Search Results - (( using optimization sensor algorithm ) OR ( data application learning algorithm ))
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Neural network algorithm development for Ion Sensitive Field Effect Transistor (ISFET) sensor / Muhammad Nasrul Hakim Adenan
Published 2013“…The ANN model performance can be optimized by altering certain parameters in the learning algorithm. …”
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Neural network algorithm development for Ion Sensitive Field Effect Transistor (ISFET) sensor: article / Muhammad Nasrul Hakim Adenan and Maizatul Zolkapli
Published 2013“…The ANN model performance can be optimized by altering certain parameters in the learning algorithm. …”
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A Survey Of Supervised Machine Learning In Wireless Sensor Network: A Power Management Perspective
Published 2013“…This survey paper is focused on the discussion of best optimal path routing algorithms in wireless sensor networks by using supervised machine learning approaches. …”
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Investigating optimal smartphone placement for identifying stairs movement using machine learning
Published 2023“…It was observed that, by using the Decision Tree and Random Forest algorithm 100% accuracy was achieved, when the smartphone was placed at the thigh during stair movement. …”
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Machine learning in botda fibre sensor for distributed temperature measurement
Published 2023“…An alternative method is proposed, utilizing machine learning algorithms. Therefore, this thesis explores the comparative analysis for BOTDA data processing using the six most suited machine learning algorithms. …”
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IOT-based fertigation system / Mohamad Amir Furqan Darus
Published 2024“…It integrates the MDD3A Cytron motor driver for water pump control and the Ph sensor to monitor soil acidity or alkalinity. Using advanced algorithms and machine learning techniques, the system nalyses the collected data to determine optimal irrigation and fertilization requirements. …”
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Water quality monitoring using machine learning and IoT: a review
Published 2024“…The paper explores various ML algorithms, including supervised and unsupervised learning and deep learning, along with their applications, and discusses the use of IoT sensors for real-time monitoring of water quality parameters such as pH, dissolved oxygen, temperature, and turbidity.…”
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An Improved Grasshopper Optimization Algorithm Based Echo State Network for Predicting Faults in Airplane Engines
Published 2020“…This problem is often formulated as a typical optimization problem. Metaheuristic algorithms are known to be excellent tools for solving optimization problems. …”
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Class binarization with self-adaptive algorithm to improve human activity recognition
Published 2018“…Two publicly activity datasets are used; Wireless Sensor Data Mining (WISDM) and Physical Activity Monitoring for Aging People (PAMAP2). …”
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A survey on the design aspects and opportunities in age-aware UAV-aided data collection for sensor networks and Internet of Things applications
Published 2023“…Numerous studies have proposed solutions to overcome the above-mentioned challenges, including adopting several optimization and machine learning (ML) algorithms for diverse architectural setups to minimize the AoI. …”
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Wearables-assisted smart health monitoring for sleep quality prediction using optimal deep learning
Published 2023“…For sleep quality prediction, the WSHMSQP-ODL model uses the deep belief network (DBN) model. To enhance the sleep quality prediction performance of the DBN model, the enhanced seagull optimization (ESGO) algorithm is used for hyperparameter tuning. …”
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Development of IoT-based automated dynamic emergency response system against fire incidents in academic building
Published 2023“…The proposed system leverages IoT technology, wireless and bluetooth sensor networks to gather real-time data from various sensors and devices installed in the site and uses machine learning algorithms to predict and prevent potential fire incidents. …”
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Web service applications and consumer environments based on ICT-driven optimization
Published 2022“…In the field of web service application and consumer environment optimization, it has been shown that the introduction of network embedding methods can effectively alleviate the problems such as data sparsity in the recommendation process. …”
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Classification of Agarwood using ANN
Published 2012“…The network developed based on three layers feed forward network and the back propagation learning algorithm was used in executing the network training. …”
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Framework for stream clustering of trajectories based on temporal micro clustering technique
Published 2018“…Many existing algorithms for stream clustering of trajectory stream data exploit the time window technique which partition trajectory stream data into time-bins and cluster the segments in each timebin separately. …”
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Automatic nutrient mixer and pH Level for hydroponic application / Hariz Akmal Azizan
Published 2024“…Future work could involve integrating advanced machine learning algorithms to optimize nutrient dosing and implement self-harvesting mechanisms, thereby enhancing plant production and contributing to the development of more efficient and sustainable hydroponic systems for agricultural practices.…”
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Bayesian Network Classifiers for Damage Detection in Engineering Material
Published 2007“…To validate the classi¯ers and the proposed algorithm, two data sets were used, the ¯rst set represents voltage amplitudes of Lamb-waves produced and col- lected by sensors and actuators mounted on the surface of laminates contain di®erent arti¯cial damages. …”
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A centralized localization algorithm for prolonging the lifetime of wireless sensor networks using particle swarm optimization in the existence of obstacles
Published 2017“…Particle Swarm Optimization has been used in the proposed algorithm to minimize the fitness function. …”
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A performance comparison study of pattern recognition systems for volatile organic compounds detection / Emilia Noorsal, Muhammad Khusairi Osman and Norfadzilah Mokhtar
Published 2007“…In this project, the networks were trained using certain types training algorithm depending on the types of networks; Levenberg Marquardt (LM) for the MLP, competitive network for the LVQ and hybrid learning for ANFIS. …”
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