Cortical neural connectivity demonstrates a small World network Topology (SW), yet its effects on neural information processing are still unclear. This work investigates the Echo State Networks (ESNs) performance in the learning task utilizing the Newman-Watts-Strogatz (NWS) graph structure as a reservoir topology. The Principal Component Analysis (PCA) technique for reservoir state dimensionality reduction effectively addresses the limitations associated with the complexity of the irregular SWT graph. Additionally, in this work, we investigate the improved ESN with SW Topology (iESN-SWT) as encoder and the Deep Neural Network (DNN) as decoder for deep readout. The iESN-SWT model is evaluated on five-time series benchmark datasets, which are Arabic Digits (AD), CMUsubject16 (CMU), Japanese Vowels (Jap. V), ECG (ECG), and Swedish Leaf (SL) datasets. Results show significant improvements in classification accuracy with 91.7 %, 93.1 %, 96.3 %, 79 %, and 65.8 %, respectively, for the datasets AD, CMU, Jap. V, ECG, and SL.

As Internet of Things (IoT) devices become increasingly interconnected, they are exposed to cybersecurity risks. A particularly critical threat is posed by poisoning attacks, where adversaries deliberately inject harmful gradients into the training process, thereby compromising the reliability and accuracy of the learned model. Existing detection methods attempt to mitigate this threat, but they often struggle to process the vast and heterogeneous data generated by IoT systems. While deep learning solutions have shown promise, they typically rely on centralized datasets, limiting scalability and effectiveness. Federated Learning (FL) has emerged as a compelling alternative, enabling decentralized model training without raw data sharing. However, FL remains highly vulnerable to distributed data poisoning, which can severely compromise the global model. In this work, we propose the Adaptive Federated Learning Approach for Detecting Poisoned Data(AFA-DPD). Our method introduces a proactive filtering layer that leverages the Isolation Forest algorithm to identify and exclude suspicious clients before training phase. Experimental results demonstrate that AFA-DPD enhances the robustness of recent state-of-the-art FL systems when combined.

Word Embeddings (WE) play a crucial role in capturing the meanings of words. They provide continuous vector representations that encode semantic and syntactic information. To accurately convert words into meaningful vectors, in this paper, we propose a novel approach called Soft EMBedding method (SoftEMB). SoftEMB combines the strengths of the Glove and Word2Vec methods through a Soft-Voting algorithm. The SoftEMD approach aims to improve the performance of word embedding, particularly in the context of Sentiment Analysis (SA) hybrid models. To evaluate the SoftEMD performance, we test it on various SA models based on CNN-LSTM, CNN-GRU, CNN-biLSTM, and CNN-bi-GRU. Our results demonstrate a substantial enhancement in accuracy when evaluating movie reviews, with scores of 88.29%, 88.33%, 88.27%, and 88.27%. Similarly, for Sentiment140 dataset, our proposal shows improved results, achieving accuracy rates of 83.27%, 82.78%, 82.76%, and 82.51%. These results highlight the significant progress made in accurately analyzing both movie reviews and the Sentiment140 dataset.

With the growth in services supplied over the internet, network infrastructure has become more exposed to cyber-attacks, particularly Distributed Denial of Service (DDoS) attacks, which can easily cause the disruption of services. The key factor for fighting against these attacks is the earlier separation and detection of the traffic in networks. In this paper, a novel approach, named Half Autoencoder-Stacked DNNs (HAE-SDNN) model, is proposed. We suggest using a Stacked Deep Neural Networks (SDNN) model. as a deep learning model, in order to detect DDoS attacks. Our approach allows feature selection from a preprocessed dataset using a Half AutoEncoder (HAE), resulting in a final set of important features. These features are subsequently used to train the DNNs that are stacked together by applying Softmax layer to combine their outputs. Experiments were performed on a benchmark cybersecurity dataset, named CICDDoS2017, containing various DDoS attack types. The experimental results demonstrate that the introduced model attained an overall accuracy rate of 99.95%. Moreover, the HAE-SDNN model outperformed existing models, highlighting its superiority in accurately classifying attacks.

This paper presents a novel method to enhance the predictive performance of the Echo State Network (ESN) model by adopting reservoir topology learning. ESNs are a type of Recurrent Neural Network (RNN) that have demonstrated considerable potential in various applications, but they can be challenging to train and optimize due to their random initialization. To improve the learning capabilities of ESNs and enhance their effectiveness in a broad range of predictive tasks, we utilize a structure learning algorithm. The proposed approach modifies the ESN reservoir's connectivity by applying techniques such as reversing, deleting, and adding new connections. We evaluate our proposal performance using both synthetic and real datasets, and our results indicate that it can substantially improve predictive accuracy compared to traditional ESNs.

Echo State Network (ESN) is a typical version of the Recurrent Neural Network model (RNN) which is characterized by sparse reservoir and simple linear output. It has been utilized in several applications, especially for time series forecasting. Nonetheless, the ESN has some drawbacks that are mainly related to the reservoir properties and initialization (weights and connection). Thus, creating an efficient ESN model represents a challenging task. Relying on the initial structure of ESN, we propose an improved version called Dual Input-ESN (DI-ESN). This work aims to optimize the prediction error. Experimental results demonstrate that the DI-ESN model outperforms existing improved ESN models in terms of prediction accuracy.

Echo State Network (ESN), as a paradigm of Reservoir Computing (RC), refers to a well-known Recurrent Neural Network (RNN). Its randomly generated reservoir represents the main reason for its ability of rapid learning. Nevertheless, designing a reservoir for a specific role constitutes a difficult task. To resolve the challenge of the reservoir structure design, in this paper, a new combination of two optimization methods, Particle Swarm Optimization (PSO) and Stochastic Gradient Descent (SGD), have been proposed to reach a higher performance. The resulted model was tested using Mackey Glass and NARMA 10 benchmarks. The experimentations proved that the suggested PSO-SGD-ESN model performs well in time series prediction tasks and outperforms the original one.

Concentric circles representation has been developed for visualizing the periodic character of temporal data set. It is particularly useful for visually interpreting periodic time-oriented patterns extracted by data mining techniques. However, this requires a cognitive study to support the transformation into the closest mental representation to reality. In this paper, the proposed information visualization tool is enhanced taking into account key human factors for temporal patterns perception and cognition. This allows facilitating visual analysis of data space to make the right decision by exerting a minimum of cognitive load. We based our work on the taxonomy proposed by Bloom of cognitive domain to improve the concentric circles technique.

The presence of large quantities of temporal data requires interactive analysis for decision-making. Interactive decision support system (DSS) based on knowledge discovery in databases (KDD) process proves to be useful. Temporal data visualization techniques are used in the KDD stages to increase the user participation as well as its confidence in the result in order to improve the decision support quality. Our applicative context is the fight against nosocomial infections in the intensive care unit.