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A behavior change support system to help employees protect organizational information security 3

A behavior change support system to help employees protect o...

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3rd International Workshop on Behavior Change Support Systems, BCSS 2015 - co-located with the 10th International Conference on Persuasive Technology, PERSUASIVE 2015

作者： Busch, Marc Patil, Sameer Regal, Georg Hochleitner, Christina Fröhlich, Peter Tscheligi, Manfred AIT-Austrian Institute of Technology GmbH Austria Department of Computer Science and Engineering Polytechnic School of Engineering New York University United States

Digital information is an important asset in the corporate world. Organizations typically devise policies and guidelines to help employees protect the security of such information. Complying with these policies can often be confusing and difficult and may obstruct the task at hand, thus potentially leading employees to circumvent or ignore these policies. Commercial technology and training programs to mitigate this issue suffer from various shortcomings. To overcome these limitations, we present a Behavior Change Support prototype that implements six persuasive features: Security Points, Security Quiz, Challenges, Statistics, Personalization, and Risk Communication. Evaluation of the prototype established persuasive security as a promising approach for influencing user attitudes and behaviors regarding secure work practices. We apply the findings to offer suggestions for how the six persuasive features could be further enhanced.

关键词： Security of data

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Feature Selection for Peer-to-Peer Lending Default Risk Using Boruta and mRMR Approach 20

Feature Selection for Peer-to-Peer Lending Default Risk Usin...

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20th IEEE India Council International Conference, INDICON 2023

作者： Anusha Hegde, H. Bhowmik, Biswajit National Institute of Technology Karnataka Ishwarchandra Vidyasagar Ait Lab Brics Lab Department of Computer Science and Engineering Mangalore India

ISBN: (纸本)9798350305593

Peer-to-peer (P2P) lending in the Financial Technology (FinTech) sector is increasingly gaining attention from people where the online platform enables lenders to offer loans to borrowers. The platform as a much needed mechanism targets to reduce the risk of default and increase profitability for lenders and the platform. Each loan record maintains a variety of attributes, including details about the loan, the borrower, their credit history, their finances, and public data. If all the features are considered, the performance of the lending platform may decline. Finding the necessary characteristics more helpful in forecasting loan default is a concern. This paper investigates essential features of the P2P lending mechanism with adequate performance in lending money to individuals or businesses. We employ two algorithms to find the pertinent features: Boruta and Max-Relevance and Min-Redundancy (mRMR). Further, we use two classifiers-decision tree and XGBoost that exercise the selected elements to predict the loan defaults. © 2023 IEEE.

关键词： Accuracy Boruta Algorithm Feature Selection FinTech mRMR Algorithm P2P Lending

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Modified WINKLER Model on SAP 2000 V.19.2.1 : Building Fixed & Flexible Base 2

Modified WINKLER Model on SAP 2000 V.19.2.1 : Building Fixed...

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2nd Asian Conference on Innovation in Technology, ASIANCON 2022

作者： Sharma, Sugandha Jindal, Harsh Jha, Atul Chauhan, Vandana Jain, Amit Singh, Vaibhav Chandigarh University Department of Computer Science & Engineering Punjab Mohali India Chandigarh University Department of Computer Science & Engineering- Ait Punjab Mohali India Chandigarh University University Institute of Computing Punjab Mohali India

ISBN: (数字)9781665468510

ISBN: (纸本)9781665468510

Now a day's irregular building contribute to modern infrastructure. It is important to know the behavior of building subjected to irregularity. Irregularity deals with configuration means deals with arrangement of the parts of structure. If the arrangement of parts of structure are irregularthen how it effects the seismic analysis of structure. Therebuilding configuration types are regular, horizontal and other is vertical. Configuration. Horizontal and Vertical configuration deals with how the parts are arranged horizontally and vertically. The configuration of structure effects the building during sever earthquake and causes damage. Many of the previous damage shows us that it is important to study about irregular structures. The damage happens even with good design. The building is analyzed with the base known as fixed in the available study whereas, in reality, the building rests on the soil. The software chosen should analyze the building considering soil structure interaction and nonlinear *** this study a. 3D model is analyzed using SAP 2000V19.2.1software. Two models are considered for analysis one with a base fixed and the other with a base of flexible soil (MWM). Analysis is performed on a 10 storey, 3D vertically irregular building. The building with fixed base and with flexible soil base(MWM) is analyzed for nonlinear analysis. Results obtained for irregularly shaped buildings are compared for fixed and flexible foundations. Note the differences in displacement, base force, hinge formation and duration. Buildings that design improved returns may be selected for further use. © 2022 IEEE.

关键词： Nonlinear analysis

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Enhancing Movie Recommendation Systems with MapReduce Genetic Algorithms: Addressing Scalability and Accuracy Challenges

Enhancing Movie Recommendation Systems with MapReduce Geneti...

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2024 International Conference on Smart Electronics and Communication Systems, ISENSE 2024

作者： Patidar, Priyansh Posa, Sathwik Venkata Girish, K. Rao, Shaifali Bhowmik, Biswajit National Institute of Technology Karnataka Ishwarchandra Vidyasagar AIT Lab BRICS Lab Department of Computer Science and Engineering Mangalore India

ISBN: (纸本)9798331528126

In the world of big data, the efficacy of movie recommendation systems is crucial for personalizing user experiences in digital entertainment. Traditional methods, including collaborative and content-based filtering, often encounter limitations such as data sparsity, cold start problems, and scalability issues. This paper introduces a novel approach that integrates MapReduce technology with Genetic Algorithms (GAs) to address these chal-lenges. Utilizing the Hadoop framework, our MapReduce Genetic Algorithm (MRGA) efficiently processes extensive datasets by distributing tasks across a cluster of machines. The genetic algorithm component optimizes recommendation accuracy through advanced techniques like selection, crossover, and mutation. Our experimental results, based on the MovieLens 100K dataset, demonstrate that the MRGA approach outperforms traditional collaborative filtering methods in terms of recommendation accuracy and scalability. By leveraging MapReduce's distributed computing power and the GA's optimization capabilities, this research offers a robust solution to improve movie recommendations and handle large-scale data efficiently. © 2024 IEEE.

关键词： MapReduce

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Monkeypox Outbreak and Recent Advancements

Monkeypox Outbreak and Recent Advancements

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2023 IEEE International Conference on Distributed Computing, VLSI, Electrical Circuits and Robotics, DISCOVER 2023

作者： Gavaniya, Dixitkumar Bhowmik, Biswajit National Institute of Technology Karnataka Ishwarchandra Vidyasagar Ait Lab Brics Lab Department of Computer Science and Engineering Mangalore India

ISBN: (纸本)9798350340129

With the fast spreading of the Monkeypox disease worldwide, it would become an alarming situation for countries around the globe. The case might become worsen, leading to another global pandemic. Therefore, early detection and analysis of Monkeypox from various points of view become necessary. This paper provides a detailed study of the recent spreading of the Monkeypox disease. The study involves multiple perspectives like information about Monkeypox disease, its origin, general symptoms that a person affected with it can experience, its global impact, and recent developments in the state-of-the-art. © 2023 IEEE.

关键词： Diagnosis

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Image Fusion of Optical and Microwave Satellite Imagery to Develop Soil Moisture Maps 9

Image Fusion of Optical and Microwave Satellite Imagery to D...

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9th IEEE International Women in engineering (WIE) Conference on Electrical and computer engineering, WIECON-ECE 2023

作者： Kaur, Ravneet Maini, Raman Tiwari, Reet Kamal Punjabi University Department of Computer Science Engineering Patiala India AIT Chandigarh University India Indian Institute of Technology Department of Civil Engineering Ropar India

ISBN: (纸本)9798350319651

Remote Sensing is an efficient solution to acquire the earth's surface from a distance in an electromagnetic spectrum. The data is generally acquired using optical or microwave satellite sensors. Each sensor has its benefits and challenges which may limit the applicability of the potential of the sensor. Generally, the optical sensors acquire multispectral information in the presence of sunlight and may be affected due to the presence of clouds. On the other hand, microwave sensors acquire the data even in the absence of light but the resolution is very limited. Moreover, soil moisture is one of the essential components to assess crop yield predictions. In this article, soil moisture maps have been generated with the fusion of optical sensor imagery and microwave sensor imagery using the nearest neighbour-based approach. The dataset was acquired over a part of India (Punjab State) using the optical sensor i.e., MODIS and microwave sensor i.e., SCATSAT-1. The experimental outcomes confirmed that artificial neural network (ANN) based classified maps have been generated with an accuracy of 94.92% (Kappa coefficient 0.9891) achieved from MODIS and SCATSAT-1 satellite datasets. The study allows the monitoring of the crop growth, crop yield estimation and fertility rate of the soil. © 2023 IEEE.

关键词： Neural networks

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Early Detection of Melanoma Skin Cancer Recognition using CNN 2

Early Detection of Melanoma Skin Cancer Recognition using CN...

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2nd International Conference on Automation, Computing and Renewable Systems, ICACRS 2023

作者： Khan, Akash Hasan, Anas Rout, Ashish Nadar, Diwakar Aggarwal, Alankrita AIT-CSE Chandigarh University Punjab Gharuan India Department of Computer Science & Engineering IT Chandigarh University Punjab Mohali India

ISBN: (纸本)9798350340235

This is research work intends to revolutionize melanoma diagnosis by developing a user-friendly web-based application. Leveraging the Flask framework and Convolutional Neural Network (CNN) architecture, the application enables users to submit images of skin lesions for analysis. Through advanced image processing, the CNN model assesses these images for potential melanoma indicators, providing rapid and accurate risk assessments. The interface prioritizes usability, guiding users through the process seamlessly. By bridging the gap between medical expertise and accessibility, this innovation has the potential to significantly impact early detection rates, ultimately leading to timely interventions, improved patient outcomes, and heightened awareness of skin health. © 2023 IEEE.

关键词： Benign Convolutional Neural Network (CNN) Diagnosis Early detection Flask Framework Image Analysis Malignant Melanoma Skin Cancer

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Exploiting the domain of Deep Learning for Brain Tumor Classification: A Comprehensive Overview

Exploiting the domain of Deep Learning for Brain Tumor Class...

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2024 International Conference on Computational Intelligence and Computing Applications, ICCICA 2024

作者： Verma, Vikram Aggarwal, Alankrita Chandigarh University Cse Department Mohali India Cse Department Piet Samlakha Panipat India Ait Department Of Computer Science And Engineering Mohali India

ISBN: (数字)9798350306446

ISBN: (纸本)9798350306446

Brain tumors (BT) constitute a formidable challenge globally, affecting both adults and children and exhibiting poor survival rates. Their diverse types, characterized by location, texture, and morphology, underscore the importance of accurate classification to guide appropriate treatment decisions early on. Magnetic resonance imaging (MRI) emerges as a pivotal modality for BT differentiation. However, manual analysis of MRI data is time-intensive and prone to human errors, necessitating the development of automated computer-Assisted Diagnosis (CAD) systems. Recent strides in image classification, particularly deep learning (DL) hold promise in this realm. To this end, we employed Convolutional Neural Networks (CNN)s to devise a novel model comprising multiple layers for MRI brain tumor classification. Through experimental validation across three datasets, our approach demonstrated compelling performance compared to existing methodologies. © 2024 IEEE.

关键词： Magnetic resonance imaging

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Advancements in Credit Scoring, Profit Scoring, and Portfolio Optimization for P2P Lending 3

Advancements in Credit Scoring, Profit Scoring, and Portfoli...

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3rd International Conference on Communication, Control, and Intelligent Systems, CCIS 2024

作者： Nayaka, Premkumar Hegde, Anusha Bhowmik, Biswajit National Institute of Technology Karnataka Ishwarchandra Vidyasagar Ait Lab Brics Laboratory Department of Computer Science and Engineering Surathkal India National Institute of Technology Karnataka Brics Laboratory Department of Computer Science and Engineering Surathkal India

ISBN: (纸本)9798331528201

The Peer-to-peer (P2P) lending platform allows borrowers to connect directly with lenders outside traditional banking systems. Therefore, for the sustainability of these platforms, they must accurately assess the credit risk and profitability of the loans. Various credit scoring techniques, including Logistic Regression, neural networks, and ensemble methods, can be used to estimate the likelihood of borrower default. It is imperative to analyze the profit the lenders generated and enhance the credit scoring so that the investors face minimum loss. Once the profit analysis is done, then it is crucial to advise the investors about the portfolio of loans. This paper presents recent credit scoring, profit scoring, and portfolio optimization trends for P2P lending. We highlight the significant issues in incorporating machine learning models into credit scoring systems. The analysis emphasizes the need for a data-driven approach to perfecting lending practices, thus benefiting both borrowers and investors in the rapidly changing P2P landscape. © 2024 IEEE.

关键词： Decentralized finance

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Money Laundering Detection in Banking Transactions using RNNs and Hybrid Ensemble 15

Money Laundering Detection in Banking Transactions using RNN...

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15th International Conference on Computing Communication and Networking Technologies, ICCCNT 2024

作者： Girish, K.K. Bhowmik, Biswajit National Institute of Technology Karnataka Ishwarchandra Vidyasagar Ait Lab Brics Lab Department of Computer Science and Engineering Mangalore Surathkal India

ISBN: (纸本)9798350370249

The financial sector has witnessed significant transformations due to the emergence of financial technology (FinTech), transitioning from traditional paperbased processes to a dynamic digital ecosystem. Despite the industry's advancements driven by FinTech innovations, concerns persist, particularly regarding financial fraud, notably money laundering. Perpetrators exploit modern technologies to launder illicitly obtained funds, posing a global threat to economies. Effective detection mechanisms for money laundering are crucial. This paper introduces a novel approach utilizing a recurrent neural network (RNN) for detecting money laundering in banking transactions. The proposed framework exercises standalone RNN models such as LSTM, GRU, BiLSTM, and stacked RNN models for the detection. Additionally, the effectiveness of hybrid ensemble models combining RNNs with XGBoosts is investigated. The evaluation achieves standard performance metrics, with the stacked RNN model achieving 92% accuracy. Surpassing it, the ensemble model achieves an impressive 95%. These results underscore the superiority of hybrid ensemble models over standalone RNNs, particularly in accurately detecting money laundering activities. © 2024 IEEE.

关键词： Banking Deep Learning FinTech Fraud Detection Money Laundering Recurrent Neural Network

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