Blockchain is a disrupting technology that has the capability to completely alter the design, activities, and product flows in logistics and supply chain networks. It provides assurance of openness, immutability, transparency, security, and neutrality for all supply chain agents and stakeholders. In this paper, we explore the improvements and tradeoffs introduced by using blockchains in logistics management in terms of the sustainability of society, the environment, and economic dimensions of the supply chain. Blockchain technology makes it much more difficult to counterfeit products by providing indisputable and immutable proof of the provenance of the raw materials, products, and sale to the end consumer. This can potentially enhance the trust of the consumer in the product and financially benefit the manufacturer through the protection of their intellectual property rights. This paper explores the benefits, applications, and issues related to the usage of blockchain and smart contracts for logistics and supply-chain management. We focus on the implementation, deployment, audit, and operational aspects of smart contracts in the blockchain applied to terrestrial, maritime, and aerial logistics networks. The paper also discusses opportunities and challenges that arise due to the use of smart contracts in these sectors.

Supporting vehicular emergency applications requires fast access to infrastructure so vehicles can call for help. Because of their environment's poor wireless qualities, vehicle-infrastructure communication paths lack security. Modern authentication systems used to close security vulnerabilities require a lot of computing and storage power from the vehicle's OBU. Thus, a 5G automobile network emergency situations Chebyshev polynomial using fog computing and authentication is proposed. The Chebyshev chaotic mapping algorithm uses a chaotic map, rotation, and XOR to generate a one-way hash and eliminate modular multiplication index or scalar multiplication on the elliptic curve. The proposed scheme has four stages: installation system, initialization, enrollment, mutual authentication, and emergency request. Critical to emergency services, the suggested protocol works better in resource-limited settings like car systems. The security analysis reveals that the suggested approach guarantees message authenticity and integrity, non-repudiation, traceability, unlinkability, identity privacy, certificate independence, and emergency response. The proposed method cannot execute forgery, impersonation, or replay attacks. Proposed method has lower computational, communication, and storage overhead than earlier efforts.

Breast cancer is one of the most prevalent cancer types with a high mortality rate in women worldwide. This devastating cancer still represents a worldwide public health concern in terms of high morbidity and mortality rates. The diagnosis of breast abnormalities is challenging due to different types of tissues and textural variations in intensity. Hence, developing an accurate computer-aided system (CAD) is very important to distinguish normal from abnormal tissues and define the abnormal tissues as benign or malignant. The present study aims to enhance the accuracy of CAD systems and to reduce its computational complexity. This paper proposes a method for extracting a set of statistical features based on curvelet and wavelet sub-bands. Then the binary grey wolf optimizer (BGWO) is used as a feature selection technique aiming to choose the best set of features giving high performance. Using public dataset, Digital Database for Screening Mammography (DDSM), different experiments have been performed with and without using the BGWO algorithm. The random forest classifier with 10-fold cross-validation is used to achieve the classification task to evaluate the selected set of features’ capability. The obtained results showed that when the BGWO algorithm is used as a feature selection technique, only 30.7% of the total features can be used to detect whether a mammogram image is normal or abnormal with ROC area reaching 1.0 when the fusion of both curvelet and wavelet features were used. In addition, in case of diagnosing the mammogram images as benign or malignant, the results showed that using BGWO algorithm as a feature selection technique, only 38.5% of the total features can be used to do so with high ROC area result at 0.871.

Epilepsy is one of the most prevalent neurological issues faced by a large population around the globe. Epilepsy is marked by intermittent seizures, the detection of which can be a challenging problem. Therefore, reliably detecting the onset of seizures has evoked the interest of researchers over the last few years. Major leaps in the domain of machine learning, signal processing methods and computational capabilities have made it a tractable task. In this paper, we apply multi-resolution dynamic mode decomposition (MRDMD), which is a data-driven dimensionality reduction technique, on the problem of epileptic seizure detection. This method can effectively separate a complex non-linear system into a collection of time-scale components at different resolutions. We have applied this algorithm on two different scalp EEG datasets, i.e., CHB-MIT and KU Leaven datasets. We have applied necessary post-processing steps to reduce the false alarm rate and boost the sensitivity and specificity. A detailed analysis of the results has been presented for the proposed method applied to both the datasets. The algorithm achieves a sensitivity of 0.937 and 0.96, specificity of 0.99 and 0.99, false alarm rates of 0.587 and 0.413 per hour, and latency of 3.12 seconds and 2.75 seconds for CHB-MIT and KU Leuven, respectively. The results indicate that multi-resolution analysis yields a significant improvement in sensitivity as compared to the basic dynamic mode decomposition Keywords– Epilepsy, seizure detection, EEG, dynamic mode decomposition, multi-resolution analysis, decision trees, RUSBoost.

A network of agents constituted of multiple unmanned aerial vehicles (UAVs) is emerging as a promising technology with myriad applications in the military, public, and civil domains. UAVs’ power, memory, and size constraints, ultra-mobile nature, and non-trusted operational environments make them susceptible to various attacks, including physical capturing and cloning attacks. A robust and resilient security protocol should be lightweight and resource-efficient in addition to providing protection against physical and tampering threats. This paper proposes an authentication protocol for a UAV-based multi-agent system robust against various threats and adversaries, including strong resistance against cloning and physical attacks. The proposed protocol is based on a physical unclonable function (PUF), a well-known hardware security primitive that is utilized for low-cost authentication and cryptographic key generation. The analysis of the proposed approach shows that it provides strong protection against various attacks, including tampering and cloning, and exhibits scalability and energy efficiency.