Search

Accurate iris centre localization is crucial in many computer vision and facial biometric applications such as gaze estimation, human–computer interaction, iris recognition, and liveness detection. However, it is challenging in an uncontrolled environment due to variations like pose, scale, rotation, specular reflection, and image quality. Therefore, a cascaded deep learning framework for iris centre localization in facial images is proposed that is robust to the abovementioned variations. The proposed approach consists of (i) YOLOv3 for eye detection, (ii) UNet for iris segmentation, and (iii) statistical modelling for iris centre localization. The eyes are first detected using the YOLOv3, and subsequently, iris segmentation is performed within the detected eyes using the UNet. Following iris segmentation, statistical modelling is employed to enhance the localization accuracy of the iris centre. Experiments were performed on benchmark databases, resulting in a standardized error measure SED of 3.405 pixels for BioID and 3.259 pixels for GI4E databases. In addition, the robustness of the proposed eye detection model was further evaluated on the Yale B for illumination variations and the CAS‐PEAL for pose variations. [ABSTRACT FROM AUTHOR]

In most regions of the world, tuberculosis (TB) is classified as a malignant infectious disease that can be fatal. Using advanced tools and technology, automatic analysis and classification of chest X-rays (CXRs) into TB and non-TB can be a reliable alternative to the subjective assessment performed by healthcare professionals. Thus, in the study, we propose an automatic TB detection system using advanced deep learning (DL) models. A significant portion of a CXR image is dark, providing no information for diagnosis and potentially confusing DL models. Therefore, in the proposed system, we use sophisticated segmentation networks to extract the region of interest from multimedia CXRs. Then, segmented images are fed into the DL models. For the subjective assessment, we use explainable artificial intelligence to visualize TB-infected parts of the lung. We use different convolutional neural network (CNN) models in our experiments and compare their classification performance using three publicly available CXR datasets. EfficientNetB3, one of the CNN models, achieves the highest accuracy of 99.1%, with a receiver operating characteristic of 99.9%, and an average accuracy of 98.7%. Experiment results confirm that using segmented lung CXR images produces better performance than does using raw lung CXR images. [ABSTRACT FROM AUTHOR]

Background: Refractive error is the leading cause of visual impairment worldwide. Biometric components change with age. This study aimed to determine the association of biometric components with refractive error in participants aged 5 to 25 years. Methods: This descriptive cross-sectional study was conducted at the Department of Ophthalmology at the Arif Memorial Teaching Hospital Lahore, Pakistan, from April 2021 to October 2021. Ninetysix subjects aged 5 to 25 participated using a non-probability convenience sampling technique. All subjects with refractive error were examined for best-corrected visual acuity (BCVA) using the Snellen chart and keratometry. A-scan biometry was used to measure the axial length (AL), anterior chamber depth (ACD), and lens thickness (LT). Results: Ninety-six participants, aged 5 to 25 years, were examined. There were 28 (29.2%) male and 68 (70.83%) female patients. The mean age of the participants was 18.38±3.823 years. The prevalence of low myopia in the right and left eyes among males and females was 22 (78.6%), 23 (82.1%), and 42 (61.8%) in both eyes, respectively. The prevalence of moderate myopia among males and females was 1 (3.6%) and 10 (14.7%), respectively. The prevalence of astigmatism in the right and left eyes among males and females was 6 (21.4%), 4 (14.3%), and 16 (23.5%) in both eyes, respectively. The maximum increase in the LT of the right eyes was noted for moderate myopia, while the maximum increase in the LT of the left eyes was noted for low myopia. The average AL mm and mean corneal power (CP) diopter of the left eyes were greater than those of the right. There was a significant association between the refractive error and mean AL (23.4056 ± 0.98781) and mean CP (44.1695 ± 1.52516) of left eyes (p<0.05). Conclusion: The AL and mean CP of left eyes were associated with refractive error. The AL, ACD, and mean CP significantly increased toward moderate myopia. The LT of the right eyes significantly increased towards moderate myopia, but the LT of the left eyes increased towards low myopia. The increase in ocular biometric components changes the refractive status of the eye. [ABSTRACT FROM AUTHOR]

The brain–computer interface (BCI) is an emerging technology that has the potential to revolutionize the world, with numerous applications ranging from healthcare to human augmentation. Electroencephalogram (EEG) motor imagery (MI) is among the most common BCI paradigms that have been used extensively in smart healthcare applications such as post-stroke rehabilitation and mobile assistive robots. In recent years, the contribution of deep learning (DL) has had a phenomenal impact on MI-EEG-based BCI. In this work, we systematically review the DL-based research for MI-EEG classification from the past ten years. This article first explains the procedure for selecting the studies and then gives an overview of BCI, EEG, and MI systems. The DL-based techniques applied in MI classification are then analyzed and discussed from four main perspectives: preprocessing, input formulation, deep learning architecture, and performance evaluation. In the discussion section, three major questions about DL-based MI classification are addressed: (1) Is preprocessing required for DL-based techniques? (2) What input formulations are best for DL-based techniques? (3) What are the current trends in DL-based techniques? Moreover, this work summarizes MI-EEG-based applications, extensively explores public MI-EEG datasets, and gives an overall visualization of the performance attained for each dataset based on the reviewed articles. Finally, current challenges and future directions are discussed. [ABSTRACT FROM AUTHOR]

Early illness detection enables medical professionals to deliver the best care and increases the likelihood of a full recovery. In this work, we show that computer-aided design (CAD) systems are capable of using chest X-ray (CXR) medical imaging modalities for the identification of respiratory system disorders. At present, the COVID-19 pandemic is the most well-known illness. We propose a system based on explainable artificial intelligence to detect COVID-19 from CXR images by using several cutting-edge convolutional neural network (CNN) models, as well as the Vision of Transformer (ViT) models. The proposed system also visualizes the infected areas of the CXR images. This gives doctors and other medical professionals a second option for supporting their decision. The proposed system uses some preprocessing of the images, which includes the segmentation of the region of interest using a UNet model and rotation augmentation. CNN employs pixel arrays, while ViT divides the image into visual tokens; therefore, one of the objectives is to compare their performance in COVID-19 detection. In the experiments, a publicly available dataset (COVID-QU-Ex) is used. The experimental results show that the performances of the CNN-based models and the ViT-based models are comparable. The best accuracy was 99.82%, obtained by the EfficientNetB7 (CNN-based) model, followed by the SegFormer (ViT-based). In addition, the segmentation and augmentation enhanced the performance. [ABSTRACT FROM AUTHOR]

Human falls, especially for elderly people, can cause serious injuries that might lead to permanent disability. Approximately 20–30% of the aged people in the United States who experienced fall accidents suffer from head trauma, injuries, or bruises. Fall detection is becoming an important public healthcare problem. Timely and accurate fall incident detection could enable the instant delivery of medical services to the injured. New advances in vision-based technologies, including deep learning, have shown significant results in action recognition, where some focus on the detection of fall actions. In this paper, we propose an automatic human fall detection system using multi-stream convolutional neural networks with fusion. The system is based on a multi-level image-fusion approach of every 16 frames of an input video to highlight movement differences within this range. This results of four consecutive preprocessed images are fed to a new proposed and efficient lightweight multi-stream CNN model that is based on a four-branch architecture (4S-3DCNN) that classifies whether there is an incident of a human fall. The evaluation included the use of more than 6392 generated sequences from the Le2i fall detection dataset, which is a publicly available fall video dataset. The proposed method, using three-fold cross-validation to validate generalization and susceptibility to overfitting, achieved a 99.03%, 99.00%, 99.68%, and 99.00% accuracy, sensitivity, specificity, and precision, respectively. The experimental results prove that the proposed model outperforms state-of-the-art models, including GoogleNet, SqueezeNet, ResNet18, and DarkNet19, for fall incident detection. [ABSTRACT FROM AUTHOR]

The fractional generalized Bagley–Torvik equation (FGB-TE) is a mathematical description of the motion of an immersed plate in a Newtonian fluid. The analytical study of the FGB-TE with uncertain initial conditions and two independent fractional orders is usually complex and difficult. Therefore, it is necessary to develop a proper and effective technique to solve the fuzzy fractional generalized Bagley–Torvik equation (FFGB-TE) analytically. This paper presents the analytical fuzzy solution of the FFGB-TEs based on the concept of the fuzzy Caputo generalized Hukuhara differentiability (g H -differentiability) using the fuzzy Laplace transform (FLT) technique. The closed-form solution of FFGB-TEs is presented for both the homogeneous and non-homogeneous cases in terms of the Mittag-Leffler function (MLF) involving double series. Several significant results are introduced and proven with true reasoning. We illustrate our proposed analytical approach with the help of several demonstrative examples. To enhance the novelty of the proposed work, we solved the FFGB-TE as an application of the motion of an immersed plate and visualized their graphs to support the theoretical results. • Analytical solutions of fuzzy fractional Bagley-Torvik equation are investigated. • The potential solutions are extracted using fuzzy Caputo g H -differentiability. • Closed-form solutions of proposed scheme are presented using Mittag-Leffler function. • Several significant results are presented using the proposed analytical approach. • The real-world problem is solved as an application of the proposed study. [ABSTRACT FROM AUTHOR]

Brain signals can be captured via electroencephalogram (EEG) and be used in various brain–computer interface (BCI) applications. Classifying motor imagery (MI) using EEG signals is one of the important applications that can help a stroke patient to rehabilitate or perform certain tasks. Dealing with EEG-MI signals is challenging because the signals are weak, may contain artefacts, are dependent on the patient's mood and posture, and have low signal-to-noise ratio. This paper proposes a multi-branch convolutional neural network model called the Multi-Branch EEGNet with Convolutional Block Attention Module (MBEEGCBAM) using attention mechanism and fusion techniques to classify EEG-MI signals. The attention mechanism is applied both channel-wise and spatial-wise. The proposed model is a lightweight model that has fewer parameters and higher accuracy compared to other state-of-the-art models. The accuracy of the proposed model is 82.85% and 95.45% using the BCI-IV2a motor imagery dataset and the high gamma dataset, respectively. Additionally, when using the fusion approach (FMBEEGCBAM), it achieves 83.68% and 95.74% accuracy, respectively. [ABSTRACT FROM AUTHOR]

Electroencephalography-based motor imagery (EEG-MI) classification is a critical component of the brain-computer interface (BCI), which enables people with physical limitations to communicate with the outside world via assistive technology. Regrettably, EEG decoding is challenging because of the complexity, dynamic nature, and low signal-to-noise ratio of the EEG signal. Developing an end-to-end architecture capable of correctly extracting EEG data's high-level features remains a difficulty. This study introduces a new model for decoding MI known as a Multi-Branch EEGNet with squeeze-and-excitation blocks (MBEEGSE). By clearly specifying channel interdependencies, a multi-branch CNN model with attention blocks is employed to adaptively change channel-wise feature responses. When compared to existing state-of-the-art EEG motor imagery classification models, the suggested model achieves good accuracy (82.87%) with reduced parameters in the BCI-IV2a motor imagery dataset and (96.15%) in the high gamma dataset. [ABSTRACT FROM AUTHOR]

• A comprehensive survey on IoMT-based fusion for healthcare is presented. • Existing IoMT fusion mechanisms and fusion levels are discussed. • Types of IoMT and their application for smart healthcare are elaborated. • Challenges of IoMT-based fusion and some future directions are provided. Smart healthcare is a framework that utilizes technologies such as wearable devices, the Internet of Medical Things (IoMT), sophisticated machine learning algorithms, and wireless communication technology to seamlessly access health records, link individuals, resources, and organizations, and then effectively handle and react to health environment demands intelligently. One of the main ingredients of smart healthcare is medical sensors or IoMT. Due to the complex nature of diseases, in many cases, there is a need for multimodal medical signals for their diagnoses. While using multimodal signals, the most important issue is how to fuse them – an area of burgeoning interest within the research community. This paper presents a comprehensive survey of multimodal medical signals fusion schemes that have been proposed for smart healthcare applications. Research works included in major repositories, such as IEEE Xplore, Science Direct, Springer Link, and ACM digital library have been surveyed to address several related research questions. Focus is placed on recent developments, thus only works published between 2014-2020 are considered. Finally, key research challenges and possible future directions are also provided. [ABSTRACT FROM AUTHOR]

• A light CNN model is proposed for COVID-19 screening using lung ultrasound images. • Multi-layers feature fusion is done to enhance the performance. • 5.9% absolute accuracy is improved by the fusion compared to without fusion. • Precision, recall, and area under the ROC curve are also significantly improved. COVID-19 or related viral pandemics should be detected and managed without hesitation, since the virus spreads very rapidly. Often with insufficient human and electronic resources, patients need to be checked from stable patients using vital signs, radiographic photographs, or ultrasound images. Vital signs do not often offer the right outcome, and radiographic photos have a variety of other problems. Lung ultrasound (LUS) images can provide good screening without a lot of complications. This paper suggests a model of a convolutionary neural network (CNN) that has fewer learning parameters but can achieve strong accuracy. The model has five main blocks or layers of convolution connectors. A multi-layer fusion functionality of each block is proposed to improve the efficiency of the COVID-19 screening method utilizing the proposed model. Experiments are conducted using freely accessible LUS photographs and video datasets. The proposed fusion method has 92.5% precision, 91.8% accuracy, and 93.2% retrieval using the data collection. These efficiency metric levels are considerably higher than those used in any of the state-of-the-art CNN versions. [ABSTRACT FROM AUTHOR]

The COVID-19 pandemic has had an unprecedented effect on the world. The pandemic has caused considerable death and suffering, triggered economic downturns, and led to significant job loss. Both during and after the pandemic, sophisticated and effective measures must be taken to diagnose COVID-19 patients and mitigate the effects of the virus. Emerging developments in the Internet of Things (IoT), 4G, 5G, and 6G wireless networks, artificial intelligence (AI), and blockchain technology can be harnessed to combat COVID-19. The implementation of IoT in hospitals enables highly integrated digital environments and real-time data collection, which can be utilized to identify clinical patterns, model risk interactions, and forecast effects via AI and deep learning systems. This article emphasizes the potential uses of IoT, AI, and 5G for combating pandemics similar to the COVID-19 pandemic. Then the authors propose a solution that uses federated learning and integrates these three technologies. Experiments were performed using cough sounds and chest X-ray images; the experiments yielded promising results. [ABSTRACT FROM AUTHOR]

Infectious coryza (IC) is a severe upper respiratory tract disease of birds. This research was aimed to study different aspects of Avibacterium paragallinarum causing IC in commercial birds. A total of 1000 samples were collected from IC suspected or recently dead birds. Results showed that 80.40% of the samples were positive for A. paragallinarum. All the isolates of A. paragallinarum produced the predicted size of 500 bp amplicons of HPG2 gene on PCR. The percentages of positive samples infected with infectious coryza in commercial birds were: 19.2% for layers, 18.4% for broilers, 16.0% for quails, 15.8% for chukars and 11.0% for pigeons. Among positive cases, serotype A was 24%, serotype B was 29% and serotype C was 27.40%. The isolates of A. paragallinarum were growing well at 35-37 oC, however, growth rate was declined at 24 oC, and 42 oC. Similarly, A. paragallinarum showed optimal growth between pH 5 and 9, but the superlative pH growth values were from 6 to 8 pH. Antimicrobial susceptibility test showed that all tested isolates displayed resistance against Metronidazole, Colistin sulphate, Bacitracin, Streptomycin, Chloramphenicol and Lincomycine, while they were found susceptible to Tetracycline, Erythromycin, Vancomycin, Amoxicillin, and Ciprofloxacin. Investigation of IC in commercial birds will certainly help the diagnosis of the disease, which causes considerable economic loss to the farmers. The current study was designed to report on the incidence of IC caused A. paragallinarum, frequency of occurrence of its serotypes and drug susceptibility pattern. This study will also alert poultry professionals about the disease and help determine specific medication as well as formulate prevention and control strategies. [ABSTRACT FROM AUTHOR]

This article aims to introduce and investigate the analytical fuzzy solution of the incommensurate non-homogeneous system of fuzzy linear fractional differential equations (INS-FLFDEs) using trivariate Mittag-Leffler functions. Entries of the coefficient matrix of the given system are treated as real numbers, initial-values are triangular fuzzy numbers (TFNs), and the forcing function is a fuzzy set (or a bunch) of real function. We extract the potential solution in the form of a fuzzy bunch of real functions (FBoRFs) rather than the solution of fuzzy-valued functions. We formulate the fuzzy initial value problem as a set of classical initial value problems by taking the forcing function from the class of FBoRFs and the initial value from the collection of TFNs (as a special case). The solution of this system is in the form of a trivariate Mittag-Leffler function. We interpret this solution as an element of the fuzzy solution set and assign the minimum value of membership that takes from the forcing function and the initial value in the fuzzy set. The originality of the proposed technique is that the uncertainty is smaller compared to the uncertainty extracted from other techniques. In addition, generalized derivatives increase the order and dimension of the system. Therefore, the proposed technique is better in terms of complexity because it reduces the order and dimension of the system. Finally, to grasp the proposed technique, we solve the electrical network and multiple mass-spring systems as applications and analyze their graphs to visualize and support theoretical results. [ABSTRACT FROM AUTHOR]

Stroke, particularly ischemic stroke, is a major cause of disability and one of the leading causes of adult mortality worldwide. Early and prompt management of stroke patients can reduce the severity of the disease. Doctors usually determine the severity of a stroke by focusing on the region of interest (ROI) in the MRI or CT scan images. An accurate and effective automatic image segmentation system can assist medical professionals as well as automatic detection and classification systems. Deep learning is the current advanced approach for dealing with machine learning and artificial intelligence. However, conventional deep learning requires a large amount of data for training, and the amount of labeled data in the medical field is limited. In this paper, we propose a few-shot learning strategy and integrate it with a base convolutional neural network model, which utilizes a self-attention mechanism to segment MRI for ischemic stroke. By combining the base model with self-attention, we can focus more on the ROI and disregard less important features. Additionally, the proposed system only selects slices with lesions and ignores unlesioned slices. This helps to improve efficiency and reduce the computational load by eliminating the need to tune unnecessary parameters. To achieve even better results, the system also combines two weighted images, FLAIR and DWI, as an early fusion process. Experiments have shown that this approach leads to higher performance compared to using the same system without fusion. The proposed system is evaluated using a publicly available dataset, ISLES 2015 SSIS, and compared with other state-of-the-art (SOTA) systems. It achieves a dice coefficient score of 0.68, which is significantly better than that of other SOTA systems. • An automatic ischemic stroke segmentation system using few-shot learning is proposed. • The proposed system selects only slices with lesions and excludes unlesioned slices. • We adopted prototypical networks with metric learning to achieve few-shot segmentation. • Two weighted MRIs, FLAIR, and DWI are fused in the few-shot learning paradigm. [ABSTRACT FROM AUTHOR]

New generation communication technologies and advanced deep learning models present a tremendous opportunity to develop fast, accurate, and seamless distributed systems in different sectors including the healthcare sector. in this article, we suggest a smart healthcare framework consisting of a pathology detection system, which is developed using deep learning. The pathology can be detected from electroencephalogram signals of a subject. in the framework, a smart EEG headset captures EEG signals and sends them to a mobile edge computing server. The server preprocesses the signals and transmits them to a cloud server. The cloud server does the main processing using deep learning and decides on whether the subject has pathology or not. Clients and stakeholders of the framework are connected via an authentication manager located in the cloud server. Experiment results on a publicly available database confirm the appropriateness of the proposed framework. [ABSTRACT FROM AUTHOR]

The aim of this work is to solve the linear system of equations using LU decomposition method in bipolar fuzzy environment. We assume a special case when the coefficient matrix of the system is symmetric positive definite. We discuss this point in detail by giving some numerical examples. Moreover, we investigate m × n inconsistent bipolar fuzzy matrix equation and find the least square solution of the inconsistent bipolar fuzzy matrix using the generalized inverse matrix theory. The existence of the strong bipolar fuzzy least square solution of the inconsistent bipolar fuzzy matrix is discussed. In the end, a numerical example is presented to illustrate our proposed method. [ABSTRACT FROM AUTHOR]

Mastitis is one of the most economically important diseases of dairy animals in Pakistan. The cure rate of mastitis with the use of antibiotics/antibacterials leaves a lot to be desired. In addition, the use of these agents leads to undesirable residues in milk and poses a host of problems to milk processing industry. Therefore, measures other than the use of antibiotics/antibacterials are being investigated to improve the mastitis cure rate. Keeping these considerations in perspective, the current study was planned to evaluate the therapeutic efficacy of autogenous animal-specific mastitis vaccines alone and in combination with antibiotic based rational therapy. For this purpose, preparation of autogenous mastitis vaccine was carried out from the isolates of individual animals suffering from subclinical mastitis (n=60). The selected animals were randomly divided into 3 equal groups. Group A received autogenous mastitis vaccine alone, group B received routine mastitis therapy while group C received combination of both these treatments. The efficacy of the treatments was evaluated through Surf Field Mastitis Test (SFMT) based cure rate, bacteriological cure rate and somatic cell counts (SCC). The treatments led to an SFMT based cure rate of 75%, 80% and 85% respectively. The bacteriological cure rate observed for groups A, B and C were 70%, 70% and 75% respectively, at completion of study period, whereas there was statistically non-significant difference in SCC values in all the groups. The study reveals that autogenous mastitis vaccine led to a comparable results and can be a suitable replacement for antibiotic therapy in subclinical mastitis without any fear of antibiotic residues. [ABSTRACT FROM AUTHOR]

Tactile edge technology that focuses on 5G or beyond 5G reveals an exciting approach to control infectious diseases such as COVID-19 internationally. The control of epidemics such as COVID-19 can be managed effectively by exploiting edge computation through the 5G wireless connectivity network. The implementation of a hierarchical edge computing system provides many advantages, such as low latency, scalability, and the protection of application and training model data, enabling COVID-19 to be evaluated by a dependable local edge server. In addition, many deep learning (DL) algorithms suffer from two crucial disadvantages: first, training requires a large COVID-19 dataset consisting of various aspects, which will pose challenges for local councils; second, to acknowledge the outcome, the findings of deep learning require ethical acceptance and clarification by the health care sector, as well as other contributors. In this article, we propose a B5G framework that utilizes the 5G network's low-latency, high-bandwidth functionality to detect COVID-19 using chest X-ray or CT scan images, and to develop a mass surveillance system to monitor social distancing, mask wearing, and body temperature. Three DL models, ResNet50, Deep tree, and Inception v3, are investigated in the proposed framework. Furthermore, blockchain technology is also used to ensure the security of healthcare data. [ABSTRACT FROM AUTHOR]

Mastitis is regarded as most economically important disease of dairy animals in Pakistan. The cure rate of mastitis with use of antibiotics/antibacterials is often quite poor. In addition, use of these agents leads to undesirable residues in milk and poses several problems to milk processing industry. Therefore, measures other than the use of antibiotics/antibacterials are being investigated to improve mastitis cure rate and to circumvent the problem. Keeping these considerations in view, the current study was planned to evaluate the therapeutic efficacy of a non-antibiotic, non-proprietary antibacterial preparation for the treatment of clinical bubaline mastitis. For this purpose, buffaloes (n=40) suffering with clinical mastitis were selected and randomly divided into two equal groups viz. group I and group II. The animals of group I (n=20) were treated with combination of trisodium citrate (TSC; 40 g), vitamin C (10 g), zinc sulphate (3 g) and copper sulphate (1 g) for 7 days. Group II (n=20) was treated with routine mastitis therapy i.e. tylosin at dose rate of 10 mg/kg, I/M along with amoxicillin & clavulanic acid combination at total dosage of 600 mg I/mm infusion daily for 5 days. The efficacy of the treatment was evaluated based on grading of improvement, surf field mastitis test (SFMT) scores and microbiological examination. Regarding grading of improvement, 50% animals of group I recovered clinically while corresponding value for group II was 45%. The SFMT based cure rates for clinical mastitis in groups I and II were 70.21% and 72.09%, respectively. Administration of allocated treatments to the buffaloes affected with clinical mastitis led to a quarter based bacteriological cure rate of 66.67% in group I and 65.12% in group II. It was concluded that administration of non-antibiotic, non-proprietary formulation containing trisodium citrate, vitamin C, zinc sulphate and copper sulphate resulted in almost equivalent therapeutic results in buffaloes affected with clinical mastitis compared to antibiotic therapy. [ABSTRACT FROM AUTHOR]

The notions of center and distance between two Parametric Bipolar Fuzzy Numbers (PBFNs) by giving the preference of right dominance as compared to the left dominance are presented. The solution of Bipolar Fuzzy System of Linear Equations (BFSLEs) is discussed with polynomial parametric bipolar fuzzy number coefficients matrix having crisp real variables and the right-hand side is polynomial parametric bipolar fuzzy numbers. Some of their related properties are investigated. It is proved that if the real coefficient matrix considered as crisp in an original system, while the unknown variable vectors and Right Hand Side (RHS) column vector functions are treated as PBFNs, then initially, the general BFSLEs in polynomial parametric form is solved by the addition and subtraction of the vectors of the lower and upper bound, respectively. The solution procedure is computationally efficient in a bipolar fuzzy environment, and our proposed method is simple as well as efficient to handle the BFSLEs. [ABSTRACT FROM AUTHOR]

Mastitis is one of the most significant problems to the dairy all over the world. In addition to production losses, mastitis also deteriorates the chemical, and physical properties of milk which ultimately leads to poor shelf life of processed milk. The present study was designed to evaluate a non-proprietary, non-antibiotic preparation containing trisodium citrate, Vitamin C, Zinc sulphate and Copper sulphate in treating sub-clinical mastitis in dairy cows. For this purpose, cows suffering from subclinical mastitis (n=40) were selected and randomly divided into two groups. The animals of group A (n=20) were treated orally with a combination of tri-sodium citrate (40 gm), vitamin C (10 g), zinc sulphate (3 g) and copper sulphate (1 g) for 7 days. Group B was treated with standard mastitis therapy (Tylosin at a dose rate of 10 mg/kg for three days intramuscular and amoxicillin + clavulanic acid at 600 mg intramammary infusion daily for five days). The efficacy of the treatment was evaluated through surf field mastitis test (SFMT) scores and microbiological examination. The findings of the study revealed that administration of oral non-antibiotic formulation resulted in a better quarter based SFMT cure rate. The values observed for SFMT based cure rates in sub-clinically mastitic cows at the termination of study were 90.90% and 88.46% for groups A and B, respectively. The observed bacteriological cure rate for group A (tri-sodium citrate, vit. C, ZnSO4 and CuSO4) was 72.73%, and 69.23% for group B (antibiotic). The findings of the current study showed that the use of a combination therapy of nonantibiotic chemicals has potential impact on treating sub-clinical mastitis in dairy animals. [ABSTRACT FROM AUTHOR]

With the progress of new generation wireless communication technology and machine learning algorithms to deal with big data, a variety of smart systems are realized to bring comfort to human life. Smart healthcare systems are one of the important developments recently. Such systems will become a necessary ingredient in our connected living. In this article, we propose a new smart pathology detection system using deep learning, edge computing, and cloud computing. Sensors will capture electroencephalogram (EEG) signals of a person and send the signals to a nearby edge computing server. The server will distribute a preprocessing step to available edge devices. The preprocessed signal will then be sent to a cloud computing server. In the cloud server, a proposed tree-based deep model will extract deep features from the EEG signal. The classified decision of whether the signal belongs to a normal person or a pathological person will be distributed to the stakeholders. [ABSTRACT FROM AUTHOR]

With the increasing demand for automated, remote, intelligent, and real-time healthcare services in smart cities, smart healthcare monitoring is necessary to provide improved and complete care to residents. In this monitoring, health-related media or signals collected from smart-devices/objects are transmitted and processed to cater to the need for quality care. However, it is challenging to create a framework or method to handle media-related healthcare data analytics or signals (e.g., voice/audio, video, or electroglottographic (EGG) signals) to meet the complex on-demand healthcare needs for successful smart city management. To this end, this paper proposes a cloud-oriented smart healthcare monitoring framework that interacts with surrounding smart devices, environments, and smart city stakeholders for affordable and accessible healthcare. As a smart city healthcare monitoring case study, a voice pathology detection (VPD) method is proposed. In the proposed method, two types of input, a voice signal and an EGG signal, are used. The input devices are connected to the Internet and the captured signals are transmitted to the cloud. The signals are then processed and classified as either normal or pathologic with a confidence score. These results are passed to registered doctors that make the final decision and take appropriate action. To process the signals, local features are extracted from the first-order derivative of the voice signal, and shape and cepstral features are extracted from the EGG signal. For classification, a Gaussian mixture model-based approach is used. Experimental results show that the proposed method can achieve VPD that is more than 93% accurate. [ABSTRACT FROM AUTHOR]

Highlights • An audio–visual emotion recognition system using CNNs is proposed. • A two-stage ELM based fusion after CNNs is introduced. • Two databases, one Big Data, and another not Big Data, were used. • ELM based fusion outperformed others strategies of fusion. Abstract This paper proposes an emotion recognition system using a deep learning approach from emotional Big Data. The Big Data comprises of speech and video. In the proposed system, a speech signal is first processed in the frequency domain to obtain a Mel-spectrogram, which can be treated as an image. Then this Mel-spectrogram is fed to a convolutional neural network (CNN). For video signals, some representative frames from a video segment are extracted and fed to the CNN. The outputs of the two CNNs are fused using two consecutive extreme learning machines (ELMs). The output of the fusion is given to a support vector machine (SVM) for final classification of the emotions. The proposed system is evaluated using two audio–visual emotional databases, one of which is Big Data. Experimental results confirm the effectiveness of the proposed system involving the CNNs and the ELMs. [ABSTRACT FROM AUTHOR]

A new speech feature extraction technique called moving average multi directional local features (MA-MDLF) is presented in this paper. This method is based on linear regression (LR) and moving average (MA) in the time–frequency plane. Three-point LR is taken along time axis and frequency axis, and 3 points MA is taken along 45° and 135° in the time–frequency plane. The LR captures the voice onset\offset, formant contour, while the moving average captures the dynamics on time–frequency axes which can be seen as voiceprints. The MA-MDLF performance is compared to commonly used speech features in speaker recognition. The comparison is performed in a speaker recognition system (SRS) for three different conditions, namely clean speech, mobile speech, and cross channel. MA-MDLF has shown better performance than the baseline MFCC, RASTA-PLP and LPCC. In clean and mobile speech, MA-MDLF feature performs the best and also in the cross channel task MA-MDLF performed excellent. We also evaluated the MA-MDLF using three speech databases, namely KSU, LDC Babylon and TIMITdatabases, and found that MA-MDLF outperformed the other commonly used features with speech from all the three databases. The first and second databases are for Arabic speech while third is for English speech. [ABSTRACT FROM AUTHOR]

We propose a cognitive Internet of Things (IoT)-cloud-based smart healthcare framework, which communicates with smart devices, sensors, and other stakeholders in the healthcare environment; makes an intelligent decision based on a patient's state; and provides timely, low-cost, and accessible healthcare services. As a case study, an EEG seizure detection method using deep learning is also proposed to access the feasibility of the cognitive IoT-cloud smart healthcare framework. In the proposed method, we use smart EEG sensors (apart from general healthcare smart sensors) to record and transmit EEG signals from epileptic patients. Thereafter, the cognitive framework makes a real-time decision on future activities and whether to send the data to the deep learning module. The proposed system uses the patient's movements, gestures, and facial expressions to determine the patient's state. Signal processing and seizure detection take place in the cloud, while signals are classified as seizure or non-seizure with a probability score. The results are transmitted to medical practitioners or other stakeholders who can monitor the patients and, in critical cases, make the appropriate decisions to help the patient. Experimental results show that the proposed model achieves an accuracy and sensitivity of 99.2 and 93.5%, respectively. [ABSTRACT FROM AUTHOR]

With the increased population in urban areas, it has become a challenge to a service provider to manage big data coming from various users and devices. One of the fastest growing devices used by the citizens of urban areas is the smartphone. Today we can find almost nobody without at least one smartphone in their possession. While talking through a smartphone, surrounding environmental noises are added to the speech of the talker. Sometimes these noises are very annoying to the listeners. In this article, we propose a communication framework for urban mobile big data integrating an urban environment classification system. The proposed system utilizes a deep learning approach to classify environmental noises so that an appropriate noise cancellation algorithm can be used to reduce the effect of noise while having a conversation through smartphones. The proposed framework uses mobile edge computing technology to provide low-latency and efficient transmission. Experimental results demonstrate that the proposed system is very efficient in classifying environmental noises. [ABSTRACT FROM AUTHOR]

The advancement of next-generation network technologies provides a huge improvement in healthcare facilities. Technologies such as 5G, edge computing, cloud computing, and the Internet of Things realize smart healthcare that a client can have anytime, anywhere, and in real time. Edge computing offers useful computing resources at the edge of the network to maintain low-latency and real-time computing. In this article, we propose a smart healthcare framework using edge computing. In the framework, we develop a voice disorder assessment and treatment system using a deep learning approach. A client provides his or her voice sample captured by smart sensors, and the sample goes to the edge computing for initial processing. Then the edge computing sends data to a core cloud for further processing. The assessment and management are controlled by a service provider through a cloud manager. Once the automatic assessment is done, the decision is sent to specialists, who prescribe appropriate treatment to the clients. The proposed system achieves 98.5 percent accuracy and 99.3 percent sensitivity using the Saarbrucken Voice Disorder database. [ABSTRACT FROM AUTHOR]

With the invention of new communication technologies, new features and facilities are provided in a smart healthcare framework. The features and facilities aim to provide a seamless, easy-to-use, accurate, and real-time healthcare service to clients. As health is a sensitive issue, it should be taken care of with utmost security and caution. This article proposes a new medical image forgery detection system for the healthcare framework to verify that images related to healthcare are not changed or altered. The system works on a noise map of an image, applies a multi-resolution regression filter on the noise map, and feeds the output to support-vector-machine-based and extreme-learning-based classifiers. The noise map is created in an edge computing resource, while the filtering and classification are done in a core cloud computing resource. In this way, the system works seamlessly and in real time. The bandwidth requirement of the proposed system is also reasonable. [ABSTRACT FROM AUTHOR]

With the recent development of Cognitive Internet of Things (CIoT) and the potential of Cyber Physical System (CPS), people’s daily activities become smarter, and intelligent. The combination of CIoT and CPS can greatly enhance the quality of people’s life. To this end, this article proposes CIoT-CPS that comprises of two main models: user activity cognitive model and image authentication model.The user activity cognitive model (UACM) is a machine-learning model to have the meaningful data. The image authentication model is to verify the authenticity of images captured by various devices, such as smart phones, digital cameras, and other camera-embedded portable devices. The authenticity of an image is breached when parts of images are assembled to produce a new image (known as a splicing forgery), or a part of an image is copied or pasted into another part of the same image (known as a copy-move forgery). In the proposed verification method, an opposite color local binary pattern (OC-LBP) texture descriptor is applied to a questioned image. The image is first decomposed into an RGB (red, green, blue) and a luminance and chroma color spaces. The OC-LBP measures the interrelation between pixels of different color components. The intensive computation involving six color components and a gray version is performed in the cloud, where a server can be dedicated to doing this job. The histograms of the OC-LBP are concatenated with weights to produce a final feature vector of the image. A support vector machine is applied as a classifier, which classifies the image as authentic or forged. Several experiments were performed to verify the suitability of those models or approaches. The proposed approaches show a good accuracy compared to other competing approaches. [ABSTRACT FROM AUTHOR]

Federated Learning (FL), Artificial Intelligence (AI), and Explainable Artificial Intelligence (XAI) are the most trending and exciting technology in the intelligent healthcare field. Traditionally, the healthcare system works based on centralized agents sharing their raw data. Therefore, huge vulnerabilities and challenges are still existing in this system. However, integrating with AI, the system would be multiple agent collaborators who are capable of communicating with their desired host efficiently. Again, FL is another interesting feature, which works decentralized manner; it maintains the communication based on a model in the preferred system without transferring the raw data. The combination of FL, AI, and XAI techniques can be capable of minimizing several limitations and challenges in the healthcare system. This paper presents a complete analysis of FL using AI for smart healthcare applications. Initially, we discuss contemporary concepts of emerging technologies such as FL, AI, XAI, and the healthcare system. We integrate and classify the FL-AI with healthcare technologies in different domains. Further, we address the existing problems, including security, privacy, stability, and reliability in the healthcare field. In addition, we guide the readers to solving strategies of healthcare using FL and AI. Finally, we address extensive research areas as well as future potential prospects regarding FL-based AI research in the healthcare management system. [ABSTRACT FROM AUTHOR]

A large number of the population around the world suffers from various disabilities. Disabilities affect not only children but also adults of different professions. Smart technology can assist the disabled population and lead to a comfortable life in an enhanced living environment (ELE). In this paper, we propose an effective voice pathology assessment system that works in a smart home framework. The proposed system takes input from various sensors, and processes the acquired voice signals and electroglottography (EGG) signals. Co-occurrence matrices in different directions and neighborhoods from the spectrograms of these signals were obtained. Several features such as energy, entropy, contrast, and homogeneity from these matrices were calculated and fed into a Gaussian mixture model-based classifier. Experiments were performed with a publicly available database, namely, the Saarbrucken voice database. The results demonstrate the feasibility of the proposed system in light of its high accuracy and speed. The proposed system can be extended to assess other disabilities in an ELE. [ABSTRACT FROM AUTHOR]

The integration of the IoT and cloud technology is very important to have a better solution for an uninterrupted, secured, seamless, and ubiquitous framework. The complementary nature of the IoT and the could in terms of storage, processing, accessibility, security, service sharing, and components makes the convergence suitable for many applications. The advancement of mobile technologies adds a degree of flexibility to this solution. The health industry is one of the venues that can benefit from IoT?Cloud technology, because of the scarcity of specialized doctors and the physical movement restrictions of patients, among other factors. In this article, as a case study, we discuss the feasibility of and propose a solution for voice pathology monitoring of people using IoT?cloud. More specifically, a voice pathology detection system is proposed inside the monitoring framework using a local binary pattern on a Mel-spectrum representation of the voice signal, and an extreme learning machine classifier to detect the pathology. The proposed monitoring framework can achieve high accuracy of detection, and it is easy to use. [ABSTRACT FROM PUBLISHER]

The effect of 2D and 3D educational content learning on memory has been studied using electroencephalography (EEG) brain signal. A hypothesis is set that the 3D materials are better than the 2D materials for learning and memory recall. To test the hypothesis, we proposed a classification system that will predict true or false recall for short-term memory (STM) and long-term memory (LTM) after learning by either 2D or 3D educational contents. For this purpose, EEG brain signals are recorded during learning and testing; the signals are then analysed in the time domain using different types of features in various frequency bands. The features are then fed into a support vector machine (SVM)-based classifier. The experimental results indicate that the learning and memory recall using 2D and 3D contents do not have significant differences for both the STM and the LTM. [ABSTRACT FROM PUBLISHER]

With the advent of future generation mobile communication technologies (5G), there is the potential to allow mobile users to have access to big data processing over different clouds and networks. The increasing numbers of mobile users come with additional expectations for personalized services (e.g., social networking, smart home, health monitoring) at any time, from anywhere, and through any means of connectivity. Because of the expected massive amount of complex data generated by such services and networks from heterogeneous multiple sources, an infrastructure is required to recognize a user's sentiments (e.g., emotion) and behavioral patterns to provide a high quality mobile user experience. To this end, this paper proposes an infrastructure that combines the potential of emotion-aware big data and cloud technology towards 5G. With this proposed infrastructure, a bimodal system of big data emotion recognition is proposed, where the modalities consist of speech and face video. Experimental results show that the proposed approach achieves 83.10 % emotion recognition accuracy using bimodal inputs. To show the suitability and validity of the proposed approach, Hadoop-based distributed processing is used to speed up the processing for heterogeneous mobile clients. [ABSTRACT FROM AUTHOR]

The promising potential and emerging applications of cloud gaming have drawn increasing interest from academia, industry, and the general public. However, providing a high-quality gaming experience in the cloud gaming framework is a challenging task because of the tradeoff between resource consumption and player emotion, which is affected by the game screen. We tackle this problem by leveraging emotion-aware screen effects in the cloud gaming framework and combining them with remote display technology. The first stage in the framework is the learning or training stage, which establishes a relationship between screen features and emotions using Gaussian mixture model-based classifiers. In the operating stage, a linear programming model provides appropriate screen changes based on the real-time user emotion obtained in the first stage. Our experiments demonstrate the effectiveness of the proposed framework. The results show that our proposed framework can provide a high quality gaming experience while generating an acceptable amount of workload for the cloud server in terms of resource consumption. [ABSTRACT FROM AUTHOR]

Mobile healthcare in a cloud-based system increases the easiness and the ubiquitous nature of patient-doctor relationship. One of the major issues of this healthcare is secure transmission and data authenticity. If the data is not transmitted securely or not authenticated, the clients may face embarrassment. In this paper, we propose a cloud-based healthcare framework that will authenticate speech data from a patient suspected to have Parkinson’s disease. The patient sends his or her speech signal recorded via a smart phone through Internet to the cloud. A discrete wavelet transform- (DWT-) singular value decomposition (SVD) based speech watermarking module is run in the cloud to embed watermark to the signal. In case of authentication, watermark is extracted from the questioned signal and matched with the stored watermark. Experimental results indicate that the proposed DWT-SVD based watermarking system achieves imperceptibility and is robust against attacks such as additive white Gaussian noise and filtering. [ABSTRACT FROM AUTHOR]

This study was conducted for evaluation of relative efficacy of hypertonic saline (HSS) and isotonic saline (ISS) solutions in neonatal diarrhea associated with Escherichia coli (E. coli) in buffalo calves. For this purpose, neonatal diarrhea was induced in 24 healthy male buffalo calves through oral administration of eneteropathogenic E. coli (K99). After 12 h of successful induction of diarrhea, the calves were randomly divided into two equal groups viz. A and B (n=12 each). Group A was treated with ISS @ 90 mL/kg body weight (BW), while calves of group B were administered HSS @ 4 mL/kg BW. Both groups were additionally treated with ceftiofur HCl and flunixin meglumine @ 6 and 2 mg/kg BW, IM, respectively. The relative efficacy of two treatments was evaluated on the basis of clinical parameters, hematological analyses, hemodynamic parameters, blood gas analyses and serum electrolytes. All parameters were recorded at baseline (before induction of diarrhea), during diarrhea, t=1, t=3, t=6, t=12, t=24 and t=36 h after treatment. The calves treated with HSS (group B) resuscitated from neonatal diarrhea more rapidly (within 24 h) and effectively than ISS (group A) and showed significant (P < 0.05) improvement. At t=24 h, values for mean arterial pressure, central venous pressure and arterial oxygen delivery were 108.6±9.8 and 116.3±14.2, 12±1.4 and 15.5±1.5 and 69.3±3.3 and 85.5±3.7 in groups A and B, respectively. Calves of group B also experienced decrease in hematocrit and hemoglobin concentration as well as increases in blood pH, bicarbonate concentration and serum electrolytes. On the basis of findings of this study, it was concluded that HSS offsets deleterious hemodynamic effects of hypovolemia, improves oxygenation, corrects metabolic acidosis and increases survival in E. coli associated neonatal diarrhea of buffalo calves which makes it more practical and economical alternative to the use of large volumes of isotonic saline solution for use in field settings. © 2015 Friends Science Publishers [ABSTRACT FROM AUTHOR]

The present study was planned to investigate the prevalence of streak canal and intramammary infections in buffaloes and cows lacking any mastitis control program and to determine the contemporaneous association between streak canal and intramammary infections in these two dairy species. Streak canal swab and quarter foremilk samples were collected aseptically from 20 lactating mastitis free cows and buffaloes each in their first 2 months of second to third lactation and subjected to microbiological examination as per standard procedures. Isolates were speciated using commercially available kits. Contemporaneous association was defined as co-occurrence of a specific microorganism in streak canal swab and quarter foremilk sample of a particular teat simultaneously. The prevalence of streak canal infections as well as intramammary infections was higher in cows (91.25% and 55%, respectively) than in dairy buffaloes (73.75%, and 22.5%, respectively). Of the 78 isolates recovered from streak canal swab samples of buffaloes, Staphylococcus aureus (n=18; 23.07%) was the most predominant organism followed by S. choromogenes (n=14), E. coli (n= 6), unidentified esculin positive Streptococcus spp. (n=5). S. aureus (n=34) was the most frequent isolate recovered from the streak canals of cows followed by S. choromogenes (n=11), S. hyicus (n=8), Escherichia coli (n=8). In terms of frequency of isolation, S. aureus was the most predominant organism (n=6) recovered quarter foremilk samples of buffaloes followed by unidentified coagulase negative Staphylococcus spp. (n=3), Trueperella pyogenes (Corynebacterium pyogenes; n=3), unidentified Trueperella spp. and mixed species (n=3). In the case of dairy cows, predominant organism recovered from quarter foremilk samples included S. aureus (n=13), Streptococcus agalactiae (n=4), yeast (n=4) and mixed species (n=4). Recovery of 44 and 27 isolates, respectively in cows and buffaloes was common to both streak canal swab and quarter foremilk samples (contemporaneous association). © 2015 Friends Science Publishers [ABSTRACT FROM AUTHOR]

The present study was planned to investigate the prevalence of streak canal and intramammary infections in buffaloes and cows lacking any mastitis control program and to determine the contemporaneous association between streak canal and intramammary infections in these two dairy species. Streak canal swab and quarter foremilk samples were collected aseptically from 20 lactating mastitis free cows and buffaloes each in their first 2 months of second to third lactation and subjected to microbiological examination as per standard procedures. Isolates were speciated using commercially available kits. Contemporaneous association was defined as co-occurrence of a specific microorganism in streak canal swab and quarter foremilk sample of a particular teat simultaneously. The prevalence of streak canal infections as well as intramammary infections was higher in cows (91.25% and 55%, respectively) than in dairy buffaloes (73.75%, and 22.5%, respectively). Of the 78 isolates recovered from streak canal swab samples of buffaloes, Staphylococcus aureus (n=18; 23.07%) was the most predominant organism followed by S. choromogenes (n=14), E. coli (n= 6), unidentified esculin positive Streptococcus spp. (n=5). S. aureus (n=34) was the most frequent isolate recovered from the streak canals of cows followed by S. choromogenes (n=11), S. hyicus (n=8), Escherichia coli (n=8). In terms of frequency of isolation, S. aureus was the most predominant organism (n=6) recovered quarter foremilk samples of buffaloes followed by unidentified coagulase negative Staphylococcus spp. (n=3), Trueperella pyogenes (Corynebacterium pyogenes; n=3), unidentified Trueperella spp. and mixed species (n=3). In the case of dairy cows, predominant organism recovered from quarter foremilk samples included S. aureus (n=13), Streptococcus agalactiae (n=4), yeast (n=4) and mixed species (n=4). Recovery of 44 and 27 isolates, respectively in cows and buffaloes was common to both streak canal swab and quarter foremilk samples (contemporaneous association). [ABSTRACT FROM AUTHOR]

Cloud computing brings several advantages such as flexibility, scalability, and ubiquity in terms of data acquisition, data storage, and data transmission. This can help remote healthcare among other applications in a great deal. This paper proposes a cloud based framework for speech enabling healthcare. In the proposed framework, the patients or any healthy person seeking for some medical assistance can send his/her request by speech commands. The commands are managed and processed in the cloud server. Any doctor with proper authentication can receive the request. By analyzing the request, the doctor can assist the patient or the person. This paper also proposes a new feature extraction technique, namely, interlaced derivative pattern (IDP), to automatic speech recognition (ASR) system to be deployed into the cloud server. The IDP exploits the relative Mel-filter bank coefficients along different neighborhood directions from the speech signal. Experimental results show that the proposed IDP-based ASR system performs reasonably well even when the speech is transmitted via smart phones. [ABSTRACT FROM AUTHOR]

The increasing demand for the remote monitoring of patients combined with the promising potential of cloud computing has enabled the design and development of a number of cloud-based systems and services for healthcare. The cloud computing, in combination with the popularity of smart handheld devices, has inspired healthcare professionals to remotely monitor patients' health while the patient is at home. To this end, this paper proposes a cloud-assisted speech and face recognition framework for elderly health monitoring, where handheld devices or video cameras collect speech along with face images and deliver to the cloud server for possible analysis and classification. In the framework, a patient's state such as pain, tensed, and so forth is recognized from his or her speech and face images. The patient state recognition system extracts local features from speech, and texture descriptors from face images. Then it classifies using support vector machines. The recognized state is later sent to the remote care center, healthcare professionals and providers for necessary services in order to provide seamless health monitoring. Experiments have been performed to validate the approach and to evaluate the suitability of this framework in terms of accuracy and time requirements. The results demonstrate the effectiveness of the proposed approach with regards to face and speech processing. [ABSTRACT FROM AUTHOR]

Methicillin-resistant Staphylococcus aureus (MRSA) remains a significant challenge for human and veterinary health professions especially in the developing nations. Resistance to wide array of antibiotics is a major hurdle in treating MRSA infections including mediastinitis. In present study, therapeutic efficacy of linezolid alone and in combination with rifampicin was evaluated in experimentally induced MRSA mediastinitis in rabbits. Experimental upper median sternotomy was performed on 56 rabbits divided into seven groups. Out of these seven groups, one was taken as uncontaminated control group; one as untreated contaminated group; two groups were given different doses of linezolid, one group was treated with rifampicin alone whereas remaining two groups were given combinational therapy of linezolid and rifampicin. Both 50 mg/kg and 100 mg/kg dose rates of linezolid were found to be effective whereas no added benefit was observed in rabbits given a combination of rifampicin and linezolid. Linezolid can be used in serious MRSA infections especially in those patients not responding to conventional antimicrobial therapy but the selection of correct dose of linezolid is important in each patient. [ABSTRACT FROM AUTHOR]

In this paper, we proposed a system of automatically classifying different types of dates from their images. Different dates have various distinguished features that can be useful to recognize a particular date. These features include color, texture, and shape. In the proposed system, a color image of a date is decomposed into its color components. Then, local texture descriptor in the form of local binary pattern (LBP) or Weber local descriptor (WLD) histogram is applied to each of the components to encode the texture pattern of the date. The texture patterns from all the components are fused to describe the image. Fisher discrimination ratio (FDR) based feature selection is utilized to reduce the dimensionality of the feature set. Size and shape features are appended to the texture descriptors to fully describe the date. As a classifier, we use support vector machines. The proposed system achieves more than 98% accuracy to classify the dates. [ABSTRACT FROM AUTHOR]

In this paper, a steady and incompressible Oseen's flow past a sphere is calculated using the indirect boundary element method (IDBEM). The surface of the sphere is discretized into quadrilateral elements, over which the velocity distribution is calculated. The computed results are compared with analytical results. It is found that both these results are in good agreement. [ABSTRACT FROM AUTHOR]