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2023 IEEE International Conference on Modelling, Simulation and Intelligent Computing, MoSICom 2023

作者： Priyadarshni, Sakshi Fathima, Rakshan Urolagin, Siddhaling Bongale, Anupkumar M. Dharrao, Deepak Sudhakar Birla Institute of Technology and Science Dept. of Computer Science Pilani Dubai United Arab Emirates Symbiosis Institute of Technology Dept. of Artificial Intelligence and Machine Learning Pune India Symbiosis Institute of Technology Dept. of Computer Science & Engineering Pune India

ISBN: (纸本)9798350393415

Customer segmentation plays a crucial role in business development by providing deep insights into customer behaviors and preferences, facilitating effective and personalized marketing strategies. However, traditional segmentation methods often fall short of accurately analyzing large and complex datasets. In recent years, the application of machine learning techniques has transformed the field of customer segmentation, enabling automated analysis, enhanced accuracy, and the discovery of intricate patterns and key trends. This research paper provides a detailed review of the application of K-Means clustering in credit card companies for effective customer segmentation. It explores the benefits, challenges, and practical considerations of utilizing K-Means clustering, along with real-world case studies highlighting trends and potential advancements for using K-Means clustering in credit card customer segmentation. © 2023 IEEE.

关键词： K-means clustering

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Question Answering FAQ Bot Using Natural Language Processing 11th

Question Answering FAQ Bot Using Natural Language Processing

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11th International Conference on Intelligent Computing and Applications, ICRTC 2023

作者： Vijay, K. Sowmia, K.R. Ravikumar, S. Arockia Raj, Y. Department of Computer Science and Engineering Rajalakshmi Engineering College Chennai India Department of Artificial Intelligence and Machine Learning Rajalakshmi Engineering College Chennai India Department of Computer Science and Engineering Vel Tech Rangarajan Dr. Sagunthala R&D Institute of Science and Technology Chennai India Department of Computer Science and Engineering PSNA College of Engineering and Technology Dindigul India

ISBN: (纸本)9789819717231

The BOT retorts with the precise results that help the user for the query and also requests them to rate the response to provide better results. The [frequently answered questions] BOT is a program developed through machine learning and natural language processing. It classifies the queries and results as key-value pairs and maps the response to the user query. It extricates itself by providing additional capabilities such as NLU processes and reinforcement learning. NLU helps the bot to make it convenient for an array of languages to meet the diverse expectations. Reinforcement learning retrieves user rating to provide better performance and search results. The project was developed due to a concern about fetching meticulous responses for specific service-related queries. The overall satisfaction rate of users in getting responses to the questions they post is devastating. The root cause for the proposed project has aimed at providing an environment for a people to find their answers at better accuracy and also respond to their satisfaction. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.

关键词： Reinforcement learning

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PlaySlot: learning Inverse Latent Dynamics for Controllable Object-Centric Video Prediction and Planning

arXiv

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arXiv 2025年

作者： Villar-Corrales, Angel Behnke, Sven Computer Science Institute VI – Intelligent Systems and Robotics Center for Robotics The Lamarr Institute for Machine Learning and Artificial Intelligence Germany

Predicting future scene representations is a crucial task for enabling robots to understand and interact with the environment. However, most existing methods rely on video sequences and simulations with precise action annotations, limiting their ability to leverage the large amount of available unlabeled video data. To address this challenge, we propose PlaySlot, an object-centric video prediction model that infers object representations and latent actions from unlabeled video sequences. It then uses these representations to forecast future object states and video frames. PlaySlot allows to generate multiple possible futures conditioned on latent actions, which can be inferred from video dynamics, provided by a user, or generated by a learned action policy, thus enabling versatile and interpretable world modeling. Our results show that PlaySlot outperforms both stochastic and object-centric baselines for video prediction across different environments. Furthermore, we show that our inferred latent actions can be used to learn robot behaviors sample-efficiently from unlabeled video demonstrations. Videos and code are available at https://***/PlaySlot/. © 2025, CC BY.

关键词： Stochastic systems

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On the Effectiveness of Heterogeneous Ensemble Methods for Re-Identification 23

On the Effectiveness of Heterogeneous Ensemble Methods for R...

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23rd IEEE International Conference on machine learning and Applications, ICMLA 2024

作者： Klüttermann, Simon Rutinowski, Jérôme Polachowski, Frederik Nguyen, Anh Grimme, Britta Roidl, Moritz Müller, Emmanuel Paderborn University Paderborn Germany Tu Dortmund University Dortmund Germany Lamarr Institute for Machine Learning and Artificial Intelligence Dortmund Germany Research Center Trustworthy Data Science and Security Dortmund Germany

ISBN: (纸本)9798350374889

In this contribution, we introduce a novel ensemble method for the re-identification of industrial entities, using images of chipwood pallets and galvanized metal plates as dataset examples. Our algorithms replace commonly used, complex siamese neural networks with an ensemble of simplified, rudimentary models, providing wider applicability, especially in hardware-restricted scenarios. Each ensemble sub-model uses different types of extracted features of the given data as its input, allowing for the creation of effective ensembles in a fraction of the training duration needed for more complex state-of-the-art models. We reach state-of-the-art performance at our task, with a Rank-1 accuracy of over 77% and a Rank-10 accuracy of over 99%, and introduce five distinct feature extraction approaches, and study their combination using different ensemble methods. © 2024 IEEE.

关键词： Contrastive learning

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1D CNN Based Model for Detection of DDoS Attack 2

1D CNN Based Model for Detection of DDoS Attack

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2nd IEEE International Conference on Device intelligence, Computing and Communication Technologies, DICCT 2024

作者： Padmavathy, C. Mahalakshmi, A. Vadivel, R. Kumawat, Rajat Dutta, Ritam Roy, Bhaskar Dutta, Nilanjan Computer Science and Engineering Sri Ramakrishna Engineering College Coimbatore India Computer Science and Engineering B.N.M Institute of Technology Bangalore India Computer Science and Engineering Poornima University Jaipur India Artificial Intelligence & Machine Learning Asansol Engineering College Asansol India Electronics & Comm. Engineering Asansol Engineering College Asansol India

ISBN: (纸本)9798350372847

As the technology behind informatics systems has improved, the Internet of Things (IoT) has grown in significance and application. IoT applications are becoming more popular as a result of the expansion of IoT enablers including smart watches, smart phones, security cameras, and smart sensors. Unfortunately, other issues have raised due to the unsecured nature of IoT devices. Distributed Denial of Service (DDoS) assaults are one of the deadliest cyber-attacks as an example. The untrustworthiness of IoT systems causes a number of security issues. Protecting IoT networks from dispersed denial of service attacks has been the focus of several recent research and development efforts. The primary challenge in this area of study is creating a model that is responsive to different types of DDoS assaults while also being able to distinguish between malicious and benign traffic to prevent false positives. In this study, a Convolutional Neural Network with only one dimension is used. The suggested system is employed in both statistics and cognitive science, and it is based on the central nervous system in particular. They are modelled as mathematical functions that are structured to depict complicated interactions between inputs and outputs (dependent variables), and they are exemplified by the connectivity of brain systems from a variety of input variables to the output (dependent variables) We foresee three types of assaults in our article. All models are tested with precision, recall and F-score analysis. SVM achieved nearly 86% of precision, 95.15% of recall and 90.43% of F-score, where the projected model achieved 92.53% of precision, 97.17% of recall and 91.59% of F-score. © 2024 IEEE.

关键词： Internet of things

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Image Super Resolution using U-Net architecture and SRGAN: Comparative Analysis

Image Super Resolution using U-Net architecture and SRGAN: C...

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computer, Communication and Control (IC4), International Conference on

作者： Pranali Dandekar Bhavika Bhojwani Aarya Balpande Sanskar Zanwar Ankan Deb Department of Computer Science and Engineering (Artificial Intelligence and Machine Learning Shri Ramdeobaba College of Engineering and Management Nagpur India

Super-resolution is an important image processing task that improves the resolution of high-resolution images. Low resolution is used in many different fields, including medical imaging, satellite imagery, and computer vision. This paper presents a new approach to ultra-high resolution using the U-Net architecture, a deep learning framework known for its success in image segmentation and restoration *** this study, we propose an adaptive U-Net model specifically designed for ultra-high-resolution tasks. The architecture includes an encoder-decoder network with bypass connections, enabling multi-scale feature extraction and high-resolution detail reconstruction. Our model is trained on a diverse dataset of low- and high-resolution image pairs, allowing it to learn complex relationships and patterns in *** evaluate the performance of the U-Net based superresolution method using standard image quality metrics and qualitative visual evaluation. Test results show a significant improvement in image quality, with improved sharpness, texture, and detail recovery. Furthermore, our model outperforms state-of-the-art super-resolution methods in terms of signal-to-noise ratio (PSNR) and structural similarity index (SSIM).

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CLAMBER: A Benchmark of Identifying and Clarifying Ambiguous Information Needs in Large Language Models 62

CLAMBER: A Benchmark of Identifying and Clarifying Ambiguous...

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62nd Annual Meeting of the Association for Computational Linguistics, ACL 2024

作者： Zhang, Tong Qin, Peixin Deng, Yang Huang, Chen Liang, Hongru Liu, Junhong Jin, Dingnan Lei, Wenqiang Chua, Tat-Seng College of Computer Science Sichuan University China Engineering Research Center of Machine Learning and Industry Intelligence Ministry of Education China National University of Singapore Singapore University of Electronic Science and Technology China

ISBN: (纸本)9798891760943

Large language models (LLMs) are increasingly used to meet user information needs, but their effectiveness in dealing with user queries that contain various types of ambiguity remains unknown, ultimately risking user trust and satisfaction. To this end, we introduce CLAMBER, a benchmark for evaluating LLMs using a well-organized taxonomy. Building upon the taxonomy, we construct ~ 12K high-quality data to assess the strengths, weaknesses, and potential risks of various off-the-shelf LLMs. Our findings indicate the limited practical utility of current LLMs in identifying and clarifying ambiguous user queries, even enhanced by chain-of-thought (CoT) and few-shot prompting. These techniques may result in overconfidence in LLMs and yield only marginal enhancements in identifying ambiguity. Furthermore, current LLMs fall short in generating high-quality clarifying questions due to a lack of conflict resolution and inaccurate utilization of inherent knowledge. In this paper, CLAMBER presents a guidance and promotes further research on proactive and trustworthy LLMs. Our dataset is available at https://***/SCUNLP/CLAMBER. © 2024 Association for Computational Linguistics.

关键词： Risk assessment

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Representation Matters for Mastering Chess: Improved Feature Representation in AlphaZero Outperforms Switching to Transformers 27

Representation Matters for Mastering Chess: Improved Feature...

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27th European Conference on artificial intelligence, ECAI 2024

作者： Czech, Johannes Blüml, Jannis Kersting, Kristian Steingrimsson, Hedinn Artificial Intelligence and Machine Learning Lab TU Darmstadt Germany Darmstadt Germany Centre for Cognitive Science TU Darmstadt Germany Darmstadt Germany Department of Electrical and Computer Engineering Rice University HoustonTX United States HoustonTX United States

ISBN: (纸本)9781643685489

While transformers have gained recognition as a versatile tool for artificial intelligence (AI), an unexplored challenge arises in the context of chess — a classical AI benchmark. Here, incorporating Vision Transformers (ViTs) into AlphaZero is insufficient for chess mastery, mainly due to ViTs' computational limitations. The attempt to optimize their efficiency by combining MobileNet and NextViT outperformed AlphaZero by about 30 Elo. However, we propose a practical improvement that involves a simple change in the input representation and value loss functions. As a result, we achieve a significant performance boost of up to 180 Elo points beyond what is currently achievable with AlphaZero in chess. In addition to these improvements, our experimental results using the Integrated Gradient technique confirm the effectiveness of the newly introduced features. © 2024 The Authors.

关键词： Distribution transformers

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Smart Education Case: A detection method of the driver's smoking behavior 2

Smart Education Case: A detection method of the driver's smo...

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2nd International Conference on Informatics, Networking, and Computing, ICINC 2023

作者： Xu, Yujia Shi, Rui Pan, Deyu Yang, Li He, Tingting Ding, Ling School of Computer Science Hubei University of Education Wuhan China Hubei Provincial Key Laboratory of Artificial Intelligence and Smart Learning Central China Normal University Wuhan China

ISBN: (纸本)9781510674745

This article presents a case study of our process in Smart Education. This is a case of detecting driver smoking behavior, in which intelligent image processing technology was used. Let students know how to conduct academic research through case analysis. Train students how to do image preprocessing, how to do detection algorithm, how to deal with experimental results, how to do result analysis and so on. Drivers have a kinds of common abnormal behaviors, including smoking, making phone calls, eating, and leaving the steering wheel. Among them, smoking is closely related to the fatigue state of the driver;when drivers make other abnormal behaviors, their hands will leave the steering wheel, which is easy to cause the car to get out of control. Therefore, this paper mainly focuses on the abnormal driving behaviors of smoking and the hands leaving the steering wheel. © 2024 SPIE.

关键词： Students

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Safe Steps: A Smart Security System for Elderly

Safe Steps: A Smart Security System for Elderly

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Computing, Semiconductor, Mechatronics, Intelligent Systems and Communications (COSMIC), International Conference on

作者： Sanjana Karunya KV Ushashree Veja J Shetty Gurusiddayya Hiremath Department of Computer Science & Engineering(Artificial Intelligence & Machine Learning) Sahyadri College of Engineering and Management Mangalore Karnataka India

ISBN: (数字)9798331517892

ISBN: (纸本)9798331517908

Falls are one of the primary causes of injuries among older adults, which lowers their quality of life and has detrimental effects on their health. As the global population ages, it's crucial that they maintain their independence and safety. The proposed model aims to provide a comprehensive solution to enhance senior citizen protection. Promptly identifying and avoiding falls can improve the well-being of elderly individuals and greatly reduce risks. Traditional methods of fall detection are prone to errors. Thus, the proposed model is designed to overcome such errors and accurately predict the falls. Tiny ML simplifies the deployment of machine learning models on low-power microcontrollers and edge devices, making it a great choice for lightweight and energy efficient applications by designing a small wearable device that can process data locally for quick detection and alert generation which helps to improve the safety and independence of elderly people. The proposed model also includes camera-based fall detection for indoor navigation.

关键词： Tiny machine learning Virtual assistants Smart homes Real-time systems Energy efficiency Older adults Fall detection Wearable devices Usability Monitoring

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