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Optimization of Aquaponics System Efficiency Based on Artificial Intelligence Approach 3

Optimization of Aquaponics System Efficiency Based on Artifi...

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3rd international conference on Computing and machine Intelligence (ICMI)

作者： Liu, Jayden Jiang, William Harker Sch San Jose CA 95124 USA

ISBN: (纸本)9798350372977;9798350372984

This paper aims to design and implement an MLbased approach to learn from NeuroAqua - the AI and IoT-based aquaponics system set up in our previous research at both a lab setting and larger-scale Ouroboros Aquaponics Farm (Half Moon Bay, CA) to enhance system stability and efficiency. Utilizing the data gathered from the wireless sensors, a structured database was formed to store the aquaponics environmental conditions, water quality, nutrient components, and plant images. We used the ML model to find the important factors having the largest impact on plant growth and their optimal amount levels. First, computer vision with image processing was applied to develop auto plant growth monitoring and to measure plant growth rate as the target variable more accurately and automatically for ML. Then feature engineering on the input variables was performed to enhance model performance and accuracy for a smaller dataset. ML algorithms including Linear Regression, Bagging Regressor, Decision Tree, Random Forest, XGBoost and Artificial Neural Network were applied and evaluated based on key performance metrics. The findings show that XGBoost outperformed the other models with 91.6% accuracy and also had the lowest MAE. Random Forest came in second with 90.9% accuracy and then Bagging Regressor in third with 88.5% accuracy. Lastly, according to the feature importance analysis conducted on the best model of XGBoost, Nitrogen had the largest impact on plant growth, followed by Nitrate, Nitrite, Light, and Phosphorus. Hence the initial results would recommend to closely monitor these top important factors together with plant growth in NeuroAqua's monitoring applications.

关键词： Artificial Intelligence machine learning Aquaponics IoT Image processing

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Location-free CSI-Based Lightweight Deeping learning Model for Human Activity Recognition 3

Location-free CSI-Based Lightweight Deeping Learning Model f...

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3rd international conference on Computing, Communication, Perception and Quantum Technology, CCPQT 2024

作者： Jiang, Xin Li, Bin Du, Yirui Zhai, Daosen Zhang, Ruonan School of Computer Science and Engineering Northwestern Polytechnical University Xi’an China

ISBN: (纸本)9798331528386

n recent years, activity recognition based on channel state information(CSI) has a wide range of application scenarios in the field of human-computer interaction(HCI) due to its advantages such as no need to carry special equipment, no privacy disclosure, and no light intensity. Many approaches based on traditional machine learning and deep learning have encountered two challenges. One is that the problem of activity recognition limited to fixed locations and complex backgrounds remains unsolved. The second is that some studies require complex CSI processing, which increases the network parameters, significantly lengthens the recognition time and raises the deployment cost. To this end, this study develops a Wi-Fi-based location-independent lightweight recognition model. We propose a lightweight CSI processing strategy that is able to effectively extract the main relevant features while compressing the model size. We combine 3D convolution with spatio-temporal information selection gates to extract activity-related information from CSI data, and employ knowledge distillation techniques to migrate the features learned from this model to a simple model. Extensive experimental results show that our system outperforms other deep learning models in terms of efficiency, with recognition accuracy up to 98.6%. © 2024 IEEE.

关键词： Distillation

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Advancing Fake News Detection: From Traditional machine learning to Transformer-based Approaches 3

Advancing Fake News Detection: From Traditional Machine Lear...

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3rd international conference on Intelligent data Communication Technologies and Internet of Things, IDCIoT 2025

作者： Abhinand, I.V. Ajal Krishna, K.P. Krishna, Ajay Jayadev, J. Reji Rahmath, K. Divya, T. Dept. of Computer Science and Engineering Sreepathy Institute of Management and Technology Vavannur A. P. J. Abdul Kalam Technological University kerala Thiruvananthapuram India

ISBN: (纸本)9798331527549

Fake news on online platforms is spreading at a rapid rate, posing great threats to social, political, and economic stability in linguistically diverse regions like Kerala, India. This paper reviews comprehensively the techniques of fake news detection from traditional machine learning methods to transformer-based models. Special attention is given to the detection of fake news in regional languages like Malayalam, characterized by complex morphology and syntax. The study investigates applications of ensemble-based learning as well as state-of-the-art transformer models, BERT, and XLNet, focusing on their ability to capture nuanced linguistic features and contextual relationships. Evaluation results show how significantly better the transformer-based models fare than traditional methods with respect to accuracy and robustness. This review also captures current limitations - such as resource constraints for regional languages - as well as future opportunities in developing scalable, multilingual fake news detection systems. © 2025 IEEE.

关键词： Adversarial machine learning

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Adversarial Attacks and Defenses on Deep learning Models 3

Adversarial Attacks and Defenses on Deep Learning Models

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3rd international conference for Advancement in Technology, ICONAT 2024

作者： Jhajharia, Kavita Shrivastavaa, Yash Kumar Manipal University Jaipur Information Technology Jaipur India

ISBN: (纸本)9798350354171

Deep learning, a pioneering field in machine learning, has significantly enhanced the analysis of medical images. These models utilize multi-layered neural networks to detect complex patterns, enhance tasks such as diagnosis, segmentation, and treatment planning within the images. Despite the remarkable advancements in detecting diseases and segmenting organs, deep learning models pose significant risks from adversarial attacks. These attacks manipulate input data, causing the models to make incorrect predictions and potentially leading to serious errors in medical diagnosis. This paper examines the impact of adversarial attacks on deep learning systems, focusing on medical imaging and the MNIST dataset. We implemented Fast Gradient Sign Method (FGSM) on convolutional neural network model. The findings of the study shows that accuracy of brain tumor detection was initially at 67% with correct classification but after the adversarial attack the model misclassified the tumor with 853% confidence under the perturbation with epsilon value of 0.3. for MNIST dataset the accuracy was decreased with every epsilon value increase, the prediction of digit input image was correctly classified with 87 % confidence, but after perturbation attack the model misclassified the input by 83% confidence. These findings highlight the distinct challenges of defending against adversarial attacks in medical imaging, focusing on the need for robust defense mechanisms. © 2024 IEEE.

关键词： Adversarial machine learning

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AlexNet-Microgrid: Advanced Deep learning for Islanding Detection in Microgrids 3

AlexNet-Microgrid: Advanced Deep Learning for Islanding Dete...

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3rd Odisha international conference on Electrical Power engineering, Communication and Computing Technology, ODICON 2024

作者： Choudhury, Anasuya Roy Mallick, Ranjan Kumar Nayak, Pravati Agrawal, Ramachandra Mishra, Sairam Siksha 'o' Anusandhan Deemed to Be University Electrical Engineering Bhubaneswar India Siksha 'o' Anusandhan Deemed to Be University Electrical and Electronics Engineering Bhubaneswar India

ISBN: (纸本)9798350354379

An essential component of microgrid operation is islanding detection, which guarantees the secure and dependable running of power systems-especially with the growing integration of renewable energy sources. Conventional islanding detection approaches frequently rely on passive and active procedures which can be slow or intrusive. This research employs the powerful convolutional neural network (CNN) architecture of Alex Net to provide a novel solution for islanding identification in microgrids using a deep learning-based framework. The suggested technique converts microgrid time-series data into visualizations that resemble images, which are subsequently input into AlexNet to distinguish between normal and islanding circumstances. The efficacy of the Alex Net-based method is demonstrated by extensive experiments conducted on simulated microgrid data, which yield excellent accuracy and quick detection times. The findings imply that islanding detection systems in contemporary microgrids can be greatly improved in terms of performance and dependability by using deep learning models, particularly AlexNet. © 2024 IEEE.

关键词： AlexNet Islanding Detection Microgrid Photovoltaic

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Artificial Intelligence-Based Detection of Autism Spectrum Disorder Using Linguistic Features 3

Artificial Intelligence-Based Detection of Autism Spectrum D...

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3rd international conference on Computing and machine Intelligence (ICMI)

作者： Li, Juliana Harker Sch San Jose CA 95124 USA

ISBN: (纸本)9798350372977;9798350372984

Artificial intelligence (AI) technology has recently shown promising results in detecting Autism Spectrum Disorder (ASD), but it faces significant challenges because it is still in early stages of development. This study proposes to use linguistic features in the detection of ASD using artificial intelligence. In order to systematically understand different AI model performances, two types of AI models have been explored and evaluated. Five classical ML models were used: logistic regression, Gaussian naive Bayes, random forests, k-nearest neighbors (K-NN), and support vector machines (SVMs), and two deep learning models were used: multilayer perceptron (MLP) and a convolutional neural network (CNN). All model development work is based on analyzing speech transcripts of 64 children total aged 3 to 6 from two data banks, CHILDES and ASDBank, including 30 children with ASD and 34 typically developing (TD) controls. The first step was to examine the annotations of the transcriptions, then extract various linguistic features from the texts. AI models were trained to determine whether a child has ASD based on the characteristics of the transcripts analyzed. There are three major findings of this study. First, neural network models will outperform classical models in ASD detection by around 5%-10%. The models multilayer perceptron and convolutional neural networks achieved 83% and 84% accuracy, respectively. Second, among the classical machine learning models, logistic regression, SVMs, K-NN, and random forests all achieved accuracies in the range 77%-80%, while Gaussian naive Bayes performed the worst by around 10%. Third, the linguistic feature with the highest importance in ASD detection is MLU, and the features MLU, MLT, rUtts, and rTNW show significant distinctions between age and gender demographics in addition to having high importance in ASD detection. Overall, this research recommends using deep learning for clinical applications of ASD detection.

关键词： Autism Spectrum Disorder Natural Language Processing Artificial Intelligence machine learning Deep learning Linguistics

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Ensembled machine learning Models for Antenna Optimization in Wireless Communication/ Biomedical Applications 3

Ensembled Machine Learning Models for Antenna Optimization i...

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3rd international conference on Communication, Control, and Intelligent Systems, CCIS 2024

作者： Upadhyay, Nidhi Kaur, Amanpreet Singh, Ashima Pattanayak, Arnab Thapar Institute of Engineering and Technology Department of Electronics and Communication Engineering Punjab India Thapar Institute of Engineering and Technology Department of Computer Science Engineering Punjab India

ISBN: (纸本)9798331528201

In this work, the antenna optimisation process is done by using ML algorithms such as KNN, random forest, Gradient Boost, XG Boost and MLP. Then the predicted results are ensembled using Voting Regressor. A Cylindrical Dielectric Resonator Antenna (CDRA) was designed and simulated in CST MWS'23 for this purpose. dataset is created by varying the radius of the DR using parametric sweep function of the EM simulator. A close prediction between actual values and the predicted ones is obtained using these ML models. It is observed that by ensembling these varied approaches, a model that can handle different types of relationships in the data, can be created. This algorithm can be further utilized for optimisation of other antenna parameters and thus helping in reducing time in optimising complex antenna designs. The accuracy achieved by the ensemble model is 98.6% while maintaining a small MSE of 0.001. The optimized design shows stable characteristics within the ISM band and hence can be used for biomedical as well as wireless communication applications. © 2024 IEEE.

关键词： Random forests

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Prediction of Pathogen Causing Rice Plant Disease and Recommendation using Enhanced machine learning Technique 3

Prediction of Pathogen Causing Rice Plant Disease and Recomm...

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3rd international conference on Applied Artificial Intelligence and Computing, ICAAIC 2024

作者： Priya, J Suji Priya, M Hema Iyswarya, M. Kiruthika, K. Sona College of Technology Department of Master of Computer Applications Salem India

ISBN: (纸本)9798350375190

Plant diseases pose significant challenges to agricultural productivity, impacting both the quality and quantity of crop yields. Early detection and effective management of these diseases are essential for mitigating their detrimental effects on agricultural output. However, traditional methods of disease monitoring and diagnosis are often costly and require continuous expert intervention. To address these challenges, this study proposes a novel approach that combines image processing techniques and advanced machine learning methodologies for the accurate identification and classification of rice leaf diseases. By utilizing datasets comprising images of diseased rice leaves, environmental data, and historical disease reports, the proposed system utilizes Convolutional Neural Networks (CNNs) for image analysis and feature extraction. Subsequently, a Support Vector machine (SVM) classifier is employed for disease detection and classification based on the extracted features. The integration of image processing and machine learning technologies offers a cost-effective and efficient solution for early disease detection and management in agriculture. By automating the process of disease identification, the proposed system facilitates timely interventions, thereby minimizing crop losses and promoting agricultural sustainability. © 2024 IEEE.

关键词： Convolutional neural networks

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Day Ahead Load Demand Forecasting based on LSTM machine learning Model 3

Day Ahead Load Demand Forecasting based on LSTM Machine Lear...

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3rd international conference on Power, Control and Computing Technologies, ICPC2T 2024

作者： Bareth, Rashmi Yadav, Anamika National Institute of Technology India Department of Electrical Engineering Raipur India

ISBN: (纸本)9798350349207

load demand Forecasting is a crucial part in modern energy management systems, grid operators in resource allocation and maintaining grid stability. This research paper introduces an innovative methodology for load demand forecasting employing Long Short Term Memory (LSTM) networks, a specific type of recurrent neural network (RNN) adepted by capturing long-Term dependencies within sequential data. This work proposed a LSTM-based load forecasting model that utilizes one year of historical electricity consumption data from the Chhattisgarh state of India. The model is designed to predict day ahead load demand for the subsequent year. As the energy sector advances towards a smarter and more sustainable future, the integration of sophisticated LSTM based forecasting techniques holds significant promise. The technique facilitates the optimization of energy distribution, encourage the seamless integration of renewable energy sources, and the resilience of power grids. The model performance analysis is presented by utilizing Mean Absolute Error (MAE) values as the evaluation metric. © 2024 IEEE.

关键词： Long short-term memory

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3rd international conference on Artificial-Business Analytics, Quantum and machine learning: Trends, Perspectives, and Prospects, Com-IT-Con 2023

3rd International Conference on Artificial-Business Analytic...

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3rd international conference on Artificial-Business Analytics, Quantum and machine learning: Trends, Perspectives, and Prospects, Com-IT-Con 2023

ISBN: (纸本)9789819725076

The proceedings contain 60 papers. The special focus in this conference is on Artificial-Business Analytics, Quantum and machine learning: Trends, Perspectives, and Prospects. The topics include: Particle Swarm Optimization for Efficient data Dissemination in VANETs;a Reactive Approach for High-Accuracy and data-Driven Customer Behaviour Analysis and Prediction;a Brief Survey on Fabric Defect Detection;CNN Based Real Time Detection of Words from Lip Movements and Automated into Text;application of Artificial Intelligence for the Diagnosis of Dementia (Alzheimer): A Systematic Evaluation;shortest Job First with Gateway-Based Resource Management Strategy for Fog Enabled Cloud Computing;software Vulnerability Analysis Based on Statistical Characteristics;hate Speech Detection on Twitter: A Comparative Evaluation of Different machine learning Techniques;detection of Heart Disease Using machine learning;SVM-Based Framework for Breast Cancer Detection;hand Gesture Recognition System Using machine learning;deep learning Method for Plant Disease Recognition and Prediction;Lung Cancer Detection by Using CNN Architecture Models;deep Fake Analyser: A Review Based on Detecting the Deepfakes;predicting the Outcomes of La Liga Matches;Artifact Detection and Removal in EEG: A Review of Methods and Contemporary Usage;analyzing the Employee Attrition Rate: A Comparative Study of Various machine learning Approaches to Foresee Employee Attrition;satellite Image Classification Using Deep learning for Big Earth data;tackling Misinformation Through Tweets: A Comparative Study of Various machine learning Approaches;feature-Based Anomaly Detection in Static Social Networks;framework for Optimising Supply Chain Analysis Using machine learning;evaluation of Load Forecasting in Intelligent Grid Systems Through machine learning Techniques.

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