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.     

Artificial Neural Networks for Prediction of COVID-19 in Saudi Arabia

In this study, we have proposed an artificial neural network (ANN) model to estimate and forecast the number of confirmed and recovered cases of COVID-19 in the upcoming days until September 17, 2020. The proposed model is based on the existing data (training data) published in the Saudi Arabia Coronavirus disease (COVID-19) situation—Demographics. The Prey-Predator algorithm is employed for the training. Multilayer perceptron neural network (MLPNN) is used in this study. To improve the performance of MLPNN, we determined the parameters of MLPNN using the prey-predator algorithm (PPA). The proposed model is called the MLPNN–PPA. The performance of the proposed model has been analyzed by the root mean squared error (RMSE) function, and correlation coefficient (R). Furthermore, we tested the proposed model using other existing data recorded in Saudi Arabia (testing data). It is demonstrated that the MLPNN-PPA model has the highest performance in predicting the number of infected and recovering in Saudi Arabia. The results reveal that the number of infected persons will increase in the coming days and become a minimum of 9789. The number of recoveries will be 2000 to 4000 per day.     

Simulation,Modeling, and Optimization of Intelligent Kidney Disease Predication Empowered with Computational Intelligence Approaches

Artificial intelligence (AI) is expanding its roots in medical diagnostics. Various acute and chronic diseases can be identified accurately at the initial level by using AI methods to prevent the progression of health complications. Kidney diseases are producing a high impact on global health and medical practitioners are suggested that the diagnosis at earlier stages is one of the foremost approaches to avert chronic kidney disease and renal failure. High blood pressure, diabetes mellitus, and glomerulonephritis are the root causes of kidney disease. Therefore, the present study is proposed a set of multiple techniques such as simulation, modeling, and optimization of intelligent kidney disease prediction (SMOIKD) which is based on computational intelligence approaches. Initially, seven parameters were used for the fuzzy logic system (FLS), and then twenty-five different attributes of the kidney dataset were used for the artificial neural network (ANN) and deep extreme machine learning (DEML). The expert system was proposed with the assistance of medical experts. For the quick and accurate evaluation of the proposed system, Matlab version 2019 was used. The proposed SMOIKD-FLS-ANN-DEML expert system has shown 94.16% accuracy. Hence this study concluded that SMOIKD-FLS-ANN-DEML system is effective to accurately diagnose kidney disease at initial levels.     

COVID-19 Infected Lung Computed Tomography Segmentation and Supervised Classification Approach

The purpose of this research is the segmentation of lungs computed tomography (CT) scan for the diagnosis of COVID-19 by using machine learning methods. Our dataset contains data from patients who are prone to the epidemic. It contains three types of lungs CT images (Normal, Pneumonia, and COVID-19) collected from two different sources; the first one is the Radiology Department of Nishtar Hospital Multan and Civil Hospital Bahawalpur, Pakistan, and the second one is a publicly free available medical imaging database known as Radiopaedia. For the preprocessing, a novel fuzzy c-mean automated region-growing segmentation approach is deployed to take an automated region of interest (ROIs) and acquire 52 hybrid statistical features for each ROIs. Also, 12 optimized statistical features are selected via the chi-square feature reduction technique. For the classification, five machine learning classifiers named as deep learning J4, multilayer perceptron, support vector machine, random forest, and naive Bayes are deployed to optimize the hybrid statistical features dataset. It is observed that the deep learning J4 has promising results (sensitivity and specificity: 0.987; accuracy: 98.67%) among all the deployed classifiers. As a complementary study, a statistical work is devoted to the use of a new statistical model to fit the main datasets of COVID-19 collected in Pakistan.     

Fabrication of polyethyleneimine surface-functionalized fluorescent carbon dots and its applications towards highly sensitive and selective detection of glutathione in aqueous medium and in vitro cell imaging of HeLa cells

Journal of Materials Science: Materials in Electronics - The present study aimed to synthesize polyethyleneimine (PEI) surface-functionalized fluorescent carbon dots (CDs)-based biosensor...     

Association of microstructure and electric heterogeneity in BiFeO3

BiFeO₃ ceramics was synthesized by solid state reaction technique. Synchrotron X-ray diffraction analysis was employed to investigate the phase formation and structure determination. Rietveld refinement of the diffracted data confirmed the rhombohedrally distorted perovskite structure with space group R3c. Localized atomic structure determined from fitted X-ray data showed off centered displacement of Fe³⁺ cations with a magnitude of 0.397 Å along c-axis. The FeO₆ octahedron comprised of two types of Fe–O bonds with bond lengths of ∼1.935 Å and ∼2.131 Å. Impedance spectroscopic data collected in wide temperature (300–400 K) and frequency (200 Hz–2 MHz) ranges, demonstrated two relaxation phenomena corresponding to two heterogeneous phases. The best fits to the collected impedance data were achieved by employing an equivalent circuit model R_g(R_gbC_gb)(R_eQ_e). Grain boundaries showed only p-type small polaronic hopping conduction process assisted with the oxidation of Fe³⁺ to Fe⁴⁺ in measured temperature range of 300–400 K. Grains exhibited p-type small polaronic hopping conduction mechanism up to 375 K; however, above 375 K electronic conduction becomes prominent. Conduction is dominated by short range hopping of the polarons among Fe³⁺ and Fe⁴⁺ or through the first ionized oxygen vacancy bridge between Fe³⁺ and Fe²⁺ cations.     

AC Potential-Dependent Concentration Variation and Domain Wall Pinning in Co1−x Zn x (x=0.4−0.5) Nanorods

The Co_1?x Zn _x (x=0.4?0.5) nanorods were synthesized via an AC electrochemical deposition method into anodized aluminum oxide (AAO) templates at different voltages ranging from 10 to 18 V, and nanorods of varying concentrations of Co and Zn were obtained. The characterization tools were used to examine different aspects of nanorods, e.g., shape, size, morphology, chemical composition, and magnetic behavior. Scanning electron microscope (SEM) images show that CoZn nanorods have length L=1μm and diameter d=50 nm. The grain size was calculated to be 25.4 nm using an X-ray diffraction (XRD) technique. The XRD also shows some other phases of ZnCoO. The M?H loops measured by a vibrating sample magnetometer (VSM) at room temperature show pure ferromagnetic behavior at all AC potentials. The nanorods show magnetic isotropic behavior due to strong magnetic interactions and presence of random nanorods. The potential-dependent coercivity H _c and saturation magnetization M _s show a non-linear curve which is explained on the basis of magnetic islands and domain wall pinning. This study is useful to tune the magnetic properties of nanorods by a simple and low-cost technique.     

Coverless Steganography for Digital Images Based on a Generative Model

In this paper, we propose a novel coverless image steganographic scheme based on a generative model. In our scheme, the secret image is first fed to the generative model database, to generate a meaning-normal and independent image different from the secret image. The generated image is then transmitted to the receiver and fed to the generative model database to generate another image visually the same as the secret image. Thus, we only need to transmit the meaning-normal image which is not related to the secret image, and we can achieve the same effect as the transmission of the secret image. This is the first time to propose the coverless image information steganographic scheme based on generative model, compared with the traditional image steganography. The transmitted image is not embedded with any information of the secret image in this method, therefore, can effectively resist steganalysis tools. Experimental results show that our scheme has high capacity, security and reliability.     

Polymer Grafting to Single‐Walled Carbon Nanotubes: Effect of Chain Length on Solubility,Graft Density and Mechanical Properties of Macroscopic Structures

Single‐walled carbon nanotubes are grafted with polystyrene chains employing a graft‐to protocol. Thermogravimetric analysis allows calculation of the grafted chain density and average interchain separation on the nanotube surface as a function of molecular weight. The separation scales with molecular weight as a power law with an exponent of ca. 0.588, showing the grafted chains to be in a swollen random walk conformation. This implies that chain packing is controlled by coil size in solution. In addition, the dispersed concentration of functionalized nanotubes scales with the size of the steric potential barrier that prevents aggregation of polymer functionalized nanotubes. It is also shown that the molecular weight of the grafted chains significantly affects the mechanical properties of nanotube films.     

A similarity coefficient measure and machine-parts grouping in cellular manufacturing systems

For more than three decades, similarity coefficient measures one of the important tools for solving group technology problems have gained the attention of the research community in cellular manufacturing systems. A new similarity coefficient measure that uses a set of important characteristic properties for grouping is developed here for use as an intermediate tool to form cohesive cells. A mathematical model that uses this similarity coefficient for optimally solving the cell-formation problems in cellular manufacturing is developed. A heuristic procedure that improves the optimal methodology in term of solution capability of the large instances is devised for an efficient solution. Both the optimal methodology and the heuristic are applied to some well-known problems from literature to compare the grouping efficiencies. The similarity coefficient and the solution methodologies developed are able to solve the cell formation problems efficiently.