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6th International Conference on computer Network, Electronic and Automation, ICCNEA 2023

作者： Chen, Yupeng Wang, Jianguo Xi'an Technological University School of Computer Science and Engineering Xi'an China Xi'an Technological University Research Institute of Artificial Intelligence and Data Science Xi'an China

ISBN: (纸本)9798350305388

A large amount of data is collected during geological hazard monitoring, which is extremely valuable for further data mining, hazard monitoring and decision analysis. However, in the process of data collection and transmission may be affected by interference and other factors, resulting in the generation of abnormal data. To extract higher value information from these basic data, it is necessary to improve the quality of source data first, so it is necessary to detect the abnormal data. The monitoring data of the geological disaster are time-series data, which have the characteristics of time-series correlation. The nearest neighbor difference jump anomaly detection algorithm is suitable for monitoring anomalous data of geological disaster system, but there are shortcomings in selecting floating values and correlation perception. To address these problems, the nearest neighbor difference jumping algorithm is improved and a algorithm is proposed to detect anomalous data of geological disasters, i.e., the data series are segmented by using sliding windows, the selection of floating values is improved, the calculation range of difference values is expanded to enhance the correlation of data, and the concept of change speed is incorporated to better perceive the trend of change before and after the data, and finally the anomaly is determined by using the anomaly probability and correlation. After the experimental comparison and analysis, the accuracy and recall rate of the proposed method on the detection of geological disaster data are improved and meet the expected results. © 2023 IEEE.

关键词： Time series

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Identification of Breast Cancer using Jelly fish Based Deep Wavelet Autoencoder 9

Identification of Breast Cancer using Jelly fish Based Deep ...

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9th International Conference on Advanced Computing and Communication Systems, ICACCS 2023

作者： Anbumani, K. Balamanigandan, R. Kumar, T. Rajesh Sasi Kumar, A. Mahaveerakannan, R. Sri Sai Ram Engineering College Department of EIE Chennai India Saveetha School of Engineering Computer Science and Engineering India SIMATS Saveetha School of Engineering Computer Science and Engineering Tamil Nadu Chennai602105 India Srinivas University Institute of Engineering & Technology Department of Cloud Technology & Data Science Mangalore India

ISBN: (纸本)9798350397376

Mammography, an imaging technology used to detect breast cancer, has recently received more interest from scientists. Screening mammography is the best way to detect breast cancer and other abnormalities. Breast tumours often have two distinct characteristics: mass lesions and microcalcification. A gathering of appropriate picture pre-processing, proceduresare essential for accurate cancer detection and classification in mammograms. Image processing is widely employed in the medical field to improve the diagnostic and treatment processes. When it comes to classification and segmentation, deep neural networks (DNN) have excelled so far. With this in mind, the authors of this paper suggest a deep wavelet autoencoder (DWA) method for image compression by fusing the autoencoder's basic feature reduction function with the picture decomposition capabilities of the wavelet transform. The best attributes for identifying breast cancer are chosen in this study by using the Jelly Fish Search Algorithm (JFSA). When used together, they have a dramatic impact on reducing the size of the feature set required to persist through a subsequent classification job using DNN. The suggested DWA-DNN image classifier was evaluated on two public datasets of breast cancer pictures. The DWA-DNN classifier's performance criteria was compared to that of other existing classifiers, and it was found that the suggested technique excels in comparison across a range of measures. © 2023 IEEE.

关键词： Deep neural networks

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BERD+: A Generic Sequential Recommendation Framework by Eliminating Unreliable data with Item- and Attribute-level Signals

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ACM Transactions on Information Systems 2023年第2期42卷 1-33页

作者： Sun, Yatong Yang, Xiaochun Sun, Zhu Wang, Bin School of Computer Science and Engineering Northeastern University School of Computing Macquarie University China Australia Institute of High Performance Computing Centre for Frontier AI Research A*STAR Singapore School of Computer Science and Engineering Northeastern University China National Frontiers Science Center for Industrial Intelligence and Systems Optimization China Key Laboratory of Data Analytics and Optimization for Smart Industry [Northeastern University Ministry of Education China

A preliminary work of our study was published at IJCAI’21 [47], which is substantially extended in the following aspects: (1) In Section 1, we analyze the necessity of introducing item attributes for detecting unreliable instances, together with the problems and challenges that attributes may bring in. (2) In Section 2, we add discussions about the limitations of existing attribute-aware recommender systems (Section 2.2) and denoising methods (Section 2.3) in the context of detecting unreliable instances. (3) In Section 4.2, we further conduct an in-depth analysis at the attribute level to demonstrate the capability of attributes for rectifying instance loss and uncertainty, as well as the disturbance caused by attributes. (4) We generalize BERD to a generic framework BERD+ in Section 5.1, equipped with novel modules, i.e., HU-GCN (Section 5.2) and EPE (Section 5.4), which properly incorporate item attributes while reducing their disturbance for rectifying instance uncer tainty (Section 5.5) and loss (Section 5.6). The generic BERD+ can be flexibly plugged into existing SRSs for performance enhanced recommendation via eliminating unreliable data. (5) In Section 6.2, we apply our BERD+ framework to seven state-of-the-art SRSs on five real-world datasets to illustrate its superiority. (6) To avoid unfair comparison caused by item attributes, we build and compare with the baseline that combines the original BERD and an advanced attribute-aware recommender system, KSR [19]. (7) For more comprehensive comparison, in Section 6.2.2, we compare BRED+ with two state-of-the-art denoising approaches;in Section 6.2.3, to examine the efficacy of HU-GCN and EPE, we compare HU-GCN with various attribute embedding techniques, i.e., variants of graph neural networks, and compare EPE with different attribute fusing methods, i.e., adding, concatenation, and weighted sum. (8) In Section 6.2.4, a detailed ablation study is conducted to verify the effectiveness of each module of BERD+. (

关键词： Convolution

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Secured Provisioning of Multimedia Contents in Cloud Platform Using Cryptographic Approaches

Secured Provisioning of Multimedia Contents in Cloud Platfor...

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computer science and Emerging Technologies (CSET), International Conference on

作者： S.A. Sahaaya Arul Mary Akshaj Nevgi Rhea Ranjith Nair Jehan Sharukh Bhathena Angad Chaudhry S. Sharon Jessika School of Computer Science and Engineering VIT Computer Science and Engineering Computer Science with specialization in Data Science

Cloud computing is a technology that allows the utilisation of a vast network of computers that are distributed and run in parallel with one another. The management of the multimedia files presents challenges and the significant of which is the prevalence of attackers and users with malicious intent. This article presents the conceptual foundation for a four-tiered data security paradigm that can be implemented in cloud computing. The proposed framework employs a DNA-computing-based password or secret key with a length of 1024 bits for the purpose of encrypting the user private multimedia file or correspondence. The DNABMS algorithm is applied in the cryptographic generation of digital identities as well as pseudorandom number generators. The results of the testing that was done on the model have validated both its reliability and its efficacy, demonstrating that the model that was developed is both reliable and effective. Experiments have demonstrated that the developed method is superior to other well-known existing strategies in terms of both effectiveness and efficiency.

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Improving the Accuracy of Iris Recognition Based on Attention Mechanism 6

Improving the Accuracy of Iris Recognition Based on Attentio...

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6th International Conference on computer Network, Electronic and Automation, ICCNEA 2023

作者： Wang, Zike Wang, Jianguo Zhong, Lei Xi'an Technological University School of Computer Science and Engineering Xi'an China Xi'an Technological University Research Institute of Artificial Intelligence and Data Science Xi'an China

ISBN: (纸本)9798350305388

Iris recognition algorithms are becoming more and more widely used in daily life, and the accuracy requirements for iris recognition algorithms are getting higher and higher. Therefore, in this paper, an improved Shufflenetv2 algorithm is proposed as a way to improve the speed and accuracy of current iris recognition algorithms in daily life. In this paper, deep and shallow features are filtered by using the channel attention mechanism, which enables the model to acquire feature representations of both underlying and high-level semantics. In addition, in the loss function, this paper uses the focal loss function instead of the cross entropy loss function to achieve a solution to the imbalance between the number of positive and negative samples and prevent too much learning to negative sample features. By validating on the CASIA-Iris-Thousand dataset, which is commonly used in iris recognition tasks, the proposed model achieves an equal error rate (EER) of 1.66% compared to the Shufflenetv2 model, which has an EER of 5.3%, and optimizes by 3.64%. The model proposed in this paper can effectively improve the accuracy of iris recognition. © 2023 IEEE.

关键词： Convolutional neural networks

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Earthquake Damage Prediction and Rapid Assessment of Building Damage Using Deep Learning 1

Earthquake Damage Prediction and Rapid Assessment of Buildin...

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1st IEEE International Conference on Advances in Electronics, Communication, Computing and Intelligent Information Systems, ICAECIS 2023

作者： Natarajan, Yuvaraj Wadhwa, Gitanjali Ranganathan, Preethi Akshaya Natarajan, Karthika KPR Institute of Engineering and Technology Dept. of Computer Science and Engineering Coimbatore641407 India KPR Institute of Engineering and Technology Dept. of Artificial Intelligence and Data Science Coimbatore641407 India

ISBN: (纸本)9798350348057

One of the most expensive natural disasters that affect people, earthquakes occur suddenly. As a result, earthquake prediction has grown in importance and difficulty for humanity. Although numerous existing approaches attempt to handle this problem, the majority rely on seismic indicators created by geologists or feature vectors extracted by the deep learning techniques to describe an earthquake for the earthquake prediction. Combining these two categories of characteristics to enhance ultimate Earthquake prediction performance is still a challenge. In order to achieve this, we put forth a deep learning model that successfully fuses explicit and implicit information for earthquake prediction. Here, we use a Convolutional Neural Network to extract implicit features while using eight precursory pattern-based indicators as the explicit features. After that, an attention-based approach is suggested to effectively combine these two categories of traits. A dynamic loss function is the additionally created to address the imbalance of category in seismic data. On a test picture dataset, the performances of multiple CNN models are contrasted. In order to classify earthquake damage, the model is further developed as a web application. Damage assessment values, which are determined using the Convolutional Neural Network model and gradient-weighted class activation mappings, are used to determine the extent of the damage. The web-based program can efficiently and automatically categorize earthquake-related structural damage, making it appropriate for decision-making in emergency response, resource allocation, and policy formation. © 2023 IEEE.

关键词： Earthquakes

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Adaptive weighted broad learning system for imbalanced classification 6

Adaptive weighted broad learning system for imbalanced class...

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6th IEEE Advanced Information Management, Communicates, Electronic and Automation Control Conference, IMCEC 2024

作者： Li, Yanting Gao, Yiping Jin, Junwei Chen, C. L. Philip Zhengzhou University of Light Industry College of Computer Science and Technology Zhengzhou China Henan University of Technology College of Artificial Intelligence and Big Data Zhengzhou China South China University of Technology School of Computer Science and Engineering Guangzhou China

ISBN: (纸本)9798350316520

Broad Learning System (BLS) perform well in classification tasks with good computational efficiency. However, its effectiveness decreases when faced with imbalanced data distribution. The traditional BLS cannot solve this problem effectively. In this paper, we introduce an adaptive weighted method for BLS, aiming to improve its classification performance on imbalanced data. This method can assign different weights according to the distribution characteristics of samples. There exists a closed-form solution to the problem and the transformation matrix can be solved efficiently by pseudo-inverse. Experimental results on several imbalanced datasets demonstrate that the proposed method surpasses standard BLS and many advanced imbalanced classification methods. © 2024 IEEE.

关键词： Classification (of information)

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Exploring Deep Convolutional Neural Networks: A Grad-CAM Enhanced Comparative Study for Automated COVID-19 Diagnosis from Chest X-Ray Images 3

Exploring Deep Convolutional Neural Networks: A Grad-CAM Enh...

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3rd International Conference on Smart Generation Computing, Communication and Networking, SMART GENCON 2023

作者： Reza, Md. Sazid Jahangir Kabir, Mir Md. Rakib Mollah, Md. Abdur Nahin, Nazmul Islam Data Science and Innovation Td School Department of Computer Science & Engineering India University of Technology Sydney Sydney Australia Rajshahi University of Engineering & Technology Department of Electronics & Telecommunication Engineering Rajshahi Bangladesh

ISBN: (纸本)9798350319125

The emergence of COVID-19 in late 2019 sparked a global health crisis. Identifying infections quickly is key to providing proper care and limiting transmission. The Reverse Transcription-Polymerase Chain Reaction (RT-PCR) test, a widely recognized primary diagnostic method for detecting COVID-19, faces challenges associated with cost and compatibility in resource-constrained environments. This issue is resolved by chest X-ray imaging since it is non-invasive and inexpensive. In this domain, research often utilizes pre-trained models on relatively small datasets of chest X-ray images, limiting the potential for optimal generalization and accuracy achievable with larger training sets. Also, these research works limit the exploration of crucial information on salient regions within CXR images, constraining the depth of analysis and interpretation. This study addresses current research challenges through a detailed comparative analysis of convolutional neural network (CNN) architectures, including pre-trained models like Inception V3, Xception, DenseNet-121, VGG-19, and a Custom CNN developed from scratch. The evaluation focuses on their effectiveness in detecting COVID-19 using a relatively vast and comprehensive dataset. The evaluation focuses on assessing the efficacy of these models in detecting COVID-19 across binary-class and multi-class classifications within a large dataset, offering valuable insights into their overall effectiveness. Grad-CAM is employed in this study, which visualizes picture salient areas affecting deep neural network predictions and improves model interpretability. The heatmaps reveal the model's decision-making processes, making their behavior more understandable to medical practitioners. With resulting accuracies of 99.13% and 97.66% in binary-class and multi-class classification, Inception V3 demonstrated the most optimal performance. This proposed approach surpasses existing benchmark methods, underscoring its superiority. © 2023 IEEE.

关键词： chest X-ray CNN COVID-19 Deep Learning DenseNet-121 Grad-CAM Inception V3 ResNet-50 RT-PCR VGG-19 Xception

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BankRuptcyML: A Sustainable System for Company Bankruptcy Prediction and Detection using Machine Learning Techniques 4

BankRuptcyML: A Sustainable System for Company Bankruptcy Pr...

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4th International Conference on Sustainable Expert Systems, ICSES 2024

作者： Shankar, Venkatesh Gauri Buch, Reeva V Kolambekar, Akhilesh A Devi, Bali Manipal University Jaipur Department of Information Technology Rajasthan Jaipur India Manipal University Jaipur Department of Data Science and Engineering Rajasthan Jaipur India Manipal University Jaipur Department of Computer Science and Engineering Rajasthan Jaipur India

ISBN: (纸本)9798331540364

This research study presents a comprehensive use of machine learning techniques used for bankruptcy prediction, which is crucial for financial institutions to assess creditworthiness. The study highlights methods such as logistic regression, decision trees, and ensemble learning (bagging, boosting), demonstrating the superiority of machine learning over classical statistics for smaller datasets. This study compares various strategies by considering financial statements, market trends, industry benchmarks, and macroeconomic indicators. Integrating diverse data sources helps identify risk factors and signs of financial distress, guiding proactive bankruptcy prevention measures. These include optimizing cash flow, enhancing efficiency, diversifying revenue, and implementing early warning systems. Predicting and preventing bankruptcies support financial stability, job protection, and economic growth. The proposed model employs financial ratios and relevant data to predict bankruptcy likelihood, which is evaluated using assessment and evaluation metrics. The AdaBoost model has a reported accuracy of 86.36% and an F1 score of 85.93%, which are higher than those of all the other cohorts included in the existing work. © 2024 IEEE.

关键词： Adversarial machine learning

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Brain Connectivity Using Deep Learning Algorithms 1

Brain Connectivity Using Deep Learning Algorithms

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2023 1st DMIHER International Conference on Artificial Intelligence in Education and Industry 4.0, IDICAIEI 2023

作者： Jiet, Moses Makuei Kamble, Aahash Puri, Chetan Gajanan Verma, Prateek Faculty of Engineering and Technology Department of Computer Science & Design Maharashtra Wardha442001 India Faculty of Engineering and Technology Department of Artificial Intelligence & Data Science Maharashtra Wardha442001 India

ISBN: (纸本)9798350338423

The integration of deep learning into the examination of brain connectivity marks a transformative era in understanding neural networks. This article provides a concise overview of the burgeoning field of deep learning in brain connectivity analysis. It defines the concept, explores functional and structural connectivity, outlines practical applications, delves into the algorithms involved, compares traditional methods vs advanced deep learning approaches for brain connectivity and highlights its revolutionary impact in clinical practice. In deep learning, brain connectivity investigates neural links using advanced artificial intelligence. Functional connectivity, powered by deep neural networks, reveals patterns in brain activity linked to cognition, emotion, and decision-making. Structural connectivity, often utilizing graph neural networks, reconstructs intricate neural pathways. This technology shows significant progress in diagnosing conditions like Alzheimer's, schizophrenia, and autism. As it evolves, its clinical importance becomes evident, offering early disease detection, tailored treatments, and a deeper understanding of neurological disorders. Beyond clinical applications, deep learning in brain connectivity opens avenues for advancements in brain-computer interfaces, enhanced rehabilitation therapies, and precise interventions. © 2023 IEEE.

关键词： Learning algorithms

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