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International Conference on computer and Communications Technologies (ICCCT)

作者： Sri Dhivya Krishna K Aditya R Nivetha G Ram Venkatesh K Dilli Kumaran V Department of Computer Science Engineering (Artificial) intelligence & Machine Learning) Sri Sairam Engineering College Chennai India

ISBN: (数字)9798331537579

ISBN: (纸本)9798331537586

As one of the leading causes of mortality globally today, heart disease demands the development of novel approaches to early detection and prevention. Leveraging it with artificial intelligence and machine learning, the medical industry is seeing a transformative shift, enabling it more accurate, and quickly. This paper focuses on developing predictive model for heart disease using various ML algorithms, including Gradient Boost(GB) Gaussian Naive Bayes (G-NB)and Random forest (RF) using the dataset which consists of various parameters including age, gender, cholesterol levels, and presence of angina. The chance of heart disease can be accurately predicted by the GB-based heart disease prediction (HDP) model with a 92% accuracy, while the RF-HDP and G-NB-HDP models have an 89% and 85% accuracy, respectively.

关键词： Heart Radio frequency Accuracy machine learning algorithms Computational modeling Prevention and mitigation Predictive models Predictive analytics Random forests Diseases

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IMPULSE NOISE REMOVAL BY L1 WEIGHTED NUCLEAR NORM MINIMIZATION

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Journal of Computational Mathematics 2023年第6期41卷 1171-1191页

作者： Jian Lu Yuting Ye Yiqiu Dong Xiaoxia Liu Yuru Zou Shenzhen Key Laboratory of Advanced Machine Learning and Applications College of Mathematics and StatisticsShenzhen UniversityShenzhen 518060China Guangdong Key Laboratory of Intelligent Information Processing Pazhou LabGuangzhou 510335China Department of Applied Mathematics and Computer Science Technical University of Denmark2800 Kgs.LyngbyDenmark

In recent years,the nuclear norm minimization(NNM)as a convex relaxation of the rank minimization has attracted great research *** assigning different weights to singular values,the weighted nuclear norm minimization(WNNM)has been utilized in many ***,most of the work on WNNM is combined with the l 2-data-fidelity term,which is under additive Gaussian noise *** this paper,we introduce the L1-WNNM model,which incorporates the l 1-data-fidelity term and the regularization from *** apply the alternating direction method of multipliers(ADMM)to solve the non-convex minimization problem in this *** exploit the low rank prior on the patch matrices extracted based on the image non-local self-similarity and apply the L1-WNNM model on patch matrices to restore the image corrupted by impulse *** results show that our method can effectively remove impulse noise.

关键词： Image denoising Weighted nuclear norm minimization l 1-data-fidelity term Low rank analysis Impulse noise

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Resume Parsing and Skill Extraction using Custom Pattern Matching algorithm and Gemini API

Resume Parsing and Skill Extraction using Custom Pattern Mat...

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International Conference on Computing Communication Control and Automation (ICCUBEA)

作者： Atharva Joshi Shailaja Pede Prathmesh Kolekar Aditya Metkar Department of Computer Science Engineering (Artificial Intelligence and Machine Learning) PCCOE SPPU India Pune India

ISBN: (数字)9798350391770

ISBN: (纸本)9798350391787

In the realm of contemporary recruitment, efficient resume parsing is essential for talent acquisition. This paper investigates resume parsing using advanced OCR technology and NLP methods. We begin with preprocessing, enhancing scanned resumes for legibility. Leveraging the Tesseract OCR engine, we convert images to machine-readable text. Through NLP techniques with spaCy, we extract key information like personal details, education, experience, and *** approach integrates the Gemini Pro Vision API for semantic analysis and question-answering. This enriches our framework, enabling deeper insights and implicit attribute inference. Empirical validation across diverse datasets demonstrates the robustness of our methodology. This work sheds light on performance metrics and challenges in automated resume parsing, paving the way for future enhancements.

关键词： Visualization Technological innovation machine learning algorithms Resumes Optical character recognition Semantics Robustness Data mining Pattern matching Recruitment

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Skin Diseases Detection Using LBP and WLD: An Ensembling Approach

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SN computer science 2024年第1期5卷 72页

作者： Banerjee, Arnab Sarkar, Somenath Nasipuri, Mita Das, Nibaran Department of Computer Science and Engineering Jadavpur University Jadavpur West Bengal Kolkata 700032 India Department of Computer Science and Engineering (Artificial Intelligence and Machine Learning) Dr. B. C. Roy Engineering College Jemua Road Fuljhore West Bengal Durgapur 713206 India Department of Dermatology Venereology and Leprosy Calcutta School of Tropical Medicine West Bengal Kolkata 700073 India

In all developing and developed countries globally, skin diseases are becoming a prevalent health problem for humans of all age groups. Skin diseases create anxiety, affect mental health, worsen working life, and sometimes cause social isolation. Detecting skin diseases in the early stage is very important because early treatment can be helpful in such cases. There are very few dermatologists in rural and semi-urban areas in a country like India. To overcome the problem, a machine learning technique has been developed to detect three prevalent skin diseases—Leprosy, Tinea versicolor, and Vitiligo from the images of the skin lesions. The proposed approach extracts the texture from the images of the skin regions affected by such diseases using Weber’s local descriptor and Local binary pattern. Then the representations are ensembled to prepare the feature vector. This ensemble technique achieved 91.38% accuracy using a multilevel support vector machine, using the features extracted from different regions of the skin lesions area utilising the centre of gravity based rotation invariant Weber local descriptor and Local binary pattern. Some popular deep learning networks such as MobileNet, ResNet152, GoogLeNet, DenseNet121, and ResNet101 were used to compare the performance of the proposed approach with the deep learning models. ResNet101 achieved 89% accuracy, i.e. best among all the deep learning models used in this study. The proposed ensemble technique outperformed the top performing deep learning model by 2.38%. This technique will be helpful for the early screening of these skin diseases. © 2023, The Author(s), under exclusive licence to Springer Nature Singapore Pte Ltd.

关键词： LBP Leprosy Multi-SVM Pattern recognition STM Tinea versicolor Vitiligo WLD WLDRI

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MetaGXplore: Integrating Multi-Omics Data with Graph Convolutional Networks for Pan-cancer Patient Metastasis Identification

MetaGXplore: Integrating Multi-Omics Data with Graph Convolu...

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2024 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2024

作者： Jiang, Tao Jiang, Haiyang Ma, Xinyi Xu, Minghao Liang, Yan Zhang, Wentao Peking University Center for Machine Learning Research Beijing China Science University of Queensland School of Electrical Engineering and Computer Brisbane Australia Guizhou University Institute of Entomology Guiyang China Zunyi Medical University School of Preclinical Medicine Zunyi China

ISBN: (纸本)9798350386226

Tumor metastasis, the spread of cancer cells to distant sites, significantly challenges clinical prognosis and treatment efficacy, ultimately impacting patient survival. Current predictive methods rely on extensive examinations and subjective clinical judgments, underscoring the urgent need for accurate and rapid assessments of metastasis likelihood to guide effective interventions. Moreover, identifying key genes closely associated with metastasis probability is essential for gaining insights that could facilitate the identification of metastasis-specific biomarkers. We developed MetaGXplore, an innovative Graph Convolutional Neural Network (GCN)-based framework, designed to predict metastasis probability by integrating pan-cancer multi-omic datasets with a protein-protein interaction network while identifying genes crucial to the metastatic process. By leveraging multi-omics datasets, our approach offers a comprehensive view of cancer biology, enhancing accuracy in metastasis forecasting through advanced deep learning algorithms. The efficacy of MetaGXplore was validated through extensive model evaluations, graph structural analysis, and multi-omics data assessment. We applied GNNExplainer to interpret our classification model's outcomes, providing insights into key feature contributions. Additionally, enrichment analysis of pivotal genes further elucidated the biological mechanisms underlying metastasis. © 2024 IEEE.

关键词： Lung cancer

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An Enhanced Authentication Protocol Suitable for Constrained RFID Systems

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IEEE Access 2024年 12卷 61610-61628页

作者： Hosseinzadeh, Mehdi Servati, Mohammad Reza Rahmani, Amir Masoud Safkhani, Masoumeh Lansky, Jan Janoscova, Renata Ahmed, Omed Hassan Tanveer, Jawad Lee, Sang-Woong Duy Tan University Institute of Research and Development Da Nang550000 Viet Nam Duy Tan University School of Medicine and Pharmacy Da Nang550000 Viet Nam Shahid Rajaee Teacher Training University Department of Computer Engineering Tehran16788-15811 Iran National Yunlin University of Science and Technology Future Technology Research Center Douliou Yunlin64002 Taiwan School of Computer Science Tehran19395-5746 Iran University of Finance and Administration Faculty of Economic Studies Department of Computer Science and Mathematics Prague101 00 Czech Republic University of Human Development Department of Information Technology Sulaymaniyah0778-6 Iraq Sejong University Department of Computer Science and Engineering Seoul05006 Korea Republic of Gachon University Pattern Recognition and Machine Learning Laboratory Department of Ai Software Seongnam-si13557 Korea Republic of

RFID technology offers an affordable and user-friendly solution for contactless identification of objects and individuals. However, the widespread adoption of RFID systems raises concerns regarding security and privacy. Vulnerabilities such as message tampering, interception, and eavesdropping pose significant risks to the integrity of the system. This study examines the effectiveness of two recently proposed ultra-lightweight RFID authentication protocols, URASP and KUAJB, in addressing these security challenges. Surprisingly, our findings reveal that both URASP and KUAJB protocols are susceptible to secret disclosure attacks, despite their claims of providing robust security. Consequently, the development of a truly ultra-lightweight protocol that ensures adequate security becomes a formidable task. As a potential solution, the enhancement of the URASP protocol through the integration of a simple cryptographic primitive is suggested to bolster its security measures. © 2013 IEEE.

关键词： Hash functions

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Revealing and Mitigating the Local Pattern Shortcuts of Mamba

arXiv

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arXiv 2024年

作者： You, Wangjie Tang, Zecheng Li, Juntao Yao, Lili Zhang, Min School of Computer Science and Technology Soochow University Taiwan Machine Learning Platform Department Tencent China

Large language models (LLMs) have advanced significantly due to the attention mechanism, but their quadratic complexity and linear memory demands limit their performance on long-context tasks. Recently, researchers introduced Mamba, an advanced model built upon State Space Models (SSMs) that offers linear complexity and constant memory. Although Mamba is reported to match or surpass the performance of attention-based models, our analysis reveals a performance gap: Mamba excels in tasks that involve localized key information but faces challenges with tasks that require handling distributed key information. Our controlled experiments suggest that this inconsistency arises from Mamba’s reliance on local pattern shortcuts, which enable the model to remember local key information within its limited memory but hinder its ability to retain more dispersed information. Therefore, we introduce a global selection module into the Mamba model to address this issue. Experiments on both existing and proposed synthetic tasks, as well as real-world tasks, demonstrate the effectiveness of our method. Notably, with the introduction of only 4M extra parameters, our approach enables the Mamba model (130M) to achieve a significant improvement on tasks with distributed information, increasing its performance from 0 to 80.54 points. © 2024, CC BY.

关键词： Complex networks

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Foundations of Multisensory Artificial Intelligence

arXiv

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arXiv 2024年

作者： Liang, Paul Pu Machine Learning Department School of Computer Science Carnegie Mellon University PittsburghPA United States

Building multisensory artificial intelligence systems that learn from multiple sensory inputs such as text, speech, video, real-world sensors, wearable devices, and medical data holds great promise for impact in many scientific areas with practical benefits, such as in supporting human health and well-being, enabling multimedia content processing, and enhancing real-world autonomous agents. However, the breadth of progress in multimodal research has made it difficult to identify the common themes and open questions in the field. By synthesizing a range of theoretical frameworks and application domains, this thesis aims to advance the foundations of multimodal machine learning. We start by defining three key principles of modality heterogeneity, connections, and interactions often present in multimodal problems [375]. Using these principles as a foundation, we propose a taxonomy of six core challenges in multimodal research: representation, alignment, reasoning, generation, transference, and quantification. Recent technical achievements will be presented through this taxonomy, allowing researchers to understand the similarities and differences across approaches, and identifying open problems for future research. The bulk of the thesis covers our recent progress towards tackling two key problems in multimodal learning: the machine learning foundations of multimodal interactions, as well as practical methods for building multisensory foundation models that generalize to many modalities and tasks in the real world. In the first part, we study the foundations of multimodal interactions: the basic principle of how modalities combine to give rise to new information for a task. We present a theoretical framework formalizing how modalities interact with each other to give rise to new information for a task, such as sarcasm identified from the incongruity between spoken words and vocal expressions [371]. Using this theoretical framework, we propose two practical estimators to

关键词： Information theory

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Enhanced BCI Performance using Diffusion Model for EEG Generation 46

Enhanced BCI Performance using Diffusion Model for EEG Gener...

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46th Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBC 2024

作者： Zhong, Yucun Yao, Lin Wang, Yueming Zhejiang University Moe Frontiers Science Center for Brain and Brain-Machine Integration China Zhejiang University College of Computer Science Hangzhou China Zhejiang University School of Medicine Affiliated Mental Health Center Hangzhou Seventh People's Hospital Department of Neurobiology China The College of Computer Science Hangzhou China

ISBN: (纸本)9798350371499

In the realm of Motor Imagery (MI)-based Brain-computer Interface (BCI), the widespread adoption of deep learning-based algorithms has resulted in an increased demand for a larger training sample size, thereby placing a heightened burden on users. This study advocates the utilization of one of the most advanced generative models, the denoising diffusion probabilistic model (DDPM), for the artificial synthesis of Electroencephalogram (EEG) raw signals. The quality of the generated EEG signals is evaluated through both qualitative and quantitative analyses. Through dimensionality reduction projection, we observed a notable similarity in the data distributions between the generated EEG signals and real EEG signals. Additionally, spectral analysis indicates a striking similarity in energy distribution between the two, accompanied by the presence of an event-related synchronization (ERS) phenomenon in the generated EEG signals. Quantitative analysis reveals that the accuracy of generated EEG signals for left and right-hand motor imagery tasks is 89.81 ± 2.11%, with discriminative information related to classes predominantly concentrated in the motor-sensory cortex area and alpha-beta frequency band. Furthermore, the integration of generated EEG samples contributes to a 3.17% improvement in the classification performance of BCI-deficiency subjects. These artificially generated EEG signals exhibit promising potential for application in calibrating MI-BCI deep learning models, thereby alleviating the burden on participants. © 2024 IEEE.

关键词： Deep learning

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Text to Image Generation Using Attentional Generative Adversarial Network 1

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1st International Conference on Advanced Computing, machine learning, Robotics and Internet Technologies, AMRIT 2023

作者： Rao, Supriya B. Shetty, Shailesh S. Singh, Chandra M, Srinivas P. Bekal, Anush Department of Artificial Intelligence and Machine Learning New Horizon College of Engineering Bangalore Karnataka Mangaluru India Department of Computer Science and Business System Srinivas Institute of Technology Karnataka Mangaluru India Department of Electronics & Communication Engineering Sahyadri College of Engineering & Management Karnataka Mangaluru India Department of Computer Science Engineering Sahyadri College of Engineering & Management Karnataka Mangaluru India

ISBN: (数字)9783031472213

ISBN: (纸本)9783031472206

AttnGan – Attentional Generative Adversarial Network (GAN) is a type of GANs that is used for text to image generation. Unlike other art models, it focuses on fine-grained word level information and once the model is trained by mapping the images and the corresponding text in the dataset, the model will generate image part-by-part using the image description given by the user as an input to the model. Current paper, focuses on the conversion of text into images based on the description provided by the user. This enables them to translate their ideas in the form of text into images of their required choices. The model is initially trained by the description of the text and images and when the user provides the text as an input to the system, it is used as a testing data. By analyzing the text word-by-word, the respective images pertaining to the text is generated part-by-part by considering the text description into account. This can heavily help the users in a applications such as in editing aids, documental archives, etc. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2024.

关键词： Generative adversarial networks

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