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检索条件"任意字段=5th International Conference on Applied Machine Learning and Data Analytics, AMLDA 2022"

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Classifying Incoming Customer Messages for an e-Commerce Site Using Supervised learning 5th

Classifying Incoming Customer Messages for an e-Commerce S...

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5th international conference on applied Informatics, ICAI 2022

作者： Albañil Sánchez, Misael Andrey Galpin, Ixent Facultad de Ciencias Naturales e Ingeniería Universidad de Bogotá – Jorge Tadeo Lozano Bogotá Colombia

ISBN: (纸本)9783031196461

throughout the world, the provision of online goods and services has increased significantly over the last few years. We consider the case of Tango Discos, a small company in Colombia that sells entertainment products through an e-commerce website and receives customer messages through various channels, including a webform, email, Facebook and Twitter. this dataset comprises 29,970 messages collected from 2019 to 2021. Each message can be categorized as being either being a sale, request or complaint. In this work we evaluate different supervised classification models to automate the task of classifying the messages, viz. decision trees, Naive Bayes, linear Support Vector machines and logistic regression. As the data set is unbalanced, the different models are evaluated in combination with various data balancing approaches to obtain the best performance. In order to maximize revenue, the management is interested in prioritizing messages that may result in potential sales. As such, the best model for deployment is one that minimizes false positives in the sales category, so that these are processed in a timely fashion. As such, the best performing model is found to be the Linear Support Vector machine using the Random Over Sampler balancing technique. this model is deployed in the cloud and exposed using a RESTful interface. © 2022, the Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： Support vector machines

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ARIMA vs LSTM Algorithm – A Comparative Study Based on Stock Market Prediction

ARIMA vs LSTM Algorithm – A Comparative Study Based on Stoc...

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Advances in Science and Technology (ICAST), international conference on

作者： Jaikishan Bagul Prajwal Warkhade Tanish Gangwal Nikhita Mangaonkar MCA Department Sardar Patel Institute of Technology

ISBN: (纸本)9781665492645

machine learning, in contrast to traditional algorithms and models, is a systematic and complete application of computer algorithms and statistical models that has been widely applied in a variety of industries. machine learning is primarily utilized in finance to examine the future trend of capital market prices. In this research, we used traditional models and machine learning models for forecasting the linear and non-linear problems, respectively, to predict stock time-series data. To begin, stock samples from the National Stock Exchange from 2010 to 2019 are collected. To train and predict stock price and stock price sub correlation, the ARIMA (autoregressive integrated moving average model) and LSTM (long short-term memory) neural network models are used.

关键词： machine learning algorithms Systematics Computational modeling Neural networks machine learning Predictive models Prediction algorithms

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On the Existence of Simpler machine learning Models 22

On the Existence of Simpler Machine Learning Models

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5th ACM conference on Fairness, Accountability, and Transparency, FAccT 2022

作者： Semenova, Lesia Rudin, Cynthia Parr, Ronald Duke University DurhamNC United States

ISBN: (纸本)9781450393522

It is almost always easier to find an accurate-but-complex model than an accurate-yet-simple model. Finding optimal, sparse, accurate models of various forms (linear models with integer coefficients, decision sets, rule lists, decision trees) is generally NP-hard. We often do not know whether the search for a simpler model will be worthwhile, and thus we do not go to the trouble of searching for one. In this work, we ask an important practical question: can accurate-yet-simple models be proven to exist, or shown likely to exist, before explicitly searching for them? We hypothesize that there is an important reason that simple-yet-accurate models often do exist. this hypothesis is that the size of the Rashomon set is often large, where the Rashomon set is the set of almost-equally-accurate models from a function class. If the Rashomon set is large, it contains numerous accurate models, and perhaps at least one of them is the simple model we desire. In this work, we formally present the Rashomon ratio as a new gauge of simplicity for a learning problem, depending on a function class and a data set. the Rashomon ratio is the ratio of the volume of the set of accurate models to the volume of the hypothesis space, and it is different from standard complexity measures from statistical learning theory. Insight from studying the Rashomon ratio provides an easy way to check whether a simpler model might exist for a problem before finding it, namely whether several different machine learning methods achieve similar performance on the data. In that sense, the Rashomon ratio is a powerful tool for understanding why and when an accurate-yet-simple model might exist. If, as we hypothesize in this work, many real-world data sets admit large Rashomon sets, the implications are vast: it means that simple or interpretable models may often be used for high-stakes decisions without losing accuracy. © 2022 ACM.

关键词： Decision trees

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machine learning based Unique Perfume Flavour Creation Using Quantitative Structure-Activity Relationship (QSAR) 5

Machine learning based Unique Perfume Flavour Creation Using...

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5th international conference on Computing Methodologies and Communication, ICCMC 2021

作者： Sushma, S. Sundaram, Nithish Jayapandian, N. Deemed to Be University Christ Department of CSE Bangalore India University of Applied Sciences Würzburg-Schweinfurt Faculty of Mechatronics Germany

ISBN: (纸本)9781665403603

Artificial intelligence played a vital role in brings revolutionary changes in the field of perfumery. It is much evident with events including the success of Philyra, exhibitions showcasing the ideas of this concept. machine learning made it user friendly and more comfortable for the users by means of suggestive interaction. machine learning also benefited the perfumers in helping them to choose the best combinations and likely successful outcomes. With growing concern about a healthy lifestyle, the thoughts about having an artificial intelligence to predict the user friendliness could be a huge success. this definitely would require a huge database comprising a large detail about diseases and the causes and combinational results of the various chemicals used in perfumery. this system may not be a completely successful one but would be reliable to a better extent. It would gain a positive response from various governmental health departments and would be encouraged by the consumers. Also, another possible development would be Artificial intelligence that is able to predict how long a perfume can last. this would let the consumer choose the one that suits the need. through this idea we could now get a clear idea about the progress that we have made till this day. Further we can also be driven into vague ideas about how the future of Artificial intelligence would likely grow into. machine learning and deep learning is a major pillar of artificial intelligence with larger application. Coming to our domain of discussion, artificial intelligence changed the way that things were in the past centuries about fragrance. this article proposed Quantitative structure-activity relationship (QSAR) method is used to predict the best perfume flavour. the proposed system also reduces mean absolute error (MAE). the proposed QSAR is also reducing the chemical composition and increase the perfume quality. © 2021 IEEE.

关键词： data analytics

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A machine learning Approach to Analyze Cloud Computing Attacks

A Machine Learning Approach to Analyze Cloud Computing Attac...

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international conference on Contemporary Computing and Informatics (IC3I)

作者： Sangeeta Rani Khushboo Tripathi Yojna Arora Ajay Kumar Amity School of Engineering & Technology Amity University Manesar Gurgaon India School of Engineering & Technology JECRC University Jaipur Rajasthan India

A Cloud Computing model enables convenient, on demand network access, otherwise known as cloud computing (CC) or "the Cloud". Cloud computing is delivered via internet, using cloud." Everything from mobile applications to data centers on a pay-per-use basis. It is difficult to adopt CC and edge computing for several reasons, including security concerns, client vulnerabilities, and partnership acknowledgment. An algorithm that automatically improves with experience is known as a machine learning algorithm. this paper aims to examine some of the ML algorithms applied to CC security threats. Among the algorithms, we reviewed unsupervised, supervised, semi-supervised, and reinforcement learning for addressing cloud security challenges. CC models can be secured by pursuing future research directions.

关键词： Support vector machines data centers machine learning algorithms Computational modeling Cloud computing security Reinforcement learning Mobile applications

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Intersection Identification Using Large-Scale Vehicle Trajectory data 1

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5th international conference of Transportation Research Group of India, CTRG 2019

作者： Huang, Tingting Sharma, Anuj ETALYC Inc. AmesIA United States Iowa State University AmesIA United States

ISBN: (数字)9789811682599

ISBN: (纸本)9789811682582

Intersections are critical facilities in traffic control and management with vital impact on traffic mobility and safety. Identifying the intersections in a network is the first step. As a major source of traffic data, trajectory data based on Global Positioning System (GPS) locations from probe vehicles are widely used. thus, how to automatically extract the information and identify the intersections out of massive and noisy GPS data is important. this study uses machine learning method associated with empirical rules to find the pattern in vehicle trajectories and detect intersections at a city scale. First, the turning points are extracted from massive GPS points by examining the bearing changes. then, a density-based clustering method is applied to cluster all the turning points as potential intersection area. To remove the false detections, two types of indices are developed: (a) direction discrepancy index (DDI) and (b) turning discrepancy index (TDI). By examining the two indices in each potential detection, clusters with high discrepancy in travel direction and turning direction distribution are filtered out. the methods are applied on a collection of millions GPS records. the raw trajectory data are noisy with inconsistent record intervals and imprecise locations. the results are compared with Open Street Map (OSM) database. Although the results generated from GPS data do not cover some points due to data availability, they can detect the points that are missed by OSM database. this method is easy to scale with few interventions;thus, it could be a good supplement to the open source geographical database. © 2022, the Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： database systems

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Deep learning Applications for Blockchain in Industrial IoT

Deep Learning Applications for Blockchain in Industrial IoT

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international conference on Contemporary Computing and Informatics (IC3I)

作者： Vipul Masal P. Pavithra Shiv Kant Tiwari Rajesh Singh Jeidy Panduro-Ramirez Durgaprasad Gangodkar Smt. Geeta D. Tatkare Polytechnic Roha Raigad Maharashtra Department of IT Vignan’s Institute of Information Technology Duvvada Gajuwaka Visakhapatnam Andhra Pradesh Institute of Business Management GLA University Mathura Uttar Pradesh India Uttaranchal University Dehradun Uttarakhand India Department of Business Universidad Tecnológica Del Perú Perú Department of Computer Science & Engineering Graphic Era Deemed to be University Dehradun Uttarakhand India

the engaging ambitions of regeneration & growth of manufacturing in several nations, as well as the speed, flexibility, or expense benefits that might arise from the architecture of the industrial Internet of things (IIoT), are attracting considerable interest. While blockchain or machine learning techniques, particularly deep learning, might offer the latest viable use cases for IIoT, they operate in a rather antagonistic manner. Underneath the assumption of information regulatory standards such as information protections, blockchain helps the crucial information collecting for machine learning. However, it may be susceptible to a data breach as a result of big information insights using machine learning. To enable machine learning/blockchain relevant & applicable for a variety of industrialized applications, it is of the utmost essential to have a thorough grasp of their evolution within the framework of IIoT. In this paper, we present a summary & analytics of the opportunity of blockchain as well as machine learning in the IIoT, focusing on the agreement method, preservation, or transmission. this study gives a better knowledge of the protection & confidentiality issues of a blockchain’s vital aspects from the viewpoint of machine learning, and that is beneficial for the creation of viable blockchain alternatives for IIoT.

关键词： Deep learning Industries Measurement Recurrent neural networks Blockchains Internet Informatics

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data analytics in Farming: Rice price prediction in Andhra Pradesh

Data Analytics in Farming: Rice price prediction in Andhra P...

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international conference on Multimedia, Signal Processing and Communication Technologies, (IMPACT)

作者： Pundru Chandra Shaker Reddy G. Suryanarayana LNC Prakash K Sucharitha Yadala School of Computing and Information Technology REVA University Bengalure India Department of Computer Science … Engineering Vardhaman College of Engineering Hyderabad India Department of Computer Science … Engineering CVR College of Engineering Hyderabad India Department of Computer Science … Engineering CMR Institute of Technology Hyderabad India

ISBN: (纸本)9781665476485

the variances regarding prices of horticultural commodities adversely affect the nations GDP. the ranchers are genuinely and monetarily influenced as their long stretches of difficult work go to no end. Forecast of the prices might help the farming production network in settling on vital choices in limiting and dealing with the danger in value variances. By the way of outcome of the diminution of crop yield making due to uneven atmosphere circumstances and global warming. Rice is a significant yield developed in India, with Andhra Pradesh (AP) state being second as far as creation. At present ranchers in AP are moving from rice development to different harvests on account of worth variances and environmental variation. In this research, the costs of rice in AP remain anticipated utilizing time-series using data analytics like machine learning (ML) models. the prototypes SARIMA, Holt-Winter's Periodic technique, and Long Short-Term Memory (LSTM) Neural Networks (NN)be situated and utilized, and their recital was assessed dependent on the RMSE esteem on the rice database whose costs from 2001 to 2020. the conclusions demonstrate that the recommended process, LSTM neural network method is actual expectant and finest method which is suitable for the data.

关键词： Costs data analysis Limiting Time series analysis Prototypes Production machine learning

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Survival Prediction of a Patient afterward a Heart Attack by machine learning

Survival Prediction of a Patient afterward a Heart Attack by...

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international conference on Contemporary Computing and Informatics (IC3I)

作者： Biswajit Giri Suman Kumari Agarwal Nandani Kumari Rana Majumder Sumita Gupta Anirban Mitra Meghnad Saha Institute of Technology Kolkata India Sister Nivedita University Kolkata India ASET Amity University Noida India

Heart attack is a major threat to human life. It occurs in one or more coronary arteries refilled by the oxygen-rich blood, which also supplies into the heart muscle, suddenly becomes blocked, and unfortunately, a few heart muscle sections can’t get sufficient oxygen. In past, most patients suffered heart attacks at some stage in life. Unfortunately, some of them lost their lives due to this. When the non-survival and survival variables both are examined that determines whether a patient will survive for one more year after suffering from a heart attack. A supervised learning technique has been applied to the Echocardiogram dataset. the experimental outcomes show that the proposed methodology applied with several data preprocessing approaches achieved a decent 94.74% classification accuracy.

关键词： Heart Supervised learning data preprocessing Cardiac arrest machine learning Muscles Informatics

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Ensemble Based Prediction of Cardiovascular Disease Using Bigdata analytics 5

Ensemble Based Prediction of Cardiovascular Disease Using Bi...

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5th international conference on Computing Sciences (ICCS)

作者： Krithika, D. R. Rohini, K. Vels Inst Sci Technol & Adv Studies Dept Comp Sci Chennai Tamil Nadu India

ISBN: (纸本)9781665494458

Heart attack is leading cause of death. In our body heart is hardest working organ. Risk factors for future cardiovascular is people with high cholesterol, high blood pressure, diabetes, strong family history. Heart attack is primarily caused by lifestyle. CAD is coronary arteries blood vessels supply oxygen and blood to the *** is almost cause of death worldwide. Coronary arteries normally happen when deposition of cholesterol accumulate artery walls creating plaques. Arteries being tighten and difficult to blood flow to the heart. It also causes blood clots of rhombus totally occludes blood flow, when a blockage occur it is called coronary occlusion. So, blockages are occlusion (myocardial infarction) of heart attack. contraction of heart muscle suddenly stopped. Blood viscosity is important role to maintain vascular homeostasis. Hematocrit is the packed cell volume (pcv) of blood. Few machine learning algorithms applied to large amount of data and accuracy checked. the proposed model is CVD prediction using Extreme Gradient Boost, DT, KNN, SVM, Naive bayes, Random forest, ANN, Hyper parameter tunned random forest Algorithm.

关键词： CVD - cardiovascular disease heart disease high cholesterol high blood pressure diabetes strong family history Hematocrit PCV- packed cell volume machine learning Heart attack myocardial infarction Extreme Gradient Boost DT KNN SVM Naive bayes Random forest ANN HPTRF- Hyper parameter tuned random forest Classifier

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