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

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A Study to Detect Emotions from Twitter Text Using Machine Learning Algorithms 2

A Study to Detect Emotions from Twitter Text Using Machine L...

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2nd International Conference for Innovation in Technology, INOCON 2023

作者： Anusha Shenoy, Savitha A Harish, S.V. MIT Manipal Department of Computer Science and Engineering Udupi India SMVITM Bantakal Department of Computer Science and Engineering Udupi India

ISBN: (纸本)9798350320923

One of the main factor contributing to mental illness, which has been linked to an increased risk of dying young is depression. Additionally, it significantly contributes to suicide ideation. Although there are many underlying reasons of depression, social networking sites play a key part in raising the likelihood of depression. In recent years social media has become the integral part of our daily lives. User reflects his internal life in the content he shares in his social media platform like twitter. People share happy incidents, joyful memories and sad moments through tweets. Thus it is possible to forecast depression in people using Twitter data. Various machine learning techniques have been employed to analyze these data. The algorithms employed are Naïve Bayes and Logistic Regression. Those algorithms will produce intended outcomes. © 2023 IEEE.

关键词： Sentiment analysis

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An Optimized Round Robin Central Processing Unit Scheduling Algorithm with Dynamic Time Quantum 2

An Optimized Round Robin Central Processing Unit Scheduling ...

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2nd International Conference on Smart Technologies for Smart Nation, SmartTechCon 2023

作者： Nayak, Smitha Sharma, Yogesh Kumar Nayak, Ritvik Department of Computer Science Muscat College Muscat Oman Department of Computer Science & Engineering Andhra Pradesh India Department of Computer Science & Engineering MIT Manipal India

ISBN: (纸本)9798350305418

Traffic signals ensure safe driving and regulated flow of traffic at road intersections. With increasing number of cars on the roads, it has become very essential to regulate traffic so that mobility of people residing in Oman is much smoother. Controlling road traffic has been a very challenging task especially at the intersections. Conventionally traffic signal control systems are scheduled in such a way that the vehicles at an intersection move in a fixed pre-timed slot manner. This fixed pre-timed controller approach fails to minimize the waiting time of vehicles at the traffic intersections as it does not sense the queue size of vehicles at the traffic signal. Many a times the traffic flow is not uniform, sometimes traffic is more from a particular direction or probably an accident has occurred in that lane which leads to increasing size of the queue and also the waiting time for people in that lane of intersecting road. In such cases it is often seen The Royal Oman Police (ROP) taking over the traffic signals and controlling the flow of traffic for smooth flow of traffic. To overcome this problem an adaptive and intelligent traffic signal control system is proposed in such a way that a traffic signal controller is installed with a wireless sensor which works on the basis of checking the queue size of vehicles in a particular lane and switches to green or stays in green for longer duration for that lane. It also sends an alert to the telecom companies and the radio channels in Oman to inform the slow moving traffic news to the people of Oman. © 2023 IEEE.

关键词： Apache Tomcat dynamic quantum localized sensors microcontrollers Priority Scheduling REST web service Round Robin RTOS Scheduling

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A Comprehensive Survey and Evaluation of MediaPipe Face Mesh for Human Emotion Recognition 8

A Comprehensive Survey and Evaluation of MediaPipe Face Mesh...

引用

8th IEEE International Conference on Computing, Communication, Control and Automation, ICCUBEA 2024

作者： Jakhete, Sumitra A. Kulkarni, Nilima Design and Technology University Mit Soc Mit Art Department of Computer Science and Engineering Pune India Pune Institute of Computer Technology Pune India

ISBN: (纸本)9798350391770

Human Emotion Recognition (HER) has become a crucial application within computer vision and artificial intelligence, facilitating advancements in human-computer interaction, robotics, and other domains. Among the myriad approaches, Facial Expression Recognition (FER) systems stand out as a prominent method for automated emotion detection. In this paper, we conduct an extensive survey of the diverse techniques, modalities, and datasets employed for emotion recognition, with primary emphasis on non-verbal cues, and facial expressions. We delve into the significance of facial landmark detection, a critical task in numerous applications such as face recognition and emotion detection. Using real-time data frames, we focus on leveraging the capabilities of the open-source MediaPipe Face Mesh, a real-time face geometry framework capable of estimating 468 3D face landmarks, to enhance the accuracy and efficiency of landmark detection. We evaluate its performance with two other open-source models namely DLIB and OpenPose and check their efficacy. From the results, it is observed that MediaPipe Face Mesh offers real-time performance and high accuracy, making it suitable for real-world applications with stringent latency requirements. © 2024 IEEE.

关键词： Mesh generation

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Web Based Book Recommendation System Using Collaborative Filtering 5

Web Based Book Recommendation System Using Collaborative Fil...

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5th International Conference on Emerging Smart Computing and Informatics, ESCI 2023

作者： Mankar, Ketaki Pawar, Shruti Agarwal, Harsh Sangale, Tejas Kulkarni, Smita Mit Academy of Engineering Department of Computer Science Engineering Pune India Mit Academy of Engineering Department of Electronics and Electrical Engineering Pune India

ISBN: (纸本)9781665475242

Recommender systems are tools that help end users recommend products and obtain information about their preferences by going online. Today's online bookstores compete with each other in a variety of ways. One of the most powerful ways to efficiently increase revenue by attracting customers is a referral system. This study offers a clear, understandable method for recommending books that aids readers in selecting the best book. The proposed methodology works on training of database and feedback to provide meaningful information that helps users make decisions. In this paper recommendation system is developed by using collaborative filtering method. The machine learning (ML) model KNN is proposed to categorize the books as per user preferences. The overall architecture of the proposed system is introduced and its implementation is demonstrated. © 2023 IEEE.

关键词： Recommender systems

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Genre Classification of Movie Trailers Using Audio and Visual Features: A Comparative Study of Machine Learning Algorithms 3rd

Genre Classification of Movie Trailers Using Audio and Visua...

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3rd International Semantic Intelligence Conference, ISIC 2023

作者： Vanarote, Viresh Chandre, Pankaj Mande, Uday Shafi, Pathan Mohd Dhotre, Dhanraj Nimbalkar, Madhukar Department of Computer Science and Engineering MIT School of Computing MIT Art Design and Technology University Loni Kalbhor Pune India

ISBN: (纸本)9789819773558

Movie trailers are a crucial marketing tool for the film industry and are often used to generate audience interest and anticipation. Automatic genre classification of movie trailers can assist filmmakers in targeting their intended audience and help viewers in deciding which films to watch. This research paper aims to investigate the effectiveness of various machine learning algorithms for the classification of movie genres based on audio and visual features extracted from movie trailers. We compare the performance of several classifiers, including Support Vector Machines (SVM), Random Forest (RF), Naive Bayes (NB), and K- Nearest Neighbors (KNN) on a dataset of movie trailers belonging to five different genres—action, comedy, drama, horror, and thriller. We extract both audio and visual features from the trailers, including spectrogram features, pitch, loudness, brightness, contrast, and color histograms. We then use these features to train and evaluate the different classifiers. Moreover, we observed that combining both audio and visual features improves the overall accuracy of genre classification. Our study contributes to the field of movie genre classification by providing a comparative analysis of different machine learning algorithms for the classification of movie trailers based on both audio and visual features. The findings of this research can be applied in various domains, such as movie recommendation systems, marketing strategies, and content analysis. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.

关键词： Decision trees

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Sybil Assault Detection in VANET with Efficient Network Analysis

Sybil Assault Detection in VANET with Efficient Network Anal...

引用

2024 mit Art, Design and Technology School of Computing International Conference, mitADTSoCiCon 2024

作者： Mukherjee Mathur, Shubhra Jadhav, Nagesh Gupta, Ravindra Design and Technology University Pune Sarvepalli Radakrishnan University School of Computing MIT Art Department of Computer Science and Engineering Bhopal India Design and Technology University School of Computing MIT Art Department of Computer Science and Engineering Pune India Sarvepalli Radakrishnan University School of Computing Department of Computer Science and Engineering Bhopal India

ISBN: (纸本)9798350362879

A developing technology that can be highly helpful for both intelligent transportation services and safety and security is the Automobile ad hoc network, or VANET. Nevertheless because of the great mobility and wireless connection of cars, there are numerous security risks that jeopardize people's safety and security when driving. The Sybil assault, in which the perpetrator makes up a false identity to obtain significant influence within the network and delay certain services and manipulate others by fictitious voting, is one of the main security issues. People can avoid being duped by an attacker or falling into traps when attacks are detected early. This study describes two methods for detecting Sybil attacks: the first uses the Poisson Distribution Method in order to forecast traffic on the street, whereas the latter examines network efficiency for packet delivery ratio (PDR) in both benign additionally malicious environments. According to the simulation's findings, PDR lowers the number of fictitious cars in the network. Our method is straightforward and efficient because it doesn't involve a lot of computational overhead and doesn't breach users' privacy concerns. © 2024 IEEE.

关键词： Poisson distribution

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ProHealth - Improvised E-Health Care System 1

ProHealth - Improvised E-Health Care System

引用

1st IEEE International Conference on Contemporary Computing and Communications, InC4 2023

作者： Sajjan, Rajani Chaturvedi, Tushar Bhanusghare, Dhanraj Bansal, Yash MIT ADT University Department of Computer Science and Engineering Pune India

ISBN: (纸本)9798350335774

The importance of health care is immense in society during the COVID-19 pandemic as well as the post-pandemic situation. The Indian health sector has also made a significant improvement in the healthcare system over the years. This paper presents the development of an application 'ProHealth,' for the citizens and doctors of India where they can store their medical data and access it anytime. In the ProHealth (Improvised E-Health Care System-IEHCS), users can register as patients to store their medical data in the database and registered doctors can give advice and prescription when requested for an appointment. The proposed application also provides a virtual E-Health card which has a unique user id and QR code for one-tab access. It concatenates sectors such as health, government, pharmacy, laboratory, and health insurance and makes it transparent between the user and respective sector. The application is going to be developed using Java and the platform will be Android. The database will be stored on google firebase and private cloud storage. It provides an efficient way of storing information electronically, a faster communication mechanism between patients, doctors, and other sectors, and ensures better security for the users. © 2023 IEEE.

关键词： Diagnosis

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RISK-AWARE REINFORCEMENT LEARNING WITH COHERENT RISK MEASURES AND NON-LINEAR FUNCTION APPROXIMATION 11

RISK-AWARE REINFORCEMENT LEARNING WITH COHERENT RISK MEASURE...

引用

11th International Conference on Learning Representations, ICLR 2023

作者： Lam, Thanh Verma, Arun Low, Bryan Kian Hsiang Jaillet, Patrick Department of Computer Science National University of Singapore Singapore Department of Electrical Engineering and Computer Science MIT United States

We study the risk-aware reinforcement learning (RL) problem in the episodic finite-horizon Markov decision process with unknown transition and reward functions. In contrast to the risk-neutral RL problem, we consider minimizing the risk of having low rewards, which arise due to the intrinsic randomness of the MDPs and imperfect knowledge of the model. Our work provides a unified framework to analyze the regret of risk-aware RL policy with coherent risk measures in conjunction with non-linear function approximation, which gives the first sub-linear regret bounds in the setting. Finally, we validate our theoretical results via empirical experiments on synthetic and real-world data. © 2023 11th International Conference on Learning Representations, ICLR 2023. All rights reserved.

关键词： Reinforcement learning

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FEDERATED NEURAL BANDITS 11

FEDERATED NEURAL BANDITS

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11th International Conference on Learning Representations, ICLR 2023

作者： Dai, Zhongxiang Shu, Yao Verma, Arun Fan, Flint Xiaofeng Low, Bryan Kian Hsiang Jaillet, Patrick Department of Computer Science National University of Singapore Singapore Department of Electrical Engineering and Computer Science MIT United States

Recent works on neural contextual bandits have achieved compelling performances due to their ability to leverage the strong representation power of neural networks (NNs) for reward prediction. Many applications of contextual bandits involve multiple agents who collaborate without sharing raw observations, thus giving rise to the setting of federated contextual bandits. Existing works on federated contextual bandits rely on linear or kernelized bandits, which may fall short when modeling complex real-world reward functions. So, this paper introduces the federated neural-upper confidence bound (FN-UCB) algorithm. To better exploit the federated setting, FN-UCB adopts a weighted combination of two UCBs: UCBa allows every agent to additionally use the observations from the other agents to accelerate exploration (without sharing raw observations), while UCBb uses an NN with aggregated parameters for reward prediction in a similar way to federated averaging for supervised learning. Notably, the weight between the two UCBs required by our theoretical analysis is amenable to an interesting interpretation, which emphasizes UCBa initially for accelerated exploration and relies more on UCBb later after enough observations have been collected to train the NNs for accurate reward prediction (i.e., reliable exploitation). We prove sub-linear upper bounds on both the cumulative regret and the number of communication rounds of FN-UCB, and empirically demonstrate its competitive performance. © 2023 11th International Conference on Learning Representations, ICLR 2023. All rights reserved.

关键词： Forecasting

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Mean-field Underdamped Langevin Dynamics and its Spacetime Discretization 41

Mean-field Underdamped Langevin Dynamics and its Spacetime D...

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41st International Conference on Machine Learning, ICML 2024

作者： Fu, Qiang Wilson, Ashia Department of Computer Science Yale University New HavenCT United States Department of Electrical Engineering and Computer Science MIT CambridgeMA United States

We propose a new method called the N-particle underdamped Langevin algorithm for optimizing a special class of non-linear functionals defined over the space of probability measures. Examples of problems with this formulation include training mean-field neural networks, maximum mean discrepancy minimization and kernel Stein discrepancy minimization. Our algorithm is based on a novel spacetime discretization of the mean-field underdamped Langevin dynamics, for which we provide a new, fast mixing guarantee. In addition, we demonstrate that our algorithm converges globally in total variation distance, bridging the theoretical gap between the dynamics and its practical implementation. Copyright 2024 by the author(s)

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