Electrocardiogram (ECG) and electrocardiography are essential tools for monitoring heart activity and diagnosing cardiovascular diseases. However, the accuracy of ECG signals can be compromised by various types of noise, including environmental factors, patient movement, and electrical interference. Among the most significant noise sources affecting ECG signals are Baseline Wander (BW), Electrode Movements (EM), Motion Artifacts (MA), Additive White Gaussian Noise (AWGN), and Powerline Interference (PLI). This paper provides a detailed review of the challenges posed by these noise sources and explores advanced techniques for minimizing their impact on ECG signals. By employing signal processing methods such as filtering, adaptive algorithms, and wavelet transforms, autoencoders, and generative methods, these techniques aim to improve the reliability and accuracy of ECG data analysis. Enhanced noise reduction enables healthcare professionals to make more precise diagnoses and treatment decisions, ultimately contributing to better patient outcomes in cardiovascular care.

Electrocardiogram (ECG) signal analysis is a crucial tool for enhancing the efficacy of clinical diagnosis, particularly in detecting arrhythmias. However, its performance tends to degrade under the inter-patient paradigm, especially for minority sample categories. To address this issue and enhance the detection performance of these less represented classes within the inter-patient test protocol, this paper proposes a novel hybrid framework. This framework integrates specialized blocks from convolutional networks, namely DeepArr CNN, with sequence-to-sequence BiLSTM models. The proposed approach involves extracting local ECG features from a sequence of heartbeats utilizing DeepArr CNN. These extracted feature maps are then fed into the RNN encoder-decoder to facilitate fusion with the feature maps of neighboring heartbeats. Our proposed method, which adhered to the AMII standard of the MIT-BIH arrhythmia database and operated within the inter-patient paradigm, achieved impressive accuracy rates. Specifically, it yielded accuracy rates of 99.92% and 99.81% for three and five class scenarios, respectively. The performance measures across different classes are as follows: for the N class, sensitivity (SEN) is 99.81%, positive predictive value (PPV) is 99.73%, and specificity (SPEC) is 97.81%; for the S class, SEN is 92.97%, PPV is 96.01%, and SPEC is 99.85%; for the V class, SEN is 99.97%, PPV is 99.26%, and SPEC is 99.95%; and for the F class, SEN is 97.42%, PPV is 95.70%, and SPEC is 99.97%. Even when dealing with an unbalanced dataset, our proposed solution consistently yields remarkable outcomes. Specifically, it achieves an accuracy rate of 99.24% in the three-class scenario and 98.75% in the five-class scenario. These experimental results underscore the effectiveness of our S2SDeepArr model for ECG Classification under the inter-patient paradigm, as demonstrated across various experimental cases.

Proteins interact with each other in complex ways to perform significant biological functions. These interactions, known as protein-protein interactions (PPIs), can be depicted as a graph where proteins are nodes and their interactions are edges. The development of high-throughput experimental technologies allows for the generation of numerous data which permits increasing the sophistication of PPI models. However, despite significant progress, current PPI networks remain incomplete. Discovering missing interactions through experimental techniques can be costly, time-consuming, and challenging. Therefore, computational approaches have emerged as valuable tools for predicting missing interactions. In PPI networks, a graph is usually used to model the interactions between proteins. An edge between two proteins indicates a known interaction, while the absence of an edge means the interaction is not known or missed. However, this binary representation overlooks the reliability of known interactions when predicting new ones. To address this challenge, we propose a novel approach for link prediction in weighted protein-protein networks, where interaction weights denote confidence scores. By leveraging data from the yeast Saccharomyces cerevisiae obtained from the STRING database, we introduce a new model that combines similarity-based algorithms and aggregated confidence score weights for accurate link prediction purposes. Our model significantly improves prediction accuracy, surpassing traditional approaches in terms of Mean Absolute Error, Mean Relative Absolute Error, and Root Mean Square Error. Our proposed approach holds the potential for improved accuracy in predicting PPIs, which is crucial for better understanding the underlying biological processes.

Abstract Neural network quantization is a critical method for reducing memory usage and computational complexity in deep learning models, making them more suitable for deployment on resource‐constrained devices. In this article, we propose a method called BBPSO‐Quantizer, which utilizes an enhanced Bare‐Bones Particle Swarm Optimization algorithm, to address the challenging problem of mixed precision quantization of convolutional neural networks (CNNs). Our proposed algorithm leverages a new population initialization, a robust screening process, and a local search strategy to improve the search performance and guide the population towards a feasible region. Additionally, Deb's constraint handling method is incorporated to ensure that the optimized solutions satisfy the functional constraints. The effectiveness of our BBPSO‐Quantizer is evaluated on various state‐of‐the‐art CNN architectures, including VGG, DenseNet, ResNet, and MobileNetV2, using CIFAR‐10, CIFAR‐100, and Tiny ImageNet datasets. Comparative results demonstrate that our method delivers an excellent tradeoff between accuracy and computational efficiency.

The principles of social networking and the Internet of Things were combined to create the Social Internet of Things (SIoT) paradigm. Therefore, this paradigm cannot become widely adopted to the point where it becomes a well-established technology without a security mechanism to assure reliable interactions between SIoT nodes. A Trust Management (TM) model becomes a major challenge in SIoT systems to create a trust score for the network nodes' ranking. Regarding the defined TM models methodology, this score will persist for the subsequent transaction and will only be changed after some time has passed or after another transaction. However, a trust evaluation methodology must be able to consider the different constraints of the SIoT environments (dynamism and scalability) when building trust scores. Based on both event-driven and time-driven methods for trust update solutions, this model can identify which damaging nodes should be eliminated based on their changing problematic behaviors over time. The effectiveness of our proposed model has been validated by a number of simulation-based experiments that were conducted on various scenarios.

This paper presents an automatic vision transformer pruning method that aims to alleviate the difficulty of deploying vision transformer models on resource-constrained devices. The proposed method aims to automatically search for the optimal pruned model by removing irrelevant units while maintaining the original accuracy. Specifically, the model pruning is formulated as an optimization problem using binary particle swarm optimization. To demonstrate its effectiveness, our method was tested on the DeiT Transformer model with CIFAR-10 and CIFAR-100 datasets. Experimental results demonstrate that our method achieves a significant reduction in computational cost with slight performance degradation.

In computer-based search systems, similarity plays a key role in replicating the human search process which underlies many natural abilities, such as image recovery, language comprehension, decision-making, or pattern recognition. The search for images consists of establishing a correspondence between the available images and those sought by the user, by measuring the similarity between the images. In fact, image search per content is generally based on the similarity between the visual characteristics of the images. The distance function used to evaluate the similarity between images depends not only on the criteria of the search but also on the representation of the characteristics of the image. This is the main idea of a content-based image retrieval system. In this article, we first constructed type-2 beta fuzzy membership of descriptor vectors to help manage inaccuracy and uncertainty of the characteristics extracted from the feature of images. Subsequently, the retrieved images are ranked according to the novel similarity measure, which is noted type-2 fuzzy nearness measure (IT2FNM). By analogy to type-2 fuzzy logic, and motivated by a near sets theory, we advanced a new fuzzy similarity measure (FSM) noted as IT2FNM. Then, we propose three new IT2FSMs and provide mathematical justification to demonstrate that the proposed FSMs satisfy proximity properties (i.e., reflexivity, transitivity, symmetry, and overlapping). The experimental results generated using three image databases show consistent and significant results.

Urban Traffic Networks are characterized by their high dynamics and increased traffic congestion cases, leading to a more complex road traffic management. The present research work suggests an innovative advanced vehicle guidance system based on Hierarchical Interval Type-2 Fuzzy Logic model optimized by the Particle Swarm Optimization (PSO) method. Indeed, this system allows an intelligent and prompt adjustment of the road traffic network in a dynamic way and improves the entire road network quality, particularly in case of congestions or jams, considering real-time traffic information. The best followed road is selected according to the quality of traffic and route length, together with contextual factors pertaining to the driver, the environment, and the path. The proposed system is executed and simulated using SUMO (Simulation of Urban Mobility), for which four large areas situated in the cities of Sfax, Luxembourg, Bologna and Cologne have been tested. The simulation results proved the effectiveness of learning the Hierarchical Interval Type-2 Fuzzy Logic model using PSO real time technique to accomplish multi-objective optimality regarding two criteria: number of cars that attain their destination and average travel time. The obtained results have confirmed the efficiency of the proposed system.

Traffic congestion is a problem in most large cities world wide. It occurs when the capacity of road is surpassed, resulting in augmented vehicular queuing and slower average speeds. The traffic congestion can be caused or increased by various conditions like weather, road work, road traffic incidents. To deal with these problems, we propose a novel cooperative Multi-Agent system (MAS) for Road Traffic Decision Making in Vehicular Ad-Hoc network (VANET) based on a Hierarchical Interval Type-2 Fuzzy Knowledge Representation System (CMRHFS) used for travel route guidance. Our proposal aims to increase the road safety and the quality of the entire road network, especially in case of congestions, accidents and jams, considering traffic information in real-time as well as drivers travel time to attain their destinations. The obtained simulation results have proved our suggested system efficiency compared to Dijkstra's algorithm and Hierarchical Interval Type-2 Fuzzy Logic System (HIT2FLS) regarding two criteria: average travel time and path flow.

The Social Internet of Things (SIoT) can be defined as an IoT where objects can establish social links with each other, in which the capability of humans and objects to select, determine, and use services in the IoT is improved. Hence, without a security mechanism to ensure trustworthy SIoT nodes' interactions, this paradigm cannot reach enough popularity to be a well-established technology. A Trust Management (TM) model becomes a major challenge in SIoT systems to ensure qualified and secure services. Nevertheless, the defined TM models don't show their performance towards trust related attacks performed at the aim of disrupting the management of the trust values. In this work, we propose a TM model dedicated not only to identify trustworthy nodes, but also to detect and prevent malicious attacks. Based on Machine Learning (ML) techniques, this model can identify these attacks by learning proposed trust features that are derived from the description of malicious node behaviors. Experimentation results generated with simulated data sets based on real data concur the effectiveness of our proposed model.

Abstract The evaluation of visual analytics (VA) is a challenging field enabling analysts to get insight into diverse data types and formats. It aims at understanding events described by data and supporting the knowledge discovery process by integrating different data analysis methods. Recently, the evolution of intelligent decision support systems has enabled the inductive and predictive approaches of data analysis to make important decisions faster with a higher level of confidence and lower uncertainty. This paper introduces a new and intelligent evaluation method of VA that understands the users’ work as well as the features of their environments including vagueness, uncertainty and ambiguity due to workload. To this end, we apply an adaptive neuro-fuzzy inference system (ANFIS) to get quantitative and qualitative measures and determine the lowest evaluation score with better approximation. By combining fuzzy logic, used to deal with the inaccuracies and uncertainty problems during the evaluation process, and neural network, used to solve the problem of continuous changes in assessment environments with the delivery of adaptive learning content. By using the ANFIS approach that allows accurate prediction of evaluation scores, the proposed method seems more efficient compared to the recent evaluation methodology.

Deep neural networks have proved promising results in many applications and fields, but they are still assimilated to a black box. Thus, it is very useful to introduce interpretability aspects to prevent the blind application of deep networks. This paper proposed an interpretable morphological convolutional neural network called Morph-CNN for pattern recognition, where morphological operations were incorporated using counter-harmonic mean into the convolutional layer in order to generate enhanced feature maps. Morph-CNN was extensively evaluated on MNIST and SVHN benchmarks for digit recognition. The different tested configurations showed that Morph-CNN outperforms the existing methods.

To increase health care efficiency, home care appears as the solution for providing personalized health care service. The major requirement of the home care service is the Internet of Things (IoT) which can provide a distant control via internet network. This service may add additional risks including system failure. In this paper, we propose a layered architecture that monitors patients based on connected equipment and risk prediction techniques. Our architecture relies on cooperative components for monitoring and analysis of Quality of Service (QoS). The main objective of this work is to acquire a good knowledge about the specificities of IoT, home care and Software Architecture (SA) to conserve and to dress a way for home care service improvement.

Connected objects are the key for many intelligent systems for instance, direct access to physical and physiological values and collecting information about the human body. Our research works aim to develop non-invasive methods that predict risk for dialysis patient in End-Stage Renal Disease (ESRD) at a smart home care system based on Internet of Things (IoT). However, the IoT components pose many new challenges in collecting more fine grained information called biomarkers. In this paper, we describe our work in progress to predict dialysis biomarkers from IoT sensors. To address this problem, we present our ongoing research to develop a modern data analytics environment using machine learning techniques. This paper gives also an overview about literature review and discusses open issues.

Traffic congestion leads to many problems, namely road users' dissatisfaction, air pollution and waste of time and fuel. For this reason, congestion detection at an early stage is required to perform an efficient exploitation of resources. This paper proposed a Hierarchical Type-2 Beta Fuzzy Knowledge Representation system for the selection of optimal route. Consequently, this system aims to avoid longer travel times, and to decrease traffic accidents and the number of traffic congestion situations. The selection is performed through itineraries assessment by contextual factors such as Max speed and density of a given path. For the validation, the traffic simulation was done with the open source microscopic road traffic simulator SUMO. When compared with the Dijkstra's algorithm, the proposed system showed better performance in terms of average travel time and path flow. These promising results prove the potential of our method to relieve traffic congestion.

The data warehouse is the most important component to supply a Business Intelligence system. It is at the core of the Decision Support System. It allows integrating data from different sources, often scattered and heterogeneous, with the purpose of helping managers in their decision-making. Thereby, the building of a data warehouse requires the execution of the Extraction-Transformation-Load (ETL) process. These recent years, the ETL is affected by the emergence of Big Data. This type of data sets was treated by some ETL studies in its 3V namely, the Volume, the Velocity, and the Variety. However, these studies do not treat the Variety of data types. Thus, in this paper, we introduce a new ETL tool that treats this aspect. GENUS, our proposed tool, extracts its data from different document types: text, image, and video, transform them, and load them to a document data warehouse. GENUS is implemented and validated in a commercial case study.

In a changing environment, it is necessary to update the ontology to new knowledge and user needs. However, ontology evolution is still a time-consuming and complex task. In this paper we propose an extended approach of an earlier work in ontology evolution based on an adaptive multi-agent system (AMAS). In fact, we seek to personalize the results proposed by the AMAS to the ontologist-feedback. First, we enhance the agents with an adaptive behavior enabling them to react to the ontologist's feedback. The ontologist gives his/her action (elementary and composite changes) towards the AMAS proposals. He/She can also add new terms and concepts. Then, the AMAS reacts and self-organizes to produce an updated ontology with new proposals. This process is repeated until a satisfactory state of the ontology is obtained. The experiments prove that the adaptive skills we added to agents help them to detect the uselessness of some proposals, to avoid the useless and wrong ones and to propose others.

The growing interest in cloud-based data warehousing is driven by the high return on investment. Nonetheless, the adoption of cloud computing for data warehousing faces security challenges given the proprietary nature of the enclosed data. This paper first, presents an adaptive security solution for sensitive data in cloud-based data warehouses. Second, it illustrates how the security solution can be implemented as a service through a model-driven approach.

Cloud computing offers generous computing resources needed to deploy data warehouses efficiently. However, security remains a key challenge for a widespread adoption of the Cloud for data warehousing. In this paper, we extend BPMN to provide for modelling secure Extract-Transform-Load processes deployed as web services in a Cloud environment. In addition, following a model-driven engineering approach, we outline how the ETL model can be transformed into a set of services to be deployed in the Cloud.

Currently, Decision support systems in dynamic and complex environment involves the use of visual data mining technology for interactive data analysis and visualization. This paper presents a new architecture for designing such systems. The envisaged architecture based on the Multi-Agent System to improve coordination and communication between the different system modules to generate the appropriate solution for a specific problem. In this work, we have applied the proposed architecture to develop visual intelligent clinical decision support system for the fight against nosocomial infections. The developed prototype was evaluated to show the new architecture applicability.

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.

In this paper we evaluate interactive visualizations generated by existing medical tool used to visualize patients' fixed and temporal data. It has been developed to better understand a large collection of patients' data in the Intensive Care Unit in order to daily prevent the nosocomial infection occurrence. Existing visualization evaluation studies introduce a variety of classical and novel evaluation methods but we need to integrate more intelligent techniques for the evaluation of visual analytics tasks of medical data. The proposed method consists of interpreting and analyzing initial evaluation results using fuzzy logic technique. Such technique allows intelligent analysis of the evaluation results. Our contribution tends to propose a more novel and interesting method to reach high performance in evaluation procedure.

We present a development of a new approach for automated diagnosis, based on classification of Magnetic Resonance (MR) human brain images. 2D Wavelet Transform and Spatial Gray Level Dependence Matrix (DWT-SGLDM) is used for feature extraction. For feature selection Simulated Annealing (SA) is applied to reduce features size. The next step in our approach is Stratified K-fold Cross Validation to avoid overfitting. To optimize support vector machine (SVM) parameters we use Genetic Algorithm and Support Vector Machine (GA-SVM) model. SVM is applied to construct the classifier. An intelligent classification rate of 95,6522% could be achieved using the support vector machine.

Nowadays the emergence of mobile devises and Cloud Computing can change the culture of healthcare from direct care services into Mobile Cloud computing (MCC) services. For that, the application targeted at mobile devices becoming copious with others systems in healthcare sector. Consequently, we assume that this technology have a potential effect in healthcare domain. MCC offers new kinds of services and facilities medical tasks. In this regard, we have tried to propose a new mobile medical web service system. The proposed system called Medical Mobile Cloud Multi Agent System (2MCMAS) is a hybrid system which integrates MCC and Multi Agent System in healthcare in order to make efficiency care.

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.

The last years witnessed a continued growth of the amount of data. The data analysis and exploration has become more and more difficult. So, it seems important to find means to visually represent this flood of data. Information visualization can help any user to get and understand information efficiently and implicate him/her in the data mining process thanks to our perception possibilities. The visualization domain proposes a large number of information visualization techniques which have been developed over the last decade to support the exploration of large data sets. In this paper, we propose a classification of information visualization techniques. We present also each technique, its advantages and disadvantages.

In this paper we propose an approach aiming to integrate human-computer interaction (HCI) aspects in decision support system (DSS) development. We propose an approach combining two methods: one issued from software engineering field (the rational unified process) and the other one from the HCI field (the U model). We have tested our approach in a DSS set up in the healthcare domain: the supervision of nosocomial infections in an intensive care unit.