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This study presents a description of an efficient hardware implementation of an elliptic curve cryptography processor (ECP) for modern security applications. A high‐performance elliptic curve scalar multiplication (ECSM), which is the key operation of an ECP, is developed both in affine and Jacobian coordinates over a prime field of size p using the National Institute of Standards and Technology standard. A novel combined point doubling and point addition architecture is proposed using efficient modular arithmetic to achieve high speed and low hardware utilisation of the ECP in Jacobian coordinates. This new architecture has been synthesised both in application‐specific integrated circuit (ASIC) and field‐programmable gate array (FPGA). A 65 nm CMOS ASIC implementation of the proposed ECP in Jacobian coordinates takes between 0.56 and 0.73 ms for 224‐bit and 256‐bit elliptic curve cryptography, respectively. The ECSM is also implemented in an FPGA and provides a better delay performance than previous designs. The implemented design is area‐efficient and this means that it requires not many resources, without any digital signal processing (DSP) slices, on an FPGA. Moreover, the area–delay product of this design is very low compared with similar designs. To the best of the authors’ knowledge, the ECP proposed in this study over Fp performs better than available hardware in terms of area and timing.

Breast Cancer is a serious threat and one of the largest causes of death of women throughout the world. The identification of cancer largely depends on digital biomedical photography analysis such as histopathological images by doctors and physicians. Analyzing histopathological images is a nontrivial task, and decisions from investigation of these kinds of images always require specialised knowledge. However, Computer Aided Diagnosis (CAD) techniques can help the doctor make more reliable decisions. The state-of-the-art Deep Neural Network (DNN) has been recently introduced for biomedical image analysis. Normally each image contains structural and statistical information. This paper classifies a set of biomedical breast cancer images (BreakHis dataset) using novel DNN techniques guided by structural and statistical information derived from the images. Specifically a Convolutional Neural Network (CNN), a Long-Short-Term-Memory (LSTM), and a combination of CNN and LSTM are proposed for breast cancer image classification. Softmax and Support Vector Machine (SVM) layers have been used for the decision-making stage after extracting features utilising the proposed novel DNN models. In this experiment the best Accuracy value of 91.00% is achieved on the 200x dataset, the best Precision value 96.00% is achieved on the 40x dataset, and the best F-Measure value is achieved on both the 40x and 100x datasets.

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Schizophrenia (SZ) is a mental disorder whereby due to the secretion of specific chemicals in the brain, the function of some brain regions is out of balance, leading to the lack of coordination between thoughts, actions, and emotions. This study provides various intelligent deep learning (DL)-based methods for automated SZ diagnosis via electroencephalography (EEG) signals. The obtained results are compared with those of conventional intelligent methods. To implement the proposed methods, the dataset of the Institute of Psychiatry and Neurology in Warsaw, Poland, has been used. First, EEG signals were divided into 25 s time frames and then were normalized by z-score or norm L2. In the classification step, two different approaches were considered for SZ diagnosis via EEG signals. In this step, the classification of EEG signals was first carried out by conventional machine learning methods, e.g., support vector machine, k-nearest neighbors, decision tree, naïve Bayes, random forest, extremely randomized trees, and bagging. Various proposed DL models, namely, long short-term memories (LSTMs), one-dimensional convolutional networks (1D-CNNs), and 1D-CNN-LSTMs, were used in the following. In this step, the DL models were implemented and compared with different activation functions. Among the proposed DL models, the CNN-LSTM architecture has had the best performance. In this architecture, the ReLU activation function with the z-score and L2-combined normalization was used. The proposed CNN-LSTM model has achieved an accuracy percentage of 99.25%, better than the results of most former studies in this field. It is worth mentioning that to perform all simulations, the k-fold cross-validation method with k = 5 has been used.

Breast cancer is one of the largest causes of women's death in the world today. Advance engineering of natural image classification techniques and Artificial Intelligence methods has largely been used for the breast-image classification task. The involvement of digital image classification allows the doctor and the physicians a second opinion, and it saves the doctors' and physicians' time. Despite the various publications on breast image classification, very few review papers are available which provide a detailed description of breast cancer image classification techniques, feature extraction and selection procedures, classification measuring parameterizations, and image classification findings. We have put a special emphasis on the Convolutional Neural Network (CNN) method for breast image classification. Along with the CNN method we have also described the involvement of the conventional Neural Network (NN), Logic Based classifiers such as the Random Forest (RF) algorithm, Support Vector Machines (SVM), Bayesian methods, and a few of the semisupervised and unsupervised methods which have been used for breast image classification.

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Public-key cryptosystems such as elliptic curve cryptography (ECC) and Rivest-Shamir-Adleman (RSA) are widely used for data security in computing systems. ECC provides a high level of security with a much smaller key than RSA, which makes ECC a preferred choice in many applications. This study proposes a multi-key ECC based on the residue number system. The proposed architecture employees deep pipelining to allow the concurrent encryption of 21 keys. The proposed architectures are implemented on two different field programmable gate array (FPGA) platforms and results are compared with existing ECC architectures. The proposed implementation on Virtex-7 FPGA achieves a throughput of 1816 kbps at a clock frequency of 73 MHz.