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检索条件"机构=Department of Computer Science and Engineering MIT"

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Unveiling the Invisible Threat: Using Machine Learning to Detect Credit Card Fraud 3

Unveiling the Invisible Threat: Using Machine Learning to De...

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3rd IEEE International Conference on Blockchain and Distributed Systems Security, ICBDS 2024

作者： Kinger, Shakti Kinger, Divija Thakkar, Shivam Bhake, Devashish Dr. Vishwananth Karad Mit World Peace University School of Computer Science & Engineering Department of Computer Engineering & Technolgy Pune India K J Somaiya Institute of Technology - Somaiya Vidyavihar Bombay India

ISBN: (纸本)9798350354348

Businesses, including both conventional brick-and-mortar businesses and internet firms, have faced particular hurdles as a result of the COVID-19 epidemic. Online commerce has become more prevalent as a result of the installation of movement restrictions that made it more difficult to make physical purchases. However, a rise in online credit card theft has coincided with this transformation, undermining customer confidence in online transactions. As a result, there is a compelling need to create a machine learning framework that is incredibly strong and can successfully identify almost all cases of fraudulent credit card *** research aims to identify 100% of fraudulent and scam-related credit card transactions, which is an ambitious target. Within the larger category, these fraudulent events are a sizable fraction. Our research focuses on improving data preprocessing as a solution to this. © 2024 IEEE.

关键词： Decision trees

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Student Satisfaction Index in Synchronous e-Learning a Case Study 14

Student Satisfaction Index in Synchronous e-Learning a Case ...

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14th International Conference on eLearning, eLearning 2023

作者： Kasliwal, Prabha Sunil Gunjan, Reena Shete, Virendra School of Engineering MIT Art Design and Technology University Pune India School of Electronics and Telecommunication Engineering MIT Academy of Engineering India Department of Computer Science Engineering School of Engineering MIT Art Design and Technology University Pune India Department of Electronics and Telecommunication School of Engineering MIT Art Design and Technology University Pune India

Design-Based Learning (DBL) is an educational teaching pedagogy that combines inquiry-based learning with design thinking. It aims to engage students in real-time situations and hands-on learning experiences where they apply design principles to observe the activities and identify the tasks which can be done or solved by an alternate way. DBL encourages creativity, critical thinking, collaboration, and problem-solving skills. DBL experience fosters student engagement, motivation, and a sense of accomplishment, leading to greater satisfaction with the learning process. Positive emotions, such as excitement, curiosity, and enjoyment, are often associated with higher levels of satisfaction. When students feel motivated, interested, and connected to the course, they are more likely to be satisfied and enjoy their learning experience. Effective teaching practices, supportive e-learning environments, and addressing individual student queries can all contribute to enhancing student satisfaction and overall learning outcomes. In synchronous e-learning, design thinking approach is applied during the instructional design process. In this paper the systematic review and survey aimed to explore the relationship between emotions during design thinking course in synchronous e-learning and Student Satisfaction Index (SSI). The research methodology involved a mixed-methods approach, combining quantitative data through surveys and qualitative data through interviews. The activities were designed to enhance attentiveness in class, student autonomy, active involvement in every task, opportunities for collaboration, and a sense of ownership over the learning process and maximize the learning in synchronous e-learning mode. The case study findings are investigation of activities done in the "Design Thinking" course at the First year engineering. The learner’s response to the online survey as individual and as offer valuable insights that activities conducted proved to be an effective pe

关键词： Students

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People Analyser 1

People Analyser

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1st IEEE International Conference on Data, Decision and Systems, ICDDS 2022

作者： Parikh, Ayushi Prasanth, Hridya K Kulkarni, Nilima Bhalerao, Satyam Koul, Shivam Mit School of Engineering Mit Arts Design and Technology University Department of Computer Science and Engineering Pune412201 India

ISBN: (纸本)9781665491624

People Analyzer is a platform that uses machine learning algorithm to predict personality of the user and Unity3D to illustrate scenarios where the user can interact with characters of different personalities. It is based on various platforms like Android Studio, Google colab, Unity3D etc. This platform proves to be useful for any individual who wishes to understand their own personalities as well as interact with people in different environments and also institutes and companies who want to assess their candidates. A questionnaire similar to MBTI test which classifies OCEAN personalities, that are Openness, Conscientiousness, Extraversion, Agreeableness, Neuroticism, acts as an input to a machine learning model where libraries like KMeans helps in predicting the personality. Further in our proposed work we have developed an environment where characters portraying each personality, is present for users to interact with them and understand how their reactions and actions are. © 2022 IEEE.

关键词： Learning algorithms

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Strong quantum mechanical squeezing based on nonlinear dispersive loss in semiconductor lasers

Strong quantum mechanical squeezing based on nonlinear dispe...

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2023 Conference on Lasers and Electro-Optics, CLEO 2023

作者： Pontula, Sahil Sloan, Jamison Rivera, Nicholas Soljačić, Marin Department of Physics MIT CambridgeMA United States Department of Electrical Engineering and Computer Science MIT CambridgeMA United States Department ofPhysics Harvard CambridgeMA United States

We predict that a combination of frequency-dependent outcoupling and optical non-linearity in semiconductor lasers can create sharply intensity-dependent loss that squeezes inten-sity noise far below the shot noise li... 详细信息

ISBN: (纸本)9781957171258

关键词： Semiconductor lasers

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AI Virtual Hardware

AI Virtual Hardware

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2022 IEEE Pune Section International Conference, PuneCon 2022

作者： Sravya, Tummala Bhargava, Sakshi Narendra Shravani Bodke, Rugveda Kulkarni, Nilima Mit School of Engineering Mit Arts Design and Technology University Department of Computer Science and Engineering Pune412201 India

ISBN: (纸本)9781665498975

The computer is one of the wonderful and fascinating inventions of technology and has come to significant use to humans in every sector. The existing computer technology is already advanced and modern. Even so, this proposed system will provide better ease of technology to humans. The proposed system is an Artificial Intelligence (AI) application with three combined features that are AI Virtual Mouse, Keyboard and Painter These three features (AI Virtual Mouse, Keyboard and Painter) use a common hand tracking module. Hand tracking module is a python file which has a class name Hand Detector and it contains 4 member functions that are findDistance, findHands, findPosition and fingerUp. Using this module the three features work successfully. The proposed system opens with a main window which is a GUI screen made with the help of module Tkinter. This main GUI page contains all the three features (AI Virtual Mouse, Keyboard and Painter) combined. The common libraries used for system execution are OpenCV, CVZone, numpy, autopy, mediapipe, etc. © 2022 IEEE.

关键词： Graphical user interfaces

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Defending Against Vishing Attacks: A Comprehensive Review for Prevention and mitigation Techniques

Defending Against Vishing Attacks: A Comprehensive Review fo...

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International Conference on Recent Developments in Cyber Security, ReDCySec 2023

作者： Ashfaq, Shaikh Chandre, Pankaj Pathan, Shafi Mande, Uday Nimbalkar, Madhukar Mahalle, Parikshit Information Technology Department M H Saboo Siddik College of Engineering Mumbai India Computer Science and Engineering Department MIT School of Computing MIT Art Design and Technology University Loni Kalbhor Pune India Artificial Intelligence and Data Science Department Vishwakarma Institute of Information Technology Kondhwa Pune India

ISBN: (纸本)9789819998104

Vishing attacks, or voice phishing attacks, are a type of social engineering attack in which attackers use voice communication channels, such as phone calls, to trick victims into divulging sensitive information or performing actions that compromise their security. Vishing attacks have become increasingly common in recent years and can have severe consequences for individuals, businesses, and organizations. In this review, we examine various prevention and mitigation techniques that can be used to defend against vishing attacks. We start by discussing the common tactics used by attackers and how they exploit vulnerabilities in human behaviour to achieve their goals. We then present an overview of different prevention and mitigation techniques, including education and awareness campaigns, technical solutions such as authentication mechanisms and policy-based approaches. We also evaluate the effectiveness of different techniques, highlighting their strengths and weaknesses, and provide guidance on how to implement a comprehensive vishing defence strategy. Finally, we discuss emerging trends and challenges in vishing attacks and defence, such as the use of artificial intelligence and deepfake technology, and suggest directions for future research and development. Overall, this review provides a comprehensive and up-to-date overview of the current state of vishing attacks and defence, and should be of interest to researchers, practitioners, and individuals who want to enhance their knowledge and understanding of this important topic. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.

关键词： Artificial intelligence

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Unsupervised change detection analysis using deep clustering frameworks

Unsupervised change detection analysis using deep clustering...

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2023 International Conference on Signal Processing, Computation, Electronics, Power and Telecommunication, IConSCEPT 2023

作者： Eliza Femi Sherley, S. Harshitha, K. Subetha, R.Siva Thanigaivasan, T. Prabakaran, R. Lakshmi, S.V.V. Anna University Department of Information Technology MIT Tamilnadu Chennai India Anna University Computer Centre MIT Tamilnadu Chennai India Anna University Department of Computer Science and Engineering CEG Tamilnadu Chennai India

ISBN: (纸本)9798350312126

Change detection involves quantifying temporal effects with a multi-temporal dataset. Remote sensing data have been extensively utilised for change detection in recent decades. Unsupervised learning is used to analyse satellite imagery or remote sensing data to find changes in land cover or land use over time without the use of labelled training data. Unsupervised learning is a type of machine learning that identifies patterns in data without the use of labels or prior knowledge. The general goal of change detection in remote sensing is to recognise the type of changes in specific geographic locations, and then quantify and assess changes in the regions. In this work, land change detection is analysed using various deep clustering techniques with multitemporal satellite images from different geographical locations. Two models, namely a deep-embedded clustering model, a sparse auto-encoding model are built and trained using both K-Means Clustering and FuzzyC-Means Clustering algorithms in clustering layer. The implemented models address the issue of mixed pixel clustering by using Fuzzy-C Means Clustering to determine whether region has changed over time using satellite images. The implementation is carried out in constrained environment with limited dataset and computational facilities. Deep clustering approaches necessitate high-quality data in order to produce accurate results. Poor-quality data can result in inaccurate clustering results, which can have an impact on the interpretation and application of the results. Due to cloud cover, atmospheric interference, and sensor limitations, environmental data can frequently have issues with noise, missing values, and data gaps, which can impair the quality of the clustering results and in turn, it misleads generation of changed regions. These constraints can have an impact on the quality of the data and make deep clustering approaches difficult to implement. The results of the implemented work have been assessed using

关键词： Change detection

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Scalable Image Processing with Apache Spark and Convolutional Neural Networks

Scalable Image Processing with Apache Spark and Convolutiona...

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2024 International Conference on Communication, computer sciences and engineering, IC3SE 2024

作者： Sharma, Deepika Rawat, Ruchira Alsalami, Zaid Chinthamu, Narender Varshney, Neeraj Saraswat, Manish Noida International University Department Of Computer Science And It U. P India Graphic Era Deemed To Be University Department Of Computer Science & Engineering Dehradun India College Of Technical Engineering The Islamic University Department Of Computers Techniques Engineering Najaf Iraq Mit Cto DallasTX United States Gla University Department Of Computer Engineering And Applications Mathura India Lloyd Institute Of Engineering & Technology Greater Noida India

ISBN: (纸本)9798350366846

This research presents a new framework called Spark-CNN that incorporates Apache Sparks distributed computing capabilities with Convolutional Neural Networks (CNNs) for images work processing. The results of experimental evaluation show that the proposed framework is indeed effective in handling large-scale image datasets, wherein substantial improvements have been noted regarding reduced processing time, scalability and accuracy. In Trial 1, Spark-CNN outperformed standalone -Spark (180 minutes), and all CNNs (240minutes) with an estimated processing season of 120 mins. Scale2 demonstrated the scalability of analyzing 2, sustaining constant processing times despite an increase in image dataset size from 1 million to 10 million. Further, in the third examination, Spark-CNN got an accuracy of 89%, surpassing standalone CNNs with a percentage score of 85%. Comparing the Trial 4 with Horovod-CNN, Spark-CNN showed a cutthroat processing time (120 minutes) and enhanced accuracy (89% vs., Trial 5 presented the influence of cluster setup on Spark-CNN revealing reduced execution times and better precision with growing numbers of executors beneficiaries per agent. These results establish Spark-CNN as a robust and effective solution to address scalable image processing challenges across different areas. © 2024 IEEE.

关键词： Convolution

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IoT and wireless sensor network -Based Three- Tier Architecture for Continuous Cardiac Health Monitoring and Alert System 4

IoT and wireless sensor network -Based Three- Tier Architect...

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4th International Conference on Expert Clouds and Applications, ICOECA 2024

作者： Gorde, Swapnil Gulabrao Singh, Archana Tamilselvi, M. Ravivarman, G. Rajendiran, M. Maranan, Ramya MIT College of Management MIT Art Design & Technology University Pune India Private University Maharashtra India Roever Engineering College Department of Computer Science and Engineering Tamil Nadu Perambalur India Karpagam Academy of Higher Education Department of Electrical and Electronics Engineering Tamil Nadu Coimbatore India Panimalar Engineering College Department of Computer Science and Engineering Poonamallee Tamil Nadu Chennai India Saveetha School of Engineering SIMATS Department of Research and Innovation Tamil Nadu Chennai India

ISBN: (纸本)9798350385793

This study suggests a powerful Three- Tier Architecture that combines machine learning models, Internet of Things (IoT) devices, and sophisticated sensors to provide continuous cardiac health monitoring. A range of physiological data is provided by the sensors in the Data Collection Tier, which measure blood pressure, temperature, acceleration, and electrocardiogram (ECG). In the Data Processing Tier, a local server applies complex algorithms to efficiently assess the gathered data and provide insightful outcomes. By providing easy access, authentication, and alarm production, the Application Tier acts as the interface and guarantees efficient contact with healthcare providers. The unique aspect of the system is the integration of machine learning models, which were trained on recorded data for predictive analysis. k-Nearest Neighbors (KNN), Artificial Neural Networks (ANN), Decision Trees (DT), and Recurrent Neural Networks (RNN) are some examples of these models. Impressive accuracy levels are seen in the results, with ANN leading at 96.44% The measures of accuracy, precision, recall, and F1 score all demonstrate the algorithms' capacity to anticipate possible health risks, which supports the models' effectiveness. This proactive paradigm in healthcare is made possible by the comprehensive approach of continuous heart health monitoring, which enables prompt interventions and customized treatment strategies. This study increases our knowledge of the dynamics of cardiovascular health and the rapidly growing field of health care technology by demonstrating the synergy of sensor-based monitoring, IoT connectivity, and machine learning analytics in a unified architecture. In the end, patient care is improved. © 2024 IEEE.

关键词： Blood pressure

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Thermometer: Towards Universal Calibration for Large Language Models 41

Thermometer: Towards Universal Calibration for Large Languag...

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41st International Conference on Machine Learning, ICML 2024

作者： Shen, Maohao Das, Subhro Greenewald, Kristjan Sattigeri, Prasanna Wornell, Gregory Ghosh, Soumya Department of Electrical Engineering and Computer Science Massachusetts Institute of Technology Cambridge United States MIT-IBM Watson AI Lab IBM Research United States

We consider the issue of calibration in large language models (LLM). Recent studies have found that common interventions such as instruction tuning often result in poorly calibrated LLMs. Although calibration is well-explored in traditional applications, calibrating LLMs is uniquely challenging. These challenges stem as much from the severe computational requirements of LLMs as from their versatility, which allows them to be applied to diverse tasks. Addressing these challenges, we propose THERMOMETER, a calibration approach tailored to LLMs. THERMOMETER learns an auxiliary model, given data from multiple tasks, for calibrating a LLM. It is computationally efficient, preserves the accuracy of the LLM, and produces better-calibrated responses for new tasks. Extensive empirical evaluations across various benchmarks demonstrate the effectiveness of the proposed method. Copyright 2024 by the author(s)

关键词： Calibration

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