GRA-PIN: A Graphical and PIN-Based Hybrid Authentication Approach for Smart Devices.

In many smart devices and numerous digital applications, authentication mechanisms are widely used to validate the legitimacy of users' identification. As a result of the increased use of mobile devices, most people tend to save sensitive and secret information over such devices. Personal Identification Number (PIN)-based and alphanumeric passwords are simple to remember, but at the same time, they are vulnerable to hackers. Being difficult to guess and more user-friendly, graphical passwords have grown in popularity as an alternative to all such textual passwords. This paper describes an innovative, hybrid, and much more robust user authentication approach, named GRA-PIN (GRAphical and PIN-based), which combines the merits of both graphical and pin-based techniques. The feature of simple arithmetic operations (addition and subtraction) is incorporated in the proposed scheme, through which random passwords are generated for each login attempt. In the study, we have conducted a comparative study between the GRA-PIN scheme with existing PIN-based and pattern-based (swipe-based) authentications approaches using the standard Software Usability Scale (SUS). The usability score of GRA-PIN was analyzed to be as high as 94%, indicating that it is more reliable and user friendly. Furthermore, the security of the proposed scheme was challenged through an experiment wherein three different attackers, having a complete understanding of the proposed scheme, attempted to crack the technique via shoulder surfing, guessing, and camera attack, but they were unsuccessful.

Celebrex derivatives: Synthesis, α-glucosidase inhibition, crystal structures and molecular docking studies.

Celebrex (1), commonly used as an anti-inflammatory drug, was functionalized (compounds 2-9) to identify new α-glucosidase inhibitors. Initially, all the synthesized derivatives were evaluated for anti-inflammatory activity but none was found to be active. Subsequently a random biological screening was carried out. Interestingly many of them were found to be potent α-glucosidase inhibitors in vitro. All the structures of synthesized derivatives were deduced through 1H NMR, FAB-MS, HR-MS, FT-IR analysis. The single-crystal X-ray structures of compounds 1, and 5 further confirmed the assigned structures. Compounds exhibited a potent α-glucosidase inhibitory activity (IC50 = 92.32 ± 1.530-445.20 ± 1.04 µM) against tested standard acarbose (IC50 = 875.75 ± 2.08 µM), except compounds 2 and 4, which appeared as inactive. Among them, compound 9 (IC50 = 92.32 ± 1.530 µM) was the most potent inhibitor of α-glucosidase enzyme. Molecular docking studies revealed that compounds 6, and 9 interacted with the key amino acid residues of α-glucosidase via H-bonding, and π-π stacking interactions. α-Glucosidase is a key target for the anti-diabetic drug development, and its inhibitors are known to exert anti hyperglycemic effect and help in lowering of post-prandial blood glucose levels.

ALL classification using neural ensemble and memetic deep feature optimization.

Acute lymphoblastic leukemia (ALL) is a fatal blood disorder characterized by the excessive proliferation of immature white blood cells, originating in the bone marrow. An effective prognosis and treatment of ALL calls for its accurate and timely detection. Deep convolutional neural networks (CNNs) have shown promising results in digital pathology. However, they face challenges in classifying different subtypes of leukemia due to their subtle morphological differences. This study proposes an improved pipeline for binary detection and sub-type classification of ALL from blood smear images. At first, a customized, 88 layers deep CNN is proposed and trained using transfer learning along with GoogleNet CNN to create an ensemble of features. Furthermore, this study models the feature selection problem as a combinatorial optimization problem and proposes a memetic version of binary whale optimization algorithm, incorporating Differential Evolution-based local search method to enhance the exploration and exploitation of feature search space. The proposed approach is validated using publicly available standard datasets containing peripheral blood smear images of various classes of ALL. An overall best average accuracy of 99.15% is achieved for binary classification of ALL with an 85% decrease in the feature vector, together with 99% precision and 98.8% sensitivity. For B-ALL sub-type classification, the best accuracy of 98.69% is attained with 98.7% precision and 99.57% specificity. The proposed methodology shows better performance metrics as compared with several existing studies.

White Blood Cells Classification Using Entropy-Controlled Deep Features Optimization.

White blood cells (WBCs) constitute an essential part of the human immune system. The correct identification of WBC subtypes is critical in the diagnosis of leukemia, a kind of blood cancer defined by the aberrant proliferation of malignant leukocytes in the bone marrow. The traditional approach of classifying WBCs, which involves the visual analysis of blood smear images, is labor-intensive and error-prone. Modern approaches based on deep convolutional neural networks provide significant results for this type of image categorization, but have high processing and implementation costs owing to very large feature sets. This paper presents an improved hybrid approach for efficient WBC subtype classification. First, optimum deep features are extracted from enhanced and segmented WBC images using transfer learning on pre-trained deep neural networks, i.e., DenseNet201 and Darknet53. The serially fused feature vector is then filtered using an entropy-controlled marine predator algorithm (ECMPA). This nature-inspired meta-heuristic optimization algorithm selects the most dominant features while discarding the weak ones. The reduced feature vector is classified with multiple baseline classifiers with various kernel settings. The proposed methodology is validated on a public dataset of 5000 synthetic images that correspond to five different subtypes of WBCs. The system achieves an overall average accuracy of 99.9% with more than 95% reduction in the size of the feature vector. The feature selection algorithm also demonstrates better convergence performance as compared to classical meta-heuristic algorithms. The proposed method also demonstrates a comparable performance with several existing works on WBC classification.

Leukocytes Classification for Leukemia Detection Using Quantum Inspired Deep Feature Selection.

Leukocytes, also referred to as white blood cells (WBCs), are a crucial component of the human immune system. Abnormal proliferation of leukocytes in the bone marrow leads to leukemia, a fatal blood cancer. Classification of various subtypes of WBCs is an important step in the diagnosis of leukemia. The method of automated classification of WBCs using deep convolutional neural networks is promising to achieve a significant level of accuracy, but suffers from high computational costs due to very large feature sets. Dimensionality reduction through intelligent feature selection is essential to improve the model performance with reduced computational complexity. This work proposed an improved pipeline for subtype classification of WBCs that relies on transfer learning for feature extraction using deep neural networks, followed by a wrapper feature selection approach based on a customized quantum-inspired evolutionary algorithm (QIEA). This algorithm, inspired by the principles of quantum physics, outperforms classical evolutionary algorithms in the exploration of search space. The reduced feature vector obtained from QIEA was then classified with multiple baseline classifiers. In order to validate the proposed methodology, a public dataset of 5000 images of five subtypes of WBCs was used. The proposed system achieves a classification accuracy of about 99% with a reduction of 90% in the size of the feature vector. The proposed feature selection method also shows a better convergence performance as compared to the classical genetic algorithm and a comparable performance to several existing works.

Lung cancer prediction using multi-gene genetic programming by selecting automatic features from amino acid sequences.

Lung cancer is one of the leading causes of cancer related deaths. Early diagnosis of lung cancer using automatic feature selection from large number of features is a challenging task. Conventionally, cancer diagnosis approaches use physical features that appear in later stages, while harmful effects have already been occurred due to abnormal somatic mutations. In order to extract useful novel patterns to efficiently predict cancer at early stages, we analyzed lung cancer related mutated genes that reveal useful information in protein amino acid sequences. For this, we developed a new evolutionary learning technique with biologically inspired multi-gene genetic programming algorithm using discriminant information of protein amino acids. The proposed model efficiently selects 23 discriminant features out of 1500 features. Then it combines the selected features and related primitive functions optimally for prediction of lung cancer. Hence, an efficient predictive model is constructed that helps in understanding the complex heterogeneous nature of lung cancer. The proposed system achieved area under ROC curve and accuracy values of 98.79% and 95.67%, respectively outperforming related lung cancer prediction approaches.

Prediction of human breast and colon cancers from imbalanced data using nearest neighbor and support vector machines.

This study proposes a novel prediction approach for human breast and colon cancers using different feature spaces. The proposed scheme consists of two stages: the preprocessor and the predictor. In the preprocessor stage, the mega-trend diffusion (MTD) technique is employed to increase the samples of the minority class, thereby balancing the dataset. In the predictor stage, machine-learning approaches of K-nearest neighbor (KNN) and support vector machines (SVM) are used to develop hybrid MTD-SVM and MTD-KNN prediction models. MTD-SVM model has provided the best values of accuracy, G-mean and Matthew's correlation coefficient of 96.71%, 96.70% and 71.98% for cancer/non-cancer dataset, breast/non-breast cancer dataset and colon/non-colon cancer dataset, respectively. We found that hybrid MTD-SVM is the best with respect to prediction performance and computational cost. MTD-KNN model has achieved moderately better prediction as compared to hybrid MTD-NB (Naïve Bayes) but at the expense of higher computing cost. MTD-KNN model is faster than MTD-RF (random forest) but its prediction is not better than MTD-RF. To the best of our knowledge, the reported results are the best results, so far, for these datasets. The proposed scheme indicates that the developed models can be used as a tool for the prediction of cancer. This scheme may be useful for study of any sequential information such as protein sequence or any nucleic acid sequence.