Search

The task of finding natural groupings within a dataset exploiting proximity of samples is known as clustering, an unsupervised learning approach. Density-based clustering algorithms, which identify arbitrarily shaped clusters using spatial dimensions and neighbourhood aspects, are sensitive to the selection of parameters. For instance, DENsity CLUstEring (DENCLUE)—a density-based clustering algorithm—requires a trial-and-error approach to find suitable parameters for optimal clusters. Earlier attempts to automate the parameter estimation of DENCLUE have been highly dependent either on the choice of prior data distribution (which could vary across datasets) or by fixing one parameter (which might not be optimal) and learning other parameters. This article addresses this challenge by learning the parameters of DENCLUE through the differential evolution optimisation technique without prior data distribution assumptions. Experimental evaluation of the proposed approach demonstrated consistent performance across datasets (synthetic and real datasets) containing clusters of arbitrary shapes. The clustering performance was evaluated using clustering validation metrics (e.g., Silhouette Score, Davies–Bouldin Index and Adjusted Rand Index) as well as qualitative visual analysis when compared with other density-based clustering algorithms, such as DPC, which is based on weighted local density sequences and nearest neighbour assignments (DPCSA) and Variable KDE-based DENCLUE (VDENCLUE).

Rice is one of the fundamental food items that comes in many varieties with their associated benefits. It can be sub-categorized based on its visual features like texture, color, and shape. Using these features, the automatic classification of rice varieties has been studied using various machine learning approaches for marketing and industrial use. Due to the outstanding performance of deep learning, several models have been proposed to assist in vision tasks like classification and detection. Regardless of their best results on accuracy metrics, they have been observed as overly excessive for computational resources and expert supervision. To address these challenges, this paper proposes three deep learning models that offer similar performance with 10% lighter computational overhead in comparison to existing best models. Moreover, they have been trained for end-to-end flow to demonstrate minimum expert supervision for pre-processing and feature engineering sub-tasks. The results can be observed as promising for classifying rice among five varieties, namely Arborio, Basmati, Ipsala, Jasmine, and Karacadag. The process and performance of the trained models can be extended for edge and mobile devices for field-specific tasks autonomously.

The healthcare sector is suffering from inefficiencies in handling its data. Many patients and healthcare organisations are frustrated by the numerous hurdles to obtaining current, real-time patient information. Patients are also frustrated at trying to schedule appointments at health organisations that have outdated contact information. The healthcare sector’s attention has been drawn to blockchain technology as a part of the solution, especially since this technology has been successfully applied in the financial sector to improve the security of transactions. The aspect of interoperability is resolved adequately by blockchain technology, because it has the potential to store, manage and share EMRs safely in the healthcare community. Therefore, the technology is having a positive impact on healthcare outcomes for various stakeholders. Interoperability in healthcare eases the exchange of health-related data, such as EMRs, between healthcare entities so that records may be shared and distributed among clinical systems. To handle data in this sector without violating privacy is a challenge, whether in the collection, storage, or analysis. Poor security, which increases data breaches, endangers patients both mentally, socially, and financially. A lack of data-sharing in the healthcare sector is considered a significant issue worldwide. This research focuses on this gap by investigating the benefits of using blockchain at the Ministry of Health in Saudi Arabia, providing a detailed analysis of the healthcare sector, and evaluating how blockchain technology improves data-sharing security. This research proposes a framework that identifies the factors supporting data-sharing using blockchain among healthcare organisations. It has three categories: healthcare systems factors; security factors; and blockchain factors. A triangulation technique achieved reliable results in three steps: a literature review; an expert review; and a questionnaire. This gave a comprehensive picture of the research topic, validating and confirming the results. To construct the framework, factors were comprehensively extracted from the literature then analysed, cleared of duplicates, and categorised. As a result, the final framework is confirmed as being based on the literature and expert review, and it is supported by the practitioners’ survey.

This article presents a throughput/area accelerator for elliptic-curve point multiplication over GF(2571). To optimize the throughput, we proposed an efficient hardware accelerator architecture for a fully recursive Karatsuba multiplier to perform polynomial multiplications in one clock cycle. To minimize the hardware resources, we have utilized the proposed Karatsuba multiplier for modular square implementations. Moreover, the Itoh-Tsujii algorithm for modular inverse computation is operated using multiplier resources. These strategies permit us to reduce the hardware resources of our implemented accelerator over a large field size of 571 bits. A controller is implemented to provide control functionalities. Our throughput/area accelerator is implemented in Verilog HDL using the Vivado IDE tool. The results after the place-and-route are given on Xilinx Virtex-6 and Virtex-7 devices. The utilized slices on Virtex-6 and Virtex-7 devices are 6107 and 5683, respectively. For the same FPGA devices, our accelerator can operate at a maximum of 319 MHz and 361 MHz. The latency values for Virtex-6 and Virtex-7 devices are 28.73 μs and 25.38 μs. The comparison to the state-of-the-art shows that the proposed architecture outperforms in throughput/area values. Thus, our accelerator architecture is suitable for cryptographic applications that demand a throughput and area simultaneously.

Deep learning is expanding and continues to evolve its capabilities toward more accuracy, speed, and cost-effectiveness. The core ingredients for getting its promising results are appropriate data, sufficient computational resources, and best use of a particular algorithm. The application of these algorithms in medical image analysis tasks has achieved outstanding results compared to classical machine learning approaches. Localizing the area-of-interest is a challenging task that has vital importance in computer aided diagnosis. Generally, radiologists interpret the radiographs based on their knowledge and experience. However, sometimes, they can overlook or misinterpret the findings due to various reasons, e.g., workload or judgmental error. This leads to the need for specialized AI tools that assist radiologists in highlighting abnormalities if exist. To develop a deep learning driven localizer, certain alternatives are available within architectures, datasets, performance metrics, and approaches. Informed decision for selection within the given alternative can lead to batter outcome within lesser resources. This paper lists the required components along-with explainable AI for developing an abnormality localizer for X-ray images in detail. Moreover, strong-supervised vs weak-supervised approaches have been majorly discussed in the light of limited annotated data availability. Likewise, other correlated challenges have been presented along-with recommendations based on a relevant literature review and similar studies. This review is helpful in streamlining the development of an AI based localizer for X-ray images while extendable for other radiological reports.

This paper presents a high-speed and low-area accelerator architecture for shared key generation using an elliptic-curve Diffie-Hellman protocol over GF(2233). Concerning the high speed, the proposed architecture employs a two-stage pipelining and a Karatsuba finite field multiplier. The use of pipelining shortens the critical path which ultimately improves the clock frequency. Similarly, the employment of a Karatsuba multiplier decreases the required number of clock cycles. Moreover, an efficient rescheduling of point addition and doubling operations avoids data hazards that appear due to pipelining. Regarding the low area, the proposed architecture computes finite field squaring and inversion operations using the hardware resources of the Karatsuba multiplier. Furthermore, two dedicated controllers are used for efficient control functionalities. The implementation results after place-and-route are provided on Virtex-7, Spartan-7, Artix-7 and Kintex-7 FPGA (field-programmable gate arrays) devices. The utilized FPGA slices are 5102 (on Virtex-7), 5634 (on Spartan-7), 5957 (on Artix-7) and 6102 (on Kintex-7). In addition to this, the time required for one shared-key generation is 31.08 (on Virtex-7), 31.68 (on Spartan-7), 31.28 (on Artix-7) and 32.51 (on Kintex-7). For performance comparison, a figure-of-merit in terms of throughputarea is utilized which shows that the proposed architecture is 963.3 and 2.76 times faster as compared to the related architectures. In terms of latency, the proposed architecture is 302.7 and 132.88 times faster when compared to the most relevant state-of-the-art approaches. The achieved results and performance comparison prove the significance of presented architecture in all those shared key generation applications which require high speed with a low area.

This work presents a scalable digit-parallel finite field polynomial multiplier architecture with a digit size of 32 bits for NIST-standardized binary elliptic fields. First, a dedicated digit-parallel architecture is proposed for each binary field recommended by NIST, i.e., 163, 233, 283, 409 and 571. Then, a scalable architecture having support for all variants of binary fields of elliptic curves is proposed. For performance investigation, we have compared dedicated multiplier architectures with scalable design. After this, the dedicated and scalable architectures are compared with the most relevant state-of-the-art multipliers. All multiplier architectures are implemented in Verilog HDL using the Vivado IDE tool. The implementation results are reported on a 28 nm Virtex-7 FPGA technology. The dedicated multipliers utilize slices of 1182 (for m=163), 1451 (for m=233), 1589 (for m=283), 2093 (for m=409) and 3451 (for m=571). Moreover, our dedicated designs can operate at a maximum frequency of 500, 476, 465, 451 and 443 MHz. Similarly, for all supported binary fields, our scalable architecture (i) utilizes 3753 slices, (ii) achieves 305 MHz clock frequency, (iii) takes 0.013 μs for one finite field multiplication and (iv) consumes 3.905 W power. The proposed scalable digit-parallel architecture is more area-efficient than most recent state-of-the-art multipliers. Consequently, the reported results and comparison to the state of the art reveal that the proposed architectures are well suited for cryptographic applications.