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Estimating fish weight growth in aquaponic farming through machine learning techniques

Estimating fish weight growth in aquaponic farming through m...

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Intelligent Technologies (CONIT), international conference on

作者： Purushottam Kumar Pranav Tiwari U Srinivasulu Reddy CoE in Artificial Intelligence Machine Learning & Data Analytics Lab National Institute of Technology Trichy India Computer Science and Engineering Indian Institute of Information Technology Tiruchirappalli Trichy India Department of Computer Applications Machine Learning & Data Analytics Lab National Institute of Technology Trichy India

Due to the ever-growing population, rapid urbanization, unusual environmental change, and dwindling water supply, the food production from conventional farming techniques won’t be able to keep up with increasing food demand for long, leading to severe food scarcity. Aquaponics is emerging as a viable alternative to food cultivation techniques but lacks efficiency, hindering its adoption on a large-scale production basis. The adoption of Artificial Intelligence (AI) techniques in smart aquaponic farming can further contribute to increasing the yield in aquaponics. In the study, both linear and non-linear regression algorithms such as linear regression, lasso regression, ridge regression, KNN regression, SVR, and decision tree regression were trained on aquaponic fish pond dataset, evaluated on the basis of multiple metrics like MAE, MSE, RMSE, and RMSLE, and a competitive analysis was done to determine the best algorithm for predicting the harvest weight of the fish in aquaponic ponds. The results showed that KNN regression and decision tree regression algorithms have lowest error and highest accuracy of 99.15% and 99.1% respectively, for predicting the harvest weight of the fish. The results obtained could be scaled up to commercial levels and used in regulating nutrient concentration and aquatic environmental conditions

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Towards a Health Recommender Tool using machine learning Approaches: A survey and a new idea

Towards a Health Recommender Tool using Machine Learning App...

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Electrical, Computer, Communications and Mechatronics engineering (ICECCME), international conference on

作者： Manel Hadjer Benyarou Fatima Zohra Benkaddour Noria Taghezout Département d’Informatique Laboratoire d’Informatique Oran(LIO) Université Oran1 Oran Algeria Département Des Sciences Exactes Ecole Laboratoire d’Informatique Oran(LIO) Normale Supérieure d’Oran Ammour-Ahmed Oran Algeria

A sanitary crisis is a series of health events that can affect a large number of persons. Late events (COVID 19) have shown how unprepared we are to such situations. This issue raises the need to propose a Health Recommender Tool (HRT) to help both patients and medical professionals make more efficient and accurate health-related decisions. This paper presents an overview of recommendation systems (RS) methods that can be classified into categories: content-based method, collaborative filtering and hybrid method. We provide a literature review on machine learning (ML) based RS mainly in the medical domain. This work proposes a multi-phase process where the first phase is the data pre-processing and the second is the use of ML based recommendation techniques to get predictions on the relevance of documents to the user. The last step is the comparison between each technique. Our study concerns especially Algeria where the need of this kind of tools is greatest.

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A Systematic Study About Chronic Disease Prediction Strategies using Artificial Intelligence based Algorithms

A Systematic Study About Chronic Disease Prediction Strategi...

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Automation, Computing and Renewable Systems (ICACRS), international conference on

作者： P.Priya S.Kavya Department of Computer Science and Engineering M. Kumarasamy College of Engineering

ISBN: (数字)9798331532420

ISBN: (纸本)9798331532437

The challenge of chronic diseases will take its deserved place among the top global health issues, mainly because they account for 74% of deaths every year, as reported by the WHO. This systematic review will discuss possible applications of machine learning and AI techniques to predict, diagnose, and treat CDs such as diabetes, cancer, cardiovascular diseases, and liver disease. Integration of ML algorithms: RF, SVM, DT, ANN, and boosting techniques has transformed healthcare by improving the quality of predictive information for potential cases, reducing healthcare costs, and contributing to innovative clinical decision support systems. The research focuses on several approaches of ML, namely supervised and unsupervised learning, for predicting patient-specific treatment regimens and hence better outcomes in chronic disease management. Another discussion from the study is on how to apply ML on large, structured and unstructured datasets collected from the healthcare system about the predictive powers of AI-based models in order to be able to address complications arising from CD. Research aims at providing a comprehensive overview of the understanding of predictive models (PMs) under one heading, making an amalgamation of the desired workings of PMs for chronic disease prediction, useful for knowledge collection for future reference. The paper also provides a descriptive analysis of data sources, methodologies, and algorithms adopted, accompanied by a tabular representation of previous studies to avoid redundancy and enhance accuracy.

关键词： Accuracy Liver diseases Redundancy Medical services Predictive models Prediction algorithms Wearable devices Unsupervised learning Diseases Systematic literature review

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Optimal data Sample Length for System Call Traces for Malware Detection in an IoT Ecosystem

Optimal Data Sample Length for System Call Traces for Malwar...

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Electrical, Computer, Communications and Mechatronics engineering (ICECCME), international conference on

作者： John Carter Spiros Mancoridis Pavlos Protopapas Department of Computer Science Drexel University Philadelphia PA USA Institute for Applied Computational Science Harvard University Cambridge MA USA

As with many machine learning (ML) models, parameter tuning is one of the most important steps in the training phase. In the case of IoT malware detection, many parameters are chosen through experimentation and experience with the specific data collected. One important parameter is the size of the time window used to segment the observed data. Since each IoT device, and by extension, the malware infecting it, all have different behavioral profiles, it can be difficult to select a good time window size with any degree of certainty without first going through a trial-and-error period. This work describes how important this one parameter can be to an effective ML model using kernel level system call data, which comprises a couple of types of IoT devices as well as a couple of types of malware, each within the same IoT Ecosystem framework. To our knowledge, there has not been extensive work done in this field. We show that finding an optimal data sample length can mean the difference between an efficacious and an unusable malware detector.

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Automated Heart Arrhythmia Detection and Health Advisory System with Auto ML

Automated Heart Arrhythmia Detection and Health Advisory Sys...

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Advancement in Computation & Computer Technologies (InCACCT), international conference on

作者： C. Rahgul Krishna M. Arunkumar R. Ragul Department of Information Technology IFET College of Engineering Villupuram Tamil Nadu

ISBN: (数字)9798331543891

ISBN: (纸本)9798331543907

The advanced AutoML platform shows exceptional results in heart arrhythmia detection using deep learning technologies which reach 98% accuracy. Our evaluation methods show that the system detects 98% of events while missing only 2.8% with an F1-score of 97.8%. Our product stimulates patient involvement with its real-time ECG tool as well as customized healthcare services. Users connect more actively by 30%. Our system helps doctors see more patients each day by saving 40% of planning time and creating readable records to watch patients' heart health. The system works well with healthcare procedures while keeping patient data safe and following healthcare rules. In many patient care settings this platform shows problems in the cardiovascular system sooner which leads to better management and enhanced health results for patients. This system harnesses analytical tools and creates meaningful results to substantially improve cardiovascular healthcare delivery. The system identifies patterns based on advanced machine learning technology so it improves its accuracy and performance in every medical setting.

关键词： Heart Deep learning Accuracy Arrhythmia Image edge detection User centered design Medical services Electrocardiography Automated machine learning Monitoring

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Contribution to Predicting Electricity Load using Random Forest

Contribution to Predicting Electricity Load using Random For...

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Embedded Systems and Artificial Intelligence (ESAI), international conference on

作者： Zahira Marzak Salma Mouatassim Jamal Benhra Rajaa Benabbou Department of Industrial and Logistic Engineering (GIL) ENSEM Laboratory of Advanced Research in Industrial and Logistic Engineering (LARILE) OSIL Team Hassan II University of Casablanca Casablanca Morocco

ISBN: (数字)9798331522056

ISBN: (纸本)9798331522063

The growing demand for sustainable energy solutions and the integration of renewable resources have increased the need for accurate electricity demand forecasting, which is an essential element in effective power system management. This study evaluates the application of the Random Forest (RF) algorithm, an ensemble learning technique, for predicting Short-Term Electricity Load (STEL). Using historical data including load, weather conditions, and calendar variables, we optimized the model through feature engineering and hyperparameter tuning, in an effort to capture the dynamic interactions between consumer behavior, climate effects, and seasonal patterns, all of which significantly influence electricity demand. The results demonstrate the RF model’s superiority over traditional methods, achieving high accuracy measured by Mean Absolute Percentage Error (MAPE).

关键词： Radio frequency Accuracy Electricity Power system dynamics Predictive models Ensemble learning Tuning Random forests Load modeling Meteorology

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Using machine learning Sentiment Analysis to Evaluate Students learning Impact

Using Machine Learning Sentiment Analysis to Evaluate Studen...

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Disruptive Technologies (ICDT), international conference on

作者： Santosh Kumar Methily Johri Kishan Kumar Yashmita Awasthi Barkha Kakkar Akash Nandan School of Computing Science and Engineering Galgotias University Greater Noida Uttar Pradesh India School of Commerce Finance and Account Christ University Bengaluru Karnataka School of Management Institute of Technology & Science Ghaziabad Uttar Pradesh India

ISBN: (数字)9798331519582

ISBN: (纸本)9798331519599

For educational experiences and results to be improved, learning impact assessment is essential. Students' emotional reactions, which are crucial to their involvement and understanding, are frequently missed by traditional evaluation techniques. Through a review of student feedback, conversations, and course ratings, this study investigates the use of machine learning-based sentiment analysis to assess the impact of learning. Performance evaluations were conducted on a number of sentiment categorization models, including Naïve Bayes, Support Vector machines (SVM), Logistic Regression, Random Forest, Long Short-Term Memory (LSTM), and BERT. With an accuracy of 91.7%, the results show that BERT performs better than other models and offers more accurate sentiment classification. Accuracy and insights are further improved by combining textual, auditory, and visual signals in multi-modal sentiment analysis. The results show how sentiment analysis may be used to track feedback in real time facilitating adaptive learning techniques to raise student interest. Future studies should concentrate on expanding sentiment analysis applications to traditional and hybrid learning contexts, integrating multi-modal data, and ethical implications.

关键词： Support vector machines Sentiment analysis Ethics Adaptive learning Visualization Adaptation models Accuracy Market research Real-time systems Long short term memory

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DNA Classification For Finding ***

引用

Applied Artificial Intelligence and Computing (ICAAIC), international conference on

作者： Namburi Ananya Puram Ravi Kumar D.N. Asritha Ch. Usha Kumari Dept. of ECE GRIET Hyderabad India

ISBN: (数字)9798350375190

ISBN: (纸本)9798350375206

This study presents an innovative approach to classify DNA sequences for the accurate detection of Escherichia coli (***) bacteria using machine learning. *** is a common pathogen in various environments, including food and water, making its rapid identification crucial for public health and safety. Traditional methods for *** detection are often time-consuming and labor-intensive. Our project leverages state-of-the-art machine learning techniques to automate the classification of DNA sequences as either *** or ***. We utilize a comprehensive dataset of DNA sequences, that includes Escherichia coli and Non Escherichia coli samples, to train and evaluate our classification model. The key steps of our approach involve data preprocessing, feature extraction from DNA sequences, and the development of a robust classification model. We explore different machine learning models and algorithms which also include deep learning algorithms and models, to identify the best-performing model for this specific task. We have selected SVM model and MLP model. Our outcomes shows how effictively we move forward and accurately classify DNA sequences as *** or ***. This project contribute in the field of microbial observations which provide a faster and more efficient method for *** identification, with potential applications in food safety, environmental monitoring, and healthcare. We are evaluating our model by confusion matrix and accuracy. The f1-score for SVM model is 0.96. The f1-score for MLP model is 0.93.

关键词： Support vector machines Pathogens machine learning algorithms Accuracy Biological system modeling DNA Safety

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Efficient Chromosome Abnormal Detection using Yolo-V8 model

Efficient Chromosome Abnormal Detection using Yolo-V8 model

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international Symposium on Advanced Computing and Communication (ISACC)

作者： V. UmaRani Augustian Isaac K. Premnath Kannan G Sanjay A Hari Haran.L. K Dept of Computer Science and Engineering Saveetha Engineering College Chennai TamilNadu India Dept of Artificial Intelligence & Machine Learning Saveetha Engineering College Chennai TamilNadu India

ISBN: (数字)9798331523893

ISBN: (纸本)9798331523909

Human chromosomes exhibit complex and intricate patterns, making their analysis and data interpretation challenging. Studying chromosomes is crucial for identifying genetic disorders, such as Down syndrome, and various types of cancer. Traditional karyotyping methods depend heavily on manual method, which causes human error and high cost. To address these challenges, a deep learning-based approach is proposed to automate the karyotyping process, improving both accuracy and efficiency while minimizing human involvement and errors. This research leverages advanced deep learning techniques, specifically the YOLOv8 object detection framework, to enhance chromosomal analysis. From experiments, the proposed YOLO V8 model achieves a 95.2% accuracy, surpassing other models such as DCNN, CNN, and VGG-19. This work represents a significant step toward developing a more reliable and efficient diagnostic tool for detecting genetic disorders.

关键词： Deep learning YOLO Training Analytical models Accuracy Manuals Genetics Reliability Intelligent systems Biological cells

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RNNSczNet: Recurrent Neural Network with attention and ReUL for the Detection of Schizophrenia using EEG Signals

RNNSczNet: Recurrent Neural Network with attention and ReUL ...

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Intelligent data Communication Technologies and Internet of Things (IDCIoT), international conference on

作者： L. Reetha R. Gnanajeyaraman Department of CSE Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences Chennai TN India

ISBN: (数字)9798331527549

ISBN: (纸本)9798331527556

The activation function of the Rectified Linear Unit (ReLU), integrated with Recurrent Neural Networks (RNNs), GooLuNet, and other networks, has greatly improved the early identification of schizophrenia (SZ) using EEG data. RNNs' ability to capture temporal dependencies, combined with GooLuNet's inception modules for spatial feature extraction, enables accurate classification of SZ patients. ReLU is pivotal in ensuring efficient gradient propagation, reducing training time, and preventing vanishing gradient issues, thus improving model performance. The proposed model achieves a remarkable accuracy of 98.6%, outperforming more traditional machine learning techniques like Random Forest (RF) and Support Vector machines (SVM). The architecture can also detect SZ around six months before symptoms appear, which shows that it might be used in real-time clinical settings. This combination of deep learning techniques offers a robust framework for early diagnosis, improving patient outcomes through timely intervention and treatment.

关键词： Support vector machines Accuracy Recurrent neural networks Computational modeling Computer architecture Schizophrenia Brain modeling Feature extraction Electroencephalography data models

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