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Abstract The development of innovative methods for concealing critical data in multimedia files has exploded in information security in recent years. Cryptography and steganography cannot be used alone to protect data; rather, they can be combined and used in a single system. Audio steganography is among the most important information security techniques. It involves the concealment of information within audio signals to achieve covert communication. This paper introduces a comprehensive technique that integrates chaos Henon, Baker, and Arnold maps for image encryption with audio steganography to create a robust and secure audio steganography technique. First, the target image is encrypted using chaotic maps. Then, it is embeded within the high frequencies of the cover audio signal based on the Inverse Short Time Fourier Transform (ISTFT) to be transmitted to the destination through the channel. By integrating both encryption and concealment techniques, the cover audio signal quality can be preserved. Moreover, the hidden image security and robustness are improved, making the technique resistant to many types of attacks. The simulation results confirm that the suggested technique is robust in the presence of attacks. It achieves a distinct perceptual quality with an appreciated peak signal-to-noise ratio (PSNR) of 91.2 dB and a Mean Square Error (MSE) of 7.5 × 10–10. The randomness of the resulting encrypted image has successfully passed the National Institute of Standards and Technology (NIST) statistical test suite.

Abstract Reinforcement of the Internet of Medical Things (IoMT) network security has become extremely significant as these networks enable both patients and healthcare providers to communicate with each other by exchanging medical signals, data, and vital reports in a safe way. To ensure the safe transmission of sensitive information, robust and secure access mechanisms are paramount. Vulnerabilities in these networks, particularly at the access points, could expose patients to significant risks. Among the possible security measures, biometric authentication is becoming a more feasible choice, with a focus on leveraging regularly-monitored biomedical signals like Electrocardiogram (ECG) signals due to their unique characteristics. A notable challenge within all biometric authentication systems is the risk of losing original biometric traits, if hackers successfully compromise the biometric template storage space. Current research endorses replacement of the original biometrics used in access control with cancellable templates. These are produced using encryption or non-invertible transformation, which improves security by enabling the biometric templates to be changed in case an unwanted access is detected. This study presents a comprehensive framework for ECG-based recognition with cancellable templates. This framework may be used for accessing IoMT networks. An innovative methodology is introduced through non-invertible modification of ECG signals using blind signal separation and lightweight encryption. The basic idea here depends on the assumption that if the ECG signal and an auxiliary audio signal for the same person are subjected to a separation algorithm, the algorithm will yield two uncorrelated components through the minimization of a correlation cost function. Hence, the obtained outputs from the separation algorithm will be distorted versions of the ECG as well as the audio signals. The distorted versions of the ECG signals can be treated with a lightweight encryption stage and used as cancellable templates. Security enhancement is achieved through the utilization of the lightweight encryption stage based on a user-specific pattern and XOR operation, thereby reducing the processing burden associated with conventional encryption methods. The proposed framework efficacy is demonstrated through its application on the ECG-ID and MIT-BIH datasets, yielding promising results. The experimental evaluation reveals an Equal Error Rate (EER) of 0.134 on the ECG-ID dataset and 0.4 on the MIT-BIH dataset, alongside an exceptionally large Area under the Receiver Operating Characteristic curve (AROC) of 99.96% for both datasets. These results underscore the framework potential in securing IoMT networks through cancellable biometrics, offering a hybrid security model that combines the strengths of non-invertible transformations and lightweight encryption.

The Internet of Things (IoT) represents a rapidly growing field, where billions of intelligent devices are interconnected through the Internet, enabling the seamless sharing of data and resources. These smart devices are typically employed to sense various environmental characteristics, including temperature, motion of objects, and occupancy, and transfer their values to the nearest access points for further analysis. The exponential growth in sensor availability and deployment, powered by recent advances in sensor fabrication, has greatly increased the complexity of IoT network architecture. As the market for these sensors grows, so does the problem of ensuring that IoT networks meet high requirements for network availability, dependability, flexibility, and scalability. Unlike traditional networks, IoT systems must be able to handle massive amounts of data generated by various and frequently-used resource-constrained devices, while ensuring efficient and dependable communication. This puts high constraints on the design of IoT, mainly in terms of the required network availability, reliability, flexibility, and scalability. To this end, this work considers deploying a recent technology of distributed edge computing to enable IoT applications over dense networks with the announced requirements. The proposed network depends on distributed edge computing at two levels: multiple access edge computing and fog computing. The proposed structure increases network scalability, availability, reliability, and scalability. The network model and the energy model of the distributed nodes are introduced. An energy-offloading method is considered to manage IoT data over the network energy, efficiently. The developed network was evaluated using a developed IoT testbed. Heterogeneous evaluation scenarios and metrics were considered. The proposed model achieved a higher energy efficiency by 19%, resource utilization by 54%, latency efficiency by 86%, and reduced network congestion by 92% compared to traditional IoT networks.

To facilitate energy-efficient information dissemination from multiple sensors to the sink within Wireless Sensor Networks (WSNs), in-network data fusion is imperative. This paper presents a new WSN topology that incorporates the Mobility-Efficient Data Fusion (MEDF) algorithm, which integrates a data-compression protocol with an adaptive-clustering mechanism. The primary goals of this topology are, first, to determine a dynamic sequence of cluster heads (CHs) for each data transmission round, aiming to prolong network lifetime by implementing an adaptive-clustering mechanism resilient to network dynamics, where CH selection relies on residual energy and minimal communication distance; second, to enhance packet delivery ratio (PDR) through the application of a data-compression technique; and third, to mitigate the hot-spot issue, wherein sensor nodes nearest to the base station endure higher relay burdens, consequently influencing network longevity. To address this issue, mobility models provide a straightforward solution; specifically, a Random Positioning of Grid Mobility (RPGM) model is employed to alleviate the hot-spot problem. The simulation results show that the network topology incorporating the proposed MEDF algorithm effectively enhances network longevity, optimizes average energy consumption, and improves PDR. Compared to the Energy-Efficient Multiple Data Fusion (EEMDF) algorithm, the proposed algorithm demonstrates enhancements in PDR and energy efficiency, with gains of 5.2% and 7.7%, respectively. Additionally, it has the potential to extend network lifetime by 13.9%. However, the MEDF algorithm increases delay by 0.01% compared to EEMDF. The proposed algorithm is also evaluated against other algorithms, such as the tracking-anchor-based clustering method (TACM) and Energy-Efficient Dynamic Clustering (EEDC), the obtained results emphasize the MEDF algorithm’s ability to conserve energy more effectively than the other algorithms.

Abstract Security of Internet-of-Medical-Things (IoMT) networks has evolved as a vital issue in recent years. The IoMT networks are designed to link patients with caregivers. All reports, data, and medical signals are transferred over these networks. Hence, these networks require robust and secure access strategies for patients to send their vital data or reports. Hence, hacking of these networks may lead to harmful effects on patients. One of the vulnerable points to hacking in these networks is the access point. Access to these networks could be performed with biometrics. The popular biometric traits for this purpose are biomedical signals such as Electrocardiogram (ECG) signals, as they are continuously monitored and measured for patients. A common thread between all biometric authentication systems is the possibility of losing the biometric traits forever if hacking attempts manage to concur the biometric template storage. A new trend in the field of biometric authentication is to avoid the utilization of original biometrics in access control processes. A possible alternative is to use cancelable biometrics instead. Cancelable biometrics can be generated through encryption schemes or non-invertible transforms. This paper adopts both strategies in a unified framework for cancelable ECG signal recognition that can be used in the access step of IoMT networks. The proposed framework begins with applying a non-invertible transformation on the ECG signals through fuzzy logic to change the dynamic range of the signals. As this process is non-invertible in nature, it prevents the recovery of the original ECG signals from the processed versions, which is the main target of cancelable biometric systems. After that, lightweight encryption through XOR operation with user-specific patterns is implemented. Here, the high complexity of full encryption schemes that need a large processing burden is eliminated. The addition of the encryption stage enhances the security of cancelable biometric traits, allowing a hybrid nature of the proposed cancelable biometric framework through the merging of non-invertible transforms and encryption algorithms. Moreover, an FPGA hardware implementation is introduced for real implementation of the proposed ECG-based cancelable biometric recognition framework. This hardware can accompany the user to allow access of the IoMT network when requested. Experimental results show a promising performance of the proposed framework with a large Area under the Receiver Operating Characteristic curve (AROC) of 99.5% and an Equal Error Rate (EER) of 0.058%.

Query by Image Content (QBIC), subsequently known as Content-Based Image Retrieval (CBIR), offers an advantageous solution in a variety of applications, including medical, meteorological, search by image, and other applications. Such CBIR systems primarily use similarity matching algorithms to compare image content to get matched images from datasets. They essentially measure the spatial distance between extracted visual features from a query image and its similar versions in the dataset. One of the most challenging query retrieval problems is Facial Sketched-Real Image Retrieval (FSRIR), which is based on content similarity matching. These facial retrieval systems are employed in a variety of contexts, including criminal justice. The difficulties of retrieving such sorts come from the composition of the human face and its distinctive parts. In addition, the comparison between these types of images is made within two different domains. Besides, to our knowledge, there is a few large-scale facial datasets that can be used to assess the performance of the retrieval systems. The success of the retrieval process is governed by the method used to estimate similarity and the efficient representation of compared images. However, by effectively representing visual features, the main challenge-posing component of such systems might be resolved. Hence, this paper has several contributions that fill the research gap in content-based similarity matching and retrieval. The first contribution is extending the Chinese University Face Sketch (CUFS) dataset by including augmented images, introducing to the community a novel dataset named Extended Sketched-Real Image Retrieval (ESRIR). The CUFS dataset has been extended from 100 images to include 53,000 facial sketches and 53,000 real facial images. The paper second contribution is presenting three new systems for sketched-real image retrieval based on convolutional autoencoder, InfoGAN, and Vision Transformer (ViT) unsupervised models for large datasets. Furthermore, to meet the subjective demands of the users due to the prevalence of multiple query formats, the third contribution of the paper is to train and assess the performance of the proposed models on two additional facial datasets of different image types. Recently, the majority of people have preferred searching for brand logo images, but it may be tricky to separate certain brand logo features their alternatives and even from other features in an image. Thus, the fourth contribution is to compare logo image retrieval performance based on visual features derived from each of the three suggested retrieval systems. The paper also presents cloud-based energy and computational complexity saving approaches on large-scale datasets. Due to the ubiquity of touchscreen devices, users often make drawings based on their fantasies for certain object image searches. Thus, the proposed models are tested and assessed on a tough dataset of doodle-scratched human artworks. They are also studied on a multi-category dataset to cover practically all possible image types and situations. The results are compared with those of the most recent algorithms found in the literature. The results show that the proposed systems outperform the recent counterparts.

The efficient compression and classification of medical signals, particularly electroencephalography (EEG) and electrocardiography (ECG) signals in wireless body area network (WBAN) systems, are crucial for real-time monitoring and diagnosis. This paper addresses the challenges of compressive sensing and classification in WBAN systems for EEG and ECG signals. To tackle the challenges of the compression process, a sequential approach is proposed. The first step involves compressing the EEG and ECG signals using the optimized Walsh-Hadamard transform (OWHT). This transform allows for efficient representation of the signals, while preserving their essential characteristics. However, the presence of noise can impact the quality of the compressed signals. To mitigate this effect, the signals are subsequently recovered using the Sparse Group Lasso 1 (SPGL1) algorithm and OWHT, which take into account the noise characteristics during the recovery process. To evaluate the performance of the proposed compressive sensing algorithm, two metrics are employed: mean squared error (MSE) and maximum correntropy criterion (MCC). These metrics provide insights into the accuracy and reliability of the recovered signals at different signal-to-sample ratios (SSRs). The results of the evaluation demonstrate the effectiveness of the proposed algorithm in accurately reconstructing the EEG and ECG signals, while effectively managing the noise interference. Furthermore, to enhance the classification accuracy in the presence of signal compression, a local binary pattern (LBP) tehnique is applied. This technique extracts discriminative features from the compressed signals. These features are then fed into a classification algorithm based on residual learning. This classification algorithm is trained from scratch and specifically designed to work with the compressed signals. The experimental results showcase the high accuracy achieved by the proposed approach in classifying the compressed EEG and ECG signals without the need for signal recovery. The findings of this study highlight the potential of the proposed approach in achieving efficient and accurate medical signal analysis in WBAN systems. By eliminating the computational burden of signal recovery and leveraging the advantages of compressive sensing, the proposed approach offers a promising solution for real-time monitoring and diagnosis, ultimately improving the overall efficiency and effectiveness of healthcare systems.

The security is important issue in wireless networks. This paper discusses audio watermarking as a tool to improve the security of image communication over the IEEE 802.15.4 ZigBee network. The adopted watermarking method implements the Singular-Value Decomposition (SVD) mathematical technique. This method is based on embedding a chaotic encrypted image in the Singular Values (SVs) of the audio signal after transforming it into a 2-D format. The objective of chaotic encryption is to enhance the level of security and resist different attacks. Experimental results show that the SVD audio watermarking method maintains the high quality of the audio signals and that the watermark extraction and decryption are possible even in the presence of attacks over the ZigBee network.

This paper studies a vital issue in wireless communications, which is the transmission of images over wireless networks. IEEE ZigBee 802.15.4 is a short-range communication standard that could be used for small distance multimedia transmissions. In fact, the ZigBee network is a wireless personal area network (WPAN), which needs a strong interleaving mechanism for protection against error bursts. Thispaper presents a novel chaotic interleaving scheme for this purpose. This scheme depends on the chaotic Baker map. A comparison study between the proposed chaotic interleaving scheme and the traditional block and convolutional interleaving schemes for image transmission over a correlated fading channel is presented. The simulation results show the superiority of the proposed chaotic interleaving scheme over the traditional schemes.

Cancelable biometric recognition techniques play a vital role in the privacy and security of remote surveillance systems to keep the genuine users’ confidential data safe and away from intruders. This research work presents an efficient cancelable biometric recognition framework that exploits an irreversible hybrid encryption algorithm. It incorporates Deoxyribonucleic, Ribonucleic Acid sequence (DNA and RNA) encryption technique, and an evolutionary optimization technique, namely Genetic Algorithms (GAs). These techniques are employed to create completely deformed templates from their original ones. Hence, the main contribution is introducing a novel biometric security framework that achieves unique randomness characteristics using RNA and DNA sequences and the evolutionary GA technique. The proposed framework produces entirely deformed biometric templates by ciphering the main discriminative features of the biometric traits of the authorized clients. It is firstly initialized by creating several encrypted biometric images for the original users with the logistic map. After that, the initially encrypted images are transformed into vectors of a binary array. Then, they are converted to their corresponding introns, and exons, and consequently, their relevant codons are stored in the cloud database. These relevant codons are replaced by new ones after generating encrypted RNA lists. The utilized encryption key for each template is extracted from the original biometric image through excessive permutations between pixels. The GA optimization technique is applied to select the most convenient biometric features. Finally, after employing the GA-based cross-over and mutation operations, the chosen features are used to generate the cancelable biometric traits. To assess the proposed framework, six different biometric databases are considered. These databases are Olivetti Research Laboratory (ORL) Faces (gray), CASIA v.5 Faces (color), UPOL Iris (gray), Indian Institute of Technology Delhi (IIT Delhi) Ear (color and gray), Fingerprint, and CASIA Palmprint (color and gray). The security performance of the proposed encryption algorithm is compared to those of recent studies in this field, such as Optical Scanning Holography (OSH) and Double Random Phase Encoding (DRPE). The simulation results prove the superior performance of the proposed framework in terms of all adopted evaluation metrics. The proposed framework provides high Area under the Receiver Operating Characteristic (AROC) curve that reaches 0.9990, low False Acceptance Rate (FAR) of 0.0015, more uniform histograms, high correlation values for genuine users, and completely hidden biometric features. In addition, from the security perspective, the proposed framework achieves good entropy, Unified Average Changing Intensity (UACI), and Number of Pixels Change Rate (NPCR) values that reach 7.9960, 33.55%, and 99.65%, respectively.

Multimedia Super-Resolution (SR) reconstruction is an essential and mandatory process for different visualization functions. Recently, several schemes have been suggested for single- and multi-image SR reconstruction. This work presents an effective SR reconstruction scheme for visual quality and resolution enhancement of 3D Video (3DV) sequences. The idea behind the proposed 3DV SR reconstruction scheme is the utilization of a recursive Bayesian algorithm for improving the resolution of the degraded 3DV sequences with down-sampling, blurring, and noise effects. In addition, a significant stage of histogram matching based on a visual image with a better-distributed histogram is employed. The main aim of employing the histogram matching stage for enhancing the 3DV sequence is to introduce a dynamic range modification of each 3DV frame. Hence, it presents a 3DV sequence with an enhanced distribution of intensities. This improves the whole performance efficiency of the suggested scheme. The performance of the proposed SR reconstruction scheme is compared with that of the conventional bicubic interpolation scheme. Comparisons with recent and related SR reconstruction schemes are also introduced. Simulation results reveal that the proposed scheme achieves superior outcomes in terms of Structural Similarity (SSIM) index, local contrast, average gradient, Mean Square Error (MSE), edge intensity, entropy, and Peak Signal-to-Noise Ratio (PSNR) of the resulting 3DV frames.

There exist several performance criteria for cryptographically-strong substitution boxes (S-boxes), which are often conflicting with each other. Constructing S-boxes that satisfy multiple criteria with optimal tradeoffs is one of the challenging tasks for cryptographers. In practice, the existing S-box design algorithms are used to optimize performance according to a single performance criterion, mainly the nonlinearity, which usually results in weak scores for other equally-significant criteria. To overcome this problem, a multi-objective optimization-based method is presented in this paper. In this method, $8 \times 8$ S-boxes are constructed satisfying multiple criteria of balancedness, high nonlinearity, low differential uniformity, and low auto-correlation. Multiple objectives are fulfilled by applying the chaos-assisted non-dominated sorting genetic algorithm-II to introduce the S-boxes. The performance assessment of the proposed method and the comparative analysis with available optimization tools and other state-of-the-art algorithms demonstrate its proficiency in generating significantly-better S-box solutions with good Pareto-optimal security features. Eventually, the S-boxes with minimum nonlinearity (NL) of 110, differential uniformity (DU) as low as 8, and auto-correlation function (ACF) as low as 80 are obtained after the optimization. Furthermore, the obtained Pareto-optimal S-box is utilized to put forward a medical image encryption algorithm for secure telemedicine services. The suggested encryption algorithm uses an S-box to perform the required permutation and diffusion of images. The encryption performance assessment and comparison analyses validate its effectiveness for securing medical imagery data in telemedicine networks.

Precision agriculture is a challenging task to achieve. Several studies have been conducted to forecast agricultural yields using machine learning algorithms (MLA), but few studies have used ensemble machine learning algorithms (EMLA). In the current study, we use a dataset generated by a computer simulation program, and meteorological data obtained over 30 years from Maine, United States (USA). The primary goal of this research is to increase the forecast accuracy of the best characteristics for overcoming hunger challenges. We adopted stacking regression (SR) and cascading regression (CR) with a novel combination of MLA based on the wild blueberry dataset. We used features that indicated the best regulation for wild blueberry agroecosystems. Four feature engineering selection techniques are applied, namely variance inflation factor (VIF), sequential forward feature selection (SFFS), sequential backward elimination feature selection (SBEFS), and extreme gradient boosting based on feature importance (XFI). We applied Bayesian optimization on popular MLA to obtain the best hyperparameters to achieve accurate wild blueberry yield prediction. The SR used a two-layer structure: level-0 containing light gradient boosting machine (LGBM), gradient boost regression (GBR) and extreme gradient boosting (XGBoost), and level-1 providing the output prediction using a Ridge. The CR topology is the same MLA used in SR, but in a series form that takes the new prediction as a feeder to each MLA and removes the previous prediction in each stage. We assessed the CR, and SR with outcomes according to the root mean square error (RMSE) and coefficient of determination ( $R^{2}$ ). In the results, the proposed SR showed the best performance with $R^{2}$ of 0.984 and RMSE of 179.898 compared with another study that reported $R^{2}$ of 0.938 and RMSE of 343.026 on the seven features selected by XFI. The SR achieved the highest $R^{2}$ of 0.985 on all features and the features that were selected by the SBEFS. Our SR outperformed CR, and another study on wild blueberry yield prediction.

A hybrid multi-state orbital angular momentum-multi pulse-position modulation ( $N$ OAM-MPPM) scheme over gamma-gamma free-space optical ( ${\Gamma \Gamma }$ -FSO) channel is studied in this paper. In our study, all atmospheric and pointing error impacts are taken into account. Expressions for the parameters of ${\Gamma \Gamma }$ -FSO-pointing error channel are derived. In addition, approximate-tight upper bounds on the bit-error rates (BERs) of $N$ OAM and $N$ OAM-MPPM techniques are developed over ${\Gamma \Gamma }$ -FSO-pointing error channels, considering the influences of beam divergence and pointing error (PE). The ${\Gamma \Gamma }$ -FSO-PE channel parameters and the BER expressions are evaluated numerically and verified by simulation. It turned out that the analytical results are nearly the same as those obtained from simulation under different turbulence scenarios and OAM modes. The results demonstrate that under variable turbulence conditions, the $N$ OAM-MPPM technique outperforms both ordinary $N$ OAM and MPPM systems. Furthermore, different deep learning (DL) techniques, namely random forest (RF), convolution neural network (CNN), and auto-encoder (AE), are employed to get the optimum classification accuracy using different datasets of $N$ OAM-MPPM over ${\Gamma \Gamma }$ -PE channel model. Finally, the results indicate that AE has the best performance metrics compared to other models using different datasets.

The brain tumor is an urgent malignancy caused by unregulated cell division. Tumors are classified using a biopsy, which is normally performed after the final brain surgery. Deep learning technology advancements have assisted the health professionals in medical imaging for the medical diagnosis of several symptoms. In this paper, transfer-learning-based models in addition to a Convolutional Neural Network (CNN) called BRAIN-TUMOR-net trained from scratch are introduced to classify brain magnetic resonance images into tumor or normal cases. A comparison between the pre-trained InceptionResNetv2, Inceptionv3, and ResNet50 models and the proposed BRAIN-TUMOR-net is introduced. The performance of the proposed model is tested on three publicly available Magnetic Resonance Imaging (MRI) datasets. The simulation results show that the BRAIN-TUMOR-net achieves the highest accuracy compared to other models. It achieves 100%, 97%, and 84.78% accuracy levels for three different MRI datasets. In addition, the k-fold cross-validation technique is used to allow robust classification. Moreover, three different unsupervised clustering techniques are utilized for segmentation.

Smart cities and their applications have become attractive research fields birthing numerous technologies. Fifth generation (5G) networks are important components of smart cities, where intelligent access control is deployed for identity authentication, online banking, and cyber security. To assure secure transactions and to protect user’s identities against cybersecurity threats, strong authentication techniques should be used. The prevalence of biometrics, such as fingerprints, in authentication and identification makes the need to safeguard them important across different areas of smart applications. Our study presents a system to detect alterations to biometric modalities to discriminate pristine, adulterated, and fake biometrics in 5G-based smart cities. Specifically, we use deep learning models based on convolutional neural networks (CNN) and a hybrid model that combines CNN with convolutional long-short term memory (ConvLSTM) to compute a three-tier probability that a biometric has been tempered. Simulation-based experiments indicate that the alteration detection accuracy matches those recorded in advanced methods with superior performance in terms of detecting central rotation alteration to fingerprints. This makes the proposed system a veritable solution for different biometric authentication applications in secure smart cities.

Metric learning has significantly improved machine learning applications such as face re-identification and image classification using K-Nearest Neighbor (KNN) and Support Vector Machine (SVM) classifiers. However, to the best of our knowledge, it has not been investigated yet, especially for the multimodal biometric recognition problem in immigration, forensic and surveillance applications with uncontrolled ear datasets. Therefore, it is interesting and very attractive to propose a novel framework for multimodal biometric recognition based on Learning Distance Metric (LDM) via kernel SVM. This paper considers metric learning for SVM by investigating a hybrid Learning Distance Metric and Directed Acyclic Graph SVM (LDM-DAGSVM) model for multimodal biometric recognition, where LDM and DAGSVM are two emerging techniques in dealing with classification problems. Different from existing multimodal biometric recognition methods, the proposed approach aims to learn Mahalanobis distance metric via kernel SVM to maximize the inter-class variations and minimize the intra-class variations, simultaneously. Experimental results on the uncontrolled datasets such as AR face and AWE ear datasets show that the proposed approach achieves competitive performance compared with models working on individual modalities and overperforms the state-of-the-art multimodal methods. The proposed model achieves five-fold classification accuracy around 99.85 % for the face and ear images.

Orbital angular momentum-shift keying (OAM-SK), which is the rapid switching of OAM modes, is vital but seriously impeded by the deficiency of OAM demodulation techniques, particularly when videos are transmitted over the system. Thus, in this paper, 3D chaotic interleaved multi-coded video frames (VFs) are conveyed via an N-OAM-SK free-space optical (FSO) communication system to enhance the reliability and efficiency of video communication. To tackle the defects of the OAM-SK-FSO mechanism, two efficient deep learning (DL) techniques, namely convolution recurrent neural network (CRNN) and 3D convolution neural network (3DCNN) are used to decode OAM modes with a low bit error rate (BER). Moreover, a graphics processing unit (GPU) is used to accelerate the training process with slight power consumption. The utilized datasets for OAM states are generated by applying different scenarios using a trial-and-error method. The simulation results imply that LDPC-coded VFs achieve the largest peak signal-to-noise ratios (PSNRs) and the lowest BERs using the 16-OAM-SK model. Both 3DCNN and CRNN techniques have nearly the same performance, but this performance deteriorates in the case of larger dataset classes. Moreover, the GPU accelerates the performance by almost 67.6% and 36.9% for the CRNN and 3DCNN techniques, respectively. These two DL techniques are more effective in evaluating the classification accuracy than the other traditional techniques by almost 10 – 20%.

Currently, secure multimedia applications are becoming a very hot research topic, specifically over the Internet and wireless communication networks due to their rapid progress. Several researchers have implemented various chaotic image and video encryption algorithms to achieve data stability and communication security. This paper presents a novel bit-level video frame cryptosystem that is dependent on the piecewise linear chaotic maps (PWLCMs). It is implemented for orbital angular momentum (OAM) modulation over different turbulence channels. Firstly, the mathematical model for the bit error rate (BER) of OAM modulation is derived over the gamma-gamma turbulence channel. After that, a comparison between the theoretical results from Mathematica and the simulation results from MATLAB for different turbulence strengths, signal-to-noise ratios (SNRs), and propagation distance values is presented to assure that there is a perfect match. The proposed video cryptosystem is checked using entropy analysis, histogram testing, attack analysis, time analysis, correlation testing, differential analysis, and other quality and security evaluation metrics. The simulation results and the performance analysis confirm that the proposed cryptosystem is reliable and secure for video frame encryption, and communication with different turbulence conditions in free space.

Differential equations are commonly used to model several engineering, science, and biological applications. Unfortunately, finding analytical solutions for solving higher-order Ordinary Differential Equations (ODEs) is a challenge. Numerical methods represent a leading candidate for solving such ODEs. This work presents an innovated adaptive technique that uses polynomials to solve linear or nonlinear third-order ODEs. The proposed technique adapts the coefficients of the polynomial to obtain an explicit analytical solution. A signed least mean square algorithm is exploited to enhance the adaptation process and decrease both computational requirements and time. The efficiency of the proposed Adaptive Polynomial Method (APM) is illustrated through six well-known examples. The proposed technique is compared with recent analytical and numerical methods to validate its effectiveness in terms of Mean Square Error (MSE) and computation time. An application in a thin film flow system is modeled to a third-order ODE. The proposed technique is compared with recent numerical and analytical methods in solving the thin film flow equation, and it achieves better results. Furthermore, the proposed technique provides an analytical solution with an increased dynamic range and much lower computational time than those of the conventional numerical methods.

The track of medical imaging has witnessed several advancements in the last years. Several medical imaging modalities have appeared in the last decades including X-ray, Computed Tomography (CT), Magnetic Resonance (MR), Positron Emission Tomography (PET), Single-Photon Emission Computed Tomography (SPECT) and ultrasound imaging. Generally, medical images are used for the diagnosis purpose. Each type of acquired images has some merits and limitations. To maximize medical images utilization for the purpose of diagnosis, medical imaging fusion trend has appeared as a hot research field. Different medical imaging modalities are fused to obtain new images with complementary information. This paper presents a survey study of medical imaging modalities and their characteristics. In addition, different medical image fusion approaches and their appropriate quality metrics are presented. The main aim of this comprehensive survey analysis is to contribute in the advancement of medical image approaches that can help for better diagnosis of different diseases.

Introduction: In humanity’s ongoing fight against its common enemy of COVID-19, researchers have been relentless in finding efficient technologies to support mitigation, diagnosis, management, contact tracing, and ultimately vaccination. Objectives: Engineers and computer scientists have deployed the potent properties of deep learning models (DLMs) in COVID-19 detection and diagnosis. However, publicly available datasets are often adulterated during collation, transmission, or storage. Meanwhile, inadequate, and corrupted data are known to impact the learnability and efficiency of DLMs. Methods: This study focuses on enhancing previous efforts via two multimodal diagnostic systems to extract required features for COVID-19 detection using adulterated chest X-ray images. Our proposed DLM consists of a hierarchy of convolutional and pooling layers that are combined to support efficient COVID-19 detection using chest X-ray images. Additionally, a batch normalization layer is used to curtail overfitting that usually arises from the convolution and pooling (CP) layers. Results: In addition to matching the performance of standard techniques reported in the literature, our proposed diagnostic systems attain an average accuracy of 98% in the detection of normal, COVID-19, and viral pneumonia cases using corrupted and noisy images. Conclusions: Such robustness is crucial for real-world applications where data is usually unavailable, corrupted, or adulterated.

Acute lower respiratory infection is a leading cause of death in developing countries. Hence, progress has been made for early detection and treatment. There is still a need for improved diagnostic and therapeutic strategies, particularly in resource-limited settings. Chest X-ray and computed tomography (CT) have the potential to serve as effective screening tools for lower respiratory infections, but the use of artificial intelligence (AI) in these areas is limited. To address this gap, we present a computer-aided diagnostic system for chest X-ray and CT images of several common pulmonary diseases, including COVID-19, viral pneumonia, bacterial pneumonia, tuberculosis, lung opacity, and various types of carcinoma. The proposed system depends on super-resolution (SR) techniques to enhance image details. Deep learning (DL) techniques are used for both SR reconstruction and classification, with the InceptionResNetv2 model used as a feature extractor in conjunction with a multi-class support vector machine (MCSVM) classifier. In this paper, we compare the proposed model performance to those of other classification models, such as Resnet101 and Inceptionv3, and evaluate the effectiveness of using both softmax and MCSVM classifiers. The proposed system was tested on three publicly available datasets of CT and X-ray images and it achieved a classification accuracy of 98.028% using a combination of SR and InceptionResNetv2. Overall, our system has the potential to serve as a valuable screening tool for lower respiratory disorders and assist clinicians in interpreting chest X-ray and CT images. In resource-limited settings, it can also provide a valuable diagnostic support.

Cybersecurity has been one of the interesting research fields that attract researchers to investigate new approaches. One of the recent research trends in this field is cancelable biometric template generation, which depends on the storage of a cipher (cancelable) template instead of the original biometric template. This trend ensures the confidential and secure storage of the biometrics of a certain individual. This paper presents a cancelable multi-biometric system based on deep fusion and wavelet transformations. The deep fusion part is based on convolution (Conv.), convolution transpose (Conv.Trans.), and additional layers. In addition, the deployed wavelet transformations are based on both integer wavelet transforms (IWT) and discrete wavelet transforms (DWT). Moreover, a random kernel generation subsystem is proposed in this work. The proposed kernel generation method is based on chaotic map modalities, including the Baker map and modified logistic map. The proposed system is implemented on four biometric images, namely fingerprint, iris, face, and palm images. Furthermore, it is validated by comparison with other works in the literature. The comparison reveals that the proposed system shows superior performance regarding the quality of encryption and confidentiality of generated cancelable templates from the original input biometrics.

The increasing growth in the demand for cloud computing services, due to the increasing digital transformation and the high elasticity of the cloud, requires more efforts to improve the electrical energy efficiency of cloud data centers. In this paper, an energy-efficient hybrid (EEH) framework for improving the efficiency of consuming electrical energy in data centers is proposed and evaluated. The proposed framework is based on both the request scheduling and servers consolidation approaches rather than depending only on one approach as in the existing related works. The EEH framework sorts the customers' requests (tasks) according to their time and power needs before performing the scheduling. It has a scheduling algorithm that considers power consumption when taking its scheduling decisions. It also has a consolidation algorithm that determines the underloaded servers to be slept or hibernated, the overloaded servers, the virtual machines to be migrated and the servers that will receive migrated virtual machines. In addition, the EEH framework includes a migration algorithm for transferring migrated virtual machines to new servers. Results of simulation experiments indicate the superiority of the EEH framework to the utilization of one approach only to reduce power consumption in terms of power usage effectiveness (PUE), data center energy productivity (DCEP), average execution time, throughput and cost saving.

Recently, massive research works have been accomplished for augmenting privacy and security requirements for cybersecurity applications in wireless communication networks. This is attributed to the fact that conventional security processes are not appropriate for robust, efficient, and reliable multimedia streaming over unsecure media. Therefore, this paper presents an efficient color image cryptosystem based on RC6 with different modes of operation. The proposed cryptosystem is composed of two phases: encryption and decryption. The encryption phase starts by decomposing the color plainimage with few details into its RGB components, which in turn, are segmented into 128-bit blocks. These blocks are then enciphered using RC6 with an appropriate mode of operation. After that, the corresponding enciphered blocks of RGB components are multiplexed for constructing the final cipherimage. This scenario is reversed in the decryption phase. The performance of the proposed cryptosystem is gauged via simulation using a set of encryption quality metrics. The simulation results reveal that the proposed cryptosystem with cipher block chaining (CBC), cipher feedback (CFB), and output feedback (OFB) modes can efficiently and effectively hide all information of the color images with few details even in the presence of some input blocks with similar data. On the other hand, the results show that the electronic codebook (ECB) mode is not effective at all in hiding all details of images. Finally, the obtained results ensure the applicability of the proposed cryptosystem and its efficiency in encrypting images in terms of security, encryption quality, and noise immunity.

The need for cybersecurity increases to protect the exchange of information for improving the data privacy. This paper presents an investigation of the encryption efficiency of the chaotic-based image block ciphering in the spatial and Fractional Fourier Transform (FrFT) domains. The main aim of this investigation is to examine the efficiency of different chaotic maps, while considering the parameters of the FrFT as additional keys for encryption and achieving reliable cybersecurity for robust image communication. In this paper, Cat, Baker, and Logistic map confusion approaches are applied in the spatial and FrFT domains to study and analyze the cybersecurity and ciphering efficiency of chaos-based image cryptosystems. The confusion features of the chaotic maps in spatial and FrFT domains are investigated using information entropy, differential analysis, histograms, visual observation, attack analysis, impact of noise, and encryption quality tests. Simulation results prove that the chaotic-based image encryption in the FrFT domain increases the efficiency of the confusion process and achieves a high nonlinear relation between the plainimage and the cipherimage in a symmetric ciphering approach. Moreover, the results demonstrate that the Cat-FrFT scheme is more susceptible to channel noise attacks than the Baker-FrFT and the Logistic-FrFT schemes. Hence, they can be implemented efficiently in the scenarios of noisy channels due to their high robustness to channel noise.

Multimedia cybersecurity is a prevalent research topic in the digital world due to the rapid progress of digital multimedia and Internet applications. Watermarking, encryption, and steganography schemes are employed to attain multimedia data confidentiality and robustness. However, these schemes are externally applied on trusted computers, and there has been a lack of similar schemes that can be effectively and efficiently enabled through an untrusted transmission medium. In this work, a self-embedding-based High-Efficiency Video Coding (HEVC) transmission and integrity verification framework is presented. This framework is robust and reliable for verifying the integrity of HEVC frames transmitted through insecure communication channels. Firstly, the transmitted HEVC frames are divided into a number of blocks with a certain block size. After that, a discrete transform is used for self-embedding of watermarks from each block into another one depending on a predefined mechanism. The Discrete Wavelet Transform (DWT), Discrete Cosine Transform (DCT), and Discrete Fourier Transform (DFT) are tested for this task. The watermarked HEVC frames are transmitted through a wireless communication channel, and hence they become subject to different attacks and corruptions. At the receiver side, the secret watermarks in each block are sensed with a correlation-based method to discover dubious counterfeit operations. To verify the reliability of the suggested transmission framework and its ability to achieve high protection and robust content verification of the transmitted HEVC frames over insecure communication channels, different HEVC analyses and comparisons are performed. Simulation results demonstrate the suitability of the suggested transmission framework for different multimedia cybersecurity applications. Furthermore, the comparative analysis shows that the DFT is an efficient discrete transform that can be employed with the proposed transmission framework to guarantee a higher HEVC frame integrity. It allows higher sensitivity to simple modifications in the transmitted watermarked HEVC frames. This makes the suggested cybersecurity framework applicable, secure, and appropriate for multimedia integrity verification purposes.

Since the onset of civilization, sketches have been used to portray our visual world, and they continue to do so in many different disciplines today. As in specific government agencies, establishing similarities between sketches is a crucial aspect of gathering forensic evidence in crimes, in addition to satisfying the user’s subjective requirements in searching and browsing for specific sorts of images (i.e., clip art images), especially with the proliferation of smartphones with touchscreens. With such a kind of search, quickly and effectively drawing and retrieving sketches from databases can occasionally be challenging, when using keywords or categories. Drawing some simple forms and searching for the image in that way could be simpler in some situations than attempting to put the vision into words, which is not always possible. Modern techniques, such as Content-Based Image Retrieval (CBIR), may offer a more useful solution. The key engine of such techniques that poses various challenges might be dealt with using effective visual feature representation. Object edge feature detectors are commonly used to extract features from different image sorts. However, they are inconvenient as they consume time due to their complexity in computation. In addition, they are complicated to implement with real-time responses. Therefore, assessing and identifying alternative solutions from the vast array of methods is essential. Scale Invariant Feature Transform (SIFT) is a typical solution that has been used by most prevalent research studies. Even for learning-based methods, SIFT is frequently used for comparison and assessment. However, SIFT has several downsides. Hence, this research is directed to the utilization of handcrafted-feature-based Oriented FAST and Rotated BRIEF (ORB) to capture visual features of sketched images to overcome SIFT limitations on small datasets. However, handcrafted-feature-based algorithms are generally unsuitable for large-scale sets of images. Efficient sketched image retrieval is achieved based on content and separation of the features of the black line drawings from the background into precisely-defined variables. Each variable is encoded as a distinct dimension in this disentangled representation. For representation of sketched images, this paper presents a Sketch-Based Image Retrieval (SBIR) system, which uses the information-maximizing GAN (InfoGAN) model. The establishment of such a retrieval system is based on features acquired by the unsupervised learning InfoGAN model to satisfy users’ expectations for large-scale datasets. The challenges with the matching and retrieval systems of such kinds of images develop when drawing clarity declines. Finally, the ORB-based matching system is introduced and compared to the SIFT-based system. Additionally, the InfoGAN-based system is compared with state-of-the-art solutions, including SIFT, ORB, and Convolutional Neural Network (CNN).

Recently, the demand for reliable and high-speed wireless communication has rapidly increased. Orthogonal frequency division multiplexing (OFDM) is a modulation scheme that is the newest competitor against other modulation schemes used for this purpose. OFDM is mostly used for wireless data transfer, although it may also be used for cable and fiber optic connections. However, in many applications, OFDM suffers from burst errors and high bit error rates. This paper presents the utilization of a helical interleaver with OFDM systems to efficiently handle burst channel errors and allow for Bit Error Rate (BER) reduction. The paper also presents a new interleaver, FRF, the initial letters of the authors’ names, for the same purpose. This newly proposed interleaver summarizes our previous experience with many recent interleavers. Fast Fourier transform OFDM (FFT-OFDM) and Discrete Wavelet Transform OFDM (DWT-OFDM) systems are used to test the efficiency of the suggested scheme in terms of burst channel error removal and BER reduction. Finally, the general complexity of the FRF interleaver is different from that of the helical interleaver in terms of hardware requirements. The performance of the proposed scheme was studied over different channel models. The obtained simulation results show a noticeable performance improvement over the conventional FFT-OFDM and the FFT-OFDM systems with the helical interleaver. Finally, the disadvantage of the proposed FRF interleaver is that it is more complex than the helical interleaver.

With the recent advances in the fifth-generation cellular system (5G), enabling vehicular communications has become a demand. The vehicular ad hoc network (VANET) is a promising paradigm that enables the communication and interaction between vehicles and other surrounding devices, e.g., vehicle-to-vehicle (V2V) and vehicle-to-everything (V2X) communications. However, enabling such networks faces many challenges due to the mobility of vehicles. One of these challenges is the design of handover schemes that manage the communications at the intersection of coverage regions. To this end, this work considers developing a novel seamless and efficient handover scheme for V2X-based networks. The developed scheme manages the handover process while vehicles move between two neighboring roadside units (RSU). The developed mechanism is introduced for multilane bidirectional roads. The developed scheme is implemented by multiple-access edge computing (MEC) units connected to the RSUs to improve the implementation time and make the handover process faster. The considered MEC platform deploys an MEC controller that implements a control scheme of the software-defined networking (SDN) controller that manages the network. The SDN paradigm is introduced to make the handover process seamless; however, implementing such a controlling scheme by the introduction of an MEC controller achieves the process faster than going through the core network. The developed handover scheme was evaluated over the reliable platform of NS-3, and the results validated the developed scheme. The results obtained are presented and discussed.

Epilepsy is one of the most serious disorders that affect patients' daily lives. When seizures occur, patients cannot control their behaviors, which can lead to serious injuries. With the great advances in recording both electroencephalogram (EEG) and magnetoencephalography (MEG) signals, it has become possible to analyze these signals in an automated manner for information extraction to help in seizure detection and prediction. Both EEG and MEG recordings of epilepsy patients contain spikes that can be used for the localization of epileptogenic zones, efficient onset detection, and even, in some cases, prediction. In this paper, we consider the characteristics of EEG and MEG spikes, present a discussion of the importance of spike detection in both signal modalities, and provide a review of spike detection algorithms. Since EEG signals have been widely used for decades, most of the algorithms presented in this paper cover the EEG spike detection methods. Few works in the literature are dedicated to MEG spike detection. Nevertheless, we assert that with some modifications, a considerable number of EEG spike detection algorithms can be applied to MEG signals. We classify the spike detection algorithms according to the domain used for processing the signal. Finally, we conclude with future research directions and open problems in this area.

The security of information is necessary for the success of any system. So, there is a need to have a robust mechanism to ensure the verification of any person before allowing him to access the stored data. So, for purposes of increasing the security level and privacy of users against attacks, cancelable biometrics can be utilized. The principal objective of cancelable biometrics is to generate new distorted biometric templates to be stored in biometric databases instead of the original ones. This paper presents effective methods based on different discrete transforms, such as Discrete Fourier Transform (DFT), Fractional Fourier Transform (FrFT), Discrete Cosine Transform (DCT), and Discrete Wavelet Transform (DWT), in addition to matrix rotation to generate cancelable biometric templates, in order to meet revocability and prevent the restoration of the original templates from the generated cancelable ones. Rotated versions of the images are generated in either spatial or transform domains and added together to eliminate the ability to recover the original biometric templates. The cancelability performance is evaluated and tested through extensive simulation results for all proposed methods on a different face and fingerprint datasets. Low Equal Error Rate (EER) values with high AROC values reflect the efficiency of the proposed methods, especially those dependent on DCT and DFrFT. Moreover, a comparative study is performed to evaluate the proposed method with all transformations to select the best one from the security perspective. Furthermore, a comparative analysis is carried out to test the performance of the proposed schemes with the existing schemes. The obtained outcomes reveal the efficiency of the proposed cancelable biometric schemes by introducing an average AROC of 0.998, EER of 0.0023, FAR of 0.008, and FRR of 0.003.

This generation faces existential threats because of the global assault of the novel Corona virus 2019 (i.e., COVID-19). With more than thirteen million infected and nearly 600000 fatalities in 188 countries/regions, COVID-19 is the worst calamity since the World War II. These misfortunes are traced to various reasons, including late detection of latent or asymptomatic carriers, migration, and inadequate isolation of infected people. This makes detection, containment, and mitigation global priorities to contain exposure via quarantine, lockdowns, work/stay at home, and social distancing that are focused on “flattening the curve”. While medical and healthcare givers are at the frontline in the battle against COVID-19, it is a crusade for all of humanity. Meanwhile, machine and deep learning models have been revolutionary across numerous domains and applications whose potency have been exploited to birth numerous state-of-the-art technologies utilised in disease detection, diagnoses, and treatment. Despite these potentials, machine and, particularly, deep learning models are data sensitive, because their effectiveness depends on availability and reliability of data. The unavailability of such data hinders efforts of engineers and computer scientists to fully contribute to the ongoing assault against COVID-19. Faced with a calamity on one side and absence of reliable data on the other, this study presents two data-augmentation models to enhance learnability of the Convolutional Neural Network (CNN) and the Convolutional Long Short-Term Memory (ConvLSTM)-based deep learning models (DADLMs) and, by doing so, boost the accuracy of COVID-19 detection. Experimental results reveal improvement in terms of accuracy of detection, logarithmic loss, and testing time relative to DLMs devoid of such data augmentation. Furthermore, average increases of 4% to 11% in COVID-19 detection accuracy are reported in favour of the proposed data-augmented deep learning models relative to the machine learning techniques. Therefore, the proposed algorithm is effective in performing a rapid and consistent Corona virus diagnosis that is primarily aimed at assisting clinicians in making accurate identification of the virus.

The Orthogonal Frequency Division Multiplexing (OFDM) transmission is the optimum version of the multicarrier transmission scheme, which has the capability to achieve high data rate. The key issue of OFDM system is the allocation of bits and power over a number of subcarriers. In this paper, a new power allocation algorithm based on subcarrier gain is proposed to maximize the bit rate. For OFDM systems, the Subcarrier Gain Based Power Allocation (SGPA) algorithm is addressed and compared with the standard Greedy Power Allocation (GPA). The authors demonstrate by analysis and simulation that the proposed algorithm reduces the computational complexity and achieves a near optimal performance in maximizing the bit rate over a number of subcarrier.

Multi-User Orthogonal Frequency Division Multiplexing (MU-OFDM) is an efficient technique for achieving high downlink capacity in high-speed communication systems. A key issue in MU-OFDM is the allocation of the OFDM subcarriers and power to users sharing the channel. In this paper a proportional rate-adaptive resource allocation algorithm for MU-OFDM is presented. Subcarrier and power allocation are carried out sequentially to reduce the complexity. The low complexity proportional subcarriers allocation is followed by Greedy Power Allocation (GPA) to solve the rate-adaptive resource allocation problem with proportional rate constraints for MU-OFDM systems. It improves the work of Wong et al. in this area by introducing an optimal GPA that achieves approximate rate proportionality, while maximizing the total sum-rate capacity of MU-OFDM. It is shown through simulation that the proposed GPA algorithm performs better than the algorithm of Wong et al., by achieving higher total capacities with the same computational complexity, especially, at larger number of users and roughly satisfying user rate proportionality.