Brain tumors present significant neurological challenges with high mortality rates, where early diagnosis based on magnetic resonance imaging (MRI) is clinically vital but is hindered by labor-intensive manual segmentation. To overcome limitations in existing approaches, where convolutional neural networks (CNNs) struggle with global context dependencies and Transformers face computational inefficiency, we propose a dual-branched framework called bifurcated fusion dual-encoder convolutional neural networks –frequency Transformer (BiF-DTNet). The BiF-DTNet employs a dual-encoder architecture, where the CNN encoder captures local features. In contrast, the frequency-domain vision Transformer (FViT) encoder efficiently models global context through fast Fourier transform and self-attention. The model incorporates bidirectional spatial-channel attention (BISC) blocks for effective multi-scale feature fusion, enhancing segmentation accuracy. While BiF-DTNet performs feature encoding on two-dimensional (2D) axial slices for computational efficiency, the model reconstructs the final segmentation in a volumetric manner, enabling precise and coherent three-dimensional (3D) tumor prediction. Evaluated on the brain tumor segmentation (BraTS) 2020 and BraTS 2021 benchmarks, BiF-DTNet achieves Dice scores of 80.42%/85.15%/91.34% (enhancing tumor (ET)/tumor core (TC)/whole tumor (WT)) on BraTS 2020 and 85.98%/91.25%/93.64% on BraTS 2021, outperforming state-of-the-art baselines. These results conclusively demonstrate BiF-DTNet’s superiority in precise 3D tumor segmentation, particularly for irregular boundaries and small lesions, through its synergistic integration of local and global features.

Deep Learning (DL) has significantly advanced artificial intelligence (AI) across numerous applications. However, its foundational reliance on centralized data collection introduces critical limitations concerning privacy, security, and scalability. With the continued proliferation of the Internet of Things (IoT), massive volumes of sensitive data are generated at the network edge, necessitating collaborative learning systems capable of securely sharing information without compromising confidentiality. Federated Learning (FL) partially addresses these challenges by enabling on-device model training, but its dependence on a central coordinator remains a core vulnerability, creating communication bottlenecks, fairness constraints, and susceptibility to single-point-of-failure (SPOF) risks. Swarm Learning (SL), developed in collaboration with Hewlett Packard Enterprise (HPE), represents a decentralized advancement that mitigates these limitations by eliminating central orchestration. Through the integration of blockchain technology with distributed machine learning (ML), SL establishes a secure, transparent, and fault-tolerant paradigm for peer-to-peer model exchange and aggregation. This survey provides a comprehensive overview of the architectural foundations, enabling technologies, and applications of SL in sensitive domains, including healthcare, the Internet of Vehicles (IoV), and Industrial IoT. In addition, it introduces a comparative taxonomy that systematically categorizes existing research by scope, methodological approach, and evaluation maturity. The review concludes by identifying key research directions—such as lightweight consensus protocols, energy-efficient optimization, and cross-chain interoperability—to advance the development of practical, secure, and privacy-preserving SL systems.

ABSTRACT This paper presents a new data clustering technique aimed at enhancing the performance of the trainable path‐cost algorithm and reducing the computational complexity of data clustering models. The proposed method facilitates the discovery of natural groupings and behaviours, which is crucial for effective coordination in complex environments. It identifies natural groupings within a set of features and detects the best clusters with similar behaviour in the data, overcoming the limitations of traditional state‐of‐the‐art methods. The algorithm utilises a density peak clustering method to determine cluster centers and then extracts features from paths passing through these peak points (centers). These features are used to train the support vector machine (SVM) to predict the labels of other points. The proposed algorithm is enhanced using two key concepts: first, it employs Q‐Generalised Extreme Value (Q‐GEV) under power normalisation instead of traditional generalised extreme value distributions, thereby increasing modelling flexibility; second, it utilises the random vector functional link (RVFL) network rather than the SVM, which helps avoid overfitting and improves label prediction accuracy. The effectiveness of the proposed clustering algorithm is evaluated through various experiments, including those on UCI benchmark datasets and real‐world data, demonstrating significant improvements across multiple performance metrics, including F1 measure, Jaccard index, purity, and accuracy, highlighting its capability in accurately identifying paths between similar clusters. Its average F1 measure, Jaccard index, purity, and accuracy is measured 76.87%, 56.29%, 80.29%, and 79.64%, respectively.

Fall detection systems are vital for identifying falls and ensuring prompt assistance, reducing the risk of severe injuries. As society progresses and health concerns gain more attention, extensive research has been conducted to mitigate the effects of falls. Integrating these systems with the Internet of Medical Things (IoMT) has significantly advanced healthcare and personal safety. This study proposes MKLS-Net, a deep learning model that combines multikernel (MK) convolution, long-short term memory (LSTM), and self-attention mechanism. MKLS-Net performs feature extraction through MK convolution, passing coarse-grained features to fine-grained ones, minimizing information loss, and improving differentiation between confusing activities. Both LSTM and self-attention help in extracting relatively important information from time series data. The MKLS-Net model demonstrates good fall detection performance on the three publicly available datasets, MobiAct, SisFall, and UniMib-SHAR, with best recognition accuracy of 99.51%, 99.94%, and 99.40%, respectively. In addition, for more analysis, we test the proposed model in the multiclassification stage, and it also shows a high accuracy rate in the SisFall dataset with an average accuracy of 83.91%.

The Electroencephalogram (EEG)-based Brain-Computer Interface (BCI) is important for Internet of Things (IoT) applications. EEG data can be used to control IoT devices for applications such as smart home automation or healthcare monitoring. EEG-based BCI systems are crucial for recognizing human brain thoughts and analyzing neurological diseases, enabling thought visualization, and improving accessibility for people with disabilities. With the rapid development of machine learning, including deep learning technologies, wearable BCI devices, and hybrid BCI research have advanced significantly, showcasing their remarkable advantages. Researchers have conducted extensive experiments to improve the accuracy of the system. This paper provides a comprehensive review of BCI based on EEG, highlighting the fundamental principles of EEG signals, common acquisition devices, feature extraction techniques, and classification models, with a particular focus on the latest advances in deep learning. We also summarize available datasets and discuss the latest applications of EEG-based BCI in human-computer interaction and neurological diseases. Finally, we highlight the main findings and explore future directions, offering researchers deeper insight to foster further progress in this field.

The capacity of Dataspace enables the distribution of heterogeneous data from several sources and domains and has attracted attention for resolving data integration challenges. Drone data sharing faces challenges such as protecting privacy and security, building trust and dependability, controlling latency and scalability, facilitating real-time data processing, and preserving the caliber of shared models. Therefore, sixth-generation (6G) networks provide high throughput and low latency to improve drone operations; security issues are exacerbated by the sensitive nature of shared data and the lack of centralized monitoring. To address the challenges, this paper presents a conceptual framework for a Dataspace in the Sky to enable secure and efficient drone data-sharing within 6G networks in the transition from Industry 4.0 to Industry 5.0 . The Dataspace in the Sky integrates Federated Learning (FL), a decentralized Machine Learning (ML) approach that enhances security and privacy by sharing models instead of raw data, facilitating effective drone collaboration. However, the quality of shared local models often suffers due to inconsistent data contributions and unreliable recording mechanisms, which can undermine the performance of FL. To tackle the challenges, the framework employs blockchain (BC) to decentralize and secure the Dataspace, ensuring the integrity of contribution records and improving the reliability of shared models. Dataspace in the Sky empowered decentralized data sharing which addresses latency issues by decentralizing decision-making and enhances trust and reliability by leveraging immutable and transparent BC mechanisms. The robustness of Dataspace in the Sky solution is not only secures drone-sharing operations in 6G environments but enables the development of citizen-friendly mobility services, expanding opportunities across smart environments.

Abstract To recognize the potential for colon polyps to develop into cancer over time, early diagnosis is crucial for preventative healthcare. Timely identification significantly improves the prognosis and treatment outcomes for colorectal cancer patients. Image segmentation is crucial in medical image analysis for accurate diagnosis and treatment planning. Therefore, in this study, we present an alternative multilevel thresholding polyp segmentation method (MPOA) to enhance the segmentation of polyp images. The proposed method is based on enhancing the planet optimization algorithm (POA) by integrating operators from the reptile search algorithm (RSA). The evaluation of the developed MPOA is tested with different polyp images and compared with other image segmentation approaches. The results highlight the superior capability of MPOA, as evidenced by various performance measures in effectively segmenting polyp images. Furthermore, metrics such as peak signal-to-noise ratio (PSNR), structural similarity index (SSIM), and fitness values demonstrate that MPOA outperforms the basic version of POA and other methods. The evaluation outcomes underscore the significant impact of RSA in enhancing the performance of POA for the segmentation of polyp images.

Advances in language models have enabled significant strides in developing language technologies tailored for analyzing and processing Dialectical Arabic (DA), which exhibits unique linguistic features and variations compared to standard Arabic. This progress has sparked a surge of interest in various research tasks within the Arabic Natural Language Processing (ANLP) domain, encompassing areas such as sentiment analysis, dialect identification, normalization and classification, fake news detection, and part-of-speech tagging. The primary objective of this survey paper is to provide a comprehensive overview of the advancements made in dialectical ANLP from 2014 to 2024. A thorough analysis is undertaken, covering a corpus of approximately 200 research papers, to offer insights into the latest developments, resources, and applications concerning dialectical Arabic. By identifying and discussing the challenges and opportunities for future research, this study aspires to serve as a valuable reference for researchers, practitioners, and enthusiasts interested in the subject matter. Central to the investigation are the recent strides in natural language processing techniques that pertain to dialectical Arabic, namely DA sentiment analysis, DA identification, DA classification, DA normalization, DA part-of-speech tagging, and the role of DA in fake news detection, among other applications. Each research category is meticulously examined, providing a comprehensive understanding of their respective contributions, significance, encountered challenges, and the availability of pertinent datasets. This exhaustive survey paper encompasses existing studies within dialectical Arabic research categories. As a result, readers are presented with a detailed reference source in pursuing advancements and innovations within this field.

Digital Twins are virtual representations of physical assets and systems that rely on effective Data Management to integrate, process, and analyze diverse data sources. This article comprehensively examines Data Management challenges, architectures, techniques, and applications in the context of Digital Twins. It explores key issues such as data heterogeneity, quality assurance, scalability, security, and interoperability. The paper outlines architectural approaches like centralized, distributed, cloud-based, and blockchain solutions and Data Management techniques for modeling, integration, fusion, quality management, and visualization. Domain-specific considerations across manufacturing, smart cities, healthcare, and other sectors are discussed. Finally, open research challenges related to standards, real-time data processing, intelligent Data Management, and ethical aspects are highlighted. By synthesizing the state-of-the-art, this review serves as a valuable reference for developing robust Data Management strategies that enable Digital Twin deployments.

Surface electromyography (sEMG) is a non-invasive technique that records the electrical signals generated by muscle activity. sEMG signals are widely used in the field of biomedical and health informatics for diagnosing and monitoring neuromuscular disorders, as well as in fields such as motor control, rehabilitation, and human-computer interaction. In this paper, we propose a novel model called the Triple Convolutional Neural Network and Kolmogorov-Arnold Network (TCNN-KAN) for recognizing gesture signals based on sEMG. Our approach replaces the commonly used fully connected layer with the KAN, parameterizing it as a spline function to improve classification accuracy. Specifically, when using a KAN instead, generate the TCNN-KAN-1 model. When using two KAN layers, generate the TCNN-KAN-2 model and generate the TCNN-KAN-3 model when KAN replaces all fully connected layers. Firstly, to ensure the model learns universal features, we fuse gesture signals from different individuals and segment them to create uniform window sizes. Then, the processed signal is input into the basic convolution layer of different depths for training. In order to improve the accuracy, we convert the standard fully connected layer in the convolutional layer to the KAN layer so that it has a learnable activation function in weight. Finally, we introduce unstructured pruning to reduce computational complexity and minimize overfitting by removing channels with lower feature importance. We use three datasets, NinaPro DB1, NinaPro DB5, and CSL, for evaluation. The results show that on the TCNN-KAN-2 model, each dataset has achieved the highest accuracy. Specifically, when the pruning rates were 0.2, 0.1, and 0.4, the accuracy rates reached 98.38%, 93.81%, and 75.56%, respectively.

Human activity recognition (HAR) can play a vital role in biomedical and health informatics by enabling the monitoring of human daily activities and health behaviors. Accurate HAR can provide valuable insights into patients' physical activity levels, and this helps to manage chronic conditions and promote healthy lifestyles. In this paper, we propose a deep learning model, DKInception, designed for HAR tasks. DKInception integrates deep convolutional residual networks with an attention mechanism and leverages multi-scale convolution kernels to efficiently extract temporal features for activity identification. This model is built on the Inception ResNet architecture and extends its capabilities with effective, fast convergence and robust scaling properties. To evaluate the performance of DKInception, we conduct extensive experiments on four benchmark HAR datasets: UCI-HAR, Opportunity, Daphnet, and PAMAP2. Our comparative analysis with several existing models shows that DKInception outperforms these models using various evaluation metrics. These results demonstrate the model recorded high accuracy of 95.70%, 87.48%, 94.00% and 89.72%, for UCI-HAR, Opportunity, Daphnet, and PAMAP2, respectively.

This paper reviews three main aspects of authentication protocols of Internet of Things (IoT): classifications and limitations, current trends, and opportunities. First, we explore the significance of IoT authentication protocols in ensuring secure communication and the protection of transmitted and received data, focusing on the classifications and associated limitations. Second, we discuss the latest developments and trends, such as using blockchain technology and machine learning to enhance authentication protocols. Third, we highlight the future opportunities, including the development of human-centric authentication designs and improved platform interoperability. At the end of this paper, we provided some insights gained for the new researcher, offering analyses of the trends and challenges in this field, giving recommendations for improving IoT authentication protocols, and emphasizing the need for further research and cooperation to develop advanced security solutions.

This paper studies distributed data dissemination in multiple mobile sinks wireless sensor networks. Previous studies employed separated paths to disseminate data packets from a given source to a given set of mobile sinks independently, which exhausts the constrained resources of the network. In this paper, we explore how the merged paths mechanism could rationalize utilizing network resources. To do so, we propose a protocol named Merged Path, which is implemented in four steps in a distributed manner. First, the bifurcation points (i.e., where the path is branched into multiple sub-branches) are discovered. Second, we developed a Discrete Cumulative Clustering algorithm (DCC) to divide the sinks into disjoint clusters at each bifurcation point. Third, we propose a Diagonal Virtual Line (DVL) structure to delegate the communication between thehigh-tierand low-tier nodes. Last, on top of DVL and DCC, we propose an opportunistic metric that captures multiple network-layer attributes to disseminate the data packet to the sinks through multiple branches. The simulation results showed that about 50% of the network energy could be saved by merging the paths versus the separate paths, considering an area of interest application with 20 mobile nodes each carrying a sink.

Meta-heuristic algorithms and ANFIS have extraordinarily impressive performance for predicting time-series PM2.5 air quality data. This paper explores the possibility of mainstream meta-heuristic algorithms combined with the adaptive neuro-fuzzy inference system (ANFIS) applied to PM2.5 prediction in Wuhan. It is a major difficulty to choose appropriate methods to connect the strengths of both MHA and ANFIS for data prediction. Therefore, three sorts of meta-heuristic algorithms, including ant colony optimization, particle swarm optimization, and genetic algorithm, have been constructed to update the parameters of the neural network of ANFIS. In the case study in Wuhan, modified fuzzy inference systems possess the studying capacity of neural networks and integrate the human ability for reasoning and interpretation, thereby handling the uncertainty of data prediction better.

The dramatic recent increase of the smart Internet of Everything (IoE) in Industry 4.0 has significantly\nincreased energy consumption, carbon emissions, and global warming. IoE applications in Industry\n4.0 face many challenges, including energy efficiency, heterogeneity, security, interoperability, and\ncentralization. Therefore, Industry 4.0 in Beyond the Sixth-Generation (6G) networks demands moving\nto sustainable, green IoE and identifying efficient and emerging technologies to overcome sustainability\nchallenges. Many advanced technologies and strategies efficiently solve issues by enhancing\nconnectivity, interoperability, security, decentralization, and reliability. Greening IoE is a promising\napproach that focuses on improving energy efficiency, providing a high Quality of Service (QoS), and\nreducing carbon emissions to enhance the quality of life at a low cost. This survey provides a comprehensive\noverview of how advanced technologies can contribute to green IoE in the 6G network of\nIndustry 4.0 applications. This survey provides a comprehensive overview of advanced technologies,\nincluding Blockchain, Digital Twins (DTs), Unmanned Aerial Vehicles (UAVs, a.k.a. drones), and\nMachine Learning (ML), to improve connectivity, QoS, and energy efficiency for green IoE in 6G\nnetworks. We evaluate the capability of each technology in greening IoE in Industry 4.0 applications\nand analyze the challenges and opportunities to make IoE greener using the discussed technologies.

Human activity recognition (HAR) is a rapidly growing field of research that aims to automatically identify and classify human motions and activities from different tracking devices, such as cameras and sensors. One of the most widely used sensor modalities for HAR is the smartphone, which has various sensors, such as gyroscopes, accelerometers, and GPS, that can provide rich information about a person’s movements and actions. HAR applications are essential for the Internet of Things (IoT) and smart home industries. We used the recent advances in deep learning techniques to develop a new HAR model for wearable sensors. The proposed model, MLCNNwav, relies on residual convolutional neural networks and 1-D trainable discrete wavelet transform. The multilevel CNN is designed to capture global features, whereas the wavelet transformation enhances the representation and generalization by learning activity-related features. Several deep learning approaches are compared to assess the superiority of the developed model. Four public benchmarks HAR data sets were used for the evaluation. The outcomes confirmed that the developed MLCNNwav recorded high-accuracy rates on all data sets.

Fall detection (FD) systems are crucial for identifying falls and ensuring timely assistance, thus reducing the risk of serious injuries. With the development of society and increasing attention to health issues, researchers have conducted extensive studies on falls to reduce the severe sequelae of falls. Integrating FD systems with the Internet of Things (IoT), particularly the Internet of Medical Things (IoMT), has significantly advanced healthcare and personal safety. This dynamic relationship between FD technology and IoT has opened up new vistas for monitoring and assisting individuals, particularly the elderly and those with health conditions that make them prone to falls. This article presents a review of wearable sensor-based FD techniques. We classify the detection methods into their categories from an algorithmic perspective: threshold-based, conventional machine learning-based, and deep learning-based methods. In addition, we identify and summarize the available data sets that can be used to evaluate the performance of the introduced methods. This review aims to provide researchers with a better comprehension of the FD problem, intending to foster further advancements in the field.

Hyperspectral images (HSIs) are typically utilized in a wide variety of practical applications. HSI is replete with spatial and spectral information, which provides precise data for material detection. HSIs are characterized by a high degree of variations and undesirable pixel distributions, providing major processing challenges. This article introduces the fast hyperspectral image classification (FHIC) model, a rapid model for classifying HSIs and resolving their associated challenges. It uses the enhancing transformation reduction (ETR) method to address the HSI difficulties and enhance classes’ differentiation. It also uses exponential linear units (ELU) to smooth and speed the classification processing. The structure of the FHIC model is designed to be very flexible and suitable for a range of HSIs. The model reduced execution time and RAM consumption and provided superior performance compared to seven of the most advanced analysis models, for three well-known HSIs. In some cases, it was 60% faster than other models. In addition, this work presents a new and highly effective method for measuring the performance of the compared models in terms of their accuracy and processing speed to provide an easy evaluation method. The code of the FHIC model is available at this link: https://github.com/DalalAL-Alimi/FHIC.

Human activity recognition (HAR) applications have received much attention due to their necessary implementations in various domains, including Industry 5.0 applications such as smart homes, e-health, and various Internet of Things applications. Deep learning (DL) techniques have shown impressive performance in different classification tasks, including HAR. Accordingly, in this article, we develop a comprehensive HAR system based on a novel DL architecture called Multi-ResAtt (multilevel residual network with attention). This model incorporates initial blocks and residual modules aligned in parallel. Multi-ResAtt learns data representations on the inertial measurement units level. Multi-ResAtt integrates a recurrent neural network with attention to extract time-series features and perform activity recognition. We consider complex human activities collected from wearable sensors to evaluate the Multi-ResAtt using three public datasets, Opportunity; UniMiB-SHAR; and PAMAP2. Additionally, we compared the proposed Multi-ResAtt to several DL models and existing HAR systems, and it achieved significant performance.

Recently, Unmanned Aerial Vehicles (UAVs) present a promising advanced technology that can enhance people life quality and smartness of cities dramatically and increase overall economic efficiency. UAVs have attained a significant interest in supporting many applications such as surveillance, agriculture, communication, transportation, pollution monitoring, disaster management, public safety, healthcare, and environmental preservation. Industry 4.0 applications are conceived of intelligent things that can automatically and collaboratively improve beyond 5G (B5G). Therefore, the Internet of Things (IoT) is required to ensure collaboration between the vast multitude of things efficiently anywhere in real-world applications that are monitored in real-time. However, many IoT devices consume a significant amount of energy when transmitting the collected data from surrounding environments. Due to a drone's capability to fly closer to IoT, UAV technology plays a vital role in greening IoT by transmitting collected data to achieve a sustainable, reliable, eco-friendly Industry 4.0. This survey presents an overview of the techniques and strategies proposed recently to achieve green IoT using UAVs infrastructure for a reliable and sustainable smart world. This survey is different from other attempts in terms of concept, focus, and discussion. Finally, various use cases, challenges, and opportunities regarding green IoT using UAVs are presented.

This work is devoted to solving the problem of multicriteria multihop routing in vehicular ad hoc networks (VANETs), aiming at three goals: 1) increasing the end-to-end delivery ratio; 2) reducing the end-to-end latency; and 3) minimizing the network overhead. To this end and beyond the state of the art, heuristic routing for vehicular networks (HERO), which is a distributed routing protocol for urban environments, encapsulating two main components, is proposed. The first component, road-segment selection, aims to prioritize the road segments based on a heuristic function that contains two probability distributions, namely, shortest distance distribution (SDD) and connectivity distribution (CD). The mass function of SDD is the product of three quantities: 1) the perpendicular distance; 2) the dot-production angle; and 3) the segment length. On the other hand, the mass function of CD considers two quantities: 1) the density of vehicles and 2) the interdistance of vehicles on the road segment. The second component, vehicle selection, aims to prioritize the vehicles on the road segment based on four quantities: 1) the relative speed; 2) the movement direction; 3) the available buffer size; and 4) signal fading. The simulation results showed that HERO achieved a promising performance in terms of delivery success ratio, delivery delay, and communication overhead.