This study addresses the critical gap in Arabic natural language processing by developing an effective Arabic Reverse Dictionary (RD) system that enables users to find words based on their descriptions or meanings. We present a novel transformer-based approach with a semi-encoder neural network architecture featuring geometrically decreasing layers, achieving state-of-the-art results on Arabic RD tasks. Our methodology incorporates a comprehensive dataset construction process and establishes formal quality standards for Arabic lexicographic definitions. Experiments with various pre-trained models demonstrate that Arabic-specific models significantly outperform general multilingual embeddings, with ARBERTv2 achieving the best ranking score (0.0644). Additionally, we provide a formal abstraction of the reverse dictionary task that enhances theoretical understanding and develop a modular, extensible Python library (RDTL) with configurable training pipelines. Our analysis of dataset quality reveals important insights for improving Arabic definition construction, leading to eight specific standards for building high-quality reverse dictionary resources. This work makes a significant contribution to Arabic computational linguistics and provides valuable tools for language learning, academic writing, and professional communication in Arabic.

With the increasing reliance on the biometric-based authentication systems, such as face recognition, in applications within the IoT and edge networks, guaranteeing proper service functionality while safeguarding individual biometric privacy has become a critical concern. However, most existing face privacy protection approaches mainly focus on preserving the machine-recognizable identity information, inadvertently compromising individual privacy. To tackle this challenge, a novel Face Privacy-Enhancing Network (FPE-Net) is proposed, which consists of two primary stages: biometric encryption and face reconstruction. Specifically, a linear encryption module is designed in the first stage for obfuscating the original identity information, which is later integrated into the depth features of the target face via an identity injector. Notably, the identity encryption process operates independently of the deep generative network, enabling greater flexibility and efficiency for key configuration. Then in the second stage, a face decoder is utilized to synthesize the photo-realistic face. Moreover, such face not only prevents cross-matching with biometric databases but also preserves recognition utility, owing to the linear encryption mechanism and loss design. Extensive quantitative and qualitative experimental results demonstrate the feasibility of FPE-Net model, which outperforms existing state-of-the-art approaches in terms of privacy protection.

The increasing complexity and connectivity of Internet of Things (IoT) environments have made them targets for advanced cyber-attacks. Recently, Deep Learning (DL)-based intrusion detection systems have shown remarkable success in identifying malicious activities within IoT traffic. In particular, Graph Neural Networks (GNNs) have emerged as effective solutions. However, GNNs come with inherent challenges, including high computational complexity and a black-box nature, which limit their transparency and interoperability. In this paper, we introduce a hybrid framework that combines a GNN model, named AD-MGCN, with a Large Language Model (LLM) to address these limitations. The proposed AD-MGCN leverages a Markov-based multi-step diffusion process to enhance feature propagation, reduce noisy edges, and improve classification performance across both frequent and rare attack types. In addition, a fine-tuned instruction-based LLM (Falcon-7B Instruct) generates natural language explanations that translate model predictions into human-understandable insights. We evaluated our framework on the Edge-IIoTset dataset, which includes diverse IoT attack scenarios. The experimental results show that AD-MGCN achieves an accuracy of 97.38%, significantly outperforming the baseline GCN models. Furthermore, the LLM explanations achieve an average clarity score of 4.2/5 in expert evaluations, improving the transparency of the model for cybersecurity analysts. These results demonstrate the potential of AD-MGCN as a reliable, efficient, and interpretable solution for securing modern IoT ecosystems.

Efficient route discovery is a key analytical challenge in high-mobility IoT environments such as Vehicular Ad-hoc networks (VANETs), where frequent topology changes degrade communication reliability. This study leverages intelligent data analytics to compare heuristic (A, Greedy Best-First Search (GBFS )) and non-heuristic (Dijkstra, Bellman-Ford) algorithms within a grid-based vehicular network under varying densities. Using metrics such as Route Discovery Time (RDT), Route Discovery Messages (RDM), and Path Length (PL), we evaluate their performance in both sparse and dense topologies. Results show that in sparse networks, heuristic algorithms — particularly A — offer the best trade-of between discovery efficiency and path optimality. In contrast, GBFS sacrifices accuracy for speed, while non-heuristic methods incur higher overhead. In dense scenarios, A maintains superior performance, demonstrating scalability and robustness. These insights ofer valuable predictive analytics for designing routing protocols in dynamic IoT environments, enabling intelligent decision-making for connected vehicle systems.

The Internet of Things (IoT) has garnered significant attention from both industries and the research community. The diverse nature of IoT devices makes them a prime target for cybercriminals. An intelligent intrusion detection system (IDS) can quickly identify multiple types of cyberattacks within an IoT system. However, operational efficiency and safety become challenging issues when managing limited labeled data in IoT networks. This article proposes a novel cyberattack detection scheme using few-shot learning (FSL). The proposed scheme employs a self-attention mechanism with a Siamese network that learns to recognize normal and malicious network traffic patterns through FSL. The Siamese network architecture facilitates efficient sample comparison by learning a shared representation. Incorporating an attention mechanism further enhances its ability to focus on discriminative features, improving attack detection accuracy for rapidly evolving intrusion patterns. The effectiveness of the proposed framework is evaluated through extensive experiments on the latest Edge-IIoTset dataset. The experimental findings demonstrate that the proposed IDS achieved the best accuracy of 99.75% with the complete dataset. In a few-shot performance evaluation, the designed architecture achieved the best accuracy of 78.69%, 81.19%, and 81.83% for 1 shot, 5 shots, and 10 shots, respectively.

In intelligent transportation systems (ITS), one of the main challenges is still finding efficient routes in highly dynamic vehicular ad hoc networks (VANETs). Even though traditional algorithms provide trade-offs between adaptability (responsiveness), computational complexity, and path optimality, their performance frequently deteriorates in situations that change quickly. In this study, a quantum-inspired routing algorithm based on Grover’s Search is evaluated in a traditional grid-based simulation setting. Through the simulation of quantum amplitude amplification, Grover’s method seeks to find effective paths with a shorter discovery time. Its performance is assessed against classical baselines—Dijkstra, A∗, and Greedy Best-First Search (GBFS)—under varying sparse traffic conditions, using three key metrics: Route Discovery Time (RDT), Path Length (PL), and Route Discovery Messages (RDM). Experimental results show that Grover’s Search consistently achieves lower RDT, indicating strong potential for latency-sensitive scenarios. Despite its higher message overhead, due to classical simulation of quantum processes, the approach underscores the promise of quantum-inspired routing and the need for further exploration of native quantum solutions in future ITS applications.

Abstract With the emergence of fog computing, new paradigms for data processing and management for IoT devices have been established in the quickly changing world of teaching/learning. This study addresses the complex issues brought about by the infiltration of diverse data sources by investigating novel approaches to strengthen data security and enhance access control mechanisms in fog computing environments. The commonly used cryptographic technique known as CP‐ABE is renowned for providing accurate access control. Unfortunately, current multi‐authority CP‐ABE methods have difficulties when implemented on low‐resource IoT devices. These techniques are not appropriate for resource‐constrained IoT devices since the sizes of the secret key and ciphertext grow in proportion to the number of attributes. In this paper, a novel multi‐authority CP‐ABE approach, called MA‐based CP‐ABE, efficiently tackles these issues by optimizing the length of secret keys and ciphertext. Users' secret keys are always the same size, no matter how many attributes they own. Moreover, MA‐based CP‐ABE ensures that the size of the ciphertext scales linearly with the number of authorities rather than characteristics, which makes it a sensible option for devices with restricted resources. A Generative AI approach has also been integrated along with CP‐ABE to make sure that the IoT data is secure and privacy is maintained. As per the security and experimental analysis, the proposed approach is considered secure and suitable for IoT‐based applications.

Abstract This article presents an innovative concept that harnesses the capabilities of large language models (LLMs) to revolutionize human‐robot interaction. This work aims to connect large language models with the Robot Operating System (ROS), the primary development framework for robotics applications. We develop a package for ROS that seamlessly integrates ChatGPT with ROS2‐based robotic systems. The core idea is to leverage prompt engineering with LLMs, utilizing unique properties such as ability eliciting, chain‐of‐thought, and instruction tuning. The concept employs ontology development to convert unstructured natural language commands into structured robotic instructions specific to the application context through prompt engineering. We capitalize on LLMs' zero‐shots and few‐shots learning capabilities by eliciting structured robotic commands from unstructured human language inputs. To demonstrate the feasibility of this concept, we implemented a proof‐of‐concept that integrates ChatGPT with ROS2, showcasing the transformation of human language instructions into spatial navigation commands for a ROS2‐enabled robot. Besides, we quantitatively evaluated this transformation over three use cases (ground robot, unmanned aerial vehicle, and Robotic arm) and five LLMs (LLaMA‐7b, LLaMA2‐7b, LLaMA2‐70b, GPT‐3.5, and GPT‐4) on a set of 3000 natural language commands. Our system serves as a new stride towards Artificial General Intelligence (AGI) and paves the way for the robotics and natural language processing communities to collaborate in creating novel, intuitive human‐robot interactions. The open‐source implementation of our system on ROS 2 is available on GitHub.

Alzheimer's disease is a severe brain disorder that causes harm in various brain areas and leads to memory damage. The limited availability of labeled medical data poses a significant challenge for accurate Alzheimer's disease detection. There is a critical need for effective methods to improve the accuracy of Alzheimer's disease detection, considering the scarcity of labeled data, the complexity of the disease, and the constraints related to data privacy. To address this challenge, our study leverages the power of Big Data in the form of pre-trained Convolutional Neural Networks (CNNs) within the framework of Few-Shot Learning (FSL) and ensemble learning. We propose an ensemble approach based on a Prototypical Network (ProtoNet), a powerful method in FSL, integrating various pre-trained CNNs as encoders. This integration enhances the richness of features extracted from medical images. Our approach also includes a combination of class-aware loss and entropy loss to ensure a more precise classification of Alzheimer's disease progression levels. The effectiveness of our method was evaluated using two datasets, the Kaggle Alzheimer dataset, and the ADNI dataset, achieving an accuracy of 99.72% and 99.86%, respectively. The comparison of our results with relevant state-of-the-art studies demonstrated that our approach achieved superior accuracy and highlighted its validity and potential for real-world applications in early Alzheimer's disease detection.

In the Internet of Medical Things (IoMT), de novo peptide sequencing prediction is one of the most important techniques for the fields of disease prediction, diagnosis, and treatment. Recently, deep-learning-based peptide sequencing prediction has been a new trend. However, most popular deep learning models for peptide sequencing prediction suffer from poor interpretability and poor ability to capture long-range dependencies. To solve these issues, we propose a model named SeqNovo, which has the encoding-decoding structure of sequence to sequence (Seq2Seq), the highly nonlinear properties of multilayer perceptron (MLP), and the ability of the attention mechanism to capture long-range dependencies. SeqNovo use MLP to improve the feature extraction and utilize the attention mechanism to discover key information. A series of experiments have been conducted to show that the SeqNovo is superior to the Seq2Seq benchmark model, DeepNovo. SeqNovo improves both the accuracy and interpretability of the predictions, which will be expected to support more related research.

Optimal routing in Vehicular Ad-hoc Networks (VANETs) presents significant challenges due to the dynamic nature of vehicular movements. Cluster-based routing schemes have emerged as promising strategies to manage the scalability and efficiency of routing in VANETs. However, they often face limitations in coverage due to their inherent reliance on distributive route discovery methods. This paper introduces the Software-Defined networking-enabled cluster-based Evolving Graph Routing Scheme (SE-CEGRS), designed to address these coverage challenges by integrating the flexibility and centralized control of Software-Defined Networking (SDN) with the established Cluster-based VANET-oriented Evolving Graph (CVoEG) framework. Unlike traditional CVoEG, which employs a distributive approach with Evolving Graph Dijkstra (EG-Dijkstra) for both intra- and inter-cluster communications, SE-CEGRS maintains this strategy for intra-cluster scenarios but shifts to a centralized model for inter-cluster communications. Our evaluation demonstrates that SE-CEGRS significantly outperforms conventional CVoEG in inter-cluster communication scenarios by offering reduced discovery times, lower communication overhead, and shorter average path lengths. The findings underscore SECEGRS’s potential to enhance the efficiency and scalability of routing in VANETs, pointing toward a new direction for future research and development in vehicular communication technologies.

Deep Learning (DL) has demonstrated remarkable success in various Remote Sensing Image (RSI) analysis applications. However, due to disparities in data distributions, DL models find it challenging to generalize meaningfully, especially when training and testing datasets are collected at different locations with varying resolutions, by different sensors, or due to climatic conditions. DL techniques applied to RSI have shown interest in domain adaptation as a suitable solution for addressing discordance among domains. In this study, we focus specifically on two DL approaches for Domain Adaptation (DA) in RSI: Self-Supervised Learning (SSL) and Graph Neural Networks (GNNs). First, we elucidate the motivation for utilizing DA techniques to address challenges in the field of RSI, along with their applications in conjunction with GNNs and SSL. Then, we present related surveys on domain adaptation and provide background information. This paper suggests a classification system for DL approaches and draws attention to challenges and research directions for DA in RSI. This study aims to deliver scholars in the remote sensing field with current references on DA using SSL and GNNs.

The integration of Industrial Internet of Things (IIoT) technology into the industrial sector has produced numerous significant advantages. However, the notable concern remains the absence of robust security and privacy measures in these interconnected critical environments. To secure IIoT networks, several researchers and experts employ intrusion detection systems (IDS) for detecting cyberattacks. The current systems exhibit efficient performance when handling a few categories of attack classes, even in the presence of slight imbalances. However, these models face challenges when confronted with vast categories of attack classes and highly imbalanced data. To tackle these issues, this study introduces an attention-based hybrid deep learning (AB-HDL) model designed to monitor network traffic and predict cyberattacks within the network. The proposed model comprises an attention mechanism and a hybrid deep learning model that integrates convolutional neural networks (CNN) and an autoencoder (AE). The effectiveness of the proposed AB-HDL is assessed using publicly accessible datasets: Edge-IIoTset and X-IIoTID. To ascertain the efficacy of AB-HDL, a comparative analysis is conducted with various other machine learning (ML) and deep learning (DL) algorithms. The outcome analysis indicates that the proposed AB-HDL surpasses the performance of the other algorithms and exhibits optimal efficiency in detecting cyber attacks within IIoT networks.

Recent advances in intelligent wearable devices have brought tremendous chances for the development of healthcare monitoring system. However, the data collected by various sensors in it are user-privacy-related information. Once the individuals’ privacy is subjected to attacks, it can potentially cause serious hazards. For this reason, a feasible solution built upon the compression-encryption architecture is proposed. In this scheme, we design an Adaptive Sparse Basis Compressive Sensing (ASB-CS) model by leveraging Singular Value Decomposition (SVD) manipulation, while performing a rigorous proof of its effectiveness. Additionally, incorporating the Parametric Deformed Exponential Rectified Linear Unit (PDE-ReLU) memristor, a new fractional-order Hopfield neural network model is introduced as a pseudo-random number generator for the proposed cryptosystem, which has demonstrated superior properties in many aspects, such as hyperchaotic dynamics and multistability. To be specific, a plain medical image is subjected to the ASB-CS model and bidirectional diffusion manipulation under the guidance of the key-controlled cipher flows to yield the corresponding cipher image without visual semantic features. Ultimately, the simulation results and analysis demonstrate that the proposed scheme is capable of withstanding multiple security attacks and possesses balanced performance in terms of compressibility and robustness.

Nowadays, there is global consensus on the threats to forests and their crucial role in mitigating global warming and its impact on Earth’s biodiversity. Both private and public entities, alongside governments, have engaged the most advanced technologies to safeguard and monitor forests against encroachment. This article examines the application of various drone technologies in the surveillance of forest areas. The system described herein employs drones to continuously survey forests, recording any changes, particularly in instances of encroachment or fire. The data captured are transmitted to a control unit for subsequent analysis. To circumvent the risk of task failure due to technical challenges, monitoring tasks within a predefined flight duration are allocated to the available drones. Given the critical nature of timing in the success of these tasks, this study addresses the forest monitoring challenge by seeking to minimize the maximum time required to complete all monitoring tasks. This challenge was addressed through the development of a suite of enhanced algorithms aimed at optimizing task efficiency. The primary goal of the proposed methodology is to afford the monitoring system additional time, thereby enabling the handling of an increased volume of tasks and providing support to firefighting teams in responding to forest fires. The system’s adaptability to new, unforeseen forest fire scenarios through the generation of novel solutions is also discussed. Extensive testing involving 1350 different scenarios has demonstrated the effectiveness of the proposed algorithms in reducing the maximum time needed for the completion of surveillance tasks by drones. The most effective algorithm was the two-group clustering algorithm (TGC), which achieved a success rate of 97.2%, with an average gap of less than 0.001 and an average computation time of 0.016 s. Furthermore, the application of this methodology to a case study of the Daintree Rainforest in Australia showcases the potential and real-world applicability of the proposed system, highlighting its performance and adaptability.

In the dynamic field of the Industrial Internet of Things (IIoT), the networks are increasingly vulnerable to a diverse range of cyberattacks. This vulnerability necessitates the development of advanced intrusion detection systems (IDSs). Addressing this need, our research contributes to the existing cybersecurity literature by introducing an optimized Intrusion Detection System based on Deep Transfer Learning (DTL), specifically tailored for heterogeneous IIoT networks. Our framework employs a tri-layer architectural approach that synergistically integrates Convolutional Neural Networks (CNNs), Genetic Algorithms (GA), and bootstrap aggregation ensemble techniques. The methodology is executed in three critical stages: First, we convert a state-of-the-art cybersecurity dataset, Edge_IIoTset, into image data, thereby facilitating CNN-based analytics. Second, GA is utilized to fine-tune the hyperparameters of each base learning model, enhancing the model’s adaptability and performance. Finally, the outputs of the top-performing models are amalgamated using ensemble techniques, bolstering the robustness of the IDS. Through rigorous evaluation protocols, our framework demonstrated exceptional performance, reliably achieving a 100% attack detection accuracy rate. This result establishes our framework as highly effective against 14 distinct types of cyberattacks. The findings bear significant implications for the ongoing development of secure, efficient, and adaptive IDS solutions in the complex landscape of IIoT networks.

Ambient computing is gaining popularity as a major technological advancement for the future. The modern era has witnessed a surge in the advancement in healthcare systems, with viable radio frequency solutions proposed for remote and unobtrusive human activity recognition (HAR). Specifically, this study investigates the use of Wi-Fi channel state information (CSI) as a novel method of ambient sensing that can be employed as a contactless means of recognizing human activity in indoor environments. These methods avoid additional costly hardware required for vision-based systems, which are privacy-intrusive, by (re)using Wi-Fi CSI for various safety and security applications. During an experiment utilizing universal software-defined radio (USRP) to collect CSI samples, it was observed that a subject engaged in six distinct activities, which included no activity, standing, sitting, and leaning forward, across different areas of the room. Additionally, more CSI samples were collected when the subject walked in two different directions. This study presents a Wi-Fi CSI-based HAR system that assesses and contrasts deep learning approaches, namely convolutional neural network (CNN), long short-term memory (LSTM), and hybrid (LSTM+CNN), employed for accurate activity recognition. The experimental results indicate that LSTM surpasses current models and achieves an average accuracy of 95.3% in multi-activity classification when compared to CNN and hybrid techniques. In the future, research needs to study the significance of resilience in diverse and dynamic environments to identify the activity of multiple users.

Blockchain (BC) and Information for Operational and Tactical Analysis (IOTA) are distributed ledgers that record a huge number of transactions in multiple places at the same time using decentralized databases. Both BC and IOTA facilitate Internet-of-Things (IoT) by overcoming the issues related to traditional centralized systems, such as privacy, security, resources cost, performance, and transparency. Still, IoT faces the potential challenges of real-time processing, resource management, and storage services. Edge computing (EC) has been introduced to tackle the underlying challenges of IoT by providing real-time processing, resource management, and storage services nearer to IoT devices on the network’s edge. To make EC more efficient and effective, solutions using BC and IOTA have been devoted to this area. However, BC and IOTA came with their pitfalls. This survey outlines the pitfalls of BC and IOTA in EC and provides research directions to be investigated further.

Medical imaging is an essential tool for diagnosing various healthcare diseases and conditions. However, analyzing medical images is a complex and time-consuming task that requires expertise and experience. This article aims to design a decision support system to assist healthcare providers and patients in making decisions about diagnosing, treating, and managing health conditions. The proposed architecture contains three stages: 1) data collection and labeling, 2) model training, and 3) diagnosis report generation. The key idea is to train a deep learning model on a medical image dataset to extract four types of information: the type of image scan, the body part, the test image, and the results. This information is then fed into ChatGPT to generate automatic diagnostic reports. The proposed system has the potential to enhance decision-making, reduce costs, and improve the capabilities of healthcare providers. The efficacy of the proposed system is analyzed by conducting extensive experiments on a large medical image dataset. The experimental outcomes exhibited promising performance for automatic diagnosis through medical images.

This paper proposes a method for automatically monitoring and analyzing the evolution of complex geographic objects. The objects are modeled as a spatiotemporal graph, which separates filiation relations, spatial relations, and spatiotemporal relations, and is analyzed by detecting frequent sub-graphs using constraint satisfaction problems (CSP). The process is divided into four steps: first, the identification of complex objects in each satellite image; second, the construction of a spatiotemporal graph to model the spatiotemporal changes of the complex objects; third, the creation of sub-graphs to be detected in the base spatiotemporal graph; and fourth, the analysis of the spatiotemporal graph by detecting the sub-graphs and solving a constraint network to determine relevant sub-graphs. The final step is further broken down into two sub-steps: (i) the modeling of the constraint network with defined variables and constraints, and (ii) the solving of the constraint network to find relevant sub-graphs in the spatiotemporal graph. Experiments were conducted using real-world satellite images representing several cities in Saudi Arabia, and the results demonstrate the effectiveness of the proposed approach.

The Red Palm Weevil (RPW) is a highly destructive insect causing economic losses and impacting palm tree farming worldwide. This paper proposes an innovative approach for sustainable palm tree farming by utilizing advanced technologies for early detection and management of RPW. Our approach combines computer vision, deep learning (DL), the Internet of Things (IoT), and geospatial data to effectively detect and classify RPW-infested palm trees. The main phases include; (1) DL Classification using sound data from IoT devices, (2) palm tree detection using YOLOv8 on UAV images, and (3) RPW mapping using geospatial data. Our custom DL model achieves 100% precision and recall in detecting and localizing infested palm trees. The integration of geospatial data enables the creation of a comprehensive RPW distribution map for Efficient monitoring and targeted management strategies. This technology-driven approach benefits agricultural authorities, farmers, and researchers in managing RPW infestations, safeguarding palm tree plantations’ productivity.

Given the importance of forests and their role in maintaining the ecological balance, which directly affects the planet, the climate, and the life on this planet, this research presents the problem of forest fire monitoring using drones. The forest monitoring process is performed continuously to track any changes in the monitored region within the forest. During fires, drones’ capture data is used to increase the follow-up speed and enhance the control process of these fires to prevent their spread. The time factor in such problems determines the success rate of the fire extinguishing process, as appropriate data at the right time may be the decisive factor in controlling fires, preventing their spread, extinguishing them, and limiting their losses. Therefore, this research presented the problem of monitoring task scheduling for drones in the forest monitoring system. This problem is solved by developing several algorithms with the aim of minimizing the total completion time required to carry out all the drones’ assigned tasks. System performance is measured by using 990 instances of three different classes. The performed experimental results indicated the effectiveness of the proposed algorithms and their ability to act efficiently to achieve the desired goal. The algorithm RID achieved the best performance with a percentage rate of up to 90.3% with a time of 0.088 seconds.

The increasing trend of the Industrial Internet of Things (IIoT) within industrial environments magnifies the risk of security breaches and vulnerabilities. Maintaining confidentiality is a pivotal requirement for effectively establishing the IIoT environment. To promptly detect malicious endeavors, integrating an intrusion detection system (IDS) becomes imperative for continuously monitoring IIoT activities. The sophisticated automated IDSs are built upon the foundation of machine learning (ML) and deep learning (DL). However, these algorithms encounter challenges related to heavily imbalanced training data and the need for accurate predictions in a short timeframe. This paper introduces an attention-based deep neural network (ABDNN) designed to tackle these challenges for intrusion detection within the IIoT environment. The attention mechanism plays a pivotal role in determining the significance of each attribute in the input data. Subsequently, the deep neural network (DNN) comes into play, leveraging the previously determined attribute importance to predict network behaviors. This process yields the advantage of predicting network behaviors more efficiently in less time. The performance of the proposed ABDNN model was evaluated using the X-IIoTID dataset. To validate its effectiveness, a comparison was made between the performance of the proposed model and that of state-of-the-art approaches. This comparative analysis serves to validate the superior performance of the proposed ABDNN model.

The Internet of Things (IoT) is a global network that connects a large number of smart devices. MQTT is a de facto standard, lightweight, and reliable protocol for machine-to-machine communication, widely adopted in IoT networks. Various smart devices within these networks are employed to handle sensitive information. However, the scale and openness of IoT networks make them highly vulnerable to security breaches and attacks, such as eavesdropping, weak authentication, and malicious payloads. Hence, there is a need for advanced machine learning (ML) and deep learning (DL)-based intrusion detection systems (IDS). Existing ML-based IoT-IDSs face several limitations in effectively detecting malicious activities, mainly due to imbalanced training data. To address this, this study introduces a transformer neural network-based intrusion detection system (TNN-IDS) specifically designed for MQTT-enabled IoT networks. The proposed approach aims to enhance the detection of malicious activities within these networks. The TNN-IDS leverages the parallel processing capability of the Transformer Neural Network, which accelerates the learning process and results in improved detection of malicious attacks. To evaluate the performance of the proposed system, it was compared with various IDSs based on ML and DL approaches. The experimental results demonstrate that the proposed TNN-IDS outperforms other systems in terms of detecting malicious activity. The TNN-IDS achieved optimum accuracies reaching 99.9% in detecting malicious activities.

Nowadays, developing environmental solutions to ensure the preservation and sustainability of natural resources is one of the core research topics for providing a better life quality. Using renewable energy sources, such as solar energy, is one of the solutions that can reduce the overuse of natural resources. This research aims to boost the efficiency of solar energy plants by proposing a novel approach to optimize the total flying time of battery-based drone systems to enhance the performance of solar plant systems. The contribution of the proposed approach is to solve scheduling problems based on timing constraints to monitor the solar plant. The main objective of the proposed approach is to maximize the drone’s minimum total flying time, which will increase the availability and reliability of the solar plant monitoring system. Time to empty values is calculated based on battery degradation rates. This problem is proven to be NP-hard. Four categories of enhanced algorithms were developed to solve drones’ scheduling problems in handling various tasks within multiple errands in the extent of solar parks in the monitored power plant to achieve the desired objective. Experimental results of the presented algorithms showed that the$M2S$algorithm has a stable performance behavior in all conducted experiments.

With the advancement in technology, digital images can easily be transmitted and stored over the Internet. Encryption is used to avoid illegal interception of digital images. Encrypting large-sized colour images in their original dimension generally results in low encryption/decryption speed along with exerting a burden on the limited bandwidth of the transmission channel. To address the aforementioned issues, a new encryption scheme for colour images employing convolutional autoencoder, DNA and chaos is presented in this paper. The proposed scheme has two main modules, the dimensionality conversion module using the proposed convolutional autoencoder, and the encryption/decryption module using DNA and chaos. The dimension of the input colour image is first reduced from N × M × 3 to P × Q gray-scale image using the encoder. Encryption and decryption are then performed in the reduced dimension space. The decrypted gray-scale image is upsampled to obtain the original colour image having dimension N × M × 3 . The training and validation accuracy of the proposed autoencoder is 97% and 95%, respectively. Once the autoencoder is trained, it can be used to reduce and subsequently increase the dimension of any arbitrary input colour image. The efficacy of the designed autoencoder has been demonstrated by the successful reconstruction of the compressed image into the original colour image with negligible perceptual distortion. The second major contribution presented in this paper is an image encryption scheme using DNA along with multiple chaotic sequences and substitution boxes. The security of the proposed image encryption algorithm has been gauged using several evaluation parameters, such as histogram of the cipher image, entropy, NPCR, UACI, key sensitivity, contrast, and so on. The experimental results of the proposed scheme demonstrate its effectiveness to perform colour image encryption.

Over the past few years, total financial investment in the agricultural sector has increased substantially. Palm tree is important for many countries’ economies, particularly in northern Africa and the Middle East. Monitoring in terms of detection and counting palm trees provides useful information for a variety of stakeholders; it helps in yield estimation and examination to ensure better crop quality and prevent pests, diseases, better irrigation and other potential threats. Despite their importance, these information still difficult to obtain. In this study, we systematically review research articles between 2011 and 2021 on artificial intelligence (AI) technology for smart palm tree detection. A systematic review (SR) was performed using the PRISMA approach based on a four-stage selection process. Twenty-two articles were included for the synthesis activity reached from the search strategy alongside the inclusion criteria in order to answer tow two main research questions. The study's findings reveal patterns, relationships, networks, and trends in the application of artificial intelligence in the palm tree detection over the last decade. Overall, despite the good results achieved in most of the studies, the effective and efficient management of large-scale palm plantations still a challenge. In addition, countries which their economy strongly related to intelligent palm services especially in North Africa should give more attention to this kind of studies. The results of this research could benefit both the research community and stakeholders.

Abstract In many cases, images contain sensitive information and patterns that require secure processing to avoid risk. It can be accessed by unauthorized users who can illegally exploit them to threaten the safety of people’s life and property. Protecting the privacies of the images has quickly become one of the biggest obstacles that prevent further exploration of image data. In this paper, we propose a novel privacy-preserving scheme to protect sensitive information within images. The proposed approach combines deoxyribonucleic acid (DNA) sequencing code, Arnold transformation (AT), and a chaotic dynamical system to construct an initial S-box. Various tests have been conducted to validate the randomness of this newly constructed S-box. These tests include National Institute of Standards and Technology (NIST) analysis, histogram analysis (HA), nonlinearity analysis (NL), strict avalanche criterion (SAC), bit independence criterion (BIC), bit independence criterion strict avalanche criterion (BIC-SAC), bit independence criterion nonlinearity (BIC-NL), equiprobable input/output XOR distribution, and linear approximation probability (LP). The proposed scheme possesses higher security wit NL = 103.75, SAC ≈ 0.5 and LP = 0.1560. Other tests such as BIC-SAC and BIC-NL calculated values are 0.4960 and 112.35, respectively. The results show that the proposed scheme has a strong ability to resist many attacks. Furthermore, the achieved results are compared to existing state-of-the-art methods. The comparison results further demonstrate the effectiveness of the proposed algorithm.

Abstract Wireless sensor networks (WSNs) have recently attracted greater attention worldwide due to their practicality in monitoring, communicating, and reporting specific physical phenomena. The data collected by WSNs is often inaccurate as a result of unavoidable environmental factors, which may include noise, signal weakness, or intrusion attacks depending on the specific situation. Sending high‐noise data has negative effects not just on data accuracy and network reliability, but also regarding the decision‐making processes in the base station. Anomaly detection, or outlier detection, is the process of detecting noisy data amidst the contexts thus described. The literature contains relatively few noise detection techniques in the context of WSNs, particularly for outlier‐detection algorithms applying time series analysis, which considers the effective neighbors to ensure a global‐collaborative detection. Hence, the research presented in this article is intended to design and implement a global outlier‐detection approach, which allows us to find and select appropriate neighbors to ensure an adaptive collaborative detection based on time‐series analysis and entropy techniques. The proposed approach applies a random forest algorithm for identifying the best results. To measure the effectiveness and efficiency of the proposed approach, a comprehensive and real scenario provided by the Intel Berkeley Research Laboratory has been simulated. Noisy data have been injected into the collected data randomly. The results obtained from the experiment then conducted experimentation demonstrate that our approach can detect anomalies with up to 99% accuracy.

The breakdown of vital communication infrastructures is one of the most common characteristics of all disasters. It can cause severe communication problems such as time delays and data loss, which cause deterioration in system performance. New techniques are needed to cope with such situations, many of which have been made possible due to the ongoing evolution of artificial intelligence technologies. In this study, we consider the case of a network consisting of several router allocation problems in situations of high priority and emergency data allocation. A novel network component called the scheduler is introduced and window constraints for routers are imposed. To solve the studied problem, four different algorithms are developed in this work. These algorithms were then applied in a particular scenario consisting of several routers and 2200 instances. In terms of the gap and running time, the proposed algorithms provide acceptable results. The best performances were achieved using the critical packet algorithm for 80% of instances with an average gap value of 0.009 and an average time of 0.209 s.

The COVID-19 pandemic is causing a global health crisis. Public spaces need to be safeguarded from the adverse effects of this pandemic. Wearing a facemask becomes one of the effective protection solutions adopted by many governments. Manual real-time monitoring of facemask wearing for a large group of people is becoming a difficult task. The goal of this paper is to use deep learning (DL), which has shown excellent results in many real-life applications, to ensure efficient real-time facemask detection. The proposed approach is based on two steps. An off-line step aiming to create a DL model that is able to detect and locate facemasks and whether they are appropriately worn. An online step that deploys the DL model at edge computing in order to detect masks in real-time. In this study, we propose to use MobileNetV2 to detect facemask in real-time. Several experiments are conducted and show good performances of the proposed approach (99% for training and testing accuracy). In addition, several comparisons with many state-of-the-art models namely ResNet50, DenseNet, and VGG16 show good performance of the MobileNetV2 in terms of training time and accuracy.

Recently, Convolutional Neural Networks (CNNs) have made a great performance for remote sensing image classification. Plant recognition using CNNs is one of the active deep learning research topics due to its added-value in different related fields, especially environmental conservation and natural areas preservation. Automatic recognition of plants in protected areas helps in the surveillance process of these zones and ensures the sustainability of their ecosystems. In this work, we propose an Enhanced Randomly Initialized Convolutional Neural Network (ERI-CNN) for the recognition of columnar cactus, which is an endemic plant that exists in the Tehuacán-Cuicatlán Valley in southeastern Mexico. We used a public dataset created by a group of researchers that consists of more than 20000 remote sensing images. The experimental results confirm the effectiveness of the proposed model compared to other models reported in the literature like InceptionV3 and the modified LeNet-5 CNN. Our ERI-CNN provides 98% of accuracy, 97% of precision, 97% of recall, 97.5% as f1-score, and 0.056 loss.

Constraint satisfaction problem (CSP) has been actively used for modeling and solving a wide range of complex real-world problems. However, it has been proven that developing efficient methods for solving CSP, especially for large problems, is very difficult and challenging. Existing complete methods for problem-solving are in most cases unsuitable. Therefore, proposing hybrid CSP-based methods for problem-solving has been of increasing interest in the last decades. This paper aims at proposing a novel approach that combines incomplete and complete CSP methods for problem-solving. The proposed approach takes advantage of the group search algorithm (GSO) and the constraint propagation (CP) methods to solve problems related to the remote sensing field. To the best of our knowledge, this paper represents the first study that proposes a hybridization between an improved version of GSO and CP in the resolution of complex constraint-based problems. Experiments have been conducted for the resolution of object recognition problems in satellite images. Results show good performances in terms of convergence and running time of the proposed CSP-based method compared to existing state-of-the-art methods.

In recent years, the Internet of Things (IoT) technology has led to the emergence of multiple smart applications in different vital sectors including healthcare, education, agriculture, energy management, etc. IoT aims to interconnect several intelligent devices over the Internet such as sensors, monitoring systems, and smart appliances to control, store, exchange, and analyze collected data. The main issue in IoT environments is that they can present potential vulnerabilities to be illegally accessed by malicious users, which threatens the safety and privacy of gathered data. To face this problem, several recent works have been conducted using microservices-based architecture to minimize the security threats and attacks related to IoT data. By employing microservices, these works offer extensible, reusable, and reconfigurable security features. In this paper, we aim to provide a survey about microservices-based approaches for securing IoT applications. This survey will help practitioners understand ongoing challenges and explore new and promising research opportunities in the IoT security field. To the best of our knowledge, this paper constitutes the first survey that investigates the use of microservices technology for securing IoT applications.

The amount of remote sensing (RS) data has increased at an unexpected scale, due to the rapid progress of earth-observation and the growth of satellite RS and sensor technologies. Traditional relational databases attend their limit to meet the needs of high-resolution and large-scale RS Big Data management. As a result, massive RS data management is currently one of the most imperative topics. To address this problem, this paper describes a distributed architecture for big RS data storage based on a unified metadata file, pyramid model, and Hilbert curve for data composition and indexing using NoSQL databases (i.e, Apache Hbase). In this paper, a Hadoop-based framework in AzureInsight cloud platform is designed to manage massive RS data in a parallel and distributed way. Experimental results prove that our method has the potential to overcome the weakness of traditional methods. The proposed model is suitable for massive high-resolution image data management.

Wireless sensor network (WSN) is an integral part of Internet of Things (IoT). The sensor nodes in WSN generate large sensing data which is disseminated to intelligent servers using multiple wireless networks. This large data is prone to attacks from malicious nodes which become part of the network, and it is difficult to find these adversaries. The work in this paper presents a mechanism to detect adversaries for the IEEE 802.15.4 standard which is a central medium access protocol used in WSN‐based IoT applications. The collisions and exhaustion attacks are detected based on a soft decision‐based algorithm. In case the QoS of the network is compromised due to large data traffic, the proposed protocol adaptively varies the duty cycle of the IEEE 802.15.4. Simulation results show that the proposed intrusion detection and adaptive duty cycle algorithm improves the energy efficiency of a WSN with a reduced network delay.

Taking appropriate decisions in the academic processes at a university has a great impact on improving the quality of education and can have an important benefit for students, faculty members, and the entire academic community. In this paper, we propose a decision support solution providing accurate analysis, better decision support, and reporting and planning capability to assist decision-makers in order to enhance the quality of educational processes. To achieve this goal, a set of machine learning is used. Experiments are conducted on real data describing the College of Computer Science and Engineering (CCSE) at Taibah University in Saudi Arabia. Results show that we can predict graduation rates in a real case study to support decision-making. In addition, a comparison between four techniques of machine learning namely Support Vector Machine, Naïve Bayes, Decision Tree, and Random Forest is held using accuracy, recall, precision, and F-measure.

Summary Wireless sensor networks (WSNs) consist of small sensors with limited computational and communication capabilities. Reading data in WSN is not always reliable due to open environmental factors such as noise, weakly received signal strength, and intrusion attacks. The process of detecting highly noisy data is called anomaly or outlier detection. The challenging aspect of noise detection in WSN is related to the limited computational and communication capabilities of sensors. The purpose of this research is to design a local time‐series‐based data noise and anomaly detection approach for WSN. The proposed local outlier detection algorithm (LODA) is a decentralized noise detection algorithm that runs on each sensor node individually with three important features: reduction mechanism that eliminates the noneffective features, determination of the memory size of data histogram to accomplish the effective available memory, and classification for predicting noisy data. An adaptive Bayesian network is used as the classification algorithm for prediction and identification of outliers in each sensor node locally. Results of our approach are compared with four well‐known algorithms using benchmark real‐life datasets, which demonstrate that LODA can achieve higher (up to 89%) accuracy in the prediction of outliers in real sensory data.

To be robust, decision-making process must take account the imperfection associated with models. The identification, understanding and propagation of imperfection sources are important. In general, the imperfection in land cover change (LCC) prediction process can be categorized as both aleatory and epistemic. This imperfection, which can be subdivided into parameter and structural model imperfection, is recognized to have an important impact on actual results. Previously, it has been shown that evidence theory can be applied to model aleatory and epistemic imperfection. The objective of this study is to introduce an efficient methodology for the propagation of imperfection using evidence theory in LCC prediction model, which include both parameter and structural model imperfection sources.

Satellite image classification is usually marked by several types of imperfection such as uncertainty, imprecision, and ignorance. Data fusion of additional sensors tries to overcome the types of imperfection by using probability, possibility, and evidence theories. Our approach will lead to improve classification accuracy of satellite images by choosing the optimum theory for a particular image context and proposing a theoretical framework based on a multiagent system and case-based reasoning. We validate our approach trough a set of optical images from the satellite Satellite Positioning and Tracking 4 and radar images from the European Remote Sensing satellite 2, and we show that the overall accuracy is considerably increased from 83% for maximum-likelihood classification applied to multispectral imagery to 94% with the proposed approach.

Land cover interpretation using multisensor remote sensing images is an important task that allows the extraction of information that is useful for several applications. However, satellite images are usually characterized by several types of imperfection, such as uncertainty, imprecision, and ignorance. Using additional sensors can help improve the image interpretation process and decrease the associated imperfections. Fusion methods such as the probability, possibility, and evidence methods can be used to combine information coming from these sensors. An extensive literature has accumulated during the last decade to resolve the issue of choosing the best fusion method, particularly for satellite images. In this paper, we present a semiautomatic approach based on case-based reasoning (CBR) and rule-based reasoning, allowing intelligent fusion method retrieval. This approach takes into account the advantage of data stored in the case base, allowing a more efficient processing and a decrease in image imperfections. The proposed approach incorporates three modules. The first is a learning module based on evaluating three fusion methods (probability, possibility, and evidence) applied to the given satellite images. The second looks for the best fusion method using CBR. The last is devoted to the fusion of multisensor images using the method retrieved by CBR. We validate our approach on a set of optical images coming from the Satellite Pour l'Observation de la Terre 4 and radar images coming from European Remote Sensing Satellite 2 (ERS-2) representing a central Tunisian region.