Search

AbstractThe fast growth and wide range of applications of blockchain (BC) technology in various industries is irrefutable. Generally, BC technology is still in at an infant stage but it has generated significant interests in many sectors and industries. Nonetheless, despite an uptake of interest on the application of BC technology, the extent of its adoption in various industries in many countries remains partially understood. This paper aims to assess the current status of research on adoption of BC technology in various industries, particularly in developing and emerging economies. This study systematically reviewed the applied theories and models, adoption factors considered in each study, benefits, barriers and challenges of BC adoption intention in different industries from 86 articles published in the past five years from 2019 to end of June 2023. Findings showed several popular adoption models such as the Technology Acceptance Model, Unified Theory of Acceptance and Use of Technology and Task Technology Fit in the reviewed articles. Benefits, barriers and challenges were evident from each of the industries, implying the need to further understand BC adoption and application in these industries. This review also identifies a few research gaps and provides recommendations for future researches.

In recent years, the Internet of Things (IoT) has become one of the most familiar names creating a benchmark and scaling new heights. IoT an indeed future of the communication that has transformed the objects (things) of the real world into smarter devices. With the advent of IoT technology, this decade is witnessing a transformation from traditional agriculture approaches to the most advanced ones. Limited research has been carried out in this direction. Thus, herein we present various technological aspects involved in IoT-based cultivation. The role and the key components of smart farming using IoT were examined, with a focus on network technologies, including layers, protocols, topologies, network architecture, etc. We also delve into the integration of relevant technologies such as cloud computing, big data analytics, and the integration of IoT-based cultivation. We explored various security issues in modern IoT cultivation and also emphasized the importance of safeguarding sensitive agricultural data. Additionally, a comprehensive list of applications based on sensors and mobile devices is provided, offering refined solutions for greenhouse management. The principles and regulations established by different countries for IoT-based cultivation systems are presented, demonstrating the global recognition of these technologies. Furthermore, a selection of successful use cases and real-world scenarios and applications were presented. Finally, the open research challenges and solutions in modern IoT-based cultivation were discussed.

The dynamic landscape of cloud computing design presents significant challenges regarding power consumption and quality of service (QoS). Virtual machine (VM) consolidation is essential for reducing power usage and enhancing QoS by relocating VMs between hosts. OpenStack Neat, a leading framework for VM consolidation, employs the Modified Best-Fit Decreasing (MBFD) VM placement technique, which faces issues related to energy consumption and QoS. To address these issues, we propose an Energy Efficient VM Consolidation (EEVMC) approach. Our method introduces a novel host selection criterion based on the incurred loss during VM placement to identify the most efficient host. For validation, we conducted simulations using real-time workload traces from Planet-Lab and Materna over ten days, leveraging the latest CloudSim toolkit to compare our approach with state-of-the-art techniques. For Planet-Lab’s workload, our EEVMC approach shows a reduction in energy consumption by 80.35%, 59.76%, 21.59%, and 7.40%, and fewer system-level agreement (SLA) violations by 94.51%, 94.85%, 47.17%, and 17.78% when compared to Modified Best-Fit Decreasing (MBFD), Power-Aware Best Fit Decreasing (PABFD), Medium Fit Power Efficient Decreasing (MFPED), and Power-Efficient Best-Fit Decreasing (PEBFD), respectively. Similarly, for Materna, EEVMC achieves a reduction in energy consumption by 16.10%, 61.0%, 4.94%, and 4.82%, and fewer SLA violations by 76.99%, 88.88%, 12.50%, and 48.65% against the same benchmarks. Additionally, Loss-Aware Performance Efficient Decreasing (LAPED) significantly reduces the total number of VM migrations and SLA time per active host, indicating a substantial improvement in cloud computing efficiency.

In this article, we develop a generic controlled alternate quantum walk model by combining parity-dependent quantum walks with distinct arbitrary memory lengths and propose a hash function (called QHFM-P) based on this model. The statistical properties of the proposed scheme are stable with respect to the coin parameters of the underlying controlled quantum walks, and with certain parameter values, the collision resistance property of QHFM-P is better than that of the state-of-the-art hash functions based on discrete quantum walks. Moreover, the proposed hash function can also maintain near-ideal statistical performance when the input message is of small length. In addition, we derive a type of inappropriate initial states of hash functions based on 1-D one-particle quantum walks (with ordinary shift operator) on cycles, with which all messages will be mapped to the same hash value, regardless of the angles adopted by the coin parameters.

Abstract LiNi0.5Co0.2Mn0.3O2 (NCM523) has become one of the most popular cathode materials for current lithium-ion batteries due to its high-energy density and cost performance. However, the rapid capacity fading of NCM severely hinders its development and applications. Here, the single crystal NCM523 materials under different degradation states are characterized using scanning transmission electron microscopy (STEM). Then we developed a neural network model with a two-sequential attention block to recognize the crystal structure and locate defects in STEM images. The number of point defects in NCM523 is observed to experience a trend of increasing first and then decreasing in the degradation process. The space between the transition metal columns shrinks obviously, inducing dramatic capacity decay. This analysis sheds light on the defect evolution and chemical transformation correlated with layered material degradation. It also provides interesting hints for researchers to regenerate the electrochemical capacity and design better battery materials with longer life.

We propose a Quantum inspired Hash Function using controlled alternate quantum walks with Memory on cycles (QHFM), where the $j$th message bit decides whether to run quantum walk with one-step memory or to run quantum walk with two-step memory at the $j$th time step, and the hash value is calculated from the resulting probability distribution of the walker. Numerical simulation shows that the proposed hash function has near-ideal statistical performance and is at least on a par with the state-of-the-art hash functions based on quantum walks in terms of sensitivity of hash value to message, diffusion and confusion properties, uniform distribution property, and collision resistance property; and theoretical analysis indicates that the time and space complexity of the new scheme are not greater than those of its peers. The good performance of QHFM suggests that quantum walks that differ not only in coin operators but also in memory lengths can be combined to build good hash functions, which, in turn, enriches the construction of controlled alternate quantum walks.

Abstract Polymers have been widely used in energy storage, construction, medicine, aerospace, and so on. However, the complexity of chemical composition and morphology of polymers has brought challenges to their development. Thanks to the integration of machine learning algorithms and large data resources, the data‐driven methods have opened up a new road for the development of polymer science and engineering. The emerging polymer informatics attempts to accelerate the performance prediction and process optimization of new polymers by using machine learning models based on reliable data. With the gradual supplement of currently available databases, the emergence of new databases and the continuous improvement of machine learning algorithms, the research paradigm of polymer informatics will be more efficient and widely used. Based on these points, this paper reviews the development trends of machine learning assisted polymer informatics and provides a simple introduction for researchers in materials, artificial intelligence, and other fields.

Intrusion detection systems (IDS) play a crucial role in securing networks and identifying malicious activity. This is a critical problem in cyber security. In recent years, metaheuristic optimization algorithms and deep learning techniques have been applied to IDS to improve their accuracy and efficiency. Generally, optimization algorithms can be used to boost the performance of IDS models. Deep learning methods, such as convolutional neural networks, have also been used to improve the ability of IDS to detect and classify intrusions. In this paper, we propose a new IDS model based on the combination of deep learning and optimization methods. First, a feature extraction method based on CNNs is developed. Then, a new feature selection method is used based on a modified version of Growth Optimizer (GO), called MGO. We use the Whale Optimization Algorithm (WOA) to boost the search process of the GO. Extensive evaluation and comparisons have been conducted to assess the quality of the suggested method using public datasets of cloud and Internet of Things (IoT) environments. The applied techniques have shown promising results in identifying previously unknown attacks with high accuracy rates. The MGO performed better than several previous methods in all experimental comparisons.

The great advancements in communication, cloud computing, and the internet of things (IoT) have opened critical challenges in security. With these developments, cyberattacks are also rapidly growing since the current security mechanisms do not provide efficient solutions. Recently, various artificial intelligence (AI) based solutions have been proposed for different security applications, including intrusion detection. In this paper, we propose an efficient AI-based mechanism for intrusion detection systems (IDS) in IoT systems. We leverage the advancements of deep learnings and metaheuristics (MH) algorithms that approved their efficiency in solving complex engineering problems. We propose a feature extraction method using the convolutional neural networks (CNNs) to extract relevant features. Also, we develop a new feature selection method using a new variant of the transient search optimization (TSO) algorithm, called TSODE, using the operators of differential evolution (DE) algorithm. The proposed TSODE uses the DE to improve the process of balancing between exploitation and exploration phases. Furthermore, we use three public datasets, KDDCup-99, NSL-KDD, BoT-IoT, and CICIDS-2017 to assess the performance of the developed method, which achieved higher accuracy compared to several existing approaches.

The continuous increase in the use of smart devices and the need for E2E smart2smart (S2S) services in IoT systems play effective and contemporary roles in the field of communication, and a large amount of resources is required. Thus, IoTs and cloud computing must be integrated. One of the results of this integration is the increase in the number of attacks and vulnerabilities in the E2E S2S message delivery service of such an IoT-cloud system. However, none of the traditional security solutions can be sufficiently regarded as a secure and lightweight mechanism for ensuring that the security requirements for E2E S2S message transmission in the IoT-cloud system are fulfilled. This work aims to provide an efficient and secure, lightweight E2E S2S message delivery function, which includes the E2E S2S secure key and biometric parameter exchange function, a bio-shared parameter and bio-key generation function, secure lightweight E2E S2S communication negotiation and secure E2E S2S lightweight message delivery. The secure, lightweight cryptographic communication procedure is negotiated between a pair of smart devices during each E2E session to minimize the power consumption required of limited-energy devices. Such a negotiation process prevents known attacks by providing responsive mutual authentication. Lightweight message delivery by the two smart devices can satisfy the basic security requirements of E2E communication and ensure that the computational cost required for a real-time system is as low as possible.

Vehicles authentication, the integrity of messages exchanged, and privacy-preserving are essential features in vehicular ad hoc network (VANETs) security. Most of the previously proposed VANETs security solutions do not sufficiently satisfy the security and efficiency requirements. Besides, most of those solutions are heavily dependent on the system key and long-term sensitive data stored in an ideal tamper-proof device, which may not be practical or ideal for resource-constrained onboard units (OBUs), especially in the case of an unexpected cloning or physical attack. Therefore, a robust authentication solution should consider those security issues and the nature of resource-constrained nodes. To satisfy all these requirements, we propose a lightweight multi-factor authentication and privacy-preserving security solution for VANETs. It employs a combination of physically unclonable functions (PUF) and one-time dynamic pseudo-identities as authentication factors. Furthermore, it eliminates the heavy dependency on the system key by decentralising the wide precinct of the certificate authority (CA) into regional domains and achieves robust control of domains keys. A detailed analysis demonstrates that our scheme efficiently meets the VANETs security requirements, and offers more suitable communication and computation costs and features than existing schemes.

Medical imaging techniques play a critical role in diagnosing diseases and patient healthcare. They help in treatment, diagnosis, and early detection. Image segmentation is one of the most important steps in processing medical images, and it has been widely used in many applications. Multi-level thresholding (MLT) is considered as one of the simplest and most effective image segmentation techniques. Traditional approaches apply histogram methods; however, these methods face some challenges. In recent years, swarm intelligence methods have been leveraged in MLT, which is considered an NP-hard problem. One of the main drawbacks of the SI methods is when searching for optimum solutions, and some may get stuck in local optima. This because during the run of SI methods, they create random sequences among different operators. In this study, we propose a hybrid SI based approach that combines the features of two SI methods, marine predators algorithm (MPA) and moth-?ame optimization (MFO). The proposed approach is called MPAMFO, in which, the MFO is utilized as a local search method for MPA to avoid trapping at local optima. The MPAMFO is proposed as an MLT approach for image segmentation, which showed excellent performance in all experiments. To test the performance of MPAMFO, two experiments were carried out. The first one is to segment ten natural gray-scale images. The second experiment tested the MPAMFO for a real-world application, such as CT images of COVID-19. Therefore, thirteen CT images were used to test the performance of MPAMFO. Furthermore, extensive comparisons with several SI methods have been implemented to examine the quality and the performance of the MPAMFO. Overall experimental results confirm that the MPAMFO is an efficient MLT approach that approved its superiority over other existing methods.

The image segmentation techniques based on multi-level threshold value received lot of attention in recent years. It is because they can be used as a pre-processing step in complex image processing applications. The main problem in identifying the suitable threshold values occurs when classical image segmentation methods are employed. The swarm intelligence (SI) technique is used to improve multi-level threshold image (MTI) segmentation performance. SI technique simulates the social behaviors of swarm ecosystem, such as the behavior exhibited by different birds, animals etc. Based on SI techniques, we developed an alternative MTI segmentation method by using a modified version of the salp swarm algorithm (SSA). The modified algorithm improves the performance of various operators of the moth-flame optimization (MFO) algorithm to address the limitations of traditional SSA algorithm. This results in improved performance of SSA algorithm. In addition, the fuzzy entropy is used as objective function to determine the quality of the solutions. To evaluate the performance of the proposed methodology, we evaluated our techniques on CEC2005 benchmark and Berkeley dataset. Our evaluation results demonstrate that SSAMFO outperforms traditional SSA and MFO algorithms, in terms of PSNR, SSIM and fitness value.

Coronavirus pandemic (COVID-19) has infected more than ten million persons worldwide. Therefore, researchers are trying to address various aspects that may help in diagnosis this pneumonia. Image segmentation is a necessary pr-processing step that implemented in image analysis and classification applications. Therefore, in this study, our goal is to present an efficient image segmentation method for COVID-19 Computed Tomography (CT) images. The proposed image segmentation method depends on improving the density peaks clustering (DPC) using generalized extreme value (GEV) distribution. The DPC is faster than other clustering methods, and it provides more stable results. However, it is difficult to determine the optimal number of clustering centers automatically without visualization. So, GEV is used to determine the suitable threshold value to find the optimal number of clustering centers that lead to improving the segmentation process. The proposed model is applied for a set of twelve COVID-19 CT images. Also, it was compared with traditional k-means and DPC algorithms, and it has better performance using several measures, such as PSNR, SSIM, and Entropy.

Artificial intelligence (AI) has received widespread attention over the last few decades due to its potential to increase automation and accelerate productivity. In recent years, a large number of training data, improved computing power, and advanced deep learning algorithms are conducive to the wide application of AI, including material research. The traditional trial‐and‐error method is inefficient and time‐consuming to study materials. Therefore, AI, especially machine learning, can accelerate the process by learning rules from datasets and building models to predict. This is completely different from computational chemistry where a computer is only a calculator, using hard‐coded formulas provided by human experts. Herein, the application of AI in material innovation is reviewed, including material design, performance prediction, and synthesis. The realization details of AI techniques and advantages over conventional methods are emphasized in these applications. Finally, the future development direction of AI is expounded from both algorithm and infrastructure aspects.

COVID-19 is a worldwide epidemic, as announced by the World Health Organization (WHO) in March 2020. Machine learning (ML) methods can play vital roles in identifying COVID-19 patients by visually analyzing their chest x-ray images. In this paper, a new ML-method proposed to classify the chest x-ray images into two classes, COVID-19 patient or non-COVID-19 person. The features extracted from the chest x-ray images using new Fractional Multichannel Exponent Moments (FrMEMs). A parallel multi-core computational framework utilized to accelerate the computational process. Then, a modified Manta-Ray Foraging Optimization based on differential evolution used to select the most significant features. The proposed method evaluated using two COVID-19 x-ray datasets. The proposed method achieved accuracy rates of 96.09% and 98.09% for the first and second datasets, respectively.

Developing cyber security is very necessary and has attracted considerable attention from academy and industry organizations worldwide. It is also very necessary to provide sustainable computing for the the Internet of Things (IoT). Machine learning techniques play a vital role in the cybersecurity of the IoT for intrusion detection and malicious identification. Thus, in this study, we develop new feature extraction and selection methods and for the IDS system using the advantages of the swarm intelligence (SI) algorithms. We design a feature extraction mechanism depending on the conventional neural networks (CNN). After that, we present an alternative feature selection (FS) approach using the recently developed SI algorithm, Aquila optimizer (AQU). Moreover, to assess the quality of the developed IDS approach, four well-known public datasets, CIC2017, NSL-KDD, BoT-IoT, and KDD99, were used. We also considered extensive comparisons to other optimization methods to verify the competitive performance of the developed method. The results show the high performance of the developed approach using different evaluation indicators.

Feature selection (FS) is a well-known preprocess step in soft computing and machine learning algorithms. It plays a critical role in different real-world applications since it aims to determine the relevant features and remove other ones. This process (i.e., FS) reduces the time and space complexity of the learning technique used to handle the collected data. The feature selection methods based on metaheuristic (MH) techniques established their performance over all the conventional FS methods. So, in this paper, we presented a modified version of new MH techniques named Atomic Orbital Search (AOS) as FS technique. This is performed using the advances of dynamic opposite-based learning (DOL) strategy that is used to enhance the ability of AOS to explore the search domain. This is performed by increasing the diversity of the solutions during the searching process and updating the search domain. A set of eighteen datasets has been used to evaluate the efficiency of the developed FS approach, named AOSD, and the results of AOSD are compared with other MH methods. From the results, AOSD can reduce the number of features by preserving or increasing the classification accuracy better than other MH techniques.

Pedestrian gender classification is one of the key assignments of pedestrian study, and it finds practical applications in content-based image retrieval, population statistics, human–computer interaction, health care, multimedia retrieval systems, demographic collection, and visual surveillance. In this research work, gender classification was carried out using a deep learning approach. A new 64-layer architecture named 4-BSMAB derived from deep AlexNet is proposed. The proposed model was trained on CIFAR-100 dataset utilizing SoftMax classifier. Then, features were obtained from applied datasets with this pre-trained model. The obtained feature set was optimized with ant colony system (ACS) optimization technique. Various classifiers of SVM and KNN were used to perform gender classification utilizing the optimized feature set. Comprehensive experimentation was performed on gender classification datasets, and proposed model produced better results than the existing methods. The suggested model attained highest accuracy, i.e., 85.4%, and 92% AUC on MIT dataset, and best classification results, i.e., 93% accuracy and 96% AUC, on PKU-Reid dataset. The outcomes of extensive experiments carried out on existing standard pedestrian datasets demonstrate that the proposed framework outperformed existing pedestrian gender classification methods, and acceptable results prove the proposed model as a robust model.

The Industrial Internet of Things (IIoT) has become a pivotal field of development that can increase the efficiency of real-time collection, recording, analysis, and control of the entire activities of various machines, and can actively enhance quality and reduce costs. The traditional IIoT depends on centralized architectures that are vulnerable to several kinds of cyber-attacks, such as bottlenecks and single points of failure. Blockchain technology has emerged to change these architectures to a decentralized form. In modern industrial settings, blockchain technology is utilized for its ability to provide high levels of security, low computational complexity, P2P communication, transparent logs, and decentralization. The present work proposes the use of a private blockchain mechanism for an industrial application in a cement factory, which offers low power consumption, scalability, and a lightweight security scheme; and which can play an efficient role in controlling access to valuable data generated by sensors and actuators. A low-power ARM Cortex-M processor is utilized due to its efficiency in terms of processing cryptographic algorithms, and this plays an important part in improving the computational execution of the proposed architecture. In addition, instead of proof of work (PoW), our blockchain network uses proof of authentication (PoAh) as a consensus mechanism to ensure secure authentication, scalability, speed, and energy efficiency. Our experimental results show that the proposed framework achieves high levels of security, scalability and ideal performance for smart industrial environments. Moreover, we successfully realized the integration of blockchain technology with the industrial internet of things devices, which provides the blockchain technology features and efficient resistance to common cyber-security attacks.

Battery lifetime prediction is a promising direction for the development of next-generation smart energy storage systems. However, complicated degradation mechanisms, different assembly processes, and various operation conditions of the batteries bring tremendous challenges to battery life prediction. In this work, charge/discharge data of 12 solid-state lithium polymer batteries were collected with cycle lives ranging from 71 to 213 cycles. The remaining useful life of these batteries was predicted by using a machine learning algorithm, called symbolic regression. After populations of breed, mutation, and evolution training, the test accuracy of the quantitative prediction of cycle life reached 87.9%. This study shows the great prospect of a data-driven machine learning algorithm in the prediction of solid-state battery lifetimes, and it provides a new approach for the batch classification, echelon utilization, and recycling of batteries.

Multi-level thresholding is one of the effective segmentation methods that have been applied in many applications. Traditional methods face challenges in determining the suitable threshold values; therefore, metaheuristic (MH) methods have been adopted to solve these challenges. In general, MH methods had been proposed by simulating natural behaviors of swarm ecosystems, such as birds, animals, and others. The current study proposes an alternative multi-level thresholding method based on a new MH method, a modified spherical search optimizer (SSO). This was performed by using the operators of the sine cosine algorithm (SCA) to enhance the exploitation ability of the SSO. Moreover, Fuzzy entropy is applied as the main fitness function to evaluate the quality of each solution inside the population of the proposed SSOSCA since Fuzzy entropy has established its performance in literature. Several images from the well-known Berkeley dataset were used to test and evaluate the proposed method. The evaluation outcomes approved that SSOSCA showed better performance than several existing methods according to different image segmentation measures.

Encryption is one of the best methods to safeguard the security and privacy of an image. However, looking through encrypted data is difficult. A number of techniques for searching encrypted data have been devised. However, certain security solutions may not be used in smart devices in IoT-cloud because such solutions are not lightweight. In this article, we present a lightweight scheme that can enable a content-based search through encrypted images. In particular, images are represented using local features. We develop and validate a secure scheme for measuring the Euclidean distance between two feature vectors. In addition, we use a hashing method, namely, locality-sensitive hashing, to devise the searchable index. The use of an locality-sensitive hashing index increases the proficiency and effectiveness of a system, thereby allowing the retrieval of only relevant images with a minimum number of distance evaluations. Refining vector techniques are used to refine relevant results efficiently and securely. Our index construction process ensures that stored data and trapdoors are kept private. Our system also efficiently supports multi-user authentication by avoiding the expensive traditional method, which enables data owners to define who can search for a specific image. Compared with other similarity-based encryption methods predicated upon searchability, the option presented in this study offers superior search speed and storage efficiency.

The remote direct memory access (RDMA) technology, which has been effectively utilized to enhance data transfer rates and reduce CPU utilization, has played a significant role in various domains such as cross-regional data center transfers, high-performance computing, and rapid data read/write operations. However, despite its emerging status, RDMA has been lacking in widely recognized security solutions. With the expansion of RDMA applications from dedicated network environments to general Ethernet networks, the need to address the security risks faced by these applications has become imperative. A set of protective measures were established to ensure security without compromising transmission efficiency. Furthermore, the unique underlying implementation and protocol design of RDMA technology have resulted in incompatibility with existing mature security solutions and have led to the confrontation with specific security risks. The development of RDMA attack and defense technologies and the provision of security guarantees for the forthcoming widespread application of RDMA technology were elucidated The principles of RDMA technology and its implementation were proposed, with examples drawn from the InfiniBand (IB) and the RDMA over converged Ethernet (RoCE) second edition protocols. The security risks faced by RDMA applications in relevant scenarios were investigated, and a summary of the research progress in the RDMA security field over recent years was provided. Effective security solutions addressing these risks were compiled and, after their defensive capabilities were proven, a comparison of their advantages and disadvantages was conducted through theoretical analysis and experimental data. Finally, improvement plans and prospects for technological optimization in the RDMA attack and defense field were proposed.