increments and firm size measures have brought a commensurate increment in their respective resources. Thus, monitoring these resources and coordinate investment exercises is necessary to preserve resource development. The best method to improve wealth management banks is to consider competitive preferences by designating a set of wealth management bank selections to oversee individuals’ wealth viably. This paper provides a step-by-step assessment guide for wealth management banks using multiple-criteria decision-making to illustrate the appropriateness of the proposed technique. We found that the two primary aspects of wealth management bank evaluations are transaction safety and professional financial knowledge. The proposed approach is relatively straightforward and appropriate for such key decision-making issues.

The method of the analysis of different texts styles is developed in the paper. Integer numerical sequences are used as models. The elements of the sequence are the number of letters in the words of the text. The algorithm for calculating the exact value of the fractal dimension is developed. It provides the determination of the exact value of the Hurst index. The value of the power dependence constant is calculated. The obtained indicators in the aspect of fractality completely describe the objects of research.

The study in the paper is placed in the broad context of research for increasing the efficiency of capturing and converting wind energy. The purpose of the study is to analyze some mathematical methods for maximum power point tracking in wind turbines. The mathematical methods studied are on–off control, fuzzy control, and neural predictive control. The rules developed for maximum power point tracking are presented. The related control structures and their design methods are presented. The behaviors of the control systems and their energy efficiency are analyzed. Maximum power point tracking ensures a significant increase in the energy generated compared to the unfavorable case of operation at a small and constant load torque. The differences in energy efficiency between the methods of maximum power point tracking studied are small.

Due to its simplicity, efficiency, and effectiveness, multinomial naive Bayes (MNB) has been widely used for text classification. As in naive Bayes (NB), its assumption of the conditional independence of features is often violated and, therefore, reduces its classification performance. Of the numerous approaches to alleviating its assumption of the conditional independence of features, structure extension has attracted less attention from researchers. To the best of our knowledge, only structure-extended MNB (SEMNB) has been proposed so far. SEMNB averages all weighted super-parent one-dependence multinomial estimators, therefore, it is an ensemble learning model. In this paper, we propose a single model called hidden MNB (HMNB) by adapting the well-known hidden NB (HNB). HMNB creates a hidden parent for each feature, which synthesizes all the other qualified features’ influences. For HMNB to learn, we propose a simple but effective learning algorithm without incurring a high-computational-complexity structure-learning process. Our improved idea can also be used to improve complement NB (CNB) and the one-versus-all-but-one model (OVA), and the resulting models are simply denoted as HCNB and HOVA, respectively. The extensive experiments on eleven benchmark text classification datasets validate the effectiveness of HMNB, HCNB, and HOVA.

Superixel is one of the most efficient of the image segmentation approaches that are widely used for different applications. In this paper, we developed an image segmentation based on superpixel and an automatic clustering using q-Generalized Pareto distribution under linear normalization (q-GPDL), called ASCQPHGS. The proposed method uses the superpixel algorithm to segment the given image, then the Density Peaks clustering (DPC) is employed to the results obtained from the superpixel algorithm to produce a decision graph. The Hunger games search (HGS) algorithm is employed as a clustering method to segment the image. The proposed method is evaluated using two different datasets, collected form Berkeley segmentation dataset and benchmark (BSDS500) and standford background dataset (SBD). More so, the proposed method is compared to several methods to verify its performance and efficiency. Overall, the proposed method showed significant performance and it outperformed all compared methods using well-known performance metrics.

Due to contemporary communication trends, the amount of multimedia data created and transferred in 5G networks has reached record levels. Multimedia applications communicate an enormous quantity of images containing private data that tend to be attacked by cyber-criminals and later used for illegal reasons. Security must consider and adopt the new and unique features of 5G/6G platforms. Cryptographic procedures, especially secret sharing (SS), with some extraordinary qualities and capacities, can be conceived to handle confidential data. This paper has developed a secured (k, k) multiple secret sharing (SKMSS) scheme with Hybrid Optimal SIMON ciphers. The proposed SKMSS method constructs a set of noised components generated securely based on performing hash and block ciphers over the secret image itself. The shares are created and safely sent after encrypting them through the Hybrid Optimal SIMON ciphers based on the noised images. This is a lightweight cryptography method and helps reduce computation complexity. The hybrid Particle Swarm Optimization-based Cuckoo Search Optimization Algorithm generates the keys based on the analysis of the peak signal to noise ratio value of the recovered secret images. In this way, the quality of the secret image is also preserved even after performing more computations upon securing the images.

With the development of cloud computing, more and more cloud resources are rented or purchased by users. Using an economics approach to achieve cloud resource management has been thought of as a good choice for an enterprise user to complete an application’s migration and deployment into the public cloud. During an application’s migration process, it is important but very challenging to achieve the satisfaction of both the enterprise user and the public cloud provider at the same time. In this paper, we apply an economics approach to investigate the migration optimization problem during the migration process of applications from the enterprise user’s data center to the remote public cloud. We consider the application migration time of the enterprise user and the energy consumption of physical machines, and establish a single static round optimization problem for both the enterprise user and the cloud provider on the premise of satisfying the quality of experience (QoE) based on the Stackelberg game, where the public cloud provider is leader and the enterprise user is follower. Then we propose a novel algorithm to find the optimal physical machine placement for application migration. After that, we further consider that an enterprise user needs to migrate several applications, and extend the single-round static game to the multi-round dynamic game, where the energy consumption costs of the physical machines are reduced by adjusting the states of the physical machines in each round. We finally illustrate the performance of our scheme through some simulation results.

This paper presents the study of multi-objective optimization of a pharmaceutical portfolio when both cost and return values are uncertain. Decision makers in the pharmaceutical industry encounter several challenges in deciding the optimal selection of drug projects for their portfolio since they have to consider several key aspects such as a long product-development process split into multiple phases, high cost and low probability of success. Additionally, the optimization often involves more than a single objective (goal) with a non-deterministic nature. The aim of the study is to develop a stochastic multi-objective approach in the frame of chance-constrained goal programming. The application of the results of this study allows pharmaceutical decision makers to handle two goals simultaneously, where one objective is to achieve a target return and another is to keep the cost within a finite annual budget. Finally, the numerical results for portfolio optimization are presented and discussed.

The network expansion problem is a very important practical optimization problem when there is a need to increment the flow through an existing network of transportation, electricity, water, gas, etc. In this problem, the flow augmentation can be achieved either by increasing the capacities on the existing arcs, or by adding new arcs to the network. Both operations are coming with an expansion cost. In this paper, the problem of finding the minimum network expansion cost so that the modified network can transport a given amount of flow from the source node to the sink node is studied. A strongly polynomial algorithm is deduced to solve the problem.

Computer network security is an important aspect of computer science. Many researchers are trying to increase security using different methods, technologies, or tools. One of the most common practices is the deployment of an Intrusion Detection System (IDS). The current state of IDS brings only passive protection from network intrusions, i.e., IDS can only detect possible intrusions. Due to that, the manual intervention of an administrator is needed. In our paper, we present a logical model of an active IDS based on category theory, coalgebras, linear logic, and Belief–Desire–Intention (BDI) logic. Such an IDS can not only detect intrusions but also autonomously react to them according to a defined security policy. We demonstrate our approach on a motivating example with real network intrusions.

Nowadays, manufacturing companies are characterized by complex systems with multiple products being manufactured in multiple assembly lines. In such situations, traditional costing systems based on deterministic cost models cannot be used. This paper focuses on developing a stochastic approach to costing systems that considers the variability in the process cycle time of the different workstations in the assembly line. This approach provides a range of values for the product costs, allowing for a better perception of the risk associated to these costs instead of providing a single value of the cost. The confidence interval for the mean and the use of quartiles one and three as lower and upper estimates are proposed to include variability and risk in costing systems. The analysis of outliers and some statistical tests are included in the proposed approach, which was applied in a tier 1 company in the automotive industry. The probability distribution of the possible range of values for the bottleneck’s cycle time showcase all the possible values of product cost considering the process variability and uncertainty. A stochastic cost model allows a better analysis of the margins and optimization opportunities as well as investment appraisal and quotation activities., This work is supported by: European Structural and Investment Funds in the FEDER component, through the Operational Competitiveness and Internationalization Programme (COMPETE 2020) [Project nº 39479; Funding Reference: POCI-01-0247-FEDER-39479].

There are increasing challenges for optimally distributing fresh products while adequately considering the uncertain demand of customers and maintaining the freshness of products. Taking the nature of fresh products and the characteristics of urban logistics systems into consideration, this paper proposes an improved genetic algorithm for effectively solving this problem in a computationally efficient manner. Such an algorithm can adequately account for the uncertain demand of customers to select the optimal distribution route to ensure the freshness of the product while minimizing the total distribution cost. Iterative optimization procedures are utilized for determining the optimal route by reducing the complexity of the computation in the search for an optimal solution. An illustrative example is presented that shows the improved algorithm is more effective with respect to the distribution cost, the distribution efficiency, and the distribution system’s reliability in optimally distributing fresh products.

Manufacturing for a multitude of continuous processing applications in the era of automation and ‘Industry 4.0’ is focused on rapid throughput while producing products of acceptable quality that meet customer specifications. Monitoring the stability or statistical control of key process parameters using data acquired from online sensors is fundamental to successful automation in manufacturing applications. This study addresses the significant problem of positive autocorrelation in data collected from online sensors, which may impair assessment of statistical control. Sensor data collected at short time intervals typically have significant autocorrelation, and traditional statistical process control (SPC) techniques cannot be deployed. There is a plethora of literature on techniques for SPC in the presence of positive autocorrelation. This paper contributes to this area of study by investigating the performance of ‘Copula’ based control charts by assessing the average run length (ARL) when the subsequent observations are correlated and follow the AR(1) model. The conditional distribution of yt given yt−1 is used in deriving the control chart limits for three different categories of Copulas: Gaussian, Clayton, and Farlie-Gumbel-Morgenstern Copulas. Preliminary results suggest that the overall performance of the Clayton Copula and Farlie-Gumbel-Morgenstern Copula is better compared to other Archimedean Copulas. The Clayton Copula is the more robust with respect to changes in the process standard deviation as the correlation coefficient increases.

Reversible data hiding in encrypted images (RDHEI) is a technology which embeds secret data into encrypted images in a reversible way. In this paper, we proposed a novel high-capacity RDHEI method which is based on the compression of prediction errors. Before image encryption, an adaptive linear regression predictor is trained from the original image. Then, the predictor is used to obtain the prediction errors of the pixels in the original image, and the prediction errors are compressed by Huffman coding. The compressed prediction errors are used to vacate additional room with no loss. After image encryption, the vacated room is reserved for data embedding. The receiver can extract the secret data and recover the image with no errors. Compared with existing approaches, the proposed method efficiently improves the embedding capacity.

In this paper, we consider the problem of identity document recognition in images captured with a mobile device camera. A high level of projective distortion leads to poor quality of the restored text images and, hence, to unreliable recognition results. We propose a novel, theoretically based method for estimating the projective distortion level at a restored image point. On this basis, we suggest a new method of binary quality estimation of projectively restored field images. The method analyzes the projective homography only and does not depend on the image size. The text font and height of an evaluated field are assumed to be predefined in the document template. This information is used to estimate the maximum level of distortion acceptable for recognition. The method was tested on a dataset of synthetically distorted field images. Synthetic images were created based on document template images from the publicly available dataset MIDV-2019. In the experiments, the method shows stable predictive values for different strings of one font and height. When used as a pre-recognition rejection method, it demonstrates a positive predictive value of 86.7% and a negative predictive value of 64.1% on the synthetic dataset. A comparison with other geometric quality assessment methods shows the superiority of our approach.