A new texture and shape based technique for improving meningioma classification

Over the past decade, computer‐aided diagnosis is rapidly growing due to the availability of patient data, sophisticated image acquisition tools and advancement in image processing and machine learning algorithms. Meningiomas are the tumors of brain and spinal cord. They account for 20% of all the brain tumors. Meningioma subtype classification involves the classification of benign meningioma into four major subtypes: meningothelial, fibroblastic, transitional, and psammomatous. Under the microscope, the histology images of these four subtypes show a variety of textural and structural characteristics. High intraclass and low interclass variabilities in meningioma subtypes make it an extremely complex classification problem. A number of techniques have been proposed for meningioma subtype classification with varying performances on different subtypes. Most of these techniques employed wavelet packet transforms for textural features extraction and analysis of meningioma histology images. In this article, a hybrid classification technique based on texture and shape characteristics is proposed for the classification of meningioma subtypes. Meningothelial and fibroblastic subtypes are classified on the basis of nuclei shapes while grey‐level co‐occurrence matrix textural features are used to train a multilayer perceptron for the classification of transitional and psammomatous subtypes. On the whole, average classification accuracy of 92.50% is achieved through the proposed hybrid classifier; which to the best of our knowledge is the highest. Microsc. Res. Tech. 77:862–873, 2014. © 2014 Wiley Periodicals, Inc.     

Correction of pressure drop in a heavily loaded hybrid bearing when starting by using a constant flow lubrication system- Application to a rotary cement mill

We analyzed a lubricated journal plain bearing supporting heavy loaded rotary mill. During start-up operating, after the shaft is lifted by high external pressure lubricant, the speed of the shaft grows from 0 to the operating hydrodynamic speed when, suddenly after the first thirty seconds of shaft rotation, the pressure drops in one recess causing excessive damage to the pad bearing interface. The aim was to understand and provide some answers to the pressure drop in order to give an appropriate correction. Misalignment between the shaft and bearing surfaces was considered and analyzed in first part of the study. According to the obtained results the proposed correction is to use a suitable constant flow lubrication system which avoids the pressure to drop in recesses. A real application was made on a partial pad bearing supporting a heavy rotary cement mill localized at the cement plant of Chlef in Algeria.

     

Oryctes monoceros Trapping with Synthetic Pheromone and Palm Material in Ivory Coast

Oryctes monoceros is the most serious pest in coconut plantations, causing up to 40% damage in tropical Africa, especially in Ivory Coast. With a view to reducing pest populations by olfactory trapping, field trials were carried out to assess the efficiency of a synthetic aggregation pheromone: ethyl 4-methyloctanoate (1), 4-methyloctanoic acid (2), a related volatile produced by males, and decaying palm material, either oil palm empty fruit bunches (EFB) or pieces of coconut wood (CW) of various ages. Vertical polyvinyl chloride tube traps (2 × 0.16 m with two openings in the upper half), embedded in the soil, were more efficient than 30-L pail traps 1.5 m above ground. EFB, which were inactive alone, synergized captures with synthetic pheromone. CW was more effective than EFB in comparative trials. Compound 2 did not catch any beetles when assessed with EFB, and reduced catches by 1 + EFB when tested at >10% with the pheromone. Trapping over 6 mo in 2002 and 2003 in a 19-ha coconut plot inside a 4,000-ha oil palm estate reduced damage from 3.8% in 2001 to 0.5% in 2002, then to 0.2% in 2003. Damage was 0.0% in 2004 with routine trapping using 32 traps, which caught 3369 beetles in 9 mo. The results are discussed in relation to other Dynastid palm pests and coconut protection in Ivory Coast.     

Optimization approaches and climates investigations in NZEB—A review

The conception of net zero energy buildings (NZEB) has been introduced to limit energy consumption and pollution emissions in buildings. Classification of NZEB is based on renewable energy (RE) supply options, energy measurement process, RE-sources location, and balances whether are energetic or exergetic. In general, it is traditionally agreed that there are three main steps to reach the NZEB performance, starting through the use of passive strategies, energy efficient technologies, and then RE generation systems. Then, these three steps could be accompanied with the smart integration of advanced efficient energy technologies. A state of the art shows that the main ZEB studies are related to: energy savings, reduce electric bills, energy independence, pollution reduction, and occupants comfort, in addition, others are more interested in the aesthetic aspect by combining modern technologies with innovations to achieve high energy and sustainability performance. Building optimization is a promising technique to evaluate NZEB design choices; it has been adopted to choose the perfect solution to reach the zero energy performance through the optimization of an objective function related to energy (thermal loads, RE generation, energy savings) and/or environment (CO₂ emissions) and/or economy (life-cycle cost (LCC), net-present value (NPV), investment cost). This paper starts by presenting the global energetic and pollution challenges the world faces. Moreover, it shows, to the best to the author’s knowledge, the existing NZEB definitions and the corresponding case studies investigated in 8 different climatic zones (humid continental, humid subtropical, Mediterranean, moderate continental, moderate continental, marine west coast, tropical, semi-arid and hot), the paper also focus on the importance to treat each climate separately. Even in the same country, two or more climates may co-exist. NZEBs drawbacks are also presented. Furthermore, different optimization problems are reviewed in the last section. Building energy optimization methods are employed to obtain the ideal solution for specific objective functions which are either related to energy, and/or environment and/or economy. Optimization variables are distributed between passive and/or RE generation systems. Finally, a table summarizing the most commonly used electric and thermal RE applications which yield to the zero energy balance in each climate, as well as three flowcharts are presented to summarize the whole three-stage procedure, to reach NZEB, starting from building designing, passing through the optimization procedure, and lastly categorizing the zero energy balance.     

Study of Magnetic Properties of Fe-Doped CuO: Monte Carlo Simulations

Using Monte Carlo simulations, we are studying the magnetic properties of Fe-doped CuO thin films. The total magnetizations and the susceptibilities are studied as a function of the effect doping, external magnetic field, and exchange coupling. The critical temperature is discussed as a function of the effect of iron concentration. On the other hand, we investigate the effect of increasing temperatures on the coercive field for a constant value of exchange coupling and a fixed concentration. The coercive magnetic field is found to decrease with increasing temperature values until reaching its null value. The effect of increasing the exchange coupling amount on the saturation magnetic field H _s is illustrated. A linear growth of the saturation magnetic field is found as a function of the exchange coupling interaction. To complete this study, we presented and discussed the magnetic hysteresis cycle loops.     

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%.     

Electronic,Magnetic, Electric and Magnetocaloric Properties of the Multiferroic Orthorhombic YMnO3

Journal of Superconductivity and Novel Magnetism - Multiferroic oxide materials have attracted much intention in recent years due to their application in different fields such as magnetic...     

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.