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Stacked Multi-Head Cross Attention for Image recognition 24

Stacked Multi-Head Cross Attention for Image Recognition

引用

1st international conference on Image Processing machine Learning and pattern recognition

作者： Liu, Shuaichen Du, Xiao Wu, Tieru Jilin Univ Sch Artificial Intelligence Changchun Jilin Peoples R China Jilin Univ Sch Math Changchun Jilin Peoples R China

ISBN: (纸本)9798400707032

Long-range dependency plays a critical role in extracting intricate image features particularly in tasks involving image recognition. In previous study, the significance of long-range positional dependencies has been proved in both image classification and image segmentation. Based on this, we introduce a Multi-Head Cross Attention module, namely MHCA, along with four different operators, which are designed to capture and integrate contextual information at every pixel position within feature maps, spanning both horizontal and vertical directions, with parallel fashion, thus can transfer information and share weights across multiple heads of features. Moreover, by stacking our module twice, forming MHCA(2) layer, the whole context of each pixel in feature can be captured, with more lighter computation burden than general full connection or Non-local networks, and it is designed to be seamlessly plugged into existing network architectures. By replacing specific convolution layer in convolutional networks with a MHCA(2) layer, we construct MHCA network. Through extensive experiments upon various datasets, we demonstrate the validity of our approach. Furthermore, comparative analysis with similar methodologies highlight the superior performance of our method.

关键词： Cross Attention Image Classification Image Segmentation Long-range Dependency Lightweight Architecture

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Improving Crop Recommendations with Augmented Reality and Sensor Data Analysis 2

Improving Crop Recommendations with Augmented Reality and Se...

引用

2nd international conference of Intelligent Methods, Systems and Applications, IMSA 2024

作者： Ghanem, Sahar I. Zaalouk, Moustafa Elbanna, Asmaa Pharos University in Alexandria Computer Science and Artificial Intelligence Faculty Artificial Intelligence Department Egypt

ISBN: (纸本)9798350362633

The decision about the selection of crops for agricultural production is closely related to many different aspects, such as the features of the soil and the surrounding environment. Using state-of-the-art sensor technology, these factors are measured with extreme caution, some significant metrics include soil pH, moisture level, and the surrounding temperature. These sensor-driven collected information are analyzed by expertly deploying robust machine learning algorithms, which leads to an accurate estimation of the best crop categories for the climatic circumstances. In this agricultural paradigm, augmented reality perceived as a game-changing tool that offers a fully immersive display of the sensor data, providing farmers an extensive awareness of the crop environment. In addition to automating the crop selection process, this smart combination of sensor technologies, machine learning with overall accuracy of 97%, and augmented reality offers farmers the insights they need to make appropriate choices and ensures sustainable agricultural practices. © 2024 IEEE.

关键词： Adversarial machine learning

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Shapley Additive Explanations (SHAP) for Cardiovascular Diseases Prediction 2

Shapley Additive Explanations (SHAP) for Cardiovascular Dise...

引用

2nd international conference on Sustainable Computing and Smart Systems (ICSCSS)

作者： Shirley, Mbabazi Elizabeth Kasujja, Namatovu Hasifah Marvin, Ggaliwango Makerere Univ Kampala Dept Comp Sci Kampala Uganda Makerere Univ Kampala Dept Informat Syst Kampala Uganda

ISBN: (纸本)9798350391558;9798350379990

The increased number of deaths of cardiovascular diseases among people in both Low and Middle Income countries (LMICs) and in developed countries is alarming. There are machine Learning (ML) models that have been developed for early diagnosis of CVDs, however, their success is low due to the black box nature of the models and the trust among the doctors is low thus hindering the models' acceptance. This research study focuses on developing an explainable AI model to predict CVDs over a period of ten years. The open CVD study dataset was used to develop multiple machine learning models i.e KNN, Logistic Regression, XGBoost, Catboost, Random Forest and Decision tree. The models' performance was assessed based on F1-score, Accuracy, AUC, Precision, Recall, sensitivity, specificity and the confusion matrix metrices. From this study, it is observed that the XGBoost model performs better than the other models with an accuracy of 89%. SHapley Additive exPlanations (SHAP) explainable technique was later applied to all the models to understand their prediction for breaking the black box nature of machine learning models. This research contributes to the identification of CVDs risk factors with the use of XAI and the early diagnosis of CVDs thus aiding in early intervention. The random Forest model performed better than the rest of the models with an accuracy of 98%.

关键词： Explainable Artificial intelligence (XAI) machine Learning Artificial intelligence SHapley Additive exPlanations (SHAP) Feature Learning Cardiovascular Diseases Risk Factors

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Transformers for Orbit Determination Anomaly Detection and Classification

Transformers for Orbit Determination Anomaly Detection and C...

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IEEE/CVF conference on Computer Vision and pattern recognition (CVPR)

作者： Re, Nathan Parrish Popplewell, Matthew Caudill, Michael Sullivan, Timothy Hanf, Tyler Tatman, Benjamin Parmar, Kanak Presser, Tyler Chikine, Sai Grant, Michael Poulson, Richard Adv Space LLC 1400 W 122nd Ave Westminster CO 80234 USA

ISBN: (纸本)9798350365474

This paper presents the judicious application of machine learning algorithms to solve two fundamental challenges in spacecraft orbit determination (OD): identification of systematic anomalies (nominal vs anomalous behavior) and the classification of these anomalies to explain probable causes (such as unexpected spacecraft maneuvers or mis-modeled small forces). Traditional OD is based on well-tested iterative linearization methods (variations of the Kalman filter). These are commonly understood in the astrodynamics community to require manual tuning for a given set of mission assumptions and re-tuning when those assumptions are invalidated. OD data are typically long, sparse, multi-variate sequences consisting of multiple observation phenomenologies. These characteristics make the OD problem fundamentally well-suited to the same machine learning architectures (namely, transformers) that have found success with language modeling and other sequence-based data modeling. The approach taken here is to simulate various failure modes in the traditional OD process using NASA's Monte software, then process the simulated data in three different transformer-based architectures. The three transformer architectures all act as classifiers and can be described at a high level as: 1) treat each epoch with data as a feature vector, with the input data comprising a single, long sequence;2) similar to the first, but with nested self-attention to efficiently handle longer sequences;and 3) plot the data, then classify the plot with a vision transformer (ViT) model. Model performance is studied as a function of the hyperparameter trade space.

关键词： anomaly classification anomaly detection kalman filter navigation orbit determination transformer

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Enhancing Facial recognition Accuracy and Efficiency Through Integrated CNN, PCA, and SVM Techniques 1

引用

2nd international conference on Forthcoming Networks and Sustainability in the AIoT Era (FoNeS-AIoT)

作者： Kashkool, Hawraa Jaafar Murad Farhan, Hameed Mutlag Naseri, Raghda Awad Shaban Kurnaz, Sefer Altinbas Univ Istanbul Turkiye Tikrit Univ Tikrit Iraq

ISBN: (数字)9783031628719

ISBN: (纸本)9783031628702;9783031628719

Facial recognition, as a paradigmatic instance of biometric identification, has witnessed escalating utilization across diverse domains, encompassing security, surveillance, human-computer interaction, and personalized user experiences. The fundamental premise underlying this technology resides in its capacity to extract discriminative features from facial images and subsequently classify them accurately. However, the precision and efficiency of such systems remain subject to an array of intricate challenges, necessitating innovative solutions. The overarching aim of this thesis is to enhance the performance and efficacy of facial recognition systems through the seamless integration of Convolutional Neural Networks (CNNs) for feature extraction, Principal Component Analysis (PCA) for dimensionality reduction, and Support Vector machines (SVMs) for classification. This holistic approach seeks to optimize the accuracy, efficiency, and robustness of facial recognition, thereby contributing to the advancement of computer vision and biometric identification technologies.

关键词： Face recognition artificial intelligence machine learning feature extraction dimensionality reduction

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Developing an AI-Based Library Assistant: Enhancing Book Retrieval with Natural Language Processing and machine Learning 2

Developing an AI-Based Library Assistant: Enhancing Book Ret...

引用

2nd international conference on Computational intelligence, Communication Technology and Networking, CICTN 2025

作者： Kailash Varma, Nadimpalli Madana Aryan, Adla Dhanush, Pagilla Manikanta, Rangisetti Chandhu, Nalla Arora, Gagan Deep Department of Artificial Intelligence & Machine Learning Vardhaman College of Engineering Telangana Hyderabad501218 India

ISBN: (纸本)9798331530389

The current college library system is hindered by inefficiencies in book searching, availability updates, identity verification, and checkout processes, resulting in delays and errors that negatively impact student experiences. This research paper presents an Al-based librarian system designed to address these challenges through automation and digital transformation. Leveraging a Large Language Model (LLM), the proposed system facilitates personalized interactions between students and library resources. Upon student verification, the AI librarian provides real-time information on book availability, location, and tailored recommendations, significantly reducing the time spent on manual searches. The streamlined checkout process allows for automated book issuance and instant confirmation notifications, minimizing human error and enhancing record- keeping. This innovative solution not only improves operational efficiency but also enriches the user experience by offering a user-friendly interface and timely assistance. Future enhancements, such as voice integration and mobile application support, are suggested to further modernize library services. This research underscores the potential of AI technologies to revolutionize library management and improve service delivery in academic settings. © 2025 IEEE.

关键词： Efficiency

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Rolling Bearing Fault Image recognition Method Based on GWO Optimization Darknet19 2

Rolling Bearing Fault Image Recognition Method Based on GWO ...

引用

2nd international conference on Computer Graphics and Image Processing, CGIP 2024

作者： Zhang, Jinrong Liang, Jingxin Shang, Sitong Shanghai Normal University Tianhua College School of Artificial Intelligence Shanghai China

ISBN: (纸本)9798350374186

Failure of rolling bearings is a common mechanical problem, and traditional fault diagnosis methods require experienced experts and have limited effectiveness for large-scale data processing and analysis. This study optimizes the parameters of a rolling fault image recognition model for Darknet19 network based on the Gray Wolf Optimization algorithm. The model adjusts the structure and parameters of the network by comparing different neural networks horizontally to make it more suitable for rolling bearing fault diagnosis tasks. Experimental results show that the Darknet19 network with optimized parameters by the GWO algorithm achieves significant performance improvement in rolling bearing fault diagnosis. The optimized model exhibits higher diagnostic accuracy and robustness under multiple fault categories. Therefore, the rolling bearing fault image recognition method based on GWO and Darknet19 can provide efficient and accurate automatic fault diagnosis. © 2024 IEEE.

关键词： Image recognition

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AI-Powered Personalization in E-Commerce 2

AI-Powered Personalization in E-Commerce

引用

2nd DMIHER international conference on Artificial intelligence in Healthcare, Education and Industry, IDICAIEI 2024

作者： Chaudhari, Aarti Hajare, Shital Faculty of Engineering and Technology Department of Artificial Intelligence and Data Science Maharashtra Sawangi442001 India

ISBN: (纸本)9798331528713

The nature of e-commerce is constantly changing how the business is carried out and how things are bought. It has been made possible to do so because of technological advancements and the capacity of the internet that goes as far as allowing the customers to view, buy and get products from the ease of their homes. The idea of e-commerce has developed to cut through both the retail environment and the interface of customer expectations. This is the outcome of artificial intelligence being adopted by the e-commerce sector, namely in the area of customization. More specifically, when AI algorithms are applied, machine learning is becoming more and more significant in e-commerce customization. AI technologies keep developing further and further which enables to analyse the detailed information about the shopping habits, interests and preferences of every client so that every e-commerce unit may be able to cater to every need of a customer. This document focuses on the AI methods related to the e-commerce personalization processes: machine learning, deep learning, and natural language processing (NLP) methods. It focuses on the procedures of gathering and analysing data because these are crucial in understanding the buying patterns of consumers and what they prefer. © 2024 IEEE.

关键词： Adversarial machine learning

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Business intelligence In Industrial Organization Using An Ai-Driven Competitive Analysis Framework 2

Business Intelligence In Industrial Organization Using An Ai...

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2nd international conference on Disruptive Technologies, ICDT 2024

作者： Gowrabhathini, Jitendra Tamilarasu, J. Rahithya, Sammeta Sindhu Parimala, Pappala Sai Hussain, Syed Sama Sri, Chanumolu Nitya Kl Business School Koneru Lakshmaiah Education Foundation Vaddeswaram India Department of Management Studies Erode India Business School Koneru Lakshmaiah Education Foundation Vaddeswaram India

ISBN: (纸本)9798350371055

In this research, it examine how the integration of AI and BI might assist businesses in penetrating new areas that have not yet been explored. When applied to enormous datasets, Recurrent Neural Networks (RNNs) can reveal detailed patterns, correlations, and trends that might otherwise go unnoticed when utilising more conventional methods of data analysis. These revelations are made possible by the fact that RNNs are able to learn from their own past experiences. By adding artificial intelligence (AI) and other forms of machine learning into their business intelligence (BI) systems, companies can increase their understanding of customer behaviour as well as other key aspects of their operations. In addition to this, it explores the potential applications of AI in business intelligence, such as improving decision-making, streamlining operations, and paving the way for preventative measures. © 2024 IEEE.

关键词： Artificial intelligence (AI) Business intelligence (BI) RNN DL

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LDA-LSTM based on variational entropy weight and multi-dimensional conditional grey association 24

LDA-LSTM based on variational entropy weight and multi-dimen...

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2nd international conference on Communication Networks and machine Learning, CNML 2024

作者： Lin, Longxin Fang, Jiahui Yao, Jiaxin Chai, Xiaolong School of Statistics and Mathematics Guangdong University of finance & Economics Guangdong Guangzhou China Institute of Artificial Intelligence and Deep Learning Guangdong University of finance & Economics Guangdong Guangzhou China

ISBN: (纸本)9798400711688

This paper studies the LDA-LSTM model based on variational entropy weight and multidimensional conditional grey relation, and applies it to the actual case of urban traffic congestion in China. Firstly, the paper collects traffic data from 20 cities in China, determines the weight of indicators through variational entropy weight, and innovatively integrates the multidimensional conditional grey relation model to identify the key indicators affecting urban traffic. Then, the paper proposes the LDA-LSTM hybrid model to perform topic analysis on the data, extracts 8 topic words as indicators for LDA-LSTM, and predicts the traffic congestion situation in the first quarter of 2024 in cities, finding that the prediction values fit well and overall achieve good prediction results. © 2024 Copyright held by the owner/author(s).

关键词： Traffic congestion

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