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91.
Umair Muneer Butt Hadiqa Aman Ullah Sukumar Letchmunan Iqra Tariq Fadratul Hafinaz Hassan Tieng Wei Koh 《计算机、材料和连续体(英文)》2023,74(3):5017-5033
Human Activity Recognition (HAR) is an active research area due to its applications in pervasive computing, human-computer interaction, artificial intelligence, health care, and social sciences. Moreover, dynamic environments and anthropometric differences between individuals make it harder to recognize actions. This study focused on human activity in video sequences acquired with an RGB camera because of its vast range of real-world applications. It uses two-stream ConvNet to extract spatial and temporal information and proposes a fine-tuned deep neural network. Moreover, the transfer learning paradigm is adopted to extract varied and fixed frames while reusing object identification information. Six state-of-the-art pre-trained models are exploited to find the best model for spatial feature extraction. For temporal sequence, this study uses dense optical flow following the two-stream ConvNet and Bidirectional Long Short Term Memory (BiLSTM) to capture long-term dependencies. Two state-of-the-art datasets, UCF101 and HMDB51, are used for evaluation purposes. In addition, seven state-of-the-art optimizers are used to fine-tune the proposed network parameters. Furthermore, this study utilizes an ensemble mechanism to aggregate spatial-temporal features using a four-stream Convolutional Neural Network (CNN), where two streams use RGB data. In contrast, the other uses optical flow images. Finally, the proposed ensemble approach using max hard voting outperforms state-of-the-art methods with 96.30% and 90.07% accuracies on the UCF101 and HMDB51 datasets. 相似文献
92.
Saeed Mohsen Sherif S. M. Ghoneim Mohammed S. Alzaidi Abdullah Alzahrani Ashraf Mohamed Ali Hassan 《计算机、材料和连续体(英文)》2023,75(3):5271-5286
Classification of electroencephalogram (EEG) signals for humans can be achieved via artificial intelligence (AI) techniques. Especially, the EEG signals associated with seizure epilepsy can be detected to distinguish between epileptic and non-epileptic regions. From this perspective, an automated AI technique with a digital processing method can be used to improve these signals. This paper proposes two classifiers: long short-term memory (LSTM) and support vector machine (SVM) for the classification of seizure and non-seizure EEG signals. These classifiers are applied to a public dataset, namely the University of Bonn, which consists of 2 classes –seizure and non-seizure. In addition, a fast Walsh-Hadamard Transform (FWHT) technique is implemented to analyze the EEG signals within the recurrence space of the brain. Thus, Hadamard coefficients of the EEG signals are obtained via the FWHT. Moreover, the FWHT is contributed to generate an efficient derivation of seizure EEG recordings from non-seizure EEG recordings. Also, a k-fold cross-validation technique is applied to validate the performance of the proposed classifiers. The LSTM classifier provides the best performance, with a testing accuracy of 99.00%. The training and testing loss rates for the LSTM are 0.0029 and 0.0602, respectively, while the weighted average precision, recall, and F1-score for the LSTM are 99.00%. The results of the SVM classifier in terms of accuracy, sensitivity, and specificity reached 91%, 93.52%, and 91.3%, respectively. The computational time consumed for the training of the LSTM and SVM is 2000 and 2500 s, respectively. The results show that the LSTM classifier provides better performance than SVM in the classification of EEG signals. Eventually, the proposed classifiers provide high classification accuracy compared to previously published classifiers. 相似文献
93.
Mohamed Fahim Hassan Mohamed Zribi Hamed M. K. Alazemi 《International Journal of Control, Automation and Systems》2011,9(2):237-248
This paper proposes an optimal recursive estimator to estimate the states of a stochastic discrete time linear dynamic system
when the states of the system are constrained with inequality constraints. The case when the constraints are strictly satisfied
is treated independently from the case when some of the constraints are violated. For the first case, the well known Kalman
filter estimator is used. In the second case, an algorithm which uses a series of successive orthogonalizations on the measurement
subspaces is employed to obtain the optimal estimate. It is shown that the proposed estimator has several attractive properties
such that it is an unbiased estimator. More importantly, compared to other estimator found in the literature, the proposed
estimator needs less computational efforts, is numerically more stable and it leads to a smaller variance. To show the effectiveness
of the proposed estimator, several simulation results are presented and discussed. 相似文献
94.
Detection of rapidly evolving malware requires classification techniques that can effectively and efficiently detect zero-day
attacks. Such detection is based on a robust model of benign behavior and deviations from that model are used to detect malicious
behavior. In this paper we propose a low-complexity host-based technique that uses deviations in static file attributes to
detect malicious executables. We first develop simple statistical models of static file attributes derived from the empirical
data of thousands of benign executables. Deviations among the attribute models of benign and malware executables are then
quantified using information-theoretic (Kullback-Leibler-based) divergence measures. This quantification reveals distinguishing
attributes that are considerably divergent between benign and malware executables and therefore can be used for detection.
We use the benign models of divergent attributes in cross-correlation and log-likelihood frameworks to classify malicious
executables. Our results, using over 4,000 malicious file samples, indicate that the proposed detector provides reasonably
high detection accuracy, while having significantly lower complexity than existing detectors. 相似文献
95.
Yun Sheng Alexei Sourin Gabriela Gonzalez Castro Hassan Ugail 《The Visual computer》2010,26(6-8):975-984
Three-dimensional (3D) representations of complex geometric shapes, especially when they are reconstructed from magnetic resonance imaging (MRI) and computed tomography (CT) data, often result in large polygon meshes which require substantial storage for their handling, and normally have only one fixed level of detail (LOD). This can often be an obstacle for efficient data exchange and interactive work with such objects. We propose to replace such large polygon meshes with a relatively small set of coefficients of the patchwise partial differential equation (PDE) function representation. With this model, the approximations of the original shapes can be rendered with any desired resolution at interactive rates. Our approach can directly work with any common 3D reconstruction pipeline, which we demonstrate by applying it to a large reconstructed medical data set with irregular geometry. 相似文献
96.
Stock market prediction is regarded as a challenging task in financial time-series forecasting. The central idea to successful stock market prediction is achieving best results using minimum required input data and the least complex stock market model. To achieve these purposes this article presents an integrated approach based on genetic fuzzy systems (GFS) and artificial neural networks (ANN) for constructing a stock price forecasting expert system. At first, we use stepwise regression analysis (SRA) to determine factors which have most influence on stock prices. At the next stage we divide our raw data into k clusters by means of self-organizing map (SOM) neural networks. Finally, all clusters will be fed into independent GFS models with the ability of rule base extraction and data base tuning. We evaluate capability of the proposed approach by applying it on stock price data gathered from IT and Airlines sectors, and compare the outcomes with previous stock price forecasting methods using mean absolute percentage error (MAPE). Results show that the proposed approach outperforms all previous methods, so it can be considered as a suitable tool for stock price forecasting problems. 相似文献
97.
Mehrdad Hakimi-Asiabar Seyyed Hassan Ghodsypour Reza Kerachian 《Applied Soft Computing》2010,10(4):1151-1163
Optimal multi-reservoir operation is a multi-objective problem in nature and some of its objectives are nonlinear, non-convex and multi-modal functions. There are a few areas of application of mathematical optimization models with a richer or more diverse history than in reservoir systems optimization. However, actual implementations remain limited or have not been sustained.Genetic Algorithms (GAs) are probabilistic search algorithms that are capable of solving a variety of complex multi-objective optimization problems, which may include non-linear, non-convex and multi-modal functions. GA is a population based global search method that can escape from local optima traps and find the global optima. However GAs have some drawbacks such as inaccuracy of the intensification process near the optimal set.In this paper, a new model called Self-Learning Genetic Algorithm (SLGA) is presented, which is an improved version of the SOM-Based Multi-Objective GA (SBMOGA) presented by Hakimi-Asiabar et al. (2009) [45]. The proposed model is used to derive optimal operating policies for a three-objective multi-reservoir system. SLGA is a new hybrid algorithm which uses Self-Organizing Map (SOM) and Variable Neighborhood Search (VNS) algorithms to add a memory to the GA and improve its local search accuracy. SOM is a neural network which is capable of learning and can improve the efficiency of data processing algorithms. The VNS algorithm can enhance the local search efficiency in the Evolutionary Algorithms (EAs).To evaluate the applicability and efficiency of the proposed methodology, it is used for developing optimal operating policies for the Karoon-Dez multi-reservoir system, which includes one-fifth of Iran's surface water resources. The objective functions of the problem are supplying water demands, generating hydropower energy and controlling water quality in downstream river. 相似文献
98.
Nik R Hassan 《欧洲信息系统杂志》2011,20(4):456-476
Following the Kleinian spirit, this study takes a critical view of the existing orthodoxy within information systems (IS) and reframes the ongoing discussion concerning the intellectual core, identity and disciplinary status of IS using the disciplinary analysis of Michel Foucault and Stephen Toulmin. Instead of limiting the discussion to specific paradigms, topics, subjects or content, it focuses on the characteristics, rules and goals of IS as an academic field. A disciplinary lens is used to frame what it means to be a field, discipline and science, and in the process the study uncovers four doxas that have shaped the development of the IS field: (1) the IS research community sees no difference between fields, disciplines or sciences; (2) IT changes so rapidly, and thus the IS field needs to change to remain relevant; (3) disciplines are by definition rigid, inflexible and uni-theoretical and (4) because IS is pluralistic, IS should not become a discipline. This study's analyses of the IS field's discursive formation and intellectual ideals offer novel perspectives that allow for the integration of the IS field's plurality and diversity. To transform the IS field from its multimodal existence into a vibrant, diverse, academically and socially relevant and influential discipline, the study proposes actionable strategies that include (1) agreeing on the intellectual ideals for IS, (2) focusing on conceptual formation, (3) focusing on theory construction, (4) erecting genealogical boundaries and (5) fostering the development of professional bodies. 相似文献
99.
Ramsey F. Hamade Hassan A. Artail Mohamad Y. Jaber 《Computers & Industrial Engineering》2011,61(3):709-720
The notion that ‘attitude drives behavior’ manifests itself in a variety of ways in educational and occupational settings. As applied to CAD competence development, industrial training of novice CAD users on their way to becoming competent CAD users consume a lot of corporate resources. This paper is the third paper in the line of research that attempts to answer the question having to do with what it takes to make a competent CAD user. Specifically, we examine the CAD-specific factors revolving around the trainees’ willingness-to-learn CAD.These factors are analyzed in two stages. At the start of the training, trainees’ initial attitude towards CAD is established by means of a short questionnaire. Afterwards, throughout the training, trainees’ behavior (online and offline practice) is gauged and, in turn, a relation is established to illustrate how this practice leads to the development of CAD-specific skills. For this purpose, another short questionnaire was utilized. Strong correlations were established relating the trainees’ CAD-specific behavior with the CAD-specific outcomes of learning CAD syntax.Furthermore, and in order to assess the quality of the trainees’ learning of CAD, overall competence was monitored throughout the study via performance measures that describe the time it took the trainees to build test models (speed), which reflects upon the ability to learn the syntax of the CAD tool (declarative knowledge). The sophistication of the models is also used as another measure. Correlating the trainees’ character attributes with these assessed measures, it was found that the stronger is the trainees’ will to learn CAD, the stronger is the likelihood to learn faster. Perhaps more importantly, trainees with initial favorable attitude toward CAD were shown to develop increasingly positive behavior that manifested through additional practice and other forms of visible effort. 相似文献
100.
Guillaume F. Remy Hassan B. Kazemian Phil Picton 《Neural computing & applications》2011,20(6):879-887
This paper is to introduce an application of Computational Intelligence (CI) to Moving Picture Expert Group-4 (MPEG-4) video
compression over IEEE.802.15.1 wireless communication, known as Bluetooth 1.2, in order to improve picture quality. The 2.4 GHz
Industrial, Scientific and Medical frequency band is used for the IEEE.802.15.1 standard. IEEE.802.15.1 can be affected by
noise and interference due to other neighboring wireless devices sharing the same frequency carrier. The noise and interference
create difficulties in ascertaining an accurate real-time transmission rate at the receiving end. Furthermore, the MPEG-4
codec is an object-oriented compression system and demands a high bandwidth. It is therefore difficult to avoid excessive
delay, image quality degradation and/or data loss during MPEG-4 video transmission over standard systems. A new buffer entitled
‘buffer added’ has been introduced at the input of the Bluetooth 1.2 device. This buffer is controlled by a Rule-Based Fuzzy
(RBF) logic controller at the input and a neural-fuzzy controller (NFC) at the output. The two new fuzzy rules manipulate
and supervise the flow of video over the Bluetooth 1.2 standard. The computer simulation results illustrate the comparison
between a non-CI video transmission over Bluetooth 1.2 and the proposed design, confirming that the applications of RBF and
NFC do improve the image quality, reduce data loss and reduce time delay. 相似文献