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检索条件"任意字段=2022 International Conference on Machine Learning and Data Engineering, ICMLDE 2022"

共 33943 条记录，以下是761-770 订阅

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Automatic Rainwater Sensing Umbrella

Automatic Rainwater Sensing Umbrella

引用

2022 international conference on Sustainable Computing and data Communication Systems, ICSCDS 2022

作者： Margret Sharmila, F. Sharmila, S. Vishali, V.K. Padma, Girishma P G Sns College of Engineering Department of Cse Coimbatore India

ISBN: (纸本)9781665478847

In today's world, robotics and artificial intelligence algorithms are replacing many manual works and the entire world is becoming fully automatic and autonomous in nature. In the concept of machine learning, the data sets are initially gets trained to the microcontroller regarding the threshold value, the nature of data etc. and hence the microcontroller unit understands the input data sets and it automatically takes decision with the help of artificial intelligence decision making algorithm whenever the same kind of data that enters in to it as a input values as they are trained earlier. This type of concept is called machine learning and artificial intelligence. (Abstract) © 2022 IEEE.

关键词： machine learning

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AdaptAUG: Adaptive data Augmentation Framework for Multi-Agent Reinforcement learning

AdaptAUG: Adaptive Data Augmentation Framework for Multi-Age...

引用

IEEE international conference on Robotics and Automation (ICRA)

作者： Yul, Xin Tian, Yongkai Wang, Li Feng, Pu Wu, Wenjun Shi, Rongye Beihang Univ Sch Comp Sci & Engn Beijing Peoples R China Beihang Univ Inst Artificial Intelligence Beijing Peoples R China

ISBN: (纸本)9798350384581;9798350384574

Multi-agent reinforcement learning has emerged as a promising approach for the control of multi-robot systems. Nevertheless, the low sample efficiency of MARL poses a significant obstacle to its broader application in robotics. While data augmentation appears to be a straightforward solution for improving sample efficiency, it usually incurs training instability, making the sample efficiency worse. Moreover, manually choosing suitable augmentations for a variety of tasks is a tedious and time-consuming process. To mitigate these challenges, our research theoretically analyzes the implications of data augmentation on MARL algorithms. Guided by these insights, we present AdaptAUG, an adaptive framework designed to selectively identify beneficial data augmentations, thereby achieving superior sample efficiency and overall performance in multi-robot tasks. Extensive experiments in both simulated and real-world multi-robot scenarios validate the effectiveness of our proposed framework.

关键词： Adversarial machine learning

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Target Recognition Using Pre-Trained Convolutional Neural Networks and Transfer learning 2

Target Recognition Using Pre-Trained Convolutional Neural Ne...

引用

2nd international conference on machine learning and data engineering, icmlde 2023

作者： Mishra, Gangeshwar Gupta, Prinima Tanwar, Rohit Department of CST MRU Haryana Faridabad India School of Computer Science University of Petroleum and Energy Studies Uttarakhand Dehradun India

In modern surveillance, automatic target recognition (ATR) is a critical challenge, necessitating rapid and precise object identification, especially in military and disaster response scenarios. This research presents a comprehensive framework for ground target classification, focusing on Synthetic Aperture Radar (SAR) imagery. Harnessing the Moving and Stationary Target Acquisition and Recognition (MSTAR) Mixed Targets dataset, this study integrates Convolutional Neural Networks (CNNs) into SAR image analysis. Transfer learning emerges as a key strategy, adapting insights from pre-trained CNNs to the intricacies of target recognition in SAR imagery. This approach outperforms traditional methods, enhancing efficiency amid dataset annotation challenges. Noteworthy CNN architectures including DarkNet-19, DenseNet-121, InceptionV4, ResNet-152, and VGG19 are explored. The ResNet-152 model demonstrates exceptional performance, emerging as a leading contender with a remarkable testing accuracy of 98.56% when trained from scratch. Furthermore, by employing transfer learning, the model's accuracy may be further enhanced to reach 98.81% on the testing dataset. This research signifies a transformative SAR imagery path guided by pre-trained CNNs and transfer learning. It reshapes ATR's accuracy and efficiency, pointing to a future where innovation surmounts challenges previously deemed insurmountable. © 2024 Elsevier B.V.. All rights reserved.

关键词： Efficiency

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Bayesian Optimized Supervised learning for Short-Term Net Load Forecasting in Distribution Grids with Net-Metering 34

Bayesian Optimized Supervised Learning for Short-Term Net Lo...

引用

34th Australasian Universities Power engineering conference

作者： Haseeb, Abdul Khan, Shahid Nawaz Arshad, Naveed Waseem, Muhammad Islam, Md. Rabiul Hossain, Md. Jahangir Biswas, Shuvra Prokash Lahore Univ Management Sci Dept Comp Sci Lahore 54792 Pakistan Maynooth Univ Dept Elect Engn Maynooth Kildare Ireland Univ Wollongong Sch Elect Comp & Telecom Engn Keiraville NSW 2522 Australia Univ Technol Sydney Sch Elect & Data Engn Ultimo NSW 2007 Australia Univ Wollongong Fac Engn & Informat Sci Wollongong NSW 2522 Australia

ISBN: (纸本)9798350377958;9798350377941

As power systems evolve into more intelligent, flexible, and interactive networks with a higher penetration of Renewable Energy Sources (RES) on the demand side, net load forecasting has become a cost-effective and essential technique for planning, stability, and reliability of modern power grids. This study explores both direct and indirect methodologies for short-term net load forecasting (STNLF) in distribution grids considering solar net-metering using deep learning (DL) and machine learning (ML) techniques. The proposed models begin with feature engineering and filtering using Pearson Correlation and Mutual Information (MI) indices, followed by developing a forecasting models employing a Bayesian Optimized Long Short-Term Memory (BO-LSTM) network and Bayesian Optimized Random Forest (BO-RF). The model's effectiveness was validated using historical net load data from the Lahore Electric Supply Company (LESCO) spanning January 2022 to January 2024, as LESCO has the highest number of net-metering customers among all Distribution Companies (DISCOS) in Pakistan. Results demonstrate the BO-RF model's capability to deliver accurate and robust STNLF, achieving a Mean Absolute Percentage Error (MAPE) of 3.56% for the test data and 2.21% for the train data. This study also compares the efficiency of indirect and direct methods for STNLF, demonstrating that the indirect method generally performs better than direct method in distribution grids.

关键词： Short-Term Net Load Forecasting Bayesian Optimization Long Short-Term Memory Deep learning machine learning Random Forest

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Detection and Classification of Rheumatoid Nodule using Deep learning Models

Detection and Classification of Rheumatoid Nodule using Deep...

引用

2022 international conference on machine learning and data engineering, icmlde 2022

作者： Dulhare, Uma N. Mubeen, Azmath Department of Computer Science and Artificial Intelligence Muffakham Jah College of Engineering and Technology Hyderabad500034 India Deparment of Computer Science and Engineering University College of Engineering Osmania University Hyderabad500007 India

In the past few years there have been many cases in which the patient may get some form of nodule on the body and it becomes very difficult to identify the type of nodule and its relative disease. Sometimes the nodule may be a simple skin lesion or it may be cancerous cyst or it may be due to a severe condition of rheumatoid arthritis. This research work concentrates on classifying the nodules from a given data set of images and detecting the Rheumatoid Nodule (RN) and its risks. Many studies have been conducted for detection of different nodules but specific to rheumatoid nodule identification and its risks on other organs are minimum as per my cognizance. In imaging study, Deep Convolutional Neural Networks (DCNN) have shown efficient accomplishment, hence the model proposed uses dual three-dimensional Deep Convolutional Networks design for the detection of RN and an improved Res Net for classification, respectively. Rheumatoid Nodule detection was performed through Pure Deep Convolutional Networks (PDCNN) and then feature selection was done using Particle Swarm Optimization (PSO) and later classification was done using the Residual Network (Res-Net) on proficiently learned features from Particle Swarm Optimization. The Deep learning (DL) model for Rheumatoid Nodule detection and classification, blended with medical aspects, aids in minimizing the errors and False Positive (FP) rate and help in early detection of rheumatoid nodule. The recommended system was assessed on dataset collected from different orthopaedic hospitals in the city and improved results were obtained in comparison to the prevailing approaches. © 2022 Elsevier B.V.. All rights reserved.

关键词： Classification (of information)

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PolySense Flashcards: A machine-learning-Enhanced Platform for learning the Multiple Senses of Academic Vocabulary 7

PolySense Flashcards: A Machine-Learning-Enhanced Platform f...

引用

7th international conference on Digital Arts, Media and Technology, DAMT 2022 and 5th ECTI Northern Section conference on Electrical, Electronics, Computer and Telecommunications engineering, NCON 2022

作者： Fernquest, Jon Mae Fah Luang University School of Information Technology Chiang Rai Thailand

ISBN: (纸本)9781665495103

An architecture for a flashcard system for learning vocabulary is presented here that feeds directly off the experimental work on vocabulary acquisition. In principle, experiments in vocabulary acquisition based on traditional inferential statistics applied to small once-off collected sets of data informs vocabulary pedagogy as teachers are exposed to experimental results via training courses and practical teaching-oriented literature. Recently, however, in many domains of practical application this traditional statistics approach has been replaced by machine learning algorithms feeding off large continuous streams of real time data. Here it is proposed that a flashcard app that integrates such machine learning support into its architecture can serve as an adaptive experimental platform for continual pedagogical improvement. Flashcards for learning the multiple senses of high-frequency academic vocabulary is chosen as an important initial exploratory domain of application. © 2022 IEEE.

关键词： learning algorithms

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machine learning for Product engineering in NAND Flash Memory

Machine Learning for Product Engineering in NAND Flash Memor...

引用

international conference on Electronics, Information, and Communication (ICEIC)

作者： Kim, Jongmin Kim, Earl Choe, Jeongin Song, Ki-Whan Kim, Eunkyoung Song, Jai Hyuk Samsung Elect Co Dept Flash Prod & Technol Hwaseong 18448 Gyeonggi South Korea

In this paper, we introduce the machine learning techniques for product engineering in NAND Flash memory. More specifically, we review the CORE optimization to maximize performance and the Anomaly Map Pattern Detectio... 详细信息

ISBN: (纸本)9781665409346

关键词： Manufacturing industries Mass production machine learning Flash memories Optimization Business

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Electrical Load Demand Forecast for Gujarat State of India using machine learning Models

Electrical Load Demand Forecast for Gujarat State of India u...

引用

2024 IEEE international Students' conference on Electrical, Electronics and Computer Science, SCEECS 2024

作者： Chandra, Priyanshu Kumar Bajaj, Deepak Sharma, Himanshu Bareth, Rashmi Yadav, Anamika National Institute of Technology India Department of Electrical Engineering Raipur India

ISBN: (纸本)9798350348460

The work is concentrates on forecasting electrical loads of Gujarat state of India. Day ahead load demand forecasting is proposed by utilizing historical load demand data from year 2022 to 2023. The dataset, acquired for these years, are processed using Python scripts to filter and organize the information with a 15-minute timestamp granularity. Subsequently, Regression based machine learning algorithms are applied on Load Demand data of Gujarat to forecast day ahead load demand. The proposed work involves a comparative analysis of the performance of Linear Regression, Support Vector Regression and Regression Tree algorithms with their classes models, to enhance accuracy and efficiency in load forecasting. The results are evaluated based on metrics such as Mean Absolute Error (MAE) and Mean Absolute Percentage Error (MAPE) The Robust Linear Regression model outperforms all the classes with MAE 70.252 MW of and MAPE of 0.176%. This research aims to contribute valuable insights for making decision in the power sector by offering a refined and effective methodology for load prediction, potentially saving significant resources for utility companies. © 2024 IEEE.

关键词： forecasting models Linear Regression Load demand Regression Tree

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Emotion Cause Pair Extraction by Multi Task learning on Enhanced English dataset

Emotion Cause Pair Extraction by Multi Task Learning on Enha...

引用

2022 international conference on machine learning and data engineering, icmlde 2022

作者： Khunteta, Arunima Singh, Pardeep National Institute of Technology Hamirpur H.P. Hamirpur177005 India

Emotion analysis and detection is a step further in sentiment analysis that aims to detect the emotion portrayed. A deeper task in mining opinions and emotions is to know the trigger behind the emotion or its stimuli and that is achieved through Emotion Cause Extraction. However, since it requires annotating sentences, limited research has been carried out in this area. Hence, there is a need to explore emotion cause extraction and expand its dataset in the most used language worldwide - English. This paper aims to extract causes without needing annotation of emotion by the proposed approach of Emotion Cause Pair Extraction on English language. For this, we have constructed a dataset of about 6000 English emotionally annotated sentences spanning across six primary emotions - happy, sad, fear, guilt, shame, disgust. Added to this, we have proposed algorithms to extract the causes of emotions from these sentences. The experimental models of our proposed approach gives encouraging results (a ∼9% increase from the state of the art methods) and also uses a larger dataset as compared to existing research on English language. We conclude by sharing the results, and discussing future advancements in this field. © 2022 Elsevier B.V.. All rights reserved.

关键词： Extraction

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A Low-Cost Edge Computing Device for Real-Time Detection of Motor Symptoms in Neurodegenerative Diseases Using machine learning

A Low-Cost Edge Computing Device for Real-Time Detection of ...

引用

IEEE international Instrumentation and Measurement Technology conference (I2MTC)

作者： Carissimo, C. Cerro, G. Komici, K. Miele, G. Ferrigno, L. Landi, E. Spinelli, F. Fort, A. Mugnaini, M. Univ Molise Dept Med & Hlth Sci V Tiberio Campobasso CB Italy Univ Cassino & Southern Lazio Dept Elect & Informat Engn Cassino Italy Univ Siena Dept Informat Engn & Math Siena Italy

ISBN: (纸本)9798350380903;9798350380910

Diseases like Parkinson's affect a non-negligible percentage of elder people worldwide. Affected people life quality strictly depends on the disease progression and counteractions that medical doctors decide to apply, according to periodical clinical evaluations. The possibility to have continuous monitoring of people with the help of electronic devices is a desirable opportunity for two main reasons: i) to reduce queues for medical doctors for in-person clinical assessment, ii) provide them with real-time and continuous monitoring data produced by assisted people in their home environment. To this end, the paper proposes the development and physical realization of an all-in-one lowcost device, able to both measure and process movement data in real-time, adopting miniaturized inertial sensors and machine learning capabilities. To accomplish the task, a pre-validated movement simulator is adopted to generate data concerning different kinds of movement disorders, specifically focusing on tremors related to Parkinson's disease specific medical tests. The simulator takes into consideration the metrological features of the platform later adopted in the miniaturized device. The generated data are then used to train a machine learning tool to recognize such disorders and provide an estimation of the disease's progression status. Once completed the tuning procedure, the whole process has been transferred to the developed physical device and validation tests have been carried out to prove its suitability for the described purpose. Obtained performance (minimum 98.5% mean accuracy with the emulated data) and low-energy consumption (maximum 27 mWh) allow stating as such prototype can be a robust basis for the following engineering and release phases to create a wearable object. It can be helpful for medical doctors as assisting support for correct diagnosis and prompt intervention in case of sudden worsening of people disease status.

关键词： movement disorders IMU real-time monitoring neurodegenerative diseases machine learning

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