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41st International Conference on Machine Learning, ICML 2024

作者： Jain, Shomik Creel, Kathleen Wilson, Ashia Institute for Data Systems and Society MIT United States Department of Philosophy & Religion Khoury College of Computer Sciences Northeastern University United States Department of Electrical Engineering and Computer Science MIT United States

Contrary to traditional deterministic notions of algorithmic fairness, this paper argues that fairly allocating scarce resources using machine learning often requires randomness. We address why, when, and how to randomize by proposing stochastic procedures that more adequately account for all of the claims that individuals have to allocations of social goods or opportunities. Copyright 2024 by the author(s)

关键词： Adversarial machine learning

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computer science Responses to Abuse Toward Women and Children 1

Computer Science Responses to Abuse Toward Women and Childre...

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1st International Conference on Cognitive Computing and engineering Education, ICCCEE 2023

作者： Bendale, Bhagyashree Pramod Shirke, Swati Dattatraya MIT Art Design and Technology University MIT School of Computing Department of Computer Science and Engineering Maharashtra Pune India

ISBN: (纸本)9798350332803

Worldwide public health concerns include violence towards women as well as children. One in three women globally, according to estimates, has been the victim of physical, emotional, or sexual abuse. Like adults, one in two children experiences some type of violence every year, including bullying and domestic abuse. Women and children are increasingly more susceptible to additional forms of abuse including cyberbullying or emotional harassment because of the extensive use of the Internet and social media. In recent years, computer science and associated technologies have been used to assist address this societal issue. Solutions to identify and stop violence against women and children have been developed using cloud computing, pattern recognition, mobile computing, ubiquitous artificial intelligence, as well as associated systems. This study presents a comprehensive evaluation of some of the initiatives that might assist in addressing the issue of violence against children and women. The present state of these contributions is discussed in this study along with trends, designs, technologies, and open issues. A review of the scholarly literature from 2010 to 2020 was used to produce the survey. The contributions were categorized into four application domains: education, safety, offline detection, and online detection. Digital simulation, the Internet of Things, and artificial intelligence were employed as additional categories to further divide these contributions. © 2023 IEEE.

关键词： Internet of things

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Revolutionizing the Driving Experience : Enhancing Safety and Comfort Using ADAS 7

Revolutionizing the Driving Experience : Enhancing Safety an...

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2023 7th International Conference On Computing, Communication, Control And Automation, ICCUBEA 2023

作者： Kelkar, Aditya Jondhale, Digvijay Borade, Rugved Juneja, Ishan Haran, Yuvraj Pawar, Rajendra G. School of Computer Engineering & Technology Mit World Peace University Maharashtra Pune India Mit School of Computing Mit Art Design and Technology University Department of Computer Science Engineering Pune India

ISBN: (纸本)9798350304268

Advanced Driver Assistance Systems, or ADAS for short, are a group of technologies intended to help drivers operate their automobiles more safely and effectively. These systems often employ sensors, cameras, and other gadgets to keep an eye on the surroundings of the car and give the driver alerts or help in a multitude of circumstances, like lane departure, accident avoidance, and parking. Modern vehicles are increasingly utilizing ADAS technologies, which are regarded as an important first step towards completely autonomous vehicles. © 2023 IEEE.

关键词： ADAS Haar Cascade Object-detection RaspberryPi YOLO

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Review of Reinforcement Learning in Chrome Dino Game 7

Review of Reinforcement Learning in Chrome Dino Game

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2023 7th International Conference On Computing, Communication, Control And Automation, ICCUBEA 2023

作者： Kulkarni, Advait Bapat, Pranav Kulkarni, Tanvi Pawar, Rajendra School of Computer Engineering & Technology Mit World Peace University Maharashtra Pune India Mit School of Computing Mit Art Design & Technology University Department of Computer Science Engineering Pune India

ISBN: (纸本)9798350304268

The intention of this study is to study some implementation of reinforcement learning in the environment of Google Chrome's Dino Game. Different environments were tested on Deep Q network and proximal policy optimization models. Images were provided as inputs in all trials and the highest scores were recorded and analysed to check which algorithm and environment performed better. Further work aims at including more environments and algorithms and performing training on High performance computers. © 2023 IEEE.

关键词： Deep Q network OpenAI gym Proximal Policy Optimisation Reinforcement learning Selenium

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Adaptive solar power generation forecasting using enhanced neural network with weather modulation

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Journal of Intelligent and Fuzzy Systems 2024年第4期46卷 10955-10968页

作者： Sujeeth, T. Ramesh, C. Palwe, Sushila Ramu, Gandikota Basha, Shaik Johny Upadhyay, Deepak Chanthirasekaran, K. Sivasankari, K. Rajaram, A. Department of CSE Siddartha Educational Academy Group of Institutions Andhra Pradesh Tirupati India Department of Mechanical Engineering M. Kumarasamy College of Engineering Karur India Department of Computer Engineering and Technology MIT-WPU Maharashtra India Department of Computer Science and Engineering Koneru Lakshmaiah Education Foundation Telangana Hyderabad India Andhra Pradesh Mylavaram India Department of Computer Science and Engineering Graphic Era hill univeristy Dehradun India Department of Electronics and Communication Engineering Saveetha Engineering College Chennai India Department of Electronics and Communication Engineering Akshaya College of Engineering and Technology Coimbatore India Department of Electronics and Communication Engineering E.G.S. Pillay Engineering College Nagapattinam India

Solar power generation forecasting plays a vital role in optimizing grid management and stability, particularly in renewable energy-integrated power systems. This research paper presents a comprehensive study on solar power generation forecasting, evaluating traditional and advanced machine learning methods, including ARIMA, Exponential Smoothing, Support Vector Regression, Random Forest, Gradient Boosting, and Physics-based Models. Moreover, we propose an innovative Enhanced Artificial Neural Network (ANN) model, which incorporates Weather Modulation and Leveraging Prior Forecasts to enhance prediction accuracy. The proposed model is evaluated using real-world solar power generation data, and the results demonstrate its superior performance compared to traditional methods and other machine learning approaches. The Enhanced ANN model achieves an impressive Root Mean Square Error (RMSE) of 0.116 and a Mean Absolute Percentage Error (MAPE) of 36.26%. The integration of Weather Modulation allows the model to adapt to changing weather conditions, ensuring reliable forecasts even during adverse scenarios. Leveraging Prior Forecasts enables the model to capture short-term trends, reducing forecasting errors arising from abrupt weather changes. The proposed Enhanced ANN model showcases its potential as a promising tool for precise and reliable solar power generation forecasting, contributing to the efficient integration of solar energy into the power grid and advancing sustainable energy practices. © 2024 – IOS Press.

关键词： Solar power generation

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Computationally efficient reductions between some statistical models 36

Computationally efficient reductions between some statistica...

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36th International Conference on Algorithmic Learning Theory, ALT 2025

作者： Lou, Mengqi Bresler, Guy Pananjady, Ashwin School of Industrial and Systems Engineering Georgia Tech United States Department of Electrical Engineering and Computer Science MIT United States Schools of Industrial and Systems Engineering and Electrical and Computer Engineering Georgia Tech United States

We study the problem of approximately transforming a sample from a source statistical model to a sample from a target statistical model without knowing the parameters of the source model, and construct several computationally efficient such reductions between canonical statistical experiments. In particular, we provide computationally efficient procedures that approximately reduce uniform, Erlang, and Laplace location models to general target families. We illustrate our methodology by establishing nonasymptotic reductions between some canonical high-dimensional problems, spanning mixtures of experts, phase retrieval, and signal denoising. Notably, the reductions are structure-preserving and can accommodate missing data. We also point to a possible application in transforming one differentially private mechanism to another. © 2025 M. Lou, G. Bresler & A. Pananjady.

关键词： Differential privacy

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Terahertz squeezed light and self-pulsing in semiconductor lasers with nonlinear loss

Terahertz squeezed light and self-pulsing in semiconductor l...

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2024 Conference on Lasers and Electro-Optics, CLEO 2024

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 of Physics Harvard CambridgeMA United States

We show how the combination of dispersive outcoupling and Kerr nonlinearity in semiconductor lasers creates regimes of self-pulsing and intensity-squeezed states of light from optical to terahertz wavelengths. CLEO 20... 详细信息

ISBN: (纸本)9781957171395

关键词： Semiconductor lasers

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Terahertz squeezed light and self-pulsing in semiconductor lasers with nonlinear loss

Terahertz squeezed light and self-pulsing in semiconductor l...

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CLEO: science and Innovations in CLEO 2024, CLEO: S and I 2024 - Part of Conference on Lasers and Electro-Optics

关键词： Semiconductor lasers

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Comparison of various machine learning approaches uses in heart ailments prediction 1

Comparison of various machine learning approaches uses in he...

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1st International Conference on Artificial Intelligence, Computational Electronics and Communication System, AICECS 2021

作者： Gupta, Gunjan Adarsh, U. Reddy, N.V. Subba Rao, B. Ashwath MTech Computer Science and Engineering MIT Manipal576104 India Department of Computer Science and Engineering MIT Manipal576104 India

Heart disease has been the leading cause of a huge number of deaths in recent years. As a result, an accurate and feasible system is required to diagnose this disease early to provide better treatment. Advances in machine learning have the potential to enhance healthcare access. Given the importance of a crucial organ like the heart, medical professionals and physicians have made it a priority to forecast heart failure-related events in clinical practice, nevertheless, forecasting heart failure-related events in clinical practice has generally failed to achieve high accuracy. The objective here is to demonstrate how machine learning may be used to solve the problem. By analyzing hundreds of healthcare data and other semantics, machine learning algorithms can analyze related cases with diseases and health conditions. Here a demonstration of how to load the data, generate predictions through different models from patient data is shown. The metrics are then compared for a better understanding of their function and what impact can be inferred from them. © 2022 Institute of Physics Publishing. All rights reserved.

关键词： Machine learning

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Fake News Detection from Online media using Machine learning Classifiers 1

Fake News Detection from Online media using Machine learning...

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1st International Conference on Artificial Intelligence, Computational Electronics and Communication System, AICECS 2021

作者： Pandey, Shalini Prabhakaran, Sankeerthi Reddy, N.V. Subba Acharya, Dinesh MTech CSIS Department of Computer Science and Engineering MIT MAHE Manipal India Department of Computer Science and Engineering MIT MAHE Manipal India

With the advancement in technology, the consumption of news has shifted from Print media to social media. The convenience and accessibility are major factors that have contributed to this shift in consumption of the news. However, this change has bought upon a new challenge in the form of "Fake news" being spread with not much supervision available on the net. In this paper, this challenge has been addressed through a Machine learning concept. The algorithms such as K-Nearest Neighbor, Support Vector Machine, Decision Tree, Naïve Bayes and Logistic regression Classifiers to identify the fake news from real ones in a given dataset and also have increased the efficiency of these algorithms by pre-processing the data to handle the imbalanced data more appropriately. Additionally, comparison of the working of these classifiers is presented along with the results. The model proposed has achieved an accuracy of 89.98% for KNN, 90.46% for Logistic Regression, 86.89% for Naïve Bayes, 73.33% for Decision Tree and 89.33% for SVM in our experiment. © 2022 Institute of Physics Publishing. All rights reserved.

关键词： Decision trees

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