Forecast Pupil Performance using SVM & Logistic Regression

The primary reason why machine learning has gained so much prominence nowadays is that it enables accurate and reliable decision making by extracting hidden relationships between various features present in the data. For this purpose the technique such as supervised methodologies and unsupervised methodologies are used. For this reason, machine learning can be used in almost any area of work to help in proper decision making and predictions. In our current project, we are trying to predict this student performance that utilizes supervised machine learning methodologies like support vector machines , logistic regression, random forests etc. We have also tried to publish this model to a web application so that it can be used by the academic community. The information extracted and the knowledge gained by extracting information from the educational data set would be helpful for predicting the student grades and their future performance. The main intention of the project used to predict student performance beforehand and help them get good grades in future. This would help in increasing the motivation levels of the students, improving their grades, decreasing their dropout ratio, and preparing better students for a better world

Exposing Pernicious Bots in Twitter Utilizing User Profile Attributes and Machine Learning

With the rampant usage of social media, fraudsters try to employ malevolent social bots that tend to generate counterfeit tweets and try to establish relationships with other users on the social media by acting like followers or try to generate multiple counterfeit accounts that get involved in malevolent activities. They also tend to post malevolent URLs that are used to navigate genuine users to malevolent web servers. Thus it is very essential to differentiate the bot accounts from genuine accounts. It is observed that bots can be identified by analyzing the profile based featured and URL features that they post such as redirected URL, spam data, frequency of URL sharing etc than social features. In this project, we suggest a novel approach using Deep Learning techniques that uses profile based features for exposing pernicious bots on social networks. We feed the Twitter data set to the above-mentioned model and observe that it gives better performance than other algorithms. we also tried to build a web application   that can show that the above approach gives better performance when compared to other existing models

Improved Integrity and Confidentiality by Arresting Intrusion and Insider Attacks in Public Cloud Environments

When we focus on Could Computing domain in connection to data as a service sophisticated techniques are been adopted to deal but security parameter became a crucial point to focus in order to contribute effective data services thus makes us to emphasize on privacy preserving techniques that improves  reliability. Intruder attacks have to be handled to order build tractability to data facilitators over public clouds, so that your privacy is our priority policy could be deployed effectively. To solve the problem of insider attacks on the cloud environment, we propose a novel technique to safeguard the data within the virtual machines. In the cloud environment, the machine which will have all the virtual machines is called a host machine. Hypervisor is software which will run the virtual machines. The hypervisor in general encrypts the virtual machines data and upon request in providing appropriate credentials the hypervisor decrypts the virtual machine data and makes it available to the users of public cloud. In this project we propose a novel technique in which the hypervisor keeps the cloud data in encrypted format along with the virtual machine. We elaborate this technique using a medical scenario in which the doctors and patients share data on the cloud. Thus, using this technique, the cloud infrastructure resources and data within them are protected from insider attacks. We have proposed a novel mechanism in which the virtual machines and their data on the cloud server can be safeguarded for data privacy and confidentiality with the help of hypervisors to encrypt the virtual machines data and decrypt them for the authorized people of a public cloud. We demonstrated this using medical scenario in which a patient can upload his health information in encrypted format to the cloud server. The doctor can view this health information and suggest required medicines. An insider such as an un-trusted cloud service administrator can try to modify or steal this information but that gets recorded and would be available for the cloud service provider for stringent actions

A Novel Scheme For Preserving Owner Privacy And Verifying Data Integrity Of Shared Data In Public Cloud Storage

With the emergence of cloud Technologies, it is important review the integrity of the information that is saved on the public cloud storage systems. When critical and private information which is very sensitive in nature is saved and shared with many uses, it is very much important to safeguard the details of the data owner from the auditor. It means that the auditor should not be able to get any details about the data owner while he is auditing are reviewing the cloud data. Many schemes were proposed that safeguard the user privacy while incorporating the confirmable information possession technique. However, the issue with these schemes is that they have heavy computational cost and they increase the load on the systems and in turn bring down the efficiency. To address all the above mentioned problems, we propose a novel and unique technique to up all the data owner's privacy while auditing the data. The architecture of our proposed scheme is based on identity supported encryption and hence it overcomes the problem of management of certificates and ensures the relation between the data owner and the uploaded data are not exposed to the auditor by encrypting the data  in the proof generation phase but not in the auditing phase. The encryption is also done at block level to safeguard the data from untrusted Cloud Service Provider. In this manner, our scheme provides maximum security from the Cloud Service Provider and the auditor and hence the privacy and anonymity of data is preserved in this mechanism. Experimental results show that our mechanism is effective, efficient and implementable when compared to to the existing systems

Large Formless Sets of Data for Competitor Mining

A company's success is determined by its ability to make a thing appealing to its customers rather than the competitors. We have some questions within the context of this challenge: how can competition between two parts be formalized and quantified? Who are the primary rivals of a certain item? What are an item's most distinguishing features? Despite the fact that this subject has a wide range of influence and relevance, only a little amount of effort has gone into developing a suitable answer. The competition between two items is observed formally in this project, depending on the business areas they both represent. Consumer comments and a wide range of available knowledge in a variety of fields are used in our competitiveness assessment. We provide efficient approaches for assessing competition and resolving the inherent challenge of finishing the top-known competitors of a specific object using large data sets. Finally, we test the validity and scalability of our conclusions using a variety of datasets from other domains

Predictive Genomics: A Post-genomic Integrated Approach to Analyse Biological Signatures of Radiation Exposure

The ultimate objective of radiation research is to link human diseases with the altered gene expression that underlie them and the exposure type and level that caused them. However, this has remained a daunting task for radiation biologists to indent genomic signatures of radiation exposures. Transcriptomic analysis of the cells can reveal the biochemical or biological mechanisms affected by radiation exposures. Predictive genomics has revolutionised how researchers can study the molecular basis of adverse effects of exposure to ionising radiation. It is expected that the new field will find efficient and high-throughput means to delineate mechanisms of action, risk assessment, identify and understand basic mechanisms that are critical to disease progression, and predict dose levels of radiation exposure. Previously, we have shown that cells responding to environmental toxicants through biological networks that are engaged in the regulation of molecular functions such as DNA repair and oxidative stress. To illustrate radiation genomics as an effective tool in biological dosimetry, an overview has been provided of some of the current radiation genomics landscapes as well as potential future systems to integrate the results of radiation response profiling across multiple biological levels in to a broad consensus picture. Predictive genomics represents a promising approach to high-throughput radiation biodosimetry.Defence Science Journal, 2011, 61(2), pp.133-137, DOI:http://dx.doi.org/10.14429/dsj.61.83

Inhibition of poly (ADP-Ribose) polymerase-1 in telomerase deficient mouse embryonic fibroblasts increases arsenite-induced genome instability

10.1186/2041-9414-1-5Genome Integrity1

Sudden Cardiac Death in Patients Under 49 Years Including Adolescents: A single-centre study from Oman

Objectives: This study aimed to identify the incidence of sudden cardiac death (SCD0 in adult patients under the age of 49 years, including adolescents with an out-of-hospital cardiac arrest that presented to the emergency department of a tertiary care hospital. Methods: This retrospective cross-sectional study was conducted at the Royal Hospital, Muscat, Oman, between January 2015 and December 2019. All patients with out-of-hospital cardiac arrest were enrolled. The incidence of SCD was evaluated. Information about the patient's demographic data, the site of cardiac arrest, the mode of arrival, the duration of pre-arrest symptoms and if cardiopulmonary resuscitation was performed was gathered. Survival data at 3-year follow-up was obtained. Results: A total of 117 out of 769 (15%) patients met the criteria for SCD. Male gender was predominant, with a median age of 33 years. In about 79.5% of the patients, cardiac arrest was witnessed. Only 43 patients (36.8%) received cardiopulmonary resuscitation at the arrest site; 21 patients (17.9%) had a shockable rhythm and 96 patients (82.1%) had a non-shockable rhythm. Spontaneous circulation was returned in 15 patients (12.8%). Nine patients (7.7%) were discharged from the hospital and 8 (6.8%) survived at least 36 months. Conclusion: The study findings indicate the prevalence of SCD among patients who experienced a cardiac arrest outside the hospital. Unfortunately, only a small number of patients were able to survive in the long term. By implementing preemptive screening for individuals and their families, it may be possible to prevent SCD and improve outcomes for those affected. Keywords: Death, Sudden, Cardiac; Epidemiology; Etiology; Risk Factors; Incidence; Cardiopulmonary Resuscitation; Retrospective Studies; Oman

Gene profiling identifies commonalities in neuronal pathways in excitotoxicity : evidence favouring cell cycle re-activation in concert with oxidative stress

The fulltext of this publication will be made publicly available after relevant embargo periods have lapsed and associated copyright clearances obtained.Excitotoxicity, induced by the aberrant rise in cytosolic Ca(2+) level, is a major neuropathological process in numerous neurodegenerative disorders. It is triggered when extracellular glutamate (Glu) concentration reaches neuropathological levels resulting in dysregulation and hyper-activation of ionotropic glutamate receptor subtype (iGluRs). Even though all three members of the iGluRs, namely N-methyl-d-aspartate (NMDAR), α-amino-3-hydroxy-5-methyl-4-isoxazolepropionic acid (AMPAR) and kainate (KAR) receptors are implicated in excitotoxicity, their individual contributions to downstream signaling transduction have not been explored. In this study, we report a comprehensive description of the recruitment of cellular processes in neurons upon iGluR activation during excitotoxicity through temporal (5h, 15h, and 24h) global gene profiling of AMPA, KA, NMDA, and Glu excitotoxic models. DNA microarray analyses of mouse primary cortical neurons treated with these four pharmacological agonists are further validated via real-time PCR. Bi-model analyses against Glu model demonstrate that NMDARs and KARs play a more pivotal role in Glu-mediated excitotoxicity, with a higher degree of global gene profiling overlaps, as compared to that of AMPARs. Comparison of global transcriptomic profiles reveals aberrant calcium ion binding and homeostasis, organellar (lysosomal and endoplasmic reticulum) stress, oxidative stress, cell cycle re-entry and activation of cell death processes as the main pathways that are significantly modulated across all excitotoxicity models. Singular profile analyses demonstrate substantial transcriptional regulation of numerous cell cycle proteins. For the first time, we show that iGluR activation forms the basis of cell cycle re-activation, and together with oxidative stress fulfill the "two-hit" hypothesis that accelerates neurodegeneration