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IoT-Based Smart Irrigation System in Aquaponics Using Ensemble machine learning 1

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6th international conference on Soft Computing and Signal Processing, ICSCSP 2023

作者： Singh, Aishani Bajaj, Dhruv Safa, M. Arulmurugan, A. John, A. Department of Computing Technologies SRM Institute of Science and Technology Kattankulathur Chennai India Department of Networking and Communications SRM Institute of Science and Technology Kattankulathur Chennai India Department of Computer Science and Information Engineering National Chung Cheng University Chiayi Taiwan

ISBN: (数字)9789819984510

ISBN: (纸本)9789819984503

Aquaponics is a sustainable farming method that combines aquaculture and hydroponics to grow plants and fish in a closed-loop system. In this research paper, an irrigation system based on aquaponics is proposed, which uses real-time sensor data from the fish tank and crop soil to improve the efficiency of the system. the system is designed to make informed decisions about crop irrigation needs by visualizing the data for analytics. the study compares the accuracy of three classification algorithms, KNN, Naive Bayes, and ANN, to decide when to irrigate the soil based on real-time sensor data. the proposed irrigation system includes two sets of sensors, one for the fish tank and the other for the crop soil, which is processed by an Arduino board and sent to Adafruit’s cloud platform for visualization and analytics. this cloud-based platform allows easy access to real-time data, enabling efficient monitoring and control of the irrigation system. Additionally, the study visualizes the results obtained from using regular water and lake water in the aquaponics system. © the Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.

关键词： Hydroponics

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machine learning Algorithm and Modeling in Solar Irradiance Forecasting 6

Machine Learning Algorithm and Modeling in Solar Irradiance ...

引用

6th international conference on Computer and Informatics Engineering, IC2IE 2023

作者： Sutarna, Nana Tjahyadi, Christianto Oktivasari, Prihatin Dwiyaniti, Murie Tohazen Politeknik Negeri Jakarta Master of Applied Electrical Engineering Depok Indonesia Politeknik Negeri Jakarta Informatics and Computer Engineering Depok Indonesia Politeknik Negeri Jakarta Electrical Engineering Depok Indonesia

ISBN: (纸本)9798350345162

Forecasting solar irradiance is a very important requirement for determining the potential of the energy produced, especially to maintain the continuity of the availability of electrical energy. Creating a solar irradiance forecasting modeling algorithm is important to obtain the degree of closeness between the predicted and actual results. this study proposes a novel method to solve solar irradiance prediction by using a machine learning model based on the Bidirectional Long Short-Term Memory (BiLSTM) with one hidden layer and three input data variables and involves setting hyperparameters. the processed data is primary data obtained from the measurement results of three sensors over a certain period. the training technique and model validation use the backpropagation algorithm by adjusting the learning rate value. the proposed BiLSTM model has achieved convergence at epoch 183rd and no overfitting occurs. Furthermore, the comparison between the BiLSTM model and the Baseline model shows that the BiLSTM model has better performance in predicting solar irradiance one minute ahead with mean absolute error (MAE) and root mean squared error (RMSE) values of 0.004326917696744204 W/m2 and 0.00917678491314492 W/m2 respectively. At the same time, the Baseline model shows MAE of 0.01438286076453574 W/m2 and RMSE of 0.033326815578768605 W/m2. © 2023 IEEE.

关键词： BiLSTM forecasting hyperparameter machine learning solar irradiance

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An Ensemble Approach for Cardiac Arrhythmia Detection using Multimodal Deep learning 6

An Ensemble Approach for Cardiac Arrhythmia Detection using ...

引用

6th international conference on Inventive Computation Technologies, ICICT 2023

作者： thanka, M. Roshni Gandu, Shalem Preetham Manaswini, Beereddy Snehitha, thirumal Reddy Bala Reddy, Manukonda Narmada Nandini, Kalle Computer Science and Engineering Karunya Institute of Technology and Sciences Coimbatore641114 India

ISBN: (纸本)9798350398496

Arrhythmias are irregular and possibly deadly heartbeats. To reduce mortality and morbidity, the patient must receive the proper treatment. Recent techniques in the field of machine learning and signal processing have been applied to the detection and classification of arrhythmias. However, they face several challenges in accurately detecting arrhythmias. One major challenge is the class imbalance in the training data, which can lead to overfitting or underfitting of the models. Another challenge is the variability in ECG signals due to factors such as noise, artifacts, and variations in electrode placement. the proposed objective is to develop an effective ensemble model with a network-in-network architecture based on CNN and LSTM to accurately detect arrhythmias in ECG signals. On the MIT-BH dataset, it was trained and validated to recognize five different kinds of arrhythmias. Prior to that, resampling was done to balance the data in order to prevent the model from being under- or overfit. the ensembled model performs excellent on the validation data. the outcome of the trial show that the suggested model performed remarkably well, with 100% and 99.72% accuracy in the training and testing datasets, respectively. On validation data the CNN and LSTM performed with 98.6% and 98.4% individually. the proposed method outperformed the existing methods in accuracy, proving that the ensemble model is the most effective. © 2023 IEEE.

关键词： Network architecture

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A Feature Selection Method Based on Rough Set Attribute Reduction and Classical Filter-Based Feature Selection for Categorical data Classification 6th

A Feature Selection Method Based on Rough Set Attribute Red...

引用

6th international conference on applied Informatics, ICAI 2023

作者： Oriola, Oluwafemi Kotzé, Eduan Atawodi, Ojonoka University of the Free State Bloemfontein South Africa Adekunle Ajasin University Akungba Akoko Nigeria University of Southern Mississippi Hattiesburg United States

ISBN: (纸本)9783031468124

the main objective of feature selection in machine learning classification is to reduce the size of features by removing irrelevant and noisy features, with the goal of improving the accuracy and the efficiency of the classification model. Like continuous and mixed data classification, feature selection has been applied to better categorical data classification. On large datasets with tens of features, however, existing feature selection methods perform worse in terms of accuracy metrics than baseline categorical data classification models that involve full features. this paper presents a feature selection method that integrates Rough Set Attribute Reduction and Classical Filter-based feature selection method to improve the performance of categorical data classification. Two large categorical datasets from UCI repository are used to evaluate the method. Support Vector machine, Random Forest and Multilayer Perceptron algorithms are used as machine learning classifiers. the results show that the proposed method outperforms existing feature selection models in terms of Accuracy, Precision, Recall, and F-measure for individual classes and their average weighted scores in both case studies. Benchmarking with baseline classification models, the best overall performance by the proposed method is obtained with Random Forest. © 2024, the Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： Classification (of information)

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Diabetes Prediction Model for Better Clarification by using machine learning 6

Diabetes Prediction Model for Better Clarification by using ...

引用

6th international conference on Inventive Computation Technologies, ICICT 2023

作者： Eben, J. Lysa Jayasudha, R. Ramya, S. Kaliappan, S. Aswal, Shobha Al-Salehi, Khalid Ali Salem Department of Computer Applications School of Computing Sciences Vels Institute of Science Technology and Advanced Studies Tamilnadu Chennai India Department of Mathematics Dr.N.G.P.Institute of Technology Coimbatore India Department of Computer Science and Engineering Kongu Engineering College Tamilnadu Erode India Division of Research and Development Lovely Professional University Punjab Phagwara144411 India School of Computing Graphic Era Hill University Dehradun India Biomedical Engineering Study Program Faculty of Science and Technology Airlangga University Surabaya Indonesia

ISBN: (纸本)9798350398496

Diabetes mellitus is one of the most pressing health concerns because so many people are afflicted by its disabling symptoms. Factors such as age, excess body fat, insufficient physical activity, a history of diabetes in one's family, a sedentary lifestyle, an unhealthy diet, hypertension, etc., all increase the likelihood of developing diabetes mellitus. Health complications are more common in people with diabetes, including cardiovascular disease, renal failure, stroke, blindness, and nerve injury. To validate a diagnosis of diabetes, hospitals typically perform a battery of procedures on the patient. Big data analytics has many vital applications in the healthcare sector. Numerous large computer systems are used in the healthcare sector. With the help of big data analytics, researchers can sift through mountains of data in search of previously unseen patterns and insights. Current techniques have a poor degree of precision in classification and forecast. While previous research has focused on factors such as glucose, body mass index, age, insulin, etc., the proposed model takes these into account and also the other factors that may be more relevant to the development of diabetes. the newer sample is superior to the older one based on categorization accuracy. A workflow algorithm for diabetes prognosis is also required to improve the accuracy. © 2023 IEEE.

关键词： Forecasting

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A machine learning Model for Prediction of Parkinson's Disease using Vocal Features 6

A Machine Learning Model for Prediction of Parkinson's Disea...

引用

6th IEEE international conference on Advances in Science and Technology, ICAST 2023

作者： Bangal, Snigdha Pawade, Dipti thakkar, Malay Lande, Viraj De, Neelanjaan Dedhia, Bharat K. J Somaiya College of Engineering Dept. of Information Technology Mumbai India

ISBN: (纸本)9798350359817

Parkinson's disease is a progressive neurological disorder which affects motor skills and is characterized by symptoms such as muscle rigidity, tremors and difficulties in maintaining equilibrium and coordination. Detecting Parkinson's disease involves analyzing the patient's medical background and conducting multiple imaging tests like MRI, Computed Tomography, and DaTscan, along with physical and neurological assessments. All these steps are essential to validate a definitive diagnosis. Recent researches have proved that speech variations in speech can be considered as a quantifiable indicator to detect Parkinson's disease in its early *** method is both non-invasive and economically efficient in contrast to alternative diagnostic procedures. To achieve a speech-based diagnosis system, we have tried a feature selection algorithm in combination with classification algorithms and a stacking classifier to combine these base models. In the process of feature selection, we have applied the mutual information gain method in combination with three classifiers: K-nearest neighbors(KNN), Support Vector machine (SVM) and XGBoost. To evaluate various permutations, we've made use of the speech dataset provided UCI. To address the issue of imbalanced data, we've applied the Synthetic Minority Oversampling Technique (SMOTE). the stacking classifier has shown the best performance, attaining an accuracy rate of 97.05%. © 2023 IEEE.

关键词： Support vector machines

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A Comprehensive Review on machine learning in Breast Cancer Analysis 6th

A Comprehensive Review on Machine Learning in Breast Cancer ...

引用

6th international conference on Recent Innovations in Computing, ICRIC 2023

作者： Ananthanagu, U. Ebin, P.M. Chinnaiyan, Ramasubramanian Department of Computer Science Engineering Alliance University Bangalore560102 India Pro Vice-Chancellor Lingayas Vidyapeeth Deemed-to-be University Haryana Faridabad India

ISBN: (纸本)9789819728381

One of the most feared illnesses is cancer since it sneaks up on people and you don't know how to fight it. Early and accurate detection are essential for efficient treatment and better patient outcomes for breast cancer, a critical worldwide health concern. For the investigation, Wisconsin Diagnostic Breast Cancer (Diagnostic) (WDBC) dataset was considered from the repository for machine learning at UCI. Medical records pertaining to breast cancer might produce insightful results using data Mining techniques, such as trends in behavior and frequent/rare item distribution. To determine the best model, the study has been applied to several machine learning classification algorithms -SVM, SMO, NB, Attribute Selected Classifier, Decision Strump, J48, using Weka 3.8.3. In all these algorithms, Wrapper-Subset-Evaluation is used to apply feature selection. On the breast cancer data set, it shows that the J48 method performs better than every other classifier. © the Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.

关键词： Support vector machines

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Smart Grading System for Evaluating Typed Text Answers Using Ensemble machine learning Techniques 1

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6th international conference on Soft Computing and Signal Processing, ICSCSP 2023

作者： Bhutada, Sunil Akshaya Reddy, G. Anusha, T. Lohith Reddy, G. Gadi, Siva Department of IT Sreenidhi Institute of Science and Technology Telangana Hyderabad India RGUKT Andhra Pradesh Nuzvid India

ISBN: (数字)9789819986286

ISBN: (纸本)9789819986279

the AI-based smart grading system evaluates student’s answers in the text format and analyzes the documents that help identify student performance. this smart grading system fixes the difference between human errors while evaluating students’ answers in the examinations. AI-based grading systems can analyze typed text-based assignments and reduce manual effort and time consumption to assign grades more accurately and unabashedly. AI can be used to identify patterns in the text data, such as correlations between student demographics and context. AI can also identify areas for improvement, such as specific courses that need to be addressed to improve student performance. the proposed system may utilize the data collected from multiple students (survey/web portal). the collected text documents are to be labeled. the text data will be processed and converted to an analytics dataset. the analytical dataset will be used to predict the classes of the documents using custom deep neural network algorithms. © the Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.

关键词： Deep neural networks

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A Survey on Crop Rotation Using machine learning and IoT 6th

A Survey on Crop Rotation Using Machine Learning and IoT

引用

6th international conference on Information and Communication Technology for Intelligent Systems, ICTIS 2021

作者： Patel, Nidhi Shah, Ashna Soni, Yashvi Solanki, Nikita Shahu, Manojkumar LDRP Institute of Technology and Research Gujarat Gandhinagar India

ISBN: (纸本)9789811935749

Agriculture plays a significant role in forming the economy in *** by day, as the population increases the demand for food also ***, we need more efficient ways to increase crop *** days with the surge of the Internet of things (IoT) and machine learning technologies, we can get more effective *** have proposed a system, which collects the soil data like NPK contents, pH level, and temperature from the IoT sensors in real time which is sent to the cloud using the MQTT method by Node MCU *** the cloud architecture, we have applied the k-nearest neighbor algorithm (KNN) model to the collected data to get the best suggestion for crop *** is a supervised machine learning algorithm and can predict similar things that exist in closed *** this system, we have taken different crops for the *** can also check the real-time soil contents of their farms on the dashboard. © 2023, the Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Cultivation

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Clustering Social Media data for Bitcoin Price Prediction with Transformer Model 6

Clustering Social Media Data for Bitcoin Price Prediction wi...

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6th international conference on machine learning and Natural Language Processing, MLNLP 2023

作者： Zhi, Yajing Chan, T-H. Hubert Department of Computer Science University of Hong Kong Hong Kong

ISBN: (纸本)9798400709241

this paper explores the integration of social media data and natural language processing methods, specifically utilizing the Transformer model, to predict Bitcoin price movements. We aim to evaluate the effectiveness of using social media data and the Transformer model in forecasting market trends for Bitcoin. By analyzing social media posts and incorporating them into predictive models, we demonstrate the potential of the Transformer architecture in capturing complex dependencies and patterns within sequential Bitcoin prices. Additionally, different clustering methods are applied to process the original social media data in a rolling manner. the evaluation of Transformer-based models on historical data showcases their predictive performance compared to various social media data clustering approaches. Furthermore, the impact of incorporating outliers of social media data into the Transformer model is explored to improve prediction accuracy. the results of this study demonstrate the potential of clustering on social media data and the Transformer model for forecasting market trends. © 2023 ACM.

关键词： Forecasting

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