Search Results - (( java simulation optimization algorithm ) OR ( features detection new algorithm ))

An amplitude independent muscle activity detection algorithm based on adaptive zero crossing technique and mean instantaneous frequency of the sEMG signal by Hameed, Husamuldeen Khalid, Wan Hasan, Wan Zuha, Shafie, Suhaidi, Ahmad, Siti Anom, Jaafar, Haslina

“…A new algorithm has been developed to detect the presence of muscle activities in weak and noisy sEMG signals. …”
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Enhanced grey wolf optimisation algorithm for feature selection in anomaly detection by Almazini, Hussein

“…Thus, this study proposes an enhanced binary grey wolf optimiser (EBGWO) algorithm for FS in anomaly detection to overcome the algorithm issues. …”
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Enhanced feature selections of Adaboost training for face detection using genetic algorithm by Mohd. Zin, Zalhan

“…With these new feature types and larger search space, Genetic Algorithm (GA) is used within the Adaboost framework, to find sets of features which can provide a better cascade of boosted classifiers with a shorter training time. …”
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Attribute reduction based scheduling algorithm with enhanced hybrid genetic algorithm and particle swarm optimization for optimal device selection by Nwogbaga, Nweso Emmanuel, Latip, Rohaya, Affendey, Lilly Suriani, Abdul Rahiman, Amir Rizaan

“…The simulation is implemented with iFogSim and java programming language. …”
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Enhanced AI-based anomaly detection method in the intrusion detection system (IDS) / Kayvan Atefi by Atefi, Kayvan

“…Moreover, shortage of reliable methods on a new dataset for the intrusion detection system and anomaly detection in terms of classification is an issue. …”
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Integrating genetic algorithms and fuzzy c-means for anomaly detection by Chimphlee, Witcha, Abdullah, Abdul Hanan, Sap, Noor Md., Chimphlee, Siriporn, Srinoy, Surat

“…Clustering-based intrusion detection algorithm which trains on unlabeled data in order to detect new intrusions. …”
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Enhanced feature selections of Adaboost training for face detection using genetic algorithm (GABoost) by Mohd. Zin, Zalhan, Khalid, Marzuki, Yusof, Rubiyah

“…Often some of these features are irrelevant and does not contribute directly to the face detection algorithm. …”
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A new hough transform on face detection and recognition using integrated histograms by Zahraddeen, Sufyanu, Fatma Susilawati, Mohamad, Abdulganiyu, Abdu Yusuf, Bashir, Muhammad

“…This study discovered a new feature detection and extraction using the HT. …”
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Feature Selection and Classifier Parameter Estimation for Egg Signal Peak Detection using Gravitational Search Algorithm by Zuwairie, Ibrahim, Mohd Zaidi, Mohd Tumari, Asrul, Adam, Norrima, Mokhtar, Marizan, Mubin, Mohd Ibrahim, Shapiai

“…This study focuses on using GSA method, a new computational intelligence algorithm. Moreover, a rule-based classifier is employed to distinguish a peak point based on the selected features. …”
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Enhancement of Ant Colony Optimization for Grid Job Scheduling and Load Balancing by Husna, Jamal Abdul Nasir

“…Global pheromone update is performed after the completion of processing the jobs in order to reduce the pheromone value of resources. A simulation environment was developed using Java programming to test the performance of the proposed EACO algorithm against existing grid resource management algorithms such as Antz algorithm, Particle Swarm Optimization algorithm, Space Shared algorithm and Time Shared algorithm, in terms of processing time and resource utilization. …”
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Ant colony optimization algorithm for load balancing in grid computing by Ku-Mahamud, Ku Ruhana, Mohamed Din, Aniza

“…The proposed algorithm is known as the enhance ant colony optimization (EACO). …”
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Improved intrusion detection algorithm based on TLBO and GA algorithms by Aljanabi, Mohammad, Mohd Arfian, Ismail

“…The proposed method combined the New Teaching-Learning-Based Optimization Algorithm (NTLBO), Support Vector Machine (SVM), Extreme Learning Machine (ELM), and Logistic Regression (LR) (feature selection and weighting) NTLBO algorithm with supervised machine learning techniques for Feature Subset Selection (FSS). …”
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Intrusion Detection in Mobile Ad Hoc Networks Using Transductive Machine Learning Techniques by Farhan, Farhan Abdel-Fattah Ahmad

“…The transductive conformal prediction and outlier detection have been employed for feature selection algorithm. …”
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Shot boundary detection based on orthogonal polynomial by Abdulhussain, Sadiq H., Ramli, Abd Rahman, Mahmmod, Basheera M., Saripan, Mohd Iqbal, Al-Haddad, Syed Abdul Rahman, Jassim, Wissam A.

“…The Features are derived from orthogonal transform domain (moments) to detect the hard transitions in video sequences. …”
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A new SMS spam detection method using both Content-Based and non Content-Based features by Sulaiman N.F., Jali M.Z.

Pre-processing strategies for skin detection using MLP by Chelsia Amy Doukim

“…The Sum of Weights strategy also gave 1.37% more correct detection than the best combining skin feature strategy. …”
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ABC: android botnet classification using feature selection and classification algorithms by Abdullah, Zubaile, Mohd Saudi, Madihah, Anuar, Nor Badrul

“…Due to the fast modifications in the technologies used by malicious application (app) developers, there is an urgent need for more advanced techniques for Android botnet detection. In this paper, a new approach for Android botnet classification based on features selection and classification algorithms is proposed. …”
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A multimodal biometric detection system via rotated histograms using hough lines by Fatma Susilawati, Mohamad, Zahraddeen, Sufyanu

“…The new technique can be used to detect the aforementioned biometric traits using the same feature extraction algorithm at limited time, since each biometric trait’s dimensions was drastically reduced. …”
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Hybrid weight deep belief network algorithm for anomaly-based intrusion detection system by Maseer, Ziadoon Kamil

“…The deep learning approach plays a pertinent role in tracing new attacks in cybersecurity. Recently, researchers suggested a deep belief network (DBN) algorithm to construct and build a network intrusion detection system (NIDS) for detecting attacks that have not been seen before. …”
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BFEDroid: A Feature Selection Technique to Detect Malware in Android Apps Using Machine Learning by Chimeleze C., Jamil N., Ismail R., Lam K.-Y., Teh J.S., Samual J., Akachukwu Okeke C.