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A Machine Learning Approach for Multiclass Sentiment Analysis of Twitter data: A Review 5th

A Machine Learning Approach for Multiclass Sentiment Analysi...

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5th international conference on Emerging technologies in Computer Engineering: Cognitive computing and Intelligent IoT, ICETCE 2022

作者： Chougule, Bhagyashree B. Patil, Ajit S. Shivaji University Kolhapur416234 India

ISBN: (纸本)9783031070112

Sentiment analysis or opinion mining is a prominent and most demanding research topic in today’s world. The main idea behind this research topic is to recognize the user’s opinions and emotions towards the aspect of service or product via a text basis. Sentiment analysis involves mining text, lexicon construction, extracting features and finally finding polarity of text. Even though numerous amounts of research work were conducted in this field through different methods, opinion mining is still considered a challenging field for research. Most of the prior research concentrated on the binary or ternary classification of sentiments such as positive, negative, neutral. Some studies have done an analysis of Twitter sentiment based on ordinal regression, but by turning the problem of ordinal regression into a problem of binary classification. The aim of this study is to review the multiclass sentiment analysis of Twitter text data using an automated i.e., machine learning approach. This review paper intends to focus on existing work for Twitter sentiment analysis with multiple polarity categorization and explore gaps with future scope in the said research area. © 2022, The Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： Social networking (online)

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IoT Integrated Air Quality Detection and Alert System 1st

IoT Integrated Air Quality Detection and Alert System

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1st international Research conference on computing technologies for Sustainable Development, IRCCTSD 2024

作者： Sreyas, Navin Venkatesh, Tarun Harjit, R.L. Prabha, B. SRM Institute of Science and Technology Tamil Nadu Vadapalani India

ISBN: (纸本)9783031823855

In many urban and industrial regions today, maintaining and monitoring air quality has become a major concern. Transportation, energy, fuels, and other forms of pollution all contribute to deteriorating air quality. Given the negative effects of air pollution on both human health and the environment, air quality is of paramount importance in today's world. In this era of technological advancement, the Internet of Things (IoT) emerges as a promising approach for improving healthcare systems. This research describes a novel IoT-integrated air quality detection and alert system that aims to enable real-time monitoring and alerts the user of the situation persisting. The proposed system collects extensive information on air quality using embedded sensors and monitoring equipment. Using machine learning algorithms, the system automatically analyzes collected data to detect Predicting and forecasting air quality is critical for addressing environmental and public health issues. With the increasing impact of air pollution on human health and the environment, accurate and timely air quality forecasting is crucial for managing risks. The goal of this project is to create and implement machine learning methods for air quality prediction and forecasting. The machine learning models developed are applied to real-time data to provide short- and long-term air quality forecasts, allowing for timely public awareness and pollution control measures to be implemented. This study additionally examines the prospects of using future technologies, such as IoT devices, to improve the geographical resolution of air quality predictions. This study advances environmental science and public health by introducing a comprehensive framework for predicting and forecasting air quality using machine learning. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Urban transportation

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A 4-element Dual Band MIMO Antenna with L-Shaped Decoupling Stub for 5G Applications

A 4-element Dual Band MIMO Antenna with L-Shaped Decoupling ...

引用

international conference on advances in Electrical and Computer technologies, ICAECT 2021

作者： Seetharaman, R. Varsha Poorani, B. Kumar, Kavya Santha Tharun, M. Sreeja Mole, S.S. Anandan, K. Department of ECE CEG Campus Anna University Chennai600025 India Department of Computer Engineering Texas A&M University College StationTX77801 United States Department of ECE Christu Jyothi Institute of Technology & Science Jangaon506167 India PS Division of E & E TUV SUD South Asia Pvt. Ltd. Chennai600041 India

ISBN: (纸本)9789811911101

A MIMO antenna which operates in dual frequency band for the 5G communication is designed. It consists of four antennas. It operates in dual bands like 3.3–3.6 GHz and 4.8–5.0 GHz. Miniaturization and multi-cell array antennas offer the possibility of high-speed data transmission recent days. According to the simulation results, return loss should be less than −6 dB for nominal matching, and the Isolation is better than 12 dB over two chosen ranges of 3.3–3.6 GHz and 4.8–5.0 GHz. Also, ECC value is achieved less than 0.001 which is necessary for MIMO operations. © 2022, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： 5G mobile communication systems

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A Multi-stage Multi-group Classification Model: Applications to Knowledge Discovery for Evidence-based Patient-centered Care 14

A Multi-stage Multi-group Classification Model: Applications...

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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： Lee, Eva K. Egan, Brent Center for Operations Research in Medicine and Healthcare The Data and Analytic Innovation Institute Atlanta United States Georgia Institute of Technology Atlanta United States American Medical Association Chicago United States Medical University of South Carolina United States

ISBN: (纸本)9789897586149

We present a multi-stage, multi-group classification framework that incorporates discriminant analysis via mixed integer programming (DAMIP) with an exact combinatorial branch-and-bound (BB) algorithm and a fast particle swarm optimization (PSO) for feature selection for classification. By utilizing a reserved judgment region, DAMIP allows the classifier to delay making decisions on ‘difficult-to-classify’ observations and develop new classification rules in a later stage. Such a design works well for mixed (poorly separated) data that are difficult to classify without committing a high percentage of misclassification errors. We also establish variant DAMIP models that enable problem-specific fine tuning to establish proper misclassification limits and reserved judgement levels that facilitate efficient management of imbalanced groups. This ensures that minority groups with relatively few entities are treated equally as the majority groups. We apply the framework to two real-life medical problems: (a) multi-site treatment outcome prediction for best practice discovery in cardiovascular disease, and (b) early disease diagnosis in predicting subjects into normal cognition, mild cognitive impairment, and Alzheimer’s disease groups using neuropsychological tests and blood plasma biomarkers. Both problems involve poorly separated data and imbalanced groups in which traditional classifiers yield low prediction accuracy. The multi-stage BB-PSO/DAMIP manages the poorly separable imbalanced data well and returns interpretable predictive results with over 80% blind prediction accuracy. Mathematically, DAMIP is NP-complete with its classifier proven to be universally strongly consistent. Hence, DAMIP has desirable solution characteristics for machine learning purposes. Computationally, DAMIP is the first multi-group, multi-stage classification model that simultaneously includes a reserved judgment capability and the ability to constrain misclassification rates within a single

关键词： Machine learning

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Negotiation-Based Multi-agent System for Ambient Conditions Control in Accordance with Multiple User Preferences 16th

Negotiation-Based Multi-agent System for Ambient Conditions...

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16th KES international conference on Agents and Multi-Agent Systems: technologies and Applications, KES-AMSTA 2022

作者： Mandaric, Katarina Skocir, Pavle Jezic, Gordan Lovrek, Ignac University of Zagreb Faculty of Electrical Engineering and Computing Internet of Things Laboratory Unska 3 Zagreb10000 Croatia

ISBN: (纸本)9789811933585

The Internet of Things has set the foundation for countless innovative solutions, some of which are designed specifically for homes, offices, etc., with the aim to increase user comfort. In order to automatically increase user comfort by saving the user time and energy, the system has to obtain and process information about the conditions in the smart space, and the user’s needs and wishes. Context awareness, without which ambient intelligence could not be developed, relies on data collected from connected devices. Ambient intelligence is about meeting users’ needs and desires while taking context into account and making instantaneous adjustments to their actions and other context changes. In this paper, we propose a context-aware, user-centric multi-agent system that is able to independently determine ambient settings in a smart space according to the preferences of users present without any user intervention. The main advantage is that it serves more users, i.e., it is not focused on only one user and his preferences, but takes care of all present users and satisfies their wishes and needs regarding ambient conditions while harmonizing their different preferences. To achieve this, a negotiation process between intelligent user agents is proposed and described. The presented model is described on the use case of smart lighting. © 2022, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Multi agent systems

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Home Automation With NoSQL and Node-RED Through Message Queuing Telemetry Transport 5th

Home Automation With NoSQL and Node-RED Through Message Queu...

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5th international conference on Intelligent computing and Applications, ICICA 2019

作者： Chauhan, Naman Malik, Medhavi Department of Computer Science and Engineering SRMIST Delhi-NCR Campus GhaziabadUttar Pradesh India

ISBN: (纸本)9789811555657

In the last decades, home automation technology has evolved and achieved popularity. This paper discusses the current and emerging home automation technologies that will make life easier and swifter. Most automation systems use a smartphone and a microcontroller. Current systems are mostly dependent on local Wi-Fi or Bluetooth connections for the transfer of data from the user to the microcontroller. In this paper, wireless communication through mobile data or the Internet and controlling the data through a centralized and secure NoSQL database on Google Firebase are studied. The user will be connected to the system using a mobile application built for both Android and iOS. Moreover, the advantages and drawbacks of different home automation systems are discussed. © 2021, Springer Nature Singapore Pte Ltd.

关键词： Microcontrollers

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Arrhythmia Detection and Classification with Hybrid Deep Learning with Pity Beetle Optimization Model

Arrhythmia Detection and Classification with Hybrid Deep Lea...

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international conference on advances in Information Technology (ICAIT)

作者： D. Vinod P.N. Karthikayan R N Ojashwini Srimathi S Saranya R Vigneshwaran P Department of Computing Technologies School of Computing College of Engineering and Technology SRM Institute of Science and Technology Chennai TN India Department of Information technology St Joseph's institute of technology OMR CHENNAI Department of CSE Raja Rajeswari College of Engineering Bengaluru Karnataka India Department of Bio Technology Saveetha School of Engineering SIMATS Chennai Tamilnadu India Department of CSE RMK college of Engineering and Technology Chennai Department of EEE K. Ramakrishnan College Of Engineering Trichy

ISBN: (数字)9798350383867

ISBN: (纸本)9798350383874

Arrhythmia classification is a crucial field within the domain of medical diagnostics, utilizing advanced Machine Learning (ML) and Deep Learning (DL) methods to accurately recognize and categorize different cardiac arrhythmias, and abnormalities in the heart's rhythm. This technology performs a significant part in cardiac healthcare, as arrhythmias may result in serious health problems, comprising stroke and heart attacks. By analyzing electrocardiogram (ECG) data, arrhythmia classification allows accurate diagnosis and earlier detection of irregular heart rhythms, facilitating timely involvement and tailored treatment approaches. It permits medical specialists to make informed decisions, leading to enhanced patient outcomes and improved cardiac medical management. In this study, we propose an Arrhythmia Detection and Classification with Hybrid Deep Learning with the Pity Beetle Optimization Algorithm (ADC-HDLPBO) method. The ADC-HDLPBO technique is targeted at advancing the domain of arrhythmia analysis in cardiac healthcare. The proposed ADC-HDLPBO system encompasses classification through a combination of Convolutional Long Short-Term Memory networks (CLSTM) and Autoencoders (AE), developed to take temporal dependencies and recreate important features. For precision and performance optimization, the model incorporates PBA for hyperparameter tuning, adapting to the unique dataset features. The ADC-HDLPBO algorithm marks a major leap forward in arrhythmia detection and classification, providing a robust and accurate solution for the earliest diagnosis of cardiac abnormalities.

关键词： Deep learning Adaptation models Accuracy Heart beat Arrhythmia Stroke (medical condition) Classification algorithms Optimization Tuning Medical diagnostic imaging

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A Proposal of Iot Based Evapotranspiration Irrigation System for Water Conservation 2nd

A Proposal of Iot Based Evapotranspiration Irrigation System...

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2nd international conference on Emerging technologies for computing, Communications, and Smart Cities, ETCCS 2021

作者： Walia, Simrat Sengupta, Jyotsna Department of CSE Baba Banda Singh Bahadur Engineering College Fatehgarh Sahib India Department of Computer Science Punjabi University Patiala India

ISBN: (纸本)9789811902833

Water conservation is a prime concern in arid and semi-arid regions. The farming sector consumes a lot of water in irrigation and other activities. IoT (Internet of Things) and WSN (Wireless Sensor Network) are extensively used in precision agriculture to control fundamental activities like Irrigation Scheduling, data Acquisition, data Storage and Analysis. These systems involve sensors inputs to be evaluated against some threshold values and decisions are taken. The proposed automated irrigation system involves monitoring environmental parameters and controlling the irrigation process remotely, involves short-range communication from sensors to the base station and long-range communication from the base station to the internet. The focus of the proposed system is to conserve water and other resources. The system incorporates use of sensor data, evapotranspiration of region and precipitation information from online weather forecast. The hardware components of system are microcontroller, 5v DC motor, L293 motor driver module, resistive soil moisture sensor and breadboard. The soil moisture sensor continuously records the moisture of the soil and transmits readings to the microcontroller. The DC submersible motor connected to the microcontroller through L293 motor driver module, supply water when soil moisture level is below the threshold. The system generates irrigation schedules are based on evapotranspiration of the region and sensor feedback. © 2022, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Irrigation

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Optimizing Combustion Efficiency in Cloud-Connected Smart Gasoline Engines using Gradient Boosting Machines

Optimizing Combustion Efficiency in Cloud-Connected Smart Ga...

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international conference on Energy, Power and Environment: Towards Sustainable Growth (ICEPE)

作者： M.R Pavithra S. Priyadarshini K. Sangeethalakshmi M Maria Sampoornam S. Senthil C. Srinivasan Department of Management Studies Rajalakshmi School of Business Chennai Tamil Nadu India Department of English Vinayaka Mission’s Kirupananda Variyar Arts & Science College A Constituent College of Vinayaka Mission’s Research Foundation (Deemed To Be University) Salem Tamil Nadu India Department of Electronics and Communication Engineering R.M.K. College of Engineering and Technology Chennai Tamil Nadu India Department of Information Technology J.J College of Engineering and Technology Tiruchirappalli Tamil Nadu India Department of Mathematics Vel Tech Rangarajan Dr.Sagunthala R&D Institute of Science and Technology Chennai Tamil Nadu India Department of Biomedical Engineering Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences Saveetha University Chennai Tamil Nadu India

ISBN: (数字)9798350379297

ISBN: (纸本)9798350379303

This research proposes using cloud-connected technologies and Gradient Boosting Machines (GBM) to improve smart Gasoline combustion efficiency. It uses real-time cloud data feeds to inform GBM decision-making to optimize fuel utilization and reduce emissions. A prediction model that adjusts to dynamic engine conditions was created using previous data and constant sensor inputs. It uses ensemble learning to optimize combustion parameters like spark timing and air-fuel ratio using GBM, improving engine performance. The experimental setup uses a cloud-connected smart petrol engine prototype with enhanced sensors and actuators. The engine and centralized computing system communicate seamlessly via the cloud architecture, allowing quick data analysis and model changes. Iterative model refinement and real-time modifications allow the system to adapt to changing operating conditions and maximize combustion efficiency. Compared to standard engine management systems, thorough calculations and practical testing reveal considerable fuel efficiency and pollution reductions. The cloud-connected GBM technique handles uncertainty and unexpected operating situations well, demonstrating its promise for smart engine systems. It advances intelligent engine control systems by using cloud connection and machine learning (ML) to optimize petrol engine combustion efficiency. The discoveries may help the car sector create environmentally friendly and high-performance engine solutions.

关键词： Cloud computing Pollution Atmospheric modeling Predictive models Control systems Combustion data models

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An Autonomous Intelligent System to Leverage the Post-harvest Agricultural Process Using Localization and Mapping

An Autonomous Intelligent System to Leverage the Post-harves...

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international conference on Intelligent Systems and Sustainable computing, ICISSC 2021

作者： Nanda, Amitash Ahire, Deepak Department of Instrumentation and Electronics Engineering College of Engineering and Technology Odisha Bhubaneswar India Department of Computer Science and Engineering Walchand College of Engineering Maharashtra Sangli India

ISBN: (纸本)9789811900105

Agriculture accounts for the principal source of income in a country like India. Farmers produce a prolific amount of crops yearly. Crop production involves four critical phases, harvesting, storage, processing, and market access. The post-phase of crop harvesting is very crucial for valuable production. Various fruits and vegetables remain fresh for a shorter duration of time before reaching timely to the market. Manual labor and data handling are less efficient;hence precision agriculture requires efficient crop monitoring and data collection using a mobile robot. In this work, we proposed two scenarios, one for mapped and the other for the unmapped model of the farm. The mapped model signifies the creation of a two-dimensional replica of the fruit farm. FireBird V ATmega 2560 robotics research platform is used in this research to traverse the abstraction of the fruit farm. It spans the mapped model, collects the fruit crates, and transmits container data to the truck abstraction for transportation. The robot covers the shortest path from the source to destination nodes by parallelizing Dijkstra’s algorithm using the OpenMP API. In the unmapped model of the fruit farm, the GraphSLAM algorithm generates a map, and the adaptive Monte Carlo Localization algorithm implementation occurs on the virtual robot. The simulation experiment is conducted in the Gazebo environment using the Robot Operating System platform. © 2022, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Fruits

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