Continuous user authentication featuring keystroke dynamics based on robust recurrent confidence model and ensemble learning approach

User authentication is considered to be an important aspect of any cybersecurity program. However, one-time validation of user’s identity is not strong to provide resilient security throughout the user session. In this aspect, continuous monitoring of session is necessary to ensure that only legitimate user is accessing the system resources for entire session. In this paper, a true continuous user authentication system featuring keystroke dynamics behavioural biometric modality has been proposed and implemented. A novel method of authenticating the user on each action has been presented which decides the legitimacy of current user based on the confidence in the genuineness of each action. The 2-phase methodology, consisting of ensemble learning and robust recurrent confidence model(R-RCM), has been designed which employs a novel perception of two thresholds i.e., alert and final threshold. Proposed methodology classifies each action based on the probability score of ensemble classifier which is afterwards used along with hyperparameters of R-RCM to compute the current confidence in the genuineness of user. System decides if user can continue using the system or not based on new confidence value and final threshold. However, it tends to lock out imposter user more quickly if it reaches the alert threshold. Moreover, system has been validated with two different experimental settings and results are reported in terms of mean average number of genuine actions (ANGA) and average number of imposter actions(ANIA), whereby achieving the lowest mean ANIA with experimental setting II

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

Formulation, characterization and wound-healing potential of emulgel and in-situ gel containing root extract of Saussurea lappa Clarke (Asteraceae)

Purpose: To investigate the wound-healing potential of herbal formulations (emulgels and in situ gels) containing Saussurea lappa root extract  (SLRE) via excision wound induction in albino rats. Methods: Preliminary phytochemical analysis of the methanol extract of roots of Saussurea lappa (SLRE) was performed using standard  procedures. In vitro anti-inflammatory assay of SLRE was conducted using heat-induced hemolysis method at a concentration of 100 μg/mL. Acute  toxicity of SLRE was also evaluated in mice at a single dose of 1000 mg/kg for 24 h. Emulgels and in situ gels were prepared using different concentrations of SLRE and assessed for their organoleptic and physical properties. In vitro drug release studies of the prepared formulations were carried out by Franz diffusion cell and the data fitted into various pharmacokinetic models. Wound healing was assessed using excision wound induction (380 mm2) on dorsal surface of male albino rats. Each formulation (F4, F5, F6, G1, G2 and G3) and pyodine gel (standard) were applied topically (0.5 g) for 20 days. Wound contraction was measured every fourth day. Results: SLRE showed 42.8 % inhibition in heat-induced hemolysis on erythrocyte membrane model, compared to aspirin (positive control).  Moreover, SLRE did not cause mortality in mice at the given doses. All the formulations were stable after one month stability check at 40 °C for emulgels and at 25 °C for in situ gels. All the formulations followed first order drug release pattern. In situ gel (G3) exhibited better wound healing (100 ± 0.0028) than emulgel (F6, 99 ± 0.004) containing 5 g extract and standard pyodine gel (91 ± 0.014, p <0.05). Conclusion: The results indicate that in situ gel of SLRE exhibits significant wound healing in rats. Thus, the findings present a strategy for the formulation of gel products with better wound healing potentials. Keywords: Saussurea lappa, Wound healing, Emulgel, In situ gel, Herbal formulatio

Comparative Effectiveness of Endoloop, Instrumental Tie and Ligaclip in Laparoscopy Appendectomy

Background: Appendectomy is the most commonly performed surgical procedure for patients. To examine the safety, complications, and cost-effectiveness of the instrumental tie, ligaclip and endoloop procedures have been used for the closure of the appendix stump. The objective of this study was to analyze the clinical outcomes, as well as to compare the effectiveness of the endoloop, instrumental tie, and ligaclip in laparoscopic appendectomy procedures. Methods: The Sir Syed Medical College Hospital, Karachi was the center of this Randomized Control Study, from June 2020 to December 2020. Acute appendicitis patients (n=120) having age 7-85 years were categorized into three groups: A, B, and C. Ligaclips were applied to those in Group A, Instrumental Tie to Group B, and Endolope to Group C patients. Each group consisted of 40 participants. The Kolmogorov Smirnov test was used to determine the variables for normal distribution. The Chi-square test was used to measure the association between the duration of surgery and hospital stay. Results: The statistical significance (p<0.05) of the relationship between the length of hospital stay and the duration of surgery had been established. The ligaclip demonstrates the shortest hospital stay of 2-3 days and the shortest operation time of 50-60 minutes, whereas the instrumental tie displayed the longest operation time of 72-75 minutes and the longest hospital stay of 4-5 days. Conclusion: Ligaclip application of the appendiceal base showed statistically significant outcomes (p=0.00) regarding technical comfort, cost-effectiveness and operating time compared to endoloop and surgical tie applications. Keywords: Ligaclip; Instrumental Tie; Endoloop; Laparoscopy Appendectomy

Automated deep bottleneck residual 82-layered architecture with Bayesian optimization for the classification of brain and common maternal fetal ultrasound planes

Despite a worldwide decline in maternal mortality over the past two decades, a significant gap persists between low- and high-income countries, with 94% of maternal mortality concentrated in low and middle-income nations. Ultrasound serves as a prevalent diagnostic tool in prenatal care for monitoring fetal growth and development. Nevertheless, acquiring standard fetal ultrasound planes with accurate anatomical structures proves challenging and time-intensive, even for skilled sonographers. Therefore, for determining common maternal fetuses from ultrasound images, an automated computer-aided diagnostic (CAD) system is required. A new residual bottleneck mechanism-based deep learning architecture has been proposed that includes 82 layers deep. The proposed architecture has added three residual blocks, each including two highway paths and one skip connection. In addition, a convolutional layer has been added of size 3 × 3 before each residual block. In the training process, several hyper parameters have been initialized using Bayesian optimization (BO) rather than manual initialization. Deep features are extracted from the average pooling layer and performed the classification. In the classification process, an increase occurred in the computational time; therefore, we proposed an improved search-based moth flame optimization algorithm for optimal feature selection. The data is then classified using neural network classifiers based on the selected features. The experimental phase involved the analysis of ultrasound images, specifically focusing on fetal brain and common maternal fetal images. The proposed method achieved 78.5% and 79.4% accuracy for brain fetal planes and common maternal fetal planes. Comparison with several pre-trained neural nets and state-of-the-art (SOTA) optimization algorithms shows improved accuracy

A novel bottleneck residual and self-attention fusion-assisted architecture for land use recognition in remote sensing images

The massive yearly population growth is causing hazards to spread swiftly around the world and have a detrimental impact on both human life and the world economy. By ensuring early prediction accuracy, remote sensing enters the scene to safeguard the globe against weather-related threats and natural disasters. Convolutional neural networks, which are a reflection of deep learning, have been used more recently to reliably identify land use in remote sensing images. This work proposes a novel bottleneck residual and self-attention fusion-assisted architecture for land use recognition from remote sensing images. First, we proposed using the fast neural approach to generate cloud-effect satellite images. In neural style, we proposed a 5-layered residual block CNN to estimate the loss of neural-style images. After that, we proposed two novel architectures, named 3-layered bottleneck CNN architecture and 3-layered bottleneck self-attention CNN architecture, for the classification of land use images. Training has been conducted on both proposed and original neural-style generated datasets for both architectures. Subsequently, features are extracted from the deep layers and merged employing an innovative serial approach based on weighted entropy. By removing redundant and superfluous data, a novel Chimp Optimization technique is applied to the fused features in order to further refine them. In conclusion, selected features are classified using the help of neural network classifiers. The experimental procedure yielded respective accuracy rates of 99.0% and 99.4% when applied to both datasets. When evaluated in comparison to state-of-the-art (SOTA) methods, the outcomes generated by the proposed framework demonstrated enhanced precision and accuracy