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检索条件"任意字段=International Conference on Computer Engineering and Technology"

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A Strategic Framework for the Development of a Microtext AI Chatbot for Tourism in Indian Cities

A Strategic Framework for the Development of a Microtext AI ...

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international conference on Electronics, Materials engineering and Nano-technology (IEMENTech)

作者： Adnan A. Sultan Unmesh Mukherjee Anupam Mondal Department of Computer Science & Engineering Institute of Engineering & Management University of Engineering & Management Kolkata India

ISBN: (数字)9798331520762

ISBN: (纸本)9798331520779

Tourism plays a crucial role in India's economy, with its diverse cultural, historical, and geographical richness attracting millions of domes-tic and international visitors annually. To cater to the needs of these travelers and enhance their overall experience, there is a growing need for inno-vative technological solutions. This paper presents a comprehensive literature survey, analyzing existing research, trends, and practical implementations of microtext normalization as well as the application of chatbots in the context of tourism. Furthermore, it proposes a strategic framework for the development and deployment of an effective microtext AI chatbot tailored to the requirements of tourism in India. The proposed design leverages modern machine learning and natural language processing (NLP) strategies for providing personalized, context-aware assistance to tourists in order to enhance their overall experience while exploring the various cities of India.

关键词： Surveys Urban areas Machine learning Chatbots Market research Cultural differences

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Brain Tumour Detection using MRI Images

Brain Tumour Detection using MRI Images

引用

Intelligent Systems and Computational Networks (ICISCN), international conference on

作者： Jayashree Ahmed Faraz Mallick Ashutosh Das Abhiraj Singh Shivaprakash T. Preethi Salian K Dept of Computer Science and Engineering Nitte Meenakshi Institute of Technology Bangalore India Dept of Computer Science and Engineering Nitte Meenakshi Institute of Technology Dept of Computer Science and Engineering Vijaya Vittala Institute of Technology Bangalore India Dept of Information Science and Engineering Nitte (deemed to be University) NMAMIT Udupi India

ISBN: (数字)9798331529246

ISBN: (纸本)9798331529253

An essential diagnostic technique for brain tumors, Magnetic Resonance Imaging (MRI) enables early detection and improved patient outcomes. However, manual interpretation of MRI scans can be time-consuming and subject to human error. Deep learning has revolutionized medical image analysis by providing automated and accurate classifications, significantly enhancing diagnostic processes. This study leverages the pre-trained Convolutional Neural Network (CNN) model Xception to classify MRI brain data, demonstrating its effectiveness in identifying brain tumors. The model was trained and validated using a diverse dataset, achieving a high test accuracy of 98.78% and training accuracy of 99.96%. These findings highlight the potential of the Xception model in supporting clinical decision-making and reducing diagnostic workloads. The study contributes to the field by showcasing Xception’s implementation, dataset preparation, model design, training, and evaluation. Furthermore, it emphasizes the importance of using deep learning techniques for reliable and efficient brain tumor classification, setting the groundwork for future advancements in automated diagnostic systems.

关键词： Training Deep learning Accuracy Image analysis Magnetic resonance imaging Brain tumors Medical services Manuals Brain modeling Medical diagnostic imaging

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Empowering Recommendations: Survey of LLM-Based Recommendation Systems

Empowering Recommendations: Survey of LLM-Based Recommendati...

引用

international conference on Electronics, Materials engineering and Nano-technology (IEMENTech)

作者： Rishav Kumar Titas Dey Soma Das Computer Science and Engineering (AI) Institute of Engineering & Management (IEM) University of Engineering & Management Kolkata India

ISBN: (数字)9798331520762

ISBN: (纸本)9798331520779

The rise of e-commerce has emphasized the importance of recommendation systems in enhancing user experiences. Traditional methods, like collaborative filtering and content-based techniques, often struggle with understanding user preferences. Large Language Models (LLMs), such as ChatGPT and GPT-4, offer a transformative approach by leveraging advanced natural language processing. This survey explores LLM-driven strategies in recommendation systems, focusing on their role in feature encoding, user-item interaction modeling, and explainability. By highlighting their ability to generate contextually relevant recommendations, we provide insights into their advantages over conventional methods and their potential for future advancements.

关键词： Surveys Visualization Technological innovation Large language models Collaborative filtering Knowledge based systems Market research Trajectory Stakeholders Recommender systems

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Using Survey to Investigate the Integration of Artificial Intelligence in e-Learning 22nd

Using Survey to Investigate the Integration of Artificial In...

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22nd international conference on Software engineering Research and Practice, SERP 2024, and 23rd international conference on e-Learning, e-Business, Enterprise Information Systems, and e-Government, EEE 2024, held as part of the World Congress in computer Science, computer engineering and Applied Computing, CSCE 2024

作者： Tao, Yi Yang, Zijiang Zhang, Ethan Boyi School of Information Technology York University Toronto Canada St. Robert Catholic High School Thornhill Canada

ISBN: (纸本)9783031866432

This paper focuses on analyzing data obtained from a survey conducted among 312 management undergraduate students in China. The aim of the survey is to explore their perceptions, inclinations, and attitudes towards the use of AI in e-learning. In addition, feature importance analysis is conducted to identify the most influential factors in determining students’ attitudes towards AI in e-learning. This analysis helps uncover the key factors that significantly impact students’ perceptions and evaluations of AI usage in the daily learning process. Overall, this paper provides a comprehensive and empirical analysis of the survey data. The findings contribute to a better understanding of the key factors influencing attitudes and provide valuable insights for educational practitioners, creators of AI technology, and policymakers involved in the implementation of AI technologies in the educational industry. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Students

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The Symphony of Style: An Extensive Examination of Determination Mechanisms in Deep Learning-Inspired Outfit Suggestions

The Symphony of Style: An Extensive Examination of Determina...

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Computational Intelligence, Communication technology and Networking (CICTN), international conference on

作者： Guru Prasad M S Ravatappa A Bhosagi Madhusudhana K Prithviraj Chandrappa S Vikash Kumar Department of Computer Science and Engineering Graphic Era (Deemed to be University) Dehradun India School of Computing and Information Technology Reva University Bengaluru India Dept. of Electronics & Communication Engineering SDM Institute of Technology Ujire India Department of Computer Science and Engineering Nitte (Deemed to be University) NMAMIT Mangalore India Department of Computer Science and Engineering Jain (Deemed-to-be University) Bengaluru India Department of Computer Application Graphic Era Deemed University Dehradun India

ISBN: (数字)9798331530389

ISBN: (纸本)9798331530396

In the field of the fashion industry, the introduction of deep learning technology has taken an innovative revolution by providing assistance in the construction of intellectual systems that are intelligent to deliver personalized ensemble recommendations. In this research work, an innovative ensemble recommendation structure called Deep Learning VGG16 (Visual Geometry Group 16) recommender is presented. The proposed method allows a deep learning model VGG16 to understand complex patterns and developments resulting from a vast group of fashion images. In order to increase the user experience and greater participation in the well-established fashion industry, the system offers personalized ensemble ideas. The important, core and fundamental component of the system is VGG16, consisting of 13 convolutional layers and 3 fully connected layers, which is used to support the analysis of fashion images. A cutting-edge convolutional neural network (CNN) model is improved over a large and diverse collection of fashion images. Model will extract advanced high-level information and statistics with the capability to distinguish particulars in colour patterns, clothing combinations, and stylistic elements. Using this process of development, the system becomes better at identifying user preferences and fashion trends.

关键词： Deep learning Adaptation models Visualization Virtual assistants Clothing Market research Clothing industry User experience Convolutional neural networks Recommender systems

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Scalable Machine Learning Architectures for IPA-Driven Maintenance Task Allocation in Large-Scale Building Portfolios 24

Scalable Machine Learning Architectures for IPA-Driven Maint...

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7th international conference on Information Science and Systems, ICISS 2024

作者： Huang, Zhimeng Liu, Xiaodong Romdhani, Imed Shih, Chi-Sheng School of Computing Engineering and Built Environment Edinburgh Napier University Edinburgh United Kingdom College of Electrical Engineering and Computer Science National Taiwan University Taipei Taiwan

ISBN: (纸本)9798400717567

This research presents a groundbreaking approach to Building Maintenance Management (BMM) by introducing an Intelligent Process Automation (IPA)-Driven Building Maintenance Management (IBMM) model. This innovative model harnesses the synergies between Artificial Intelligence (AI), Machine Learning (ML), and Internet of Things (IoT) technologies to transition from reactive to proactive and predictive building maintenance strategies. The study highlights the critical gap in current BMM practices—the absence of intelligent systems for anticipating and addressing maintenance issues before they escalate. Through an extensive literature review, the transformative potential of AI and IoT for enhancing building maintenance management within smart cities is explored, establishing a foundation for the IBMM model’s application. The core of this research lies in its novel application of scalable machine learning architectures to automate and optimize maintenance task allocation in large-scale building portfolios. The practicality of the IBMM model is demonstrated via a proof of concept (POC) in an industrial setting, evidencing its capacity to improve efficiency, reduce costs, and bolster sustainability in building maintenance operations. The model epitomizes a paradigm shift in BMM by integrating IPA, which combines AI and ML, facilitating automated, intelligent decision-making and task allocation. Among its advancements, the IBMM model introduces enhanced predictive maintenance through real-time data analysis, adaptive learning and optimization, automated decision-making, and human-machine collaboration, contributing to energy efficiency and alignment with smart city objectives. The paper delineates the methodology, design, and implementation of a machine learning model for engineer task assignments, culminating in a case study that validates the model’s efficacy. This research not only signifies a significant advancement in BMM by leveraging IPA technologies for autonomous p

关键词： Predictive maintenance

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Cyber-Physical Systems in Disaster Management: Real-Time Data Collection and Analysis for Improved Response

Cyber-Physical Systems in Disaster Management: Real-Time Dat...

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Automation and Computation (AUTOCOM), international conference on

作者： Pavan Chaudhary Pachayappan R Ishika Soni Anmol Pattanaik Pradeep Marwaha Gadug Sudhamsu Maharishi School of Engineering & Technology Maharishi University of Information Technology Uttar Pradesh India Department of Computer Applications (DCA) Presidency College Bengaluru India Centre of Research Impact and Outcome Chitkara University Rajpura Punjab India Department of Computer Science and Engineering Siksha ‘O’ Anusandhan (Deemed to be University) Bhubaneswar Odisha India Chitkara Centre for Research and Development Chitkara University Baddi Himachal Pradesh India School of Engineering and Technology Computer Science and Engineering JAIN (Deemed to be University) Bangalore Karnataka

ISBN: (数字)9798331542375

ISBN: (纸本)9798331542382

Disaster management is one of the applications where cyber-physical systems play a significant role by allowing real-time data collection and analysis. Such systems consist of physical, computational and communication elements and enable the automatic exchange of information between machines and humans and decision-making in disaster situations. This technology can change the method of disaster response to a more automated one, and it could reduce the amount of manual data collection and analysis. In disaster management, CPS consists of a set of interconnected devices, including sensors, drones and mobile devices monitoring different factors of the disaster: assessing damage, environmental conditions and survivor whereabouts. This information is sent to a central hub for processing and real-time analysis. This information is then fed into models to assess risk, identify hot spots and guide rescue and relief operations. CPS enables disaster response teams to utilize live data, which means decisions can be made faster and more accurately.

关键词： Wireless communication Satellites Disasters Supply chains Disaster management Data collection Cyber-physical systems Sensor systems and applications Real-time systems Remote monitoring

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Improving Image Super-Resolution Reconstruction with InceptionV3 and Dual Perceptual Loss

Improving Image Super-Resolution Reconstruction with Incepti...

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international ECTI Northern Section conference on Electrical, Electronics, computer and Telecommunications engineering (ECTI-NCON)

作者： Siriwan Chuengtheerapanit Warawut Kesornsukhon Kanabadee Srisomboon Luepol Pipanmekaporn San Ratanasanya Department of Computer and Information Science King Mongkut’s University of Norh Bangkok Bangkok Thailand Department of Electrical and Computer Engineering King Mongkut’s Universiy of North Bangkok Bangkok Thailand

ISBN: (数字)9798331543273

ISBN: (纸本)9798331543280

This paper proposes a method to improve the quality of image super-resolution (SR) by using the InceptionV3 network and Dual Perceptual Loss (DP Loss) within the Enhanced Super-Resolution Generative Adversarial Networks (ESRGAN) framework. Reconstruction of high-resolution images is enhanced by the proposed method in terms of both structural integrity and perceptual quality. The proposed method solves the limitations of previous models, including over-sharpening and color inaccuracies, which are prevalent in SR tasks. The proposed method, which replaces the ResNet Model with InceptionV3, outperforms the baseline ESRGAN-DP in Peak Signal-to-Noise Ratio (PSNR), Structural Similarity Index (SSIM), and Learned Perceptual Image Patch Similarity (LPIPS). It achieves up to 0.61% improvement in PSNR, 0.90% in SSIM, and 5.9% in LPIPS across multiple benchmark datasets.

关键词： Visualization PSNR Image color analysis Superresolution Benchmark testing Feature extraction Solids Image reconstruction Standards Biomedical imaging

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Analysis of AI Singing technology Based on Praat Software

Analysis of AI Singing Technology Based on Praat Software

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Electronics, Energy Systems and Power engineering (EESPE), IEEE international conference on

作者： Ruoyu Wang School of Computer Science Hong Kong Baptist University Hong Kong China

ISBN: (数字)9798350389579

ISBN: (纸本)9798350389586

This study explores the core technology behind many contemporary virtual singers, specifically AI singing technology, from the perspective of the Xiaoice framework. The paper compares AI singing with human singing in terms of resonance, breathing, head-chest voice transition, and articulation. Using Praat software, the study analyzes pitch, timbre, and transitions between vowels and consonants. The results reveal significant differences between AI and human singing in terms of pitch processing and articulation, which in turn affect the emotional expression and performance of songs. While certain features of AI singing are difficult for the human ear to distinguish, other features are easily identifiable. The study concludes that AI singing technology needs further refinement to surpass the expressive capabilities of the human voice. Additionally, the paper discusses the future prospects of AI singing technology, emphasizing that AI with singing capabilities, unlike AI that merely provides knowledge storage, is less likely to become obsolete and has considerable potential for development.

关键词： Power engineering Entertainment industry Ear Information age Software Resonance Timbre Human voice Artificial intelligence

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Cloud Based Automated Control System Workshops and Rooms for Controlling Parameters

Cloud Based Automated Control System Workshops and Rooms for...

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Advances in computer Science, Electrical, Electronics, and Communication Technologies (CE2CT), international conference on

作者： Dinesh Kumar Ravinder Singh Kuntal Pragya Deep Anup Shekhar Chamoli Pinki Singh Rishabh Mandal Department of Computer Science and Engineering SRM Institute of Science & Technology Ghaziabad UP Department of Mathematics Nitte Meenakshi Institute of Technology Bangalore Karnataka India Department of AI & DS IIMT College of Engineering Greater Noida India Uttaranchal School of Computing Sciences Uttaranchal University Dehradun India Department of Computer Science and Engineering Accurate College of Engineering & Management Greator Noida India Department of Computer Science and Engineering Tula's Institute Dehradun UK India

ISBN: (数字)9798331518578

ISBN: (纸本)9798331518585

Across all industries, cloud-based automated control systems have completely changed how operational and environmental parameters are tracked and controlled. With cloud-integrated systems for real-time data collection, processing, and control, this concept presents intelligent, remote-controlled workshops and control rooms. To guarantee accurate control of crucial variables like temperature, humidity, pressure, and equipment efficiency, these systems make use of IoT devices, AI techniques, and big data analytics. Through sophisticated data perspectives, the cloud-based strategy enables predictive maintenance, improves flexibility, and lowers operating expenses. Stakeholders may make prompt, data-driven choices from anywhere with remote access to control rooms. These seminars provide practical experience with cloud-controlled settings and function as training and development centres. The system's flexibility to meet a range of industrial demands is further enhanced by its compatibility with external tools and equipment. The design, deployment, and possible uses of cloud-based control systems are covered in this paper, with an emphasis on how they might promote operational excellence, energy efficiency, and smart manufacturing.

关键词： Knowledge engineering Machine control Clouds Control systems Software Machine tools Security Time factors Personnel Standards

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