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Hyperdimensional Consumer pattern Analysis with Quantum Neural Architectures using Non-Hermitian Operators 5

Hyperdimensional Consumer Pattern Analysis with Quantum Neur...

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5th international conference on Information Management and machine Intelligence, ICIMMI 2023

作者： Goyal, Shivam Singh, Sunil K. Kumar, Sudhakar Sarin, Saket Gupta, Brij B. Arya, Varsha Chui, Kwok Tai Chandigarh College of Engineering and Technology Chandigarh Chandigarh India Asia University Taichung Taiwan Hong Kong Metropolitan University Hong Kong Hong Kong

ISBN: (纸本)9798400709418

In an era inundated with high-dimensional consumer data, the need for advanced hyper-dimensional pattern analysis poses a significant computational challenge. this research pioneers the use of quantum computing to revolutionize consumer technology. Modern data streams, including images, audio, sensor data, and more, require an agile solution to overcome dimensionality challenges that traditional machine learning and classical computing struggle with. Our work combines Quantum Neural Architectures (QNAs) with Non-Hermitian Operators (NHOs), harnessing NHOs' unique non-unitary properties for quantum speedup in feature extraction, dimensionality reduction, and pattern recognition. this approach allows for simultaneous processing of large datasets through quantum parallelism, demonstrating substantial gains in efficiency and accuracy compared to classical and Hermitian quantum methods. We also explore quantum cryptography, offering insights into quantum-safe encryption and cryptographic primitives. this multidisciplinary effort represents a paradigm shift in quantum technology's application across consumer domains. this paper presents a quantum-inspired framework for hyperdimensional consumer pattern analysis, supported by mathematical rigor and empirical validation. the fusion of NHOs and QNAs heralds a new era in consumer technology, marked by exceptional computational power, robust security, and transformative potential. © 2023 ACM.

关键词： Large datasets

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A Region-Based Approach for Layout Analysis of Music Score Images in Scarce data Scenarios 18th

A Region-Based Approach for Layout Analysis of Music Score I...

引用

18th international conference on Document Analysis and recognition (ICDAR)

作者： Castellanos, Francisco J. Martinez-Esteso, Juan P. Galan-Cuenca, Alejandro Gallego, Antonio Javier Univ Alicante Univ Inst Comp Res Alicante 03690 Spain

ISBN: (纸本)9783031705458;9783031705465

this work presents a novel region-based layout analysis (LA) method for Optical Music recognition (OMR) systems, aimed at overcoming the data scarcity challenge. Contemporary OMR techniques, grounded in machine learning principles, have a critical requirement: a labeled dataset for training. this presents a practical challenge due to the extensive manual effort required, coupled with the fact that the availability of suitable data for creating training sets is not always guaranteed. Unlike other approaches, our method focuses on adapting the training and sample extraction processes within an existing neural network framework. Our approach incorporates a labeled data-driven oversampling technique, a masking layer to enable training with partial labeling, and an adaptive scaling process to improve results for varying score sizes. through comprehensive experimentation, we established the minimal labeled data necessary for an effective model and demonstrated that our method could achieve a performance comparable with the state-of-the-art with just 8 to 32 labeled samples. the implications of our research extend beyond improving LA, providing a scalable and practical solution for digitizing and preserving musical documents.

关键词： Few-shot learning Deep learning Layout Analysis Object Detection Optical Music recognition

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Research on the recognition and Classification of Recyclable Garbage in a Complex Environment Based on Improved YOLOv8s 5

Research on the Recognition and Classification of Recyclable...

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5th international conference on Control and Robotics (ICCR)

作者： Shi, Yongqiang Li, Xuyue Wang, Guorong Jin, Xin Liaoning Petrochem Univ Coll Informat & Control Engn Fushun Peoples R China

ISBN: (纸本)9798350307627

To solve the difficulty of garbage detection and classification in complex environments, an improved YOLOv8s-based recyclable garbage identification and classification method in complex environments was proposed. One of the main reasons for the difficulty of garbage detection and classification in complex environments is the lack of data. therefore, this paper constructed the Rtrash recyclable garbage data set and expanded it by adjusting contrast and saturation, introduced the mixup data enhancement method, and further improved the robustness of garbage data samples by the linear interpolation method. Second, the ShuffleAttention module is introduced to improve the model's attention to global information and perform better feature extraction. Finally, the dynamic nonmonootone focusing mechanism WIoUv2 is used to replace the regression loss function of the original network to improve the training speed and inference accuracy of the model and meet the actual detection requirements. the experiment shows that MAP can reach 96.3% after the above improvement, which is 3.9% higher than the original network, which proves that the method proposed in this paper has a good effect on the detection and classification of recyclable garbage in complex environments.

关键词： machine vision Attention Mechanisms Deep learning YOLOv8s

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A Systematic Review of machine learning Based Models for Early Diabetes Prediction 5th

A Systematic Review of Machine Learning Based Models for Ear...

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5th international conference on data Science, machine learning and Applications, ICDSMLA 2023

作者： Malchi, Sunil Kumar Davanam, Ganesh Neelima, P. Sravanthi, T. Lakshmi AP Tirupati India Department of CSE School of Computing SPMVV AP Tirupati India AP Tirupati India

ISBN: (纸本)9789819780426

Diabetes is a persistent metabolic condition that affects millions of people globally. the effective management of diabetes care is crucial in order to prevent complications and improve patient outcomes. the application of machine learning methods for the field of medical care, particularly in the management of diabetes, has significantly increased in recent years. this review seeks to offer a thorough examination of machine learning techniques used in diabetes treatment, both supervised and unsupervised Algorithms for supervised machine learning have been widely used for a variety of diabetes care activities, including risk assessment, diagnosis, and medication recommendation. these algorithms utilize labelled data to train predictive models, allowing for accurate identification of high-risk individuals, early detection of diabetes, and personalized treatment plans. In particular, support vector machines, random forests, and synthetic neural networks have produced promising outcomes in these fields of contrast, unsupervised machine learning techniques have been used for pattern identification and exploratory analysis of big datasets without specified labels. the identification of patient subgroups based on shared traits using clustering techniques like k-means and hierarchical clustering has enabled personalised therapies and precision medicine approaches in the treatment of diabetes. In order to visualize complicated data and show hidden correlations, dimensionality reduction approaches such as principal component analysis as well as t-distributed stochastic neighbor embedding have been successful. © the Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Ada Boost Decision tree Support vector classifier XGBoost

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Herbal Species Identification using Deep learning with Image Processing 5

Herbal Species Identification using Deep Learning with Image...

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5th international conference on data Intelligence and Cognitive Informatics, ICDICI 2024

作者： Anila, M. Kumar, G. Kiran Rakhee Irigela, Likith Reddy Buchala, Raghu Chaitanya Bharathi Institute of Technology Department of Computer Science and Engineering Gandipet Telangana Hyderabad India The University of West Indies Faculty of Engineering Mona campus Kingston Jamaica

ISBN: (纸本)9798350389609

Sorting medicinal plant leaves according to their classification can make it easier to link those leaves to certain illnesses and, in turn, help find possible treatments. this paper examines both the front and rear aspects of leaf images along with addition to structural traits in an effort to find the unique, optimal feature combination that increases the recognition rate. To accurately identify medicinal plants, a dataset of scanned pictures of leaves collected from various angles is created. the leaves are categorized based on sizes and shapes. Identification of medicinal plant leaves will have a significant positive impact on the health care pharmaceutical production business. though there were many methods and techniques developed by various researches using different machine learning algorithms like KNN and SVM, there is still a need for enhancement of such developed methods. this paper introduces an automated plant detection system utilizing deep convolutional neural network (DCCN), which comprises of image pre-processing, feature extraction, and recognition stages. the classifier built upon our model employs layers such as dropout, convolutional, fully connected layers and max pooling to learn intricate leaf features, that achieved 98.1% accuracy with DCNN and Advanced Xception model, which is hyperparameter tuned, observed with 99.25% accuracy on the Indian medicinal leaf's dataset. the results are compared to models like Mobilenet, MobilenetV2 and RESNET50 on the dataset comprising of around 2000 different medicinal leaf images by leveraging knowledge from various plant species. the proposed Advanced Xception system achieved remarkable accuracy with minimal losses in predicting the category of leaf images. © 2024 IEEE.

关键词： Medicinal chemistry

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Hybrid Actor-Action Relation Extraction: A machinelearning Approach 5

Hybrid Actor-Action Relation Extraction: A MachineLearning A...

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5th international conference on Innovative data Communication Technologies and Application, ICIDCA 2024

作者： Nair, Reshma P. thushara, M.G. Department of Computer Science and Engineering Amrita Vishwa Vidhyapeetham Amrita School of Computing Amritapuri India Department of Computer Science and Applications Amrita Vishwa Vidhyapeetham Amrita School of Computing Amritapuri India

Software architects and developers face challenges while trying to explain the complex functional relationships between actors and systems in the process of system design. the field of natural language processing (NLP) and information extraction is always growing. One of the biggest problems that comes up time and time again is finding relationships between entities and actions in textual data. Actor-action relation extraction (AARE) is a hybrid model that combines NLP with rule-based and machine-learning models. It effectively analyzes unstructured data and takes into account different contextual factors, saving time and reducing errors. the proposed approach extracts Actors, actions, entities, and relationships (AARE) from natural language text more accurately and completely using machine learning techniques and rule-based systems. the hybrid model handles unstructured input and adapts to changing linguistic signals using machine learning. the hybrid approach uses Named Entity recognition, rule-based extraction, and machine learning principles to convert unstructured data into structured format. It uses tokenization, part-of-speech tagging, nlp, and semantic role labeling for relationship categorization. the model has a 93% accuracy rate and is effective in extracting actor-action relations. Future research should focus on improving rule-based techniques, semantic learning, and addressing complexities in UML diagrams. © 2024 the Authors. Published by Elsevier B.V.

关键词： machine learning

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data mining Based Method for Integrating Online English SPOC Curriculum Resources 9th

Data Mining Based Method for Integrating Online English SPOC...

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9th EAI international conference on e-learning, e-Education, and Online Training, eLEOT 2023

作者： Tang, Zhou Wang, Nan Tianjin University of Fiance and Economics Pearl River College Tianjin301811 China BinHai School of Foreign Affairs of Tianjin Foreign Studies University Tianjin300300 China

ISBN: (纸本)9783031514708

In order to improve the integration effect of English SPOC course resources and improve students’ English grades, this article proposes an online English SPOC course resource integration method based on data mining. Firstly, web log preprocessing is implemented through five steps: data cleaning, user authentication, session authentication, path integration, and sequence recognition. then, use frequent pattern mining algorithms to implement online English SPOC course resource mining. Finally, design an online English SPOC course resource integration platform using a B/S structure to achieve the integration of course resources. the test results show that this method can slightly improve students’ English grades in the short term, and significantly improve students’ English grades in the long term. © ICST Institute for Computer Sciences, Social Informatics and Telecommunications Engineering 2024.

关键词： Students

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A Conceptual Overview on Earlier Methodologies Focused on Stock Price Prediction 5

A Conceptual Overview on Earlier Methodologies Focused on St...

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5th international conference on Smart Systems and Inventive Technology, ICSSIT 2023

作者： Gnanavel, R. Gnanasekar, J.M. Sri Venkateswara College of Engineering Department of Computer Science and Engineering Chennai India

ISBN: (纸本)9781665474672

data science, analytics, and mining are the subsets of data engineering, and they play a vital role in the majority of emerging applications. Stock Price Prediction (SPP) is one such application that has drawn the attention of investors ranging from individuals to large business houses. It has become the focus of study for many financial institutions that starve to park their capital in expectation of better returns. Of Late, much research has been done on this aspect, and this trend is expected to continue further. Many algorithms like Advanced conventional, AI, machine learning, and deep learning have focused on analyzing national and international stock data to predict the market movement. Sinoe the stock data is time-series data that is always volatile and unpredictable, accuracy in predicting the stock value has always been a topic of discussion in the global financial markets. this unpredictable nature of stock prices has misled genuine investors into investing in the wrong place and at the wrong time. there is now a dire need for an algorithm that could predict the stock movements as close as possible to the accurate price. Until now, research has been done on this topic using statistical methods combined with machine learning algorithms to predict stock movements. Still, they were far from giving accurate and satisfactory predictions that only resulted in diminishing returns. In an attempt to overcome this issue, this paper has analyzed various research works done so far and tried to come up with a solution in the form of a novel machine-learning algorithm. It uses historical data to make better predictions on the stock movements that are considerably nearer to the real value. this work has also aimed to highlight the challenges faced by previous researchers while framing an algorithm. It should help future researchers to get a better outline of the SPP. © 2023 IEEE.

关键词： data Analytics

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Alzheimer's Disease Detection Using Ensemble learning and Artificial Neural Networks 5th

Alzheimer's Disease Detection Using Ensemble Learning and Ar...

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5th international conference on Recent Trends in Image Processing and pattern recognition (RTIP2R)

作者： Bandyopadhyay, Ahana Ghosh, Sourodip Bose, Moinak Singh, Arun Othmani, Alice Santosh, K. C. Appl AI Res Lab Vermillion SD 57069 USA Univ Paris Est LISSI UPEC F-94400 Vitry Sur Seine France Univ South Dakota Basic Biomed Sci Vermillion SD 57069 USA Univ South Dakota Dept Comp Sci Appl AI Res Lab Vermillion SD 57069 USA

ISBN: (纸本)9783031235986;9783031235993

this paper presents an ensemble method using machine learning classification algorithms and an artificial neural network-based scheme using the popular and widely used open access series of imaging studies (OASIS) dataset for Alzheimer's disease (AD) detection. the proposed work performs an in-depth feature examination and a training-test split in a 70 : 30 ratio on the dataset and applies 8 different ML algorithms. the AD detection outcome is obtained using two procedures, first by an ensemble approach applied to different machine learning algorithms, and secondly by using an artificial neural network (ANN). the use of ANN achieves an overall test accuracy of 0.9196 whereas two ensemble techniques, namely gradient boosting and voting classifier achieve an overall test accuracy of 0.857 and 0.8304. the precision and sensitivity scores demonstrate the superior detection performance of the ANN over the ensemble method on ML algorithms.

关键词： Alzheimer's disease detection machine learning ANN Ensemble learning

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Harnessing the Power of Quantum Feature Representation for Leveraging Fused Descriptor Definition in Breast Cancer Diagnosis 5

Harnessing the Power of Quantum Feature Representation for L...

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5th international conference on Electrical, Communication and Computer Engineering, ICECCE 2024

作者： Das, Rik Dutta, Niladri Mukherjee, Debdeep Mahapatra, Saranik Mitra, Indranil Nandy, Prasun Data and Analytics PwC India Kolkata India

ISBN: (纸本)9798331529437

Breast cancer remains a global health challenge, driving the need for advanced diagnostic tools that ensure timely detection and better patient outcomes. this research introduces an innovative diagnostic method that enhances manually engineered features and deep learning-based features with quantum machine learning (QML) based feature representation techniques to improve the accuracy of breast cancer classification. Our results demonstrate that the Quantum Support Vector Classifier (QSVC) consistently outperforms other methods, particularly when paired with Uniform Local Binary pattern (uLBP) features, achieving an accuracy of 94.3%. Additionally, the fusion of Color Histogram (CH) features with QSVC assisted representation learning features achieves precision and recall rates of 96.6% and 96.3% respectively, highlighting superiority of quantum-based descriptor definition. In comparison, deep features from Inception-ResNet-V2 merely reach an accuracy of 87%, indicating limited effectiveness due to the limitations in non-linear quantum classification tasks. However, the integration of quantum representation learning across all feature sets significantly improves classification performance, particularly when fused with handcrafted or deep features. this study underscores the effectiveness of QML in capturing richer and more discriminative breast cancer characteristics. these findings pave the way for more precise, quantum-enhanced diagnostic tools in the pursuit against breast cancer, offering new avenues for clinical application and market growth. © 2024 IEEE.

关键词： Diseases

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