Framework to predict NPA/Willful defaults in corporate loans: a big data approach

Growth and development of the economy is dependent on the banking system. Bad loans which are Non-Performing Assets (NPA) are the measure for assessing the financial health of the bank. It is very important to control NPA as it affects the profitability, and deteriorates the quality of assets of the bank. It is observed that there is a significant rise in the number of willful defaulters. Hence systematic identification, awareness and assessment of parameters is essential for early prediction of willful default behavior. The main objective of the paper is to identify exhaustive list of parameters essential for predicting whether the loan will become NPA and thereby willful default. This process includes understanding of existing system to check NPAs and identifying the critical parameters. Also propose a framework for NPA/Willful default identification. The framework classifies the data comprising of structured and unstructured parameters as NPA/Willful default or not. In order to select the best classification model in the framework an experimentation is conducted on loan dataset on big data platform. Since the loan data is structured, unstructured component is incorporated by generating synthetic data. The results indicate that neural network model gives best accuracy and hence considered in the framework

Security framework for cloud based electronic health record (EHR) system

Health records are an integral aspect of any Hospital Management System. With newer innovations in technology, there has been a shift in the way of recording health information. Medical records which used to be managed using various paper charts have now become easier to organize and maintain, thereby increasing the efficiency of medical staff. The Electronic Health Records (EHR) System is becoming a high-tech medical management technology developed for the economic or emerging economic countries like India. In a national health system, the EHR integrates the Electronic Medical Records (EMR) in all collaborating hospitals through different networks. EHR gives healthcare professionals a way to share and manage patient data quickly and effectively. Due to the mass storage of confidential patient data, healthcare organizations are considered as one of the most targeted sectors by intruders. This paper proposes a security framework for EHR system, which takes into consideration the integrity, availability, and confidentiality of health records. The threats posed to the EHR system are modeled by STRIDE modeling tool, and the amount of risk is calculated using DREAD. The paper also suggests the security mechanism and countermeasures based on security standards, which can be utilized in an EHR environment. The paper shows that the utilization of the proposed methods effectively addresses security concerns such as breach of sensitive medical information

The International Workshop on Osteoarthritis Imaging Knee MRI Segmentation Challenge: A Multi-Institute Evaluation and Analysis Framework on a Standardized Dataset

Purpose: To organize a knee MRI segmentation challenge for characterizing the semantic and clinical efficacy of automatic segmentation methods relevant for monitoring osteoarthritis progression. Methods: A dataset partition consisting of 3D knee MRI from 88 subjects at two timepoints with ground-truth articular (femoral, tibial, patellar) cartilage and meniscus segmentations was standardized. Challenge submissions and a majority-vote ensemble were evaluated using Dice score, average symmetric surface distance, volumetric overlap error, and coefficient of variation on a hold-out test set. Similarities in network segmentations were evaluated using pairwise Dice correlations. Articular cartilage thickness was computed per-scan and longitudinally. Correlation between thickness error and segmentation metrics was measured using Pearson's coefficient. Two empirical upper bounds for ensemble performance were computed using combinations of model outputs that consolidated true positives and true negatives. Results: Six teams (T1-T6) submitted entries for the challenge. No significant differences were observed across all segmentation metrics for all tissues (p=1.0) among the four top-performing networks (T2, T3, T4, T6). Dice correlations between network pairs were high (>0.85). Per-scan thickness errors were negligible among T1-T4 (p=0.99) and longitudinal changes showed minimal bias (<0.03mm). Low correlations (<0.41) were observed between segmentation metrics and thickness error. The majority-vote ensemble was comparable to top performing networks (p=1.0). Empirical upper bound performances were similar for both combinations (p=1.0). Conclusion: Diverse networks learned to segment the knee similarly where high segmentation accuracy did not correlate to cartilage thickness accuracy. Voting ensembles did not outperform individual networks but may help regularize individual models.Comment: Submitted to Radiology: Artificial Intelligence; Fixed typo

A WSN based Environment and Parameter Monitoring System for Human Health Comfort: A Cloud Enabled Approach

The number and type of sensors measuring physical and physiological parameters have seen dramatic increase due to progress in the MEMS and Nano Technology. The Wireless Sensor Networks (WSNs) in turn is bringing new applications in environment monitoring and healthcare in order to improve the quality of service especially in hospitals. The adequacy of WSNs to gather critical information has provided solution but with limited storage, computation and scalability. This limitation is addressed by integrating WSN with cloud services. But, once the data enters the cloud the owner has no control over it. Hence confidentiality and integrity of the data being stored in the cloud are compromised. In this proposed work, secure sensor-cloud architecture for the applications in healthcare is implemented by integrating two different clouds. The sink node of WSN outsources data into the cloud after performing operations to secure the data. Since the SaaS and IaaS environments of Cloud Computing are provided by two different cloud service providers (CSPs), both the CSPs will not have complete information of the architecture. This provides inherent security as data storage and data processing are done on different clouds

Design and implementation of aquaculture resource planning using underwater sensor wireless network

Aquaculture is a globally fast-growing food sector, and its economic significance is increasing consistently. Currently, site selection for aquaculture is based on remote sensing and Geographic Information Systems (GIS), which provide infrastructure information. The information regarding environmental factors such as water properties are measured manually. There is a requirement for building a database on water properties at various river sites that are favourable for aquaculture. Underwater wireless sensor network (WSN) systems are being developed to measure various water parameters for aquaculture monitoring, and the mobility of sensor is considered for node deployment to increase the accuracy of measured water parameters. The present system integrates WSN nodes to extract the data and connect to cloud data server. A centralized database is created to store the information/data, which is analysed using clustering k-means algorithm. The proposed work presents an aquaculture resource planning (ARP), a SaaS (Software-as-a-Service) application; the users can access information using the application “Aquaward” and analyse various river sites that can be used for aquaculture development

Automatic Facial Expression Recognition Using DCNN

AbstractFace depicts a wide range of information about identity, age, sex, race as well as emotional and mental state. Facial expressions play crucial role in social interactions and commonly used in the behavioral interpretation of emotions. Automatic facial expression recognition is one of the interesting and challenging problem in computer vision due to its potential applications such as Human Computer Interaction(HCI), behavioral science, video games etc.In this paper, a novel method for automatically recognizing facial expressions using Deep Convolutional Neural Network(DCNN) features is proposed. The proposed model focuses on recognizing the facial expressions of an individual from a single image. The feature extraction time is significantly reduced due to the usage of general purpose graphic processing unit (GPGPU). From an evaluation on two publicly available facial expression datasets, we have found that using DCNN features, we can achieve the state-of-the-art recognition rate

Web Log Pre-processing and Analysis for Generation of Learning Profiles in Adaptive E-learning

Adaptive E-learning Systems (AESs) enhance the efficiency of online courses in education by providing personalized contents and user interfaces that changes according to learner’s requirements and usage patterns. This paper presents the approach to generate learning profile of each learner which helps to identify the learning styles and provide Adaptive User Interface which includes adaptive learning components and learning material. The proposed method analyzes the captured web usage data to identify the learning profile of the learners. The learning profiles are identified by an algorithmic approach that is based on the frequency of accessing the materials and the time spent on the various learning components on the portal. The captured log data is pre-processed and converted into standard XML format to generate learners sequence data corresponding to the different sessions and time spent. The learning style model adopted in this approach is Felder-Silverman Learning Style Model (FSLSM). This paper also presents the analysis of learner’s activities, preprocessed XML files and generated sequences

Prediction of Learner’s Profile based on Learning Styles in Adaptive E-learning System

The major requirement of present e-learning system is to provide a personalized interface with adaptiveness. This is possible to provide by analyzing the learning behaviors of the learners in the e-learning portal through Web Usage Mining (WUM). In this paper, a method is proposed where the learning behavior of the learner is captured using web logs and the learning styles are categorized according to Felder-Silverman Learning Style Model (FSLSM). Each category of FSLSM learner is provided with the respective content and interface that is required for the learner to learn. Fuzzy C Means (FCM) algorithm is used to cluster the captured data into FSLSM categories. Gravitational Search based Back Propagation Neural Network (GSBPNN) algorithm is used to predict the learning styles of the new learner. This algorithm is a modification of basic Back Propagation Neural Network (BPNN) algorithm that calculates the weights using Gravitation Search Algorithm (GSA). The algorithm is validated on the captured data and compared using various metrics with the basic BPNN algorithm. The result shows that the performance of GSBPNN algorithm is better than BPNN. Based on the identified learning style, the adaptive contents and interface can be provided to the learner

Mitigation of insider and outsider DoS attack against signature based authentication in VANETs

International audienc