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2023 7th International Conference On Computing, Communication, Control And Automation, ICCUBEA 2023

作者： Selva Jeba, G. Chitra, P. Thiagarajar College of Engineering Dept. of Computer Science and Engineering Madurai India Thiagarajar College of Engineering Dept. of Computer Applications Madurai India

ISBN: (纸本)9798350304268

Floods occur when water overflows onto normally dry land and are a destructive natural disaster. In recent times, deep learning models have demonstrated their remarkable capabilities in identifying objects and classifying images. As a result, the utilization of deep learning in remote sensing, especially for the crucial task of flood detection, has become an increasingly prominent and promising area of research and application. This study proposes a lightweight yet powerful feature extraction framework by leveraging a self-attention(SA) based EfficientNetV2. The EfficientNetV2 architecture serves as the backbone of the model and has exhibited effectiveness in image classification tasks. By incorporating self-attention mechanisms, the model can capture spatial relationships within remote sensing images, enhancing its accuracy and efficiency in flood detection. The model was trained and assessed using the SEN12-FLOOD dataset, and the outcomes demonstrate that the suggested framework exhibits enhanced flood detection performance, achieving an accuracy of 95.9%. © 2023 IEEE.

关键词： attention mechanism deep learning EfficientNetV2 flood detection remote sensing

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Advancements in Left Ventricular Myocardium Analysis: A Comprehensive Review of Myocardium Segmentation, Fibrosis Detection and Wall Motion Estimation Using Cardiac Magnetic Resonance Imaging

Advancements in Left Ventricular Myocardium Analysis: A Comp...

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2024 IEEE International Conference on Emerging Technologies in computer science for Interdisciplinary Applications, ICETCS 2024

作者： Sunanda, H.G. Kavitha, K.S. Bangalore Institute of Technology Dept. of Computer Science and Engineering Bengaluru560004 India Visvesvaraya Technological University Belagavi590018 India Dayananda Sagar College of Engineering Dept. of Computer Science and Engineering Bengaluru560078 India

ISBN: (纸本)9798350372502

Cardiac Magnetic Resonance Imaging (CMRI) has established itself as a cornerstone in cardiovascular diagnostics, enabling precise assessment of left ventricular (LV) myocardium. This review meticulously examines the multifaceted aspects of LV myocardium analysis, focusing on three pivotal components: myocardium segmentation, fibrosis detection, and wall motion estimation. By exploring cutting-edge techniques and methodologies in these domains, this article sheds light on the profound impact CMR has had on the accurate diagnosis, prognosis and structuring treatment plans for diseases of the heart. In the realm of cardiac diagnostics, Left Ventricular (LV) myocardium analysis utilizing CMRI has undergone a transformation advancement. This review encapsulates the comprehensive exploration of LV myocardium analysis, Myocardium segmentation techniques, ranging from traditional methods to contemporary machine learning-driven approaches, are scrutinized for their accuracy and efficiency. The detection and characterization of myocardial fibrosis, crucial in diagnosing various cardiac conditions, are dissected through cutting-edge CMR techniques such as Late Gadolinium Enhancement (LGE) imaging and quantitative mapping methods. Additionally, advanced methods of wall motion estimation, including feature tracking and strain imaging, highlighting their applications in assessing myocardial function. Real-world clinical scenarios underscore the transformation impact of these techniques on diagnosing heart diseases, guiding treatment decisions, and improving patient outcomes. The current landscape of LV myocardium analysis, emphasizing the pivotal role of CMR in revolutionizing cardiac healthcare. © 2024 IEEE.

关键词： Motion estimation

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Reviewlogy: An Interface for Analyzing Sentiments 3

Reviewlogy: An Interface for Analyzing Sentiments

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3rd IEEE World Conference on Applied Intelligence and Computing, AIC 2024

作者： Shyam, Radhey Khanna, Shilpi Srivastava, Kavya Srivastava, Ajay Kumar Chaudhary, Ishika Bbd University Dept. of Computer Science & Engineering Lucknow India Srmcem Dept. of Information Technology Lucknow India

ISBN: (纸本)9798350384598

This paper investigates the impact of early reviews on product sales by analyzing the traits of initial reviewers on major e-commerce platforms. It segments product life-cycles into three phases: early, majority, and laggards, focusing on early reviewers who post reviews during the initial stages. The paper introduces an innovative interface for sentiment analysis to identify and predict early reviewers. Quantitative analysis reveals that early reviewers often provide more insightful comments and give higher overall ratings. Their ratings and helpfulness scores significantly affect product popularity. The methodology, tested on extensive datasets from Amazon and Yelp, extracts crucial attributes of early reviewers, including purchasing patterns and social impact. The interface employs a hybrid machine learning approach combining Naive Bayes, margin-based embedding ranking models (MERM), and support vector machines (SVMs) to predict potential early reviewers accurately. This approach balances probabilistic reasoning with robust classification, ensuring comprehensive forecasting. Evaluation metrics such as precision and accuracy demonstrate the effectiveness of the models over baseline methods. Furthermore, the study enhances the understanding of how early reviews influence consumer behavior and product success in e-commerce. The interface aids in refining product marketing strategies by enabling precise targeting of early reviewers, accelerating product adoption, amplifying sales, and optimizing marketing tactics in the competitive e-commerce landscape. © 2024 IEEE.

关键词： Support vector machines

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Onboard Near-Real-Time Detection of Harmful Algal Blooms Using a Convolutional Neural Network and Multispectral Imagery from a Satellite in Low Earth Orbit

Onboard Near-Real-Time Detection of Harmful Algal Blooms Usi...

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OCEANS 2024 - Halifax, OCEANS 2024

作者： Parsons, Jonathan D. Seto, Mae L. Dalhousie University Computer Science Dept. Halifax Canada Dalhousie University Mechanical Engineering Dept. Halifax Canada

ISBN: (纸本)9798331540081

Freshwater harmful algal blooms (HABs) pose significant ecological and public health risks worldwide. Detecting HABs soon after they form is critical to managing the damage they cause. While in-situ measurements are more accurate at detecting and measuring their toxicity levels, satellite imagery is more adept.at capturing the spatial and temporal dynamics of these blooms over large geographic regions. Satellites can also more persistently monitor for HABs. In the past, empirical methods and machine learning methods have used multispectral satellite imagery to estimate HAB biomass. To build upon the current body of research, this paper investigates an approach to expedite HAB detection by utilizing a convolutional neural network (CNN) deployed onboard a CubeSat in low Earth orbit to detect HABs in near-real-time. The CNN is trained with multispectral imagery from the Sentinel-2 satellite constellation aggregated with in-situ cyanobacteria cell counts from the Seabass CAML dataset. The results successfully demonstrated the capability of a CNN to detect cyanobacterial blooms using multispectral imagery. After classifying HAB predictions into 5 severity classes, the best performing model achieved a RMSE of 1.33 between HAB severity levels. Training the CNN on 30m GSD imagery with RGB and red edge (B05) bands achieved a RMSE of 1.83 between HAB severity levels, which was inadequate for detecting HABs in small inland water bodies. Improved performance was observed with 10m ground sample distance (GSD) band combinations. The best performing networks utilized all of Sentinel-2's 10m and 20m spectral bands. © 2024 IEEE.

关键词： Satellite imagery

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qCrop: An IoT Based Framework to Enhance Crop Productivity in Smart Agriculture 32

qCrop: An IoT Based Framework to Enhance Crop Productivity i...

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32nd IFIP/IEEE International Conference on Very Large Scale Integration, VLSI-SoC 2024

作者： Shamsa, Mahdi Rachakonda, Laavanya Mohanty, Saraju P. Kougianos, Elias University of North Texas Dept. of Computer Science and Engineering Denton United States University of North Carolina Wilmington Dept. of Computer Science Wilmington United States University of North Texas Dept. of Electrical Engineering Denton United States

ISBN: (纸本)9798331539672

Food is crucial in our life. Despite food manufacturers' efforts to meet consumers' needs with manufactured food, it cannot attain the identical level of quality and flavor as natural food. Some fruits and vegetables are classified as food that cannot be manufactured. Also, they play an essential role worldwide. This paper focuses on how farmers can protect their farms from an object that might threaten the crops and cause damage to them. It proposes a framework that farmers may utilize to safeguard the crops against any species of birds. Considering the adverse impact of bird attacks on crop production, this system effectively addresses this problem and consistently enhances the quality and quantity of them. It utilizes computer vision technology to establish a secure environment for the crop. This system is called Quality of Crop Device (qCrop), and it works with a You Only Look Once (Yolov8m) model to detect birds with high accuracy to protect farms. © 2024 IEEE.

关键词： Agri-food

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Towards Early Diagnosis and Intervention: An Ensemble Voting Model for Precise Vital Sign Prediction in Respiratory Disease

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IEEE Journal of Biomedical and Health Informatics 2023年 PP卷 1-13页

作者： Ahmed, Usman Lin, Jerry Chun-Wei Srivastava, Gautam Department of Computer Science Electrical Engineering and Mathematical Sciences Western Norway University of Applied Sciences Bergen Norway Dept. of Math and Computer Science Brandon University Canada

Worldwide, cardiovascular and chronic respiratory diseases account for approximately 19 million deaths annually. Evidence indicates that the ongoing COVID-19 pandemic directly contributes to increased blood pressure, cholesterol, as well as blood glucose levels. Timely screening of critical physiological vital signs benefits both healthcare providers and individuals by detecting potential health issues. This study aims to implement a machine learning-based prediction and classification system to forecast vital signs associated with cardiovascular and chronic respiratory diseases. The system predicts patients' health status and notifies caregivers and medical professionals when necessary. Utilizing real-world data, a linear regression model inspired by the Facebook Prophet model was developed to predict vital signs for the upcoming 180 seconds. With 180 seconds of lead time, caregivers can potentially save patients' lives through early diagnosis of their health conditions. For this purpose, a Naïve Bayes classification model, a Support Vector Machine model, a Random Forest model, and genetic programming-based hyper tunning were employed. The proposed model outdoes previous attempts at vital sign prediction. Compared with alternative methods, the Facebook Prophet model has the best mean square in predicting vital signs. A hyperparameter-tuning is utilized to refine the model, yielding improved short- and long-term outcomes for each and every vital sign. Furthermore, the F-measure for the proposed classification model is 0.98 with an increase of 0.21. The incorporation of additional elements, such as momentum indicators, could increase the model's flexibility with calibration. The findings of this study demonstrate that the proposed model is more accurate in predicting vital signs and trends. IEEE

关键词： Decision trees

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Leveraging AI-Driven Strategies to Mitigate Employee Turnover in Commercial Industries 26

Leveraging AI-Driven Strategies to Mitigate Employee Turnove...

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26th International Conference on computer and Information Technology, ICCIT 2023

作者： Hossain, Md. Shakib Hossain, Md. Rakib Hasan, S. M. Mahedy Akter, Farzana Srizon, Azmain Yakin Faruk, Md. Farukuzzaman Islam, Hedayatul Dept. of Electronics & Telecommunication Engineering Rajshahi Bangladesh Dept. of Computer Science & Engineering Rajshahi Bangladesh

ISBN: (纸本)9798350359015

Employee attrition remains a significant obstacle in the corporate sector, adversely influencing organizational coherence and productivity. This article introduces a well-rounded system, meticulously crafted to curtail employee turnover. An extensive analysis employing a diverse set of machine learning models, namely, Support Vector Machine, Logistic Regression, K-Nearest Neighbors, Random Forest, CatBoost, and XGBoost, has been performed to construct an efficacious predictive architecture. Despite the widespread adoption of machine learning in forecasting attrition, the interpretational challenges persist, largely attributable to the dependency on feature sets. This research underscores that the Min-Max Scaler empowers the Random Forest classifier to attain an exceptional 88.24% accuracy utilizing a condensed three-feature subset, underscoring the efficacy of feature reduction amidst expansive datasets. The study employs LIME and SHAP frameworks to delve into the interpretability of models, examining the influence of risk variables meticulously. SHAP illuminates both overarching and specific insights, while LIME focuses on dissecting local black-box outputs. This scholarly contribution extends beyond offering a potent AI resolution to the pressing challenge of employee attrition. It accentuates the paramountcy of model interpretability in deriving insightful HR decisions. Furthermore, the investigation accentuates the capability of AIinfused systems to bolster workforce consistency and efficiency in the corporate sphere. It unfolds actionable intelligence regarding the determinants of attrition, marking a significant stride towards a more informed and strategic approach to employee retention and organizational productivity. © 2023 IEEE.

关键词： Employee Turnover LIME Machine Learning Random Forest SHAP

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Artificial Neural Network and ARIMA model to estimate ICT sector investment, a comparative approach 11

Artificial Neural Network and ARIMA model to estimate ICT se...

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11th International Symposium on Telecommunication, IST 2024

作者： Mansourifar, Leila Moradhasel, Niloofar Borghei, Matin Sadat Heidari, Arman Dept. Strategic Studies and Digital Economics Center Tehran Iran Tehran Dept. Computer Science and Engineering Iran

ISBN: (纸本)9798350356250

The purpose of this research is to predict the required ICT sector for the time period leading up to . The ICT sector was predicted based on Autoregressive Integrated Moving Average (ARIMA) and Artificial Neural Networks (ANN). The major difference between the results of the two models indicates the difference between these two methods in forecasting, and it was shown that the ARIMA model is not a suitable method for program-oriented forecasts because it is not possible to consider variables in its calculations like economic growth. On the other hand, the neural network method can provide a more reliable prediction due to the inclusion of the economic growth variable in the learning process. © 2024 IEEE.

关键词： Neural network models

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Diabetic Retinopathy Prediction and Analysis Using Ensemble Classifier in Deep Learning Technique

Diabetic Retinopathy Prediction and Analysis Using Ensemble ...

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2022 OPJU International Technology Conference on Emerging Technologies for Sustainable Development, OTCON 2022

作者： Hemanth, S.V. Saravanan, A. Dept. of Computer Science and Engineering Tamil Nadu India

ISBN: (纸本)9781665492942

The primary cause of vision impairment is diabetic retinopathy (DR). Diabetes can cause diabetic retinopathy, a disorder that damages the retinal blood vessels and affects the eyes. According to the analysis, 90% of sight-threatening cases may be cured if it is discovered early. We proposed an Ensemble algorithm to predict diabetes from retinal images. The primary goal of this work is to enhance the accuracy and predict the result with minimum time consumption. The proposed system is a two-phase work in which, the initial bootstrap resampling method is employed for extracting the subsamples. Next, the classified decision tree establishes a voting system for finalizing the prediction result. The performance accuracy achieved by the proposed Ensemble algorithm is compared with the logistic regression and Deep neural network. The observation shows proposed enhanced Ensemble mechanism attained 96% accuracy, which is more prominent than the other models. © 2023 IEEE.

关键词： Decision trees

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Machine Learning Framework for Women Safety Prediction using Decision Tree 5

Machine Learning Framework for Women Safety Prediction using...

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5th International Conference on Smart systems and Inventive Technology, ICSSIT 2023

作者： Sowmika, P. Sareena Rao, S.Siva Nageswara Rafi, Shaik Dept. of Computer Science and Engineering Andhra Pradesh India

ISBN: (纸本)9781665474672

In every city, harassment and violence becomes one of the major problems for women. Further, women's personal life is suffered by the bullying and abusive content presented in Online Social Networking (OSN). Therefore, it is necessary to identify the women safety in OSN environment. When it came to predicting the maximum safety analysis, however, traditional methodologies came up short. This study, then, employs a decision tree (WSP-DT) classifier to make predictions about women's safety. After considering the Twitter dataset for system implementation, it is pre-processed to get rid of the blanks and the unknowns. The tweets were then processed by a natural language toolkit (NLTK) that handled tasks including tokenization, case-conversion, stop-word detection, stemming, and lemmatization. Next, we create a text blob protocol to determine the positive, negative, and neutral polarity of pre-processed tweets. To further extract the data characteristics based on word and character frequency, term frequency-inverse document frequency (TF-IDF) is used. At last, a decision tree classifier was used, based on several rounds of training, to determine if a tweet was phoney or real. Testing on the Twitter dataset demonstrates that the proposed WSP-DT classifier outperforms the competition in simulations. © 2023 IEEE.

关键词： Decision trees

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