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Role of Artificial intelligence and machine Learning in scalable Internet of Things Applications 3

Role of Artificial Intelligence and Machine Learning in scal...

引用

3rd international conference on Advancement in Electronics and Communication Engineering, AECE 2023

作者： Singla, Neeraj Chitkara University Institute of Engineering and Technology Chitkara University Punjab India

ISBN: (纸本)9798350330724

Anything non-living that can take decisions on its own is called artificially intelligent. It is a technique designed to mimic human behaviour. On the other hand machine learning if a subset of artificial intelligence. It is a statistic method used to enable machine to improve with experience. They both go hand in hand. Now IoT has been there in existence for an enormous amount of time but during recent times due to large scale good connectivity for efficient data transfer, machine learning and analytics and Conversional Artificial intelligence has made IoT possible. © 2023 IEEE.

关键词： Internet of things

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Siamese Contrastive Reverse Distillation for Industrial Anomaly Localization 24

Siamese Contrastive Reverse Distillation for Industrial Anom...

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3rd international conference on Frontiers of Artificial intelligence and machine Learning (FAIML)

作者： Liang, Dong Du, Yuji Tan, Shan Huazhong Univ Sci & Technol Sch Artificial Intelligence & Automat Wuhan Peoples R China

ISBN: (纸本)9798400709777

The unsupervised anomaly localization based on feature distillation has demonstrated outstanding performance in industrial anomaly localization. It relies on the feature discrepancies between the student network and the teacher network to achieve anomaly localization. In these methods, the student network exclusively learns from the normal features of the teacher network during training, overlooking the explicit constraining of prior anomaly knowledge. This results in uncertainty in the feature discrepancy between the student and teacher for abnormal inputs, leading to a decrease in prediction accuracy. To inject prior anomaly knowledge into the student network during training, this paper proposes Fine-Grained CutPaste (FG-CutPaste) data augmentation strategy and Siamese Contrastive Reverse Distillation Network (SCrd). FG-CutPaste provides pseudo-abnormal samples and corresponding pixel-level pseudo-abnormal labels during the training phase of SCrd. SCrd introduces the Siamese network paradigm along with Contrastive Distillation (CD) loss. The CD loss, utilizing pseudo-abnormal samples and labels, not only reduces the discrepancy of normal features between the student and teacher networks of SCrd but also increases the discrepancy of their abnormal features, achieving explicit constraint on the abnormal features of the student. Experimental results indicate that SCrd achieves outstanding anomaly localization performance, yielding more refined visualizations for anomaly localization.

关键词： anomaly localization contrastive learning knowledge distillation

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A Systematic Survey of Human Ear Biometrics recognition by using 2D images 3

A Systematic Survey of Human Ear Biometrics Recognition by u...

引用

3rd IEEE international Maghreb Meeting of the conference on Sciences and Techniques of Automatic Control and Computer Engineering, MI-STA 2023

作者： Elmarzaki, Howayda Abedallah Gargoum, Bothaina F. Abdo, Amina A University of Benghazi Department of Software Engineering Benghazi Libya University of Benghazi Department of Computer Science Benghazi Libya Collage of Computer Technology Department of Artificial Intelligence Benghazi Libya

ISBN: (纸本)9798350319897

Nowadays, one of the most serious issues is secure verification, especially with the advent of artificial intelligence and machine learning and deep learning algorithms. As a result, the research field of recognizing human biometrics has grown significantly, and various types of algorithms have been proposed to help with security. From the available literature, we noticed that researchers were focusing on various systematic surveys for the recognition of human biometrics, including faces, eyes, finger prints, palms, and etc. Despite the popularity of machine and deep learning methods in the field of human hearing prediction, surveys of such methods are rare. In this work, we present an up-to-date survey of the ear recognition field by using 2D images and display the most relevant papers from the most popular databases by applying different techniques based on several criteria. This review presents an overview of the similarities and differences between different works and provides a statistical analysis view of common techniques commonly used in ear recognition literature. The results presented in this report provide insight on the patterns of this field's research. © 2023 IEEE.

关键词： Biometrics

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Design and Implementation of Marketing Classification System Based on Artificial intelligence 3

Design and Implementation of Marketing Classification System...

引用

3rd IEEE international conference on Electrical Engineering, Big Data and Algorithms, EEBDA 2024

作者： Tian, Shuo Shanghai University Shanghai China

ISBN: (纸本)9798350380989

This paper mainly introduces some machine learning methods used in the field of data mining. The method of data mining is discussed by taking market segmentation algorithm as an example. This paper presents an improved support vector machine method. By judging the similarity between each sampling point and its closest class, the classifier is obtained by training it with support vector machine. A method based on stepwise approximation method is proposed to solve the problem of too large size of KNN training samples. The experimental results show that non-SVM is superior to SVM in classification accuracy and classification speed. In addition, this project will also adopt artificial intelligence technology to build a data analysis system for high-end wine, analyze, compare and match massive information data of high-end wine through data mining, and discover the comparison information and market potential of main ingredients in high-end wine. It is compared with the chemical composition of high-grade red wine, and the relevant marketing treatment of high-grade red wine is obtained. It can help high-grade red wine manufacturers to analyze the composition content and quality of high-grade red wine. © 2024 IEEE.

关键词： artificial intelligence data analysis data mining high-end red wine machine learning marketing classification algorithm

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Using Generative Adversarial Networks for Efficient Constrained Multi-objective Optimization 24

Using Generative Adversarial Networks for Efficient Constrai...

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3rd international conference on Frontiers of Artificial intelligence and machine Learning (FAIML)

作者： Chao, Wang Jing, Zhang Zheng, Zhabang Liang, Zhushou Anhui Univ Informat Mat & Intelligent Sensing Lab Anhui Prov Hefei 230601 Anhui Peoples R China Anhui Univ Sch Artificial Intelligence Hefei 230601 Anhui Peoples R China

ISBN: (纸本)9798400709777

To address constrained multi-objective optimization problems through evolutionary algorithms, both the constraint handling technique and the search operator are crucial. However, existing approaches often prioritize the former for identifying promising solutions, while the critical role of the latter in producing feasible solutions is frequently overlooked. This paper shifts the focus to the search operator's contribution by introducing an offspring generation technique that is guided by a model-driven approach leveraging adversarial learning. The method involves dividing the population into groups of feasible and infeasible solutions, which are then used as adversarial examples during the model's training phase. If the solution crafted by the generator is impractical, the feasible subset guides the evolution towards the feasible solution space, while the infeasible subset helps define the boundaries of the feasible space. This adversarial training technique progressively replicates the distribution of feasible regions within the decision space. By utilizing adversarial learning, this innovative method generates offspring with significant potential, thereby accelerating the convergence of the population towards the optimal feasible region.

关键词： Constrained multi-objective optimization Generation adversarial Network Offspring generation Evolutionary algorithm

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Identification Method of Dress pattern Drawing based on machine Vision Algorithm 3

Identification Method of Dress Pattern Drawing based on Mach...

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3rd international conference on Computer Vision, Image and Deep Learning and international conference on Computer Engineering and Applications, CVIDL and ICCEA 2022

作者： Lyu, Ke Yan, Haizhang Zhejiang Sci-Tech University School of International Education Zhejiang310000 China Xidian University School of Computer Science and Technology Xi'an71000 China

ISBN: (纸本)9781665459112

This paper uses the machine vision method to identify the skirt module. We have constructed three kinds of machine recognition models of skirt profile processing, structure analysis of style drawing, and size estimation. The author constructs a relatively complete image recognition system for dress pattern drawing. In addition, we conducted an effect evaluation with a certain number of samples at the later stage of the experiment. This study has A good effect in distinguishing an A-type skirt from an H-type skirt, identifying the reasonable degree and length of the skirt, and determining the quantity statistics of each component element in the skirt pattern diagram. © 2022 IEEE.

关键词： Image recognition

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Double-frame rate-based cheat detection 3

Double-frame rate-based cheat detection

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3rd international conference on Computer Vision and pattern Analysis, ICCPA 2023

作者： Wang, HongXia Yan, XiangYu Li, Hui Xu, Fei School of Big Data and Artificial Intelligence Anhui Xinhua University Hefei China

ISBN: (纸本)9781510667563

The relationship between modern education development and artificial intelligence is getting closer and closer, but the technology of online examination and test detection needs to be improved. Most of the existing technologies are aimed at the comparison of pictures per frame, head detection, limb detection, key part detection, etc., although this effect is ideal, but at the same time the requirements for conditions are high. In this paper, for online exam detection, it is proposed to use face recognition and Euclidean distance combined with Yolov3 to complete the cheating detection. © 2023 SPIE.

关键词： Face recognition

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A Review on EMG-based pattern Identification Methods for Effective Controlling of Hand Prostheses 3

A Review on EMG-based Pattern Identification Methods for Eff...

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3rd international conference on Innovative Mechanisms for Industry Applications, ICIMIA 2023

作者： Ramkumar, S. Rema, Dhanusha Devi, T. Archana Elavarasi, K. Selvaganapathi, T. Gokila, S. Department of Computer Science Bangalore India Karpagam Academy of Higher Education Department of Food Technology Coimbatore India Karpagam Academy of Higher Education Department of Electrical and Electronics Engineering Coimbatore India Karpagam Institute of Technology Department of CSE Coimbatore India

ISBN: (纸本)9798350343632

The ability of amputees to do daily duties is significantly restricted by upper limb amputation. The myoelectric prosthesis uses impulses from the surviving muscles in the stump to gradually restore function to such severed limbs. Such myosignals are unfortunately tedious and challenging to gather and employ. The process of transforming it into a user control signal after it has been acquired often consumes a significant amount of processing resources. By modifying machine learning strategies for pattern recognition, the factors that influence the traditional electromyography (EMG)-pattern identification approaches may be significantly minimized. Although more recent developments in intelligent pattern recognition algorithms could discern between a variety of degrees of freedom with high levels of accuracy, their usefulness in practical (amputee) applications was less obvious. This review paper examined how well various pattern recognition algorithms for hand prostheses performed. Finally, we discussed the current difficulties and offered some suggestions for future research in our paper's conclusion. © 2023 IEEE.

关键词： Classification Algorithms Deep Learning Electromyography Hand Prosthesis recognition Accuracy

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Applications of Artificial intelligence and machine Learning for Accurate Forecasting and Optimization of Renewable Energy Generation 3

Applications of Artificial Intelligence and Machine Learning...

引用

3rd international conference on Electronics and Renewable Systems, ICEARS 2025

作者： Rajarajeswaran, Jayakumar Saveetha Institute of Medical and Technical Sciences Simats Engineering Department of Nanobiomaterials Chennai602105 India

ISBN: (纸本)9798331509675

Accurate forecasting of renewable energy production is critical for integrating variable energy sources like solar and wind into the power grid. This study explores the application of Artificial intelligence (AI) and machine Learning (ML) techniques to improve the accuracy and reliability of renewable energy forecasting. Historical meteorological and energy production data were preprocessed and analyzed using advanced ML algorithms, including Long Short-Term Memory (LSTM) networks, Gradient Boosting machines (GBM), and Support Vector machines (SVM). Results demonstrated the superior performance of LSTM networks, achieving an R-squared value of 0.96, with minimal forecasting errors. Feature importance analysis revealed solar irradiance and wind speed as key determinants of energy production. This research highlights the potential of AI-driven approaches to address the inherent variability of renewable energy sources, thereby enhancing grid stability, reducing operational costs and supporting sustainable energy integration. The findings provide actionable insights into the deployment of AI-based forecasting tools in energy management systems that help in reliable and efficient renewable energy utilization. © 2025 IEEE.

关键词： Clean energy

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Artificial intelligence In Field of Medical Imaging Informatics 3

Artificial Intelligence In Field of Medical Imaging Informat...

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3rd international conference on Advance Computing and Innovative Technologies in Engineering, ICACITE 2023

作者： Jha, Medha Hasija, Yasha Delhi Technological University Department of Biotechnology Delhi India

ISBN: (纸本)9798350399264

This paper reviews about application of artificial intelligence in medical image informatics. Additionally, it may enhance therapeutic results and increase the value of medical image analysis in yet-to-be-determined ways. Different techniques of imaging like anatomical (x-ray, MRI, ultrasound) and system generated imaging techniques (microscopy, PACs, SPECT) has been discussed in this paper. Deep learning and machine learning approaches for imaging is discussed at later stage. Deep learning techniques in particular are receiving a lot of attention due to their outstanding efficiency in image-recognition tasks in artificial intelligence (AI). They can more effectively and accurately diagnose patients by performing an automated quantitative assessment of complicated medical image properties. Deep learning, particularly image categorization, is being used more and more in the realm of medical images. machine learning to find tumor image is discussed for brain tumor. For the collection and storage of image data, the Digital Imaging and Communication in medicine is frequently utilized. How OMERO and DICOM database is used for digital imaging is discussed further. © 2023 IEEE.

关键词： Medical imaging

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