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Training a model using batch learning requires uniform data storage in a repository. This approach is intrusive, as users have to expose their privacy and exchange sensitive data by sending them to central entities to be preprocessed. Unlike the aforementioned centralized approach, training of intelligent models via the federated learning (FEDL) mechanism can be carried out using decentralized data. This process ensures that privacy and protection of sensitive information can be managed by a user or an organization, employing a single universal model for all users. This model should apply average aggregation methods to the set of cooperative training data. This raises serious concerns for the effectiveness of this universal approach and, therefore, for the validity of FEDL architectures in general. Generally, it flattens the unique needs of individual users without considering the local events to be managed. This paper proposes an innovative hybrid explainable semi-personalized federated learning model, that utilizes Shapley Values and Lipschitz Constant techniques, in order to create personalized intelligent models. It is based on the needs and events that each individual user is required to address in a federated format. Explanations are the assortment of characteristics of the interpretable system, which, in the case of a specified illustration, helped to bring about a conclusion and provided the function of the model on both local and global levels. Retraining is suggested only for those features for which the degree of change is considered quite important for the evolution of its functionality. [ABSTRACT FROM AUTHOR]

This paper introduces a diagnostic model that effectively diagnoses in fourteen different stages, by fusing functional magnetic resonance imaging (fMRI) and structural MRI (sMRI) information. Georgios Theodoridis and Athanasios Tsadiras from the Aristotle University of Thessaloniki, Greece, have authored the seventh paper which is entitled "Applying machine learning techniques to predict and explain subscriber churn of an online drug information platform." Machine learning (ML) and more specifically deep learning (DL) algorithms are considered among the most paramount technologies of both artificial intelligence (AI) and 4 SP th sp industrial revolution. This paper provides an in-depth comparison of various machine learning (ML) techniques and advanced preprocessing methods, in an effort to successfully perform online subscriber churn prediction. [Extracted from the article]

The growing need to use online services has made it necessary to ensure protection against all kinds of cyber-threats. This research effort aims to tackle network security problems as follows: It introduces the hybrid intrusion detection system COREM2 that successfully detects nine cyber-attacks. Its architecture comprises of a two-dimensional convolutional neural network (2-D CNN), a recurrent neural network with long short-term memory layers and a multilayer perceptron. The COREM2 was successfully tested against the timely Kitsune Network Attack Dataset, achieving an overall accuracy of 98.64% and 98.92% in the training and testing phases, respectively. Since this is a multiclass classification effort, the "one-versus-all strategy" was employed to validate the introduced model, which has proved its ability to generalize. COREM2 outperforms other state-of-the-art approaches achieving overall accuracy above 98%, rare for field cyber-security intrusion. We strongly suggest that it can be safely used as a prototype for further research on network security enhancement. Furthermore, this research introduces a holistic approach for cyber intrusion detection, using the COREM2 in order to classify network traffic as benign or malicious. It captures network flow packets in the form of PCAP files (packet capture), and it stores them in.csv files and it evaluates them in order to perform classification in ten classes as provided by the Kitsune Dataset. If the malicious traffic exceeds a certain limit, the model notifies the user to take all necessary actions. The proposed method has an average processing power of 10,000 packets per 8 s. It potentially can be used in any device that has Internet access. [ABSTRACT FROM AUTHOR]

Data-driven methods are implemented using particularly complex scenarios that reflect in-depth perennial knowledge and research. Hence, the available intelligent algorithms are completely dependent on the quality of the available data. This is not possible for real-time applications, due to the nature of the data and the computational cost that is required. This work introduces an Automatic Differentiation Variational Inference (ADVI) Restricted Boltzmann Machine (RBM) to perform real-time anomaly detection of industrial infrastructure. Using the ADVI methodology, local variables are automatically transformed into real coordinate space. This is an innovative algorithm that optimizes its parameters with mathematical methods by choosing an approach that is a function of the transformed variables. The ADVI RBM approach proposed herein identifies anomalies without the need for prior training and without the need to find a detailed solution, thus making the whole task computationally feasible. [ABSTRACT FROM AUTHOR]

The fifth generation (5G) of telecommunications networks is currently commercially deployed. One of their core enabling technologies is cellular Massive Multiple-Input-Multiple-Output (M-MIMO) systems. However, future wireless networks are expected to serve a very large number of devices and the current MIMO networks are not scalable, highlighting the need for novel solutions. At this moment, Cell-free Massive MIMO (CF M-MIMO) technology seems to be the most promising idea in this direction. Despite their appealing characteristics, CF M-MIMO systems face their own challenges, such as power allocation and channel estimation. Deep Learning (DL) has been successfully employed to a wide range of problems in many different research areas, including wireless communications. In this paper, a review of the state-of-the-art DL methods applied to CF M-MIMO communications systems is provided. In addition, the basic characteristics of Cell-free networks are introduced, along with the presentation of the most commonly used DL models. Finally, future research directions are highlighted. [ABSTRACT FROM AUTHOR]

It is a fact that natural disasters often cause severe damage both to ecosystems and humans. Moreover, man-made disasters can have enormous moral and economic consequences for people. A typical example is the large deadly and catastrophic explosion in Beirut on 4 August 2020, which destroyed a very large area of the city. This research paper introduces a Geo-AI disaster response computer vision system, capable to map an area using material from Synthetic Aperture Radar (SAR). SAR is a unique form of radar that can penetrate the clouds and collect data day and night under any weather conditions. Specifically, the Memory-Augmented Deep Convolutional Echo State Network (MA/DCESN) is introduced for the first time in the literature, as an advanced Machine Vision (MAV) architecture. It uses a meta-learning technique, which is based on a memory-augmented approach. The target is the employment of Deep Reservoir Computing (DRC) for domain adaptation. The developed Deep Convolutional Echo State Network (DCESN) combines a classic Convolutional Neural Network (CNN), with a Deep Echo State Network (DESN), and analog neurons with sparse random connections. Its training is performed following the Recursive Least Square (RLS) method. In addition, the integration of external memory allows the storage of useful data from past processes, while facilitating the rapid integration of new information, without the need for retraining. The proposed DCESN implements a set of original modifications regarding training setting, memory retrieval mechanisms, addressing techniques, and ways of assigning attention weights to memory vectors. As it is experimentally shown, the whole approach produces remarkable stability, high generalization efficiency and significant classification accuracy, significantly extending the state-of-the-art Machine Vision methods. [ABSTRACT FROM AUTHOR]

The complexity of threats in the ever-changing environment of modern industry is constantly increasing. At the same time, traditional security systems fail to detect serious threats of increasing depth and duration. Therefore, alternative, intelligent solutions should be used to detect anomalies in the operating parameters of the infrastructures concerned, while ensuring the anonymity and confidentiality of industrial information. Blockchain is an encrypted, distributed archiving system designed to allow for the creation of real-time log files that are unequivocally linked. This ensures the security and transparency of transactions. This research presents, for the first time in the literature, an innovative Blockchain Security Architecture that aims to ensure network communication between traded Industrial Internet of Things devices, following the Industry 4.0 standard and based on Deep Learning Smart Contracts. The proposed smart contracts are implementing (via computer programming) a bilateral traffic control agreement to detect anomalies based on a trained Deep Autoencoder Neural Network. This architecture enables the creation of a secure distributed platform that can control and complete associated transactions in critical infrastructure networks, without the intervention of a single central authority. It is a novel approach that fuses artificial intelligence in the Blockchain, not as a supportive framework that enhances the capabilities of the network, but as an active structural element, indispensable and necessary for its completion. [ABSTRACT FROM AUTHOR]

The backbone of the economy, security and sustainability of a state is inseparably linked to the security of its critical infrastructure. Critical infrastructures define goods, systems or subsystems that are essential to maintain the vital functions of society, health, physical protection, security plus economic and social well-being of citizens. The digital security of critical infrastructures is a very important priority for the well-being of every country, especially nowadays, because of the direct threats dictated by the current international conjuncture and due to the emerging interactions or interconnections developed between the National Critical Infrastructures, internationally. The aim of this research is the development and testing of an Anomaly Detection intelligent algorithm that has the advantage to run very fast with a small portion of the available data and to perform equally well with the existing approaches. Such a system must be characterized by high efficiency and very fast execution. Thus, we present the Gryphon advanced intelligence system. Gryphon is a Semi-Supervised Unary Anomaly Detection System for big industrial data which is employing an evolving Spiking Neural Network (eSNN) One-Class Classifier (eSNN-OCC). This machine learning algorithm corresponds to a model capable of detecting very fast and efficiently, divergent behaviors and abnormalities associated with cyberattacks, which are known as Advanced Persistent Threat (APT). The training process is performed on data related to the normal function of a critical infrastructure. [ABSTRACT FROM AUTHOR]

The need to protect the environment and biodiversity and to safeguard public health require the development of timely and reliable methods for the identification of particularly dangerous invasive species, before they become regulators of ecosystems. These species appear to be morphologically similar, despite their strong biological differences, something that complicates their identification process. Additionally, the localization of the broader space of dispersion and the development of invasive species are considered to be of critical importance in the effort to take proper management measures. The aim of this research is to create an advanced computational intelligence system for the automatic recognition, of invasive or another unknown species. The identification is performed based on the analysis of environmental DNA by employing machine learning methods. More specifically, this research effort proposes a hybrid bio-inspired computational intelligence detection approach. It employs extreme learning machines combined with an evolving Izhikevich spiking neuron model for the automated identification of the invasive fish species ' Lagocephalus sceleratus' extremely dangerous for human health. [ABSTRACT FROM AUTHOR]

Several machine learning models were used to predict interior spruce wood density using data from open-pollinated progeny testing trial. The data set consists of growth (height and diameter which were used to estimate individual tree volume) and wood quality (wood density determined by X-ray densitometry, resistance to drilling, and acoustic velocity) attributes for a total of 1146 trees growing on comparable sites in interior British Columbia. Various machine learning models were developed for estimating wood density. The multilayer feed-forward artificial neural networks and gene expression programming provided the highest predictability as compared to the other methods tested, including those based on classical multiple regression which was considered as the comparisons benchmark. The utilization of machine learning models as a credible method for estimating wood density using available growth data as an indirect method for determining trees wood density is expected to become increasingly helpful to forest managers and tree breeders. [ABSTRACT FROM AUTHOR]

A heuristic wood density prediction model has been developed by means of artificial neural networks (ANNs). Four populations of 32-year-old coastal Douglasfir (Pseudotsuga menziesii (Mirb.) Franco var. menziesi) trees representing 20 full-sib families growing on comparable sites were in focus of this study. Tree height, diameter, volume, wood density, and acoustic velocity data from 632 trees were considered for the calculations. Two different ANN platforms were developed employing different classes and architectures, namely, the multilayer feed-forward (MLFF) and modular (MOD) models. After establishing the optimal configuration of the model, a MLFF network and a MOD neural network (with the obtained optimal structure) were developed and tested without cross-validation by employing a typical training and testing set methodology. To this purpose, the data set was divided in 480 trees for training and 152 trees for validation. A significant relationship between actual and predicted wood density was obtained with R² values of 0.50 and 0.52 for the two networks, respectively, demonstrating their predictive potential for wood density estimation. A classic multiple regression analysis produced substantially lower predictive power with an R² of 0.23. The application of ANNs as a viable predictive tool in determining wood density using growth and acoustic velocity data without additional intrusive sampling and laboratory work was demonstrated. An additional work including other species is required for these approaches. [ABSTRACT FROM AUTHOR]

Abstract: This research effort has two orientations. First it aims in the application of hybrid soft computing techniques for the estimation of wood loss factor which is considered a very important feature for wood industry. This has been performed by employing fuzzy weighted Support Vector Machines (SVM), Global SVM and Artificial Neural Networks. The second part of this research focuses in the evaluation of the produced models by employing an innovative fuzzy logic method developed by our research team [10]. For this purpose, experimental data for two different wood species were used. The estimation of the dielectric properties of wood was done by using soft computing algorithms as a function of both ambient electro-thermal conditions applied during drying of wood and basic wood chemistry. The best fit neural models that were developed previously were compared to the current approaches in order to determine the optimal ones. [Copyright &y& Elsevier]

This study examines a hybrid Artificial Intelligence modelling approach in terms of its classification efficiency in the abdominal pain disease, especially in childhood. The classification model consists of a series of efficient Artificial Neural Network (ANN) architectures which have been evaluated by the application of an innovative Fuzzy Algebraic Information System (FAIS) [10]. FAIS offers a flexible approach by employing fuzzy sets and relations, fuzzy intensification and dilution techniques towards the assessment of neural models under different levels of accuracy and under various perspectives. The fact that FAIS produces an overall ANN evaluation index and also individual partial evaluation indices corresponding to each separate output neuron, makes it very useful for the specific disease where even the slightest error can cause the unnecessary operative treatment of the disease. In the examined cases, the produced ANN models have proven to perform classification with success. The whole approach comprises of a mixture of ANN development techniques together with Fuzzy modelling functions and relations and thus it can be considered as a Hybrid one. [ABSTRACT FROM AUTHOR]

This paper introduces the prototype of a generic fuzzy algebraic framework, that aims to serve as a holistic modeling approach of kinematics. Moreover, it detects paradoxes and uncertainties when the involved features of the timeseries have "unconventional" values. All well accepted models are perfectly capturing and clearly describing the spatiotemporal characteristics of a moving object's (MO) status, when its actual distance from the observer is conventional, i.e., "insignificant compared to the magnitude of light years". Let us consider the concept that emerges by the following Boolean expression1 (BE1): "Velocity is significant compared to the speed of light (SIV_cSL) AND distance between observer and moving body is significant compared to light years (SID_cLY)". The only restriction in the above BE1 Boolean expression is that velocity would always be less than C. So far, BE1 is not considered to be true in the models that are employed to build our scientific physics studies. This modeling effort performs mining of kinematics phenomena for which BE1 is true. This approach is quite innovative, in the sense that it reveals paradoxes and uncertainties, and it reaches the following conclusions: When a particle is moving inside hypersurfaces characterized by any type of BE1′s negation, our existing kinematics' models can survive. In the opposite case, we are gradually led to paradoxes and uncertainties. The gradual and smooth transition from the one state to the other as well as the importance of the aforementioned limitations, can be inferred-modeled by employing fuzzy logic. [ABSTRACT FROM AUTHOR]

This is a preliminary attempt towards a wider use of Artificial Neural Networks in the management of mountainous water supplies. It proposes a model to be used effectively in the estimation of the average annual water supply, in each mountainous watershed of Cyprus. This is really a crucial task, especially during the long dry summer months of the island. On the other hand the evaluation of the potential torrential risk due to high volume of water flow in the winter season is also very important. Data (from 1965–1993) from 78 measuring stations located in the 70 distinct watersheds of Cyprus were used. This data volume was divided in the training subset comprising of 60 cases and in the testing subset containing 18 cases. The input parameters are the area of the watershed, the average annual and the average monthly rain-height, the altitude and the slope in the location of the measuring station. Consequently three structural and two dynamic factors are considered. After several and extended training-testing efforts a Modular Artificial Neural Network was determined to be the optimal one. [Copyright &y& Elsevier]

Abstract: Forecasting, using historic time-series data, has become an important tool for fisheries management. ARIMA modeling, Modeling for Optimal Forecasting techniques and Decision Support Systems based on fuzzy mathematics may be used to predict the general trend of a given fish landings time-series with increased reliability and accuracy. The present paper applies these three modeling methods to forecast anchovy fish catches landed in a given port (Thessaloniki, Greece) during 1979–2000 and hake and bonito total fish catches during 1982–2000. The paper attempts to assess the model''s accuracy by comparing model results to the actual monthly fish catches of the year 2000. According to the measures of forecasting accuracy established, the best forecasting performance for anchovy was shown by the DSS model (MAPE=28.06%, RMSE=76.56, U-statistic=0.67 and R 2 =0.69). The optimal forecasting technique of genetic modeling improved significantly the forecasting values obtained by the selected ARIMA model. Similarly, the DSS model showed a noteworthy forecasting efficiency for the prediction of hake landings, during the year 2000 (MAPE=2.88%, RMSE=13.75, U-statistic=0.19 and R 2 =0.98), as compared to the other two modeling techniques. Optimal forecasting produced by combined modeling scored better than application of the simple ARIMA model. Overall, DSS results showed that the Fuzzy Expected Intervals methodology could be used as a very reliable tool for short-term predictions of fishery landings. [Copyright &y& Elsevier]

Describes the basic design principles of a heuristic expert system for forest fire forecasting in Greece. Construction of a mathematical model to describe the annual number of forest fires in the country; Factors affecting the number of forest fires; Importance and effectiveness of the expert system in the prevention planning of forest fires.

One of the most critical aspects in the seismic behavior or reinforced concrete (RC) structures pertains to beam–column joints. Modern seismic design codes dictate that, if failure is to occur, then this should be the ductile yielding of the beam and not brittle shear failure of the joint, which can lead to sudden collapse and loss of human lives. To this end, it is imperative to be able to predict the failure mode of RC joints for a large number of structures in a building stock. In this research effort, various ensemble machine learning algorithms were employed to develop novel, robust classification models. A dataset comprising 486 measurements from real experiments was utilized. The performance of the employed classifiers was assessed using Precision, Recall, F1-Score, and overall Accuracy indices. N-fold cross-validation was employed to enhance generalization. Moreover, the obtained models were compared to the available engineering ones currently adopted by many international organizations and researchers. The novel ensemble models introduced in this research were proven to perform much better by improving the obtained accuracy by 12–18%. The obtained metrics also presented small variability among the examined failure modes, indicating unbiased models. Overall, the results indicate that the proposed methodologies can be confidently employed for the prediction of the failure mode of RC joints. [ABSTRACT FROM AUTHOR]

Deep learning architectures are the most effective methods for analyzing and classifying Ultra-Spectral Images (USI). However, effective training of a Deep Learning (DL) gradient classifier aiming to achieve high classification accuracy, is extremely costly and time-consuming. It requires huge datasets with hundreds or thousands of labeled specimens from expert scientists. This research exploits the MAML++ algorithm in order to introduce the Model-Agnostic Meta-Ensemble Zero-shot Learning (MAME-ZsL) approach. The MAME-ZsL overcomes the above difficulties, and it can be used as a powerful model to perform Hyperspectral Image Analysis (HIA). It is a novel optimization-based Meta-Ensemble Learning architecture, following a Zero-shot Learning (ZsL) prototype. To the best of our knowledge it is introduced to the literature for the first time. It facilitates learning of specialized techniques for the extraction of user-mediated representations, in complex Deep Learning architectures. Moreover, it leverages the use of first and second-order derivatives as pre-training methods. It enhances learning of features which do not cause issues of exploding or diminishing gradients; thus, it avoids potential overfitting. Moreover, it significantly reduces computational cost and training time, and it offers an improved training stability, high generalization performance and remarkable classification accuracy. [ABSTRACT FROM AUTHOR]

This study emphasizes the explainability of machine learning (ML) models in predicting the seismic response of rigid rocking structures, specifically using the LightGBM algorithm. By employing SHapley Additive exPlanations (SHAP), partial dependence plots (PDP), and accumulated local effects (ALE), a comprehensive feature importance analysis has been performed. This revealed that ground motion parameters, particularly peak ground acceleration (PGA), are critical for predicting small rotations, while structural parameters like slenderness and frequency are more significant for larger rotations. Utilizing an extensive dataset generated from nonlinear time history analyses, the trained LightGBM model demonstrated high accuracy in estimating the maximum rotation angle of rigid blocks under natural ground motions. The study also examined the sensitivity of model performance to lower bound thresholds of the target variable, revealing that reduced feature sets can maintain predictive performance effectively. These findings advance ML-based modeling of seismic rocking responses, providing interpretable and accurate models that enhance our understanding of rocking structures' dynamic behavior, which is crucial for designing resilient structures and improving seismic risk assessments. Future research will focus on incorporating additional parameters and exploring advanced ML techniques to further refine these models. [ABSTRACT FROM AUTHOR]

The analysis along with the modeling of passenger demand dynamic, which deem to have vital implications on the management and the operation within the entire aviation industry, are regarded to be an extreme challenge. However, air passenger demand introduces reliably complex non-linearity and non-stationarity. In this paper, we have tried to forecast aviation demand with the use of time series and deep learning techniques. We have developed air travel demand estimation and forecasting models, using classical Autoregressive Integrated Moving Average methods (ARIMA), Seasonal approaches (SARIMA) and Deep Learning Neural Networks (DLNN). Moreover, this research has performed a qualitative comparison of the aforementioned techniques aiming to serve as a guideline toward the choice of the optimal modeling approach. The experimental results have shown that the proposed approaches can provide significant assistance in forecasting air travel demand, by producing both accurate and robust results. Therefore, this approach can be utilized as a tool to be reliably employed for air passenger demand forecasting. [ABSTRACT FROM AUTHOR]

Time-series analysis and forecasting problems are generally considered as some of the most challenging and complicated problems in data mining. In this work, we propose a new complete framework for enhancing deep learning time-series models, which is based on a data preprocessing methodology. The proposed framework focuses on conducting a sequence of transformations on the original low-quality time-series data for generating high-quality time-series data, "suitable" for efficiently training and fitting a deep learning model. These transformations are performed in two successive stages: The first stage is based on the smoothing technique for the development of a new de-noised version of the original series in which every value contains dynamic knowledge of the all previous values. The second stage of transformations is performed on the smoothed series and it is based on differencing the series in order to be stationary and be considerably easier fitted and analyzed by a deep learning model. A number of experiments were performed utilizing time-series datasets from the cryptocurrency market, energy sector and financial stock market application domains on both regression and classification problems. The comprehensive numerical experiments and statistical analysis provide empirical evidence that the proposed framework considerably improves the forecasting performance of a deep learning model. [ABSTRACT FROM AUTHOR]

Pursuing "intelligent justice" necessitates an impartial, productive, and technologically driven methodology for judicial determinations. This scholarly composition proposes a framework that harnesses Artificial Intelligence (AI) innovations such as Natural Language Processing (NLP), ChatGPT, ontological alignment, and the semantic web, in conjunction with blockchain and privacy techniques, to examine, deduce, and proffer recommendations for the administration of justice. Specifically, through the integration of blockchain technology, the system affords a secure and transparent infrastructure for the management of legal documentation and transactions while preserving data confidentiality. Privacy approaches, including differential privacy and homomorphic encryption techniques, are further employed to safeguard sensitive data and uphold discretion. The advantages of the suggested framework encompass heightened efficiency and expediency, diminished error propensity, a more uniform approach to judicial determinations, and augmented security and privacy. Additionally, by utilizing explainable AI methodologies, the ethical and legal ramifications of deploying intelligent algorithms and blockchain technologies within the legal domain are scrupulously contemplated, ensuring a secure, efficient, and transparent justice system that concurrently protects sensitive information upholds privacy. [ABSTRACT FROM AUTHOR]

Mining hidden knowledge from available datasets is an extremely time-consuming and demanding process, especially in our era with the vast volume of high-complexity data. Additionally, validation of results requires the adoption of appropriate multifactor criteria, exhaustive testing and advanced error measurement techniques. This paper proposes a novel Hybrid Fuzzy Semi-Supervised Forecasting Framework. It combines fuzzy logic, semi-supervised clustering and semi-supervised classification in order to model Big Data sets in a faster, simpler and more essential manner. Its advantages are clearly shown and discussed in the paper. It uses as few pre-classified data as possible while providing a simple method of safe process validation. This innovative approach is applied herein to effectively model the air quality of Athens city. More specifically, it manages to forecast extreme air pollutants’ values and to explore the parameters that affect their concentration. Also it builds a correlation between pollution and general climatic conditions. Overall, it correlates the built model with the malfunctions caused to the city life by this serious environmental problem. [ABSTRACT FROM AUTHOR]

The analysis of air quality and the continuous monitoring of air pollution levels are important subjects of the environmental science and research. This problem actually has real impact in the human health and quality of life. The determination of the conditions which favor high concentration of pollutants and most of all the timely forecast of such cases is really crucial, as it facilitates the imposition of specific protection and prevention actions by civil protection. This research paper discusses an innovative threefold intelligent hybrid system of combined machine learning algorithms HISYCOL (henceforth). First, it deals with the correlation of the conditions under which high pollutants concentrations emerge. On the other hand, it proposes and presents an ensemble system using combination of machine learning algorithms capable of forecasting the values of air pollutants. What is really important and gives this modeling effort a hybrid nature is the fact that it uses clustered datasets. Moreover, this approach improves the accuracy of existing forecasting models by using unsupervised machine learning to cluster the data vectors and trace hidden knowledge. Finally, it employs a Mamdani fuzzy inference system for each air pollutant in order to forecast even more effectively its concentrations. [ABSTRACT FROM AUTHOR]

Air pollution is the problem of adding harmful substances or other agents into the atmosphere and it is caused by industrial, transport or household activities. It is one of the most serious problems of our times and the determination of the conditions under which we have extreme pollutants' values is a crucial challenge for the modern scientific community. The innovative and effective hybrid algorithm designed and employed in this research effort is entitled Easy Hybrid Forecasting (EHF). The main advantage of the EHF is that each forecasting does not require measurements from sensors, other hardware devices or data that require the use of expensive software. This was done intentionally because the motivation for this work was the development of a hybrid application that can be downloaded for free and used easily by everyday common people with no additional financial cost, running in devices like smart phones. From this point of view it does not require data from sensors or specialized software and it can offer people reliable information about extreme cases. [ABSTRACT FROM AUTHOR]

Over the last few years, one major research direction of information retrieval includes user behaviour prediction. For that reason many models have been proposed aiming at the accurate evaluation of information retrieval systems and the best prediction of web search users׳ behaviour. In this paper we propose a new evaluation metric for information retrieval systems which employs two relevance factors; a relevance judgement grade and a popularity grade that represents users׳ vote for a document. We show that this new metric performs better than other evaluation metrics when expressing user behaviour. Moreover, in order to test the performance of our metric on different and scalable ranking algorithms, we develop a pairwise text similarity algorithm using cosine similarity, implemented on the MapReduce model, and then perform experiments on rankings generated by the algorithm. [ABSTRACT FROM AUTHOR]

Abstract: We present a syntactic recognition theory for graphs with fuzzy edges and fuzzy nodes by employing the algebraic structure of magmoids. Several examples are illustrated and it is proved that the -cut of a recognizable set of fuzzy graphs is not always recognizable as a set of crisp graphs. The described theory is then applied for the recognition of fuzzy cognitive maps and the recognizability of interesting examples of sets of fuzzy cognitive maps is examined. [Copyright &y& Elsevier]

Abstract: Self-Organizing Feature Maps (SOFMs) are extensively used for dimensionality reduction and rendering of inherent data structures. A novel model of a SOFM based on the notion of aggregate/reduced ordering (R-ordering) of vector sets is proposed and applied to the segmentation of color images. The so-called Cross-Order Distance Matrix is defined in order to measure the similarity between local histograms corresponding to ordered sets of color vectors. Color images are regarded as two-dimensional (2-D) vector fields. Basic image processing algorithms are modified since color is represented as a vector instead of a scalar gray level variable. Operators utilizing several distance and similarity measures are adopted in order to quantify the color distribution within a sliding window. The proposed window-based SOFM uses sets of one, two and more color vectors in order to approximate local color distributions within sliding windows. Each set represents a separate node of the SOFM that is trained according to a sequence of ordered input sets of color vectors. A 3×3 window is used to capture color components in uniform color space (L ⁎ u ⁎ v ⁎). The color vectors within the sliding window are R-ordered. The neuron featuring the smallest aggregated distance (similarity) is activated during training. Segmentation results suggest that clustered nodes represent populations of pixels in rather compact segments of the images featuring similar texture. [Copyright &y& Elsevier]

Wood properties, including bending stiffness and strength, basic density and microfibril angle were experimentally obtained for six aspen and six hybrid poplar clones grown in Western Canada. Data analysis attempted to establish a relationship between wood mechanical properties and intrinsic wood attributes by means of artificial neural networks (ANN) and ε-regression support vector machines (ε-rSVM) employing a 5-fold cross validation approach (5-fold CV). Initial results for strength were acceptable, but require further improvement. Estimations of stiffness results (MOE) were inferior to those of strength (MOR) due to the fact that in several regression cases, the developed model worked well for narrow windows of data, but failed on a large scale due to the high variations in the values of the input data vectors. In such cases, the result is probably the development of regression with uneven performance throughout the input data set, and therefore the modeling capacity is poor. To avoid this predicament, different neural networks with one output neuron were developed in order to estimate either the stiffness or the strength, and at the same time the approximation capabilities of ε-rSVM were employed. In both methods, 5-fold CV was carried out in order to attain a more generalized solution by eliminating the boundary effect phenomena and by avoiding local behavior of the global support vector regression. The resultant models were evaluated by common metrics. The best ANN for the estimation of strength in combination with 5-fold CV, was a modular back propagation with average R2=0.70, and mean root mean square error (MRMSE) equal to 0.19 and mean average percent error (MAPE) equal to 12.5%. The Gaussian kernel 5-fold CV ε-rSVM estimated MOR with similar accuracy. The best 5-fold CV ANN for MOE estimation was a feed forward back propagation one, with average R2=0.60, MRMSE equal to 0.23 and MAPE equal to 41.5%, which was better than all other kernel methods employed. [ABSTRACT FROM AUTHOR]

Abstract: The present manuscript is the result of research conducted towards a wider use of artificial neural networks in the management of mountainous water supplies. The novelty lies on the evolutionary clustering of time-series data which are then used for the training and testing of a neural object, applying meta-heuristics in the neural training phase, for the management of water resources and for torrential risk estimation and modelling. It is essentially an attempt towards the development of a more credible forecasting system, exploiting an evolutionary approach used to interpret and model the significance which time-series data pose on the behavior of the aforementioned environmental reserves. The proposed model, designed such as to effectively estimate the average annual water supply for the various mountainous watersheds, accepts as inputs a wide range of meta-data produced via an evolutionary genetic process. The data used for the training and testing of the system refer to certain watersheds spread over the island of Cyprus and span a wide temporal period. The method proposed incorporates an evolutionary process to manipulate the time-series data of the average monthly rainfall recorded by the measuring stations, while the algorithm includes special encoding, initialization, performance evaluation, genetic operations and pattern matching tools for the evolution of the time-series into significantly sampled data. [Copyright &y& Elsevier]