Optimal Deep Learning Model Enabled Secure UAV Classification forIndustry 4.0

Emerging technologies such as edge computing, Internet of Things (IoT), 5G networks, big data, Artificial Intelligence (AI), and Unmanned Aerial Vehicles (UAVs) empower, Industry 4.0, with a progressive production methodology that shows attention to the interaction between machine and human beings. In the literature, various authors have focused on resolving security problems in UAV communication to provide safety for vital applications. The current research article presents a Circle Search Optimization with Deep Learning Enabled Secure UAV Classification (CSODL-SUAVC) model for Industry 4.0 environment. The suggested CSODL-SUAVC methodology is aimed at accomplishing two core objectives such as secure communication via image steganography and image classification. Primarily, the proposed CSODL-SUAVC method involves the following methods such as Multi-Level Discrete Wavelet Transformation (ML-DWT), CSO-related Optimal Pixel Selection (CSO-OPS), and signcryption-based encryption. The proposed model deploys the CSO-OPS technique to select the optimal pixel points in cover images. The secret images, encrypted by signcryption technique, are embedded into cover images. Besides, the image classification process includes three components namely, Super-Resolution using Convolution Neural Network (SRCNN), Adam optimizer, and softmax classifier. The integration of the CSO-OPS algorithm and Adam optimizer helps in achieving the maximum performance upon UAV communication. The proposed CSODL-SUAVC model was experimentally validated using benchmark datasets and the outcomes were evaluated under distinct aspects. The simulation outcomes established the supreme better performance of the CSODL-SUAVC model over recent approaches.     

Combining docking, scoring and molecular field analyses to probe influenza neuraminidase-ligand interactions

In this project, several docking conditions, scoring functions and corresponding protein-aligned molecular field analysis (CoMFA) models were evaluated for a diverse set of neuraminidase (NA) inhibitors. To this end, a group of inhibitors were docked into the active site of NA. The docked structures were utilized to construct a corresponding protein-aligned CoMFA models by employing probe-based (H+, OH, CH3) energy grids and genetic partial least squares (G/PLS) statistical analysis. A total of 16 different docking configurations were evaluated, of which some succeeded in producing self-consistent and predictive CoMFA models. However, the best model coincided with docking the ionized ligands into the hydrated form of the binding site via PLP1 scoring function (r2LOO=0.735, r2PRESS against 24 test compounds=0.828). The highest-ranking CoMFA models were employed to probe NA-ligand interactions. Further validation by comparison with a co-crystallized ligand-NA crystallographic structure was performed. This combination of docking/scoring/CoMFA modeling provided interesting insights into the binding of different NA inhibitors.     

Determination of methyl gallate in Bauhinia retusa extract by high-performance liquid and thin-layer chromatography

Two stability-indicating chromatographic methods are reported for the determination of methyl gallate in crude extracts of Bauhinia retusa. Separation by high performance thin layer chromatography was conducted on silica gel aluminum sheets using 9.5:0.5:0.2 (v/v/v) chloroform:methanol:acetic acid at 280 nm. The results from the 2–40 µg/band were used to prepare a linear calibration graph. The limits of detection and quantitation were 0.5 and 1.5 µg/band, respectively. The reverse phase high performance liquid chromatographic isolation of methyl gallate was performed at ambient temperature with an injection volume of 10 μL. The mobile phase consisted of 40:60 (v/v) methanol:0.1% ortho-phosphoric acid. The separation was performed at 1 mL/min using a detection wavelength of 280 nm. The calibration graph for methyl gallate was rectilinear from 0.02–40 µg/mL with limits of detection and quantitation of 0.004 and 0.010 µg/mL, respectively. For both methods, intra-day and inter-day precision were evaluated and the relative standard deviation was less than 2%, indicating good precision. The robustness was evaluated by making small and deliberate changes to appropriate parameters and the calculated relative standard deviation was less than 2%.The chromatographic methods were employed to determine methyl gallate in crude Bauhinia retusa extracts.     

IoMT-Based Smart Monitoring Hierarchical Fuzzy Inference System for Diagnosis of COVID-19

The prediction of human diseases, particularly COVID-19, is an extremely challenging task not only for medical experts but also for the technologists supporting them in diagnosis and treatment. To deal with the prediction and diagnosis of COVID-19, we propose an Internet of Medical Things-based Smart Monitoring Hierarchical Mamdani Fuzzy Inference System (IoMTSM-HMFIS). The proposed system determines the various factors like fever, cough, complete blood count, respiratory rate, Ct-chest, Erythrocyte sedimentation rate and C-reactive protein, family history, and antibody detection (lgG) that are directly involved in COVID-19. The expert system has two input variables in layer 1, and seven input variables in layer 2. In layer 1, the initial identification for COVID-19 is considered, whereas in layer 2, the different factors involved are studied. Finally, advanced lab tests are conducted to identify the actual current status of the disease. The major focus of this study is to build an IoMT-based smart monitoring system that can be used by anyone exposed to COVID-19; the system would evaluate the user’s health condition and inform them if they need consultation with a specialist for quarantining. MATLAB-2019a tool is used to conduct the simulation. The COVID-19 IoMTSM-HMFIS system has an overall accuracy of approximately 83%. Finally, to achieve improved performance, the analysis results of the system were shared with experts of the Lahore General Hospital, Lahore, Pakistan.     

Autonomous Parking-Lots Detection with Multi-Sensor Data Fusion Using Machine Deep Learning Techniques

The rapid development and progress in deep machine-learning techniques have become a key factor in solving the future challenges of humanity. Vision-based target detection and object classification have been improved due to the development of deep learning algorithms. Data fusion in autonomous driving is a fact and a prerequisite task of data preprocessing from multi-sensors that provide a precise, well-engineered, and complete detection of objects, scene or events. The target of the current study is to develop an in-vehicle information system to prevent or at least mitigate traffic issues related to parking detection and traffic congestion detection. In this study we examined to solve these problems described by (1) extracting region-of-interest in the images (2) vehicle detection based on instance segmentation, and (3) building deep learning model based on the key features obtained from input parking images. We build a deep machine learning algorithm that enables collecting real video-camera feeds from vision sensors and predicting free parking spaces. Image augmentation techniques were performed using edge detection, cropping, refined by rotating, thresholding, resizing, or color augment to predict the region of bounding boxes. A deep convolutional neural network F-MTCNN model is proposed that simultaneously capable for compiling, training, validating and testing on parking video frames through video-camera. The results of proposed model employing on publicly available PK-Lot parking dataset and the optimized model achieved a relatively higher accuracy 97.6% than previous reported methodologies. Moreover, this article presents mathematical and simulation results using state-of-the-art deep learning technologies for smart parking space detection. The results are verified using Python, TensorFlow, OpenCV computer simulation frameworks.     

Convolutional Neural Network Based Intelligent Handwritten Document Recognition

This paper presents a handwritten document recognition system based on the convolutional neural network technique. In today’s world, handwritten document recognition is rapidly attaining the attention of researchers due to its promising behavior as assisting technology for visually impaired users. This technology is also helpful for the automatic data entry system. In the proposed system prepared a dataset of English language handwritten character images. The proposed system has been trained for the large set of sample data and tested on the sample images of user-defined handwritten documents. In this research, multiple experiments get very worthy recognition results. The proposed system will first perform image pre-processing stages to prepare data for training using a convolutional neural network. After this processing, the input document is segmented using line, word and character segmentation. The proposed system get the accuracy during the character segmentation up to 86%. Then these segmented characters are sent to a convolutional neural network for their recognition. The recognition and segmentation technique proposed in this paper is providing the most acceptable accurate results on a given dataset. The proposed work approaches to the accuracy of the result during convolutional neural network training up to 93%, and for validation that accuracy slightly decreases with 90.42%.     

Comparison of homogeneous and heterogeneous thorium fuel blocks with four drivers in advanced high temperature reactors

Thorium is an attractive potential fuel owing to its abundance and unique thermal, neutronic, and chemical properties. One way to utilize thorium in block-type advanced high temperature reactors (AHTRs) is to homogenously mix the driver fuel (eg, U-233) with thorium in every fuel kernel of the tristructural-isotropic particles in a fuel block, which is the mixed oxide fuel (MOX) concept. Application of the seed and blanket (S&B) concept, that is, driver fuel in the seed region of a fuel block and thorium in the blanket region, is also another method to utilize thorium. To investigate the differences in the utilization of thorium using the MOX and S&B concepts in AHTRs, their multiplication factors (k_∞), discharge burnups, conversion ratios, and reactivity temperature coefficients are compared. The investigated drivers include U-233 (U3), weapons-grade uranium (WU), weapons-grade plutonium (WPu), and reactor-grade plutonium (RPu). The results demonstrate that the MOX block with plutonium drivers has a higher discharge burnup in comparison with the S&B block. When the heavy metal mass is 6 kg and the initial mass of the fissile materials is 0.65 kg per block, the MOX block achieves 27% higher discharge burnup than the S&B block with the RPu driver. In contrast, the S&B block achieves higher discharge burnup in the case of uranium drivers (U3 and WU). The MOX block achieves a higher conversion ratio for all the drivers. Furthermore, the MOX block achieves a stronger negative moderator temperature coefficient of reactivity than the S&B block for all the driver fuels.     

Immobilized Enzymes-Based Biosensing Cues for Strengthening Biocatalysis and Biorecognition

Catalysis Letters - The versatility of immobilized enzyme systems enables rapid recovery of both product and enzyme, numerous re-uses of enzymes, continuous enzymatic processes, quick reaction...     

Modelling Intelligent Driving Behaviour Using Machine Learning

In vehicular systems, driving is considered to be the most complex task, involving many aspects of external sensory skills as well as cognitive intelligence. External skills include the estimation of distance and speed, time perception, visual and auditory perception, attention, the capability to drive safely and action-reaction time. Cognitive intelligence works as an internal mechanism that manages and holds the overall driver’s intelligent system.These cognitive capacities constitute the frontiers for generating adaptive behaviour for dynamic environments. The parameters for understanding intelligent behaviour are knowledge, reasoning, decision making, habit and cognitive skill. Modelling intelligent behaviour reveals that many of these parameters operate simultaneously to enable drivers to react to current situations. Environmental changes prompt the parameter values to change, a process which continues unless and until all processes are completed. This paper model intelligent behaviour by using a ‘driver behaviour model’ to obtain accurate intelligent driving behaviour patterns. This model works on layering patterns in which hierarchy and coherence are maintained to transfer the data with accuracy from one module to another. These patterns constitute the outcome of different modules that collaborate to generate appropriate values. In this case, accurate patterns were acquired using ANN static and dynamic non-linear autoregressive approach was used and for further accuracy validation, time-series dynamic backpropagation artificial neural network, multilayer perceptron and random sub-space on real-world data were also applied.     

SAT‐based and CP‐based declarative approaches for Top‐Rank‐K closed frequent itemset mining

Top‐Rank‐K Frequent Itemset (or Pattern) Mining (FPM) is an important data mining task, where user decides on the number of top frequency ranks of patterns (itemsets) they want to mine from a transactional dataset. This problem does not require the minimum support threshold parameter that is typically used in FPM problems. Rather, the algorithms solving the Top‐Rank‐K FPM problem are fed with $K$, the number of frequency ranks of itemsets required, to compute the threshold internally. This paper presents two declarative approaches to tackle the Top‐Rank‐K Closed FPM problem. The first approach is Boolean Satisfiability‐based (SAT‐based) where we propose an effective encoding for the problem along with an efficient algorithm employing this encoding. The second approach is CP‐based, that is, utilizes Constraint Programming technique, where a simple CP model is exploited in an innovative manner to mine the Top‐Rank‐K Closed FPM itemsets from transactional datasets. Both approaches are evaluated experimentally against other declarative and imperative algorithms. The proposed SAT‐based approach significantly outperforms IM, another SAT‐based approach, and outperforms the proposed CP‐approach for sparse and moderate datasets, whereas the latter excels on dense datasets. An extensive study has been conducted to assess the proposed approaches in terms of their feasibility, performance factors, and practicality of use.