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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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Enhanced Brain Tumor Segmentation: A Study on the Efficacy of Multi-Model Ensembling Utilizing Diverse Backbones 26

Enhanced Brain Tumor Segmentation: A Study on the Efficacy o...

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

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

ISBN: (纸本)9798350359015

Gliomas account for approximately 80% of all malignant brain tumors and are associated with the most unfavourable survival rates amongst all brain tumor types. Segmenting these tumors is integral to evaluating their dimensions and morphology, formulating tailored treatment plans, and estimating patients' life expectancy. Various approaches for brain tumor segmentation have been proposed, yet the model described herein stands out, offering promising results in this vital task. A comprehensive examination of three eminent convolutional neural network (CNN) models, namely Resnext50, Resnet50, and InceptionV3, was conducted, each characterised by its own unique structural framework. The enhancement of the segmentation approach's effectiveness was a focal point, leading to the exploration of ensemble modeling. This approach leverages the collective strengths of the aforementioned models, facilitated by softmax averaging. This collaborative initiative culminated in the creation of the Ensemble model, which outshoned its individual counterparts in performance metrics. Furthermore, the Ensemble model is poised to instigate significant advancements in brain tumor diagnostic procedures. The ensemble methodology is instrumental in refining the precision and automation of brain tumor diagnoses. Deep learning, as evidenced by the research, is a transformative element in medical image analysis, elevating the accuracy and efficiency of brain tumor segmentation. Consequently, this refined approach is set to become an invaluable asset for medical professionals in enhancing the accuracy of diagnoses and the efficacy of treatments for patients afflicted with brain tumors. © 2023 IEEE.

关键词： Backbone Architectures Brain Tumor Segmentation Deep Learning Ensemble Learning Evaluation Metrics Medical Image Analysis MRI Images U-Net

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Predictive Analytics in Agriculture: Unraveling the Determinants of Crop Yield with Machine Learning 26

Predictive Analytics in Agriculture: Unraveling the Determin...

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

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

ISBN: (纸本)9798350359015

In the context of Bangladesh, the precision in forecasting crop yields has been enhanced significantly through the integrative approach of amalgamating historical data, environmental variables, and cutting-edge technology. This integrative methodology underscores the fortification of the nation's agricultural domain, integral to both its economic fabric and the populace's sustenance. This study presents an integrative approach leveraging historical data, environmental variables, and advanced technology to enhance crop yield forecasting. Traditional agricultural methods, while valuable, often lack comprehensive understanding of climatic factors, limiting agricultural refinement. Employing an Advanced computer-Aided Decision Support System, this research meticulously analyzes extensive datasets to uncover intricate correlations between historical yields and environmental nuances. In this study, a novel interpretative machine learning model is formulated, emphasizing data pre-processing and optimal feature transformation protocols tailored to diverse model archetypes. The study delves into the influence of distinct variables on predictive acumen and the impact of individual observational values on forecast outcomes. The study's contributions include a transparent machine learning model elucidating key variables affecting crop yield predictions, paving the way for informed agricultural practices. Future endeavors could focus on leveraging real-time data and expanding datasets to further refine predictive models in this crucial domain. © 2023 IEEE.

关键词： Crop Yield Prediction Feature Importance Machine Learning Parameter Optimization

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Utilizing Advanced Machine Learning Techniques for Effective Prediction and Control of Pandemic Outbreaks 2

Utilizing Advanced Machine Learning Techniques for Effective...

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2nd IEEE International Conference on Trends in Quantum Computing and Emerging Business Technologies, TQCEBT 2024

作者： Rajasekaran, R. Thalapathi Shanmugavalli, H. Jayalakshmi, P. Ambika, S. Balaji, V. Saveetha School of Engineering Simats Dept. of Computer Science and Engineering Chennai India Grt Institute of Engineering and Technology Dept. of Computer Science and Engineering Tamil Nadu Tiruttani India Sri Krishna College of Engineering Dept. of Computer Science and Engineering Arakkonam India E.G.S. Pillay Engineering College Department of Information Technology Tamil Nadu Nagapattinam India Chennai India

ISBN: (纸本)9798350384277

The health and social implications of pandemic epidemics are substantial. Accurate forecasting and management of such epidemics are of utmost importance to lessen the blow of such epidemics. To tackle this pressing problem, we employ state-of-the-art machine learning methods in our research. In this paper, we investigate the potential of cutting-edge algorithms for modeling, predicting, and controlling the spread of pandemics. These algorithms include LSTM, Random Forest, GNN, RL, DRL, Bayesian Networks, and SVM. We run a battery of experiments and analyses using real-world data sources, such as COVID-19 case data, mobility data, contact tracking data, vaccination distribution data, intervention strategy data, and resource allocation data. These advanced machine-learning approaches have the ability to enhance the accuracy of pandemic predictions, optimize the allocation of resources, decrease transmission rates, and enable more effective intervention measures, according to our findings. For example, our RL algorithm optimized vaccination distribution by 20% while our LSTM model averaged a 92% success rate in forecasting new cases each day. Our research further emphasizes the significance of early detection and intervention. With a 90% success rate, the SVM-based early detection system could spot epidemics ten days before conventional approaches could, buying precious time for focused and efficient responses. © 2024 IEEE.

关键词： COVID-19

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