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analysis of Sleep Habits: Prediction of Quality of Sleep and Reasoning Ability (Cognitive Skills) 24

Analysis of Sleep Habits: Prediction of Quality of Sleep and...

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13th international conference on Bioinformatics and Biomedical Science, ICBBS 2024

作者： Bhat, Abhishek Hemachandra, Chinthimi Vidhathri, V. Anand, M.S. Pes University Karnataka Bengaluru India Computer Science Pes University Karnataka Bengaluru India

ISBN: (纸本)9798400717956

Sleep is a vital process for overall health and well-being. this is a critical physiological state categorised by altered consciousness, reduced sensory activity and minimal voluntary muscle movement. During this time our body engages in myriad of restorative processes such as repairing damaged tissues, synthesizing proteins, release of growth hormones, muscle recovery and overall cellular regeneration. Without proper sleep our physical and mental health will deteriorate rapidly which leads to a weakened immune system and illness. Beyond restoration, sleep plays an important role in cognitive functions. Chronic lack of sleep leads to a large number of health problems including obesity, diabetes, cardiovascular disease etc. Sleep deprivation impairs cognitive function leading to decreased productivity and poor decision-making. Maintaining healthy sleep patterns have a huge impact on emotional resilience and overall mental well-being. It also helps in enhancing our ability to concentrate, boosts creativity and social interactions. the research paper begins with an extensive literature review on sleep instability and impact of sleep on cognitive skills. We highlight the challenges, consequences of sleep instability by analysis of individual daily habits. the following sections and methodologies provide a detailed information on the process adopted for dataset collection and analysis. In this study we consider several daily habits such as smoking, consumption of alcohol, caffeine to develop few predictive models. Later, the study presents the results of the detailed analysis of sleep habits and reasoning scores. the results of this study will provide insights to the quality of sleep and its effect on reasoning score. © 2024 Copyright held by the owner/author(s). Publication rights licensed to ACM.

关键词： Sleep research

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Testing the Security of Deep Learning Systems Based on Chest X-ray images 24

Testing the Security of Deep Learning Systems Based on Chest...

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13th international conference on Software and computer Applications, ICSCA 2024

作者： Li, Yang Liu, Shaoying Guo, Lisen Graduate School Of Advanced Science And Engineering Hiroshima University Japan School Of Computer Science South China Business College Of Guangdong University Of Foreign Studies China

ISBN: (纸本)9798400708329

In recent years, deep learning has experienced rapid advancement and significant success, particularly in the field of medical image analysis. However, emerging studies have highlighted a critical vulnerability: deep learning systems are susceptible to adversarial attacks, which employ slight perturbations to deceive these systems. this vulnerability is notably pertinent to medical deep learning systems, thereby raising substantial concerns regarding their security. In our research, we focus on medical deep learning systems developed for chest X-ray image analysis. We subject these systems to adversarial attack and further examine the transferability of this attack across various medical deep learning models. Given the direct correlation between the accuracy of medical image diagnoses and patient health outcomes, enhancing the safety and reliability of medical deep learning systems before their practical implementation is of paramount importance and profound significance. © 2024 ACM.

关键词： Learning systems

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Image Based, Bearing Fault Diagnosis, Using Convolutional Neural Networks 13

Image Based, Bearing Fault Diagnosis, Using Convolutional Ne...

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13th international conference on Power Electronics, Machines and Drives, PEMD 2024

作者： Hambly, Paul Griffo, Antonio Department of Electrical Engineering University of Sheffield United Kingdom

this research investigated the possibilities of implementing a novel method for diagnosing bearing faults in electrical machines. the research utilised the electrical current output of an electrical machine as a data source enabling deep learning algorithms to predict and diagnosis a variety of distinct bearing faults. When bearing faults occur in a machine, corresponding rotational anomalies occur in the rotor, inducing variation in the characteristics of electrical current output. Each bearing fault type will in theory manifest a unique change in electrical output characteristics;however, the changes can be very subtle and difficult to detect using traditional time and frequency domain signal processing techniques. this research approach converted time-based signals into corresponding 2d texture and polar images, texture images are further processed using local binary patterns serving as feature extraction and dimensionality reduction tool. the processed images are subsequently used to train a ‘Convolutional Neural Network’(CNN) enabling bearing accurate fault diagnosis to be made. CNN’s are renowned for their ability to extract spatial features and detect hidden patterns at a level that is superior to other fault detection methods more commonly in use. Initial accuracy results have proved promising, ranging between 60 and 90 percent on a variety of bearing types. © the Institution of Engineering & Technology 2024.

关键词： Frequency domain analysis

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Preprocessing Techniques for Rectal Cancer Diagnosis using MR images 24

Preprocessing Techniques for Rectal Cancer Diagnosis using M...

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13th international conference on Software and computer Applications, ICSCA 2024

作者： D'Almeida, Sonal Patreena Kamath, Surekha Rajagopal, K.V. Manjunath, K.N. Department Of Instrumentation And Control Engineering Manipal Institute Of Technology Manipal Academy Of Higher Education Manipal Karnataka576104 India Department Of Radiodiagnosis And Imaging Kasturba Medical College Manipal Academy Of Higher Education Manipal Karnataka576104 India Department Of Computer Science And Engineering Manipal Institute Of Technology Manipal Academy Of Higher Education Manipal Karnataka576104 India

ISBN: (纸本)9798400708329

the use of magnetic resonance (MR) Image has become more significant when treating rectal cancer. Rectal cancer can be staged more accurately with MRI, which serves as a great tool for choosing the most suitable course of action. Image pre-processing is used to improve the quality of image and making it better for analysis and further processing. the study carried out in this paper is concentrated on pre-processing methods like Gaussian and Sobel filtering. the filtering techniques were implemented on Pelvic MR images obtained from Kasturba Medical College (KMC), Manipal. the findings suggest that Sobel filtering had a superior output image quality, indicating its potential as a preferred preprocessing technique for enhancing Pelvic MR images in rectal cancer diagnosis and treatment planning. the output image efficiency is calculated by Peak to Signal Noise Ratio (PSNR) and Mean Square Error (MSE). © 2024 Owner/Author.

关键词： Mean square error

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Classification of benign and malignant tumors in Digital Breast Tomosynthesis images using Radiomic-based methods 13

Classification of benign and malignant tumors in Digital Bre...

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13th international conference on computer and Knowledge Engineering, ICCKE 2023

作者： Moghadam, Farangis Sajadi Rashidi, Saeid Islamic Azad University Faculty of Medical Sciences and Technologies Science and Research Branch Tehran Iran

ISBN: (纸本)9798350330151

Breast cancer arises from the uncontrolled proliferation of abnormal cells, leading to the formation of a mass in the breast tissue. Digital Breast Tomosynthesis (DBT), a three-dimensional imaging technology, has enhanced both screening and diagnostic outcomes. It provides supplementary information that mitigates the confounding effects of tissue overlap and enhances the detection, identification, and localization of abnormalities. the objective of this research is to classify the benign or malignant nature of masses in DBT images using Radiomic features. this analysis utilizes an open database from TCIA consisting of 224 lesion bounding boxes. To effectively extract relevant features, a two-dimensional central slice of the DBT image encompassing a significant anatomical portion of the breast tumor is utilized. During the pre-processing stage, the rescale intensity method is employed to enhance contrast and improve image quality. Subsequently, a binary mask is utilized to segment the breast tissue mass. Four categories of Radiomic features are then extracted. the study investigates the suitability of these features for benign-malignancy classification. Furthermore, the impact of feature selection, feature balancing, and feature normalization is explored in conjunction with eight different learning algorithms. With this setting, the best result of the evaluation metrics in terms of mean AUC, accuracy, sensitivity and specificity are equal to 88.56%, 88.67%, 77.12 % and 75.11% for Quadratic Discriminant analysis (QDA), respectively. © 2023 IEEE.

关键词： Feature extraction

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Capturing Local and Global Features in Medical images by Using Ensemble CNN-Transformer 13

Capturing Local and Global Features in Medical Images by Usi...

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13th international conference on computer and Knowledge Engineering, ICCKE 2023

作者： Kaleybar, Javad Mirzapour Saadat, Hooman Khaloo, Hooman University College of Nabi Akram Department of Computer Engineering Tabriz Iran Qazvin Branch Department of Electrical Engineering Iran Sharif University School of Technology Tehran Iran

ISBN: (纸本)9798350330151

this paper introduces a groundbreaking classification model called the Controllable Ensemble Transformer and CNN (CETC) for the analysis of medical images. the CETC model combines the powerful capabilities of convolutional neural networks (CNNs) and transformers to effectively capture both high and low frequency features present in medical images. the model architecture comprises three main components: a transformer classification block (TCB), a transposed-convolutional decoder block (TDB), and a convolutional encoder block (CEB). the CEB is responsible for capturing multi-local features at different scales and draws upon components from VGGNet, ResNet, and MobileNet as backbones. By leveraging this combination, the CEB is able to effectively detect and encode local features. the TDB, on the other hand, consists of sub-decoders that decode and sum the captured features using ensemble coefficients. this enables the model to efficiently integrate the information from multiple scales. Finally, the TCB utilizes the SwT backbone and a specially designed prediction head to capture global features, ensuring a comprehensive understanding of the entire image. the paper provides detailed information on the experimental setup and implementation, including the use of transfer learning, data preprocessing techniques, and training settings. the CETC model is trained and evaluated using two publicly available COVID-19 datasets. Moreover, the model is able to outperform existing state-of-the-art models across a variety of evaluation metrics, which is encouraging. Experimental results clearly demonstrate that the CETC model performs better than other deep learning models, emphasizing its potential for accurate and efficient image analysis in the field of medicine. © 2023 IEEE.

关键词： Convolutional neural networks

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Disease Classification in Wheat from images Using CNN 13

Disease Classification in Wheat from Images Using CNN

引用

13th international conference on Cloud Computing, Data Science and Engineering, Confluence 2023

作者： Suri, Dhruv Saksenaa, Shivie Sehgal, Umang Garg, Rakesh Amity University Amity School of Engineering and Technology Uttar Pradesh India

ISBN: (纸本)9781665462631

Wheat is a staple crop for large sections of the global population. However, like all crops, a major impediment to its yield and expansion is the prevalence of diseases in wheat that cause significant losses to the crop each year. One of the methods that such losses can be prevented is by identifying and responding to the same appropriately and swiftly. Given that a majority of farmers in developing countries cannot afford specialist help for the same, and the fact that manual identification of disease in wheat is a challenging task to begin with, this problem may be aided by using computer vision methods to identify the diseases, after which the farmer needs to only refer to expert sources for solutions. this can greatly reduce the challenge and costs, as well as increase the availability of specialists to deal with the same. It can also be used as an early detection mechanism for the advent of plant epidemics. this review paper aims to perform a comparative analysis of some of the popular deep learning architectures-namely VGG16, MobileNet, ResNet50-for the problem of disease identification and classification in wheat. © 2023 IEEE.

关键词： Image classification

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Data Mining Techniques In Bioinformatics Enhanced by HPC and AI Convergence 13

Data Mining Techniques In Bioinformatics Enhanced by HPC and...

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13th IEEE international conference on Communication Systems and Network Technologies, CSNT 2024

作者： Kannagi, A. Patil, Shashikant Jain, Sandeep Kumar Department of Computer Science and Information Technology Bangalore India Atlas SkillTech University Department of UGDX Maharashtra Mumbai India Vivekananda Global University Department of Electrical Engineering Jaipur India

ISBN: (纸本)9798350305463

High-Performance Computing (HPC) and Artificial Intelligence (AI) have come together to usher in a new age of data mining in the field of bioinformatics. Using high-performance computing (HPC) and artificial intelligence (AI), this research presents a suggested strategy for improving data mining techniques and then evaluates it alongside six more conventional approaches. Discovering latent patterns in high-dimensional biological data is the goal of the proposed technique, which combines Principal Component analysis (PCA), Convolutional Neural Networks (CNN), and Random Forest (RF). the suggested technique consistently outperforms the state-of-the-art methods in our comparisons, including in terms of accuracy, precision, recall, and F1 score. these performance criteria are essential for effective data mining in bioinformatics, and the system's shown equilibrium between them is rather impressive. the suggested strategy also drastically decreases execution time, which is a crucial aspect in effectively managing large-scale biological information. Our findings highlight the need for AI-driven data mining methods in the field of bioinformatics. the application of PCA offers dimensionality reduction, assisting in feature selection and boosting model performance. When it comes to classifying and ranking the value of features, RF provides ensemble learning capabilities, whereas CNNs excel at extracting complicated patterns from pictures and sequences. the suggested technique is holistic in nature, drawing on the advantages of several algorithms to improve data mining and provide a complete service to the field of biological research. Our results show that the suggested approach has considerable potential to further bioinformatics studies and applications. It helps scientists speed up drug development, improve the accuracy of diagnostics, and unearth new biological insights. In addition, the faster execution time suggests better use of computer resources, which is useful for

关键词： Genome

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Deep Learning Based K-Line Chart Recognition for Financial Quantitative Investment analysis 18th

Deep Learning Based K-Line Chart Recognition for Financial Q...

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18th international conference on Neural Networks (ISNN)

作者： Luo, Yamei Zhang, Zhijun Jiang, Rongzhun Liu, Yu South China Univ Technol 381 Wushan Rd Guangzhou 510000 Peoples R China

ISBN: (纸本)9789819743988;9789819743995

In financial quantitative investment analysis field, most of existing artificial intelligence analysis methods are based on sequential data, and very few research apply expert intuitive image experience based intelligence method to analyze many K-line patterns. In this paper, a Faster R-CNN based image recognition method is proposed to analyze and predict the financial image (e.g., w-bottom patterns, pivots in entanglement theory, trend and consolidation patterns, etc.). the proposed method can intuitively recognize the K-line pattern images effectively and accurately. To do so, a Faster R-CNN is firstly constructed. the financial data images are then fed into the constructed neural network for training. What is more, the trained network model is used to predict some new financial images. the proposed method can not only recognize a financial K-line pattern feature, but also recognize multiple-features image patterns. the experimental results show that the recognition algorithm based on Faster -R-CNN greatly improves the accuracy of K-line pattern recognition, and the method is effective.

关键词： Neural Network Data analysis K-line Pattern Image Faster R-CNN Image Recognition

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A Novel Ensemble Strategy With Enhanced Cross Attention Encoder-Decoder Framework for Tumor Segmentation in Whole Slide images 13

A Novel Ensemble Strategy With Enhanced Cross Attention Enco...

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13th international conference on Information and Communication Systems (ICICS)

作者： Abdel-Nabi, Heba Awajan, Arafat Ali, Mostafa Princess Sumaya Univ Technol Dept Comp Sci Amman Jordan Jordan Univ Sci & Technol Fac Comp & Informat Technol Irbid Jordan

ISBN: (纸本)9781665480970

Due to the rapid increase in the quality of the generated medical images combined with the development of computational algorithms, computer-Aided Diagnostic (CAD) systems, which are empowered with artificial intelligence to analyze these images, provide an objective decision to assist and guide the medical staff. In cancer diagnosis and staging, the golden standard is the microscopic examination of suspicious tissue either in the laboratory using the conventional glass slides or using their digital scanned counterparts called Whole Slide images (WSI). However, the vast search space in these slides and the existing heterogeneous patterns in the sampled tissue make their analysis process labor-intensive and time-consuming. therefore, this paper proposed a novel encoder-decoder framework with a new ensemble strategy to address the difficulties in semantically segmenting tumors in WSI. We introduced new enhancements to the base U-Net, including cross attention to aggregate information from different portions of the network. Moreover, we manipulated each stained slide to produce three new slides with different characteristics and fed them to separate models and injected their predictions together at the inference stage through our proposed ensemble strategy to form the final segmentation prediction. We tested and validated the proposed cross attention U-Net model with the ensemble strategy on the task of viable tumor segmentation of the challenging hepatocellular carcinoma in liver WSIs in the PAIP2019 dataset. the experimental results have demonstrated that the proposed ensemble strategy outperformed the single conventional handling of the WSI and improved the segmentation accuracy.

关键词： Whole-Slide images Medical Image Segmentation PAIP2019 Ensemble Strategy computer-Aided Diagnosis

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