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Cultural heritage image classification using transfer learning for feature extraction: a comparison 1

Cultural heritage image classification using transfer learni...

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1st international Virtual Conference on Visual pattern Extraction and recognition for Cultural Heritage Understanding, VIPERC 2022

作者： Babić, Radmila Janković Mathematical Institute of the Serbian Academy of Sciences and Arts Kneza Mihaila 36 Belgrade Serbia

Image classification in the domain of cultural heritage becomes extremely important with the development of digitisation practices. This study aims to analyze how classification performance on the small dataset representing cultural heritage changes depending on the feature extraction method. The dataset comprised of 150 images belonging to three classes: (i) archaeological sites, (ii) frescoes, and (iii) monasteries. Five transfer learning architectures were used to extract the features from images, while classification was per-formed using four traditional machine learning algorithms, mainly Random forest, Naïve Bayes, Decision tree, and Multilayer perceptron classifier. The results suggest that Random forest and Multilayer perceptron are the most suitable algorithms for classification of cultural heritage images, especially when used in combination with the DenseNet121 pre-trained architecture. Naïve Bayes also performed well, with a maximum accuracy of 100% obtained when features are extracted using EfficientNetB0. However, the Decision tree algorithm reached only moderate performance. © 2022 Copyright for this paper by its authors.

关键词： Image classification

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ACM KDD AI4Cyber: The 1st workshop on Artificial Intelligence-enabled Cybersecurity Analytics 21

ACM KDD AI4Cyber: The 1st Workshop on Artificial Intelligenc...

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27th ACM SIGKDD international Conference on Knowledge Discovery and data mining (KDD)

作者： Samtani, Sagar Yang, Shanchieh Chen, Hsinchun Indiana Univ Dept Operat & Decis Technol Bloomington IN 47405 USA Rochester Inst Technol Dept Comp Engn Henrietta NY USA Univ Arizona Dept Management Informat Syst Tucson AZ 85721 USA

ISBN: (纸本)9781450383325

Despite significant contributions to various aspects of cybersecurity, cyber-attacks remain on the unfortunate rise. Increasingly, internationally recognized entities such as the National Science Foundation and National Science & Technology Council have noted Artificial Intelligence can help analyze billions of log files, Dark Web data, malware, and other data sources to help execute fundamental cybersecurity tasks. Our objective for the 1st workshop on Artificial Intelligence-enabled Cybersecurity Analytics (half-day;co-located with ACM KDD) was to gather academic and practitioners to contribute recent work pertaining to AI-enabled cybersecurity analytics. We composed an outstanding, inter-disciplinary Program Committee with significant expertise in various aspects of AI-enabled Cybersecurity Analytics to evaluate the submitted work. Significant contributions to the half-day workshop were made in the areas of CTI, vulnerability assessment, and malware analysis.

关键词： Cybersecurity artificial intelligence analytics machine learning

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Deep learning Classification Model for Analysing and Predicting Road Accidents 1st

Deep Learning Classification Model for Analysing and Predict...

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1st international Conference on Intelligent Systems in Computing and Communications, ISCComm 2023

作者： Udupi, Prakash Kumar Jose, Manju Middle East College Muscat Oman Majan University College Muscat Oman

ISBN: (纸本)9783031756047

The Road traffic accidents are one of the major concerns in developing and developed countries, primarily due to fatalities. Rapid urbanisation, improved economic conditions, better roads, large number of people owing the vehicles resulted into more number of accidents in the recent past. Despite the implementation of various safety measures, whether in automobiles, while driving or in the surrounding traffic environment, accidents are unavoidable due to human errors, adverse weather conditions or influence of other factors such as alcohol consumption, rash driving or wrong judgements. Capturing of various accident parameters in terms of data, identifying and evaluating the patterns or pattern recognition are carried out, which are hidden in these data can help to understand various influencing factors for causing accidents. machine learning is a technique in computer science, that facilitates the machine to learn from the input data using algorithms and predict the results. Deep learning is a subset of machine learning and artificial intelligence, used to automate the prediction of results using network models, that imitates the functioning of human brain. This research evaluates accident dataset and explores deep learning classification algorithms to predict the type of accidents. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Deep learning

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Deep Interaction Feature Fusion for Robust Human Activity recognition 4th

Deep Interaction Feature Fusion for Robust Human Activity R...

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4th international workshop on Deep learning for Human Activity recognition, DL-HAR 2024, and 1st international workshop on Anomaly Detection with Foundation Models, ADFM 2024, Held in Conjunction with the international Joint Conference on AI, IJCAI 2024

作者： Oh, YongKyung Kim, Sungil Bui, Alex A. T. Los AngelesCA90024 United States Ulsan44919 Korea Republic of

ISBN: (纸本)9789819790029

Human activity recognition (HAR) is vital in healthcare settings, facilitating personalized care and early intervention. Recent advancements in sensor technology and machine learning have paved the way for more accurate and reliable HAR systems. However, the integration of multimodal data and the extraction of informative features remain significant challenges. In this paper, we propose a novel data fusion approach that leverages deep, nonlinear interactions between static and dynamic modalities to enhance the performance of HAR systems. Our method combines three types of interaction features derived from static data, latent representations learned by deep neural networks, and statistical features extracted from time series data. We explore two data fusion strategies: feature-level fusion and decision-level fusion. We conduct extensive experiments on six publicly available datasets, demonstrating the superiority of our approach compared to traditional methods. Our research highlights the importance of capturing complex interactions between multimodal data and opens up new possibilities for the development of advanced HAR systems in healthcare applications. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Sensor data fusion

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Leveraging Semi-Supervised learning to Enhance data mining for Image Classification under Limited Labeled data

Leveraging Semi-Supervised Learning to Enhance Data Mining f...

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Electronic Information Engineering and Computer Communication (EIECC), international Conference on

作者： Aoran Shen Minghao Dai Jiacheng Hu Yingbin Liang Shiru Wang Junliang Du University of Michigan Ann Arbor USA Columbia University New York USA Tulane University New Orleans USA Northeastern University Seattle USA Dartmouth College Hanover USA Shanghai Jiao Tong University Shanghai China

ISBN: (数字)9798331534622

ISBN: (纸本)9798331534639

In the 21st-century information age, with the development of big data technology, effectively extracting valuable information from massive data has become a key issue. Traditional data mining methods are inadequate when faced with large-scale, high-dimensional and complex data. Especially when labeled data is scarce, their performance is greatly limited. This study optimizes data mining algorithms by introducing semi-supervised learning methods, aiming to improve the algorithm's ability to utilize unlabeled data, thereby achieving more accurate data analysis and pattern recognition under limited labeled data conditions. Specifically, we adopt a self-training method and combine it with a convolutional neural network (CNN) for image feature extraction and classification, and continuously improve the model prediction performance through an iterative process. The experimental results demonstrate that the proposed method significantly outperforms traditional machine learning techniques such as Support Vector machine (SVM), XGBoost, and Multi-Layer Perceptron (MLP) on the CIFAR-10 image classification dataset. Notable improvements were observed in key performance metrics, including accuracy, recall, and F1 score. Furthermore, the robustness and noise-resistance capabilities of the semi-supervised CNN model were validated through experiments under varying noise levels, confirming its practical applicability in real-world scenarios.

关键词： Support vector machines Visualization Accuracy Semisupervised learning Feature extraction Classification algorithms Behavioral sciences data mining Convolutional neural networks Image classification

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Cardiac Arrhythmia Diagnosis via Multichannel Independent Component Analysis: An Approach Towards a Better Health Care System 1st

Cardiac Arrhythmia Diagnosis via Multichannel Independent Co...

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1st international Conference on Emerging Technology Trends in Internet of Things and Computing (TIOTC)

作者： Sarfraz, Mohammad Khan, Mudassir Hasan Mohammed, Duraid Yahya Hussain, Mays Dheya Zidan, Khamis A. Aligarh Muslim Univ Ctr Interdisciplinary Biomed & Human Factors Engn Dept Elect Engn Aligarh 202002 Uttar Pradesh India AL Iraqia Univ Coll Engn Comp Engn Dept Baghdad Iraq

ISBN: (纸本)9783030972554;9783030972547

With the evolving lifestyle, many cardiac ailments are becoming more frequent, and it has become necessary to provide detailed surveillance of the heart's functioning to ensure healthy living. ECG signals provide details regarding the various forms of arrhythmias. However, owing to the complexities and non-linearity of ECG signals, it is impossible to examine these signals manually. Conventional approaches for specialist inspection of ECGs on paper or television are inadequate for ambulatory, long-term monitoring, and sports ECGs. Automated applications that use signal processing and pattern recognition would be extremely beneficial. Identification of arrhythmias from ECGs is an essential branch of biomedical signal processing and pattern recognition. Motion-induced artifacts are well-known to be a major source of misrecognition and misdiagnosis. On the other hand, the feature extraction method has a significant effect on the reliability and performance of ECG pattern recognition. This paper proposes new approaches and algorithms for pre-processing multi-channel ECG signals and neural networks for arrhythmia classification using independent component analysis (ICA) with two distinct goals: (1) to eliminate motion-induced or associated artifacts, and (2) to better select the features and allow more effective pattern recognition. When processing noisy ECG data with the MIT dataset, cross-validation reveals a major improvement. For noisy signals, classification sensitivity of 97.9% and positive predictivity of 98.1% was achieved in this work. A tenfold neural validation rule was used to achieve 99.3% accuracy in arrhythmia classification. The lower the signal-to-noise level, the more significant is the improvement. This proposed algorithm would be a valuable method for physicians in justifying their diagnosis. With its quick reaction time, the proposed algorithm can be easily integrated into an automated healthcare management system.

关键词： Health care Independent component analysis machine learning Arrhythmia computer-aided diagnosis Feature extraction Neural network

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2021 1st international Conference on Artificial Intelligence and data Analytics, CAIDA 2021

2021 1st International Conference on Artificial Intelligence...

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1st international Conference on Artificial Intelligence and data Analytics, CAIDA 2021

ISBN: (纸本)9780738131771

The proceedings contain 54 papers. The topics discussed include: an intelligent approach for food recipe rating prediction using machine learning;hardware implementation of IP-enabled wireless sensor network using 6LoWPAN;an efficient machine learning-based approach for android v.11 ransomware detection;robotics to enhance the teaching and learning process;an efficient pattern recognition based method for drug-drug interaction diagnosis;enhancing the prediction of MERS-CoV survivability using stacking-based method;a new solution to the brain state permanency for brain-based authentication methods;towards efficient detection and crowd management for law enforcing agencies;and AI support marketing: understanding the customer journey towards the business development.

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A shallow neural net with model-based learning for the virtual restoration of recto-verso manuscripts 1

A shallow neural net with model-based learning for the virtu...

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1st international Virtual Conference on Visual pattern Extraction and recognition for Cultural Heritage Understanding, VIPERC 2022

作者： Savino, Pasquale Tonazzini, Anna Istituto di Scienza e Tecnologie dell’Informazione Consiglio Nazionale delle Ricerche Via G. Moruzzi 1 Pisa56124 Italy

We propose a fast procedure based on neural networks (NN) to correct the typically complex background of recto-verso historical manuscripts, where the texts of the two sides often appear mixed. The purpose is to eliminate the interfering, shining-through text, to facilitate both the work of philologists and paleographers and the automatic analysis of the linguistic contents. We adapt the learning phase of a very simple shallow NN to exploit the information of the registered recto and verso sides of the manuscript without the need for a large class of other similar manuscripts. Hence, the training set is self-generated from the data images based on a theoretical mixing model that accounts for ink spreading through the paper fiber and for ink saturation in the text superposition areas. Operationally, we select pairs of patches containing clean text from the manuscript and then mix them symmetrically using the model with varying parameters that span the allowed range. This makes the NN able to generalize to diverse amounts of ink seeping and then classify different manuscripts. We show comparisons between the results obtained on heavily damaged manuscripts with this NN and other approaches. From a qualitative point of view, the proposed method seems quite promising. © 2022 Copyright for this paper by its authors.

关键词： Multilayer neural networks

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DroBoost: An Intelligent Score and Model Boosting Method for Drone Detection 21st

DroBoost: An Intelligent Score and Model Boosting Method for...

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21st international Conference on Image Analysis and Processing (ICIAP)

作者： Eryuksel, Ogulcan Ozfuttu, Kamil Anil Akyon, Fatih Cagatay Sahin, Kadir Buyukborekci, Efe Cavusoglu, Devrim Altinuc, Sinan OBSS Technol OBSS AI 1606 Cad 4-1-307 Cyberpk Cyberplaza C Blok 3 Kat Ankara Turkey

ISBN: (纸本)9783031133244;9783031133237

Drone detection is a challenging object detection task where visibility conditions and quality of the images may be unfavorable, and detections might become difficult due to complex backgrounds, small visible objects, and hard to distinguish objects. Both provide high confidence for drone detections, and eliminating false detections requires efficient algorithms and approaches. Our previous work, which uses YOLOv5, uses both real and synthetic data and a Kalman-based tracker to track the detections and increase their confidence using temporal information. Our current work improves on the previous approach by combining several improvements. We used a more diverse dataset combining multiple sources and combined with synthetic samples chosen from a large synthetic dataset based on the error analysis of the base model. Also, to obtain more resilient confidence scores for objects, we introduced a classification component that discriminates whether the object is a drone or not. Finally, we developed a more advanced scoring algorithm for object tracking that we use to adjust localization confidence. Furthermore, the proposed technique won 1st Place in the Drone vs. Bird Challenge (workshop on Small-Drone Surveillance, Detection and Counteraction Techniques at ICIAP 2021).

关键词： Drone detection Deep learning Object tracking Object detection Synthetic data

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Enhancing OCR Accuracy Using Convolutional Neural Networks: A study on Spelling Correction in Handwritten Numerals

Enhancing OCR Accuracy Using Convolutional Neural Networks: ...

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Advances in Computing, Communication and Networking (ICAC2N), international Conference on

作者： Priyanka Kaushik Inder Singh Dewal Sajjan Singh Rathore Vinay Singh Sani Kumar P.M. Sindhu Bhavani Dept. of Computer Science Engineering Chandigarh University Chandigarh India Dept. of Humanities & S.Science Madhav University Pindwara India Dept. of Com. & Information Technology Usha Martin University Jharkhand India Dept. of Computer Eng. & Applications Mangalayatan University Aligarh India Dept. of Humanities & Science Meenakshi A.H.Edu. & Research Chennai India

ISBN: (数字)9798350356816

ISBN: (纸本)9798350356823

Artificial neural networks have transformed machine learning and had a significant impact on a wide range of industries, including robotics, deep learning, healthcare, sports, and surveillance. Convolutional Neural Networks (CNNs) are unique among these developments because they combine cutting-edge deep learning methods with conventional Artificial Neural Networks (ANNs). CNNs are essential for tasks like data analysis, text classification, speech and face recognition, and pattern recognition in a variety of fields. In order to improve spelling correction, this article uses a dataset that was upgraded from the National Institute of standards & Technology (NIst) and includes 70,000 grayscale pictures of handwritten numbers.

关键词： Deep learning Computer aided software engineering Accuracy Service robots Surveillance Text categorization Speech recognition Convolutional neural networks standards Sports

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