Search Results - (( features detection device algorithm ) OR ( java application using algorithm ))

A malware analysis and detection system for mobile devices / Ali Feizollah by Ali, Feizollah

“…We then used feature selection algorithms and deep learning algorithms to build a detection model. …”
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Comparison of Feature Extractors for Real-Time Object Detection on Android Smartphone by Saipullah, Khairul Muzzammil

“…However due to the development of embedded hardware and object detection algorithm, current embedded device may be able to execute the object detection algorithm in real-time. …”
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ANALYSIS OF REAL-TIME OBJECT DETECTION METHODS FOR ANDROID SMARTPHONE by Saipullah, Khairul Muzzammil

“…However due to the development of embedded hardware and object detection algorithm, current embedded device may be able to execute the object detection algorithm in real-time. …”
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Detection of muscle activities in the sEMG signal by using frequency features and adaptive decision threshold by Hameed, Husamuldeen Khalid, Wan Hasan, Wan Zuha, Shafie, Suhaidi, Ahmad, Siti Anom, Jaafar, Haslina, Inche Mat, Liyana Najwa

“…Moreover, the decision threshold value must be adaptive to the changes in the sEMG signal characteristics to reduce the number of false alarms that may arise with the fixed threshold and lead to unintended movements to these devices. In this paper, an amplitude-independent algorithm had been developed with an adaptive decision threshold; the algorithm employed only frequency features of the sEMG signal to detect muscle activities. …”
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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

“…The algorithm does not employ any amplitude features in the detection process and employs only frequency features of the sEMG signal; therefore it is amplitude independent and can detect muscle activities in signals that have low signal to noise ratio. …”
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Bio-inspired for Features Optimization and Malware Detection by Mohd Faizal, Ab Razak, Nor Badrul, Anuar, Fazidah, Othman, Ahmad, Firdaus, Firdaus, Afifi, Rosli, Salleh

“…A static analysis technique with machine learning classifier is developed from the permission features noted in the Android mobile device for detecting the malware applications. …”
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Bio-inspired for Features Optimization and Malware Detection by Razak, Mohd Faizal Ab, Anuar, Nor Badrul, Othman, Fazidah, Firdaus, Ahmad, Afifi, Firdaus, Salleh, Rosli

“…A static analysis technique with machine learning classifier is developed from the permission features noted in the Android mobile device for detecting the malware applications. …”
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Detection of muscle activities in the sEMG signal by using frequency features and adaptive decision threshold by Hameed, Husamuldeen Khalid, Wan Hasan, Wan Zuha, Shafie, Suhaidi, Ahmad, Siti Anom, Jaafar, Haslina, Inche Mat, Liyana Najwa

“…Moreover, the decision threshold value must be adaptive to the changes in the sEMG signal characteristics to reduce the number of false alarms that may arise with the fixed threshold and lead to unintended movements to these devices. In this paper, an amplitude-independent algorithm had been developed with an adaptive decision threshold; the algorithm employed only frequency features of the sEMG signal to detect muscle activities. …”
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Android malware detection using PMCC heatmap and Fuzzy Unordered Rule Induction Algorithm (FURIA) by Nur Khairani, Kamarudin, Ahmad Firdaus, Zainal Abidin, Azlee, Zabidi, Ferda, Ernawan, Syifak, Izhar Hisham, Mohd Faizal, Ab Razak

“…However, in machine learning intelligence detection, too many insignificant features will decrease the percentage of the detection’s accuracy. …”
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Hybrid intelligent approach for network intrusion detection by Al-Mohammed, Wael Hasan Ali

“…Feature selection has decreased the features from 41 to 21 features for intrusion detection and later normalization method is employed to perform and reduce the differences among the data. …”
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Automated feature selection using boruta algorithm to detect mobile malware by Che Akmal, Che Yahaya, Ahmad Firdaus, Zainal Abidin, Salwana, Mohamad, Ernawan, Ferda, Mohd Faizal, Ab Razak

“…This research proposed automated feature selection using Boruta algorithm to detect the malware. …”
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Anomaly detection in system log files using machine learning algorithms / Zahedeh Zamanian by Zahedeh, Zamanian

“…Moreover, log files have a lot of irrelevant and redundant features that act as noise. Also, log files are heterogenous and cannot fed them directly in machine learning algorithms. …”
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An enhanced android botnet detection approach using feature refinement by Anwar, Shahid

“…These features are able to disclose the sensitive information stored on the Android mobile devices. …”
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Autonomous anomaly detection using density-based features in streaming data / Muhammmad Yunus Iqbal Basheer by Iqbal Basheer, Muhammmad Yunus

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Classification of hand gestures from EMG signals / Diaa Albitar by Albitar, Diaa

“…Neighbourhood Component Analysis (NCA) are used as features selection technique has reduced the features to fourteen. …”
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Data fusion and multiple classifier systems for human activity detection and health monitoring: Review and open research directions by Nweke, Henry Friday, Teh, Ying Wah, Mujtaba, Ghulam, Al-Garadi, Mohammed Ali

“…Recently, deep learning algorithms for automatic feature representation have also been proposed to lessen the burden of reliance on handcrafted features and to increase performance accuracy. …”
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Android mobile malware detection model based on permission features using machine learning approach by Sharfah Ratibah, Tuan Mat

“…Chi-square and information gain algorithms were used for features selection. The aim is to learn the behaviour of permission features that react to the accuracy according to the number of features. …”
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A Cryptojacking Detection System With Product Moment Correlation Coefficient (Pmcc) Heatmap Intelligent by Kong, Jun Hao

“…This study aims to develop a cryptojacking detection system based to the random forest algorithm.…”
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E2IDS: an enhanced intelligent intrusion detection system based on decision tree algorithm by Bouke, Mohamed Aly, Abdullah, Azizol, ALshatebi, Sameer Hamoud, Abdullah, Mohd Taufik

“…The massive volume of data with various dimensions and security features, on the other hand, can influence detection accuracy and raise the computation complexity of these systems. …”
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Automated content sizing using accelerometer by Ng, Yun Hao

“…The purpose of this research is to study the technology of mobile accelerometer in mobile zooming feature. Accelerometer is a kind of device that built in mobile device in order to detect motion. …”
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