Discharge summaries are free-text notes that outline the hospital stay of a patient, including diagnoses, treatments, and follow-up care recommendations. Through the use of International Classification of Diseases (ICD) coding, they play a vital role in communicating clinical data exchange and reimbursement. However, manually assigning ICD codes to discharge summaries can be labor-intensive and prone to errors due to the unstructured narrative format, variations in terminology, potential inaccuracies in documentation, and the limitations of the ICD coding system in capturing the complexity of patient conditions. This paper aims to improve the results of state-of-the-art automated medical coding machine learning models with two preprocessing techniques. The first technique involves data management to maximize the amount of usable data by mapping ICD-9 codes in the MIMIC-IV dataset to their corresponding ICD-101. With a similar goal, the second technique leverages large language models (LLMs) for the generation of multiple synthetic discharge summaries corresponding to ICD codes with few appearances in the dataset. The results of this extensive preprocessing are evaluated with the use of several well-established and state-of-the-art deep learning models, which present significant improvement.

In this work, we investigate the relation between weather and atmospheric conditions, and the biomarkers Platelet-Lymphocyte Ratio (PLR) and Neutrophil-Lymphocyte Ratio (NLR). In more detail, we are interested in identifying how significant weather and atmospheric changes can affect people’s health over time. For this purpose we introduce a new dataset that combines data from a recent population study along with historical meteorological measurements. We initially conduct timeseries exploratory analysis and then focus on changepoint detection and analysis of multivariate time series data in an effort to identify major incidents and their consequences. Interestingly, the results showed that the NLR and PLR biomarkers are significantly related to the major changes in weather and atmospheric conditions.

This paper introduces a comprehensive framework for clustering analysis, centered on a novel incremental silhouette score calculation designed specifically for hierarchical clustering. This innovative method significantly reduces the computational complexity of silhouette evaluation, transforming the process from O(K N) to effectively O(N) for K hierarchical configurations (demonstrated by an over 100-fold speedup in our tests) making it feasible for large-scale datasets and enabling efficient cluster number estimation within hierarchical clustering scenarios. Building on this, we revisit and enhance the Principal Direction Divisive Partitioning (IPDDP) algorithm, proposing principal component analysis-maximum margin divisive clustering (PCA-MMDC), which utilizes multiple principal components for more accurate data partitioning, and PCA-MMDC-sc, which incorporates a scatter-based cluster selection for improved balance. These are integrated into a hybrid clustering strategy that combines the strengths of incremental silhouette calculation and the enhanced algorithms, allowing for robust cluster identification and effective management of noise and outliers. Experimental results on synthetic and real-world datasets demonstrate notable improvements in clustering accuracy (achieving an average Adjusted Rand Index (ARI) increase of over 10 percentage points on custom noisy synthetic datasets compared to K-Means) and computational efficiency. While the choice of principal components in PCA-MMDC presents a parameter, the overall framework offers a scalable and robust solution for complex clustering tasks, with future work aimed at adaptive parameter selection and extending incremental calculations to other validation metrics.

This research looks at the use of long-short-term memory (LSTM) networks to predict psychosis, in patients within the schizophrenia spectrum, based on Heart Rate Variability (HRV) data acquired from wearable devices. Our primary objective is to test whether the personalized relapse prediction remains accurate when eliminating the artifact-removal preprocessing. We first analyzed 7 patients sleep HRV recordings (7–113 days each), and then validated the methodology on a separate 30-patient psychosis cohort from another clinical setting. In this framework, HRV characteristics are computed directly from the unprocessed time series for each patient, without artifact correction, at any stage prior to feature extraction. HRV features are then, organized into sequential inputs, where the model uses the first n−1 steps to predict the nth step. This structure allows the model to learn from temporal relationships and individual physiological trends in HRV. The sequence length used by the LSTM is optimized for each patient, allowing the model to account for individual physiological patterns. Through this, on the 7 patient cohort, the LSTM model reaches a mean F1 score of 0.9817, marking its strength across diverse patient profiles. The method provides predictions for each individual by learning from their own HRV history. Results using both traditional and state-of-the-art noise-removal techniques, like wavelet and GAN-based denoising, showed that omitting these data cleaning steps did not reduce, and in some cases even improved, prediction accuracy. These findings indicate that, for psychosis prediction based on wearable HRV data, additional data cleaning may not be necessary.

Abstract The utilization of large language models (LLMs) in research is becoming increasingly prevalent, as they offer advanced capabilities in processing and generating human-like text. However, this advancement comes with a significant trade-off in terms of time and computational costs. In this paper, we demonstrate that analyzing large text datasets with the use of LLMs can be performed efficiently in terms of both time and energy. For this purpose, we utilize the Llama pre-trained model. In more detail, we study the topic modeling task where the goal is to discover and identify topics in large text corpora. The basis of our approach is a hierarchical divisive clustering technique that clusters the data based on their semantic similarity, after employing a Sentence-BERT encoder, pre-trained on a variety of data across different tasks. Then, using an LLM, we identify topics for representative samples from each cluster. Additionally, we introduce a new evaluation method that leverages the capabilities of LLMs to assess the alignment between discovered topics and ground truth labels, providing a robust validation metric. Our findings indicate that it is possible to effectively reduce the computational cost of the topic modeling process compared to the direct application of LLMs and BERTopic, while simultaneously enhancing inference time and overall efficiency, thereby surpassing the current state-of-the-art capabilities of BERTopic.

Single-cell RNA sequencing (scRNA-seq) represents a paradigm shift in understanding the complexities of cellular functions and states at an individual level. Despite its transformative potential in revealing cellular heterogeneity and mechanisms, scRNA-seq data poses significant analytical challenges due to its high dimensionality, noise, and sparsity. This paper introduces Hyperdimensional Computing (HDC) as an alternative classification approach, suited for addressing these challenges. HDC, characterized by its robustness to noise and efficiency in high-dimensional spaces, accommodates the sparsity and variability inherent in scRNA-seq for the classification and analysis of scRNA-seq data offerring a promising pathway. This study aims to explore the application of HDC in scRNA-seq data classification, benchmarking its performance against traditional methods, and discussing its potential to enhance single-cell transcriptomic analysis. This work not only contributes to advancing the computational methodologies available for scRNA-seq analysis but also establishes a foundation for future research into scalable and robust data-driven approaches in genomics.

In the quest to enhance predictive models for schizophrenia spectrum disorders, Long Short-Term Memory networks (LSTM) have been pivotal due to their adept han-dling of temporal data sequences. This ability to process time-dependent data, enhances the monitoring of longitudinal patterns that are indicative of relapse. This study leverages LSTMs to analyze Heart Rate Variability (HRV), a key marker in neuropsychiatric evaluations, focusing on individual patient data. Emphasizing individualization, each LSTM model is carefully tai-lored to reflect the unique behavioral and physiological patterns of the patient. By customizing the analysis to accommodate the distinct fluctuations in HRV, LSTMs have shown a remarkable capacity to decode the complex, patient-specific patterns, as evidenced by a notable predictive average score of$0.972\pm 0.068$. This figure not only validates the LSTM model's effectiveness in grappling with the diverse aspects of psychosis but also highlights its exceptional ability to adapt to the unique temporal dynamics inherent in each patient's mental health progression.

The rapid growth of biomedical literature necessitates efficient Information Extraction systems able to identify relevant knowledge for various biological applications, such as understanding gene regulation by microRNA (miRNA). In this study, we employed a Large Language Model, specifically GPT-3.5 (version 0301), in conjunction with BERN2 for miRNA-gene interaction extraction from paper titles and abstracts. We optimized our approach using an initial dataset of about a thousand molecular biology papers and subsequently evaluated its performance on a manually curated dataset of 400 papers, achieving an accuracy of 82-85%. Driven by the promising results and the practical utility of our method, we applied the system to a large dataset of 39,000 papers. The extracted miRNA-gene interactions, combined with a Natural Language Processing approach, were included in the TarBase v9 database. Our findings demonstrate the potential of Large Language Models in biomedical Information Extraction tasks and highlight the limitations of the current gene and miRNA recognition systems, which hinder further improvements in accuracy.

The usage of social media is increasing daily. Users tend to interact with social media content through a plethora of devices, from laptops and personal computers to smartphones, tablets, and wearables. However, these devices have different technical characteristics that affect the overall user experience. In this work, we introduce SocialNetView, a mobile application to acquire and present the user with information based on their usage of social media and provide recommendations on the available and most efficient network connection. We developed the application for smartphones and smartwatches. In order to examine its performance, we conducted experiments with 30 users on a University campus. We evaluate the efficiency of the proposed approach while user location and historical social media interaction are used for the network selection. According to experimental data, SocialNetView can deliver higher Quality of Experience (QoE) to the user, when it is used for network recommendation.

Ensemble learning has been proven effective in enhancing classification accuracy by aggregating predictions from multiple base classifiers. This paper introduces a novel approach to augmenting weak projection-based classifiers using a Neural Network within a stacking ensemble framework. The proposed method capitalizes on the diverse strengths of both linear and complex models, harnessing the interpretability of projection-based classifiers, while leveraging the pattern recognition capabilities of Neural Networks. We present a comprehensive algorithm involving dataset selection, preprocessing, base model training, meta-feature generation, and Neural Network architecture design and training. Extensive experiments demonstrate the efficiency of our approach on a variety of high-dimensional biomedical datasets. Our results showcase significant accuracy improvements over standalone projection-based classifiers and conventional ensemble methods. We analyze the interpretability of the hybrid ensemble, shedding light on the insights drawn from its Neural Network component. This work not only advances the field of ensemble learning, but also underscores the potential of combining disparate classifier paradigms to achieve superior predictive performance. The code for this study is available1.1.https://github.com/panagiotisanagnostou/NNv-MRPV

The plethora of online applications and mobile communication systems helps in the increase of the everyday usage of social networks. More people tend to use social media and join social networks. At the same time, several people suffer from mental stress while they either receive or create social media content. In this work, we examine the feasibility of detecting stress related to social media content, with the use of wearable devices. We use Electrodermal Activity (EDA) signals collected from wrist-based devices and we examine any correlation between them and the social media content. We conducted experiments in different environments with self-reported data from the users. According to preliminary results, the relationship between EDA and stress levels related to social media content can be identified.

One of the major concerns regarding currently proposed public blockchain systems relates to the feasible transaction processing rate. It is common for such systems to limit this rate to maintain the required levels of security and decentralisation. State channels are an approach to overcome this limitation, as they aim to decrease the required on-chain transactions for a given application and thus indirectly increase the capacity (in terms of applications) of public blockchain systems. In the present paper, we propose a state channel design that, through the use of RSA accumulators, operates on a compact state structure. This scheme is optimal for applications whose state includes large sets of elements. The novel state channel design is presented by analysing all state channel operations and how they have to be revised. The security of the design is discussed, while a practical use case scenario regarding the use of the design for an on-chain asset (e.g., non-fungible tokens) exchange application is also analysed.

The Covid-19 pandemic made a significant impact on society, including the widespread implementation of lockdowns to prevent the spread of the virus. This measure led to a decrease in face-to-face social interactions and, as an equivalent, an increase in the use of social media platforms, such as Twitter. As part of Industry 4.0, sentiment analysis can be exploited to study public attitudes toward future pandemics and sociopolitical situations in general. This work presents an analysis framework by applying a combination of natural language processing techniques and machine learning algorithms to classify the sentiment of each tweet as positive, or negative. Through extensive experimentation, we expose the ideal model for this task and, subsequently, utilize sentiment predictions to perform time series analysis over the course of the pandemic. In addition, a change point detection algorithm was applied in order to identify the turning points in public attitudes toward the pandemic, which were validated by cross-referencing the news report at that particular period of time. Finally, we study the relationship between sentiment trends on social media and, news coverage of the pandemic, providing insights into the public's perception of the pandemic and its influence on the news.

In recent years, feature selection has become an increasingly active field of data science and machine learning research. Most of the datasets that are being used nowadays for various machine learning tasks consist of thousands of features (columns), which make them extremely complex and difficult to work with. In this paper, we propose a feature selection methodological pipeline that can be used to reduce the complexity of high dimensional datasets through the elimination of redundant and/or non-informative features as well as to improve the performance of machine learning models which are trained on high dimensional datasets. The proposed method has been applied to high-dimensional biomedical data and compared against a classic filter-based feature selection algorithm. Specifically, the method was applied to gene expression profiles of a single-cell RNA-seq dataset from healthy and infected by covid-19 human samples.

The task of understanding human behavior through intelligent systems is crucial in various domains from medical health and well-being to financial and social platforms. In this work, we propose a complete framework that takes advantage of collected sensor accelerometer data to generate a human activity behavioral model that can be supportive in predicting future development of human movement disabilities such as Osteoarthritis or even in the individual's rehabilitation after a surgery for Osteoarthritis. More precisely, we focus on estimating uncommon behaviors within daily activities as an indication for further examination. Challenge-point of the proposed methodology is the agnostic knowledge of different behaviours of individual's movement. Based on accelerometer sensor data collected from mobile devices, the proposed framework utilizes state-of-the-art Machine Learning models for Human Activity Recognition and introduces new Deep Hybrid Models for outlier detection suggesting a solid basis for further developments and wider applicability.

We are in the era of single-cell RNA sequencing technology, which offers a great potential for uncovering cellular differences with a higher resolution, shedding light in various complex biological processes and complex human diseases. However, such studies create extremely high dimensional data isolating expression profiles for thousands or even millions of cells. Consequently, dealing with single-cell RNA-seq (scRNA-seq) data is considered the main challenge for unsupervised clustering, which can be used in order to identify grouped cell types. Towards this direction, we present a framework that enhances hierarchical clustering utilizing Proximity Learning on Random Projected spaces (PLRP). The proposed method's efficiency lies in the fact that we exploit the distances from multiple significantly lower dimension spaces defined by Random Projections using ensembles of k-nearest neighbor searches. In the transformed data we applied hierarchical agglomerative clustering (HAC) improving significantly its performance when compared against using the original space. The performance of the proposed PLRP was evaluated in a publicly available experimental dataset with scRNA-seq expression profiles, against three well-established clustering tools. The results showed that our approach greatly enhances clustering performance exposing its applicability in ultra-high dimensions and imposing further development towards this direction.

We are in the big data era which has affected several domains including biomedicine and healthcare. This revolution driven by the explosion of biomedical data offers the potential for better understanding of biology and human diseases. An illustrative example is the emerging single-cell sequencing technologies, which isolate and measure each cell individually, taking a step beyond the traditional techniques where consider their measurements from a bulk of cell. Although big single-cell RNA sequencing (scRNA-seq) data promises valuable insights into the cellular level, their volume poses several challenges related to the ultra-high dimensionality. Furthermore, to further elucidate the potential of these data, more insight into gene regulatory networks (GRN) is required. Network-based approaches can tackle part of the inherent complexity of human diseases, however, the challenges related to the ultra-high dimensionality are increased. Towards this direction, we propose the NIRP, an algorithm that copes with the high dimensionality of scRNA-data using a workflow based on fast multiple random projections and a radius-based nearest neighbors search. NIRP infers a gene regulatory network (GRN) from big scRNA-seq data by transforming the original data space to a lower dimensions space and capturing the similarities among gene expressions. The network is further analyzed using a random walk approach in order to achieve dense subgraphs, active to the case under study. The performance of NIRP is evaluated in a real single-cell experimental study among three well-established GRN tools. Our results make NIRP a reliable tool, able to handle big single-cell data with ultra-high dimensionality and complexity. he main advantage of this method is that it is not affected by the volume, as much as it increases, since it transforms the data space to a specific low dimensional space.

Single-cell sequencing technologies offer a platform to explore deeper the complex diseases and biological processes. On parallel, Systems Biology approaches can tackle part of this complexity, since they are based on biological networks elucidating genotype to phenotype relationships in a more comprehensive framework. Hence, research community is shifting towards singlecell systems biology approaches to identify and validate disease-specific biomarkers. Under this perspective, we propose a methodological framework aimed to infer disease-specific subpathway activities, using cell signaling pathway topology and transcriptomics data derived from single-cell RNA-seq technologies. More specifically, we concatenated and integrated pathway network refining with single-cell expression profiles. Linear subpathways were extracted and a subpathway profile score was calculated taking into account the pathway information flow. The core of our method is XGBoost supervised classification based on the aforementioned score in order to identify important subpathways that control the rest of the network and cause major changes from a control to a disease state under study. The proposed methodology was applied to a real experimental study with single-cell RNA-seq expression profiles revealing changes in type 2 diabetes. Exported subpathways showed promising results, suggesting that the proposed approach can reliably identify subpathways, acting as potential disease-specific biomarkers.

The recent advent in Next Generation Sequencing has created a huge data source which offers a great potential for elucidating complex disease mechanisms and biological processes. A recent technology is the single-cell RNA sequencing, which allows transcriptomics measurements in individual cells, having promising results. However, such studies measure the entire genome for thousands of cells, creating datasets with extremely high dimensionality and complexity. Following this perspective, we propose a dimensionality reduction approach, called RGt-SNE, which visualizes single-cell RNA-seq data in two dimensions. Initially, RGt-SNE defines a cell-cell distance matrix based on Random Projections and Geodesic Distances. The first is used to define the pairwise cells distances in a low dimensional projected space avoiding the difficulties that exist in data with ultra-high dimensionality. The latter is used to better define the large pairwise cells distances. Subsequently, the t-SNE method is applied in the customized distance matrix for two dimensional visualization. RGt-SNE was evaluated in two real experimental single-cell RNA-seq data against three well-known methods, such as t-SNE, Multidimensional scaling, and ISOMAP. Outcomes provide the superiority of RGt-SNE suggesting it as a reliable tool for single-cell RNA-seq data analysis and visualization.

Convolutional Neural Networks (CNNs) have been established as substantial supervised methods for classification problems in many research fields. However, a large number of parameters have to be tuned to achieve high performance and good classification results. One of the most crucial parameter for the performance of a CNN is the learning rate (step) of the training algorithm. Although the heuristic search to tune the learning rate is a common practice, it is extremely time-consuming, considering the fact that CNNs require a significant amount of time for each training, due to their complex architectures and high number of weights. Approaches that integrate the adaptation of the initial learning rate in the optimization algorithm, manage to converge to high quality solutions and have been embraced by the research community. In this work, we propose an improvement of the recently proposed Adaptive Learning Rate algorithm (AdLR). The proposed learning rate adaptation algorithm (e-AdLR) exhibits excellent convergence properties and classification accuracy, while at the same time is fast and robust.

Biomedicine is undergoing a revolution driven by the explosion of biomedical data, which are generated by emerged medical imaging, sensor technologies and high-throughput technologies. An indicative example is the single cell sequencing technology which concerns the genome sequencing examination of hundreds of separate cells in a single tumor. Consequently, open challenges arising from this emerged technology and generally from the evolution of biomedical technologies under the big data perspective. Also, given the fact that approaches based on high-performance computing require high computing resources and advanced developers, solutions that reduce the problem complexity remain very attractive. Following this direction, in this paper a classification scheme based on Multiple Random Projections and Voting is presented. Random Projections offer a platform not only for a low computational time analysis by significantly reducing the data dimensionality, but also for an accurate analysis which may well exceed classical classification approaches. The proposed method was applied on real biomedical high dimensional data and compared against well-known classification schemes as to Random Projection-based cutting-edge methods. Specifically, we applied it on expression profiles for single-cell RNA-seq data from non-diabetic and type 2 diabetic human samples. Experimental results showed that based on simplistic tools we can create a computationally fast, simple, yet effective approach for biomedical Big Data analysis and knowledge discovery.

Recent sequencing technology breakthroughs have resulted in a dramatic increase in the amount of available sequencing data, enabling major scientific advances in biology and medicine. Nowadays, sequencing transcriptome data of single cells (scRNA-seq) are growing rapidly, posing new challenges in their analysis, mostly due to their high dimensionality. In this paper, we study the problem of visualizing such high-dimensional scRNA-seq data. A new visualization scheme is presented based on a customized distance matrix retrieved by applying independently Nearest Neighbors search through multiple Random Projections. The proposed method is compared against well-known dimensionality reduction and visualization techniques showing its capabilities and performance.

In the past few years, there has been a huge growth in Twitter sentiment analysis having already provided a fair amount of research on sentiment detection of public opinion among Twitter users. Given the fact that Twitter messages are generated constantly with dizzying rates, a huge volume of streaming data is created, thus there is an imperative need for accurate methods for knowledge discovery and mining of this information. Although there exists a plethora of twitter sentiment analysis methods in the recent literature, the researchers have shifted to real-time sentiment identification on twitter streaming data, as expected. A major challenge is to deal with the Big Data challenges arising in Twitter streaming applications concerning both Volume and Velocity. Under this perspective, in this paper, a methodological approach based on open source tools is provided for real-time detection of changes in sentiment that is ultra efficient with respect to both memory consumption and computational cost. This is achieved by iteratively collecting tweets in real time and discarding them immediately after their process. For this purpose, we employ the Lexicon approach for sentiment characterizations, while change detection is achieved through appropriate control charts that do not require historical information. We believe that the proposed methodology provides the trigger for a potential large-scale monitoring of threads in an attempt to discover fake news spread or propaganda efforts in their early stages. Our experimental real-time analysis based on a recent hashtag provides evidence that the proposed approach can detect meaningful sentiment changes across a hashtags lifetime.

This paper proposes a novel methodology for automatic detection and localization of gastrointestinal (GI) anomalies in endoscopic video frame sequences. Training is performed with weakly annotated images, using only image-level, semantic labels instead of detailed, and pixel-level annotations. This makes it a cost-effective approach for the analysis of large videoendoscopy repositories. Other advantages of the proposed methodology include its capability to suggest possible locations of GI anomalies within the video frames, and its generality, in the sense that abnormal frame detection is based on automatically derived image features. It is implemented in three phases: 1) it classifies the video frames into abnormal or normal using a weakly supervised convolutional neural network (WCNN) architecture; 2) detects salient points from deeper WCNN layers, using a deep saliency detection algorithm; and 3) localizes GI anomalies using an iterative cluster unification (ICU) algorithm. ICU is based on a pointwise cross-feature-map (PCFM) descriptor extracted locally from the detected salient points using information derived from the WCNN. Results, from extensive experimentation using publicly available collections of gastrointestinal endoscopy video frames, are presented. The data sets used include a variety of GI anomalies. Both anomaly detection and localization performance achieved, in terms of the area under receiver operating characteristic (AUC), were >80%. The highest AUC for anomaly detection was obtained on conventional gastroscopy images, reaching 96%, and the highest AUC for anomaly localization was obtained on wireless capsule endoscopy images, reaching 88%.

The recent technological advancements in cloud computing and the access in increasing computational power has led in undertaking the data processing derived by mobile devices. In particular, when these data are high dimensional this is indispensable, since the mobile device has to balance its processing functionalities to additional services. However, developing efficient algorithms could allow various types of analysis to be performed locally, avoiding the necessity of a constantly connected device. In this work, we present a methodology that combines lightweight dimensionality reduction and change detection techniques. The experimental results justify its impressive performance and subsequently its usefulness in several tasks.

This paper pursues a course of investigation of an approach to combine Evolutionary Computation and Data Mining for the location and computation of multiple local and global optima of an objective function. To accomplish this task we exploit the spatial concentration of the population members around the optima of the objective function. Such concentration regions are determined by applying clustering algorithms on the actual positions of the members of the population. Subsequently, the evolutionary search is confined in the interior of the regions discovered. To enable the simultaneous discovery of more than one global and local optima, we propose the use of clustering algorithms that also provide intuitive approximations for the number of clusters. Furthermore, the proposed scheme has often the potential of accelerating the convergence speed of the Evolutionary Algorithm, without the need for extra function evaluations.

In this work, we present a system, which uses computer vision techniques for human silhouette segmentation from video in indoor environments and a parametric 3D human model, in order to recognize the posture of the monitored person. The video data are acquired indoors from a fixed fish-eye camera in the living environment. The implemented 3D human model collaborates with a fish-eye camera model, allowing the calculation of the real human position in the 3D-space and consequently recognizing the posture of the monitored person. The paper discusses briefly the details of the human segmentation, the camera modeling and the posture recognition methodology. Initial results are also presented for a small number of video sequences.

The aim of this paper is the combination of an Evolutionary Algorithm and a Data Mining technique for the location and computation of multiple local and global optima of an objective function. To accomplish this task we exploit the spatial concentration of the population members around the optima of the objective function. Such concentration regions are determined by applying clustering algorithms on the actual positions of the members of the population. Subsequently, the evolutionary search is confined in the interior of the regions discovered. To enable the simultaneous discovery of more than one global and local optima, we propose the use of unsupervised clustering algorithms that also provide intuitive approximations for the number of clusters. Furthermore, as shown by the experimental analysis, the proposed scheme has often the potential of accelerating the convergence speed of the Evolutionary Algorithm, without the need for extra function evaluations.

A new family of Differential Evolution mutation strategies (DE/nrand) that are able to handle multimodal functions, have been recently proposed. The DE/nrand family incorporates information regarding the real nearest neighborhood of each potential solution, which aids them to accurately locate and maintain many global optimizers simultaneously, without the need of additional parameters. However, these strategies have increased computational cost. To alleviate this problem, instead of computing the real nearest neighbor, we incorporate an index-based neighborhood into the mutation strategies. The new mutation strategies are evaluated on eight well-known and widely used multimodal problems and their performance is compared against five state-of-the-art algorithms. Simulation results suggest that the proposed strategies are promising and exhibit competitive behavior, since with a substantial lower computational cost they are able to locate and maintain many global optima throughout the evolution process.

An active research direction in Particle Swarm Optimization (PSO) is the integration of PSO variants in adaptive, or self-adaptive schemes, in an attempt to aggregate their characteristics and their search dynamics. In this work we borrow ideas from adaptive filter theory to develop an “online” algorithm adaptation framework. The proposed framework is based on tracking the parameters of a multinomial distribution to capture changes in the evolutionary process. As such, we design a multinomial distribution tracker to capture the successful evolution movements of three PSO variants. Extensive experimental results on ten benchmark functions and comparisons with five state-of-the-art algorithms indicate that the proposed framework is competitive and very promising. On the majority of tested cases, the proposed framework achieves substantial performance gain, while it seems to identify accurately the most appropriate algorithm for the problem at hand.

Clustering of high dimensional data is a very important task in Data Mining. In dealing with such data, we typically need to use methods like Principal Component Analysis and Projection Pursuit, to find interesting lower dimensional directions to project the data and hence reduce their dimensionality in a manageable size. In this work, we propose a new criterion of direction interestingness, which incorporates information from the density of the projected data. Subsequently, we utilize the Differential Evolution algorithm to perform optimization over the space of the projections and hence construct a new hierarchical clustering algorithmic scheme. The new algorithm shows promising performance over a series of real and simulated data.

Differential evolution is a very popular optimization algorithm and considerable research has been devoted to the development of efficient search operators. Motivated by the different manner in which various search operators behave, we propose a novel framework based on the proximity characteristics among the individual solutions as they evolve. Our framework incorporates information of neighboring individuals, in an attempt to efficiently guide the evolution of the population toward the global optimum, without sacrificing the search capabilities of the algorithm. More specifically, the random selection of parents during mutation is modified, by assigning to each individual a probability of selection that is inversely proportional to its distance from the mutated individual. The proposed framework can be applied to any mutation strategy with minimal changes. In this paper, we incorporate this framework in the original differential evolution algorithm, as well as other recently proposed differential evolution variants. Through an extensive experimental study, we show that the proposed framework results in enhanced performance for the majority of the benchmark problems studied.

In recent years, the Particle Swarm Optimization has rapidly gained increasing popularity and many variants and hybrid approaches have been proposed to improve it. Motivated by the behavior and the proximity characteristics of the social and cognitive experience of each particle in the swarm, we develop a hybrid approach that combines the Particle Swarm Optimization and the Differential Evolution algorithm. Particle Swarm Optimization has the tendency to distribute the best personal positions of the swarm near to the vicinity of problem's optima. In an attempt to efficiently guide the evolution and enhance the convergence, we evolve the personal experience of the swarm with the Differential Evolution algorithm. Extensive experimental results on twelve high dimensional multimodal benchmark functions indicate that the hybrid variants are very promising and improve the original algorithm.

While data clustering has a long history and a large amount of research has been devoted to the development of clustering algorithms, significant challenges still remain. One of the most important challenges in the field is dealing with high dimensional datasets. The class of clustering algorithms that utilises information from Principal Component Analysis has proven very successful in such datasets. Unlike previous approaches employing principal components, in this paper we propose a technique that uses a quality criterion to select the most important dimension (projection). This criterion permits us to formulate the problem as an optimisation task over the space of projections. However, in high dimensional spaces this problem is hard to solve and analytic solutions are not available. Thus, we tackle this problem through the use of an evolutionary algorithm. The experimental results indicate that the proposed techniques are effective in both simulated and real data scenarios.

This papers proposes a novel self-adaptive scheme for the evolution of crucial control parameters in evolutionary algorithms. More specifically, we suggest to utilize the differential evolution algorithm to endemically evolve its own control parameters. To achieve this, two simultaneous instances of Differential Evolution are used, one of which is responsible for the evolution of the crucial user-defined mutation and recombination constants. This self-adaptive differential evolution algorithm alleviates the need of tuning these user-defined parameters while maintains the convergence properties of the original algorithm. The evolutionary self-adaptive scheme is evaluated through several well-known optimization benchmark functions and the experimental results indicate that the proposed approach is promising.

The hybridization and composition of different Evolutionary Algorithms to improve the quality of the solutions and to accelerate execution is a common research practice. In this paper we propose a hybrid approach that combines differential evolution mutation operators in an attempt to balance their exploration and exploitation capabilities. Additionally, a self-balancing hybrid mutation operator is presented, which favors the exploration of the search space during the first phase of the optimization, while later opts for the exploitation to aid convergence to the optimum. Extensive experimental results indicate that the proposed approaches effectively enhance DEpsilas ability to accurately locate solutions in the search space.

The Differential Evolution algorithm uses an elitist selection, constantly pushing the population in a strict downhill search, in an attempt to guarantee the conservation of the best individuals. However, when this operator is combined with an exploitive mutation operator can lead to premature convergence to an undesired region of attraction. To alleviate this problem, we propose the Non-Monotone Differential Evolution algorithm. To this end, we allow the best individual to perform some uphill movements, greatly enhancing the exploration of the search space. This approach further aids algorithm's ability to escape undesired regions of the search space and improves its performance. The proposed approach utilizes already computed pieces of information and does not require extra function evaluations. Experimental results indicate that the proposed approach provides stable and reliable convergence.

This paper investigates the performance of trading strategies identified through computational intelligence techniques. We focus on trading rules derived by genetic programming, as well as, generalized moving average rules optimized through differential evolution. The performance of these rules is investigated using recently proposed risk-adjusted evaluation measures and statistical testing is carried out through simulation. Overall, the moving average rules proved to be more robust, but genetic programming seems more promising in terms of generating higher profits and detecting novel patterns in the data.

The hybridization and combination of different Evolutionary Algorithms to improve the quality of the solutions and to accelerate execution is a common research practice. In this paper, we utilize Genetic Programming to evolve novel Differential Evolution operators. The genetic evolution resulted in parameter free Differential Evolution operators. Our experimental results indicate that the performance of the genetically programmed operators is comparable and in some cases is considerably better than the already existing human designed ones.

In this paper a new clustering operator for evolutionary algorithms is proposed. The operator incorporates the unsupervised k-windows clustering algorithm, utilizing already computed pieces of information regarding the search space in an attempt to discover regions containing groups of individuals located close to different minimizers. Consequently, the search is confined inside these regions and a large number of global and local minima of the objective function can be efficiently computed. Extensive experiments shown that the proposed approach is effective and reliable, and greatly accelerates the convergence speed of the considered algorithms.

Networks of spiking neurons can perform complex non-linear computations in fast temporal coding just as well as rate coded networks. These networks differ from previous models in that spiking neurons communicate information by the timing, rather than the rate, of spikes. To apply spiking neural networks on particular tasks, a learning process is required. Most existing training algorithms are based on unsupervised Hebbian learning. In this paper, we investigate the performance of the parallel differential evolution algorithm, as a supervised training algorithm for spiking neural networks. The approach was successfully tested on well-known and widely used classification problems.

Recent advances in microarray technologies have allowed scientists to discover and monitor the mRNA transcript levels of thousands of genes in a single experiment. The data obtained from microarray studies present a challenge to data analysis. In this paper, we design an expression-based classification method for acute leukemia. Different dimension reduction techniques are considered to tackle the very high dimensionality of this kind of data. Subsequently, the classification system employs artificial neural networks. The comparative results reported, indicate that high classification rates are possible and moreover that subsets of features that contribute significantly to the success of the neural classifiers can be identified.

A parallel, multi-population differential evolution algorithm for multiobjective optimization is introduced. The algorithm is equipped with a domination selection operator to enhance its performance by favouring non-dominated individuals in the populations. Preliminary experimental results on widely used test problems are promising. Comparisons with the VEGA approach are provided and discussed.

Parallel processing has emerged as a key enabling technology in modern computing. Recent software advances have allowed collections of heterogeneous computers to be used as a concurrent computational resource. In this work we explore how differential evolution can be parallelized, using a ring-network topology, so as to improve both the speed and the performance of the method. Experimental results indicate that the extent of information exchange among subpopulations assigned to different processor nodes, bears a significant impact on the performance of the algorithm. Furthermore, not all the mutation strategies of the differential evolution algorithm are equally sensitive to the value of this parameter.

A novel generalized theoretical result is presented that underpins the development of globally convergent first-order batch training algorithms which employ local learning rates. This result allows us to equip algorithms of this class with a strategy for adapting the overall direction of search to a descent one. In this way, a decrease of the batch-error measure at each training iteration is ensured, and convergence of the sequence of weight iterates to a local minimizer of the batch error function is obtained from remote initial weights. The effectiveness of the theoretical result is illustrated in three application examples by comparing two well-known training algorithms with local learning rates to their globally convergent modifications.

We present deterministic nonmonotone learning strategies for multilayer perceptrons (MLPs), i.e., deterministic training algorithms in which error function values are allowed to increase at some epochs. To this end, we argue that the current error function value must satisfy a nonmonotone criterion with respect to the maximum error function value of the M previous epochs, and we propose a subprocedure to dynamically compute M. The nonmonotone strategy can be incorporated in any batch training algorithm and provides fast, stable, and reliable learning. Experimental results in different classes of problems show that this approach improves the convergence speed and success percentage of first-order training algorithms and alleviates the need for fine-tuning problem-depended heuristic parameters.

A new theorem for the development and convergence analysis of supervised training algorithms with an adaptive learning rate for each weight is presented. Based on this theoretical result, a strategy is proposed to automatically adapt the search direction, as well as the step-size length along the resultant search direction. This strategy is applied to some well known local learning algorithms to investigate its effectiveness.

Abstmct- Evolutionary neural network training algorithms are presented. These algorithms are applied to train neural networks with weight values confined to a narrow band of integers. We constrain the weights and biases in the range [-2"-l + 1, 2k-1- 11, for k = 3,4,5, thus they can be represented by just k bits. Such neural networks are better suited for hardware implementation than the real weight ones. Mathematical operations that are easy to implement in software might often be very burdensome in the hardware and therefore more costly. Hardware-friendly algorithms are essential to ensure the functionality and cost effectiveness of the hardware implementation. To this end, in addition to the integer weights, the trained neural networks use threshold activation functions only, so hardware implementation is even easier. These algorithms have been designed keeping in mind that the resulting integer weights require less bits to be stored and the digital arithmetic operations between them are easier to be implemented in hardware. Obviously, if the network is trained in a constrained weight space, smaller weights are found and less memory is required. On the other hand, as we have found here, the network training procedure can be more effective and efficient when larger weights are allowed. Thus, for a given application a trade off between effectiveness and memory consumption has to be considered. Our intention is to present results of evolutionary algorithms on this difficult task. Based on the application of the proposed class of methods on classical neural network benchmarks, our experience is that these methods are effective and reliable. 1

Presents neural network training algorithms which are based on the differential evolution (DE) strategies introduced by Storn and Price (J. of Global Optimization, vol. 11, pp. 341-59, 1997). These strategies are applied to train neural networks with small integer weights. Such neural networks are better suited for hardware implementation than the real weight ones. Furthermore, we constrain the weights and biases in the range [-2/sup k/+1, 2/sup k/-1], for k=3,4,5. Thus, they can be represented by just k bits. These algorithms have been designed keeping in mind that the resulting integer weights require less bits to be stored and the digital arithmetic operations between them are more easily implemented in hardware. Obviously, if the network is trained in a constrained weight space, smaller weights are found and less memory is required. On the other hand, the network training procedure can be more effective and efficient when large weights are allowed. Thus, for a given application, a trade-off between effectiveness and memory consumption has to be considered. We present the results of evolution algorithms for this difficult task. Based on the application of the proposed class of methods on classical neural network benchmarks, our experience is that these methods are effective and reliable.

Training algorithms suitable to work under imprecise conditions are proposed. They require only the algebraic sign of the error function or its gradient to be correct, and depending on the way they update the weights, they are analyzed as composite nonlinear successive overrelaxation (SOR) methods or composite nonlinear Jacobi methods, applied to the gradient of the error function. The local convergence behavior of the proposed algorithms is also studied. The proposed approach seems practically useful when training is affected by technology imperfections, limited precision in operations and data, hardware component variations and environmental changes that cause unpredictable deviations of parameter values from the designed configuration. Therefore, it may be difficult or impossible to obtain very precise values for the error function and the gradient of the error during training.

A mathematical framework for the convergence analysis of the well known Quickprop method is described. The convergence of this method is analyzed. Furthermore, we present modifications of the algorithm that exhibit improved convergence speed and stability and at the same time, alleviate the use of heuristic learning parameters. Simulations are conducted to compare and evaluate the performance of a proposed modified Quickprop algorithm with various popular training algorithms. The results of the experiments indicate that the increased convergence rates, achieved by the proposed algorithm, affect by no means its generalization capability and stability.