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检索条件"任意字段=5th International Conference on Machine Learning and Data Mining in Pattern Recognition"

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Hyperdimensional Consumer pattern Analysis with Quantum Neural Architectures using Non-Hermitian Operators 23

Hyperdimensional Consumer Pattern Analysis with Quantum Neur...

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Proceedings of the 5th international conference on Information Management & machine Intelligence

作者： Shivam Goyal Sunil K. Singh Sudhakar Kumar Saket Sarin Brij B. Gupta Varsha Arya Kwok Tai Chui CSE Chandigarh College of Engineering and Technology India Department of Computer Science and Information Engineering (CSIE) Asia University Taichung Taiwan Taiwan Department of Business Administration Asia University Taiwan CSE Hong Kong Metropolitan University China

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.

关键词： Consumer Applications

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Prostate cancer prognosis-a comparative approach using machine learning techniques 5

Prostate cancer prognosis-a comparative approach using machi...

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5th international conference on Intelligent Computing and Control Systems, ICICCS 2021

作者： Bellad, Sagar.C. Mahapatra, Ananya Ghule, Sahil Dilip Shetty, Satvik Sridhar Sountharrajan, S. Karthiga, M. Suganya, E. VIT Bhopal University Sehore Madhya Pradesh India VIT Bhopal University Sehore India Bannari Amman Institute of Technology Sathyamangalam India Anna University Chennai India

ISBN: (纸本)9781665412728

Recently machine learning have been a field of interest for many researchers. machine learning holds applications in tremendous sectors like healthcare, e-commerce and so on. It plays a vital role in the field of healthcare by their significant contribution through its ability to read from the historic/ past data especially in the diagnosis/predicting of diseases. Many classification algorithms have been proposed for the prediction process. the problem with classifiers is their vast variety and difficulty in choosing an appropriate method for the particular problem. To overcome the issue of selecting the appropriate method a comprehensive study on various classifiers should be done. this paper focuses on the working of various classifiers for prediction of prostate Cancer in calculating the level of efficiency in prediction and this helps in selecting the best method. © 2021 IEEE.

关键词： data mining

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Predictive Analytics for Heart Diseases Using machine learning

Predictive Analytics for Heart Diseases Using Machine Learni...

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Emerging Technology (INCET), international conference for

作者： Swati Kale Abhishek Shimpukade Aditya Mohan Yadav Sushant Dhavane Dept of Electronics & Telecommunication JSPM's Rajarshi Shahu College of Engineering Tathawade Pune MS India Savitribai Phule Pune University Pune

ISBN: (数字)9798350361155

ISBN: (纸本)9798350361162

the diagnosis and prediction of cardiovascular illness are critical medical responsibilities that assist cardiologists correctly classify patients and treat them accordingly. Since ML algorithms are here to identify a similar pattern in the patient data. Misdiagnosis in diagnosis of CV diseases is also decreased by the doctorsusing ML patterns. In order to bring down the death rate from cardiovascular disorders. In the creation of this paper the used model can accurately forecast the CVDs conditions. this paper suggests a Fresh beginning k-modes clustering approach that can increase classification accuracy. there are several models that are employed, including DT, RFClassifier, K-Nearest Neighbor, SVM etc., was utilized to hyper tune employed ML model's parameters. On real-world dataset of 300+ instances from Kaggle, the suggested model is used.

关键词： Heart Support vector machines Radio frequency Accuracy machine learning algorithms Prediction algorithms data models

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Optimization of cross-border intelligent e-commerce platform based on data flow node analysis 5

Optimization of cross-border intelligent e-commerce platform...

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5th international conference on Trends in Electronics and Informatics, ICOEI 2021

作者： Wang, Jin Yang, Lei Zhang, Shuqin Business School Xi'an Siyuan University Shaanxi Xi'an710038 China

ISBN: (纸本)9781665415712

Optimization of the cross-border intelligent e-commerce platform based on data flow node analysis is studied in this manuscript. In the era of the popularization of big data applications, the application of big data technology has gradually shown its own advantages, and also the penetration field has gradually expanded. In our designed model, the 2 aspects of the novelty are reflected. (1) the data flow node analysis is conducted. We apply the machine learning algorithms and the graph theory for the work of node modelling and pattern analysis. the intelligent coding theory is applied for the implementation of the model. (2) the cross-border intelligent e-commerce platform is implemented with the optimal solution. We apply the platform into the applications. © 2021 IEEE.

关键词： Big data

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A Conceptual Overview on Earlier Methodologies Focused on Stock Price Prediction

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

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international conference on Smart Systems and Inventive Technology (ICSSIT)

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

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.

关键词： Deep learning Economics machine learning algorithms Statistical analysis Prediction algorithms Market research Distance measurement

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Investigation on the Bi-direction Fusion of data and Knowledge in Prognostic and Health Management

Investigation on the Bi-direction Fusion of Data and Knowled...

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System Reliability and Safety Engineering (SRSE), international conference on

作者： Tong Zhang Zhihao Liu Yuanxing Huang Bofeng Cui Xin Zhu Laifa Tao China State Ship-Building Corporation Limited Marine Design and Research Institute of China Shanghai China School of Reliability and Systems Engineering Beihang University Beijing China

data and knowledge fusion are hot topics of machine learning, and the important working direction of Prognostic and Health Management technology (PHM). In order to crack up the schismatically situation of data and knowledge in industrial community, we propose the bi-direction fusion of data and knowledge in PHM including knowledge mining and knowledge embedding. We present an overall framework and conduct exploratory studies through actual project cases. In terms of knowledge mining, a KMAHP (knowledge mining analytic hierarchy process) test system decision method for structural health monitoring is proposed. And for knowledge embedding, a physical-based health baseline construction method is proposed for critical telemetry health status monitoring of aircraft. the results of the two cases are in line with expectations, which show that the proposed fusion framework and the concrete implementation plan are both effective exploration research and engineering practice for PHM.

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Robust Domain Generalization for Multi-modal Object recognition

Robust Domain Generalization for Multi-modal Object Recognit...

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Artificial Intelligence and Electromechanical Automation (AIEA), international conference on

作者： Yuxin Qiao Keqin Li Junhong Lin Rong Wei Chufeng Jiang Yang Luo Haoyu Yang Department of Computer Information Technology Northern Arizona University Flagstaff AZ USA Department of Computer Science AMA University Quezon Philippines Electrical Engineering & Computer Science Department Massachusetts Institute of Technology Cambridge MA USA Academy for Advanced Interdisciplinary Studies Peking University Beijing China Department of Computer Science The University of Texas at Austin Austin TX USA Department of Computer Science University of Southern California Los Angeles CA USA College of Computing Georgia Institute of Technology Atlanta GA USA

ISBN: (数字)9798350366174

ISBN: (纸本)9798350366181

In multi-label classification, machine learning encounters the challenge of domain generalization when handling tasks with distributions differing from the training data. Existing approaches primarily focus on vision object recognition and neglect the integration of natural language. Recent advancements in vision-language pre-training leverages supervision from extensive visual-language pairs. this allows learning across diverse domains and enhances recognition in multi-modal scenarios, showcasing superior transfer learning capabilities in methods like CLIPood. However, CLIPood has several limitations: differences in the utilized loss, loss of generality in evaluating only a single backbone, and neglect of class-aware visual *** address these, we propose this paper that infers the actual loss based on the implementation, broadens evaluations to larger vision-language backbones, and introduces Mixup-CLIPood with a novel mix-up loss for enhanced class-aware visual fusion.

关键词： Visualization Automation Instruments Transfer learning Natural languages Training data learning (artificial intelligence) Benchmark testing Robustness Object recognition

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Development Of Food Tracking System Using machine learning 5

Development Of Food Tracking System Using Machine Learning

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5th international conference on Electrical, Electronics, Communication, Computer Technologies and Optimization Techniques (ICEECCOT)

作者： Jijesh, J. J. Jinesh, J. J. Bolla, Dileep Reddy Sruthi, P., V Dileep, M. R. Keshavamurthy Sri Venkateshwara Coll Engn Dept E&CE Bangalore Karnataka India L&T Technol Serv Ltd Aerosp Mysore Karnataka India NITTE Meenakshi Inst Technol Dept CSE Bangalore Karnataka India Govt Coll Engn Dept CSE Kanpur Uttar Pradesh India Atria Inst Technol Dept E&CE Bengaluru India

ISBN: (纸本)9781665432726

the use of technology has played a vital role in the person's Food intake, its quality and calorie measurement. A successful framework will enhance better leaving of the individuals and allows individual to consume the food in a well optimized manner. A real-time framework is developed to monitor eating pattern of the individuals during their food intake. Food recognition and tracking is implemented using machine learning based on the support vector machines (SVM). When the food consumption among the people increases, the problem related to the food also increases. Hence in this paper we introduce a food recognition system in which calories are measured along with the food detection. We create a dataset for both the food images and calories present in that food. Food and calories detected are sent through the mail for the user.

关键词： Support Vector machines (SVM) Calorie measurement Scale Invariant Feature Transform (SIFT) Speeded Up Robust Features (SURF) Renesas microcontroller

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Smart Healthcare Systems: IoT and AI for Heart Disease Prediction

Smart Healthcare Systems: IoT and AI for Heart Disease Predi...

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Electronics and Sustainable Communication Systems (ICESC), 2020 international conference on

作者： Vishnu K thomas Sebastian Binu P K Department of Computer Science and Applications Amrita School of Computing Amrita Vishwa Vidyapeetham Amritapuri India

ISBN: (数字)9798350379945

ISBN: (纸本)9798350379952

Fast food consumption and changes in lifestyle are associated with an increase in heart related problems. this study looks at how cloud computing, the Internet of things and machine learning can be combined for CVD risk prediction and real-time monitoring. the IoT-ML-Cloud paradigm aims to increment the precision of Cardio Vascular Disease prediction by merging these technologies. the system comprises three main modules: Prediction, data collection and User engagement. Utilizing Arduino and ESP32 modules. An Android application facilitates patient data input and real-time monitoring and predictions using ML models deployed on the Microsoft Azure cloud. Bluetooth technology utilised to facilitates real-time data transfer to the cloud. this paper proposes Bi-LSTM algorithm with an accuracy of 96.55% for the heart disease prediction. Along with highlighting significant discoveries from numerous ML algorithms in the diagnosis of Cardio Vascular Disease, it also highlights the significance of cloud computing and data mining for large-scale medical data analysis.

关键词： Heart Cloud computing machine learning algorithms Predictive models Prediction algorithms Real-time systems Hardware data models Internet of things Medical diagnostic imaging

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Self-supervised learning for Sketch-Based 3D Shape Retrieval 5th

Self-supervised Learning for Sketch-Based 3D Shape Retrieval

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5th Chinese conference on pattern recognition and Computer Vision (PRCV)

作者： Chen, Zhixiang Zhao, Haifeng Zhang, Yan Sun, Guozi Wu, Tianjian Nanjing Univ Posts & Telecommun Sch Comp Sci Nanjing 210023 Peoples R China Jinling Inst Technol Sch Software Engn Nanjing 211169 Peoples R China Jiangsu Hoperun Software Co Ltd Nanjing 210012 Peoples R China Nanjing Normal Univ Sch Comp Sci & Technol Nanjing 210023 Peoples R China

ISBN: (纸本)9783031189067;9783031189074

In recent years, sketch-based 3D shape retrieval (SB3DR) gets a lot of attentions in the computer vision community. the task is challenging due to the large domain gap between sketches and 3D shapes. Most of the existing methods solve the problem by the supervised learning to extract discriminative features in the common feature space. However, these methods rely on the good quality of training data annotations which is costly in practical scenarios. To address this problem, we propose a self-supervised learning approach for SB3DR. Motivated by the idea of instance discrimination, we regard the multiple views of a 3D shape as positive pairs, and model the relation of multi-views in a contrastive self-supervised learning framework which requires only positive samples to extract features. the proposed approach needs neither additional supervised dependencies nor ground-truth labels of 3D shapes, leading to a simpler training process. Moreover, we construct an enhanced triple loss to mitigate the gap between sketch and 3D shape domains. the experimental results on benchmark datasets show our approach can achieve the state-of-the-art performance at a comparable level.

关键词： 3D shape Sketch Sketch based 3D shape retrieval Self-supervised learning

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