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Enhanced Multi-Class Skin Cancer Classification Using Attention-Based Convolutional Neural Network with HOG Feature Extraction and ROI-Centred Pre-processing

Enhanced Multi-Class Skin Cancer Classification Using Attent...

引用

2024 International conference on Computing and Intelligent Reality Technologies, ICCIRT 2024

作者： Hariarasi, A. Banumathi, J. Faculty of Information and Communication Engineering University College of Engineering Anna University Nagercoil629001 India Department of Information Technology University College of Engineering Anna University Nagercoil629001 India

ISBN: (纸本)9798331510299

Skin cancer, a common increasingly prevalent disease, encompasses eight distinct diagnostic categories, posing significant challenges for dermatologists due to the highly similar visual characteristics of these categories. Traditional diagnostic accuracy for multiclass skin cancer detection ranges between 70% and 99%, often falling short in multi-class scenarios compared to binary classification performance. To address this, an Attention-Based Convolutional Neural Network (CNN) designed this research for multiclass skin cancer classification. The proposed work begins with a robust pre-processing stage involving Min-Max scaling for normalization and Region of Interest (ROI) cropping, which effectively reduces computational complexity. RGB images are transformed into single-channel grayscale, and ROI cropping emphasizes relevant regions, aiding in the model's localization capability. Following feature extraction, Histogram of Oriented Gradients (HOG) is used to capture important material details, improving the quality of input features. The classification stage utilizes the Attention based CNN to focus on diagnostically relevant areas, thereby improving classification accuracy across multiple skin cancer types. The proposed method demonstrates a significant improvement in diagnostic performance, offering a promising solution for efficient and accurate cancer classification. The accuracy of the proposed method is achieved as 96% and the specificity is of 0.92. © 2024 IEEE.

关键词： Convolutional neural networks

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Efficiency Enhancement and Mode Change of Intelligent Media communication under the Interaction of Artificial Intelligence Technology 22

Efficiency Enhancement and Mode Change of Intelligent Media ...

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7th International conference on Multimedia Systems and Signal processing, ICMSSP 2022

作者： Zhao, Lili College of Humanities and Social Sciences Heilongjiang Institute of Technology Harbin China

ISBN: (纸本)9781450396424

The development of artificial intelligence in the fields of machine learning and deep learning has made it possible to use artificial intelligence technology in human information production and dissemination activities. At present, artificial intelligence has also made a series of achievements in artificial intelligence media, natural language processing and interpretation, human-computer interaction, visual language processing, etc., which are closely related to the field of information production and dissemination, providing an expandable boundary for the intelligent development of media and intelligent media dissemination. Therefore, in this paper, the efficiency enhancement of intelligent media communication and the transformation of communication mode under the progress of artificial intelligence technology are discussed and analyzed, and the conclusion is drawn that the development of intelligent media has become an inevitable trend. © 2022 ACM.

关键词： visual languages

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Darkroom image enhancement algorithm based on SSR adaptive adjustment

Darkroom image enhancement algorithm based on SSR adaptive a...

引用

2023 International conference on Computer Graphics, Artificial Intelligence, and Data processing, ICCAID 2023

作者： Bao, Shangfeng Lin, Hongyi Gao, Jianping Zhang, Fengcheng School of Optoelectronics and Communication Engineering Xiamen University of Technology Xiamen361024 China Xiamen Lilin Technology Co. Ltd Xiamen361021 China

ISBN: (纸本)9781510677685

Aiming at the problems of poor brightness effect and poor recognition, a darkroom image enhancement algorithm based on Retinex algorithm is proposed, which replaces Gaussian filter with bilateral filter and adaptively adjusts the saturation of the darkroom image. The algorithm firstly converts the darkroom image from the three-primary color model (RGB) to the hue saturation value model (HSV). The Retinex algorithm is used to obtain the incident component and the reflection component of the darkroom *** reflection component is sharpened by Laplace algorithm to enhance the brightness of the image and retain the color sense of the original image, and the incident component is processed by bilateral filtering. In this way, the visual display effect of images can be improved. Finally, the adaptive adjustment strategy of correlation coefficient is adopted for the saturation component in HSV space, so that the saturation component can adjust itself and maintain the global color of the image. Compared with histogram equalization (HE) algorithm and single-scale Retinex(SSR) algorithm, this algorithm can effectively solve the problems of color loss and edge information loss in darkroom images. © 2024 SPIE.

关键词： Luminance

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Zérosdetect: Phishing URL Detection with Quantum-Driven Zero-shot Learning 9

Zérosdetect: Phishing URL Detection with Quantum-Driven Zer...

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9th International conference on Signal processing and communication, ICSC 2023

作者： Bangroo, Ishan Shivansh Kumar, Ravi Department of Computer and Information Science and Engineering University of Florida FL United States Department of Electronics and Communication Engineering Jaypee University of Engineering & Technology M.P. India

ISBN: (纸本)9798350383201

Phishing, a prevalent cyber danger in contemporary times, involves the fraudulent impersonation of legitimate websites with the intention of deceiving users into divulging confidential information. The insufficiency of classic phishing detection tools has become apparent as fraudsters continue to develop new strategies;these approaches mostly depend on variables such as URL character sequences, site content, and visual resemblance. The present study highlights the "Zérosdetect" approach, a quantum-powered technique for detecting phishing URLs using zero-shot learning, a robust model that makes use of both the zero-shot learning's adaptability and the computational advantageous nature of quantum computing, it mitigates the need for extensive previous knowledge of phishing URL attributes. In order to detect previously undiscovered phishing attacks, quantum neural network have been deployed to convert URL data into quantum spaces, therefore using the computational benefits of quantum systems. Quantum layers embedded inside Qnodes are a key part of the present system, calculating gradients at a faster pace, optimizing the network performance by rapidly calculating gradients, making the solution both efficient and forward-thinking. The present study lays the groundwork for future cybersecurity initiatives in the era of quantum computing, enhancing the potential to predict new cases of phishing. © 2023 IEEE.

关键词： Learning systems

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A Multimodal Target Speech Extraction Algorithm Based on Long Short Term Attention Mechanism

A Multimodal Target Speech Extraction Algorithm Based on Lon...

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2023 IEEE International conference on Signal processing, communications and Computing, ICSPCC 2023

作者： Li, Yue Li, Ruwei Li, Man Beijing University of Technology Facult of Information Technology Department of Information and Communication Engineering Beijing China

ISBN: (纸本)9798350316728

Most of the existing audio-video fusion mechanisms are fused by directly splicing or summing audio-visual cues, which cannot make full use of both kinds of information to work together. In order to solve this problem, a multimodal target speech extraction algorithm based on long short term attention mechanism is proposed in this paper. In this algorithm, firstly, the features of audio and lip are extracted by convolutional neural network and chunked with an overlap factor of 50% on the time axis. Secondly, the short term correlation and the long term correlation between the sequences are calculated by the long short term attention mechanism (LSTA). Finally, the obtained target speech mask sequence is multiplied with the target speech sequence and passed through the decoder to obtain the estimated target speaker speech. Experimental results show that compared with contrast algorithms, the proposed method performs better in the scale-invariant signal-to-noise ratio improvement (SI-SNRi) and perceptual evaluation of speech quality (PESQ), and achieves consistent improvement in cross-dataset evaluation. © 2023 IEEE.

关键词： Extraction

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Fast Collection of Target Tags information for Large-Scale RFID Systems 20

Fast Collection of Target Tags Information for Large-Scale R...

引用

20th International Computer conference on Wavelet Active Media Technology and information processing, ICCWAMTIP 2023

作者： Chu, Chu Fu, Ning Xu, Rui College of Computer Science Sichuan Normal University Chengdu610101 China The Visual Computing and Virtual Reality Key Laboratory of Sichuan Province Chengdu610101 China School of Network and Communication Engineering Chengdu Technological University Chengdu611730 China School of Information and Communication Engineering University of Electronic Science and Technology of China Chengdu611731 China

ISBN: (纸本)9798350318982

Radio Frequency Identification (RFID) technology is greatly supporting a variety of life and industry. Enhanced by sensors, RFID tags can monitor real-time information about attached objects and the environment, making efficient collection of tag information an important task. However, most works focus on collecting all tags but lack effective mechanisms to avoid interfering from irrelevant tags when the application only cares about a part of the tags in some cases. Besides, in more common scenarios with multiple RFID readers, existing works fail to effectively determine the surrounding tags for each reader, increasing the execution time. To conquer these defects, we propose a new solution called the efficient Vector-based target tag information Collection (VIC) protocol to accelerate the collection rate. First, we introduce a composite vector to silence irrelevant tags and determine the local tags. During this process, the expected empty slots are skipped and the useless information transmitted by tags is reduced. Second, we use an allocation vector to collect tag information by resolving tag collision, thus improving the time efficiency. Finally, we provide theoretical analyses to optimize the parameter settings. Extensive experiments prove that VIC outperforms existing solutions by up to 32% increase in time efficiency. © 2023 IEEE.

关键词： Efficiency

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Employing Tensor Decomposition and Contextual Inference for Reducing the Semantic Discrepancy in Image processing 2

Employing Tensor Decomposition and Contextual Inference for ...

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2nd IEEE International conference on communication, Security and Artificial Intelligence, ICCSAI 2023

作者： Suneetha, K. Gupta, Namit University Karnataka Bangalore India College of Computing Science and Information Technology Teerthanker Mahaveer University Uttar Pradesh Moradabad India

ISBN: (纸本)9798350369960

Despite the growing need for effective image tagging in both commercial and research domains, the inconsistency between image content and text annotations remains a significant issue. We propose an innovative approach that leverages deep learning and tensor decomposition to enhance the quality of image labelling and eliminate the reliance on labor-intensive manual tagging. Our methodology transforms images into tensors, facilitating the formation of a standardized feature space. We employ a sophisticated five-layer neural network architecture that integrates three-level tensor decomposition to match images with their context groups accurately. This structure is adept at capturing the nuanced patterns in data, which is crucial for reducing the semantic discrepancy between visual and textual information. We have rigorously evaluated our algorithm using the Corel-10K dataset, focusing on the specific case of penguin images, to demonstrate the efficacy of our model. The results showcase a marked improvement in the relevance of generated tags, thereby enhancing the retrieval accuracy and mitigating the issue of inappropriate tag assignment. This work not only contributes to the fields of Machine Learning and Data Mining by refining feature extraction and context estimation techniques but also has significant implications for the development of NLP algorithms that can better interpret and generate descriptive tags for visual content. Moreover, our approach sets the stage for future explorations into the integration of AI-driven image tagging systems with robotic systems for autonomous data handling and organization. © 2023 IEEE.

关键词： Contextual Estimation Image Understanding Techniques Semantic Gap Minimization Tensor-Based Image Analysis visual Data Interpretation

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8th International conference on information and communication Technology for Intelligent Systems, ICTIS 2024

8th International Conference on Information and Communicatio...

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8th International conference on information and communication Technology for Intelligent Systems, ICTIS 2024

ISBN: (纸本)9789819758098

The proceedings contain 65 papers. The special focus in this conference is on information and communication Technology for Intelligent Systems. The topics include: A Malmquist Index for Measuring Productivity and Efficiency of ICTs on Literacy Rate Versus Life Expectancy;importance of Cybersecurity Education to Reduce Risks in Academic Institutions;learn with Love Application to Support the Development of Reading Skills in People with Down Syndrome;boosting Accuracy and Reducing Time in Laser Shooting Practices: Video processing Solutions for Impact Evaluation;differential Performance of Satellite Navigation in Urban and Rural Andes of Ecuador;IP to Satellite Adaptation Level for a Bent Pipe Network Using DVB-S2/RCS2 Standards;performance Analysis for Image Rendering on Different Operating Systems;Classification of Motor Imagery Tasks Using EEG Signal Analysis and Linear Discriminant Analysis;unveiling Mortality Patterns in Ecuador: A K-Modes Clustering Approach to Categorical Data Analysis;FFAD: A Novel Metric for Assessing Generated Time Series Data Utilizing Fourier Transform and Auto-Encoder;deTroll—Leveraging Graph Neural Networks with Attention Mechanism to Detect State-Sponsored Trolls;cancer Ease: You Are Not Alone!;experimental Development of Scripts for Data Network Automation, Using the Python Programming Language and Open-Source Tools;a Novel Two-Stage Classification Architecture Integrating Machine Learning and Artificial Immune System for Intrusion Detection on Balanced Dataset;identification and Functional Annotation of Potential Biomarkers Associated with Thalassemia Using Machine Learning-Based Knowledge Discovery;Artificial Intelligence in Colorectal Cancer Diagnosis: "SYNERGY-NET" in Campania FESR-POR (European Fund of Regional Development—Regional Operative Program) Research Project;A New Optimization of Agricultural Greenhouse’s PAYLOAD Applying Light Weighted, Green Intelligent, and Secured Solutions Based on visual Watermarking of Objective an

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Aesthetic visual Question Answering of Photographs

Aesthetic Visual Question Answering of Photographs

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2023 IEEE International conference on Multimedia and Expo Workshops, ICMEW 2023

作者： Jin, Xin Li, Yuchen Zhou, Wu Zhou, Xinghui Yang, Hongtao Beijing Electronic Science and Technology Institute Beijing100070 China University of Science and Technology Hefei230026 China School of Information and Communication Engineering Communication University of China Beijing100024 China School of Electrical and Information Engineering Beijing Polytechnic College Beijing100042 China

ISBN: (纸本)9798350313154

Aesthetic assessment of images can be categorized into two main forms: numerical assessment and language assessment. In this paper, we propose a new task of aesthetic language assessment: aesthetic visual question and answering (AVQA) of images. We use images from ***. The objective QA pairs are generated by the proposed aesthetic attributes analysis algorithms. Moreover, we introduce subjective QA pairs that are converted from aesthetic numerical labels and sentiment analysis from large-scale pre-train models. We build the first aesthetic visual question answering dataset, AesVQA, that contains 72,168 high-quality images and 324,756 pairs of aesthetic questions. This is the first work that both addresses the task of aesthetic VQA and introduces subjectiveness into VQA tasks. The experimental results reveal that our methods outperform other VQA models on this new task. © 2023 IEEE.

关键词： Sentiment analysis

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8th International conference on information and communication Technology for Intelligent Systems, ICTIS 2024

8th International Conference on Information and Communicatio...

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8th International conference on information and communication Technology for Intelligent Systems, ICTIS 2024

ISBN: (纸本)9789819766741

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