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共 2833 条记录，以下是461-470 订阅

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6th international conference on Information and Communication Technology for Intelligent Systems, ICTIS 2021

6th International Conference on Information and Communicatio...

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6th international conference on Information and Communication Technology for Intelligent Systems, ICTIS 2021

ISBN: (纸本)9789811935749

the proceedings contain 149 papers. the special focus in this conference is on Information and Communication Technology for Intelligent Systems. the topics include: COVID-19 Triggers a Paradigm Shift in Technology for Insurance Industry;internet of things in Saudi Arabia Universities: State of the Art, Future Opportunities, and Open Challenges;study of Security in Cryptocurrency;study and Analysis of Network Steganography Methods;preface;Review of Different ML Techniques Used for Heart Disease Prediction;survey on Driver Fatigue Detection Using Sensors, Big data Analytics and machine learning Techniques;on Characteristics of κ–Q–Fuzzy Translation and κ–Q–Multiplication in T-Ideal in T-Algebras;blockchain-driven Smart Healthcare System: Challenges, Technologies and Future Research;a Future to the Blockchain Technology and Its Concepts;Impacts of DDoS Attacks in Software-Defined Networks;ioT-Based Agriculture Trend Prediction Based on Weather data;EEG in Optic Nerves Disorder Based on FSVM Using Kernel Membership Function;DDoS Detection in ONOS SDN Controller Using Snort;classification of Keratoconus Using Corneal Topography pattern with Transfer learning Approach;intelligent Heuristic Keyword-Based Search Methodologies Applied to Cryptographic Cloud Environment;image Enhancement by Various Segmentation Techniques Using machine learning;Surakhsha Kavach: ML-Based Cross-platform Application for COVID-19 Vulnerability Detection;Malware Family Categorization Using Genetic Algorithm-CNN-Based Image Classification Technique;master data Management Maturity Evaluation: A Case Study in Educational Institute;malware Family Classification Using Music Information Retrieval Techniques;text-Based Prediction of Heart Disease Doctor Chatbot Using machine learning;intelligent Proctoring System;project-Based learning: A Contemporary Approach to Blend theory and Practical Knowledge of database Management Course;student’s Employability Concern for Colleges and Universities.

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Web Development and Evaluation of Instructional Objectives’ Quality Using Natural Language Processing 6th

Web Development and Evaluation of Instructional Objectives’...

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6th international conference on Computational Intelligence in pattern recognition, CIPR 2024

作者： Mohanta, Jyotiprava Jain, Mahim Jain, Mahim M. Mandal, Shyamal Kumar Das Advance Technology Development Centre Indian Institute of Technology Kharagpur India Aerospace Engineering Indian Institute of Technology Kharagpur India

ISBN: (纸本)9789819780891

Outcome-based education is currently recommended in every field of education by the accreditation bodies. Outcome-based education is the process of developing the curriculum in a well-defined framework. the stated outcome or the instructional objective should be properly defined for successful implementation. Here, a methodology is proposed where the curriculum designer can check whether the instructional objective is properly designed. the methodology consists of website development and a machine learning model (ML) using natural language processing. the input to the model is an instructional objective statement, and the result will be in three categories: poor, good, and perfect. the website consists of a Flask-written backend and a Next. js-developed frontend. the backend was integrated with the ML model. For the model, the Bidirectional Encoder Representations from Transformers model is used. the designed model was applied to 50 instructional objective datasets for training the model and 40 datasets for testing the prediction. the BERT model was found to be effective. © the Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Contrastive learning

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Tool Support for learning Architectural Guidance Models from Architectural Design Decision Models 28

Tool Support for Learning Architectural Guidance Models from...

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28th European conference on pattern Languages of Programs, EuroPLoP 2023

作者： Amiri, Amirali Ntentos, Evangelos Zdun, Uwe Geiger, Sebastian University of Vienna Faculty of Computer Science Software Architecture Group Vienna Austria University of Vienna Doctoral School Computer Science Vienna Austria Siemens Vienna Austria

ISBN: (纸本)9798400700408

this paper presents an approach to architectural knowledge management that does not assume existing architectural design decisions or pattern applications are documented as architectural knowledge, but benefits from more existing data. We drew inspiration from manual qualitative research methods for mining patterns and architectural knowledge and created a guideline model of which the ADD models are instances. We evaluated our approach on 11 cases from the gray literature. We found that it can provide suitable recommendations after modeling only a single case and reaches theoretical saturation and recommendations with low to very low errors after only 6-8 cases. Our approach shows that creating a reusable architectural design space is possible based only on limited case data. Our approach not only provides a novel approach to architectural knowledge management but can also be used as a tool for pattern mining. © 2023 Copyright held by the owner/author(s).

关键词： Knowledge management

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An Approach Towards learning K-Means-Friendly Deep Latent Representation 27th

An Approach Towards Learning K-Means-Friendly Deep Latent Re...

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27th international conference on pattern recognition, ICPR 2024

作者： Roy, Debapriya Institute of Engineering and Management Kolkata India

ISBN: (纸本)9783031781650

Clustering is a long-standing problem area in data mining. the centroid-based classical approaches to clustering mainly face difficulty in the case of high dimensional inputs such as images. With the advent of deep neural networks, a common approach to this problem is to map the data to some latent space of comparatively lower dimensions and then do the clustering in that space. Network architectures adopted for this are generally autoencoders that reconstruct a given input in the output. To keep the input in some compact form, the encoder in AE’s learns to extract useful features that get decoded at the reconstruction end. A well-known centroid-based clustering algorithm is K-means. In the context of deep feature learning, recent works have empirically shown the importance of learning the representations and the cluster centroids together. However, in this aspect of joint learning, recently a continuous variant of K-means has been proposed;where the softmax function is used in place of argmax to learn the clustering and network parameters jointly using stochastic gradient descent (SGD). However, unlike K-means, where the input space stays constant, here the learning of the centroid is done in parallel to the learning of the latent space for every batch of data. Such batch updates disagree with the concept of classical K-means, where the clustering space remains constant as it is the input space itself. To this end, we propose to alternatively learn a clustering-friendly data representation and K-means based cluster centers. Experiments on some benchmark datasets have shown improvements of our approach over the previous approaches. © the Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： K-means clustering

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Restaurant Queuing Time Prediction Using Random Forest Regression 12

Restaurant Queuing Time Prediction Using Random Forest Regre...

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12th international conference on pattern recognition Systems (ICPRS)

作者： Xue, Yijia Zhang, Xiang DukeKunshan Univ Suzhou 215316 Peoples R China Southeast Univ Nanjing 210096 Peoples R China

ISBN: (数字)9781665466943

ISBN: (纸本)9781665466943

Unknown queuing time often brings negative consumer experience in restaurant service. Prediction of queuing time has been a critical task to benefit both consumers and restaurants. However, current research mainly focuses on the queuing theory, which requires collecting prior knowledge and survey. this paper proposes a queuing time prediction model based on Random Forest Regression with features selection method to ease the limitations of previous research. Random Forest Algorithm have achieved enormous success in various fields. Experiments on a real-life restaurant queuing database validate the practicality of the model. the proposed method outperforms the baseline model on the experimental data. We conclude the intrinsic information of the queuing model with the feature selection method, which will help improve other application models in queuing scenarios.

关键词： Queuing time prediction feature selection random forest regression data mining machine learning

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A Review of Deep learning Techniques for Early Detection and Categorization of Lung Cancer 6th

A Review of Deep Learning Techniques for Early Detection and...

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6th international conference on Intelligent Communication, Control and Devices, ICICCD 2024

作者： Joshi, Swati Mishra, Raj Gaurav Sivagaminathan, P.G. School of Engineering Ajeenkya D Y Patil University MH Pune India Deemed-to-be-University MP Indore India

ISBN: (纸本)9789819783281

Lung cancer becomes critical to human wellness because of its fast-rising rates of disease and loss of life. As a valuable method for increasing the diagnosis precision and detecting cancer earlier, low-dose computed tomography (LDCT) assessment is developed, which leads to decreasing death rates. Designing a computer-aided detection (CADe) method is of great medical significance when using CT data because of its unpredictable nature. Such system would benefit radiologists by helping them to automatically analyze nodules, increasing accuracy of their diagnoses, and enabling prompt patient interventions. An outline of artificial intelligence (AI) systems for pulmonary nodule detection is presented in this work. Findings show the increasing usage of machines and deep learning methods, demonstrating their effectiveness in detecting, classifying, and are tested in. this study confirms that AI helps identify and analyze lung cancer and reduces exposure. With remarkable advances in deep learning, medical imaging studies have occurred as a major research field. this paper provides a broad review of the richness of lung image evaluation by means of deep learning techniques, focusing on different pattern recognition tasks such as classification and local/identification, distribution, and registration. © the Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Lung cancer

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Minimal data for Maximum Impact: An Indonesian Part-of-Speech Tagging Case Study 6th

Minimal Data for Maximum Impact: An Indonesian Part-of-Speec...

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6th international conference on Soft Computing and data mining (SCDM)

作者： Chua, Chi Log Lim, Tong Ming See, Kwee Teck Tunku Abdul Rahman Univ Management & Technol Fac Comp & Informat Technol Kuala Lumpur Malaysia Tunku Abdul Rahman Univ Management & Technol Ctr Business Incubat & Entrepreneurial Ventures Kuala Lumpur Malaysia

ISBN: (纸本)9783031669644;9783031669651

Annotating low-resource languages is challenging due to its time-consuming nature and high costs. In response to these challenges, this study investigates the potential of training models with minimal annotated data (520 tagged words) and abundant unannotated data (419,055 sentences), using Indonesian Part-of-Speech (POS) tagging as a case study. For the first time, we apply Stratos and Collins' algorithm for Indonesian POS tagging, using four classifiers: Support Vector machine, Naive Bayes, Decision Tree, and K-Nearest Neighbor. Our approach not only improved precision, recall, Fl-score, and accuracy by approximately 1-5% compared to a baseline model that uses only the minimal annotated data, but also achieved a high performance for an Indonesian POS model, attaining an accuracy of 84% using this small amount of annotated data. this is a significant achievement in the context of using a small amount of annotated data to train the Indonesian POS model, as previous researchers have not achieved this level of accuracy with such a limited dataset. the method proved particularly beneficial for low-resource languages with limited high-quality annotated data but abundant unannotated data. It also reduced the workload of manual annotation as high-performance models required only a small amount of annotated data. Building on this efficiency, future work will focus on developing methods that further optimize the annotation process for low-resource language data.

关键词： Semi-supervised learning Part-of-Speech tagging Indonesian language Low-resource languages Natural language processing

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PostAugment: Adversarial data Augmentation with Hard Sample Suppression by Incorrect Class Likelihood 27th

PostAugment: Adversarial Data Augmentation with Hard Sample...

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27th international conference on pattern recognition, ICPR 2024

作者： Sawada, Azusa Shibata, Takashi Yokoyama, Keiko Yachida, Shoji Hosoi, Toshinori NEC Corporation Nakahara-ku Shimonumabe Kawasaki1753 Japan

ISBN: (纸本)9783031781919

We propose an adversarial automatic data augmentation with hard sample suppression by incorrect class likelihood for time series data. Automatic data augmentation (ADA) is a practical framework when the number of training data is small. In particular, an ADA based on adversarial loss, such as TeachAugment, can improve classification accuracy in optimizing complex transformation parameters with class and instance dependencies. In this paper, we aim to extend adversarial ADA for image recognition to time series data. the critical challenge is that data augmentation for time series data is more difficult than that for images in designing a transformation function that does not cross the discriminant class boundary. When existing ADA frameworks are naively applied to time series data, the transformation function often generates augmented data similar to the incorrect class. As a result, this overly hard-to-identify data degrades the accuracy of a target classifier. In general, this hard-to-identify data near the class boundary has a high likelihood both in the correct and incorrect classes. the proposed method can reduce this hard-to-identify data by introducing a novel fidelity loss that suppresses only the likelihood of the incorrect class. Comprehensive experiments on two datasets demonstrate the effectiveness of the proposed method. the project code is included in the supplemental material. © the Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Adversarial machine learning

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Fall Detection System Based on Millimeter Wave Radar and machine learning 6

Fall Detection System Based on Millimeter Wave Radar and Mac...

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6th international conference on Robotics and Automation Sciences (ICRAS)

作者： Liang, Jiye Huang, Yu Huang, Zhuo Univ South China Sch Elect Engn Hengyang Peoples R China

ISBN: (数字)9781665467841

ISBN: (纸本)9781665467841

Nowadays, falling is a growing threat to the elderly. this paper combines millimeter wave radar technology, machine learning algorithm, wireless communication technique and cloud platform to realize a fall detection system. In this project, the millimeter wave radar is used to sample the human posture point cloud data, and we create a data set that consist of point of clouds of two different human poses. Random forest and BP neural network are used to train the fall detection model. the system will send the human posture point data to the trained model and realize the fall detection. Besides, the system will use 4G communication technology to transform the data to the web cloud platform. this web page serves as an warning function, which can report the acceleration, speed and other information. According to our experiments, the millimeter wave radar system that we built in this paper can effectively detect human point cloud, and can send human point cloud data packets to the recognition model to detect human falls. In machine learning part, both Random forest and BP neural network models show very strong robustness after repeated adjusting parameters. Random forest has the advantages of light weight and interpretability, which can reach 93% recognition accuracy. the recognition accuracy of BP neural network is even up to 95%, which is higher than other detection models in previous works. Besides, BP neural network model also has higher recall rate in the categories of human falls, which fully meets the requirements of this project.

关键词： millimeter-wave radar random forest BP neural network fall detection

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A Big Wave of Deep learning in Medical Imaging - Analysis of theory and Applications 6

A Big Wave of Deep Learning in Medical Imaging - Analysis of...

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6th international conference on Intelligent Computing and Control Systems, ICICCS 2022

作者： Senthil, G.A. Prabha, R. Razmah, M. Sridevi, S. Roopa, D. Asha, R.M. Dept of Electronics and Communication Engineering Chennai India Department of Computer Science and Engineering Chennai India Sri Sairam Institute of Technology Dept of Computer Science and Engineering Chennai India Sri Sairam Institute of Technology Dept of Civil Engineering Chennai India

ISBN: (纸本)9781665410359

Deep learning models have become the province of cutting-edge machine learning models that are widely used in medical imaging in multiple kinds of ranging from image recognition to natural language processing. this article presents a brief overview on the recent developments and some relevant issues in medical image processing and image analysis as they related to machine learning. the proposed article will not cover the entire application landscape because it is becoming a very large and rapidly growing field, but will instead focus primarily on deep learning theory and techniques in medical imaging. Our goal is threefold: (i) To provide such a brief overview to deep medical imaging learning;(ii) To represent different approaches as well as comparisons of how deep learning has been accomplished, from image retrieval to segmentation to disease prediction;(iii) Provide the current medical imaging-related deep learning applications. © 2022 IEEE.

关键词： Big data

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