Deep neural network battery life and voltage prediction by using data of one cycle only

In this work, a multiple user deep neural network-based non-orthogonal multiple access (NOMA) receiver is investigated considering channel estimation error. The decoding of the symbol in the case of the NOMA system follows the sequential order and decoding accuracy depends on the detection of the previous user. Without estimating the throughput, a deep neural network-based NOMA orthogonal frequency division multiplexing (OFDM) system is proposed to decode the symbols from the users. Firstly, the deep neural network is trained. Secondly, the data are trained and lastly, the data are tested for various users. In this work, for various values of signal to noise ratio, the performance of the deep neural network is investigated, and the bit error rate (BER) is calculated on a per subcarrier basis. The simulation results show that the deep neural network is more robust to symbol distortion due to inter-symbol information and will obtain knowledge of the channel state information using data testing.

Download Full-text

Enhanced Indonesian Ethnic Speaker Recognition Using Data Augmentation Deep Neural Network

Journal of King Saud University - Computer and Information Sciences ◽

10.1016/j.jksuci.2021.04.002 ◽

2021 ◽

Author(s):

Kristiawan Nugroho ◽

Edi Noersasongko ◽

Purwanto ◽

Muljono ◽

Moses

Keyword(s):

Neural Network ◽

Speaker Recognition ◽

Deep Neural Network ◽

Data Augmentation ◽

Using Data

Download Full-text

Smart Application Division and Time Allocation Policy for Computational Offloading in Wireless Powered Mobile Edge Computing

Mobile Information Systems ◽

10.1155/2021/9993946 ◽

2021 ◽

Vol 2021 ◽

pp. 1-13

Author(s):

Abdullah Numani ◽

Zaiwar Ali ◽

Ziaul Haq Abbas ◽

Ghulam Abbas ◽

Thar Baker ◽

...

Keyword(s):

Neural Network ◽

Energy Consumption ◽

Time Allocation ◽

Deep Neural Network ◽

Time Slot ◽

Optimal Number ◽

Edge Computing ◽

Battery Life ◽

Mobile Edge Computing ◽

Allocation Policy

Limited battery life and poor computational resources of mobile terminals are challenging problems for the present and future computation-intensive mobile applications. Wireless powered mobile edge computing is one of the solutions, in which wireless energy transfer technology and cloud server’s capabilities are brought to the edge of cellular networks. In wireless powered mobile edge computing systems, the mobile terminals charge their batteries through radio frequency signals and offload their applications to the nearby hybrid access point in the same time slot to minimize their energy consumption and ensure uninterrupted connectivity with hybrid access point. However, the smart division of application into k subtasks as well as intelligent partitioning of time slot for harvesting energy and offloading data is a complex problem. In this paper, we propose a novel deep-learning-based offloading and time allocation policy (DOTP) for training a deep neural network that divides the computation application into optimal number of subtasks, decides for the subtasks to be offloaded or executed locally (offloading policy), and divides the time slot for data offloading and energy harvesting (time allocation policy). DOTP takes into account the current battery level, energy consumption, and time delay of mobile terminal. A comprehensive cost function is formulated, which uses all the aforementioned metrics to calculate the cost for all k number of subtasks. We propose an algorithm that selects the optimal number of subtasks, partial offloading policy, and time allocation policy to generate a huge dataset for training a deep neural network and hence avoid huge computational overhead in partial offloading. Simulation results are compared with the benchmark schemes of total offloading, local execution, and partial offloading. It is evident from the results that the proposed algorithm outperforms the other schemes in terms of battery life, time delay, and energy consumption, with 75% accuracy of the trained deep neural network. The achieved decrease in total energy consumption of mobile terminal through DOTP is 45.74%, 36.69%, and 30.59% as compared to total offloading, partial offloading, and local offloading schemes, respectively.

Download Full-text

Prediction and Detection of Urban Trajectory Using Data Mining and Deep Neural Network

10.1109/bdacs53596.2021.00017 ◽

2021 ◽

Author(s):

Fu Ning

Keyword(s):

Neural Network ◽

Data Mining ◽

Deep Neural Network ◽

Using Data

Download Full-text

Stock Market Prediction using Data Mining Techniques

International Journal of Scientific Research in Computer Science Engineering and Information Technology ◽

10.32628/cseit206290 ◽

2020 ◽

pp. 310-313

Author(s):

Karunesh Makker ◽

Prince Patel ◽

Hrishikesh Roy ◽

Sonali Borse

Keyword(s):

Neural Network ◽

Stock Market ◽

Prediction Model ◽

Efficient Market Hypothesis ◽

Deterministic System ◽

Vast Number ◽

Factors Influencing ◽

Number Of Factors ◽

Using Data ◽

Robust Prediction

Stock market is a very volatile in-deterministic system with vast number of factors influencing the direction of trend on varying scales and multiple layers. Efficient Market Hypothesis (EMH) states that the market is unbeatable. This makes predicting the uptrend or downtrend a very challenging task. This research aims to combine multiple existing techniques into a much more robust prediction model which can handle various scenarios in which investment can be beneficial. Existing techniques like sentiment analysis or neural network techniques can be too narrow in their approach and can lead to erroneous outcomes for varying scenarios. By combing both techniques, this prediction model can provide more accurate and flexible recommendations. Embedding Technical indicators will guide the investor to minimize the risk and reap better returns.

Download Full-text