Informatics Publishing Limited

Kunjali Pawar ¹, Madhuri Patil ²

Affiliations
1 Dr. D.Y. Patil School of Engineering and Technology, Pune, IN
2 Dr. D. Y. Patil School of Engineering and Technology, Pune, IN

Abstract

Statistical spam filters are vulnerable to the adversarial attacks. An e-mail is classed as spam if a minimum of one instance within the corresponding bag is spam, and as legitimate if all the instances in its square measure legitimate. These systems based on the design methods and classical methods which do not take into account adversarial settings. In this paper, the security evaluation framework is proposed to avoid the detection in the Spam filtering with the help of Multiple Instance Logistic Regression i.e. MILR. In addition to define the model of Adversary with the guidelines for simulating attack scenarios The principal theme of the framework is to develop an enhanced model which anticipates the attacks by utilizing a data distribution.

Keywords

Adversary, Multiple Instance Learning, Multiple Instance Logistic Regression (MILR), Spam Filtering.

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Kunjali Pawar , Madhuri Patil

Abstract

The Pattern classification system is an annex of the Machine learning on which the focal point is the recognition of patterns in the data. In case adversarial applications use, for example Spam Filtering, the Network Intrusion Detection System (NIDS), Biometric Authentication, the pattern classification systems are used. Spam filtering is and adversary application in which data can be employed by humans to attenuate perspective operations. To appraise the security issue related Spam Filtering voluminous machine learning systems. We presented a framework for the experimental evaluation of the classifier security in an adversarial environments, that combines and constructs on the arms race and security by design, Adversary modelling and Data distribution under attack. Furthermore, we presented a MILR classifier with SVM and LR classifier for classification to categorize among legitimate and spam emails on the basis of their textual content.

Keywords

Adversary Model, Pattern Classification, Security Evaluation, Spam Filtering.

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Beena Joshi ¹, Gazala Hasan ², Gulrez Tyebkhan ³, Shobha Udipi ⁴, Anushree Patil ⁵, Duru Shah ⁶, Madhuri Patil ⁷, Roda Dalal ⁸, Rama Vaidya ⁹

Affiliations
1 Scientist F, Department of Operational Research, ICMR-National Institute for Research in Reproductive Health, Mumbai, IN
2 Senior Research Fellow, Department of Operational Research, ICMR-National Institute for Research in Reproductive Health, Mumbai, IN
3 Hon Consultant Dermatologist, Medical Research Centre-Kasturba Health Society; Hon Dermatologist, Saifee Hospital, Mumbai, IN
4 Hon Director Integrative Nutrition and Ayurceuticals, Medical Research Centre-Kasturba Health Society, Mumbai, IN
5 Scientist D, Department of Clinical Research, ICMR- National Institute for Research in Reproductive Health, Mumbai, IN
6 President, PCOS Society, India. Scientific Director, Gynaecworld Assisted Fertility Center, Mumbai, IN
7 Vice-President, PCOS Society, India; Clinical Director, Dr. Patil’s Fertility and Endoscopy Clinic, Bengaluru, IN
8 Hon Editorial Consultant and Communications Associate, Medical Research Centre-Kasturba Health Society, Mumbai, IN
9 Director, Division of Endocrine and Metabolic Disorders, Medical Research Centre-Kasturba Health Society, Mumbai, IN

Abstract

Background: Polycystic Ovary Syndrome (PCOS) diagnosis is very complex and due to overlapping presentation of symptoms, women with PCOS may approach various practitioners, who might be using varied definitions to diagnose and manage PCOS.

Objectives: To assess knowledge about PCOS diagnosis and management by Dermatologists, Gynecologists, Endocrinologists and Family Physicians (FPs).

Methods: This cross-sectional study was done among 529 health care providers (HCPs) practicing in Mumbai metropolitan region using a convenient sample. A self-administered pretested questionnaire was filled by the HCPs after obtaining informed consent. Pearson’s Chi-square test and Analysis of Variance (ANOVA) was used to study the differences within each group.

Results: All HCPs reported that women with PCOS consulted them with a varied spectrum of symptoms. Among FPs, Allopaths had better knowledge about diagnosis and Homeopaths were more confident about PCOS management. Among Specialists, Endocrinologists were more stringent in diagnosing cases as per defined criteria, along with screening and comprehensive management of the metabolic syndrome. Although the need for multidisciplinary management was perceived, in practice holistic management was not common.

Conclusions: This study reveals the need for training and disseminating algorithms for comprehensive PCOS management across disciplines. It highlights women’s preferences in seeking health care for symptom complex of PCOS. These findings would help health policymakers to develop community-based awareness modules and India-specific management guidelines for early screening and a continuum of care for PCOS patients.

Keywords

Polycystic ovary syndrome, healthcare providers, knowledge assessment, practice, management

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