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Deep learning is a promising way to get relevant information from IoT service sensor data embedded in complex situations. Due to its multifaceted structure, deep learning is better suited to the nature of computer drag. So, in the course of this article, we start by introducing deep IoT metrics into the computer environment. Because there is a limited amount of available bandwidth, we are designing a separate load-filling strategy to optimize the performance of deep IoT learning systems using computers (World Health Organization Epilepsy, http://www.who.int/mediacentre/factsheets/fs999/en/)

The identification of Hate Speech in Social Media is of great importance and receives much attention in the text classification community. There is a huge demand for research for languages other than English. The HASOC track intends to stimulate development in Hate Speech for Hindi, German and English. Three datasets were developed from Twitter and Facebook and made available. Binary classification and more fine-grained subclasses were offered in 3 subtasks. For all subtasks, 321 experiments were submitted. The approaches used most often were LSTM networks processing word embedding input. The performance of the best system for identification of Hate Speech for English, Hindi, and German was a Marco-F1 score of 0.78, 0.81 and 0.61, respectively.

Nowadays, we have many remote data storage systems available which allow clients to store their data on remote server. But these systems possess many security problems like integrity, availability, and privacy. Unauthorized modification of files must not be possible, but this problem should be resolved. For this, one existing solution called Provable Data Possession (PDP) is available to eliminate integrity problem. This PDP works on public key infrastructure (PKI) and it generates large size keys and requires public key certificates to publish public keys. These characteristics of PDP introduce computation and management overhead on devices having limited computation power. A possible solution to these problems could be usage of Identity-based encryption instead of typical PKI. This solution will generate small-size public keys and eliminate the requirement of public key certificates to publish them.

Classification models have proven useful for predicting clinical interventions and patient outcomes. One of the key issues that affect the predictive ability of supervised learning frameworks in the healthcare scenario is imbalance in data sets. In addition, non-uniform data collection processes in clinical scenarios lead to poor quality data sets. We designed a novel approach to predict Intensive Care Unit (ICU) transfers based on a weighted-similarity measure for patients outside of ICUs. The approach uses similarity between patient vital signs as input features for training the model. To address the data quality issues, we demonstrate the use of various up-sampling and down-sampling techniques to handle imbalanced data sets and train a classifier on a re-sampled data set. The data set used for testing the approach is derived from the MIMIC III database. We compare our results with the clinically accepted methodology to capture patient's health state, assisting in clinical decision making. Our model outperforms the standard methodology used in clinical decision making in standard scoring metrics such as F1-score, False Positive Rate and Mathew's Correlation Coefficient [MCC].

Computing is concerned with applying the heteregenous resources of many computers to solve a single problem and involves managing the diverse resources towards a common objective. Successful utilization of grid infrastructure to solve resource intensive and computing problems requires performance modeling and evaluation to meet the QoS requirements of end users. Resource management and scheduling is the most important component of grid systems. A Grid scheduler must make resource selection decisions in an environment where it has no control over the local resources, the resources are distributed, and information about the systems is often limited or dated. Grid Resource broker or meta-scheduler uses local schedulers of the different grid middleware and local schedulers of clusters to allocate jobs to distributed resources. To address different issues in grid scheduling, different scheduling approaches and algorithms have been proposed in the literature. However, evaluation and comparative analysis of these algorithms and research experiments are often difficult to perform due to problems like large number of heterogeneous resources, dynamic nature of grid, not able to create different types of realistic workloads and jobs with different parameters and lack of certain functionalities in available resource management systems like advance reservation (AR) and grid usage accounting. This paper focuses on modeling and simulation of grid towards achieving various grid performance metrics and QoS requirements. This paper also presents the experimental results on grid simulation and performance evaluation different grid resource brokering approaches using synthetic workloads. Different simulation experiments are used to compare different aspects of scheduling using different types of grid job, input resources and workloads. The simulation results indicate the effect of resource brokering approach used on efficient execution of grid jobs, success ratio of jobs with QoS requirement, resource utilization and load balancing of grid system.

Identity theft, privacy invasion, loss of key information is the major reasons for which E-mail security is breached these days. Hence, it is very essential that effective security prevention measures are taken. This paper, proposes many such hacking prevention measures with approaches to recover a hacked account. It also puts forward a novel CAPTCHA (Completely Automated Public Turing Test to tell Computers and Humans Apart) based mechanism, which can protect the crucial information even if the account is hacked.

N-Acetylaspartylglutamate in human brain has been measured with difference editing at 7 T. The CH(2) proton resonances (∼ 2.5 ppm) of the aspartyl groups of N-acetylaspartylglutamate and N-acetylaspartate were difference edited (MEGA) using 20-msec gaussian radiofrequency pulses for selective 180 ° rotations of the coupling partners at 4.61 and 4.38 ppm, respectively. The echo time of the editing sequence, 108 msec, was obtained in phantom tests. Single-voxel localized in vivo measurements were conducted in the medial prefrontal and right frontal cortices of five healthy volunteers. The gray and white matter fractions within the voxels were obtained from T(1)-weighted image segmentation. Using linear regression of the metabolite concentration vs. fractional white matter contents within the voxels, the N-acetylaspartylglutamate-to-N-acetylaspartate concentration ratios in gray and white matter were estimated to be 0.13 and 0.28 by difference editing (95% confidence intervals 0.07-0.19 and 0.22-0.34), respectively, assuming identical relaxation effects between the metabolites.

Grid computing involves harnessing the diverse heterogeneous resources to solve compute and resource intensive problems. Such types of distributed systems are inherently unreliable with high chances of different types of failure events. The `best-effort' approach is inappropriate if grids are to be used as the infrastructure for "real world" commercial applications and complex scientific applications like simulations and modeling. The QoS oriented grid enhances and leverages the basic job submission functionality provided by grid middleware like Globus and Sun N1 grid engine with QoS support. QoS oriented grid provides a grid resource broker (or meta scheduler) to abstract the users from complexity of managing diverse distributed resources (under control of local schedulers) and take scheduling decisions based on users QoS constraints. To address the QoS grid problem, QoS oriented grid model and architectural framework has been proposed. Prototype implementation of the QoS grid model and architecture has been done in our grid test bed. To validate the QoS grid model, we have executed high performance grid applications using QoS scheduler like Bioinformatics Sequence Alignment problem and Histogram calculation and have obtained favorable experiment results.

In current scenario, wired and wireless networks are widely used in educational organizations to meet the various needs of education institutions. New types of security threats and vulnerabilities are increasing day by day, making wired and wireless networks insecure and unreliable. In this case study paper, a survey of different types of security threats and security mechanisms in educational environment and ways to counteract them has been discussed. To address the security issue, we have proposed an integrated model for security in wired and wireless network. The model includes network topology and associated network security mechanisms. The network model incorporates the concept of Bring Your Own Device (BYOD) with its security implications. The proposed model is generalized and holistic to fulfill the network requirements and address emerging security issues of any type of educational organization. The proposed model is implemented in our educational organization and initial encouraging results have been obtained.