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An Innovative Machine Learning Pipeline for Stroke Prediction on Imbalanced Data 2

An Innovative Machine Learning Pipeline for Stroke Predictio...

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2nd International Conference on Information and Communication technology for Sustainable Development, ICICT4SD 2023

作者： Ullah, Md. Oli Raju, Shakil Ahmed Nazir, Md. Imran Akter, Afsana Rahman, Md. Saifur Bangladesh University of Business and Technology Dept. of Computer Science and Engineering Dhaka Bangladesh

ISBN: (纸本)9798350358667

Stroke requires immediate medical care. The WHO states stroke is the leading cause of death and disability world-wide. Thus, early stroke detection is essential. Few studies projected with CNNs or other stacking approaches. CNN-ensemble classifier. This study used unequal Kaggle data. Random over-sampling balances data. After identifying the most important features, several machine learning algorithms are applied, including Logistic Regression, Support Vector Machine, Decision Tree Classifier, Random Forest Classifier, K-Nearest Neighbor's Classifier, Gaussian Naive Bayes, Bernoulli Naive Bayes, and Stacking for Six Algorithms (Support Vector Machine, Decision Tree Classifier, Random Forest Classifier, KNN, BernoulliNB, GaussianNB). Training and testing evaluate each algorithm's stroke prediction accuracy. Stacking six algorithms gives the maximum accuracy, testing is \mathbf{99.9}\pm\% and training is 100%. Machine learning improves this classifier's stroke prediction. © 2023 IEEE.

关键词： Decision trees

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Stripe Removal from Hyperspectral Food Images acquired by Handheld Camera using 2,1 Norm Minimization and SSTV Regularization

Stripe Removal from Hyperspectral Food Images acquired by Ha...

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2023 IEEE International Conference on Industry 4.0, Artificial Intelligence, and Communications technology, IAICT 2023

作者： Shanthini, K.S. George, Sudhish N George, Sony Devassy, Binu Melit National Institute of Technology Dept. of Electronics and Communication Engineering Calicut India Norwegian University of Science and Technology Dept. of Computer Science Norway

ISBN: (纸本)9798350313635

Hyperspectral imaging offers the capacity to quickly and noninvasively monitor a food product's physical, chemical and morphological properties. Specim IQ is a handheld push broom camera with basic data handling and data analysis capabilities within the camera software. However, the recordings of the Specim IQ camera showed a line pattern (stripes) that was evident in all images. Stripes significantly reduce the visual quality of the images and lower the results of further processing. Hence an efficient destriping model is developed, which specifically addresses this issue. The proposed model uses a spatial gradient term to analyze the directional characteristics and group sparsity to describe the structural characteristics of the stripe component. In addition to this, a spatial spectral total variation regularization is used to ensure piecewise smoothness in the spatial and spectral domains and to remove Gaussian noise. The ensuing optimisation problem is solved using the alternating direction method of multipliers (ADMM). The proposed method is tested in real stripe noise environments, and the findings demonstrate that it outperforms some of the best approaches in terms of visual quality and quantitative evaluations. When compared with the other approaches, the proposed method attained the highest noise reduction (NR) and lowest mean relative deviation (MRD) values (NR=1.67, MRD=1.02%). © 2023 IEEE.

关键词： Noise abatement

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An Overview of Machine Learning Applications in Blockchain for Green Energy Exchanges 33

An Overview of Machine Learning Applications in Blockchain f...

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33rd Southern African Universities Power engineering Conference, SAUPEC 2025

作者： Nemakonde, Pfano Nemangwele, Fhulufhelo Ratshitanga, Mukovhe University of Venda Faculty Science Engineering and Agriculture Dept. of Physics Thohoyandou South Africa Cape Peninsula University of Technology Dept. of Electrical Electronic and Computer Engineering Bellville South Africa

ISBN: (纸本)9798331535162

There is an increasing demand for affordable, decentralised and distributed electricity, which is one of the key motivating factors that has incentivised this review article's writing. Industrial 4.0 has been a conduit through which the conventional grids shift towards a self-autonomous system, and blockchain platforms are emerging as viable solutions to facilitate that paradigm. With the advent of peer-to-peer (P2P) energy trading, community-based energy exchanges have significantly contributed to reduced grid dependency. Machine learning (ML) is being implemented in predictive analytics for energy forecasting (EF), thereby aiding both distribution network operators (DNOs) and aggregators to precisely and predictably monitor, optimise and stabilise grid operations through smart agents. The main aim of this review paper is to provide a detailed analysis of the existing research and implementation approaches on blockchain-based P2P energy trading and a comparative overview of how ML applications aid predictive analytics in community-based energy exchanges. © 2025 IEEE.

关键词： Predictive analytics

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DeepFuzzLoc Positioning: A Unified Fusion of Fuzzy Clustering and Deep Learning for Scalable Indoor Localization Using Wi-Fi RSSI 13

DeepFuzzLoc Positioning: A Unified Fusion of Fuzzy Clusterin...

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Joint 13th International Conference on Soft Computing and Intelligent Systems and 25th International Symposium on Advanced Intelligent Systems, SCIS and ISIS 2024

作者： Mahali, Muhammad Izzuddin Leu, Jenq-Shiou Achmad Sulistyo Putro, Nur Widyawan Prakosa, Setya Avian, Cries National Taiwan University of Science and Technology Dept. of Electronic and Computer Engineering Taipei Taiwan Universitas Negeri Yogyakarta Dept. of Electronics and Informatics Engineering Education Yogyakarta Indonesia Universitas Gadjah Mada Dept. of Computer Science and Electronics Yogyakarta Indonesia

ISBN: (纸本)9798350373332

This research introduces a novel method for indoor positioning by integrating Fuzzy C-Means clustering with deep learning autoencoders, leveraging Wi-Fi RSSI fingerprinting for precise location prediction and building and floor level classification within a unified framework. The approach combines the comprehensibility of Fuzzy C-Means (FCM) with the robust feature extraction capabilities of deep autoencoders through a dual-phase strategy: extensive offline training utilizing RSSI fingerprint data followed by real-time online position prediction. The model's performance was rigorously tested using 5-Fold cross-validation, examining its effectiveness both with and without the integration of FCM on various cluster numbers. Evaluation metrics, including Euclidean distance error for regression and accuracy, F1-score, precision, and specificity for classification effectiveness, were used for comparative analysis. The results were visualized through regression plots, confusion matrices, and learning curves, supplemented by t-SNE visualizations that provide profound insights into the feature space. These detailed analyses highlight the considerable promise of this integrated approach in enhancing the accuracy and reliability of indoor positioning systems. © 2024 IEEE.

关键词： Fuzzy clustering

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Enhancing Cabbage Plant Protection: CNN Algorithm for Insect Identification

Enhancing Cabbage Plant Protection: CNN Algorithm for Insect...

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2024 International Conference on Information technology Research and Innovation, ICITRI 2024

作者： Senjaya, Remi Musa, Purnawarman Sularsih, Puji Novrina, Novrina Aryunani, Witari Suhartini, Suhartini Gunadarma University dept. of Informatics Engineering Depok Indonesia Gunadarma University dept. of Electrical Engineering Depok Indonesia Gunadarma University dept. of Information Technology Depok Indonesia Gunadarma University dept. of Computer Science Depok Indonesia

ISBN: (纸本)9798350376210

People frequently find it straightforward to identify insects that feed on cabbage plants based on the insects' characteristics. However, challenges arise when employing a computer for precise bug categorization and identification. The implementation of Deep Learning (DL) utilizes the Convolutional Neural Network (CNN) technique, renowned for its effectiveness in categorizing and recognizing objects in digital photo data. The CNN algorithm delivers notable results in photo identification, simulating the human visual brain's image recognition through artificial neural networks in the Python programming language. To enrich the user experience, frameworks like Keras and TensorFlow are employed in neural network development. Our research will use the CNN algorithm to determine insect species in cabbage images. Achieving high accuracy involves coupling the CNN algorithm with the VGGNet architectural model. Notably, utilizing an epoch value of 150 resulted in a 97.75% accuracy rate during the training model technique;however, the validation accuracy was slightly lower at 94.25%. The corresponding loss and validation rates were 0.0613% and 0.2945%, respectively. Moreover, based on the model (150 epochs), the testing outcomes in the study, involving 48 images beyond the dataset and incorporating additional information, resulted in an accuracy rate of 91.66%. © 2024 IEEE.

关键词： Convolutional neural networks

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A Self-Supervised Semantic Segmentation Method for Identifying Barrett' s Esophagus in Endoscopic Images 24

A Self-Supervised Semantic Segmentation Method for Identifyi...

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6th Asia Pacific Information technology Conference, APIT 2024

作者： Lin, Guo-Shiang Chang, Ku-Yaw Hsu, Wen-Teng Yang, Sheng-Chih Yan, Shen-Lei Dept. of Artificial Intelligence and Computer Engineering National Chin-Yi University of Technology Taiwan Dept. of Computer Science and Information Engineering National Chin-Yi University of Technology Taiwan Dept. of Internal Medicine Chang Bing Show Chwan Memorial Hospital Taiwan

ISBN: (纸本)9798400716218

Barrett's esophagus is considered a precancerous condition that may lead to esophageal cancer. The condition is usually diagnosed through an endoscopy with biopsy. This careful imaging examination is quite labor-intensive and usually lacks of diagnostic consistency. In this paper, a computer-aided diagnosis (CAD) method was proposed to assist pathologist in Barrett's esophagus diagnosis from endoscopic images. The proposed semantic segmentation was built based on the U-Net architecture, which features capturing detailed spatial information and context to produce accurate pixel-wise predictions. An autoencoder, which is quite capable of learning a compact and meaningful representation of the input data, is incorporated to achieve self-supervised learning. A two-stage pretraining for the encoder and the decoder is adopted for better performance and reduced data requirements. The proposed method can extract features and patterns from unlabeled data without requiring human annotation or labels, which is very suitable in many biomedical image analyses. The experimental results show that the segmentation accuracy with mean pixel accuracy, mean dice coefficient, and mIoU reaches 98.20%, 87.96%, and 83.18% respectively, indicating that the proposed method has a good performance on the identification of Barrett's esophagus in endoscopic images. © 2024 Owner/Author.

关键词： Semantic Segmentation

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Advancing Image Security Through Deep Learning and Cryptography in Healthcare and Industry 4

Advancing Image Security Through Deep Learning and Cryptogra...

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4th International Conference on Emerging Trends in Networks and computer Communications, ETNCC 2024

作者： Kumar, Prajwal Laroia, Ankur Kumar, Mehul Laroia, Avi Upreti, Kamal Parashar, Jyoti Maharshi Dayanand University Computer Science Engineering Haryana India University of Texas at Dallas Ms in Business Analytics TX United States Amity University Computer Science Engineering Uttar Pradesh India Birla Institute of Technology Civil Engineering Pilani India Christ University Dept. of Computer Science Delhi NCR Campus India Dept. of Computer Science and Engineering Delhi India

ISBN: (纸本)9798350353266

Securing electronic health records (EHRs) in the Internet of Medical Things (IoMT) ecosystem is a key concern in healthcare due to the sector's differed environment. As the evolution of technology continues, ensuring the confidentiality, integrity, and accessibility of EHRs becomes more and more challenging. To enhance the confidentiality of healthcare picture data, this study explores the combined use of deep learning and cryptography methods. Through the utilization of weight analysis for improving encryption strength and the combination of chaotic systems to generate undetectable encryption patterns, it explores how deep neural networks can be modified for use in encryption. It also provides a survey of the present scenario of deep learning-based image detection of anomalies methods in working environments, such as network typologies, supervision levels, and assessment norms. Techniques in cryptography provide an effective means to protect confidential medical picture data while it's being transmitted and stored. Deep learning, on the other hand, has the ability to entirely change cryptography by providing robust encryption, resolution augmentation, and detection capabilities for medical image security. The paper outlines future research approaches to overcome these problems and tackles the opportunities and obstacles in medical image cryptography and industrial picture anomaly detection. Through this work, picture privacy in the healthcare and industrial sectors is advanced, opening the door to enhanced privacy, integrity, and availability of vital image data by overcoming the gap between deep learning and cryptography. © 2024 IEEE.

关键词： Electronic health record

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Proposed Model for Prediction of Stock Market Price of Netflix

Proposed Model for Prediction of Stock Market Price of Netfl...

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2023 International Conference on Artificial Intelligence and Applications, ICAIA 2023 and Alliance technology Conference, ATCON-1 2023

作者： Patwal, Pratap Singh Srivastava, Anoop Kumar Laxmi Devi Institute of Engineering & Technology Dept. of Computer Science & Engineering Rajasthan Alwar India Aced Alliance University Dept. of Computer Science & Engineering Karnataka Bengaluru India

ISBN: (纸本)9781665456272

Accurate prediction of stock market price is highly challenging. This paper presents a proposed model for prediction of stock market price of Netflix. We have considered a five-year data set (April, 2017 - April, 2022) of Netflix. An Exploratory Data Analysis (EDA) of Netflix's stock price data for predicting its stock market prices using time series is done. The implementation of the model is done using Python language. We imported five-years data and applied several techniques: importing libraries, calculating stock return, line plot, plot all, plot return year wise, plot histogram, plot kernel density, plot box plot, differencing method, resample daily to monthly data etc. EDA proved that using time series technique achieved better results in prediction of stock price and visualizing. © 2023 IEEE.

关键词： Forecasting

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Optimizing Road Extraction in Remote Sensing Imagery: A Lightweight Dilated Inception Deep Residual U-Net with Channel Attention (LDIDRU-CAN) Model and Pruning Strategies 15

Optimizing Road Extraction in Remote Sensing Imagery: A Ligh...

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15th International Conference on Computing Communication and Networking Technologies, ICCCNT 2024

作者： Sharma, Palvi Kumar, Rakesh Gupta, Meenu Obaid, Ahmed J. Chandigarh University Dept. of Computer Science & Engineering Punjab India University of Kufa Faculty of Computer Science & Mathematics Najaf Iraq National University of Science and Technology College of Technical Engineering Dhi Qar Iraq

ISBN: (纸本)9798350370249

The major challenge in Road Extraction is to extract accurate and complete road network and to get high accuracy while minimizing computational power and ensuring faster prediction. It plays important role in urban planning, disaster management, automatic vehicle navigation and GPS navigation. Several earlier studies used complex deep networks that required large amounts of computational power and large storage. In order to address issues such as high storage requirements, computational load, access to memory delays, and low hardware deployment efficiency this research work presents a novel network model called LDIDRU-CAN. The proposed model is fast, compact, and efficient and requires less storage. The objective of this work, is to compress the original model using structured pruning strategy. This strategy simplified the original model and reduced their computation cost as well as storage requirements. For the reduction of the parameters, we focused on the Row Scale Regular Pruning (RSRP) and Low Learning Rate (LLR) retraining. After applying RSRP pruning approach it reduces storage requirements, parameters, and also contributes to a more than 50% reduction in hardware resource consumption and successfully enhances the parallelism of the model. Testing and validations are performed on the Massachusetts Road Dataset. With only 45 M parameters and 180 MB model size, the suggested network achieves remarkable performance with 87.91% precision, 85.51% recall, 86.69% F1-score and 98.91% accuracy. © 2024 IEEE.

关键词： Channel Attention LLR Pruning Road Extraction RSI RSRP

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Breathing+: A Game-based System for Enhancing Respiratory Training and Motivation 6

Breathing+: A Game-based System for Enhancing Respiratory Tr...

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6th IEEE International Conference on Knowledge Innovation and Invention, ICKII 2023

作者： Lin, Chang Yu Chu, Edward T.-H. National Yunlin University of Science and Technology Dept. of Computer Science and Information Engineering Yunlin Taiwan

ISBN: (纸本)9798350323535

Breathing difficulties, such as shortness of breath or rapid breathing, can significantly harm health and diminish the quality of life. Breathing exercises are widely recognized for their effectiveness in resolving such difficulties. They increase lung function, ease discomfort, and alleviate stress. However, current breathing-assisted methods, such as the incentive spirometer, do not accurately track the duration and frequency of training despite their advantages. Furthermore, the process of performing breathing exercises is tedious over time, making users feel bored and reducing their motivation. Thus, we presented Breathing+, a breathing video game designed to address these limitations by providing a series of game challenges. Breathing+ incorporated sensors to detect breathing signals and provided users with real-time feedback on a screen. According to the experimental results, Breathing+ achieved a breathing recognition accuracy of 95%. In addition, the response time to the user's breathing was around 0.23 s. © 2023 IEEE.

关键词： Motivation

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