Multiple Levels of Abstraction in the Simulation of Microthreaded Many-Core Architectures

Uddin, Imad

doi:10.4236/ojmsi.2015.34017

Cited by 7 publications

(9 citation statements)

References 36 publications

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“…In every country, dealing with terrorism is the top most priority of the government. ey seek for techniques to Input: the whole dataset of GTD along with labels Output: optimized values of W and b Data: GTD Datasets (1) W [1..L] � random numbers //Glorot Uniform initializer (2) b [1..L] � random numbers (3) while i ≤ num iteration do (4) k ⟵ 1 (5) while j ≤ L do (6) Z [j] � W [j]T .A [j− 1] + b [j] 7A [j] � g(Z [j] )//g(Z) � max(0, z) (8) increment j by 1 (9) L(A [L] , Y) � − (1/m) m i�0 Y i log(A [L] i ) //Binary cross-entropy loss (10) k ⟵ L (11) while k ≥ 0 do (12) W [k] � W [k] − αzL/zW[k] (13) b [k] � b [k] − αzL/zb[k] (14) decrement k by 1 ALGORITHM 2: e training of deep neural network using gradient descent optimization algorithm. Complexity understand the different factors involved in terrorism and how to deal with those factors in order to completely stop or reduce terrorist activities.…”

Section: Resultsmentioning

confidence: 99%

“…e curve shows two parameters: true-positive rate (TPR) and false-positive rate (FPR). ese parameters are defined in equation (8).…”

Section: Roc Curvementioning

confidence: 99%

“…NN and particularly DNN are getting popularity mainly because of the fact that a huge amount of labelled data is available recently. e advancements in computer technologies [7][8][9] have been able to create much powerful computer systems to perform the required computation in DNN. In this paper, NN and DNN models are used to make predictions of different factors that lead to terrorist activities.…”

Section: Introductionmentioning

confidence: 99%

“…e training of neural network with gradient descent optimization algorithm 8. Complexity size equal to number of classes squared.…”

mentioning

confidence: 99%

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Prediction of Future Terrorist Activities Using Deep Neural Networks

et al. 2020

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One of the most important threats to today’s civilization is terrorism. Terrorism not only disturbs the law and order situations in a society but also affects the quality of lives of humans and makes them suppressed physically and emotionally and deprives them of enjoying life. The more the civilizations have advanced, the more the people are working towards exploring different mechanisms to protect the mankind from terrorism. Different techniques have been used as counterterrorism to protect the lives of individuals in society and to improve the quality of life in general. Machine learning methods have been recently explored to develop techniques for counterterrorism based on artificial intelligence (AI). Since deep learning has recently gained more popularity in machine learning domain, in this paper, these techniques are explored to understand the behavior of terrorist activities. Five different models based on deep neural network (DNN) are created to understand the behavior of terrorist activities such as is the attack going to be successful or not? Or whether the attack is going to be suicide or not? Or what type of weapon is going to be used in the attack? Or what type of attack is going to be carried out? Or what region is going to be attacked? The models are implemented in single-layer neural network (NN), five-layer DNN, and three traditional machine learning algorithms, i.e., logistic regression, SVM, and Naïve Bayes. The performance of the DNN is compared with NN and the three machine learning algorithms, and it is demonstrated that the performance in DNN is more than 95% in terms of accuracy, precision, recall, and F1-Score, while ANN and traditional machine learning algorithms have achieved a maximum of 83% accuracy. This concludes that DNN is a suitable model to be used for predicting the behavior of terrorist activities. Our experiments also demonstrate that the dataset for terrorist activities is big data; therefore, a DNN is a suitable model to process big data and understand the underlying patterns in the dataset.

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Section: Resultsmentioning

confidence: 99%

“…e curve shows two parameters: true-positive rate (TPR) and false-positive rate (FPR). ese parameters are defined in equation (8).…”

Section: Roc Curvementioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

“…e training of neural network with gradient descent optimization algorithm 8. Complexity size equal to number of classes squared.…”

mentioning

confidence: 99%

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Prediction of Future Terrorist Activities Using Deep Neural Networks

et al. 2020

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“…e main objective of this research work is the novel technique of identifying different parameters that can contribute to the absenteeism, preprocess the data to be processed by deep learning algorithms efficiently, and then devise a deep learning algorithm with most recent optimization techniques that can make prediction of absenteeism with reasonable accuracy. Even though many researchers have worked on absenteeism and have demonstrated to find Artificial Intelligence-based solutions for it, no one has studied an effective mechanism of understanding factors of absenteeism using deep learning, which is becoming very popular recently with the increased data and increased computational [58][59][60][61][62] power. According to the knowledge of the authors, no comprehensive work is dedicated to absenteeism prediction using deep learning algorithms.…”

Section: Backward Propagationmentioning

confidence: 99%

An Enhanced Deep Neural Network for Predicting Workplace Absenteeism

et al. 2020

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Organizations can grow, succeed, and sustain if their employees are committed. The main assets of an organization are those employees who are giving it a required number of hours per month, in other words, those employees who are punctual towards their attendance. Absenteeism from work is a multibillion-dollar problem, and it costs money and decreases revenue. At the time of hiring an employee, organizations do not have an objective mechanism to predict whether an employee will be punctual towards attendance or will be habitually absent. For some organizations, it can be very difficult to deal with those employees who are not punctual, as firing may be either not possible or it may have a huge cost to the organization. In this paper, we propose Neural Networks and Deep Learning algorithms that can predict the behavior of employees towards punctuality at workplace. The efficacy of the proposed method is tested with traditional machine learning techniques, and the results indicate 90.6% performance in Deep Neural Network as compared to 73.3% performance in a single-layer Neural Network and 82% performance in Decision Tree, SVM, and Random Forest. The proposed model will provide a useful mechanism to organizations that are interested to know the behavior of employees at the time of hiring and can reduce the cost of paying to inefficient or habitually absent employees. This paper is a first study of its kind to analyze the patterns of absenteeism in employees using deep learning algorithms and helps the organization to further improve the quality of life of employees and hence reduce absenteeism.

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Efficient Processing of Image Processing Applications on CPU/GPU

Naz

Malik

Khurshid

et al. 2020

Mathematical Problems in Engineering

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Heterogeneous systems have gained popularity due to the rapid growth in data and the need for processing this big data to extract useful information. In recent years, many healthcare applications have been developed which use machine learning algorithms to perform tasks such as image classification, object detection, image segmentation, and instance segmentation. The increasing amount of big visual data requires images to be processed efficiently. It is common that we use heterogeneous systems for such type of applications, as processing a huge number of images on a single PC may take months of computation. In heterogeneous systems, data are distributed on different nodes in the system. However, heterogeneous systems do not distribute images based on the computing capabilities of different types of processors in the node; therefore, a slow processor may take much longer to process an image compared to a faster processor. This imbalanced workload distribution observed in heterogeneous systems for image processing applications is the main cause of inefficient execution. In this paper, an efficient workload distribution mechanism for image processing applications is introduced. The proposed approach consists of two phases. In the first phase, image data are divided into an ideal split size and distributed amongst nodes, and in the second phase, image data are further distributed between CPU and GPU according to their computation speeds. Java bindings for OpenCL are used to configure both the CPU and GPU to execute the program. The results have demonstrated that the proposed workload distribution policy efficiently distributes the images in a heterogeneous system for image processing applications and achieves 50% improvements compared to the current state-of-the-art programming frameworks.

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Multiple Levels of Abstraction in the Simulation of Microthreaded Many-Core Architectures

Cited by 7 publications

References 36 publications

Prediction of Future Terrorist Activities Using Deep Neural Networks

Prediction of Future Terrorist Activities Using Deep Neural Networks

An Enhanced Deep Neural Network for Predicting Workplace Absenteeism

Efficient Processing of Image Processing Applications on CPU/GPU

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