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Advancing Data Science Using AI-Driven Processes

Advancing Data Science Using AI-Driven Processes

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2023 international conference on Power Energy, Environment and Intelligent Control, PEEIC 2023

作者： Jahir Pasha, M. Gaurav, Kumar Bhanja, Amit Kumar Shamout, Mohamed Dawood Mupparaju, Chinna Babu Manohar Bhanushali, Mahesh G.Pullaiah College of Engineering and Technology Computer Science and Engineering Kurnool India Lovely Professional University Phagwara India School of Management Sciences Department of Management Varanasi India College of Business Administration University of Sharjah Department of Management Sharjah United Arab Emirates University of Central Missouri Masters in Computer Science warrensburg United States Management Studies University of Mumbai VPM's Dr.V.N.Bedekar Institute of Management Studies Thane India

ISBN: (纸本)9798350357769

Advances in AI-pushed facts technological know-how can be seen in an expansion of different regions. Examples of those consist of herbal language processing (NLP), machine learning (ML), Deep gaining knowledge of (DL), pc vision (CV), computerized data evaluation, and plenty *** technology is used to technique and extract significant records from textual content-based totally datasets. This era uses semantic algorithms, natural language processing tools, and rule-based totally era to apprehend and interpret textual content information. machine studying (ML) is now being used to automate positive obligations such as predicting monetary trends, figuring out consumer styles, and greater. ML technology can be used to identify relationships among facts points that have been previously no longer seen. Deep gaining knowledge of (DL) is a sub field of the device getting to know that applies layers of neural networks to identify patterns inside facts units. This generation gives an exceptionally powerful manner of analyzing records, allowing machines to stumble on complicated relationships that may have previously been too hard to come *** imaginative and prescient (CV) is another region in which advances in AI-pushed information science are being visible. CV generation lets in for computerized evaluation of snap shots and movies, making it viable to become aware of gadgets, come across tendencies, and discover anomalies. Computerized facts analysis is one of the maximum powerful approaches. © 2023 IEEE.

关键词： Analyzing Records Computerized Data Evaluation Semantic Algorithms

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The Role of natural language processing in Detecting Insurance Fraud 4

The Role of Natural Language Processing in Detecting Insuran...

引用

4th international conference on Communication, Computing and Industry 6.0, C216 2023

作者： Saddi, Venkata Ramana Gnanapa, Bhagawan Boddu, Swetha Logeshwaran, J. ACE American Insurance - Chubb Group RaleighNC United States AI/ML Architect SmartTrak AI Holly SpringsNC United States Perficient Inc. RaleighNC United States Department of ECE Sri Eshwar College of Engineering Tamil Nadu Coimbatore India

ISBN: (纸本)9798350327328

Coverage fraud is an illegal hobby that costs the coverage enterprise billions of bucks yearly and can be difficult to detect without the proper equipment. One technology that could assist in detecting fraud is natural language processing (NLP). NLP is a shape of synthetic intelligence (AI) used to research written claims and locate styles or signs that might advise coverage fraud. NLP uses a mixture of machine-gaining knowledge and algorithms to interpret written text and locate positive keywords or phrases that may be associated with fraudulent claims. This data can create regulations and versions to locate suspicious conduct inside the *** can provide insurers insights into the claims evaluation manner that are impossible using manual strategies. With the power of NLP, insurers can automate the statistics analysis procedure to detect coverage fraud more quickly and correctly than earlier. It can assist in identifying ability fraud cases fast, without difficulty, and as it should be, and save time and money on labor costs related to manually reviewing claims. Using NLP in insurance fraud detection also enables compliance because it offers insurers higher insight into their enterprise procedures, client conduct, and data *** can also help offer context to claims records, and with extra sources, insurers can, in addition, beautify the technique of detecting coverage fraud. © 2023 IEEE.

关键词： Insurance

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Smart Music Recommendation System Integrating NLP and Hybrid Algorithms 2

Smart Music Recommendation System Integrating NLP and Hybrid...

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2nd international conference on Advances in Computation, Communication and Information Technology, ICAICCIT 2024

作者： Kumar, S. Roshan Subashka Ramesh, S.S. Vardan, Harsha Adithya Yuvan, V. SRM IST Ramapuram Chennai India Department of Computer Science and Engineering SRM IST Ramapuram Chennai India

ISBN: (纸本)9798331541217

In an era where technological growth gives a massive amount of knowledge in music, intuitive and personalized music delivery forms the cornerstone for better user experience and satisfaction. The increasing reliance on voice-driven interfaces can be leveraged to create newer music recommendation systems. This paper explores the development of the Smart Music Recommendation System, which will use natural language processing (NLP), speech recognition, and hybrid algorithms to recommend music to the user based on his mood input through voice. The system leverages Google Speech Recognition API for real-time voice interaction ensuring a seamless interaction between the user and the system. The system additionally detects the user's emotional state from voice commands, while search algorithms are employed to fetch and recommend songs. A hybrid recommendation approach combines content-based filtering for emotion-specific playlist generation for every mood for an enhanced listening experience. An exceptional feature in our system is offline download using Yt-dlp to any local device and playback processes using an automated threading mechanism. The system proposed here was tested and gave efficient performance in voice interaction, song suggestion according to mood, and coordination with external platforms for retrieving and playing songs. © 2024 IEEE.

关键词： Hidden Markov models

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Revolutionizing User Experience for Product Quality Evaluation using AR/VR and NLP 2

Revolutionizing User Experience for Product Quality Evaluati...

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2nd international conference on Distributed Computing and Optimization Techniques, ICDCOT 2024

作者： Sri, Bande Bhavya Amulya, Kadamati Dharani Naga Sai Pallapothu, Beulah Rao, M.V.L.N. Raja Siva Satya Sreedhar, P. Abirami, A. Seshadri Rao Gudlavalleru Engineering College Department of Information Technology Andhra Pradesh India Sona College of Technology Department of Mathematics Tamil Nadu Salem India

ISBN: (纸本)9798350382952

The research work proposes an important change technique that could transform User Experience (UX) evaluation by overcoming the limits of existing systems. Existing systems, which depend on traditional testing and surveys, usually fail to provide a complete understanding of user interactions. The proposed system creates immersive interfaces using Augmented Reality (AR), Virtual Reality (VR), and natural language processing (NLP), while also obtaining qualitative feedback from user comments. Algorithmic analytics and machine learning algorithms enable reliable quantification of user activity. Real-time monitoring and iterative improvement cycles allow for dynamic responsiveness. A comparative investigation shows that the suggested system outperforms AR/VR immersion, NLP sentiment analysis, and algorithmic analytics. Efficiency measures such as accuracy (0.90), precision (0.92), recall (0.88), and F1 Score (0.90) demonstrate that it exceeds systems. The study proposes an innovative, user-centric solution set for rethinking UX evaluation, resulting in an in-depth knowledge of product quality in an ever-changing digital environment. © 2024 IEEE.

关键词： Augmented reality

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A Machine Learning Based Classification of Students' Algebraic Responses Using MathBERT Embeddings 5

A Machine Learning Based Classification of Students' Algebra...

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5th IEEE international conference for Emerging Technology, INCET 2024

作者： Injeti, Anees Sajid Rupsica, G. Neha Reddy, G. Praneetha Balakrishnan, Roshni M. Pati, Peeta Basa Dept of Computer Science and Engineering Amrita School of Computing Amrita Vishwa Vidhyapeetham Bengaluru India

ISBN: (纸本)9798350361155

Conduction of assessments and analysing the students' knowledge is the most important part of any teaching learning process. Traditional manual grading of written mathematical solutions can be time-consuming and laborious, especially for a larger group of students. It takes lot of time and effort to correct these answer scripts. In order to evaluate students' open responses for algebraic inquiries automatically, this project explores the use of MathBERT embeddings, an advanced natural language processing technique with different machine learning classifiers. The model tries to classify the student's responses to either correct or wrong. XGBoost Classifier on the MathBERT embeddings exhibited the best accuracy for this model with 75.3% mean accuracy with standard deviation of 0.016. © 2024 IEEE.

关键词： Students

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Custom Dataset Text Classification: An Ensemble Approach with Machine Learning and Deep Learning Models 3

Custom Dataset Text Classification: An Ensemble Approach wit...

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3rd international conference on Innovative Mechanisms for Industry Applications, ICIMIA 2023

作者： Valluri, Deekshitha Manne, Suneetha Tripuraneni, Nikitha Velagapudi Ramakrishna Siddhartha Engineering College Dept of Information Technology Vijayawada India

ISBN: (纸本)9798350343632

In academic institutions, commercial enterprises, research centers, technology-heavy businesses, and government funding agencies, maintaining consistent data is a major difficulty. For an entity, which might be anything from an object to a place or thing, most data are irregular. These days, to identify significant patterns that represent the data, entity links in a dataset are investigated by text mining or data analytics. With this knowledge, alternatives are then taken. Analytics creates data and finds patterns by turning words into numbers. In the end, better data organization results in better conclusions. However, classifying and processing each piece of data by hand is difficult. As a result, in the domain of natural language processing (NLP), which looks at grammatical and lexical patterns, intelligent text processing systems have emerged. Before mining, it's imperative to examine and comprehend the nature of the data. Text categorization requires automation because of the increasing volume of data and the requirement for accuracy or precision. It is an interesting study opportunity to develop automatic classifying texts with deep learning methods to handle difficult NLP tasks with semantic constraints. Text categorization is founded on data analytics, which can facilitate information discovery. The majority of the advantages can be obtained by applying these insights to emerging applications that support decision-making, improve resources. Improved techniques for parameter optimization demonstrating effective knowledge discovery will be the focus of future research studies. © 2023 IEEE.

关键词： Data Deep Learning Machine Learning NLP Text Analytics Text Classification

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"A PIECE OF CAKE" - SUSTAINABLE EDUCATION PRACTICES WITH TRANSFORMER MODELS 17

"A PIECE OF CAKE" - SUSTAINABLE EDUCATION PRACTICES WITH TRA...

引用

17th international conference on e-Learning and Digital Learning 2023, ELDL 2023 and 11th international conference on Sustainability, Technology and Education 2023, STE 2023

作者： de Arriba-Pérez, Francisco García-Méndez, Silvia González-Castaño, Francisco J. Information Technologies Group atlanTTic University of Vigo School of Telecommunications Engineering Maxwell street Vigo36310 Spain

ISBN: (纸本)9789898704511

New advances in Artificial Intelligence (AI), particularly transformer language models, have disruptively changed the current education and training practices. The latter advancements have forced a shift towards a more practical and sustainable learning process with the ultimate objective of creating active, high-quality, and continuous educational services. However, human intervention is still essential to keep control over the outcomes of AI-based solutions. Thus, this work contributes with a fully functional and well-designed Conversational Assistant for knowledge Enhancement (CAKE), which integrates a transformer model in a chatbot platform with empathetic capabilities. The proposed environment is fully immersive and incorporates gamification. natural language processing techniques are exploited through prompts. The most relevant features of CAKE are its flexibility regarding language, areas and topics covered, and the education level of the end users. Results obtained in two evaluation scenarios endorse the performance of the proposed solution and motivate us to continue this research line toward real-time monitoring of smart learning. © 2023 17th international conference on e-Learning and Digital Learning 2023, ELDL 2023 and 11th international conference on Sustainability, Technology and Education 2023, STE 2023. All rights reserved.

关键词： Computational linguistics

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Unsupervised Feature Selection for Multivariate Time Series based on Improved FINCH 6

Unsupervised Feature Selection for Multivariate Time Series ...

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6th international conference on Machine Learning and natural language processing, MLNLP 2023

作者： Wang, Ling Liu, Wei Chu, Zhongkun Shunde Innovation School University of Science and Technology of Beijing China School of Automation and Electrical Engineering University of Science and Technology of Beijing Key Laboratory of Knowledge Automation for Industrial Process Ministry of Education Beijing China

ISBN: (纸本)9798400709241

Feature selection is a crucial step in the preprocessing of data mining for multivariate time series. In order to remove irrelevant and redundant features in the multivariate time series, an unsupervised feature selection method based on improved First Integer Neighbor Clustering Hierarchy (FINCH) is proposed to select a compact subset of features containing the most important features. Firstly, the time-lagged cross-correlation (TLCC) is used to measure the redundancy between features, and Inverse Fast Fourier Transform (IFFT) is performed to reduce the computational complexity. Secondly, a new correlation-based clustering centroid and stopping criterion are defined to speed up hierarchical clustering. Finally, feature filtering is performed using the proposed improved FINCH to remove redundant features. Experiments on six benchmark datasets show that the proposed method achieve higher efficiency in dimensional reduction under the same experimental conditions. © 2023 ACM.

关键词： Feature Selection

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"Why My Essay Received a 4?": A natural language processing Based Argumentative Essay Structure Analysis 24th

"Why My Essay Received a 4?": A Natural Language Processing ...

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24th international conference on Artificial Intelligence in Education (AIED)

作者： Yang, Bokai Nam, Sungjin Huang, Yuchi Univ Wisconsin Eau Claire WI 54701 USA ACT Iowa City IA 52243 USA

ISBN: (纸本)9783031362712;9783031362729

Writing argumentative essays is a critical component of students' learning. Previous works on automatic assessments on essay writing often focused on providing a holistic score for the input essay, which only summarized the essay's overall quality. However, to provide more pedagogical value and equitable educational opportunities for all students, an automatized system needs to provide detailed feedback on students' essays. To address this issue, we developed an essay argumentative structure feedback system to support educators and students. We employed natural language processing (NLP) and data mining techniques to explore the association between argumentative structure and essay scores. First, we proposed a cross-prompt, sentence-level ensemble model to classify the argumentative elements and extract the argumentative structures from the essay. The model worked across multiple datasets and achieved high performance. Second, after applying the classification model on the ACT writing tests, we performed a sequential mining process to extract representative argumentative structures. Our findings highlight the role of organizational argumentative structure in essay scoring. Furthermore, we found a common argumentative structure used by the high-scored essays. Finally, with the knowledge of argumentative elements and structures used in the previous essays, we proposed a feedback tool design to complement the current AES systems and help students improve their argument writing skill.

关键词： natural language processing Educational data mining Argument mining Teaching analytics Automated feedback

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Development of Aero AI for Initial Flight Shape Design of Small Fixed-Wing UAV 4

Development of Aero AI for Initial Flight Shape Design of Sm...

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4th IEEE international conference on Electronic Communications, Internet of Things and Big Data, ICEIB 2024

作者： Ye, Yu-Lin Tseng, Shih-Ting Huang, Ching-Huei Tsai, Kun-Lin Feng Chia University Department Automatic Control Engineering Taichung Taiwan Electrical Engineering Tunghai University Taichung Taiwan

ISBN: (纸本)9798350360721

We created Aero Artificial Intelligence (Aero AI), an innovative AI platform for optimal Unmanned Aerial Vehicle (UAV) flight shape design. Aero AI guides designers to select UAV shape parameters intuitively. It assembles an initial UAV model based on selected parameters for shape evaluation. Then, it utilizes Stanford University Unstructured (SU2) for aerodynamic analysis and employs AI for neural network deep learning to generate reliable aerodynamic parameters without extensive wind tunnel tests for design. Aero AI reduces reliance on wind tunnel tests, saves energy and resources, and expedites the shape design process. AI platforms have permeated human life at an expeditious pace in recent years. Aero AI furnishes instantaneous and personalized responses to aerodynamic parameters. The development of such platforms facilitates a diverse spectrum of aerodynamic configurations and designs from commonplace knowledge-based inquiries to intricate domain-specific topics. By employing natural language processing methodologies, user interrogatives and proffer corresponding resolutions are enabled. The Aero AI platform applies continuous learning algorithms to augment response accuracy and relevance. Being intelligent inquiry-based, ubiquitous applications across myriad domains are possible, including customer service provisioning on web-based platforms, educational instruction aids, enterprise knowledge management, and issue resolution paradigms. © 2024 IEEE.

关键词： Deep learning

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