Reconfigurable Intelligent Surfaces (RIS) and Cognitive Radio (CR) offer solutions to improve spectral efficiency and mitigate spectrum scarcity. This article proposes a hybrid Dynamic Spectrum Management (DSM) paradigm combining Opportunistic Spectrum Access (OSA) and Concurrent Spectrum Access (CSA) models for spectrum allocation in RIS-supported CR environments. We frame the spectrum access issue as a game where each secondary user (SU) acts as a player to secure a communication channel. Our findings reveal that RIS technology significantly enhances spectrum allocation efficiency, quality of wireless communications, and spectrum sensing performance, providing high channel capacity and energy efficiency.

Software modeling relies heavily on UML class diagrams, essential tools for structuring a system’s entities, behaviors, and relationships. Yet, manually developing them from textual specifications remains a time-consuming task and subject to interpretation. This study proposes the creation of a corpus annotated according to a customized IOB schema, intended to train Named Entity Recognition (NER) models for the automatic extraction of UML elements from text. The schema integrates specific labels to accurately capture classes, attributes, methods, and relationships (association, aggregation, composition, inheritance), including their compound forms. The current corpus, built from 132 documents from various sources, includes more than 900 sentences and 11,000 manually annotated tokens. Particular attention was paid to the syntactic and semantic diversity of the texts, as well as to the linguistic quality, to ensure good generalization of the models. The empirical evaluation conducted with six Transformers models (BERT, RoBERTa, SpanBERT, XLNet, MiniLM and Electra) shows promising results, especially for classes and their relationships. This work thus lays the foundation for a reliable automation of UML class diagram generation from textual specifications, with strong potential for integration into software engineering environments and MDA processes.

Soil moisture prediction requires the integration of multisource data, including satellite observations, ground-based sensors, and airborne systems, each contributing critical information for modeling Earth’s hydrological cycles. The complexity of this task necessitates an analytical framework capable of reconciling general modeling principles with the intricate variability of climatic factors to ensure reliable predictions. This study explores the application of Spiking Neural Networks (SNNs) as an advanced approach beyond conventional methodologies, utilizing array-sensed data from the ERA5 dataset. SNNs are distinguished by their ability to merge computational efficiency with biologically inspired dynamics, employing Leaky Integrate-and-Fire neurons to process spatial and temporal information effectively. The model’s adaptability and precision in handling large-scale climatic datasets were evaluated using an 80-20 data split, achieving a Mean Squared Error (MSE) of 0.0003, an R 2 value of 0.8919, and a Pearson correlation coefficient of 0.9449, reinforcing its predictive capability and ability to capture intrinsic dependencies within soil moisture dynamics. This novel implementation of SNNs enhances prediction accuracy while offering a computationally efficient solution for soil moisture forecasting, addressing key challenges in environmental and agricultural applications. The findings provide a foundation for future research aimed at optimizing hydrological models through biologically inspired neural architectures.

This paper proposes a novel framework for climate anomaly detection by integrating antenna array-derived environmental data with Spiking Neural Networks (SNNs), a biologically inspired computational approach. The methodology systematically captures key climatic parameters, including temperature, wind, and moisture, allowing a precise and structured analysis. By encoding temporal and spatial dynamics, SNNs provide an advanced mechanism for detecting subtle patterns and anomalies that conventional methods may overlook. The study demonstrates the effectiveness of simulated antenna-based climate data in identifying temperature trend anomalies, reinforcing the potential of neural architectures in climate variability analysis. The results highlight the ability of SNNs to improve anomaly detection through efficient processing of time-sensitive and spatially complex datasets. The proposed pipeline not only addresses methodological gaps but also improves the broader understanding of climate disturbances by incorporating innovative technology-driven solutions. This research underscores the importance of integrating computational intelligence into climate studies, contributing to more accurate and scalable environmental monitoring systems.

The tourism sector has recently undergone a significant transformation with the integration of chatbots, enabling users to interact with services through natural language. At the heart of these systems lies the Natural Language Understanding (NLU) component, which processes user input through intent classification and entity extraction. A major challenge, however, is selecting the most effective machine learning method to build robust NLU systems tailored to tourism applications. This study evaluates the performance of various machine learning algorithms for intent classification in tourism-focused chatbots. The models under investigation include Support Vector Machine (SVM), LightGBM, XGBoost, and Random Forest. A tourism-specific dataset was developed for this comparative analysis, with evaluation based on metrics such as accuracy and weighted F1-score. The experimental results indicate that XGBoost, LightGBM, and Random Forest achieve the highest training accuracy in intent classification. These outcomes offer valuable insights for developing effective NLU components in tourism chatbots, improving their ability to interpret user queries accurately.

Chatbots are tools designed to interact with users through natural language. They are widely used in various sectors, such as education, tourism, and transportation. These systems perform several common tasks, such as enhancing customer service, providing information permanently, and answering frequently asked questions. They can be classified into two main categories: rule-based chatbots like Eliza, which rely on predefined rules and intents to handle specific tasks, and AIbased chatbots like ChatGPT, which use advanced technologies like deep learning and Natural Language Processing (NLP) to interpret and respond to user inputs dynamically. However, their development faces challenges due to constraints specific to their development tools, such as high-cost NLP services. In addition, the absence of a dedicated chatbot development platform for the transportation domain remains a significant limitation. To bridge this gap, this study conducts a comparative analysis of existing metamodels for chatbot development and identifies their concepts and relations. The outcome is the design of a unified metamodel specifically tailored to the transportation sector, serving as the abstract syntax for constructing a DomainSpecific Language (DSL) that accelerates chatbot development for the transportation domain and reduces costs associated with NLP services.

Smart grids are confronted with growing cybersecurity threats by Advanced Persistent Threats (APTs) targeting vulnerabilities of cyber-physical systems with stealthy, multi-stage attacks. Conventional signature-based rule-driven detection mechanisms cannot detect these advanced threats. This paper presents an active behavior detection system using Generative Adversarial Networks and Autoencoders for benign network behavior modeling and anomaly detection characteristic of APTs. Tested on actual smart grid data, our hybrid solution is 96.5 % accurate and has an$\text{F 1}$-score of$\text{96.59 \%}$, surpassing baseline MLPs and state-of-the-art techniques. The main innovations are adversarial training for generating attack patterns and Autoencoder reconstruction for anomaly detection. Experiments show the framework's robustness to stealthy APTs with few false positives. This research propels adaptive defense technologies for critical infrastructure, bridging the gaps in scalability and dynamic threat modeling.

Abstract Conversational assistants (CAs) and Task‐oriented ones, in particular, are designed to interact with users in a natural language manner, assisting them in completing specific tasks or providing relevant information. These systems employ advanced natural language understanding (NLU) and dialogue management techniques to comprehend user inputs, infer their intentions, and generate appropriate responses or actions. Over time, the CAs have gradually diversified to today touch various fields such as e‐commerce, healthcare, tourism, fashion, travel, and many other sectors. NLU is fundamental in the natural language processing (NLP) field. Identifying user intents from natural language utterances is a sub‐task of NLU that is crucial for conversational systems. The diversity in user utterances makes intent detection (ID) even a challenging problem. Recently, with the emergence of Deep Neural Networks. New State of the Art (SOA) results have been achieved for different NLP tasks. Recurrent neural networks (RNNs) and Transformer architectures are two major players in those improvements. RNNs have significantly contributed to sequence modelling across various application areas. Conversely, Transformer models represent a newer architecture leveraging attention mechanisms, extensive training data sets, and computational power. This review paper begins with a detailed exploration of RNN and Transformer models. Subsequently, it conducts a comparative analysis of their performance in intent recognition for Task‐oriented (CAs). Finally, it concludes by addressing the main challenges and outlining future research directions.

ABSTRACT Chatbots have emerged as ubiquitous tools for enhancing user interaction across various platforms, from customer service to personal assistance. They are computer programs that simulate and process human conversation, either written, spoken or both. However, developing efficient chatbots remains a challenge, primarily due to the intricate nature of critical components of chatbots like natural language understanding (NLU) requiring a subscription from intent recognition providers like Dialogflow and Amazon Lex. This makes chatbots closely linked to NLP services and can be locked in. Recently, various research studies have provided solutions to reduce the workload of developers and designers. These approaches have proposed model‐driven development via domain‐specific languages (DSLs), which make the chatbot development process more accessible and more automated. This advancement aims to enhance effectiveness in chatbot development by leveraging DSLs. This study aims to provide a comprehensive overview of DSLs for developing chatbots, with the first contribution comprising various research topics, tools, approaches, and technologies employed to implement DSLs. Second, this work aims to assess and contrast the primary DSLs currently available for chatbot development, focusing on presenting the key elements used in constructing these DSLs. Third, this study identifies the challenges and limitations of using DSLs in chatbot development.

Smart tourist destinations are increasingly using technology to manage interactions with tourists. Chatbots, supported by artificial intelligence and natural language processing, have demonstrated greater capability and effectiveness in various conversational scenarios, offering assistance to tourists before, during, and after their visits. However, the effectiveness of chatbots can be improved, as these applications only cover some tourism functionalities. This article analyzes chatbots, accompanied by an exhaustive study of their evolution. We used the Web of Science and Scopus databases to gather relevant papers using a specific search query. This study provides a comprehensive overview of advances and trends in tourism chatbots over the past ten years (2013-2023), highlighting current failings and opportunities for future developments.

This research paper presents a novel formal framework designed for piloting human resources performance and fostering agile HR management within organizations. The framework facilitates the systematic collection of HR data, enabling the computation of sensitive Key Performance Indicators (KPIs) essential for predictive analytics and data-driven decision-making. Through this framework, top management gains a clear understanding of recruitment and training strategies, as well as the ability to distinguish between easily-replaceable and critical resources. The framework empowers organizations to optimize resource allocation, enhance operational efficiency, and mitigate risks associated with human capital management. The integration of predictive analytics enables the development of comprehensive dashboards, providing actionable insights to guide strategic HR initiatives and ensure organizational success in dynamic environments.

Zero-shot learning (ZSL) is a machine learning technique that identifies the target classes without any observed data, using semantic information from some source classes as the basis of knowledge transfer. ZSL has emerged as a new paradigm in machine learning to solve the constraints of classical supervised learning. This study explores applying ZSL techniques to a plant species dataset containing rich attribute descriptions for each class. Our study uses the multimodal capabilities of Contrastive Language–Image Pre-training to predict unseen plant species classes without needing labeled examples during training. Our objective is to contribute to advancing biodiversity conservation efforts and promoting environmental sustainability by facilitating the automated recognition of plant species based on semantic attributes.

The creation of software documentation is widely recognized as a critical and demanding undertaking within the rapidly changing realm of software development. This study introduces a novel method for generating software documentation by leveraging Large Language Models (LLM). The paper presents a novel system that extracts use cases from UML Use Case Diagrams and employs a Generative AI Model to generate descriptive text for each extracted use case. This approach aims to reduce the amount of time dedicated to documentation and encourage uniformity in the description of software functions. The results suggest that the level of manual labor and time needed can be substantially decreased by upholding elevated levels of clarity and comprehensiveness in software documentation. This study presents a use-case scenario that showcases the practical application of our methodology in real-world situations. The purpose of this example is to demonstrate the practicality and effectiveness of the method.

In today’s world, chatbots have become a significant advancement in Artificial Intelligence (AI). They are extensively utilized to provide users convenient access to 24/7 services using natural language. The development of these conversational applications is evolving rapidly and necessitating specific knowledge and practical experience to successfully exploit all the functionalities of chatbot development platforms and frameworks. The heterogeneity of chatbot development tools and their need for NLP services makes it challenging to build chatbots. Thus, one possible solution to these problems is to construct a domain-specific language (DSL) to accelerate the development of Chatbots. A Domain Specific Language (DSL) is a programming language that provides expressive power within a specific problem domain by using appropriate abstraction notations. Abstract syntax, concrete syntax, and semantics are the three components that describe it. Furthermore, it is necessary to utilize generation templates to construct a chatbot for an already established platform. Through the use of a Model-Driven Architecture (MDA), which is an approach that focuses on modeling software systems at different levels of abstraction, from high-level requirements to platform-independent designs, this work aims to define a sketch of an independent language of the chatbot development platform by providing the components needed for our DSL, like metamodel for modeling conversations and developing transformations between models to generate the source code for a chatbot conforming to a specific implementation platform. This will facilitate the automatic generation of code.

Each new wave of technological progress sparks debates about the effects of automation on the future of employment. Current debates on artificial intelligence (AI) and employment are reminiscent of those raised by mechanization in the 19th century, the generalization of electricity, and the introduction of computers in the 20th century: some consider new technologies as a way to relieve workers of the most challenging tasks, and others are alarmed by the imminent threat to employment. This article aims to contribute to the ongoing debate on the potential changes that may arise from the recent emergence of Generative AI in job markets. It is based on a historical analysis of technological revolutions and a literature review of technology’s impact on employment. The purpose of this study is not to gather general statistics but rather to analyze potential changes and help design suitable policy responses. This analysis will also consider the possible impact on job quality. The study emphasizes the potential implications for various professional categories but does not predetermine the outcomes of technological transition. The decision to incorporate such technologies is driven by humans, and it is their responsibility to guide the transition process.

This paper examines the critical function of Field-Programmable Gate Arrays (FPGAs) in speeding Spiking Neural Networks (SNNs) for real-time edge neuromorphic computing. Our work systematically evaluates the integration of FPGA technology for the optimization and speeding of SNN models. The analysis covers the power efficiency, low latency processing, and parallelism that are intrinsic benefits of FPGAs, emphasizing their relevance for edge computing applications. We discuss the smooth transfer of trained SNN models to FPGA platforms. Using an extensive analysis of state-of-the-art architectures, we demonstrate the efficiency benefits of using FPGA to accelerate SNNs. We derive more insights into the real-world applications of this FPGA-SNN integration in various fields. The analysis supports advances in edge computing and neuromorphic processing paradigms by adding to the collective knowledge of how FPGA enhances the real-time processing capabilities of Spiking Neural Networks.

This study examines the variability of upwelling along eastern boundaries, specifically focusing on two upwelling systems in the northern hemisphere: the Moroccan Atlantic coast and the Californian coast. In this study, we use a specialized archive that is both highly efficient and robust. The archive is specifically designed to tackle the challenge of detecting upwelling phenomena in satellite data. We ensure accurate and reliable results by prioritizing cloud processing in upwelling zones and questionable pixels. The description and analysis of upwelling dynamics from 2000 to 2019 include the interannual and seasonal variability, which are examined using different upwelling indices. Furthermore, this study proposes a novel index to evaluate the agreement between Sea Surface Temperature (SST) and Chlorophyll-a concentration (Chl-a) as indicators of upwelling. This index successfully captures the relationship between biological and physical components at various spatio-temporal scales, indicating its potential for use in other upwelling systems.

A dialogue state tracker is a component in a task-oriented dialogue system that monitors the current state of a conversation and gives information about its context and history to other system components. The dynamic and open-ended character of human interactions is one of the primary obstacles in dialogue state monitoring, necessitating robust and adaptable models to keep up with the quick context changes. Recently, numerous deep learning-based algorithms have been developed for this purpose. Still, these models are typically heavily-engineered and conceptually sophisticated, making them challenging to deploy, debug, and maintain in a production environment. To overcome these challenges, we offer the BERT-SIAM-DST model, a unique way to dialogue state monitoring employing a Siamese network with BERT as the base network. This model uses the robust representation capabilities of BERT and the ability of Siamese networks to record correlations between inputs to make accurate predictions regarding the current state of the discussion. In addition, the number of parameters does not increase proportionally with the size of the ontology, and the model is adaptable to alterations in the domain ontology. We test the performance of the BERT-SIAM-DST model on the standard WoZ 2.0 dataset of annotated dialogues and compare it to other approaches. Compared to numerous baseline models, the BERT-SIAM-DST model is effective at tracking the state of discussions, demonstrating the promise of BERT-based Siamese networks for this purpose.

This paper discusses an Internet of Things (IoT)-based energy meter for photovoltaic systems (PV) to forecast energy production at industrial locations. Based on an ESP-32 card, the proposed IoT device collects energy consumption data from the sub-meter and delivers it to the cloud. The data is used to monitor the values of a typical PV system installed in Benguerir (Morocco). The method involves conducting an analysis of the solar resource available at the site in Benguerir, as well as conducting an investigation, evaluation, and selection of the components of the solar station using simulation software such as the PVSYST tool. The proposed solution will improve the management of a PV system. Additionally, the method involves the development of a datalogger that is used for monitoring solar panels’ energy production, storing data in the cloud, and displaying results on a web interface. Lastly, we apply an artificial neural network for solar energy forecasting to future production.

As a rapidly evolving paradigm, digital transformation (DT) remains one of today's most significant challenges. Consequently, numerous businesses devote a substantial portion of their resources to this captivating transition. However, this investment's return is difficult to quantify or even unexplored by others. This observation constituted the foundation for Solow's productivity paradox. Even if they are aware of how the KPI measurement affects their performance, managers continue to disregard this evaluation. In terms of academic literature, this field of study is still inadequately developed. To address this limitation, this study uses the NVivo software to conduct a systematic literature review highlighting the KPIs, the methodology used in the corpus, the evolution of related works, and their type. According to our analysis, several works cite this term without describing it or defining a general approach to DT metrics. In addition, the financial and commercial performance KPIs are the most specific. Following a summary of the primary KPIs in the literature, we classified them into two prominent families: generic and specific. This classification represents the originality of this paper. The generic KPIs are multidimensional and applicable to a variety of businesses, while the specific ones pertain to a particular function or industry.

Link prediction is a challenging issue in practical applications such as recommendation systems. The purpose of such applications is to predict the presence of links between distinct objects based on examining structured data within a network. In this paper, we develop a graph convolutional neural network (GCNN) model to address this problem while incorporating interaction relationships and content information on various elements. The suggested GGCN improves prediction accuracy by constraining the consistencies of the graph embedding from multiple perspectives, in contrast to existing strategies that directly combine hybrid approaches based on interaction and content information into a single display. Experimental results were tested on three datasets, including Facebook, Google+, and Twitter, using various hyperparameters.

This research paper presents a deep learning-based predictive model for classifying currency trends using technical indicators. The model is trained on a dataset generated from three technical indicators: relative strength index (RSI), moving average convergence divergence (MACD), and stochastic. The dataset consists of historical currency data along with the corresponding values of the technical indicators. The deep learning model can accurately classify the trends of a given currency based on the importance of these indicators. The model's performance is evaluated using standard metrics, and the results demonstrate its effectiveness in classifying currency trends. The proposed model provides a valuable tool for traders and investors in the foreign exchange market by helping them make informed decisions about the direction of currency prices.

The retina is affected by glaucoma and diabetic retinopathy (DR). Glaucoma must be detected early because it is irreversible and one of the leading causes of blindness. A delayed diagnosis will result in permanent vision loss. It is characterized primarily by ganglion cell dysfunction, which changes the thickness of the retinal nerve fiber layer and the shape of the optic nerve head. As a result, early detection of glaucoma is critical for preventing vision loss. This study employs a hybrid approach to glaucoma diagnosis by combining its powerful Multichannel Gabor filtering and Principal Component Analysis (PCA) capabilities with various transfer learning architectures such as MobileNet, MobileNetV2, and NASNet. These classifiers divide the source retinal images into two groups: glaucoma and non-glaucoma. The suggested approaches are used and evaluated on a dataset of Retinal Fundus Images. For the glaucoma diagnostic method, this strategy yields 99% Precision, 97% Recall, and 98% Accuracy.

Cognitive radio (CR) is considered a primary technology for spectrum usage efficiency and dynamic spectrum management. Spectrum sensing is an essential cognitive radio cycle function. Recently, reconfigurable intelligent surfaces (RIS) technology has emerged as a promising new enabler of 6G wireless communication, with the ability to control signal propagation and increase signal coverage and spectrum management. This paper investigates how integrating RISs with CR can improve spectral efficiency. First, we optimize the RIS parameters by identifying the optimal transmit powers and number of RIS antennas. Then, we compare the novel RIS technology with the traditional decode-and-forward (DF) relaying technology. Next, we investigate how adopting RIS can enhance spectral efficiency. Finally, we demonstrate how integrating the novel technology of reconfigurable intelligent surfaces with CR can significantly increase spectrum detection, the most crucial step in the cognitive radio process. Simulation studies indicate that RIS is more energy-efficient than SISO and DF-relay communications in various setups. Moreover, by boosting spectrum sensing capability in CR situations, RISs can significantly enhance the efficiency of frequency spectrum usage.

The next generation of the Internet of Things will rely heavily on RFID and sensor technology. Compared to existing systems, the development of various RFID and sensor technologies is expected to increase IoT applications, especially in the field of healthcare. This research summarizes the fundamentals of the Internet of Things (IoT) based RFID technology. RFID sensors are critically compared by separating them into near-field and far-field configurations. The two categories are then analyzed for their operating mechanism. RFID sensing via tags equipped with chips is now a sophisticated technological device that continuously increases its presence in the market and several application scenarios; therefore, adopting RFID tag sensors for healthcare applications is discussed in this work. The advantages and limitations of several IoT-based RFID configurations for healthcare are reviewed. In addition, a summary of the most appropriate application scenarios of the RFID sensors is then illustrated. Finally, a look at overall machine learning (ML) satisfactory solutions for RFID antenna design is highlighted.

Conversational Systems are increasingly substituting humans in many service industries. They aim to provide human-like interaction with users for task completion or chitchat in a conversation style. User sentiment analysis is an important task that can help better understand users’ behavior and satisfaction in conversations. Although some researchers have studied the problem of sentiment analysis, most of the existing methods are oriented toward general felds. To overcome the challenges of sentiment analysis, we propose a BE-Att-BiLSTM, which stands for an attention-based Bidirectional Long Short-Term Memory (BiLSTM) model. The proposed model uses pre-trained BERT, contextual embeddings and a combination of BiLSTM and attention mechanism for efficient sentiment analysis in conversations. In addition, text-augmentation techniques are leveraged to enhance the performance of the proposed model. Experimental results on a public benchmark dataset show an improved accuracy of 68.00% and an F1-score of 67.50%.

The smart city has become a persistent need and is no longer just a concept. The concept of smart cities heavily relies on collecting enormous amounts of data. This paper proposes a data-management-based solution for smart city, which is labeled Smart Systems Oriented Big Data Architecture. Big data technologies have become essential to the functioning of cities. The architecture includes complex components to be implemented based on the architectural requirements. A data migration strategy was proposed to handle the various data sources such as IoT devices, video cameras, and drones. The proposed approach also takes into account data processing and data storage. The technical constraints related to data processing in a big-data environment are also studied. We also consider data modeling from a business intelligence point of view and a data science perspective. Our main goal is to favor the facilitation of the daily life practices in the context of a smart city by providing the city administrators with a solution that helps them maintain their city smartly and effectively.

Spectrum sensing is the first step in the cognitive cycle and represents the most critical function in cognitive radio-based dynamic spectrum management. Recently, a new technology termed reconfigurable smart surfaces has emerged as a promising enabler of smart radio environments to control the signal propagation further and improve signal coverage and spectrum management capabilities. This paper investigates how the adoption of reconfigurable intelligent surfaces (RISs) can increase spectral efficiency in a cognitive radio environment. To this end, we optimize the parameters of the new RIS technology by determining the optimal transmit powers and the optimal number of these elements. Next, we derive expressions for the false alarm and detection probabilities of the cognitive radio (CR) node and the transmission probability and throughput. Finally, we demonstrate how the detection phase of the CR spectrum can be improved by employing RIS technology at the UP. Simulation results show that RIS achieves higher energy efficiency than SISO communication for different configurations. In addition, the RISs can significantly improve the wireless communication quality and spectrum sensing performance in a CR environment.

This paper presents a deep artificial neural network approach based currency market volatility based recommendation engine. Since deep learning classification needs labeled data set that we don't have, an approach is designed specially for that point in order to generate labeled data set from non labeled one. This is a major innovative aspect in this contribution in addition to the recommendation service. It is based on Gaussian kernel density and Monte Carlo simulation. The main goal of the proposed approach is to predict - for each hour of the day - the volatility behaviour of the selected currency pair. The proposed model has a range of applications in financial market specially the algorithmic trading. Deep neural network was trained and evaluated and testing process gave good convergence rate.

With the rapid development of artificial intelligence (AI), many companies are moving towards automating their services using automated conversational agents. Dialogue-based conversational recommender agents, in particular, have gained much attention recently. The successful development of such systems in the case of natural language input is conditioned by the ability to understand the users’ utterances. Predicting the users’ intents allows the system to adjust its dialogue strategy and gradually upgrade its preference profile. Nevertheless, little work has investigated this problem so far. This paper proposes an LSTM-based Neural Network model and compares its performance to seven baseline Machine Learning (ML) classifiers. Experiments on a new publicly available dataset revealed The superiority of the LSTM model with 95% Accuracy and 94% F1-score on the full dataset despite the relatively small dataset size (9300 messages and 17 intents) and label imbalance.

Machine learning (ML) and deep learning (DL) techniques have been used to study the changes in human physiological and non-physiological properties. DL has proven his efficiency when perceiving positive emotions (joy, surprise, pride, emotion) and negative emotions (anger, sadness, fear, disgust). Furthermore, the DL is used to identify the emotions accordingly. First, this paper describes the different DL and ML algorithms applied in the emotion recognition field. Then, as a perspective, it proposes a three-layered emotion recognition architecture that leverages the massive data generated by IoT devices such as mobile phones, smart homes, and health monitoring. Finally, the potential of emerging technologies, such as 5G and 6G communication systems in a parallel Big Data infrastructure, were discussed.

Nowadays, there is an important need for fault-tolerant and energy-efficient self-organization systems, especially within smart cities. Internet of Things (IoT) proved capable of observing and examining the environment, generating & processing data. IoT is now applicable to almost every industry, including transportation and logistics, utilities, agriculture, smart cities, and more. In these industries, various types of meters, sensors, and trackers are used to constantly monitor activities, automate processes and optimize tasks. With the help of big data analytics, they can drive decision-making systems based on observations. As a result, the cities-management challenges are growing. The smart cities requirements are increasing to remedy the challenges, which requires a self-organized network composed of a sizeable number of nodes distributed across an area of interest. The traditional communication systems show limitations, especially when dealing with massive data rates, latency, the explosive growth of vehicular communication, and dynamic mobility. In this study, we explore a way to leverage the capabilities of wireless communication and big data analytics in favor of Smart Cities.

Smart city applications are using extensively artificial intelligence for decision-making. Among the fields of application are facial recognition and intrusion detection. The subject is old, but processing techniques and hardware are constantly evolving. This paper will review the most widely known practices and apply them to a smart parking and intrusion detection system using the “JetsonNano” board. Nowadays, quality assurance for machine learning systems is becoming increasingly important. This article focuses on detecting bugs in implementing two classical face recognition algorithms: Eigenface (EF) and Local binary pattern histogram (LBPH). We tested the efficiency of our system using metamorphic testing depending on many factors: weather conditions, pixel noise, and distortion.

This paper reports the characterization procedure of a radiofrequency identification tag powered by meandered L-matching configuration and placed directly on the planar layered anatomical model of a human arm. The tag antenna component and its matching system interface to the RFID chip are designed with the help of electromagnetic simulators. A new optimal tag structure is combining multiconductor strips and a meandering schema used to achieve the required inductance. The folded configuration of the proposed tag adds stretchability and more reduction of the antenna size, especially when attached to the non-uniform as the human body. It is demonstrated that the tag can communicate with a reader. The simulated performances indicate the robustness of the proposed tag structure and its ability to be deployed in several healthcare sensing applications.

Reconfigurable Intelligent Surfaces (RISs) have been recently considered in communication environments for focusing signal reflections to create smart radio environments. As it can create favorable propagation conditions by controlling the phase shifts of the reflected signals at the surface to enhance the communication quality. However, the current proofs of concept require complex operations for the RIS configuration, which are mainly realized via wired control connections. When used in wireless networks, RISs may contribute enhance wireless communications. The present paper, compares the RIS technology with the SISO case and evaluates the performance of RIS assisted communication system by giving an approximation of the achievable data rate and the energy efficiency, and investigates the effect of the achievable data rate and distances on the energy efficiency. In this work, based on a work by Emil Björnson [18], we have studied the impact of RIS on the wireless sensor networks by modifying some parameters and giving more interpretations. In particular, we illustrate numerical results that highlight the spectral efficiency gains of RISs when their size is sufficiently large as compared with the wavelength of the radio waves. In addition, we discuss key open issues that need to be addressed for unlocking the potential benefits of RISs for application to wireless communications and networks.

People hope that computers can be in constant intelligence development. Just like humans, they can ”see” the world and ”recognize” a visual event. We propose an approach based on computer vision methods to recognize Human-Object interaction(HOI). The technique stands on aggregating significant contextual features Human-Object interactions and scene recognition. We design a branch architecture consisting of the main branch for HOI detection and a supplementary branch for scene recognition. We explore the deep learning models through the knowledge distillation method and the Cross Branch Integration mechanism for encoding models into graph neural network architecture. We construct a knowledge graph to merge between high-level context information. When trained collaboratively, those models allow computing efficiency, strong context knowledge.

The main goal ofNew Radio 5G (NR) mobile technology is to support three generic service categories, each with very specific requirements. The first category is enhanced Mobile Broadband (eMBB), the second category relates to massive Machine-Type Communications (mMTC), and the third category relates to ultra-Reliable Low Latency Communications (URLLC). The slicing of the radio part of 5G network access network has greatly contributed to the emergence of these three categories of service with different qualities of service. This division therefore enabled the network to reserve the necessary resources for each category of services, orthogonally, and according to the performance required. In this article, we have dealt with the problem of Call Admission Control (CAC) in 5G networks where we have considered the case of the only two categories eMBB and uRLLC, which their users are served by a single cell. We calculated the maximum eMBB users admitted into the system with guaranteed data rate, while allocating power, bandwidth, and beamforming directions to all uRLLC users whose latency requirements and reliability are always guaranteed. We only considered the downlink communication, and we used the case of the multiple-input single-output (MISO) system. This CAC problem is formulated as a minimization problem l0 which is known as NP-hard problem. We therefore chose to use Sequential Convex Programming (SCP) to find a suboptimal solution to the problem.

Abstract Embedded systems and wireless sensor networks (WSN) are found today in increasingly critical areas of applications. They have become integrated and embedded in nearly all aspects of everyday life, including manufacturing, healthcare, education, critical infrastructure, and entertainment. The number of connected devices continues to grow, and due to the insecure nature of these devices, the amount of risk continues to grow as well. These risks, however, can be mitigated with the creation and adoption of WSN security standards developed to create an environment of safety, security, and confidence in the technology. Designing the security policy for WSNs requires asking some preliminary questions. These questions are particularly important in the case of WSNs because their use is highly decentralized. Blockchain's ability on governing decentralized networks makes it especially suitable for designing a self‐managing system on WSN devices. This article proposes a routing protocol that uses Blockchain technology to offer a shared memory between the network's nodes. The simulation results have shown that this solution could be applicable and could resolve the issues cited above.

Summary The International Telecommunications Union defines in its IMT‐2020 recommendations three types of use of 5G services: mMTC (massive Machine‐type Communications), eMBB (enhanced Mobile Broadband), and uRLLC (ultra‐Reliable Low Latency Communications). The mMTC service allows a considerable number of machines and devices to communicate while guaranteeing a good quality of service. The eMBB service allows very high data throughput, even at the cell border. The uRLLC service is used for ultra‐reliable communication for critical needs requiring very low latency. These services are provided separately in a given cell. However, the number of connected objects is starting to increase rapidly as well as the bit rates and energy consumption. The 5G network must make it possible to provide access to a vast number of users of its different service categories. Call admission control (CAC) techniques focus more on availability in terms of bit rate and coverage. In this article, we suggest an algorithm for modeling CAC in an area served by the three categories of services in a 5G access network, mainly based on minimum energy consumption. This technique will allow connected objects that consume low energy to connect to the network with an adequate quality of service and enable the development of the Internet of Things.

Wireless body area networks (WBANs) have emerged as a promising technology for health monitoring due to their high utility and important role in improving human health. WBANs consist of a number of small battery‐operated biomedical sensor nodes placed on the body or implanted, which are used to monitor and transmit important parameters such as blood pressure, electrocardiogram (ECG), and electroencephalogram (EEG). WBANs have strict requirements on energy efficiency and reliability during data collection and transmission. The most appropriate layer to address these requirements is the MAC layer. Medium access control protocols play an essential role in controlling the operation of radio transceivers and significantly affect the power consumption of the whole network. In this paper, we present a comprehensive survey of the most relevant and recent MAC protocols developed for WBANs. We discuss design requirements of a good MAC protocol for WBANs. We further review the different channel access mechanisms for WBANs. Then, we investigate the existing designed MAC protocols for WBANs with a focus on their features along with their strengths and weaknesses. Finally, we summarize the results of this work and draw conclusions.

The new concept of Industry 4.0 has been developed: it includes both Internet of Things (IoT) structure and the local networks that are still needed to carry out real-time tasks. Genetic algorithms are successfully used for decoding some classes of error correcting codes, and offer very good performances when solving large optimization problems. This article proposes a decoder based on parallel Genetic Algorithms (PGAD) for Decoding Low Density Parity Check (LDPC) codes. The proposed algorithm gives large gains over the Sum-Product decoder, which proves its efficiency, the best performances are obtained for Ring Crossover (RC) as a type of crossover and the tournament as a type of selection. Furthermore, the performances of the new decoder are improved using Multi-criteria method. For the LDPC code, simulation results showed that our Proposed PGAD exceeds the sum-product by a gain of 1.5 dB at BER = 10-4, and the PGAWS exceeds the sum-product by 2.5 dB.

The massive growth of wireless communications in recent years is mostly due to new connectivity demands and advances in technology development of low power) transceivers. An example of the unique demands is the increasing exchange of data in Internet services, which has led to wireless network deployment for data transmissions. The coordination of the IoT devices, smart systems, and agriculture can contribute directly to the development of the farmer’s practices by building their farm more intelligent and digital. However, enhancing farming practices requires inspecting farm equipment and farmer’s experiences, which can be analyzed through the interconnectedness of IoT objects to collect farm data over the Internet to launch smart digital agriculture. It is challenging to control all farming processes (especially in real-time), this remaining as the main limitation of traditional farming. In this work, we focus on how wireless sensors can play a vital role in smart farm systems and allow processing the large amount of data generated in batches or real-time to analyze it, retrieve insights from it, and create a Smart Digital Farm. This paper proposes hierarchical-logic mapping and deployment algorithms to tackle the problem of poor network connectivity and sensing coverage in random IoT deployment.

Recent advances in sensing modules and radio technology will enable small but smart sensors to be deployed for a wide range of environmental monitoring applications. They collect data from different environment or infrastructures in order to send them to the cloud using different communications platforms. These data can be used to provide smarter services. However, they are various issues and challenges related to the ubiquitous sensors that should be solved. In this paper we interest on analysis of wireless sensor network from an energy management perspective. The idea behind the energy-efficiency wireless sensor networks is that each node can only transmit to a limited number of other nodes directly. The limited resources of nodes imply that the transmission range is limited. In order to transfer the data to the final destination, the traffic must be relayed using intermediate nodes, creating a multi-hop route. The total energy consumption associated with an end-to-end transmission over such a route can be significantly reduced if the nodes are correctly configured. In this paper, underground mine monitoring system is presented with an overview of the related issues and challenges such as reliability, cost, and scalability.

The dramatically development of wireless technologies in the last few decades, leads to the growth of channel resources demand in a limited spectrum with inextensible character. Cognitive radio network (CR) is a promising technology that provides solutions for the spectrum management and optimization problems via dynamic spectrum management. The spectrum resources management and optimization are an important part of the future network performances. In this paper, we propose an efficient algorithm to examine the design specification issues regarding the choice of optimal power, optimal speed, and optimal amount of information in a wireless network along with studying the effect of different parameters on the obtained results. Our objectives are to guarantee the protection on licensed users (Primary users ‘PU’) from harmful interference caused by the unlicensed users (Secondary users ‘SU’), more especially, to optimize the quality of communication link, Transmission levels, and battery life of the wireless devices. Results show that our proposed work leads to an efficient utilization of radio spectrum and strongly contributes to alleviating the spectrum scarcity problem.

Spectrum sensing is an essential step in cognitive radio-based dynamic spectrum management. Spectrum sensing to detect the presence of the licensed signals in a particular frequency band is one of the most important research topics in cognitive radio. To identify primary user (PU) presence, we propose a low cost and low power consumption implementation of spectrum sensing operation based on real signals. These signals are generated by smart embedded devices at 433 MHz wireless transmitter using ASK (Amplitude-Shift Keying) and FSK (Frequency-Shift Keying) modulation type. The reception interface is constructed using an RTL-SDR dongle connected to MATLAB software. The signal detection is done by using four techniques: the artificial neural network (ANN), support vector machine (SVM), Decision Trees (TREE), and k-nearest neighbors (KNN). This article comparatively analyzed the performance of the classifiers to identify the best method for spectrum sensing between the three techniques. The performance evaluation of our proposed model is the probability of detection (Pd) and the false alarm probability (Pfa). Results show also that the sensing is susceptible to signal to noise ratio value. This comparative study has been demonstrated that the spectrum sensing operation by ANN and SVM can be more accurate than KNN, TREE, and some other classical detectors.

New generations of mobile telephony succeed every decade, each bringing an evolution or even a revolution. Nowadays, the Internet of Things and the tactile Internet are starting to grow, and 5G technology is there to enable these services. 5G technology has introduced three types of services, namely eMBB (for services requiring very high bit rates), mMTC (for massive connection of user equipment), and uRLLC (for critical services requiring very high reliability and extremely reduced latency). In this paper, we have dealt with some issues encountered by uRLLC services for tactile Internet services. In this article, we have studied the transmission of very small packets as required by the 5G uRLLC services. We also examined the probability of transmission error and its variation concerning the transmission delay and the length of the packet transmitted. This study was conducted considering its application in the Tactile Internet.

Cloud computing Concept has been recognized as the next generation computing infrastructure. In addition, the explosive expansion of mobile devices usage around all over the world leads to more requirements in terms of resources, environment, and security. Therefore, mobile cloud computing (MCC) paradigm emerges, and it is considered as the future technology for mobile services and applications. Furthermore, Network Coding (NC) is a novel technique that aims to improve performance and throughput in wireless communication. Our work's concern is to build a delay and throughput optimized network coding scheme with mobile cooperation for broadcast based Mobile clouds applications. Hence, after a deep literature study, we proposed a scheme that introduces the use of cooperation with Instantly Decodable Network Coding. In order to evaluate our proposition, we carried out simulation experiments to test it and compare it with relevant existing solutions. Finally, the results are very encouraging and the proposed scheme gives a satisfactory delay compared to the existing solutions.

Vehicular Ad-Hoc Network (VANET) has recently become an attractive area of research in the wireless networking. The mobility model is considered one of the most critical issues in a simulation of VANET, which can create realistic moving behavior of each vehicle in the network. Thus, the mobility model play a vital role in determining the performance of VANET system. This paper will tackle five types of mobility models, proposed in the recent research literature, based on the Impulse Radio Ultra wide-Band (IR-UWB) technology to enable communication between vehicles and evaluate their impact on packet delivery carried out in OMNeT++ simulator.

In this paper, based on a set of Ultra Wide Bandwidth Channel (UWB) measurements the dependance between Path Loss and frequency is investigated. The measurements are provided in frequency domain over a channel bandwidth 6 GHz (from 3 to 9 GHz) in different locations. A Vector Network Analyzer is used to measure the frequency channel response of the frequency bandwidth of interest. An Omni-directional antennas are used for both the transmitter (Tx) and receiver (Rx) antennas. Probability Density Function (PDF) and Cumulative Distribution Function (CDF) of received signal, power variation and path loss fluctuations are evaluated in frequency domain. An investigation of path loss shows no dependency between frequency and the path loss, but the path loss and central frequency present a correlation.

1Next generation wireless personal area networks (WPAN) are intended for a variety of applications. The Multiband OFDM Alliance (MBOA) is currently developing a new physical layer (PHY) and medium access control (MAC) protocol, which fit the needs of this mass market. The MBOA standards provide wireless technology offering data rates of up to 480 Mbit/s [1]. Motivated by the sub-band approach, this paper presents measurement analysis of the mean (excess) delay, RMS delay spread and number of significants paths for different locations. First, we present the channel measurements based in this study. Secondly, the interpretation of the obtained results show a correlation between the channel bandwidth and the tow main dispersion parameters namely τrmsand τmin all tested settings, Laboratory, Corridor and Outdoor. Our measurement included terminal separations of 1 – 14 meters, the based data are for terminal separation 6 m.

The paper aims at characterizing the second order statistics of indoor ultra-wideband (UWB) channels using channel sounding techniques. We present measurement results for different scenarios conducted in a laboratory setting at Institut Eurecom. These are based on a eigendecomposition of the channel autocovariance matrix, which allows for determining the growth in the number of significant degrees of freedom of the channel process as a function of the signaling bandwidth as well as the statistical correlation between different propagation paths. We show empirical eigenvalue distributions as a function of the signal bandwidth for both line-of-sight and non line-of-sight situations. Furthermore, we give examples where paths from different propagation clusters (possibly arising from reflection or diffraction) show strong statistical dependence.