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This paper examines economic growth in 52 African countries for 1961–2016 and seeks to find if there is common growth. As all African countries have their particular features, concerning climate, harvest, industry, size, politics, and infrastructure, and more, it seems best to rely on a non-parametric method. Dynamic Time Warping is such a convenient method, also as it allows leads and lags across countries to vary over time, and as it can easily be incorporated into a clustering technique. Five clusters are found, two of which concern Equatorial Guinea and Botswana, and the three other clusters have common growth rates of about 0, 2 and 4 over more than five decades. [ABSTRACT FROM AUTHOR]

The development and integration of technologies such as the Internet of Things (IoT) or edge computing devices is contributing to the formation of an increasingly digital, intelligent and connected world. As a result, there is a massive flow of data in different sectors of human activity. One example is intelligent buildings, where thousands of components, devices, systems and suppliers interact. In this context, failures in control and monitoring systems are frequent. To analyze this situation, this paper presents as a case study the problem of fault-tolerant robust adaptive monitoring control with state prediction performance for a class of IoT temperature systems subject to uncertainties of precision states and external disturbances. The authors propose a new control strategy based on consensus game theory and prediction of future precision states to reduce tracking error and improve algorithm efficiency. The authors present the development of a new algorithm that improves the functioning of monitoring and control of parcel networks. This has the purpose of increasing the energy efficiency of the same and ensure the effectiveness of our adaptive temperature control algorithm, compared to existing results. With the simulation presented in this research, it is possible to conclude that a new fault tolerant error tracking algorithm ensures robust monitoring of the reference model. It was shown that the predicted temperature signal is limited by a small range close to the collected temperature data. A case study result is provided to demonstrate the effectiveness of the proposed fault-tolerant adaptive monitoring control algorithm. [ABSTRACT FROM AUTHOR]

One critical point to improve the economic and technical results of every industrial process lies on the fact of achieving a good optimization, and applying a smart maintenance plan. In this context, the tools development for detecting the appearance of any kind of anomaly represents an important challenge. For this reason, the implementation of classifiers for anomaly detection tasks has been a significant trend in the scientific community. However, since the behavior of the potential anomalies that may occur in a plant is unknown, it is necessary to generate artificial outliers to assess these classifiers. This paper proposes the performance checking of different intelligent one-class techniques to detect anomalies in an industrial plant, used to obtain the main material for wind generator blades production. These classifiers are tested using anomaly data generated, giving successful results. [ABSTRACT FROM AUTHOR]

Risk plays an important role in the decision making process. To assess the severity and likelihood of impairment to human health from exposure to a substance or activity that under plausible circumstances can cause harm to human health is the main purpose of risk assessment. It is important to know the nature and characteristic of all available information, data or model parameters which are more generally tainted with aleatory and epistemic uncertainty or both type of uncertainty. In some situation, model parameters are affected by aleatory uncertainty and simultaneously other some parameters are affected by epistemic uncertainty, how far computation of the risk is concern, two ways to deal with the situation either transform all the uncertainties to one type of format or need for joint propagation of uncertainties. In this paper, an effort has been made to combine probability distributions, generalized fuzzy numbers, normal fuzzy numbers and generalized interval valued fuzzy numbers within the same framework. [ABSTRACT FROM AUTHOR]

This paper deals with the problem of estimating the Minimum Initial Marking (MIM) of Labeled Petri Nets (L-PN). By the observation of a sequence of labels, we determine the set of possible MIMs related to a given L-PN through an approach based on GRASP (Greedy Randomized Adaptive Search Procedure) inspired method – GMIM. The objective is to get the maximum of feasible MIMs by exploring the search space and giving best solutions for real time cyber systems in short time. We consider four basic assumptions during the reasoning: (i) the L-PN structure is known; (ii) for each transition of L-PN, a label is associated, (iii) the label sequence is known, and (iv) all transitions of L-PN are observable. We show the validity and efficiency of our approach by applying the proposed GMIM metaheuristic to two validation examples: Initialization of two parallel machines (example widely cited in literature) and resources allocation in a monitoring problem via mobile robot network. [ABSTRACT FROM AUTHOR]

RPA is used to complete repetitive tasks usually completed by humans. A robot is a software programed to mimic the operations of humans on computer systems. Robots are configured to accomplish these tasks using algorithms from the machine learning and artificial intelligence field. Using RPA leads to reduced errors in the processing of tasks and reduced completions times to carry out the tasks. This paper calculates the number of software licenses required to carry out thousands of transactions for financial services and proposes an assignment of the transactions to the robots. A two-phase linear integer optimization process is proposed. The first phase calculates the number of robots required to complete all of the tasks and the second phase assigns the tasks to the robots. Many different transaction types for financial services are to be completed, and each type has precise market operating hours, maximal delays in their treatment, different processing times and different volumes. The objective function minimizes the total number of robots, but also limits the number of robot startups and reconfigurations, since these operations take time to complete. The key behind the fast computing times to solve the problem is the formulation, where periods are defined at intersections of market operating hours. Preliminary tests are conducted on a real case provided by a bank in North America and a synthetic test case and show that the problem is solved quickly. [ABSTRACT FROM AUTHOR]

This paper addresses usability problems of data entry techniques and patterns in web systems and web sites. In order to carry out a comparative analysis of the solutions a dedicated web application was developed as an experimental tool for performing usability testing. The application was designed in the form of a test divided into scenarios and exercises on filling up selected models. Furthermore, the application recorded usability metrics, questionnaires, open and closed questions, as well as users' personal data. 40 participants, who took part remotely in the experiment, were split into two distinct groups and completed two separate tests that differed in input data and tested solutions. Some measures referring to the effectiveness, efficiency, and user satisfaction were collected. The results were thoroughly analyzed and the recommendations for the usage of particular models were formulated. [ABSTRACT FROM AUTHOR]

Cognitive Vision Systems have gained a lot of interest from industry and academia recently, due to their potential to revolutionize human life as they are designed to work under complex scenes, adapting to a range of unforeseen situations, changing accordingly to new scenarios and exhibiting prospective behavior. The combination of these properties aims to mimic the human capabilities and create more intelligent and efficient environments. Contextual information plays an important role when the objective is to reason such as humans do, as it can make the difference between achieving a weak, generalized set of outputs and a clear, target and confident understanding of a given situation. Nevertheless, dealing with contextual information still remains a challenge in cognitive systems applications due to the complexity of reasoning about it in real time in a flexible but yet efficient way. In this paper, we enrich a cognitive system with contextual information coming from different sensors and propose the use of stream reasoning to integrate/process all these data in real time, and provide a better understanding of the situation in analysis, therefore improving decision-making. The proposed approach has been applied to a Cognitive Vision System for Hazard Control (CVP-HC) which is based on Set of Experience Knowledge Structure (SOEKS) and Decisional DNA (DDNA) and has been designed to ensure that workers remain safe and compliant with Health and Safety policy for use of Personal Protective Equipment (PPE). [ABSTRACT FROM AUTHOR]

Retrieving relevant information from Twitter is always a challenging task given its vocabulary mismatch, sheer volume and noise. Representing the content of text tweets is a critical part of any microblog retrieval model. For this reason, deep neural networks can be used for learning good representations of text data and then conduct to a better matching. In this paper, we are interested in improving both representation and retrieval effectiveness in microblogs. For that, a Hybrid-Deep Neural-Network-based text representation model is proposed to extract effective features' representations for clustering oriented microblog retrieval. HDNN combines recurrent neural network and feedforward neural network architectures. Specifically, using a bi-directional LSTM, we first generate a deep contextualized word representation which incorporates character n-grams form FasText. However, these contextual embedded existing in a high-dimensional space are not all important. Some of them are redundant, correlated and sometimes noisy making the learning models over-fitting, complex and less interpretable. To deal with these problems, we proposed a Hybrid-Regularized-Autoencoder-based method which combines autoencoder with Elastic Net regularization for an effective unsupervised feature selection and extraction. Our experimental results show that the performance of clustering and especially information retrieval in microblogs depend heavily on features' representation. [ABSTRACT FROM AUTHOR]

Industry 4.0 aims at providing a digital representation of a production landscape, but the challenges in building, maintaining, optimizing, and evolving digital models in inter-organizational production chains have not been identified yet in a systematic manner. In this paper, various Industry 4.0 research and technical challenges are addressed, and their present scenario is discussed. Moreover, in this article, the novel concept of developing experience-based virtual models of engineering entities, process, and the factory is presented. These models of production units, processes, and procedures are accomplished by virtual engineering object (VEO), virtual engineering process (VEP), and virtual engineering factory (VEF), using the knowledge representation technique of Decisional DNA. This blend of the virtual and physical domains permits monitoring of systems and analysis of data to foresee problems before they occur, develop new opportunities, prevent downtime, and even plan for the future by using simulations. Furthermore, the proposed virtual model concept not only has the capability of Query Processing and Data Integration for Industrial Data but also real-time visualization of data stream processing. [ABSTRACT FROM AUTHOR]

This paper presents a novel method for computing the Nash bargaining equilibrium for finite, ergodic and controllable Markov chains games. To solve the bargaining process we first set the disagreement point as the Nash equilibrium of the problem, then to find the new agreement point we follow the bargaining model presented by Nash. We exemplify the game formulation in terms of nonlinear programing equations implementing the Lagrange principle. For ensuring the convergence of the game to an equilibrium point we employ the Tikhonov's regularization method. For solving the bargaining problem we make use of the extraproximal optimization approach. Finally, we validate the proposed method by a numerical example for a three-person bargaining situation. [ABSTRACT FROM AUTHOR]

The aim of the paper is to develop new aggregation operators using Bonferroni means, ordered weighted averaging (OWA) operators and some distance measures. We introduce the Bonferroni-Hamming weighted distance (BON-HWD), Bonferroni OWA distance (BON-OWAD), Bonferroni OWA adequacy coefficient (BON-OWAAC) and Bonferroni distances with OWA operators and weighted averages (BON-IWOWAD). The main advantages of using these operators are that they allow the consideration of different aggregations contexts to be considered and multiple comparison between each argument and distance measures in the same formulation. An application is developed using these new algorithms in combination with Pichat algorithm to solve a group decision-making problem. Creative personality is taken as an example for forming creative groups. The results show fuzzy dissimilarity relations in order to establish the maximum similarity subrelations and find groups according to each individual's creative personality similarities. [ABSTRACT FROM AUTHOR]

Is the intelligence of a collective larger than the intelligence of its members? How does one determine the knowledge of a collective on the basis of the knowledge of its members? In this paper, we try to answer these questions. Many examples show that the knowledge of a collective is not a usual union of the knowledge of its members. If we assume that the members of a collective possess their knowledge states about some common real world, and these states reflect to some degree the proper (real) state of the knowledge about the real world, then a question arises: How does one determine the knowledge of the collective, and what is its quality? In this paper, we use Consensus Theory to solve this problem and to show that, in many cases, the collective knowledge state is more proper than the knowledge states of the collective intelligence. [ABSTRACT FROM AUTHOR]

The objective of this paper is to find an adaptive control strategy which would enable us to estimate the parameters of the ARMAX model as accurately as possible, along with consuming less controlling energy, while keeping the output of the system below a specified level of variability. Using the self-tuning tracker, this paper establishes global convergence of a stochastic adaptive control algorithm for discrete linear system, and the adaptive controller may converge to the one-step-ahead optimal controller at the same time. [ABSTRACT FROM AUTHOR]

This paper presents a comparative study of several well-known and thoroughly tested techniques for the segmentation of textured images, including two algorithms belonging to the adaptive Bayesian family of restoration and segmentation methods, and a novel approach based on the recently introduced concept of the frequency histogram of connected elements (FHCE). The paper first introduces the parameters that define a connected element and then details the sensitivity analysis of these parameters, showing that the grayscale intensity histogram of a digital image is a particular case of the FHCE. The application domain chosen for comparison purposes is the problem of medical images segmentation and, more specifically, as a particularly illustrative case the segmentation of digital angiograms is analyzed in detail. To get a comparative evaluation of FHCE performance, two well-established adaptive or contextual Bayesian segmentation algorithms have been applied to the segmentation of digital angiograms as well. The paper ends with a brief discussion of the comparative performances. [ABSTRACT FROM AUTHOR]

Artificial intelligence for law is a thriving area of research as of June 2003. The article discusses contributions made by different authors in the field of artificial intelligence. Artificial intelligence as such has a wide scope and usability in wide spectrum.

Imitation is an important learning mechanism of widespread utility and common occurrence. This article presents a theory and working computational model of the detailed mechanisms of imitation. The model is in the restricted domain of the learning of pencil and paper procedures. The task that is modelled is of a teacher demonstrating the steps of a procedure, such as long division to a student by means of one or more examples. Such a task can be learned by an imitation-learning mechanism, but the mechanism has a much wider range of application. Imitation is treated as a four-stage process: the events performed by the teacher are segmented by the learner; the events are encoded and explained in terms of spatial relations between objects; repeated patterns in the events are recognized; and finally, different examples are merged together. This model is implemented as a computer program learning algorithms from worked examples (LAWE). [ABSTRACT FROM AUTHOR]

This paper aims to present the authors' experiences in building up knowledge-base systems. The systems were worked out using the authors' own method based on concurrent engineering philosophy with elements of fuzzy logic in the local area network (LAN). The accepted method ensures the following: 1. efficiency of rule creation; 2. reduced number of errors in testing the graphical user interface (GUI); 3. ease of creation and verification of the membership function when engineers cooperate with experts. This paper presents examples of domains and ways of creating the system with special emphasis on the membership function. [ABSTRACT FROM AUTHOR]

Resource allocation and scheduling are the key areas in the operation of complex industrial systems. One can argue that the systems have grown in complexity over the years mainly due to the increased striving for performance enhancing combined with a greater degree of uncertainty and imprecision in system's external and internal environments. This complexity, always present in real life systems, makes the application of quantitative scheduling and allocation tools as problem solvers questionable in many instances. In this paper simulation technique is proposed as an analysis tool that proved to be an adequate, effective and economically efficient problem solver in the case of scheduling and resource allocation and utilization for a complex manufacturing system. The implementation described in the paper was developed using SLAMSYSTEM modelling environment. [ABSTRACT FROM AUTHOR]

Pattern modelling in time-series prediction refers to the process of identifying past relationships and trends in historical data for predicting future values. This paper describes the development of a new pattern-matching technique for univariate time-series forecasting. The pattern modelling technique outperforms frequently used statistical methods such as exponential smoothing on different error measures and predicting the direction of change in time-series. The paper discusses the prediction results on popular benchmarks and the real U.S. S&P index for financial markets. [ABSTRACT FROM AUTHOR]

To solve the problem of the poor solution precision and convergence speed in the artificial bee colony (ABC) algorithm, in this article we propose a modified algorithm called ABC algorithm with self-adaptive extended memory (ABCSEM) algorithm. First, the extended memory is introduced to store employed bees’ historical information comprising recent food sources, personal best food sources, and global best food sources. Furthermore, the extended memory is added to the solution search equation to improve the exploitation capability. Experimental results conducted on a set of numerical benchmark functions show that the ABCSEM algorithm can outperform the ABC algorithm in most of the tested functions. [ABSTRACT FROM AUTHOR]

Recycling, reusing, and reducing electronic garbage (E-waste) may be the sole methods for managing E-waste in use today. In essence, there is no ideal method of managing E-waste. This process required more labor and resources. E-waste classification is fulfilled using fractional Henry gas optimization-based deep convolutional neural network (FHGO-based deep CNN) is shown in this manuscript. To predict the optimal path the E-waste images are conversed through the energy-aware FHGO routing algorithm. The feature extraction procedure is performed to cut out the features for example the gray level co-occurrence matrix (GLCM) feature, local Gabor binary pattern (LGBP) and histogram of oriented gradient (HOG) and the pre-processing phase is concluded with a median filter. To supplement the extracted feature size the data augmentation is fulfilled. Moreover, the E-waste classification is done based on deep CNN, which is trained using a FHGO algorithm. FHGO is exhibited by the merging of Henry gas solubility optimization (HGSO) algorithm, fractional calculus (FC). Comparing to the existing approaches like deep learning, tensor flow deep learning, Cuckoo search-based neural network and machine learning the accuracy of the proposed method is 19.49%, 18.05%, 12.77%, and 7.89% privileged. [ABSTRACT FROM AUTHOR]

Second phase, the prediction in Minimum Support Price (MSP) is made through detected species. The required data in the price is gathered through online resources that are utilized for the data normalization stage in further. These normalized data are used in the deep feature extraction stage by utilizing the autoencoder and the Conventional Neural Network (CNN) to extract the required deep feature; it is then fused in that stage. Further, these fused features are fed into the optimal feature selection stage to choose the important optimal features by utilizing the newly developed algorithm Intensity-based Barnacle Mating Honey Badger Algorithm (I-BMHBA). Then, the selected features are fed into the Optimized Radial Basis Function + Gated Recurrent Unit (RBF + GRU) to predict MSP prices based on the crop type. The predicted prices are then divided into four shares as well as made the profit suggestion is through training the regional and location crops data. In the end, the Optimized RBF + GRU provide better profitable harvest outcomes. From these investigations, it is proven that the proposed profit suggestion via species prediction and MSP prediction method is better when assimilated with another existing model by utilizing the various performance metrics. [ABSTRACT FROM AUTHOR]

Cancer is a disease affecting humans around the world. Exploring a deep understanding of the state/parameter estimation based on the drug prescription provides an awareness of the patient's experience during the treatment. Applying controllers to such systems with the primary aim of reducing cancer cells with the fewest side effects is a very interesting field in the past decade. The usual assumptions followed when designing a controller are that all system parameters are constant and all states are measurable. However, this is not the case in reality. In this article, based on a five-states differential equation model, which models a combined therapy of chemotherapy and anti-angiogenic, the state and/or parameters are estimated using an unscented Kalman filter while a nonlinear adaptive controller produces drug injection rate as the control signals utilizing the estimated state/parameter. The advantage of this estimation method is that, unlike in the traditional extended Kalman filter, nonlinear dynamics can be used without any linearization. The results show that the state and parameter estimation with UKF was successful. This idea can be used to assign a mathematical cancer dynamic for any individual, and state/parameter estimation can be studied and verified throughout a patient's treatment. [ABSTRACT FROM AUTHOR]

The degenerative neurological condition known as Parkinson's disease is distinguished by tremors, stiffness, and difficulties with walking, balance, and motor coordination. Parkinson's disease is a progressive condition. Identifying whether this guy suffers from Parkinson's disease is the purpose of this puzzle. Early detection and effective treatment of Parkinson's disease can significantly improve the quality of life for those affected. This highlights the significance of early disease detection. The objective of this initiative is to facilitate the identification of Parkinson's disease through the use of hand-drawn graphics. Three unique Machine Learning algorithms are used in this article. The adaptability of the model has been demonstrated through the usage of a variety of designs, including but not limited to spirals, waves, cubes, and triangles. Experiment results showed that datasets 89.7% correct, and proposed XGBOOST algorithm is 98.75% accurate. [ABSTRACT FROM AUTHOR]

The development of several popular social networks and the publication of social networks' data have led to the risk of leakage of sensitive and confidential information of individuals. This requires the preservation of privacy before the publication of a user's data available from his Online Social Network (OSN) presence. Numerous algorithms have been proposed in the area of preserving the privacy of social network users' information such as K-anonymity and L-diversity. Previous work has shown good results based on the concept of adding edges and noise nodes for achieving K-anonymity and L-diversity. K-anonymization techniques are able to prevent identity disclosure of users but are not sufficient to prevent the disclosure of sensitive information of users. In this direction, a number of techniques for preserving the sensitive information of social network users have been proposed. Although these techniques have shown reasonably good results to achieve anonymity, but they also lead to a substantial change in the original structure of the OSNs. In this article, the problems of preventing sensitive attribute disclosure and reducing the noisy nodes have been addressed by perturbing the sensitive attributes. Existing research uses L-diversity for preventing sensitive attribute disclosure resulting in skewness and similarity attacks. We have addressed the skewness attacks by removing the duplicate noisy nodes from the final dataset to be published for stakeholders by the OSN service providers. All the information of duplicate nodes has been stored in a table named Reference Attribute Table (RAT). This table will be accessible only to the service providers for the purpose of de-anonymizing the data of users. The proposed technique has been extensively evaluated using five metrics viz. APL, ACSPL, RRTI, number of noisy nodes, and information loss using four real-time datasets collected for OSNs namely CORA, ARNET, DBLP, and Twitter. Results of evaluation parameters viz. APL and RRTI show that there is less change in the structure of datasets after anonymization. Results of ACSPL show that our proposed technique is able to preserve sensitive attributes in the datasets. The maximum number of noisy nodes amongst all four datasets is 5.4% and the maximum information loss is 2.2%. Evaluation results make it evident that our proposed technique ensures privacy preservation with less loss of information and thus preserving the utility of published data. [ABSTRACT FROM AUTHOR]

Indoor object detection and recognition (IODR) helps to progress the quality life of the visually weakened person by helping them in indoor navigation along with their day-to-day activities. This research introduced the African Adam Optimization-based SqueezeNet (AAO-based SqueezeNet) for object detection and recognition. Moreover, Generative Adversarial Network (GAN) is exploited to detect the thing in the images with the assistance of a generator and discriminator in GAN. The features, such as texture and Resnet features are extracted in the detected objects. In addition, the objects are recognized using SqueezeNet based on the extracted features. Moreover, the training process of SqueezeNet is done using devised AAO algorithm, which is the concatenation of the African Vultures Optimization Algorithm (AVOA) and Adam optimizer (AO). Analysis is done based on the metrics, such as testing accuracy, precision, and recall using the Mendeley dataset and it accomplished the values of 0.905, 0.902, and 0.939, respectively. [ABSTRACT FROM AUTHOR]

Inter-dependency among the decision criteria and difficulty of handling the information in form of "yes," "abstain," "no," and "refusal" are two important issues to be addressed in multicriteria group decision-making (MCGDM) problems in the environment of uncertainty. Recently, spherical fuzzy set (SFS) has gained the attention of the researchers for MCGDM problems due to their capability of handling decision makers' preferences in the form "yes," "abstain," "no," and "refusal" more efficiently than picture fuzzy set. Choquet integral operator has an edge over traditional aggregation operator in the modeling of interaction among the preferences of decision makers and decision criteria and therefore, we define Choquet integral operator for SFS. The main objective of this study is to define the spherical fuzzy Choquet integral (SFCI) operator and to extend the VIsekriterijumska optimizacija I Kmpromisno Resenje (VIKOR) for spherical fuzzy environment. In this study we define spherical fuzzy Choquet average, spherical fuzzy Choquet geometric and spherical fuzzy Choquet integral distance (SFCID) operators and propose SFCI-based VIKOR method for MCGDM problems. Proposed SFCI-based VIKOR method is applied on warehouse selection problems and results are compared with existing MCGDM methods. [ABSTRACT FROM AUTHOR]

In recent years, with the rapid growth of the internet of things (IoT), the number of devices connected to the network has increased significantly. The rising trend of mass data that objects send to the network has become a big challenge for cloud networks. The next generation IoT applications require low delay and real-time responses. Hence, IoT applications need a new processing paradigm to be able to access distributed cloud services at the network edge. In recent years, fog computing has emerged as a distributed computing model compatible with heterogeneous environments that can provide cloud services with low delay at the network edge. Meanwhile, services provided by the IoT suffer from varying workload changes over time, and approaches are needed to automatically provision resources and satisfy quality of service (QoS) requirements. With this motivation, this study presents an improved political optimizer-based resource management scheme for fog-enabled cloud environments, which is named IPORM. IPORM seeks to efficiently allocate resources to IoT requests with the purpose of maximizing resource utilization. In addition, other objectives such as energy, bandwidth, and delay are also included in IPORM. Simulation results show that our scheme significantly improves network performance in terms of various metrics compared to its counterparts. [ABSTRACT FROM AUTHOR]

One of the most significant goals that must be guaranteed for high-speed railway systems is safety. A new model is constructed for high-speed railway accident analysis with interval type-2 fuzzy DEMATEL method based on the 24Model. Specifically, the 24Model is introduced to identify the accident factors from four stages at the individual and organizational levels. The DEMATEL method is combined with interval type-2 fuzzy sets to analyze the key causations of the accident considering the interrelationships between factors under an uncertain environment. A specific case of the "7.23" Yong-Tai-Wen railway accident is employed to validate the proposed model. The analysis results show that poor management of the ministry of railways, imperfect rules and standards and little supervision or inspection are core organizational factors that lead to accidents. Besides, unsafe material conditions and unsafe human conditions are the direct causes of the accident which are easily influenced by other accident factors. The relevant managers need to strengthen the inspection to ensure the safe operation of the high-speed railway. [ABSTRACT FROM AUTHOR]

The main contribution of the proposed work is to minimize the capacity of the canal region and functional step for the head gate of the supply canal depending on the canal constraint. Due to the communication and interconnection over the sensors in IoT, the required decision variables are obtained for the operation of water supply scheduling. Then, the proposed model utilizes several decision variables such as start time, and discharge rate. In various sections, the decision variables change from the head of the canal to the tail, which is also to be gradually decreased as well. To overcome this constraint, the Modified Exploration-based Artificial Gorilla Troops Optimizer (ME-AGTO) algorithm is proposed in this article, where the start time, and discharge rate is efficiently optimized. The newly developed ME-AGTO algorithm is applied for the two-way water supply and acquires the optimal scheduling of the water supply. Hence, the canal capacity is considered with optimized variables at different sections throughout the head and tail parts of the canal. Thus, the schedules are created for the inflow of water with the aid of the rotation period and head gate. The performance is estimated to obtain the desired results of less conveyance loss. [ABSTRACT FROM AUTHOR]

One of the common techniques to reduce the scalability problem in collaborative filtering (CF)-based recommender systems is the clustering technique, which accelerates finding the nearest neighbor users in the recommendation process. Different clustering algorithms lead to improved accuracy and diversity in recommender systems. It is challenging to develop recommender systems based on clustering with decreasing scalability and simultaneously increasing accuracy. This article proposes a new clustering-based recommender system that takes into account the theoretical properties of the Learning Automata (LA) technique. The presented clustering novelty lies in the fact that employing LA technique for the user clustering in a CF-based recommender system has not been set forth so far in a way that addresses the scalability while improving accuracy. In addition, a novel similarity metric is embedded in the proposed algorithm to measure the similarity value between users. This metric is developed as like/dislike (LD) which can significantly improve the accuracy by reducing the computational cost. Extensive simulations have been performed on real-world datasets such as MovieLens and FilmTrust, which confirm the effectiveness of the proposed algorithm. In this regard, the proposed algorithm has improved the precision between 5 and 16% on average compared to the existing state-of-the-art methods such as GA-GELS, NUSCCF, NNMF, and KL-KM. [ABSTRACT FROM AUTHOR]

Several traditional methods were tested on standard datasets to evaluate client emotions transmitted via internet portals. Customers, on the other hand, continue to have difficulty obtaining aspect-oriented viewpoints voiced by other customers, and the accuracy of the current model is insufficient. The suggested Aspect-Based Sentimental Analysis (ABSA) starts with pre-processing, which includes "stop word and punctuation removal, lower case conversion, and stemming." Aspect extraction, which entails dividing the nouns and adjectives, as well as verbs and adverbs, is the following step. The weighted polarity features from the "Vader sentiment intensity analyzer, as well as the word2vector and Term Frequency-Inverse Document Frequency (TF-IDF)" are concatenated. OIDL stands for Optimized Integrated Deep Learning, which combines two types of deep learners. The first is the combination of concatenated features with "Convolutional Neural Network (CNN) and Recurrent Neural Network (RNN)," while the second is the combination of concatenated features with RNN. The Improved Coyote Optimization Algorithm (ICOA) improves both deep learners, and the conclusion of sentiment analysis result is considered both models. Thus, the suggested model surpasses standard methodologies regarding precision and accuracy, according to the results of the experiments. [ABSTRACT FROM AUTHOR]

Distributed Denial of Service (DDoS) attack is a severe type of attack. It affects the cloud in terms of loss of repudiation, service outage, financial loss, and data loss. Hence, the optimal feature selection and the hybrid learning-based classifier are developed for evading the normal function disruption in the cloud server. After pre-processing, the resultant data is subjected to the ensemble feature selection. The first feature is selected optimally by the Probability of Fitness-based Billiards-Inspired Optimization (PF-BIO) algorithm, the second feature selection is acquired through the Fisher discriminant, and the last feature selection is carried out by feature correlation with class. Consequently, the resultant of these feature selections is concatenated with the weight parameter to provide weighted fused features, where the weight is getting optimized by the PF-BIO algorithm. Finally, a newly developed hybrid-based learning model, where the Deep Belief Network with the Gated Recurrent Unit (DBN-GRU), in which the hyper parameters are tuned and optimized by the PF-BIO algorithm. Thus, the performance is compared with the existing approaches. Throughout the result analysis, the accuracy of the designed method is 97.05%. Hence, the proposed model proves the efficiency for the detection of DDoS attacks precisely. [ABSTRACT FROM AUTHOR]

This article has developed a novel dual-data sanitization and dual-data restoration model for privacy preservation in mobile cloud computing (MCC) environment. The original medical data have been sanitized at the transmitter end utilizing the newly suggested dual-data sanitization technique. The wireless channel transports the sanitized data from the transmitter to the receiver. The dual-sanitized input has been recovered at the other end. More significantly, the exact key generation, which has been found by a novel hybrid optimization model, is essential to both operations (dual-data cleaning and dual-data restoring). Electric fish customized dolphin swarm optimization (ECDO) is a suggested hybrid optimization model that is a conceptual merger of the electric fish optimization (EFO) and the dolphin swarm method. Ultimately, to confirm the effectiveness of the anticipated strategy, a study has been made. The present architecture has been contrasted against the previous models, including the DSOA, EFO, SMO, WOA, and J-SSO, respectively. The restoration effectiveness of the projected model is 57.84%, 26.3%, 5.26%, 31.5%, and 15.7% improved over DSOA, EFO, SMO, WOA, and J-SSO, respectively. As a result of the overall assessment, the proposed model appears to be extremely secure during data transfer. [ABSTRACT FROM AUTHOR]

Multi-objective integer nonlinear programming (MOINLP) problems are multi-objective integer programming problems with at least one nonlinear objective function or constraint. To date, MOINLP problem has not been exactly solved. Although traditional ε-constraint method can be used to solve MOINLP problem, the obtained solution may not be Pareto-optimal. To overcome this shortcoming, a basic ε-constraint method (BEM) is proposed to solve MOINLP problem exactly. However, the time complexity of BEM is as high as O ((p − 1) M 2 N) , where p, M, and N are the numbers of objectives, Pareto-optimal solutions, and feasible solutions, respectively. For this reason, an improved BEM (IBEM) is developed whose time complexity is O ( M 2 N). That is, the time complexity of IBEM for solving MOINLP problem is equal to solving the single-objective one. Finally, to avoid using all the feasible solutions (N) in obtaining each Pareto-optimal solution, three methods to eliminate dominated solutions effectively are used before performing IBEM. The test results illustrate that our method can not only solve MOINLP problem exactly but also has high efficiency. [ABSTRACT FROM AUTHOR]

The hazard of landslides has been demonstrated over time with numerous events causing damage to human lives and high material costs. Several previous studies have shown that one of the predominant factors in landslides is intensive rainfall. The present work proposes the use of data generated by weather stations to predict landslides. We give special treatment to precipitation information as the most influential factor and whose data are accumulated in time windows (3, 5, 7, 10, 15, 20, and 30 days) looking for the persistence of meteorological conditions. To optimize the dataset composed of geological, geomorphological, and climatological data, a feature selection process is applied to the meteorological variables. We use filter-based feature ranking and Self-Organizing Map (SOM) with Clustering as supervised and unsupervised machine learning techniques, respectively. This contribution was successfully verified by experimenting with different classification models, improving the test accuracy of the prediction, and obtaining 99.29% for Multilayer Perceptron, 96.80% for Random Forest, and 88.79% for Support Vector Machine. To validate the proposal, a geographical area sensitive to this phenomenon was selected, which is monitored by several meteorological stations. Practical use is a valuable tool for risk management decision making, can help save lives and reduce economic losses. [ABSTRACT FROM AUTHOR]

Unmanned vehicles are gaining the attention of professional operators and the general public. The implementation of unmanned vehicles is evident in, among other fields, emergency management, agriculture, traffic monitoring, post-disaster operations, and delivery of goods. Naturally, a group of unmanned vehicles can cooperatively complete operations more proficiently than a single vehicle. However, several issues must be resolved before a stable and reliable group of unmanned vehicles can be generally deployed to solve tasks in civil infrastructures and in industrial facilities. Here, a framework for the guidance of a fleet of unmanned surface vehicles is proposed. The framework utilizes several levels of control, namely Global Planning Level, Local Planning Level, and Low-Level Control. While the individual vehicles are completely autonomous in their operational locomotion and obstacle avoidance (low-level control and local planning), the task assignment for each vehicle (or group of them) is provided by a global planning process, based on the genetic algorithm. The framework provides a concept to solve complex tasks for the fleet of unmanned surface vehicles (USVs). This includes, but is not necessarily limited to, a dynamically changing environment, different types of USVs with special abilities, multiple types of areal restrictions and obstacles, different restrictions for individual USVs, cooperation of multiple USVs to solve their subtasks, energy consumption optimization, etc. The framework can be advantageously applied to tasks such as warehouse logistics, surface maintenance, area exploration, etc. At the end of the study, the application of the framework is presented using a simulated example of cooperative problem solving using six vehicles. [ABSTRACT FROM AUTHOR]

Here we expand on a previous paper concerning the role of logic in information retrieval IR modelling. In that paper, among other things, we had pointed out how different ways of understanding the contribution of logic to IR have sprung from the always unstated adherence to either the total or the partial knowledge assumption. Here we make our analysis more precise by relating this dichotomy to the notion of vividness, as used in knowledge representation, and to another dichotomy that has had a profound influence in database theory, namely, the distinction between the proof-theoretic and the model-theoretic views of a database, spelled out by Reiter in his "logical reconstruction of database theory." We show that precisely the same distinction can be applied to logical models of IR developed so far. The strengths and weaknesses of the adoption of either approach in logical models of IR are discussed. [ABSTRACT FROM AUTHOR]

In this paper, we propose the use of subspace clustering to detect the states of dynamical systems from sequences of observations. In particular, we generate sparse and interpretable models that relate the states of aquatic drones involved in autonomous water monitoring to the properties (e.g., statistical distribution) of data collected by drone sensors. The subspace clustering algorithm used is called SubCMedians. A quantitative experimental analysis is performed to investigate the connections between i) learning parameters and performance, ii) noise in the data and performance. The clustering obtained with this analysis outperforms those generated by previous approaches. [ABSTRACT FROM AUTHOR]

With the increasing interest in autonomous shipping technology, to ensure ship's safety it is vital to equip the ship with a navigational algorithm to continuously track the moving vessels in the area of interest and subsequently avoid any collisions. To address this problem, in this paper, an intelligent and robust tracking algorithm using the Kalman filter (KF) and Interacting Multiple Model (IMM) scheme has therefore been designed. The algorithm is able to provide accurate motion information of other ships in constrained environments using least navigational information. The capability of the proposed algorithm has been tested and verified in a number of computer-based simulations. [ABSTRACT FROM AUTHOR]

The recent spike in malware dissemination rate in various striving organizations necessitate the stringent demand for the establishment of suitable counter-measures for safeguarding Information Technology assets against customized malware attacks. The Anti-virus (AV) communities perform a vital role in the threat management of such dissemination by detecting new malware in the wild and assigning labels to them. However during an extensive study conducted on malware, a huge heterogeneity in their categorization has been observed. It adversely affects research communities, business organizations, and AV companies as this creates confusion and difficulty to study malicious programs. Thus, there is an immediate need of a system where malware is determined not on the basis of their 'confusing' names given by AV vendors but by its activities so that its damage potential on the victim machine can be determined. This paper presents malware naming conventions being used by AV vendors and characterizes malware capabilities on the basis of their characteristic features obtained after performing static and dynamic malware analysis. It proposes a novel technique, an initiative to solve the issue of inconsistencies that will assess the level of different capabilities of a malware using Fuzzy logic paradigm. This approach would remove the misunderstandings and confusion that arises due to inconsistent naming convention and improve clarity towards their mature and new attributes. The proposed approach is tested on a set of well-known real malware samples and the results obtained are compared with that of fuzzy clustering. [ABSTRACT FROM AUTHOR]

Fictional robots in literature and film have shaped our cultural image of robots. This paper studies what these images can contribute to our relationship with the real robots that are now entering our everyday lives. A review of science fiction literature and films comes to the conclusion that the predominant theme of the fictional robots – mostly androids and replicants – has been human identity, not robotics itself. Non-humanoid "just-robots" are presented as unproblematic sidekicks. It is argued that this focus has as consequence a tendency toward humanizing even non-humanoid robots in their presentation to the public. This tendency leads to a) breakdowns where technology contradicts the humanoid narrative, and b) a lack of productive narratives about emerging, more complex human-robot relationships. [ABSTRACT FROM AUTHOR]

The paper presents the relationship module (RM) being a part of the semantic knowledge base used for the efficient semantic modeling of relations between concepts. The main contribution of presented solutions represents an approach based on the hypergraph-based data model expressed upon the association-oriented metamodel. The RM has been designed in accordance with a number of assumptions related to the representation of relationships, such as defining types of relations in terms of their characteristics, arity and hierarchy, transferring attribute among related concepts, instancing specific relationships, and determining potentially permitted or prohibited connections between concepts. [ABSTRACT FROM AUTHOR]

Inventory management deals with a tradeoff between the benefits of keeping stocks of goods that allows fulfillment of the customer's demand, and the cost of carrying inventory. Inventory control techniques are very important components and the most organizations can substantially reduce their costs associated with the flow of materials. This paper presents new inventory management model based on particle swarm optimization and pure adaptive search global optimization algorithm in production-inventory system. The proposed model is focusing on planned level of demand for finished goods, production and raw materials cost, production capacity as the norm, change of the production cost and inventory capital cost, all of which are typical factors in automobile manufacture industry. The model determines different factors such as the minimizing inventory quantity, minimizing inventory value, and minimizing production cost based on demand for production items. The model is tested with original real-world dataset obtained from the automotive company Lear from US and its factory in Novi Sad, Serbia. [ABSTRACT FROM AUTHOR]

Introduces series of articles published in the January issue of the journal "Cybernetics and Systems: An International Journal." Presentation of several speech-to-speech translation systems based on finite-state models; Problem of machine vision applications, deriving a suitable method to deal with the effects of illumination changes; Introduction of a novel approach to image segmentation that is applied to digital angiograms analysis.

Knowledge-based engineering systems are founded upon integration of knowledge into computer systems and are one of the core requirements for the future Industry 4.0. This paper presents a system called smart innovation engineering (SIE) capable of facilitating product innovation process semi-automatically. It enhances decision-making processes using the explicit knowledge of formal decision events. The SIE system carries the promise to support the innovation processes of manufactured products in a quick and efficient way. It stores and reuses past decisional events or sets of experiences related to innovation issues, which significantly enhances innovation progression. The analysis of basic concepts and implementation method proves that SIE system is an advanced form of cyber physical systems. It is flexible, systematic, fast, and supports customization. It can play a vital role toward Industry 4.0 development. [ABSTRACT FROM AUTHOR]

Knowledge and experience are important requirements for product development. The aim of this paper is to propose a systematic approach for industrial product development. This approach uses smart knowledge management system comprising of set of experience knowledge structure and decisional DNA (DDNA) along with virtual engineering tools (virtual engineering object, virtual engineering process, and virtual engineering factory). This system provides a new direction to researchers working on product development, especially designers and manufacturers. It will reduce their communication gap by allowing them to work on the same platform. The proposed system adopts an early consideration of manufacturing issues. Therefore, it can shorten product development cycle time, minimize overall development cost, and ensure a smooth transition into production. The proposed system is dynamic in nature because it updates itself after every time a new decision related to product development activity is made. Product development process can be performed systematically and efficiently using this system as it stores knowledge of experiences of different activities. [ABSTRACT FROM AUTHOR]

Twitter has become a popular microblogging service that allows millions of active users share news, emergent social events, personal opinions, etc. That leads to a large amount of data producing every day and the problem of managing tweets becomes extremely difficult. To categorize the tweets and make easily in searching, the users can use the hashtags embedding in their tweets. However, valid hashtags are not restricted which lead to a very heterogeneous set of hashtags created on Twitter, increasing the difficulty of tweet categorization. In this paper, we propose a hashtag recommendation method based on analyzing the content of tweets, user characteristics, and currently popular hashtags on Twitter. The proposed method uses personal profiles of the users to discover the relevant hashtags. First, a combination of tweet contents and user characteristics is used to find the top-k similar tweets. We exploit the content of historical tweets, used hashtags, and the social interaction to build the user profiles. The user characteristics can help to find the close users and enhance the accuracy of finding the similar tweets to extract the hashtag candidates. Then a set of hashtag candidates is ranked based on their popularity in long and short periods. The experiments on tweet data showed that the proposed method significantly improves the performance of hashtag recommendation systems. [ABSTRACT FROM AUTHOR]