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International Conference on Contemporary Computing and Informatics (IC3I)

作者： Shubham Dhananjay Shende Aniruddha Shrawankumar Singh Shubham Sushil Shah Mayuresh Mohan Shinde Siddhant Rahul More Bharati Ainapure Vishwakarma University Pune India

In this academic research, we make an effort to put machine learning methods for stock price prediction. Stock price forecasting uses machine learning effectively. In order to make wiser and more accurate financial decisions, machine learning methods can be examined. The scope of this research work revolves around financial ratios published by companies. This research paper puts forth a stock market price forecasting technique based on the fundamental study of stocks. The technique examines the financial ratios or parameters of stocks over a specific period of time and forecasts whether they will experience a gain or loss. We found that strategies like random forest and the LSTM algorithm function best throughout the system of thinking about various strategies and factors that should be taken into account.

关键词： machine learning algorithms Forestry Companies Prediction algorithms Forecasting Stock markets Informatics

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A Comparative Analysis of machine learning algorithms for Agricultural Drought Forecasting

A Comparative Analysis of Machine Learning Algorithms for Ag...

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International Conference on Issues and Challenges in Intelligent Computing Techniques (ICICT)

作者： Jayavrinda Vrindavanam Tina Babu Harika Gandiboina Gopika Jayadev Department of AI & ML School of Engineering Dayananda Sagar Univeristy Bengaluru India Department of Computer Science and Engineering School of Engineering Dayananda Sagar Univeristy Bengaluru India

The occurrence of drought is a climatic feature and is a phenomenon that happens over time. Depending on the severity, it can last for a short or long time. Farming households are trying to meet due to rising agricultural operating costs that hinder the country's development. This study aims to forecast the severity of the drought over time. Drought scores vary from 0 to 5, with 0 and 5 indicating the least and highest intensity drought conditions. This is done using weather and soil data of a region consisting of Precipitation, Surface Pressure, Humidity, Temperature, Wind Speed, and Soil data. The main reasons for the cause of drought are first identified. These features are used to train the multivariate time series models like Prophet, VAR (Vector Auto-Regression), LSTM (Long short-term memory), and Comparison of actual v/s predicted values. The results were promising. The study has done an analysis comparing different machine learning algorithms for agricultural drought forecasting and it was found that the LSTM model performed better than VAR and Prophet models.

关键词： Reactive power Analytical models machine learning algorithms Biological system modeling Wind speed Droughts Time series analysis

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AI-based Diagnostic Tool for Liver Disease using machine learning algorithms

AI-based Diagnostic Tool for Liver Disease using Machine Lea...

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International Conference on Humanoid, Nanotechnology, Information Technology, Communication and Control, Environment and Management (HNICEM)

作者： Rex Paolo C. Gamara Antipas T. Teologo Romano Q. Neyra Argel A. Bandala Electrical and Electronics Engineering Department FEU Institute of Technology Manila Philippines Senior Director for Engineering Office FEU Institute of Technology Manila Philippines Department of Electronics and Computer Engineering De La Salle University – Manila Manila Philippines

The liver is the human body's largest internal organ. Globally, liver disease is considered the cause of approximately 2 million yearly death – whereas the 11th and 16th worldwide leading causes of death are cirrhosis and liver cancer. In the Philippines, according to the Department of Health (DOH), liver cancer is ranked as the 3rd leading cause of death. In most cases, surgery may be considered a possible cure if detected at an early stage. However, there is no efficient early detection method for liver cancer. In this paper, multiple machine learning methodologies are modeled to provide diagnosis classification of liver disease based on the laboratory parameter readings. Based on the results for all models, the most accurate prediction is made by ANN at 89%, followed by SVM at 79.5%. The results establish that AI-based machine learning approaches may be utilized for assisting medical-related diagnosis.

关键词： Support vector machines Liver cancer machine learning algorithms Liver diseases Surgery machine learning learning (artificial intelligence)

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The electrocardiogram endeavour: from the Holter single-lead recordings to multilead wearable devices supported by computational machine learning algorithms

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EUROPACE 2020年第1期22卷 19-23页

作者： Vardas, Panos Cowie, Martin Dagres, Nikolaos Asvestas, Dimitrios Tzeis, Stylianos Vardas, Emmanuel P. Hindricks, Gerhard Camm, John Hygeia Hosp Grp Heart Sect Athens Greece Imperial Coll London London England Univ Leipzig Heart Ctr Leipzig Dept Electrophys Leipzig Germany Mitera Gen Hosp Hygeia Grp Cardiol Dept Athens Greece 251 Airforce Gen Hosp Athens Greece St Georges Univ London London England

This review aims to provide a comprehensive recapitulation of the evolution in the field of cardiac rhythm monitoring, shedding light in recent progress made in multilead ECG systems and wearable devices, with emphasis on the promising role of the artificial intelligence and computational techniques in the detection of cardiac abnormalities.

关键词： Electrocardiogram Electrocardiography machine learning algorithms Multilead wearable devices

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Intent Classification Using machine learning algorithms and Augmented Data

Intent Classification Using Machine Learning Algorithms and ...

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Data Science and Intelligent Computing (ICDSIC), International Conference on

作者： Alaa T. Al-Tuama Dhamyaa A. Nasrawi College of Computer Science and Information Technology University of Kerbala Karbala Iraq

An essential step in a conversational agent is an intent classification of user-generated text input. The purpose of building the intent classifier for a chatbot is to understand the intention of user queries to respond fast and accurately. Robust chatbots necessitate more utterances for an improved training model. Nevertheless, acquiring and annotating data is time-consuming and expensive. This paper investigates machine learning techniques and data augmentation for addressing intent classification. Experiments were conducted on office product's question answering of Amazon using Random Forest, Multinomial Naïve Bayes, Logistic Regression, and Support Vector machine (SVM). Contextual word embedding with BERT was used for generating new synonym utterances. The main experiments are the comparison of the performance of these methods after augmenting new data. In general, SVM and random forest yield comparable results. Followed by logistic regression. However, the f1 score of the multinomial naïve base is the lowest. Additionally, we discovered that augmenting new utterances had a simple effect on the performance of models.

关键词： Support vector machines Training machine learning algorithms Computational modeling Buildings Bit error rate Data science

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Software Supply Chain Vulnerabilities Detection in Source Code: Performance Comparison between Traditional and Quantum machine learning algorithms

Software Supply Chain Vulnerabilities Detection in Source Co...

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IEEE International Conference on Big Data

作者： Mst Shapna Akter Md Jobair Hossain Faruk Nafisa Anjum Mohammad Masum Hossain Shahriar Nazmus Sakib Akond Rahman Fan Wu Alfredo Cuzzocrea Department of Computer Science Kennesaw State University USA Department of Information Technology Kennesaw State University USA Department of Applied Data Science San Jose State University USA Department of Computer Science and Software Engineering Auburn University USA Department of Computer Science Tuskegee University USA iDEA Lab University of Calabria Rende Italy

ISBN: (纸本)9781665480468

The software supply chain (SSC) attack has become one of the crucial issues that are being increased rapidly with the advancement of the software development domain. In general, SSC attacks execute during the software development processes lead to vulnerabilities in software products targeting downstream customers and even involved stakeholders. machine learning approaches are proven in detecting and preventing software security vulnerabilities. Besides, emerging quantum machine learning can be promising in addressing SSC attacks. Considering the distinction between traditional and quantum machine learning, performance could be varies based on the proportions of the experimenting dataset. In this paper, we conduct a comparative analysis between quantum neural networks (QNN) and conventional neural networks (NN) with a software supply chain attack dataset known as ClaMP. Our goal is to distinguish the performance between QNN and NN and to conduct the experiment, we develop two different models for QNN and NN by utilizing Pennylane for quantum and TensorFlow and Keras for traditional respectively. We evaluated the performance of both models with different proportions of the ClaMP dataset to identify the f1 score, recall, precision, and accuracy. We also measure the execution time to check the efficiency of both models. The demonstration result indicates that execution time for QNN is slower than NN with a higher percentage of datasets. Due to recent advancements in QNN, a large level of experiments shall be carried out to understand both models accurately in our future research.

关键词： machine learning algorithms Computational modeling Supply chains Qubit Artificial neural networks machine learning Big Data

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Diseases Detection System based on machine learning algorithms and Internet of Things Technology used in Viticulture

Diseases Detection System based on Machine Learning Algorith...

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E-Health and Bioengineering Conference (EHB)

作者： Cristina Balaceanu Robert Streche Roxana Roscaneanu Filip Osiac Oana Orza Sabina Bosoc George Suciu R&D Department Beia Consult International Bucharest Romania

ISBN: (数字)9781665485579

ISBN: (纸本)9781665485586

The concept of smart farming has begun to take hold in all sectors of agriculture, with the wine sector keeping pace with recent technological developments. Thanks to the advantages offered by new technologies, winegrowers are turning to IoT technology, sensor networks, robots, drones and artificial intelligence to monitor their vineyards in order to increase production and improve quality. Therefore, a study was carried out using sensor networks (WSN) to monitor the main environmental parameters that can affect grape production and quality, as well as ML algorithms to analyse the health status of vine leaves. With high-precision WSN sensors we can monitor in real time the condition of the plants as well as the environment. Artificial intelligence-based algorithms communicate to the user the susceptibility of plants to certain diseases. With the help of drones, images of the vines are captured to be analysed by the ML-based algorithm for the detection of diseases such as powdery mildew, downy mildew, and grey rot.

关键词： Wireless sensor networks machine learning algorithms Production Robot sensing systems Turning Biomedical monitoring Monitoring

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United Neurological study of disorders: Alzheimer's disease, Parkinson's disease detection, Anxiety detection, and Stress detection using various machine learning algorithms

United Neurological study of disorders: Alzheimer's disease,...

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International Conference on Signal and Information Processing (IConSIP)

作者： Sahaja Dixit Akash Gaikwad Vibha Vyas Mahesh Shindikar Ketaki Kamble Department of electronics and telecommunication College Of Engineering Pune Pune Maharashtra India Department of Applied Science College Of Engineering Pune Pune Maharashtra India

ISBN: (纸本)9781728168869

The biomedical study of large-scale multimodal neuroimaging data is popular nowadays due to advancements in deep learning and machine learning algorithms. This article has studied four different types of neurocircuitry disorders or diseases that help human beings prevent or detect them in the early stages for easily starting the treatment. a) Early detection and classification of Alzheimer's disease (AD) b) Parkinson's detection (a neurocircuitry disorder that had slowed the activity) c) Generalized anxiety disorder d) stress prediction for patients of stress) Here, this work briefly reviews the important literature on neurological diseases and explores how deep learning can help researchers to diagnose the disease at its primary stages

关键词： Deep learning Neurological diseases Neuroimaging machine learning algorithms Parkinson's disease Magnetic resonance imaging Anxiety disorders

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A Study on machine learning algorithms for Lung Cancer Classification and Prediction

A Study on Machine Learning Algorithms for Lung Cancer Class...

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International Conference on Signal Processing and Communication (ICSC)

作者： Sai Sri Ramya Koppisetti Sistla Sri Sai Venkata Chaitanya T. Sudha Rani B. S. Kiruthika Devi Department of Computer Science and Engineering Aditya Engineering College Surampalem A.P India CL Educate Ltd New Delhi India

ISBN: (纸本)9781665454315

machine learning (ML) is an AI subfield that helps programs improve their predictive abilities without being explicitly taught to do so. This ML incorporates a number of prediction methods, and it makes use of past outcomes to calculate predicted values for the future. Lung cancer is the biggest cancer killer, accounting for a disproportionately large number of cancer-related deaths. Unchecked cell growth leads to tumor development. The cancerous tumor multiplies and metastasizes throughout the body. Worldwide, lung cancer is the leading cause of death, accounting for around 7.6 million deaths annually. No symptoms are apparent until the disease has progressed significantly. Patients with this cancer sometimes don’t seek medical help until they’ve already gone past the point of no return. If cancer is detected and treated when still in its early stages, survival rates can increase by 47%. Worldwide mortality now sits at 17%. Although the causes of this malignancy are not fully understood, it is clear that accurate diagnosis and early prognosis are crucial for lowering mortality rates. In this paper, we discuss several methods for diagnosing lung cancer ahead of time.

关键词： machine learning algorithms Lung cancer Prediction methods machine learning Signal processing Bayes methods Prognostics and health management

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Comparative Analysis of machine learning algorithms for Securing IoT Enabled Environment

Comparative Analysis of Machine Learning Algorithms for Secu...

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International Conference on Computing, Communication, and Intelligent Systems (ICCCIS)

作者： Amit Sagu Nasib Singh Gill Preeti Gulia Deepti Rani Ayushi Chahal Department of Computer Science & Applications Maharshi Dayanand University Rohtak India

ISBN: (纸本)9781665462013

The IoT (Internet of Things) link system, various applications, data storage like cloud, and another service area that possibly will be a fresh entry for attackers as they uninterruptedly offer services in the organization. At this time, threats to users’ privacy and malware pose significant challenges to the integrity of the Internet of Things. These extortions might lead to the loss of important information, which in turn could cause a company’s finances and reputation to suffer. In this paper, we have identified anomalous activity throughout the IoT ecosystem by utilizing a variety of machine learning approaches. The results from the experiment indicate that the categorization capabilities of machine learning techniques may serve as an alternative strategy for ensuring the safety of communication inside the IoT.

关键词： Support vector machines Privacy machine learning algorithms Memory machine learning Organizations Malware

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