Multi-view clustering has become increasingly pervasive and prominent as multiple sources often provide different representations of information. However, existing multi-view clustering algorithms still encounter challenges since most multi-view data do not exhibit clear cluster boundaries, meaning cluster boundaries may locally overlap. Consequently, effectively characterizing and unveiling the imprecise and uncertain cluster structures in multi-view clustering remains an unresolved issue. Inspired by the robust capabilities of neutrosophic clustering in modeling imprecise and uncertain information, this paper introduces two novel multi-view neutrosophic c -means clustering algorithms , which can be regarded as derivatives of NCM in multi-view scenarios. The proposed algorithms are designed to represent the imprecision and uncertainty in cluster assignment of multi-view data while also autonomously discerning the importance of each view to boost clustering performance. We craft two objective functions and develop the corresponding optimization strategies to derive the neutrosophic partition matrix , view weight vector , and cluster centers matrix. Through extensive testing on both synthetic and real-world datasets, we demonstrate the practicality and effectiveness of our proposed algorithms.

Time series forecasting task predicts future trends based on historical information. Transformer-based U-Net architectures, despite their success in medical image segmentation, have limitations in both expressiveness and computation efficiency in time series forecasting as evidenced in YFormer. To tackle these challenges, we introduce Kernel-U-Net, a flexible and kernel-customizable U-shape neural network architecture. The kernel-U-Net encoder compresses the input series into latent vectors, and its symmetric decoder subsequently expands these vectors into output series. Specifically, Kernel-U-Net separates the procedure of partitioning input time series into patches from kernel manipulation, thereby providing the convenience of executing customized kernels. Our method offers two primary advantages: 1) Flexibility in kernel customization to adapt to specific datasets; and 2) Enhanced computational efficiency, with the complexity of the Transformer layer reduced to linear. Experiments on seven real-world datasets, demonstrate that Kernel-U-Net’s performance either exceeds or meets that of the existing state-of-the-art model in the majority of cases in channel-independent settings. The source code for Kernel-U-Net will be made publicly available for further research and application.

Deep learning and big data analytics can be attributed to recent trends and opportunities in many research activities and areas such as bioinformatics, beyond 5G and 6G communications, healthcare, internet of things (IoT), manufacturing business and social networks. Big data analytics and deep learning are sought-after and fastest-growing techniques for the enhancement of information and communication technology (ICT), and recent approaches are providing unexpected solutions that once seemed unachievable. Applications of big data analytics and deep learning in 5G and 6G are able to facilitate many new features in network management and operations, and 5G and beyond communication systems are expected to provide services with massive connectivity, ultra-low latency, extremely high security, extremely low energy consumption, and ultra-high data-rate. The main focus of this special issue is on deep learning and big data analytics to process and analyze data in 5G and 6G applications. The special issue includes novel studies about big data, IoT, Industry 4.0 applications, machine learning, deep learning solutions, 5th generation, and 6th generation technologies. There were in total nineteen papers accepted for publication in this special issue through careful peer review and revisions, and all are covered under the overarching theme of deep learning-based intelligent communication systems. The summary of every topic is given below. However, it is strongly encouraged to read the full paper if interested. Saeed et al., in their paper 'A comprehensive review on the users' identity privacy for 5G networks', aim to shed light on the survey about user privacy for 5G networks, which continues the identity and location privacy. Also, it discusses most of the studies which handle the user identifications in authentication, paging, and location update. The paper discusses various privacy issues in 5G network which use IMSI in clear text or join the temporary identities: TMSI and C-RNTI with IMSI to disclose the privacy of user indentity. After that the paper studies many proposed solutions which discuss user privacy (identity and location) and concludes that each of these studies has advantages and disadvantages for its proposed solutions. Elfatih et al., in their paper 'Internet of vehicle's resource management in 5G networks using AI technologies: Current status and trends', discuss and provide a comprehensive detail for resource allocation and management for IoV over 5G RAN network utilizing AI techniques. In addition, an extensive discussion of AI technologies that promise to be adopted and contributed to IoV and V2X applications is presented. The presented reviews in these areas have not taken into account the significance of integrating the multi-layers of vehicular network architecture for each AI strategy and how to be tailored for rapid and dynamic topology problems. Hence, this paper adresses these problems by describing how sophisticated and deep vehicle network architecture can be enhanced by AI techniques for layer-by-layer resources management and allocation problems. Hasan et al., in their paper 'A review on security threats, vulnerabilities, and counter measures of 5G enabled internet-of-medical-things', review the applications of the internet of medical things (IoMT) that has gained major attention as an ecosystem of connected clinical systems, computing systems, and medical sensors geared towards improving the quality of healthcare services. The 5G based AI technology can revolute the perception of healthcare and lifestyle. In light of the importance of IoT platforms and 5G networks, the purpose of this proposed research work is to identify threats that could undermine the integrity, privacy, and security of IoMT systems. Also, the novel blockchain-based approaches can help in improving the confidentiality of the IoMT network. It has been discovered that IoMT is vulnerable to various types of attacks, including denial of service (DoS), malware, and eavesdropping attack. In addition, IoMT is exposed to various vulnerabilities, such as security, privacy, and confidentiality. Le in his paper 'A comprehensive survey of imbalanced learning methods for bankruptcy prediction', gives a review about imbalanced learning methods. This study first reviews several state-of-the-art approaches for handling this problem in bankruptcy prediction, including an over sampling based (OSB) framework, a cost-sensitive method (the C Boost algorithm), a combination of resampling techniques and a cost-sensitive framework, and an ensemble-based model (the XGBS algorithm). The author also conducts empirical experiments to evaluate the methods surveyed here in terms of two performance metrics; the area under the ROC curve and the geometric mean. The results show that the ensemble-based model outperforms other methods in terms of bankruptcy prediction on the KB dataset. Poongodi et al., in their paper '5G Based blockchain network for authentic and ethical keyword search engine', carry out a proposed 5G-based blockchain network architecture for an encrypted keyword search engine. The suggested model permits to play out all connections amongst different users and mini-base stations through the use of diverse access nodes points and network brokers. It also helps to comprehend the complete application of blockchain technology, wherein the distribution of numerous digital ledgers and smart contracts were acknowledged between each network entity. Moreover, complete utilization of cryptocurrency is realized at essential points of the network layer to lessen the effect of interference rate and streamline the spectrum sharing when requested by the user. Alshammari et al., in their paper 'Technology-driven 5G enabled e-healthcare system during COVID-19 pandemic', reveal that most people receive information from social networking sites, health professionals, and television without facing any challenges. The analysis shows that, during the COVID-19 pandemic, about 42% of respondents felt tense always or most of the time on a daily basis. Only 28.6% of respondents felt tense sometimes, whereas the remainder (about 30%) did not feel tense in relation to the COVID-19 crisis. Satisfaction with COVID-19-related information is also positively correlated with COVID-19-related information literacy (r = 0.53, p < 0.01) that is also positively correlated with depression or emotion, anxiety, and stress (r = -0.15, p < 0.05). The long-term pandemic is creating several psychological symptoms including anxiety, stress, and depression, irrespective of age. Natarajan et al., in their paper 'An IoT and machine learning-based routing protocol for reconfigurable engineering application', present an upgradable cross-layer routing protocol based on CR-IoT to improve routing efficiency and optimize data transmission in a reconfigurable network. In this context, the system is developing a distributed controller which is designed with multiple activities, including load balancing, neighbourhood sensing and machine-learning path construction. The proposed approach is based on network traffic and load and various other network metrics including energy efficiency, network capacity and interference, on average of 2 bps/Hz/W. The trials are carried out with conventional models, demonstrating the residual energy and resource scalability and robustness of the reconfigurable CR-IoT Pandey et al., in their paper 'Lyapunov optimization machine learning resource allocation approach for uplink underlaid D2D communication in 5G networks', formulate the maximization of uplink and overall system capacity with resource management, which guarantees the signal to interference noise ratio for the D2D users. The optimization is a mixed-integer non-linear problem that uses the Lyapunov optimization method to optimize the BER value and an iterative algorithm to optimize the power value with different constraints. After attaining the optimized value, SVM (support vector machine) technique is utilized to ensure the spectral efficiency of the overall system in autonomous mode. Simulation results show that the proposed method provides higher reliability and power efficiency with higher system capacity in comparison to prevailing technologies. Liang et al., in their paper 'A new model path for the development of smart leisure sports tourism industry based on 5G technology', adopt the literature method to learn the theoretical basis of 5G technology and smart tourism in depth, establish a multi-dimensional resource allocation model for the smart leisure sports tourism industry, and conduct research on the influencing factors, information sources, channel factors and other aspects of the tourism industry. The general public's search for tourism strategies and attractions, food and specialty products, the use of online search information channels accounted for 70.3% and 69.3%, which further shows that the development of 5G technology has promoted the transformation and development of the sports tourism industry. Khan et al., in their paper '3D convolutional neural networks based automatic modulation classification in the presence of channel noise', consider the problem of multiclass (eight classes) classification of modulated signals (binary phase shift keying, quadrature phase shift keying, 16 and 64 quadrature amplitude modulation corrupted by additive white Gaussian noise, Rician and Rayleigh fading channels) using architectures in both frequency and spatial domains while deploying three approaches for data augmentation, such as random zoomed in/out, random shift and random weak Gaussian blurring augmentation techniques with a cross-validation (CV) based hyperparameter selection statistical approach. Simulation results testify the performance of 10-fold CV without augmentation in the spatial domain to be the best while the worst performing method happens to be 10-fold CV without augmentation in the frequency domain and learning in the spatial domain to be better than learning in the frequency domain. Chen et al., in their paper 'Resource electronic database for measuring regional cultural influence based on machine learning big data', aim to build a resource electronic database for measuring regional cultural influence through the current hot big data technology, and to provide some reference suggestions and data resources for the harmonious development of regional culture. In this article, the authors investigate the current cultural development in various regions of China and its impact on the development of Chinese culture and the culture of the world through literary research. Considering the current state of cultural development in the region, this article determines the key functional requirements for building an electronic database of cultural impact measurement resources in the region. In the specific process of designing the database, big data mining algorithms and machine learning classification and prediction algorithms are used to collect, categorize and process the data resources of the regional cultural influence measurement database. In the analysis of the measurement of regional cultural influence, this paper uses the regional cultural pattern index to evaluate and predict the distribution, concentration, prosperity and influence of regional culture. Duggal et al., in their paper 'A sequential roadmap to Industry 6.0: Exploring future manufacturing trends', scroll through patent pathways and intellectual developments throughout industrial revolutions listing significant products and services that landmarked each revolution up to Industry 4.0. The research pools of Industry 4.0 are classified and explored. A lack of human–machine workforce synergy in Industry 4.0 and the nascent 'customized manufacturing' concept is addressed in subsequent sections. The paper classifies two expected phases of Industry 5.0, highlighting the subdomains touted to be its focal areas. Gourisaria et al., in their paper 'Data science appositeness in diabetes mellitus diagnosis for healthcare systems of developing nations' use various machine learning, deep learning, and data dimensionality reduction techniques to detect diabetes mellitus. The research is principally conducted on two datasets, first from the Frankfurt hospital, Germany, second from the UCI repository. Models such as support vector machines, naïve Bayes, and random forests are implemented to classify diabetic patients from non-diabetic ones. Subsequently, after hyperparameter tuning, a comparative study on the results is done and the most prominent model promoted. This process is repeated for the datasets with reduced dimensionality using linear discriminant analysis (LDA) and principal component analysis (PCA). For the Frankfurt, Germany dataset, k-nearest neighbours showed the best accuracy of 98.2%, and the random forest classifier for the UCI repository showed 99.2%. Abbasi et al., in their paper 'An intelligent method for reducing the overhead of analyzing big data flows in OpenFlow switch', focus on developing a dynamic replacement method. This intelligent method utilizes the statistical features of the traffic flows in the table to select a table for replacement and makes use of the popularity of flows in the flow table for replacing entries and updating the flow table. The method aims to evaluate the existing entries according to the history of the activities of the flow, which was neglected in previous studies. For this purpose, the author uses the 'importance' feature which has been introduced in OpenFlow 1.4. Mohanty et al., in their paper 'Identification and evaluation of the effective criteria for detection of congestion in a smart city', propose a novel congestion detection system based on the combination of k-means clustering and analytical hierarchy process. A transport network is created in the simulation of urban mobility (SUMO) simulator. After receiving the parameters of vehicles from the simulator in a congested junction area, the key parameters are extracted by using the k-means clustering technique and mathematical mean algorithm. This key parameter is utilized in analytical hierarchy process to detect the highest priorities parameter. Based on that parameter the congestion is detected in a particular lane. Yadav et al., in their paper 'A secure data transmission and efficient data balancing approach for 5G based IOT data using UUDIS-ECC and LSRHS-CNN algorithms', propose a technique that contains authentication, destination selection, validation, secure DT, and also LB phases. The user and also the device are permitted to send the data towards the destination if they are authenticated. The UDDIS-ECC is employed for secure DT. For improving the SL, the SiP hash function is utilized and the 5G IoT data is balanced by employing the LSRHS-CNN algorithm. By deeming the input data's tasks, the LB is managed. Afterward, the performance analysis is conducted. Analysis for secure DT and also LB are the two parts wherein the analysis is handled. Centred upon the ET, DT, along with SL, the proposed UUDIS-ECC is analogized with the existent ECC, RSA, DES, and ECDSA in the secure DT analysis. Moorthy et al., in their paper 'Reduction of satellite images size in 5G networks using machine learning algorithms', propose a method which is implemented with a combination of intra-coding and machine learning algorithms. The standard compression technique does not give better results due to degradation of pixels, lack of spatial and spectral information. This paper enriches progressive results by reducing satellite images for transmission of data in IoT and 5G wireless networks, which qualitative results are compared by standard compression technique with suitable parameters. Li in his paper 'SWOT analysis of e-commerce development of rural tourism farmers' professional cooperatives in the era of big data', analyzes the e-commerce development strategy of China's rural tourism cooperatives in detail, and uses the analytic hierarchy process to analyze the establishment of the green development of e-commerce tourism business, affecting external opportunities and threats. This makes it possible to explore the sustainable development path for the follow-up development of e-commerce tourism business, which is conducive to the sustainable development path of rural tourism e-commerce tourism, and achieves multi-win business, environmental and social benefits. The experimental results of this paper show that through the calculation of the quadrangle of my country's tourism e-commerce enterprise development strategy, M1 = 0.0089, M2 = 0.0029, M3 = 0.0012, M4 = 0.0038, and M1 > M4 > M2 > M3 can be obtained. Singh et al., in their paper 'LoRa based intelligent soil and weather condition monitoring with internet of things for precision agriculture in smart cities', present the design of an intelligent irrigation system based on soil and weather conditions. The soil and weather parameters are selected through various research articles in Agriculture 4.0 and ML. The paper also juxtaposes the designed weather station with various patents developed. The system developed in this paper provides a cost-effective and state-of-the-art solution to local weather monitoring. Rohit Sharma is an associate professor in the Department of Electronics and Communication Engineering, SRM Institute of Science and Technology, India. He is an active member of ISTE, IEEE, ICS, IAENG, and IACSIT. He is an editorial board member and reviewer for more than 12 international journals and conferences, including IEEE Access and IEEE Internet of Things Journal. He has served as a book editor for seven different titles to be published by CRC Press, Taylor & Francis Group, USA and Apple Academic Press, CRC Press, Taylor & Francis Group, USA, and Springer. He has received the Young Researcher Award at the 2nd Global Outreach Research and Education Summit & Awards 2019 hosted by the Global Outreach Research & Education Association (GOREA). He has served as a guest editor in the SCI journal of Elsevier. He has actively organized various international conferences. He has served as an editor and organizing chair to the 3rd Springer International Conference on Microelectronics and Telecommunication (2019), IEEE International Conference on Microelectronics and Telecommunication (2018), IEEE International Conference on Microelectronics and Telecommunication (ICMETE-2016), and technical committee member of CSMA2017, EEWC 2017, IWMSE2017, ICG2016, and ICCEIS2016. Qin Xin received his PhD from the Department of Computer Science at the University of Liverpool, UK in December 2004. Currently, he is working as a professor of Computer Science and Faculty Research Leader in the Faculty of Science and Technology at the University of the Faroe Islands (UoFI), Faroe Islands. Prior to joining UoFI, he had held various research positions in world-leading universities and research laboratories including a Senior Research Fellowship at Universite Catholique de Louvain, Belgium, Research Scientist/Postdoctoral Research Fellowship at Simula Research Laboratory, Norway and Postdoctoral Research Fellowship at the University of Bergen, Norway. His main research focus is on design and analysis of sequential, parallel and distributed algorithms for various communication and optimization problems in wireless communication networks, as well as cryptography and digital currencies including quantum money. Moreover, he also investigates the combinatorial optimization problems with applications in Bioinformatics, Data Mining and Space Research. Currently, Prof. Dr. Xin is serving on the management committee board of Denmark for several EU ICT projects. Prof. Dr. Xin has produced more than 111 peer reviewed scientific papers. His works have been published in leading international conferences and journals, such as ICALP, ACM PODC, SWAT, IEEE MASS, ISAAC, SIROCCO, IEEE ICC, Algorithmica, Theoretical Computer Science, Distributed Computing, IEEE Transactions on Computers, Journal of Parallel and Distributed Computing, IEEE Transactions on Dielectrics and Electrical Insulation, IEEE Transactions on Sustainable Computing, ACM Transactions on Internet Technology, IEEE Transactions on Network Science and Engineering, ACM Transactions on Asian and Low-Resource Language Information Processing, and Advances in Space Research. He has been very actively involved in the services for the community in terms of acting (or acted) on various positions (e.g., Session Chair, Member of Technical Program Committee, Symposium Organizer and Local Organization Co-chair) for numerous international leading conferences in the fields of distributed computing, wireless communications and ubiquitous intelligence and computing, including IEEE MASS, IEEE LCN, ACM SAC, IEEE ICC, IEEE Globecom, IEEE WCNC, IEEE VTC, IFIP NPC, IEEE Sarnoff and so on. He is the organizing committee chair for the 17th and 18th Scandinavian Symposium and Workshops on Algorithm Theory (SWAT 2020 and SWAT 2022, Torshavn, Faroe Islands). Currently, he also serves on the editorial board for more than ten international journals. Patrick Siarry received the Ph.D. degree in computer science and optimization from University Paris VI, France, in 1986, and the Doctorate of Sciences (Habilitation) degree in computer science and optimization from University Paris XI, France in 1994. He was first involved in the development of analog and digital models of nuclear power plants with Electricité de France, Paris. Since 1995, he has been a Professor of Automatics and Informatics with Université Paris-Est Créteil, France. His main research interests include computer-aided design of electronic circuits, cognitive intelligence, and the applications of new stochastic global optimization heuristics to various engineering fields, also including the fitting of process models to experimental data, the learning of fuzzy rule bases, and of neural networks. Wei-Chiang Hong is a professor in the Department of Information Management at the Oriental Institute of Technology, Taiwan. His research interests mainly include computational intelligence (neural networks and evolutionary computation) and applications of forecasting technology (ARIMA, support vectorregression, and In his paper was as by In he was to be as Professor by the Science and Technology In the he was the Young Researcher by in the of and and In he was in the of In 2017, he was in the of Deep Communication Big Data

Abstract This paper proposes a unique disturbance rejection factor (DRF) based design of direct stable adaptive fuzzy logic controllers (AFLCs) for a class of non‐linear systems with large and fast disturbances. The proposed AFLCs are realized by employing hybrid combinations of Lyapunov theory based local adaptation and harmony search algorithm based global optimization technique. These hybrid AFLCs are designed with the objective of optimizing both the structure and free parameters of it with guaranteed stability and, at the same time, simultaneously achieving satisfactory tracking performance and disturbance rejection. The novelty of the proposed work lies in the fact that, in a bid to perform the disturbance rejection, the nature of the disturbance itself is used in designing the tracking control law. The proposed DRF based hybrid stable AFLCs are implemented for several benchmark case studies and extensive performance evaluations demonstrate their usefulness.

The new frontier research era and convergence of cognitive data science methods and models with reference to the Internet of Things (IoT) and big data systems have brought about various challenges in industrial systems that need to be addressed in the current scenario. Cognitive science will lead to a high level of fluidity to analytics. This special section aims to explore the domain knowledge and reasoning of data science technologies and cognitive methods with the IoT over the big data systems. Data science techniques have been adopted to improve the IoT in terms of data throughput, optimization, and management, and to have a major impact on the future of IoT networking systems. The main focus is the design of best cognitive embedded data science technologies to process and analyze the large amount of data collected through industrial IoT systems and help for good decision making.

Population based metaheuristics (e.g. Genetic Algorithm, Particle Swarm Optimization, ...) deal with a dichotomy between exploration (discover unexplored areas) and exploitation (dig around a good solution). The consequence is a wide exploration of the search space. A lot of information about the link between the objective function and the input variables is collected during the algorithm. Sensitivity analysis methods allow to transform this information in order to characterize the effect of an input variable on the objective function: linear impact, nonlinear impact, negligible impact. We propose to integrate a sensitivity analysis method in the optimization process in order to increase or decrease the shift length when offsetting a variable according to its behavior. The offset of a variable with a nonlinear impact has to be small in order to catch possible local optima of the objective function. On the contrary, the offset of a variable with a linear impact has to be high in order to move faster the variable toward its best position. A toy example is used to illustrate the interest of the method.

Deep learning (DL) has been expensively applied in multiple fields like computer vision, speech recognition and natural language processing. The field of Epileptic seizure prediction didn't receive the deserved attention by DL community, even though, deep neural networks can handle the challenging task of onsets prediction whilst achieving the highest rates of sensitivity, despite the complex nature of EEG signals. In the literature, this issue was addressed differently most of the time using handcrafted temporal and spectral features, machine learning techniques and rarely deep learning with extracted features. In this paper, we introduce an LSTM model designed to address the chaotic nature of an EEG signal in order to predict pre-ictal and inter-ictal states. Our model is evaluated on the publicly available CHBMIT database. We achieved an average sensitivity rate of 0.84 using a Raw EEG data segment as input to the LSTM model.

Image segmentation is one of the most critical tasks in Magnetic Resonance (MR) images analysis. Since the performance of most current image segmentation methods is suffered by noise and intensity non-uniformity artifact (INU), a precise and artifact resistant method is desired. In this work, we propose a new segmentation method combining a new Hidden Markov Random Field (HMRF) model and a novel hybrid metaheuristic method based on Cuckoo search (CS) and Particle swarm optimization algorithms (PSO). The new model uses adaptive parameters to allow balancing between the segmented components of the model. In addition, to improve the quality of searching solutions in the Maximum a posteriori (MAP) estimation of the HMRF model, the hybrid metaheuristic algorithm is introduced. This algorithm takes into account both the advantages of CS and PSO algorithms in searching ability by cooperating them with the same population in a parallel way and with a solution selection mechanism. Since CS and PSO are performing exploration and exploitation in the search space, respectively, hybridizing them in an intelligent way can provide better solutions in terms of quality. Furthermore, initialization of the population is carefully taken into account to improve the performance of the proposed method. The whole algorithm is evaluated on benchmark images including both the simulated and real MR brain images. Experimental results show that the proposed method can achieve satisfactory performance for images with noise and intensity inhomogeneity, and provides better results than its considered competitors.

The authors have proposed a new automatic classification scheme based on the conjoint use of the multi‐layer perceptron (MLP) neural network and an enhanced particle swarm optimisation (EPSO) algorithm for its training. In this work, six predominant categories of heartbeats from MIT‐BIH database are considered, which are: normal, premature ventricular contraction, atrial premature contraction, right bundle branch block, left bundle branch block and paced beats. First, the authors have applied the standard particle swarm optimisation (PSO) algorithm to select the network structure for each features vector. Then, the relevant electrocardiogram (ECG) features to the studied arrhythmias were chosen, which suited to the optimised training performance of the classifier. The recognition performance of the proposed EPSO‐MLP classification system is evaluated considering two different versions of the EPSO algorithm. In the first version (EPSOw), the inertia weight factor of the PSO algorithm is proposed to be a variable with iterations. However, two PSO parameters are taken to be variables in the second version of the improved learning algorithm (EPSOwc). The obtained experimental results prove the enhancement of the convergence ability of the MLP neural network and confirm the superiority of the proposed EPSO‐MLP classification scheme on comparison with the other last published classification systems.

This paper describes a novel particle swarm optimizer algorithm. The focus of this study is how to improve the performance of the classical particle swarm optimization approach, i.e., how to enhance its convergence speed and capacity to solve complex problems while reducing the computational load. The proposed approach is based on an improvement of particle swarm optimization using evolutionary game theory. This method maintains the capability of the particle swarm optimizer to diversify the particles' exploration in the solution space. Moreover, the proposed approach provides an important ability to the optimization algorithm, that is, adaptation of the search direction, which improves the quality of the particles based on their experience. The proposed algorithm is tested on a representative set of continuous benchmark optimization problems and compared with some other classical optimization approaches. Based on the test results of each benchmark problem, its performance is analyzed and discussed.

This paper discusses a novel conceptual formulation of the fractional-order variational framework for retinex, which is a fractional-order partial differential equation (FPDE) formulation of retinex for the multi-scale nonlocal contrast enhancement with texture preserving. The well-known shortcomings of traditional integer-order computation-based contrast-enhancement algorithms, such as ringing artefacts and staircase effects, are still in great need of special research attention. Fractional calculus has potentially received prominence in applications in the domain of signal processing and image processing mainly because of its strengths like long-term memory, nonlocality, and weak singularity, and because of the ability of a fractional differential to enhance the complex textural details of an image in a nonlinear manner. Therefore, in an attempt to address the aforementioned problems associated with traditional integer-order computation-based contrast-enhancement algorithms, we have studied here, as an interesting theoretical problem, whether it will be possible to hybridize the capabilities of preserving the edges and the textural details of fractional calculus with texture image multi-scale nonlocal contrast enhancement. Motivated by this need, in this paper, we introduce a novel conceptual formulation of the fractional-order variational framework for retinex. First, we implement the FPDE by means of the fractional-order steepest descent method. Second, we discuss the implementation of the restrictive fractional-order optimization algorithm and the fractional-order Courant-Friedrichs-Lewy condition. Third, we perform experiments to analyze the capability of the FPDE to preserve edges and textural details, while enhancing the contrast. The capability of the FPDE to preserve edges and textural details is a fundamental important advantage, which makes our proposed algorithm superior to the traditional integer-order computation-based contrast enhancement algorithms, especially for images rich in textural details.

In this paper, we present an Artificial Bee Colony (ABC) algorithm coupled with a sensitivity analysis method to guide its multidimensional search process. This sensitivity analysis method can evaluate the weight of each dimension of the problem on the objective function computation. We propose a new method for selecting a random neighbor during ABC search phase, using the information of the sensitivity analysis computation. As the algorithm is running, we collect information on the solutions visited and their evaluations. When a sufficient number of evaluation is reached, we launch our sensitivity analysis process to evaluate the influence of each dimension on the objective function result. A weight is then computed on each dimension and promotes the search following these dimensions. The result of this analysis drives the algorithm towards significant dimensions of the search space to improve the discovery of the global optimum.

The application of fractional calculus to signal processing and adaptive learning is an emerging area of research. A novel fractional adaptive learning approach that utilizes fractional calculus is presented in this paper. In particular, a fractional steepest descent approach is proposed. A fractional quadratic energy norm is studied, and the stability and convergence of our proposed method are analyzed in detail. The fractional steepest descent approach is implemented numerically and its stability is analyzed experimentally.

Most range-free techniques for indoor localization depend on the received signal strength (RSS) fingerprints. Their performances are relied to the structure of the considered indoor environments. We consider in this paper RSS-based methods: Multi-Layer Perceptron Neural Network (MLPNN), and K-nearest neighbor (KNN), and compare their performance under the same indoor environment. One of the advantages focused by the choice of these techniques is their robustness against external disturbances that may affect the received RSS signal. Moreover, we propose a new metric to enhance the performance of the KNN method, called d-nearest neighbor. In order to test the different techniques, we build a heterogeneous fingerprint database with different resolutions. The obtained results show the efficiency of the proposed enhancement in the case of a heterogeneous high resolution database.

In this work, we deal with a class of problem of trajectory planning taking into account the smoothness of the trajectory. We assume that we have a set of positions in which the robot must pass. These positions are not assigned in the time axis. This kind of result can be found in many works of trajectory planning. Thus, this result is not complete in the sense that we do not have the complete trajectory and, we do not have any idea about the whole time to move. In this work, we propose a formulation of this problem, where the total length of the trajectory and the total time to move from the initial to the final position are minimized simultaneously. In order to avoid abrupt movement, we should ensure the smoothness of the trajectory at the position, the velocity and the acceleration levels. Thus, the position function must be at least two times differentiable. We use a genetic algorithm to resolve this problem and we show the efficiency of the proposed technique by simulation.

While the static indoor geo-location of mobile terminals (MTs) has been extensively studied in the last decade, the prediction of the trajectory of an MT is still a major problem when designing mobile location (tracking) systems (TSs). In fact, Global Positioning System (GPS) works quite well in outdoor conditions and relatively unobstructed spaces, but falls short in many urban conditions and other realistic use cases. It is important to augment mobile geo-location architectures with a prediction dimension to deal with distortions caused by obstacles, and ultimately produce a more accurate positioning system. Different prediction approaches have been proposed in the literature, the most common is based on prediction filters such as linear predictors (LPs), Kalman filters (KFs), and particle filters (PFs). In this paper, we take the prediction one step further by using digital fractional integration (DFI) to predict the actual trajectory of MTs. We evaluate the performance of our proposed DFI prediction in two indoor trajectory scenarios inspired by typical user mobility patterns in typical indoor conditions (museum visit and hospital doctor walk). To illustrate the efficiency of the proposed method in particularly noisy environments, we consider two other MT trajectory scenarios, namely spiral and sinusoidal trajectories. Experimental results show a significant performance improvement over most common predictors in the relevant literature, particularly in noisy cases. Extensive study of short-archive principle using 5, 10, and 25 previous estimated positions, showed the benefit of using DFI operator with only the most recent locations of an MT.

Many real-world optimization problems are dynamic (time dependent) and require an algorithm that is able to continuously track a changing optimum over time. In this paper, we investigate a recently proposed algorithm for dynamic continuous optimization, called MLSDO (Multiple Local Search algorithm for Dynamic Optimization). MLSDO is based on several coordinated local search agents and on the archiving of the optima found over time. This archive is used when a change occurs in the objective function. The performance of the algorithm is evaluated on the set of benchmark functions provided for the IEEE WCCI-2012 Competition on Evolutionary Computation for Dynamic Optimization Problems.

While the static indoor location of a mobile terminal (MT) has been extensively studied on last decade, the prediction of the trajectory of a MT still is the major problem for building mobile location (tracking) systems (TSs). This problem is solved for outdoor TSs using global positioning system (GPS), however, it remains an essential obstacle to construct reliable indoor TSs. Different approaches were proposed in the literature, the most used is that based on prediction filters, such as linear filters (LF), Kalman filters (KF) and particle filters (PF). In this paper, we propose to enhance the performance of the predictors using digital fractional differentiation (DFD) to predict a MT trajectory. To illustrate the obtained results, three indoor trajectory scenarios inspired from real daily promenades are simulated (museum visit, hospital doctor walking and shopping in the market). Experimental results show a significant improvement of the performance of the classical predictors, particularly in noisy cases.

In this paper, we propose a multiagent based evolution strategy algorithm, called CMADO, to evaluate the amplitudes of the deformations of the walls of the third cerebral ventricle on a brain cine-MR imaging. CMADO based segmentation technique is applied on a 2D+t dataset to detect the contours of the region of interest (i.e. lamina terminalis). Then, the successive segmented contours are matched using a procedure of global alignment. Finally, local measurements of deformations are derived from the previously determined matched contours. The validation step is realized by comparing our results to the measurements achieved on the same patients through a manual segmentation provided by an expert using Ethovision ®software.

This paper presents a performance study of two versions of a unidimensional search algorithm aimed at solving high-dimensional optimization problems. The algorithms were tested on 11 scalable benchmark problems. The aim is to observe how metaheuristics for continuous optimization problems respond with increasing dimension. To this end, we report the algorithms' performance on the 50, 100, 200 and 500-dimension versions of each function. Computational results are given along with convergence graphs to provide comparisons with other algorithms during the conference and afterwards.

Many real-world problems are dynamic and require an optimization algorithm that is able to continuously track a changing optimum over time. In this paper, a new multiagent algorithm for solving dynamic problems is studied. This algorithm, called MADO, is analyzed using the Moving Peaks Benchmark, and its performances are compared to those of competing dynamic optimization algorithms on several instances of this benchmark. The obtained results show the efficiency of MADO, even in multimodal environments.

In France, nuclear energy provides about 80% of the whole electricity production. A modulation of the nuclear power plants must be able to respond to the demand on the network. The pressurized water nuclear reactor has to yield correctly a load set point. Fundamentally, two parameters are concerned in leading this task to a successful conclusion: the power axial-offset and the control rods position. The focus of this study is the automation of the control of the power axial-offset by adding soluble boron and by minimizing the volume flows through the water pump. It is also important to take into consideration the liquid waste volume. Water or boron is injected into the reactor primary circuit. At the present time this task is still performed manually by an operator, for all previous attempts to automate it failed. A nonfuzzy device, earlier developed by Electricite de France, was intensively tested at Cruas, France, power plant and allowed us to prove the feasibility of automating the boration-dilution function. But it could not be definitely adopted because it was too difficult to be tuned for industrial purposes. That device, sketchily described in the paper, gave rise to the development of a real-time fuzzy controller for the power axial-offset and the control rods insertion in a pressurized water reactor (PWR). The fuzzy controller, which is the main subject of the paper, expresses more naturally the human expertise, thus avoiding the previous issue of empirical tunings. It was implemented in simulation using Matlab-Simulink on a Sun workstation. Two realistic tests discussed show that the fuzzy controller runs as efficiently as an expert operator does.

A new global optimization algorithm for functions of many continuous variables is presented, derived from the basic Simulated annealing method. Our main contribution lies in dealing with high-dimensionality minimization problems, which are often difficult to solve by all known minimization methods with or without gradient. In this article we take a special interest in the variables discretization issue. We also develop and implement several complementary stopping criteria. The original Metropolis iterative random search, which takes place in a Euclidean space R n , is replaced by another similar exploration, performed within a succession of Euclidean spaces R p , with p << n . This Enhanced Simulated Annealing (ESA) algorithm was validated first on classical highly multimodal functions of 2 to 100 variables. We obtained significant reductions in the number of function evaluations compared to six other global optimization algorithms, selected according to previously published computational results for the same set of test functions. In most cases, ESA was able to closely approximate known global optima. The reduced ESA computational cost helped us to refine further the obtained global results, through the use of some local search. We have used this new minimizing procedure to solve complex circuit design problems, for which the objective function evaluation can be exceedingly costly.

This paper presents the results of a systematic investigation of the thermodynamic ("simulated annealing") method applied to the placement of rectangular blocks on a chip. A new presentation of the fundamental ideas underlying this technique is proposed. It is shown that the analogies with physics, which have been at the origin of the method, may be partially forgotten, but that they are still useful to understand some results. Several simple examples are investigated, and the influence of various parameters is studied. Typical complex industrial applications are subsequently presented. Finally, an interactive implementation of the thermodynamic optimization algorithm, based on the results of the present investigation, is proposed.