New Meaning to it Alignment

One of the perennial top issues in surveys of IT management concerns is IT alignment. It always seems to be in the top-ten, but cyclically rises to number-one at times of economic decline. When the economy turns down, information services departments are called upon to redouble their vigilance on spending and to focus their attention on projects and initiatives that are in line with the near-term objectives of the overall business. the enterprises that have developed collaborative management processes between the business and information services organizations do this best and with the least amount of disruption. in fact, it seems that there has been tremendous progress in the past ten or fifteen years in terms of leadership and management practices on the part of both business and information services organizations that have contributed to a dramatic improvement in the handling of downward economic adjustments. Upward economic change, on the other hand, poses more perplexing challenges.     

An Enhanced Security Framework for Secured Data Storage and Communications in Cloud Using ECC,Access Control and LDSA

Wireless Personal Communications - The Virtual Private Network (VPN) service offered by the Multi-Protocol Label Switching Protocol (MPLS) is more and more solicited by clients thanks to its...     

Brain Tumor Identification Using Data Augmentation and Transfer Learning Approach

A brain tumor is a lethal neurological disease that affects the average performance of the brain and can be fatal. In India, around 15 million cases are diagnosed yearly. To mitigate the seriousness of the tumor it is essential to diagnose at the beginning. Notwithstanding, the manual evaluation process utilizing Magnetic Resonance Imaging (MRI) causes a few worries, remarkably inefficient and inaccurate brain tumor diagnoses. Similarly, the examination process of brain tumors is intricate as they display high unbalance in nature like shape, size, appearance, and location. Therefore, a precise and expeditious prognosis of brain tumors is essential for implementing the of an implicit treatment. Several computer models adapted to diagnose the tumor, but the accuracy of the model needs to be tested. Considering all the above mentioned things, this work aims to identify the best classification system by considering the prediction accuracy out of AlexNet, ResNet 50, and Inception V3. Data augmentation is performed on the database and fed into the three convolutions neural network (CNN) models. A comparison line is drawn between the three models based on accuracy and performance. An accuracy of 96.2% is obtained for AlexNet with augmentation and performed better than ResNet 50 and Inception V3 for the 120th epoch. With the suggested model with higher accuracy, it is highly reliable if brain tumors are diagnosed with available datasets.     

Membrane-based parallel plate denuder for the collection and removal of soluble atmospheric gases

A continuously wetted cellulose acetate membrane-based parallel plate diffusion denuder is described. This is the first membrane-based denuder that has a small enough internal liquid holdup volume to permit reasonably rapid response time (10 --> 90% rise time of approximately 1.2 min for a transient event at a liquid flow rate of 500 microL/min) while permitting quantitative removal of common soluble atmospheric trace gases at flow rates up to 1.7 L/min. The latter attribute permits the use of the device as the first element in a particle sampling and analysis system for the quantitative removal of potentially interfering soluble trace gases. Particle losses in the denuder range from 0.9 to 2.9% over an aerodynamic diameter range of 0.38-3.48 microm, averaging 1.8%. However, only approximately 0.5% of the particles actually appears in the denuder effluent liquid. The relatively compact (300 mm H x 57 mm W x 26 mm D) wet denuder should be attractive in a number of applications. We show excellent agreement for HONO measurements with a conventional larger parallel plate wetted denuder in field measurements.     

Microscale continuous ion exchanger

A microscale continuous ion exchanger based on two liquid streams flowing in parallel is presented. The ion exchange reaction occurs through diffusional transfer of molecules between the ion exchanger phase and the eluent phase and is applied for conductivity suppression. Two approaches are demonstrated. In the first approach, a liquid ion exchanger (i.e. a strongly basic compound, e.g., tetraoctylammonium hydroxide, or a secondary amine, e.g., Amberlite IA-2) is dissolved in an organic solvent immiscible with the aqueous eluent. The system allows for sensitive suppressed conductivity detection of various inorganic cations. When the weakly basic secondary amine is used, conductometric detection of heavy metals is possible. In the second approach, a suspension of finely ground ion-exchange resin is used as the ion exchanger phase. In this case, the suspension need not involve an organic solvent. Theoretical models and computations are presented along with experimental results. The potential of such a system as a chip-scale post-separation suppressor/reactor is evident.     

Chaotic Whale Optimized Fractional Order PID Controller Design for Desalination Process

The main aim of this work is to design a suitable Fractional Order Proportionl Integral Derivative (FOPID) controller with Chaotic Whale Optimization Algorithm (CWOA) for a RO desalination system. Continuous research on Reverse Osmosis (RO) desalination plants is a promising technique for satisfaction with sustainable and efficient RO plants. This work implements CWOA based FOPID for the simulation of reverse osmosis (RO) desalination process for both servo and regulatory problems. Mathematical modeling is a vital constituent of designing advanced and developed engineering processes, which helps to gain a deep study of processes to predict the performance, more efficiently. Numerous approaches have been employed for mathematical models based on mass and heat transfer and concentration of permeable flow rate. Incorporation of FOPID controllers is broadly used to improve the dynamic response of the system, at the same time, to reduce undershoot or overshoot, steady state error and hence improve the response. The performances of the FOPID controller with optimization is compared in terms of measures such as Integral Time Absolute Error (ITAE) and Integral Square Error (ISE). Simulation results with FOPID on desalination process achieved rise time of 0.0311 s, settling time of 0.0489 s and 0.7358% overshoot, better than the existing techniques available in the literatures.     

Histogram Matched Chest X-Rays Based Tuberculosis Detection Using CNN

Tuberculosis (TB) is a severe infection that mostly affects the lungs and kills millions of people’s lives every year. Tuberculosis can be diagnosed using chest X-rays (CXR) and data-driven deep learning (DL) approaches. Because of its better automated feature extraction capability, convolutional neural networks (CNNs) trained on natural images are particularly effective in image categorization. A combination of 3001 normal and 3001 TB CXR images was gathered for this study from different accessible public datasets. Ten different deep CNNs (Resnet50, Resnet101, Resnet152, InceptionV3, VGG16, VGG19, DenseNet121, DenseNet169, DenseNet201, MobileNet) are trained and tested for identifying TB and normal cases. This study presents a deep CNN approach based on histogram matched CXR images that does not require object segmentation of interest, and this coupled methodology of histogram matching with the CXRs improves the accuracy and detection performance of CNN models for TB detection. Furthermore, this research contains two separate experiments that used CXR images with and without histogram matching to classify TB and non-TB CXRs using deep CNNs. It was able to accurately detect TB from CXR images using pre-processing, data augmentation, and deep CNN models. Without histogram matching the best accuracy, sensitivity, specificity, precision and F1-score in the detection of TB using CXR images among ten models are 99.25%, 99.48%, 99.52%, 99.48% and 99.22% respectively. With histogram matching the best accuracy, sensitivity, specificity, precision and F1-score are 99.58%, 99.82%, 99.67%, 99.65% and 99.56% respectively. The proposed methodology, which has cutting-edge performance, will be useful in computer-assisted TB diagnosis and aids in minimizing irregularities in TB detection in developing countries.