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Shopping Tech Cart using Arduino and RFID

Shopping Tech Cart using Arduino and RFID

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Image Processing and Capsule Networks (ICIPCN), international conference on

作者： Anita Joshi Tanishq thuse Prashant thorve Raj thakur Isha thakur Anshul thote Department Of Engineering Science and Humanities Vishwakarma Institute Of Technology Pune India

ISBN: (数字)9798350367171

ISBN: (纸本)9798350367188

this Smart Tech Cart system integrates Arduino and RFID technology to not only streamline retail and inventory management but also focuses on analyzing user behavior. By leveraging RFID for precise item identification, the system enables real-time user behavior analysis, pattern identification, and prediction of future behaviors. the Arduino microcontroller processes data to enhance user experience by personalizing recommendations and optimizing store layouts based on observed patterns. this study assesses the feasibility and benefits of integrating behavior analysis into retail operations, emphasizing the potential for achieving improved shopping experiences.

关键词： machine learning algorithms Microcontrollers Heuristic algorithms Layout Clustering algorithms machine learning Inventory management

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Sleep Staging Based on Cardiopulmonary Coupling Features Derived from Single-Lead ECG Signal

Sleep Staging Based on Cardiopulmonary Coupling Features Der...

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pattern recognition and machine learning (PRML), IEEE international conference on

作者： Ye Lin Yidan Dai School of Artificial Intelligence South China Normal University Foshan China School of Computer Science South China Normal University Foshan China

ISBN: (数字)9798350355925

ISBN: (纸本)9798350355932

Sleep staging is an important tool for diagnosing various sleep disorders. However, polysomnography (PSG), which serves as the gold standard for sleep detection, is challenging to acquire in non-clinical settings. Electrocardiogram (ECG) signals are readily obtainable and have been utilized in sleep staging research. the majority of existing studies have not taken into account neighborhood information and cardiopulmonary coupling technology. Actually, medical experts usually consider about 30 minutes or 1 hour of contextual information when staging a segment of ECG, so neighborhood segments can provide auxiliary information. In addition, clinical studies have proved that cardiopulmonary coupling technology plays a key role in sleep staging research. In view of these, we propose a sleep staging method based on ECG, cardiopulmonary coupling technology and deep neural network. More specifically, we first utilize the CNN model for automatic feature extraction and fusion of cardiopulmonary coupling features and the original ECG. then, noises effects caused by neighborhood information are removed through an efficient channel attention (ECA) mechanism. Finally, sleep stages are determined for signal segments. To verify the robustness of the model, we evaluate it on four publicly available datasets and a private dataset. Experiments show that our method outperforms other benchmarks.

关键词： Couplings Attention mechanisms Noise Artificial neural networks Electrocardiography Feature extraction Robustness Sensors pattern recognition Monitoring

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Framework for Prediction of Diabetes using FireFly Swarm Intelligence Algorithm, Fuzzy C Mean and SVM Algorithm 5

Framework for Prediction of Diabetes using FireFly Swarm Int...

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5th international conference on Inventive Computation Technologies (ICICT)

作者： Kalpana, C. Booba, B. Vels Univ Dept CSE VISTAS Chennai Tamil Nadu India

ISBN: (数字)9781665408370

ISBN: (纸本)9781665408387;9781665408370

Diabetes is a metabolic sickness that remains to be a major universal problem because it influences the health of the entire population. Over the years several researchers have attempted to build a model for predicting diabetes accurately. However, this field study remains a challenge because of the unavailability of dataset and prediction models which forces the researchers to utilize ML (machine learning) algorithm and Big data analytics. the primary objective of the study was to identify how ML and Big data analytics can be adopted in diabetes. the evaluation of outcomes demonstrates that the proposed ML-based technique might achieve 8.6% of accuracy. the authors performed a review of literature on the ML models and came out with the suggestion of an Automatic Intelligent method for the prediction of diabetes based on the findings. the authors explored ML approaches and proposed and developed an intelligent ML-based architecture for diabetes prediction. In this work, the authors used fuzzy mean, SVM learning models for the prediction of diabetes. A firefly algorithm optimized classifies was utilized for selecting the best features. It is proposed in the work, that an exclusive automatic intelligent optimize diabetic prediction model is built by utilizing ML approaches. the framework was created following a thorough analysis of existing prediction approaches in the literature and assessing their relevance to diabetes. the authors presented the training methods, model evaluation strategies, and the challenges allied to the prediction of diabetes also the remedies they provide by using the framework. Health professionals, students, researchers, and stakeholders working in diabetes forecast research development may benefit from the findings of the study. the proposed work achieves an accuracy of 86 percent with low error rates.

关键词： Fuzzy C-Means Clustering algorithm diabetes diagnosis medical data mining classification firefly algorithm

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Jaśkowski–Fitch Natural Deduction Proofs for Metadata Extraction through machine learning Systems

Jaśkowski–Fitch Natural Deduction Proofs for Metadata Ext...

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

作者： Cuconato, Simone Department of Humanities University of Calabria CS Rende87036 Italy LUISS University - Rome Rome00198 Italy

ISBN: (纸本)9789819780921

In the present paper, I will focus on propositional knowledge, proof theory, and the deductive transmission of information in data mining. the application of proof theory to machine learning-based metadata extraction systems will allow us to intend metadata extraction as mathematical objects. More in detail, I will consider a recent first-order logic (FOL)-based applied ontology for metadata extraction, by the name MAke Decision for Metadata Extraction (MADME), and I will build a specific logical system for declarative sentences applied to the extraction of metadata through machine learning systems. MADME’s strength is that it bases its decision-making procedure on a rigorous logical structure. the objective of this paper is to demonstrate how the logic in MADME can be optimized using Jaśkowski–Fitch natural deduction proofs. Jaśkowski–Fitch systems will determine new operations among the best machine learning-based metadata extraction systems from heterogeneous documents sources and, consequently, will support the research community in knowledge extraction and management. © the Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Decision making

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5th international conference on Cognitive Computing and Information Processing, CCIP 2023

5th International Conference on Cognitive Computing and Info...

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5th international conference on Cognitive Computing and Information Processing, CCIP 2023

ISBN: (纸本)9783031607240

the proceedings contain 14 papers. the special focus in this conference is on Cognitive Computing and Information Processing. the topics include: MVR Delay: Cooperative Light Weight Detection and Prevention of False Emergency Message Dissemination in VANET;Antarctic Ice Sheet Surface Mass Balance Using UAV-Based Digital Elevation Model;estimation of Glacier Velocity of Ny-Ålesund, Arctic Using Sentinel-1 Remote Sensing data;Implementation of ECPM for High-Speed Area-Efficient Processor on FPGA;comparative Analysis of machine learning Approaches to Assess Stability in Micro Grid;cloud-Based Real-Time Sign Language Detection and Voice Synthesis for Impaired Individuals Using machine learning;machine learning Approaches for Analysing Sentiment in Reviews on Massive Open Online Courses;Deep Belief Network Optimization Using PSOGAA Algorithm for Efficient Crop Recommendation;a Practical Solution Towards Development of Real-Time Face Attendance System;Ancient Kannada Handwritten Character recognition from Palm Leaf Manuscripts Using PyTesseract-OCR Technique;Anisotropic Guided Filtering and Multi-level Disintegration Method for NIR and Visible Image Fusion;an Approach for Detecting and Restoring Tampering in Digital Image Watermarking.

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Deep learning: Exploring the Depths of Artificial Neural Networks for Advanced data Analysis and pattern recognition

Deep Learning: Exploring the Depths of Artificial Neural Net...

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Global conference for Advancement in Technology (GCAT)

作者： Rajesh Jagadeesan Ravikumar A. Ammupriya Devi.P.P V.Samuthira Pandi Akhila Akula K. Sankar Software Engineering Senior Advisor Evernorth Health Services Chattanooga TN Department of MBA St Joseph's College of Engineering Chennai Department of Artificial Intelligence and Machine Learning Vel Tech Rangarajan Dr. Sagunthala R & D Institute of Science and Technology Avadi Chennai Centre for Advanced Wireless Integrated Technology Chennai Institute of Technology Chennai Tamil Nadu India Department of ECE MLR Institute of Technology Hyderabad Department of Biomedical Engineering Rajalakshmi Engineering College Chennai

ISBN: (数字)9798350376685

ISBN: (纸本)9798350376692

this research provides a comprehensive exploration of LSTMs, CNNs, and RNNs, with a focus on their application in advanced data analysis and pattern recognition. We begin by examining the theoretical underpinnings of each architecture, highlighting their unique features and the mechanisms by which they process and learn from data. In the process of collecting long-term relationships in sequential data, LSTMs have been demonstrated to perform exceptionally well, making them an excellent choice for applications such as language modeling and time series prediction. CNNs are discussed in the context of their ability to extract hierarchical features from spatial data, rendering them particularly effective for image and video analysis. RNNs, with their inherent ability to process sequences of varying lengths, are analyzed for their applications in speech recognition and machine translation. Following this, the research begins to conduct a comparative examination of different architectures, examining how well they perform on benchmark datasets as well as challenges that are encountered in the real world. We assess their strengths and limitations, considering factors such as accuracy, computational efficiency, and scalability. Additionally, we explore the challenges associated with training deep neural networks, including the need for extensive data preprocessing, the risk of overfitting, and the importance of hyperparameter tuning. Finally, we discuss the implications of these findings for future research and practical applications in data analysis and pattern recognition. the study concludes that while each architecture has its niche, the combination of LSTMs, CNNs, and RNNs offers a powerful toolkit for tackling complex problems across diverse domains. the research underscores the potential of deep learning to unlock insights from data at unprecedented scales and complexities, paving the way for innovative solutions in fields ranging from healthcare to finance.

关键词： Deep learning Training Technological innovation Time series analysis Computer architecture Speech recognition Feature extraction Computational efficiency Tuning Overfitting

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Architecture Design of Emergency Rehabilitation Management System for Large-scale Public Health Events Based on machine learning 5

Architecture Design of Emergency Rehabilitation Management S...

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5th international conference on Electronics, Communication and Aerospace Technology, ICECA 2021

作者： Yin, Weida China University of Mining and Technology School of Public Administration Jiangsu Xuzhou China Law and Politicals School Suqian University Jiangsu Suqian223800 China

ISBN: (纸本)9781665435246

Our country is in the transitional period of economic transition society, facing complex contradictions, coupled with natural disasters, accidents and disasters, public health and social security exposure of the inharmonious issues, there is the possibility of various public emergencies anytime and anywhere. therefore, no matter from the perspective of field, frequency, scale, organization, etc., my country is in a period of frequent public emergencies at this stage. this paper studies the architecture design of a large-scale public health incident emergency rehabilitation management system based on machine learning. © 2021 IEEE.

关键词： data mining

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Chinese Nested Named Entity recognition in the Medical Field Based on BiLSTM and Multi-Head Global Pointer

Chinese Nested Named Entity Recognition in the Medical Field...

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pattern recognition and machine learning (PRML), IEEE international conference on

作者： Xinke Zhao Hankiz Yilahun Askar Hamdulla School of Computer Science and Technology Xinjiang University Urumqi China

ISBN: (数字)9798350355925

ISBN: (纸本)9798350355932

the technology of named entity recognition has been gradually refined. However, current research is primarily focused on non-nested named entity recognition, with less attention devoted to nested named entity recognition (NNER). the challenge of accurately extracting nested entities from complex medical texts remains. To address the issue of low accuracy in identifying nested entities in medical named entity recognition, we propose a Chinese nested named entity recognition model (Multi-ABGP) based on adversarial training, bidirectional long short-term memory (BiLSTM) and multi-headed global pointer network. this model employs a BiLSTM network to extract bidirectional and long-distance text features, enhancing the ability of the global pointer network to extract features from longer text. Furthermore, adversarial training is employed to reduce the impact of noise on the model's performance and enhance its robustness. this enables the model to ignore irrelevant features introduced by noise and instead focus on entity-related features. Finally, a multi-head global pointer network is utilized to fuse the features of multiple global pointer networks. this reduces the erroneous prediction of a single pointer network and improves the overall performance of the model. the experimental results demonstrate that the proposed model achieves Micro-F1, Micro-P, and Micro-R values of 73.71%, 74.96%, and 72.50%, respectively, on the CMeEE-V2 dataset of Chinese nested named entity recognition in the medical specialty. this significantly improves the effectiveness of Chinese nested entity recognition in the medical field. And we also compare it with the large language model (LLM), the experimental results show that the model outperforms the ChatGLM in the nested entity recognition task. Additionally, it exhibits favorable results on the non-nested entity recognition dataset People's Daily.

关键词： Training Accuracy Large language models Noise Semantics Named entity recognition Predictive models Feature extraction Robustness pattern recognition

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Sign Language recognition of Autistic Children using Wearable Sensors Signals Features Selection

Sign Language Recognition of Autistic Children using Wearabl...

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international conference on Advanced Communication Technologies and Networking (CommNet)

作者： Farman Ullah Mohamed Isa Ahmed Abdulraheim Alhammadi Abdalla Ahmed Ali Alyassi Shumayla Yaqoob Ihtesham Jadoon College of Information Technology United Arab Emirates University Al-Ain Abu Dhabi

ISBN: (数字)9798350367027

ISBN: (纸本)9798350367034

Children with autism spectrum disorder (ASD) often face difficulties and challenges in social interactions, communication, and eye contact due to neurological disorders. this neurological disorder causes children to have a lack of social interaction, sensory processing issues, repetitive behaviours, trouble adapting to change, and executive functioning problems. this article proposes the autistic children’s gesture recognition framework using wearable sensors feature selection-based machine learning techniques. the system records and analyzes individuals’ gestures, providing valuable data for understanding their behaviour. A dataset of $\mathbf{2 4}$ gestures from 10 children with ASD was collected, and various machine learning models, such as KNN-Manhattan, KNN-Euclidean, neural networks, and random forests, were trained using time- and frequency-domain features extracted from the sensor data. Cross-validation techniques were applied to evaluate the models’ accuracy, with most classifiers achieving around $\mathbf{8 8 \%}$ and the highest accuracy of about $\mathbf{9 1 . 5 \%}$. this approach demonstrates the potential of wearable technology and machine learning in improving the understanding and support of individuals with ASD.

关键词： Neurological diseases Sign language Accuracy Frequency-domain analysis Face recognition Feature extraction data models data mining Wearable sensors Random forests

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IBDR-Net: A Computerized Framework for Irregular Brain Signal data recognition 13th

IBDR-Net: A Computerized Framework for Irregular Brain Signa...

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13th international conference on Health Information Science, HIS 2024

作者： Alvi, Ashik Mostafa Manami, Nishat Tasnim Siuly, Siuly Sun, Lili Victoria University MelbourneVIC Australia

ISBN: (纸本)9789819655960

Brain signal data are records of the electrical activity of the brain made with electroencephalography (EEG). EEG data mining is one of the key topics which has received lots of attention. State-of-art machine learning (ML) methods are incompetent to dive deep and learn the EEG data pattern. Moreover, additional feature extraction methods are employed for feature extraction when a classical ML is in action which adds up additional computational burden. Lastly, the multi-class EEG data classification performance is not quite satisfactory. To overcome these issues, we propose a computerized framework for efficient and effective irregular brain signal data recognition network (IBDR-Net). Our suggested IBDR-Net is made up of four steps: (1) EEG data acquisition, (2) Pre-processing, (3) recognition of irregular brain signal data using IBDR-Net, and (4) Performance evaluation. IBDR-Net framework has been tested and trained with real human EEG dataset and this study has set a new milestone for the EEG researchers and boosted the multi-class performance. © the Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Deep learning

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