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Advancements in Chatbot Technology: Enhancing User Experience through AI and NL

Advancements in Chatbot Technology: Enhancing User Experienc...

2024 International Conference on Artificial Intelligence and Quantum Computation-Based Sensor Applications, ICAIQSA 2024

作者： Arora, Yojna Singh, Ajeet Sharma, Abhay Aggarwal, Nitin Bansal, Nidhi Sharda University Department of Computer Science & Engineering Greater Noida India Moradabad Institute of Technology Department of Computer Science & Engineering Uttar Pradesh Moradabad India School of Computing and Intelligent System Manipal University Jaipur Department of IoT & IS Rajasthan Jaipur India Supervisor Oracle Development Database Admin Data Science PA United States Pune India

ISBN: (纸本)9798331517953

A chatbot is an AI-powered software or application designed to communicate with people. This technology can perform a variety of tasks, including providing instant responses and answers to users, delivering information, assisting with purchasing products, and enhancing customer service. In my paper, I present the fundamental principles and essential concepts of computing related to chatbots and its applications various sectors like Education, E commerce, Entertainment, Healthcare, Telecommunication, Customer Service etc. The paper also mentions detailed literature explain the contribution by various researchers in this fields. Architecture and step wise approach of creating a chatbot is presented lately. © 2024 IEEE.

关键词： Chatbots

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system for Talent Acquisition: Integrating AI, Automation, and Data Analysis in HR 6

System for Talent Acquisition: Integrating AI, Automation, a...

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6th International Conference on Artificial Intelligence in Information and Communication, ICAIIC 2024

作者： Abitova, Gulnara Serikov, Ayanbek Nikulin, Vladimir Rakhimzhanova, Mira Shuteyeva, Gulnur Kulniyazova, Korlan Astana IT University Dep. Computer Engineeering Astana Kazakhstan Binghamton University Dept. Electical Engineering and Computer Science Binghamton United States Eurasian National University Dept. System Anslysis and Managemnt Astana Kazakhstan

ISBN: (纸本)9798350344349

This article presents a novel system designed for human resources (HR) companies, aiming to predict an individual's personality based on resume analysis through the integration of advanced information technology tools. At the core of this system is Airable, serving as the central database, which is seamlessly I integrated with various other tools such as *** for document handling, GitHub for code repository management, and OpenAI for advanced AI algorithms. The integration and automation of these diverse tools are skillfully orchestrated using Make, a no-code solution that streamlines the workflow and enhances efficiency. This innovative approach allows HR companies to leverage technology for more accurate personality predictions, thereby improving their recruitment processes and decision-making strategies. © 2024 IEEE.

关键词： Machine learning

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Toward Precision Diagnosis of Otitis Media: Introducing A Novel EARnet-AR Model 2

Toward Precision Diagnosis of Otitis Media: Introducing A No...

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2nd International Conference on Ambient Intelligence in Health Care, ICAIHC 2023

作者： Shakil, Rashiduzzaman Shamrat, F. M Javed Mehedi Sharmin, Sharmin Akter, Bonna Rubi, Maksuda Akter Dutta, Anik Daffodil International University Department of Computer Science and Engineering Dhaka Bangladesh University of Malaya Department of Computer System and Technology Kuala Lumpur Malaysia Daffodil International University Department of General Educational Development Dhaka Bangladesh Guru Nanak Institute of Technology Department of Computer Science and Engineering Kolkata India

ISBN: (纸本)9798350344882

Otitis media represents inflammation or infection of the middle ear, often stemming from colds, sore throats, or respiratory infections. To address diagnostic challenges, this study introduces the EARnet-AR method, a novel advanced approach for automatically classifying otoscope tympanic membrane images. We employed 1100 otoscopic augmented images were utilized to conduct this research. To enhance the clarity and accuracy of otoscopic images, our research incorporated median filtering, an effective noise reduction technique, and gamma correction to adjust the image's luminance, ensuring a more consistent visual representation. In our initial evaluations, we tested the capabilities of five renowned pre-trained models using a comprehensive set of evaluation metrics, including accuracy, sensitivity, specificity, precision, f1-score, FPR, and FNR. The confusion matrices also detailed the model's capabilities in classifying each specific ear condition. Moreover, our proposed novel model EARnet-AR, enriched with Attention and RNN features, surpasses the other models, achieving training, testing and validation accuracies of 97.5%, 96.0%, and 96.5%, respectively. With such precise diagnostic tools, medical professionals can potentially achieve more timely and accurate treatments, enhancing patient care and outcomes in otitis media cases. © 2023 IEEE.

关键词： Diagnosis

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Evaluation of the Software Development Life Cycle Model with Value of Testing and Quality Control 1

Evaluation of the Software Development Life Cycle Model with...

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1st IEEE International Interdisciplinary Humanitarian Conference for Sustainability, IIHC 2022

作者： Singh, Vikram Annamalai, S. School of Computer & System Sciences Department of Computer & System Sciences Jaipur National University Jaipur India Galgotias University Department of Computer Science and Engineering Uttar Pradesh Greater Noida India

ISBN: (纸本)9781665456876

One of the most noteworthy, essential, and important issues in the PC world is software project development. It is a portion of the life cycle for developing frameworks (SDLC). The goal of the SDLC is to reduce the likelihood of failure and disappointment while improving the quality of the software product. The structure that includes multiple tasks and assignments to be completed during the software development process is known as the SDLC. In order to examine existing models, have included five of these SDLC models in this paper: the cascade model, the angular model, the twisting model, and the iterative model. As a result, support and upgrades for the software should be based on current requirements for long-term and stable usage. This study illustrates the concept of testing and its role in quality assurance, experimentation and test levels, and the ongoing planning, execution, and observation of tests. This outline was created with the intention of focusing on the current SDLC sequence.. Approx 300 circulations were selected in addition to the crucial accentuation carried out with embeddings inquiry toward the open-source data collections. © 2022 IEEE.

关键词： Quality control

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Rolling Horizon Co-evolution for Snake AI Competition 13th

Rolling Horizon Co-evolution for Snake AI Competition

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13th IFIP TC 12 International Conference on Intelligent Information Processing, IIP 2024

作者： Li, Hui Zhou, Jiayi Zhang, Qingquan Research Institute of Trustworthy Autonomous System Southern University of Science and Technology Shenzhen518055 China Guangdong Provincial Key Laboratory of Brain-Inspired Intelligent Computation Department of Computer Science and Engineering Southern University of Science and Technology Shenzhen518055 China

ISBN: (纸本)9783031578076

The Snake game, a classic in the gaming world, gains new dimensions with the Snake AI competition, where two players controlled by AI algorithms can now compete simultaneously in the same game session. This competition holds significance in advancing our understanding of artificial intelligence (AI) algorithms. In the 2020 and 2021 Snake AI competitions, popular algorithms, using graph-based search or heuristic strategies, demonstrate competitive performance, such as the A* algorithm, Monte Carlo Tree Search (MCTS). Contrary to these heuristic approaches, the Rolling Horizon Co-evolution Algorithm (RHCA), characterised by its core principles of rolling horizon evaluation and co-evolution, maintains two populations, one for each player, to co-evolve with each other without reliance on heuristics. RHCA has been verified its effectiveness in a two-player spaceship game. In this paper, we extend the RHCA application to the two-player Snake AI game, comparing it with other state-of-the-art methods. Additionally, we introduce various obstacles to create different complex scenarios, ensuring a comprehensive analysis. Experimental results reveal RHCA’s superior and stable performance, especially in resource-constrained and complex scenarios. Furthermore, an analysis of RHCA’s behaviours across maps with diverse obstacle scenarios highlights its ability to make intelligent decisions in competing with state-of-the-art methods. © IFIP International Federation for Information Processing 2024.

关键词： Trees (mathematics)

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Improved Density Peaking Algorithm for Community Detection Based on Graph Representation Learning

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computer systems science & engineering 2022年第12期43卷 997-1008页

作者： Jiaming Wang Xiaolan Xie Xiaochun Cheng Yuhan Wang Guangxi Key Laboratory of Embedded Technology and Intelligent System Guilin541006GuangxiChina School of Information Science and Engineering Guilin University of TechnologyGuilin541006GuangxiChina Department of Computer Science Middlesex UniversityLondon

There is a large amount of information in the network data that we canexploit. It is difficult for classical community detection algorithms to handle network data with sparse topology. Representation learning of network data is usually paired with clustering algorithms to solve the community detection ***, there is always an unpredictable distribution of class clusters outputby graph representation learning. Therefore, we propose an improved densitypeak clustering algorithm (ILDPC) for the community detection problem, whichimproves the local density mechanism in the original algorithm and can betteraccommodate class clusters of different shapes. And we study the communitydetection in network data. The algorithm is paired with the benchmark modelGraph sample and aggregate (GraphSAGE) to show the adaptability of ILDPCfor community detection. The plotted decision diagram shows that the ILDPCalgorithm is more discriminative in selecting density peak points compared tothe original algorithm. Finally, the performance of K-means and other clusteringalgorithms on this benchmark model is compared, and the algorithm is proved tobe more suitable for community detection in sparse networks with the benchmarkmodel on the evaluation criterion F1-score. The sensitivity of the parameters ofthe ILDPC algorithm to the low-dimensional vector set output by the benchmarkmodel GraphSAGE is also analyzed.

关键词： Representation learning data mining low-dimensional embedding community detection density peaking algorithm

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An Investigation of the Current State of Digital Platforms and Their Prospects for the Future 1

An Investigation of the Current State of Digital Platforms a...

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1st IEEE International Interdisciplinary Humanitarian Conference for Sustainability, IIHC 2022

作者： Rao, Mahainder Kumar, S Rakesh School of Computer & System Sciences Department of Computer & System Sciences Jaipur National University Jaipur India Galgotias University Department of Computer Science and Engineering Uttar Pradesh Greater Noida India

ISBN: (纸本)9781665456876

Researchers in the field of information systems are becoming more interested in this subject as a result of the increased use of digital platforms as a new way to organise social and commercial activities. As a result, over the past ten years, the literature on digital platforms has greatly increased. However, as platforms change and a number of problems remain unsolved, academics call for greater research on the subject. Therefore, a review of the existing literature is necessary to better understand what has been accomplished and the potential avenues for future study. A thorough review of the literature on digital platforms, which included 100 publications from IS and management journals, was done by our team. The study's key findings point to some ambiguity in how digital platforms are currently conceptualised. They also emphasise how multifaceted digital platforms are, which reflects the variety of ways they are used in real life. © 2022 IEEE.

关键词： Human resource management

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Multi-Agent Deep Reinforcement Learning for Access Point Activation Strategy in Cell-Free Massive MIMO Networks

Multi-Agent Deep Reinforcement Learning for Access Point Act...

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2023 IEEE INFOCOM Conference on computer Communications Workshops, INFOCOM WKSHPS 2023

作者： Sun, Li Hou, Jing Chapman, Richard Auburn University Department of Computer Science and Software Engineering AuburnAL36849 United States California State University San Marcos Department of Computer Science and Information System San MarcosCA92096 United States

ISBN: (纸本)9781665494274

Cell-free massive MIMO network is a promising solution to support 5G communication. However, as an ultra-dense network (UDN), it suffers from two major issues: power consumption and scalability, which hinders it to be widely used in practice. The increasing and time-varying user demand, the dynamic propagation environment, and the huge amount of access points (APs) make it a challenging task to address these issues. For this purpose, we propose a distributed solution to solve the access point activation (APA) problem in cell-free massive MIMO networks to reduce power consumption considering dynamic user demand. We leverage the user-centric characteristic and design a multi-agent deep reinforcement learning (MADRL) algorithm by which each AP independently decides whether it needs to be switched on or off. The simulation results show that the MADRL outperforms the centralized and the random strategies. This research demonstrates the ability of distributed learning in solving the APA problem in cell-free massive MIMO networks. © 2023 IEEE.

关键词： Reinforcement learning

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Prediction of Post-COVID-19 Pandemic Learning Mode Preferences Using Machine Learning 14

Prediction of Post-COVID-19 Pandemic Learning Mode Preferenc...

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14th International Conference on Computing Communication and Networking Technologies, ICCCNT 2023

作者： Pratondo, Agus Novianty, Astri Putro, Budi Laksono Komala Sari, Siska Telkom University School of Applied Sciences Department of Multimedia Engineering Bandung Indonesia Telkom University School of Electrical Engineering Department of Computer Engineering Bandung Indonesia Indonesia University of Education Department of Computer Science Education Bandung Indonesia Telkom University School of Applied System Diploma Program of Information System Bandung Indonesia

ISBN: (纸本)9798350335095

The COVID-19 epidemic has had a huge impact on the educational landscape, prompting the adoption of online and remote learning as viable alternatives to conventional in-person instruction. In order to create effective educational methods as institutions prepare for the post-pandemic period, it is essential to understand students' preferences for various learning modes. With the aid of machine learning techniques, this study seeks to forecast preferences for learning styles in post-pandemic higher education. The study made use of a dataset compiled from a wide range of college students, which included information on the institution type (public or private), program type (bachelor's or diploma), academic discipline (science or humanities), gender, year of study, internet use, communication costs, comorbidity status, vaccination history, gadget and laptop ownership, and preferred learning mode (online, in-person, or both). For the aim of prediction, a Support Vector Machine (SVM) was used. Its kernel functions included linear, polynomial, radial basis function (RBF), and sigmoid. Performance criteria like accuracy, precision, recall, and F1 score were used to assess the models after the dataset was randomly partitioned into training and testing sets. The findings showed that for identifying the preferred mode of learning, SVM with the RBF kernel had the highest prediction accuracy (91.67%). Further proving the RBF kernel's efficacy, the confusion matrix analysis showed that high values in the diagonal represented accurate predictions for each of the three classes (both online and onsite). The results imply that machine learning approaches, in particular SVM with the RBF kernel, may accurately forecast students' preferences for learning modes in the post-pandemic situation. In a post-pandemic society, the findings provide insightful information that may be used to build adaptable and inclusive learning models and put them into practice. © 2023 IEEE.

关键词： Support vector machines

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Towards Temporal Dependency Identification based on Multivariate Time Series IIoT Data 14

Towards Temporal Dependency Identification based on Multivar...

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14th International Conference on Information and Communication Technology Convergence, ICTC 2023

作者： Isah, Abubakar Shin, Hyeju Hassan, Sani Ahmad Oh, Seugmin Kim, Jinsul Chonnam National University Department of Ict Convergence System Engineering Gwangju Korea Republic of Ahmadu Bello University Department of Computer Science Zaria Nigeria

ISBN: (纸本)9798350313277

In multivariate time series data, the analysis of temporal dependencies is crucial for comprehending complex relationships, forecasting trends, and streamlining diverse applications. In this study, we proposed a method for improving multivariate time series data using temporal dependence modeling. Our approach is based on utilizing cutting-edge machine learning methods, particularly Long Short-Term Memory (LSTM) networks, that are well known for their capacity to record complicated temporal correlations. We want to reveal hidden patterns and dependencies within multivariate time series data by employing LSTM networks. We use benchmark metrics Mean Absolute Percentage Error (MAPE), Mean Absolute Error (MAE), and Mean Squared Error (MSE) to assess the effectiveness of our strategy. We run comprehensive tests on various datasets to demonstrate the accuracy and robustness of our model. © 2023 IEEE.

关键词： LSTM multivariate temporal dependency

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