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

共 33684 条记录，以下是4991-5000 订阅

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Weather Based Future Rain Prediction Using machine learning with Flask Framework

Weather Based Future Rain Prediction Using Machine Learning ...

引用

2022 international conference on data Science, Agents and Artificial Intelligence, ICDSAAI 2022

作者： Sathya, R. Rastogi, Arpit Kumar, Ankit Singh, Shubham SRM Institute of Science and Technology Ramapuram Campus Chennai India

ISBN: (纸本)9798350333848

Changes in weather patterns have an impact on a wide range of industries. Weather prediction that is intelligent and sophisticated is critical for reducing the influence of weather patterns. Agriculture is a big industry that is impacted by changing weather patterns. The amount of sunlight and rain that falls on particular crops has an impact on their production. To anticipate crop yield, various machine learning approaches have been developed. The major goal of this project is to create a smart weather forecast system that will improve crop output and raise agricultural profitability by utilizing various machine learning techniques. datasets are gathered from a variety of sources and analyzed from a variety of angles in order to identify the many weather patterns that affect the crop yield. Currently, a variety of agricultural chores are carried out manually. Weather patterns are anticipated with the help of satellites orbiting the globe. The information is acquired from the satellites and analyzed by the competent authorities in the meteorological departments, who then take the necessary actions. All of this necessitates a significant amount of human effort and time. weather prediction is complete. © 2022 IEEE.

关键词： Crops

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A Survey Paper on an IoT-based machine learning Model to Predict Air Pollution Levels 4

A Survey Paper on an IoT-based Machine Learning Model to Pre...

引用

4th international conference on Advances in Computing, Communication Control and Networking, ICAC3N 2022

作者： Kasetty, Sai Bhargav Nagini, S. Vnr Vjiet Department of Computer Science and Engineering Hyderabad India

ISBN: (纸本)9781665474368

In today's quickly developing world, air pollution is a major worry. As pollution rises in the earth's atmosphere every day, so do its rates. The problem is typically caused by toxic fumes released into the air as a result of a broad range of human activities. It is necessary for the current generation to understand the risks posed by these circumstances and to take effective measures to minimize them before the ecosystem is destroyed. This study examines studies of machine learning approaches (MLAs or MLTs) and Internet of Things in predicting air quality, as well as forecasting air pollution levels so individuals can take action to reduce pollution. © 2022 IEEE.

关键词： Internet of things

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Interpretable Disease Name Estimation based on Learned Models using Semantic Representation learning of Medical Terms 14

Interpretable Disease Name Estimation based on Learned Model...

引用

14th international conference on Knowledge Discovery and Information Retrieval, KDIR 2022 as part of 14th international Joint conference on Knowledge Discovery, Knowledge engineering and Knowledge Management, IC3K 2022

作者： Keshi, Ikuo Daimon, Ryota Hayashi, Atsushi AI & IoT Center Fukui University of Technology 3-6-1 Gakuen Fukui Fukui Japan Electrical Electronic and Computer Engineering Course Department of Applied Science and Engineering Fukui University of Technology 3-6-1 Gakuen Fukui Fukui Japan Department of Ophthalmology University of Toyama 2630 Sugitani Toyama Toyama Japan

ISBN: (纸本)9789897586149

This paper describes a method for constructing a learned model for estimating disease names using semantic representation learning for medical terms and an interpretable disease-name estimation method based on the model. Experiments were conducted using old and new electronic medical records from Toyama University Hospital, where the data distribution of disease names differs significantly. The F1-score of the disease name estimation was improved by about 10 points compared with the conventional method using a general word semantic vector dictionary with a faster linear SVM. In terms of the interpretability of the estimation, it was confirmed that 70% of the disease names could provide higher-level concepts as the basis for disease name estimation. As a result of the experiments, we confirmed that both interpretability and accuracy for disease name estimation are possible to some extent. Copyright © 2022 by SCITEPRESS – Science and Technology Publications, Lda. All rights reserved.

关键词： machine learning

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SAMM: Situation Awareness with machine learning for Misbehavior Detection in VANET 22

SAMM: Situation Awareness with Machine Learning for Misbehav...

引用

17th international conference on Availability, Reliability and Security (ARES)

作者： Abdelmaguid, Mohammed A. Hassanein, Hossam S. Zulkernine, Mohammad Queens Univ Sch Comp Kingston ON Canada

ISBN: (纸本)9781450396707

Vehicular Ad hoc Network (VANET) is a foundation stone for connected vehicles. As vehicles' safety depends heavily on the exchanged data's accuracy, VANET has a low tolerance for false data. The process of intentionally exchanging inaccurate data is called misbehaving. machine learning (ML)-based solutions were heavily invested in detecting misbehavior messages. However, they also have some limitations with respect to how much they can detect. To overcome such limitations, we introduce situation awareness (SA) as a powerful concept that can break the limits of the used ML models, leading to more accurate and reliable solutions. Situation awareness uses environmental elements and events to gain a holistic view of the system at any given time. In this paper, we propose using SA to predict the trust of the surrounding cars and consequently reevaluate the outcome of the used ML model. Based on the collected data and SA information, we may reject a message classified as benign by the ML model or vice versa. We used VeReMi dataset to evaluate the proposed approach called SAMM (Situation Awareness with machine learning for Misbehavior Detection in VANET) on different ML models with a wide range of features. The results show that the proposed approach improves the system's accuracy for various misbehavior attacks by enhancing the recall rate up to 24% and 50% in some cases.

关键词： VANET security Situation Awareness (SA) machine learning (ML) VeReMi dataset misbehavior detection trust measurement

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Single stock trading with deep reinforcement learning: A comparative study 2022

Single stock trading with deep reinforcement learning: A com...

引用

14th international conference on machine learning and Computing, ICMLC 2022

作者： Ge, Jun Qin, Yuanqi Li, Yaling Huang, Yanjia Hu, Hao Research Center for Intelligent Social Governance Zhejiang Lab China School of Mathematics Xi'An Jiaotong-Liverpool University China School of Engineering Westlake University China

ISBN: (纸本)9781450395700

In this paper, we apply Deep Reinforcement learning (DRL) methods to automate the trading of single stock. The A2C, PPO, DDPG, TD3 and SAC deep reinforcement learning models are built and studied comparatively. Shanghai Composite Index (SH00001) is used as the trading stock, where the stock data before the Covid-19 is used as the training set, and the data after the Covid-19 is used as the testing (trading) set to back-test the performance of these models. Experimental results show that the DDPG, TD3, and SAC models outperform the benchmark, among which the DDPG model shows the most obvious advantages in returns and risk control, achieving a cumulative return rate of 25%, while the TD3 and SAC models achieve a cumulative return rate of 16-17%. The A2C and PPO models have inferior performance comparing to the benchmark. © 2022 ACM.

关键词： Reinforcement learning

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Quantum-Inspired Neural Network on Handwriting datasets 4th

Quantum-Inspired Neural Network on Handwriting Datasets

引用

4th international conference on Trends in Cognitive Computation engineering, TCCE 2022

作者： Shah, Manik Ratna Sarraf, Jay Pattnaik, Prasant Kumar Bandyopadhyay, Anjan School of Computer Engineering KIIT Deemed to Be University Odisha Bhubaneswar751024 India

ISBN: (纸本)9789811994821

With the advancement of machine learning for classifying and categorizing larger sets of data, there is a high need for greater computational power. Quantum computing in machine learning advances to solve this in a lesser time, consuming less energy and power. This paper includes the comparative analysis of Traditional Neural Networks with Quantum Neural Networks using handwritten digits from the simplified version of the MNIST dataset. © 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： machine learning

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e-SMARTS: A System to Simulate Intelligent Traffic Management Solutions (Demo Paper) 22

e-SMARTS: A System to Simulate Intelligent Traffic Managemen...

引用

30th ACM SIGSPATIAL international conference on Advances in Geographic Information Systems (ACM SIGSPATIAL GIS)

作者： Gunarathna, Udesh Borovica-Gajic, Renata Karunasekera, Shanika Tanin, Egemen Univ Melbourne Melbourne Vic Australia

ISBN: (纸本)9781450395298

Intelligent traffic management solutions that leverage machine learning have gained a lot of interest in recent years. These techniques, however, cannot be deployed in real-world settings at a desirable pace due to technological barriers. Thus, easily customizable, realistic simulation environments are needed to train and verify the effectiveness of machine learning algorithms for traffic control. We propose an easily extendable traffic simulation system named e-SMARTS to allow researchers to experiment with novel data-driven traffic management algorithms in a setup that mimics real-world traffic conditions. We demonstrate the flexibility of e-SMARTS using widely researched traffic management solutions for Autonomous Intersection Management (AIM). In the demonstration, we present several pluggable algorithms for AIM and show that these computationally efficient algorithms can achieve effective and safe results.

关键词： Simulations Spatial-temporal data Reinforcement learning

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Comparison of Performance of machine learning Algorithms for Cervical Cancer Classification 2

Comparison of Performance of Machine Learning Algorithms for...

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2nd IEEE international conference on Advances in Electrical, Computing, Communication and Sustainable Technologies (IEEE ICAECT)

作者： Karani, Hamza Gangurde, Ashish Dhumal, Gauri Gautam, Waidehi Hiran, Samiksha Marathe, Abha Vishwakarma Inst Technol Dept Comp Engn Pune Maharashtra India Vishwakarma Inst Technol Dept Elect & Telecommun Engn Pune Maharashtra India

ISBN: (纸本)9781665411202

Cervical cancer, which is the fourth leading cause of mortality among women, displays no symptoms in its early stages. Cervical cancer is currently diagnosed using only a few approaches using machine learning techniques. Certain approaches such as PAP Test, HPV Test, Colposcopy and Biopsy require medical staff intervention and cancer is not detected until a certain stage is reached. These procedures are also too costly in developing countries. Detection of Cervical Cancer using machine learning and Deep learning techniques come into play to solve this issue. A few to name are: CervDetect[1], a hybridized model using a combination of Random Forest and Shallow Neural Networks, ResNet50 – A Convolutional Neural Network’s pre-trained model works effectively on classification of cervical cancer cells using images. This research paper experiments and analyses two Support Vector machine (SVM) techniques as well as K-Nearest Neighbor (KNN), Random Forest(RF), Logistic Regression and Gaussian Naïve Bayes (GNB) algorithms for cervical cancer diagnosis. The dataset used is Cervical cancer (Risk Factors) data Set from UCI Repository[2] . There are 32 risk factors and four target variables in cervical cancer dataset: Citology, Hinselmann, Schiller and Biopsy. The two SVM-based techniques namely SVM Linear and SVM Radial, KNN, RF, Logistic Regression and GNB have diagnosed and categorized all four targets respectively. Following that, a comparison between these six methods is done and inferences are drawn on which algorithm performs better on each of the targets.

关键词： Cervical Cancer machine learning Classification Support Vector machine Random Forest Logistic Regression

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An Overview of machine learning Approaches to Software Development Cost Estimation 8

An Overview of Machine Learning Approaches to Software Devel...

引用

8th international conference on Contemporary Information Technology and Mathematics, ICCITM 2022

作者： Maher, Mohammed Alneamy, Jamal Salahaldeen University of Mosul Dept. of Software Engineering Mosul Iraq

ISBN: (纸本)9798350334869

Software cost estimation still presents a real challenge to software development firms and practitioners. One of the crucial activities in project management is software cost estimation. The accuracy of the estimated cost has a critical impact on the project's success or failure. Overestimation and underestimation are serious challenges to project managers, especially in today's competitive marketplace. Inaccurate estimation may lead to over budget, missing deadlines, business loss, and even project failure. The precise cost estimation will enable the project manager to forecast and allocate the necessary resources to deliver a successful product. Many researchers have published research papers in this area. In this paper, the study's goal is to outline the recent machine learning approaches to software cost estimation. The study will address the problems with conventional cost estimation models and provide a comprehensive review of the researches regarding software cost estimation with machine learning. The current study will discuss the intelligence models for software cost estimation and highlight the models that excel in this area. © 2022 IEEE.

关键词： machine learning

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Automated Handling of Anaphoric Ambiguity in Requirements: A Multi-solution Study 22

Automated Handling of Anaphoric Ambiguity in Requirements: A...

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ACM/IEEE 44th international conference on Software engineering (ICSE)

作者： Ezzini, Saad Abualhaija, Sallam Arora, Chetan Sabetzadeh, Mehrdad Univ Luxembourg Luxembourg Luxembourg Deakin Univ Geelong Vic Australia Univ Ottawa Ottawa ON Canada

ISBN: (纸本)9781450392211

Ambiguity is a pervasive issue in natural-language requirements. A common source of ambiguity in requirements is when a pronoun is anaphoric. In requirements engineering, anaphoric ambiguity occurs when a pronoun can plausibly refer to different entities and thus be interpreted differently by different readers. In this paper, we develop an accurate and practical automated approach for handling anaphoric ambiguity in requirements, addressing both ambiguity detection and anaphora interpretation. In view of the multiple competing natural language processing (NLP) and machine learning (ML) technologies that one can utilize, we simultaneously pursue six alternative solutions, empirically assessing each using a collection of approximate to 1,350 industrial requirements. The alternative solution strategies that we consider are natural choices induced by the existing technologies;these choices frequently arise in other automation tasks involving natural-language requirements. A side-by-side empirical examination of these choices helps develop insights about the usefulness of different state-of-the-art NLP and ML technologies for addressing requirements engineering problems. For the ambiguity detection task, we observe that supervised ML outperforms both a large-scale language model, SpanBERT (a variant of BERT), as well as a solution assembled from off-the-shelf NLP coreference resolvers. In contrast, for anaphora interpretation, SpanBERT yields the most accurate solution. In our evaluation, (1) the best solution for anaphoric ambiguity detection has an average precision of approximate to 60% and a recall of 100%, and (2) the best solution for anaphora interpretation (resolution) has an average success rate of approximate to 98%.

关键词： Requirements engineering Natural-language Requirements Ambiguity Natural Language Processing (NLP) machine learning (ML) Language Models BERT

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