A Controlled Environment Model for Dealing with Smart Phone Addiction

Uddin, Imad; Baig, Adeel; Minhas, Abid Ali

doi:10.14569/ijacsa.2018.090973

Cited by 9 publications

(13 citation statements)

References 21 publications

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“…at is, ITN, is the development of nets, defining the population coverage (a ITN ∈ (0, 1]). e second (3) Initialize target network Q′ with weight θ Q′ ⟵ θ Q (4) Initialize target network μ′ with weight θ μ′ ⟵ θ μ (5) Initialize replay buffer R (6) while For every episode do (7) Randomly initialize N for exploration (8) Get initial observation state s 1 (9) while For every step in the episode do //Repeat until s is terminal (10) Section action a t � μ(s t | θ μ ) + N t as per the current policy and exploration strategy (11) Perform action a t and monitor rewards r t and new states t+1 (12) Store (s t , a t , r t , s t+1 ) in R (13) Sample a randomly selected minibatch of N transition (s i , a i , r i , S i+1 ) from R (14)…”

Section: Simulation and Discussionmentioning

confidence: 99%

“…e parameter x is tuned for policy by actor as given in equation (10). Using Temporal Difference error, the policy computed by the action is evaluated by critic as demonstrated in equation (11). e policy decided by the actor is shown by v. e idea of experience replay and separate target network as utilized by Deep Q Network (DQN) [83] is used by DDPG.…”

Section: Deep Deterministic Policymentioning

confidence: 99%

“…When a mosquito sets on the net, it dies due to the insecticide, which disrupts the reproductive cycle. In addition to ITNs, the other malaria preventive policies include Indoor Residual Spraying (IRS) [6], larvicide [7] in bodies of water, and malaria vaccination [8][9][10][11].…”

Section: Introductionmentioning

confidence: 99%

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Optimal Policy Learning for Disease Prevention Using Reinforcement Learning

Khan

Feng

Uddin

et al. 2020

Scientific Programming

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Diseases can have a huge impact on the quality of life of the human population. Humans have always been in the quest to find strategies to avoid diseases that are life-threatening or affect the quality of life of humans. Effective use of resources available to human to control different diseases has always been critical. Researchers are recently more interested to find AI-based solutions to control the human population from diseases due to the overwhelming popularity of deep learning. There are many supervised techniques that have always been applied for disease diagnosis. However, the main problem of supervised based solutions is the availability of data, which is not always possible or not always complete. For instance, we do not have enough data that shows the different states of humans and different states of environments, and how all different actions taken by humans or viruses have ultimately resulted in a disease that eventually takes the lives of humans. Therefore, there is a need to find unsupervised based solutions or some techniques that do not have a dependency on the underlying dataset. In this paper, we have explored the reinforcement learning approach. We have tried different reinforcement learning algorithms to research different solutions for the prevention of diseases in the simulation of the human population. We have explored different techniques for controlling the transmission of diseases and its effects on health in the human population simulated in an environment. Our algorithms have found out policies that are best for the human population to protect themselves from the transmission and infection of malaria. The paper concludes that deep learning-based algorithms such as Deep Deterministic Policy Gradient (DDPG) have outperformed traditional algorithms such as Q-Learning or SARSA.

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Section: Simulation and Discussionmentioning

confidence: 99%

Section: Deep Deterministic Policymentioning

confidence: 99%

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Optimal Policy Learning for Disease Prevention Using Reinforcement Learning

Khan

Feng

Uddin

et al. 2020

Scientific Programming

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“…e deep learning algorithm 6 Complexity was used to learn the pattern of this big data available by GTD using most recent optimization techniques and make reasonable predictions and classifications. Even though many researchers have worked in the domain of using AI solutions for counterterrorism, no one has studied an effective mechanism of understanding factors of terrorism using deep learning, which is becoming very popular recently with the increased data and increased computational [53,54] power. To the best of the authors' knowledge, no comprehensive work is dedicated to predict and classify factors of terrorism using deep learning algorithms.…”

Section: Dealing With Unbalanced Classesmentioning

confidence: 99%

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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“…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

Self Cite

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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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A Controlled Environment Model for Dealing with Smart Phone Addiction

Cited by 9 publications

References 21 publications

Optimal Policy Learning for Disease Prevention Using Reinforcement Learning

Optimal Policy Learning for Disease Prevention Using Reinforcement Learning

Prediction of Future Terrorist Activities Using Deep Neural Networks

An Enhanced Deep Neural Network for Predicting Workplace Absenteeism

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