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检索条件"任意字段=International Conference on Advances in Data-driven Computing and Intelligent Systems, ADCIS 2022"

共 558 条记录，以下是11-20 订阅

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Mapping Sentiment: A Geospatial Analysis of Twitter data in Indian Premier League 2023 1

引用

2nd international conference on advances in data-driven computing and intelligent systems, adcis 2023

作者： Bhatt, Mukesh Singh, Vijay Singh, Ashwini Kumar Dehradun India

ISBN: (数字)9789819995240

ISBN: (纸本)9789819995233

This paper presents application of machine learning and geospatial analysis to examine sentiments from Twitter data during the 2023 season of the Indian Premier League (IPL). Utilizing machine learning models (linear SVM and logistic regression), we effectively categorized sentiments into negative, neutral, and positive classes, attaining impressive overall accuracy of 96.8% and 97.1%, respectively. The geospatial analysis led to map sentiment across various geographical locations in India, reflecting diverse public sentiment throughout the IPL season. We noticed intriguing temporal and spatial variations in sentiment distribution across March, April, and May in 2023, visually represented via a heatmap. This comprehensive examination of sentiment distribution, linked to a significant event like the IPL, opens new horizons for understanding public perception and emotional response to large-scale sporting events. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.

关键词： Sentiment analysis

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Predictive Analytics in Marketing Using Artificial Intelligence 1

引用

2nd international conference on advances in data-driven computing and intelligent systems, adcis 2023

作者： Shukla, Rishi Prakash Juneja, Divya Monga, Shriya AIT-Management Centre of Excellence Chandigarh University Chandigarh India The Business School RIMT International University Hanoi Viet Nam Chandigarh University Chandigarh India

ISBN: (数字)9789819995318

ISBN: (纸本)9789819995301

In today’s dynamic and rapidly evolving business landscape, the implementation of artificial intelligence (AI) in marketing has become imperative for achieving swift and effective outcomes. AI marketing encompasses the utilization of AI technology to gather data, glean valuable consumer insights, predict future consumer behaviours, and autonomously devise marketing strategies. This study aims to investigate the impact of AI integration in marketing on a company’s sales performance. To achieve this objective, four distinct independent variables were considered: brand communication, brand loyalty, PPC advertisement, and customer insights. These variables were chosen to elucidate their influence on the company’s sales figures, given its utilization of AI technology in its marketing endeavours. Employing a numerical survey methodology, 40 responses were collected from diverse companies using a basic random sampling approach. data analysis was conducted using the PLS-SEM technique through Smart-PLS. The findings underscore the company’s adoption of AI technology in marketing as a strategic move to enhance customer understanding and formulate data-driven marketing strategies. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.

关键词： Predictive analytics

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Fundamental Frequency Extraction by Utilizing the Modified Weighted Autocorrelation Function in Noisy Speech 1

引用

2nd international conference on advances in data-driven computing and intelligent systems, adcis 2023

作者： Rahman, Moinur Saifur Rahman, Md. Parvin, Nargis Rahman, Maqsudur Department of ICT Comilla University Cumilla Bangladesh Department of CSE Bangladesh Army International University of Science and Technology Cumilla Cantonment Cumilla Bangladesh

ISBN: (数字)9789819995318

ISBN: (纸本)9789819995301

This research suggests an efficient method that is better suited for speech processing applications for retrieving the accurate fundamental frequencies from speech in noisy conditions. This research describes a modified weighted autocorrelation function for fundamental frequency extraction that is noise-resistant. To gain more accuracy in extracting the fundamental frequency, we have focused on reducing the noise by using the third-order statistics of the noisy speech signal. The proposed fundamental frequency extraction method is composed of two stages: generating third order statistics of speech signal and applying weighted autocorrelation function on it. From experimental results, it is observed that the proposed method shows better performance in noisy environments compared to other conventional methods. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.

关键词： Speech processing

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PBDPA: A Task Scheduling Algorithm in Containerized Cloud computing Environment

PBDPA: A Task Scheduling Algorithm in Containerized Cloud Co...

引用

international conference on advances in data-driven computing and intelligent systems, adcis 2022

作者： Verma, Himanshukamal Shrivastava, Vivek School of Computer Science & IT Devi Ahilya University Madhya Pradesh Indore452001 India International Institute of Professional Studies Devi Ahilya University Madhya Pradesh Indore452001 India

ISBN: (纸本)9789819909803

The container is a lightweight and agile virtualization solution that effectively addresses VM issues in cloud infrastructures such as overhead and compatibility. Users’ tasks are assigned to containers in the form of cloudlets to ensure efficient management of cloud resources. Task scheduling with large job sizes and strict deadlines is a challenging job in cloud environments. Several jobs may need to be effectively assigned on several containers while minimizing makespan and optimizing resource efficiency. This paper presents a PROMETHEE-II-Based Dynamic Priority Algorithm (PBDPA) that incorporates multi-criteria-based optimization for dealing with multi-objective task scheduling issues. The suggested technique is evaluated using the CloudSim simulator. Experimental results on 3000 tasks successfully demonstrate that the proposed PBDPA algorithm maximizes performance in terms of time, revenue, resource utilization, and quality of service (QoS). © 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Cloud computing

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Predictive Models for Equipment Fault Detection: Application in Semiconductor Industry 1

引用

international conference on advances in data-driven computing and intelligent systems, adcis 2022

作者： Duy, Tran Quang Vi, Tran Duc School of Industrial Engineering and Management International University. Vietnam National University Block 6 Linh Trung Ward Thu Duc District HCM City Viet Nam

ISBN: (数字)9789819909810

ISBN: (纸本)9789819909803

Predictive maintenance (PdM) in manufacturing using machine learning (ML) techniques has received much attention from many researchers and practitioners in recent years, particularly in the capital-intensive industries such as semiconductor manufacturing. Developing a decision tool that detects problems in equipment or processes early to maintain high process efficiencies is a critical step in process control for cost reduction. In this study, we conducted experiments on real semiconductor manufacturing data (SECOM). Our experimental results show that performance of predictive models using all the data balancing techniques that we studied including both the data-driven and algorithmic-based techniques outperformed models using unbalanced data. The total of 30 combinations of experiments are conducted for this study (six scenarios of data balancing techniques × 5 ML classification algorithms), we find the best model providing the optimal performance with the highest area under the ROC curve (AUC) is support vector machine (SVM) in combination with cost sensitive learning. © 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Fault detection

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data-driven Prediction of Effluent BOD5 from an Institutional Wastewater Treatment Plant 1

引用

international conference on advances in data-driven computing and intelligent systems, adcis 2022

作者： Jain, Shubham K. Kumar, Ashwani Kumar, Sudhir Kumar, Amit Choudhary, Aditya Department of Civil Engineering Malaviya National Institute of Technology Jaipur Jaipur India

ISBN: (数字)9789819909810

ISBN: (纸本)9789819909803

The study develops a data-driven based model to predict biological oxygen demand of the treated wastewater from an institutional wastewater treatment plant. First, the data was collected for six parameters (i.e. pH, conductivity, temperature, total suspended solids, total organic carbon and biological oxygen demand) from the selected wastewater treatment plant by collecting and analysing samples over a period of more than two months. The quality parameters were then combined in seven different scenarios for training of machine learning models. These scenarios were tested using four machine learning models, i.e. multiple linear regression, artificial neural network, support vector machine and random forest. Finally, these models were compared to select the best model for prediction of biological oxygen demand using a set of indicators, i.e. coefficient of determination and root mean square error. © 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Wastewater treatment

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Simulation Tools for Cloud computing: A Comparative Study 1

引用

international conference on advances in data-driven computing and intelligent systems, adcis 2022

作者： Umar, Talha Nadeem, Mohammad Sajid, Mohammad Department of Computer Science Aligarh Muslim University Aligarh India

ISBN: (数字)9789819909810

ISBN: (纸本)9789819909803

computing is evolving toward a paradigm that consists of commoditized services supplied in a way similar to conventional utilities. A variety of computing paradigms have been proposed to realize this goal, the most recent of which is known as cloud computing. It is capable of performing complex and massive scale computing, which has changed roles and responsibilities in IT services. It relieves the consumers from the burden of maintaining massive computing hardware, applications, and data storage systems. As many people have moved to cloud computing because of its elastic resource allocations and pay-for-per-usage features, and also because the cloud environment is very complex and sensitive, it is essential to study, analyze, and evaluate the performance, security, and other features that might be confronted in cloud computing. It is expensive to set up and perform these tests in actual cloud systems. However, modeling and simulation tools are often viable alternatives to cloud computing environment emulation. There are numerous simulation programs available, and they all have different features and applications. As a contribution to knowledge, in this paper, we first present a brief introduction to cloud computing, describe its fundamentals, and present a detailed comparative study of some popular cloud computing simulation tools. © 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Application programs

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Comparative Analysis of Malware Classification Using Supervised Machine Learning Algorithms 1

引用

2nd international conference on advances in data-driven computing and intelligent systems, adcis 2023

作者： Mahato, Anisha Goswami, R.T. Dutta, Ambar Sister Nivedita University Kolkata India Techno International New Town Kolkata India Amity University Kolkata India

ISBN: (数字)9789819995189

ISBN: (纸本)9789819995172

Privacy is a myth, a statement persistently encountered when talking about the world of Internet. Malwares are a constant, ominous threat to data which cripples the cyberspace today. The myth of digital privacy began with the conception and subsequent proliferation of malwares. Any device connected through the Internet is a potential target and runs the risk of its security being breached and information being compromised. In this paper, a benchmarked dataset Big 2015 is used for the malware classification experiment. Seven different machine learning models namely Random Forest, Support Vector Machines, Logistic Regression, Naïve Bayes, AdaBoost, Gradient Boost and Bagging, are used to train and test the dataset and to establish the one that performs the best. The performance metrics put in place are Accuracy, Precision, Recall and F1-score. It is seen that ensemble machine learning approach, namely Random Forest, Bagging and Gradient Boost performed better in accordance to the performance parameters considered. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.

关键词： Malware

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Conceptual Model for data Collection and Processing in a Smart Medical Ward 1

引用

2nd international conference on advances in data-driven computing and intelligent systems, adcis 2023

作者： Levonevskiy, Dmitriy Motienko, Anna Saint Petersburg199178 Russia

ISBN: (数字)9789819995240

ISBN: (纸本)9789819995233

The study deals with modeling and automation of obtaining, collecting and analyzing health data in smart medical wards. The authors made a review of various existing solutions in the domain of interest. Such solutions increase safety and comfort of the patients, enhance quality of their treatment in hospitals, reduce the costs and the influence of human errors. However, the existing solutions are aimed at solving particular tasks. The use of different tools and heterogeneous modules causes problems of compatibility, security risks and increased complexity and redundancy of systems, so they become more expensive to support and maintain. To overcome these obstacles, the smart ward can be built within a general framework that supports typical modules, interfaces and operations. At the first stage of solving the problem the authors proposed a conceptual model of a smart ward, and make a description of its components and operation scenarios. Then, the authors considered a sample scenario for disease stratification based on obtaining values of health indicators and performing calculations according to a defined methodology. An algorithm for data collection and analysis for diagnosis making and disease stratification was developed, and a simulation example of smart ward functioning was carried out. Numerical experiments demonstrated that data collection and processing using smart components of the model could significantly reduce the assessment time. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.

关键词： data acquisition

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IDTCKS: An intelligent Integrative Approach for Disaster Tweet Classification and Disaster-Related Document Classification Using Knowledge-driven Hybrid Semantics 2nd

IDTCKS: An Intelligent Integrative Approach for Disaster Twe...

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2nd international conference on advances in data-driven computing and intelligent systems, adcis 2023

作者： Nama, Vihaan Deepak, Gerard Department of Computer Science Engineering RV College of Engineering Bengaluru India Department of Computer Science Engineering Manipal Institute of Technology Bengaluru Manipal Academy of Higher Education Manipal India

ISBN: (纸本)9789819995172

There is an immediate requirement for a strategic semantically inclined, semantic web accommodating framework for disaster tweet classification and disaster document and newsfeed documents recommendation to perform disaster risk mitigation and management. This framework proposes an IDTCKS framework for disaster management as a domain of choice, wherein an agent-driven model for integrating auxiliary knowledge from several news and Twitter APIs is encompassed. Subsequently, the TF-IDF discovers informative terms from the documents. The frameworks encompass two distinct classifiers, namely the AdaBoost Classifier to classify the disaster tweets using the upper ontology for the disaster domain as the features and upper ontology is considered by using the LDA to the enriched disaster terms. Subsequently, the upper ontology and the LDA-enriched disaster terms are also used as a feature-driven logistic regression classifier that helps in classifying the dataset. Semantic relevance is achieved using Twitter Semantic Similarity and Horn’s Index with a threshold and step deviance measure and overall precision of 97.39, an accuracy of 98.24, and a very low False Negative Rate of 0.01 has been achieved by the IDTCKS model for disaster classification while an overall precision of 96.57, accuracy of 97.65, and a very low False Negative Rate of 0.02 has been achieved by the IDTCKS model for document classification, which performs much better than other baseline models. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.

关键词： Disasters

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