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International Joint conference on neural networks (IJCNN)

作者： Minagawa, Koki Saito, Taisei Kojima, Sena Ando, Kota Asai, Tetsuya Hokkaido Univ Grad Sch Informat Sci & Technol Sapporo Japan Hokkaido Univ Fac Informat Sci & Technol Sapporo Japan

ISBN: (纸本)9798350359329;9798350359312

Out-of-distribution (OOD) data detection is critical when using machine learning models for practical applications. Many methods have been proposed to estimate the uncertainty of machine learning models for input data. Therefore, the purpose of this paper is to propose and verify toward elucidation of the nature and the practical use. We proposes data preprocessing to improve OOD image data detection performance using Bayesian neural networks (BNNs) to estimate two types of uncertainties. The aim of the proposed preprocessing is to increase the usefulness of the two uncertainties by providing more information to the BNN. The proposed preprocessing extracts high-frequency components of the input image by wavelet transform and adds the channel direction to create new input data. The contributions of this paper are as follows. First, we show that in training FashionMNIST, the BNN has a high performance in detecting OOD data by epistemic uncertainty that can be estimated. Second, we infer the relationship between the degree of BNN learning and OOD data detection performance. Finally, the proposed pre-processing is demonstrated to improve the OOD data detection performance. The validation results show that the average area under the receiver operating characteristic (AUROC) increases from 0.936 to 0.970 with the proposed preprocessing for BNNs trained in 10 epochs. These contributions reveal some aspects of the nature of uncertainty and provide clues to approaches for practical applications.

关键词： bayesian neural network out-of-distribution detection wavelet transform uncertainty

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Convolutional neural Network Approach for Early Skin Cancer Detection

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JOURNAL OF ELECTRICAL SYSTEMS 2023年第3期19卷 1-14页

作者： Raut, Roshani Gavali, Niraj Amate, Prathamesh Amode, Mihir Ajay Malunjkar, Shraddha Borkar, Pradnya Pimpri Chinchwad Coll Engn Dept Informat Technol Pune Maharashtra India Symbiosis Inst Technol Nagpur Maharashtra India

The field of medical image processing is rapidly adopting artificial intelligence. Its use is required for many applications in the healthcare industry. A machine can learn from experience without explicit programming thanks to computer education. It is an area within AI. Deep learning, a kind of machine learning, infers critical features for image processing via multiple layer processing and mathematical operations based on artificial neural networks. In the field of healthcare, which encompasses medicine and dentistry, artificial intelligence has several *** melanoma skin cancer identification is necessary for effective therapy. Melanoma, among the various types of skin cancer, has recently gained international recognition as the most deadly one since it is much more likely to spread to other body regions if detected and treated quickly. Clinical diagnosis of various ailments is increasingly using non-invasive medical computer vision or medical image processing. These methods offer an automatic image processing tool that makes it possible to examine the lesion quickly and precisely. The procedures used in this study included building a database of dermoscopy images, preprocessing, segmenting using thresholding, extracting statistical features using asymmetry, border, colour, diameter, etc., and choosing features based on the total dermoscopy score, principal component analysis (PCA), and convocation neural network classification (CNN). According to the findings, a classification accuracy of 90.1% was attained.

关键词： AlexNet Benign Convolutional neural Network Data pre-processing Feature extraction image Augmentation Malignant Maxpooling Training Testing Resnet VGG.

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AI Innovator: Text to image Generation using GAN

AI Innovator: Text to Image Generation using GAN

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2024 IEEE International Students' conference on Electrical, Electronics and Computer Science, SCEECS 2024

作者： Nimbarte, Mrudula Hussain, Alfiya Budhlani, Kanak Bansod, Chirag Ramdham, Mayur Kanhe, Om Khullar, Amit S.B. Jain Institute of Technology Management and Research Department of Computer Science and Engineering MS Nagpur India

ISBN: (纸本)9798350348460

This review provides a comprehensive review of the latest approaches and advances in text-to-image processing using artificial neural networks (GANs). The work under review uses GAN architecture, where generators and classifiers work together to extract corresponding images from descriptions. This research includes a comprehensive review of the GAN framework, delving into the complexities of the generator and discrimination during training. The project solves problems related to converting text input into visual images using a combination of natural language processing and computer vision. This article reviews the main concepts of the architecture as well as the use of advanced neural networks for text understanding and image synthesis. Particular attention is paid to the optimization process the generator uses to improve the quality and integrity of the resulting images. Additionally, the review highlights the evolution of different labels and discusses their implications for real-world applications. © 2024 IEEE.

关键词： Adversarial Training Computer Vision Generative Adversarial networks (GANs) image Synthesis Multimodal Learning Natural Language processing (NLP) neural Network Architecture Text to image Generation

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Cervical cancer classification based on a bilinear convolutional neural network approach and random projection

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ENGINEERING applications OF artificial INTELLIGENCE 2024年第PartA期127卷

作者： Abd-Alhalem, Samia M. Marie, Hanaa Salem El-Shafai, Walid Altameem, Torki Rathore, Rajkumar Singh Hassan, Tarek M. Delta Univ Sci & Technol Fac Artificial Intelligence Gamasa 35712 Egypt Delta Univ Sci & Technol Fac Engn Gamasa 35712 Egypt Menoufia Univ Fac Elect Engn Dept Elect & Elect Commun Engn Menoufia 32952 Egypt King Saud Univ Community Coll Comp Sci Dept POB 11433 Riyadh Saudi Arabia Cardiff Metropolitan Univ Cardiff Sch Technol Dept Comp Sci Llandaff CampusWestern Ave Cardiff CF5 2YB Wales

Cervical cancer (CC) is one of the most prevalent malignancies affecting women globally, with a particularly notable impact and high mortality in regions with limited economic resources. This underscores the imperative for the expeditious development of techniques that facilitate timely and precise detection, thereby augmenting treatment efficacy, enhancing survival rates, and mitigating the burden of healthcare expenditure. The intricacies of cervical cancer detection are inherently aligned with the challenges of fine-grained visual classification. This study focuses on the integration of bilinear pooling within convolutional neural networks (CNNs) and addresses the problem of the computational complexity of bilinear features with the fortification of the bilinear CNN with a random projection paradigm (RP-BCNN). The aim is the simultaneous achievement of improved classification precision and streamlined processing temporalities. The proposed methodology entails the introduction of a dyadic feature extraction protocol in which the input cellular image is subjected to twin feature extraction modalities. The feature maps obtained from this process undergo element-wise multiplication via an outerproduct operation, thereby engendering composite feature representations. Subsequently, a judiciously designed random projection procedure is invoked to reduce dimensionality, yielding a more succinct yet informative image descriptor. Empirical evaluations of the introduced model predicted on the RP-BCNN framework yielded commendable outcomes. Notably, an accuracy of 0.9983 was achieved for the dual-label classification scenarios, and an accuracy of 0.9530 was realized in the context of multiclass classification encompassing seven distinct labels. The proposed model achieves an optimal equilibrium between classification accuracy and processing efficiency, thus constituting a potent instrument for the classification of cervical cancer. Further, it holds promise for the refinemen

关键词： Cervical cancer Bilinear pooling Random projection Convolutional neural networks RP-BCNN image classification

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Tracking the Nodal Point of Weakly Electric Fish Using artificial neural networks 31

Tracking the Nodal Point of Weakly Electric Fish Using Artif...

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31st IEEE conference on Signal processing and Communications applications (SIU)

作者： Catalbas, Bahadir Elikuru, Dogukaan Aydin, Emin Yusuf Uyanik, Ismail Hacettepe Univ Elekt & Elekt Muhendisligi Bolumu Ankara Turkiye Yildiz Tekn Univ Kontrol & Otomasyon Muhendisligi Bolumu Istanbul Turkiye

ISBN: (纸本)9798350343557

In living beings, there is a closed-loop system called sensorimotor transformation, in which the signals received by the sensory organs from the environment are processed in the nervous system, and the necessary motor signals are transmitted to the muscular system. Unfortunately, the closed-loop nature of the structure makes it difficult to decipher the relationship between inputs and outputs. Since the nodal point on the anal fin of the weakly electric fish (Eigenmannia virescens) may be an output of the sensorimotor control system, precisely monitoring its position is important. The method used in the literature to find the nodal point so far is to mark each video frame manually. However, manual marking creates a significant workload and causes a waste of time. This study aims to determine the position of the node that has been manually marked so far by using artificial neural networks more easily and effectively.

关键词： artificial neural network deep learning weakly electric fish sensorimotor control

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Explainable AI for Medical Data: Current Methods, Limitations, and Future Directions

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ACM COMPUTING SURVEYS 2025年第6期57卷 1-46页

作者： Hossain, Md Imran Zamzmi, Ghada Mouton, Peter r. Salekin, Md Sirajus Sun, Yu Goldgof, Dmitry Univ S Florida Dept Comp Sci & Engn ENB 4202 E Fowler Ave Tampa FL 33620 USA SRC Biosci 1810 W Kennedy Blvd Tampa FL 33606 USA

With the power of parallel processing, large datasets, and fast computational resources, deep neural networks (DNNs) have outperformed highly trained and experienced human experts in medical applications. However, the large global community of healthcare professionals, many of whom routinely face potentially life-or-death outcomes with complex medicolegal consequences, have yet to embrace this powerful technology. The major problem is that most current AI solutions function as a metaphorical black-box positioned between input data and output decisions without a rigorous explanation for their internal processes. With the goal of enhancing trust and improving acceptance of artificial intelligence- (AI) based technology in clinical medicine, there is a large and growing effort to address this challenge using eXplainable AI (XAI), a set of techniques, strategies, and algorithms with an explicit focus on explaining the "hows and whys" of DNNs. Here, we provide a comprehensive review of the state-of-the-art XAI techniques concerning healthcare applications and discuss current challenges and future directions. We emphasize the strengths and limitations of each category, including image, tabular, and textual explanations, and explore a range of evaluation metrics for assessing the effectiveness of XAI solutions. Finally, we highlight promising opportunities for XAI research to enhance the acceptance of DNNs by the healthcare community.

关键词： Explainability medical data responsible AI deep neural networks pretable AI

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Revisiting DCT in Deep Learning Era: An Initial Denoising Application 32

Revisiting DCT in Deep Learning Era: An Initial Denoising Ap...

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32nd IEEE Signal processing and Communications applications conference (SIU)

作者： Karaoglu, Hasan H. Eksioglu, Ender M. Istanbul Tech Univ Elekt & Haberlesme Muhendisligi Bolumu Istanbul Turkiye

ISBN: (纸本)9798350388978;9798350388961

In the classical era of image denoising, the methods working in transform domain have achieved high performance results. However, most of the deep neural networks that have been proposed in the last decade and have shown better noise removal performance try to denoise the noisy image in pixel domain. In deep learning literature, there are few deep neural networks that work in transform domain. Most of them have not chosen the discrete cosine transform (DCT), which is known to provide a very good representation for most images. This is the result of convolution layer, which is often used in deep networks, searching in vain for a relationship between neighboring values of an image's uncorrelated global and block DCT coefficients. On the other hand, it is known that working with transform coefficients of overlapped image blocks improves noise removal performance. Recent studies have shown that the convolution of an image with 2D DCT basis images is a meaningful ordering of the DCT coefficients of overlapping image blocks. Hence, in this paper, a deep neural network is proposed to remove noise in the DCT domain. Experiments on color images indicate that the proposed network is quantitatively and qualitatively successful in noise removal.

关键词： image denoising deep learning discrete cosine transform

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Blind quality-based pairwise ranking of contrast changed color images using deep networks

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SIGNAL processing-image COMMUNICATION 2024年 121卷

作者： Chetouani, Aladine Qureshi, Muhammad Ali Deriche, Mohamed Beghdadi, Azeddine Univ Orleans Lab PRISME Orleans France Islamia Univ Bahawalpur Bahawalpur 63100 Pakistan Ajman Univ Coll Engn Artificial Intelligence Res Ctr Ajman U Arab Emirates Univ Sorbonne Paris Nord Inst Galilee L2TI Villetaneuse France

Next-generation multimedia networks are expected to provide systems and applications with top Quality of Experience (QoE) to users. To this end, robust quality evaluation metrics are critical. Unfortunately, most current research focuses only on modeling and evaluating mainly distortions across the pipeline of multimedia networks. While distortions are important, it is also as important to consider the effects of enhancement and other manipulations of multimedia content, especially images and videos. In contrast to most existing works dedicated to evaluating image/video quality in its traditional context, very few research efforts have been devoted to image Quality Enhancement Assessment (IQEA) and more specifically, Contrast Enhancement Evaluation (CEE). Our contribution fills this gap by proposing a pairwise ranking scheme for estimating and evaluating the perceptual quality of image contrast change (contrast enhancement and/or contrast-distorted images) process. We propose a novel Deep Learning-based Blind Quality pairwise Ranking scheme for Contrast-Changed (Deep-BQRCC) images. This method provides an automatic pairwise ranking of a set of contrast-changed images. The proposed framework is based on using a pair of Convolutional neural networks (CNN) together with a saliency-based attention model and a color-difference visual map. Extensive experiments were conducted to validate the effectiveness of the proposed workflow through an ablation analysis. Different combinations of CNN models and pooling strategies were analyzed. The proposed Deep-BQRCC approach was evaluated over three dedicated publicly available datasets. The experimental results showed an increase in performance within a range of 3-10% compared to state-of-the-art IQEA measures.

关键词： Contrast enhancement evaluation (CEE) Contrast change Contrast enhancement (CE) image quality enhancement evaluation (IQEA) Convolutional neural network (CNN) Visual saliency image ranking

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Non-symmetric over-time pooling using pseudo-grouping functions for convolutional neural networks

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ENGINEERING applications OF artificial INTELLIGENCE 2024年第PartE期133卷

作者： Ferrero-Jaurrieta, Mikel Paiva, Rui Cruz, Anderson Bedregal, Benjamin De Miguel, Laura Takac, Zdenko Lopez-Molina, Carlos Bustince, Humberto Univ Publ Navarra Dept Stat Comp Sci & Math Pamplona Spain Inst Fed Educ Ciencia & Tecnol Ceara Fortaleza Brazil Univ Fed Rio Grande Do Norte Inst Metropole Digital Natal Brazil Univ Fed Rio Grande Do Norte Dept Informat & Matemat Aplicada Natal Brazil Slovak Univ Technol Fac Mat Sci & Technol Trnava Slovakia Univ Publ Navarra UPNA IdiSNA Navarrabiomed Hosp Univ Navarra HUN Pamplona 31008 Spain

Convolutional neural networks (CNNs) are a family of networks that have become state-of-the-art in several fields of artificial intelligence due to their ability to extract spatial features. In the context of natural language processing, they can be used to build text classification models based on textual features between words. These networks fuse local features to generate global features in their over-time pooling layers. These layers have been traditionally built using the maximum function or other symmetric functions such as the arithmetic mean. It is important to note that the order of input local features is significant (i.e. the symmetry is not an inherent characteristic of the model). While this characteristic is appropriate for image-oriented CNNs, where symmetry might make the network robust to image rigid transformations, it seems counter-productive for text processing, where the order of the words is certainly important. Our proposal is, hence, to use non-symmetric pooling operators to replace the maximum or average functions. Specifically, we propose to perform over-time pooling using pseudo-grouping functions, a family of non-symmetric aggregation operators that generalize the maximum function. We present a construction method for pseudo-grouping functions and apply different examples of this family to over-time pooling layers in text-oriented CNNs. Our proposal is tested on seven different models and six different datasets in the context of engineering applications, e.g. text classification. The results show an overall improvement of the models when using non-symmetric pseudo-grouping functions over the traditional pooling function.

关键词： Pseudo-grouping function Over-time pooling Text classification Feature fusion Aggregation function

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Invisible and Multi-triggers Backdoor Attack Approach on Deep neural networks through Frequency Domain

Invisible and Multi-triggers Backdoor Attack Approach on Dee...

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9th International conference on Signal and image processing (ICSIP)

作者： Sun, Fengxue Pei, Bei Chen, Guangyong Southeast Univ Sch Cyber Sci & Engn Nanjing Peoples R China Natl Engn Res Ctr Classified Protect & Safeguard Shanghai Peoples R China

ISBN: (纸本)9798350350920

In recent years, the security of deep neural networks (DNNs) has become a research hotspot with widespread deployments of machine learning models in our daily life. Backdoor attack is an emerging security threat to DNNs, where the infected model will output malicious targets for the images containing specific triggers. However, most existing backdoor attack approaches have only single trigger, and the triggers are often visible to human eyes. In order to overcome these limitations, in this paper, we propose an invisible and multi-triggers backdoor attack (IMT-BA) approach to simultaneously generate four invisible triggers. Firstly, in our IMT-BA approach, we divide the whole images into four blocks and apply Discrete Cosine Transform (DCT) algorithm to generate four invisible triggers aiming at four targets. Secondly, our IMT-BA approach can be easily deployed in real world without any knowledge of the hyperparameters and architectures of the DNNs models. Finally, we do the experiments with MNIST and CIFAR-10 datasets and the experiment results show our IMT-BA approach can fool both DNNs models and Human Visual System (HVS) with high success rate.

关键词： artificial intelligence security backdoor attack frequency domain discrete cosine transform

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