Search Results - (( parameter optimization sensor algorithm ) OR ( pattern detection learning algorithm ))*

Neural network algorithm development for Ion Sensitive Field Effect Transistor (ISFET) sensor / Muhammad Nasrul Hakim Adenan by Adenan, Muhammad Nasrul Hakim

“…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 by Hakim Adenan, Muhammad Nasrul, Zolkapli, Maizatul

“…The ANN model performance can be optimized by altering certain parameters in the learning algorithm. …”
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Classification of heart sound signals for the detection of heart diseases / N. Shamsuddin and M. N. Taib by Shamsuddin, N., Taib, M. N.

“…The obtained frequency pattern features were fed to the FFNN and trained using Resilient Backpropagation (RPROP) algorithm. …”
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Smart waste management system with IoT monitoring by Kalitazan, Sachein, Muniswaran, Suvarshan, Velan, Sheshan, Muniswaran, Suvathithan, Shah, Dhanesh

“…Through advanced data analytics and machine learning algorithms, the platform predicts waste accumulation patterns, optimizes collection routes, schedules pickups based on fill-level data, and detects any abnormal conditions. …”
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Bat Algorithm for Complex Event Pattern Detection in Sentiment Analysis by Kabir Ahmad, Farzana, Kamaruddin, Siti Sakira, Yusof, Yuhanis, Yusoff, Nooraini

“…Thus, this study proposed a Bat Algorithm (BA) to address the complex learning structure of DBN in detecting sentiment patterns. …”
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An algorithm for Elliott Waves pattern detection by Vantuch, T., Zelinka, I., Vasant, P.

“…The Random Decision Forest and the Support Vector Machine are the machine learning algorithms employed for this task. The accuracy of trend prediction above 70 proves the relevancy of EW patterns on stock market data as well as the validity of the algorithm as a tool for detection of such patterns. …”
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An algorithm for Elliott Waves pattern detection by Vantuch, T., Zelinka, I., Vasant, P.

“…The Random Decision Forest and the Support Vector Machine are the machine learning algorithms employed for this task. The accuracy of trend prediction above 70 proves the relevancy of EW patterns on stock market data as well as the validity of the algorithm as a tool for detection of such patterns. …”
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Multi-mobile agent itinerary planning algorithms for data gathering in wireless sensor networks: a review paper by Qadori, Huthiafa Q., Ahmad Zukarnain, Zuriati, Mohd Hanapi, Zurina, Subramaniam, Shamala

“…The review shows that most of the algorithms used one parameter to find the optimal number of mobile agents in multi-agent itinerary planning without utilizing other parameters. …”
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Parameter adaptation for ant colony system in wireless sensor network by Abdul Nasir, Husna Jamal, Ku-Mahamud, Ku Ruhana, Kamioka, Eiji

“…The Ant Colony System (ACS) algorithm has been applied in solving packet routing problems in Wireless Sensor Networks (WSNs). …”
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Whale optimization algorithm based on tent chaotic map for feature selection in soft sensors by AlRijeb, Mothena Fakhri Shaker, Othman, Mohammad Lutfi, Ishak, Aris, Hassan, Mohd Khair, Albaker, Baraa Munqith

“…One of the powerful optimization algorithms that is used for feature selection is the Whale Optimization Algorithm (WOA), which is a nature-inspired metaheuristic optimization algorithm that mimics the social behavior of humpback whales. …”
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Delay and energy-aware routing for efficient data collection in wireless sensor networks / Ihsan Ali by Ihsan , Ali

“…The IDBA-AFW evaluates the fitness of relay nodes based on multiple criteria, such as energy efficiency, throughput, and end-to-end delay. Both Bat Algorithm parameters and AFW parameters are adaptively tuned to balance exploration and exploitation throughout the optimization process. …”
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A Review of Unsupervised Machine Learning Frameworks for Anomaly Detection in Industrial Applications by Usmani, U.A., Happonen, A., Watada, J.

“…Without human input, these algorithms discover patterns or groupings in the data. …”
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Algorithm enhancement for host-based intrusion detection system using discriminant analysis by Dahlan, Dahliyusmanto

“…Misuse detection algorithms model know attack behavior. They compare sensor data to attack patterns learned from the training data. …”
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An empirical study of pattern leakage impact during data preprocessing on machine learning-based intrusion detection models reliability by Bouke, Mohamed Aly, Abdullah, Azizol

“…In this paper, we investigate the impact of pattern leakage during data preprocessing on the reliability of Machine Learning (ML) based intrusion detection systems (IDS). …”
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Optimization of user comfort index for ambient intelligence using dynamic inertia weight artificial bees colony optimization algorithm by Farah Nur Arina, Baharudin, Nor Azlina, Ab. Aziz, Mohamad Razwan, Abdul Malek, Zuwairie, Ibrahim

“…In this work, these parameters are optimized using dynamic inertia weight artificial bees colony (DIW-ABC) optimization algorithm. …”
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A new routing mechanism for energy-efficient in bluetooth mesh-low power nodes based on wireless sensor network by Almuslehi, Hussein Saad Mohammed

“…Compared to the Ant Colony Optimization-Genetic Algorithm (ACO-GA) and Ant Colony Optimization- Hierarchical Clustering Mechanism (ACOHCM), the ACO algorithm shows superior power savings and efficiency. …”
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A study on advanced statistical analysis for network anomaly detection by Ngadi, Md. Asri, Idris, Mohd. Yazid, Abdullah, Abd. Hanan

“…Misuse detection algorithms model know attack behavior. They compare sensor data to attack patterns learned from the training data. …”
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Deployment of Optimized Algorithm for MPEG-4 Data Over Wireless Multimedia Sensor Network by Hashim, Norlezah, Idris, Fakrulradzi, Syed Ariffin, Dr Sharifah Hafizah, Yunus, Farizah, Fisal, Norsheila

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Single Fitness Function to Optimize Energy using Genetic Algorithms for Wireless Sensor Network by Ismail Abdullah, Kalid Abdlkader Marsal

Random Undersampling on Imbalance Time Series Data for Anomaly Detection by Saripuddin M., Suliman A., Syarmila Sameon S., Jorgensen B.N.