Prediction Model Using Recurrent Neural Networks

Jahan, Israt; Sajal, Sayeed; Nygard, Kendall E.

doi:10.1109/eit.2019.8834336

Cited by 11 publications

(7 citation statements)

References 12 publications

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“…In literature, hardware trojan is a deliberate and unwanted alteration of an integrated circuit (Jahan, Sajal & Nygard, 2019;Salmani, Tehranipoo & Plusquellic, 2011). They present emerging security concerns and can have devastating effects when used in sensitive environments.…”

Section: Literature Reviewmentioning

confidence: 99%

Implementation of a Hardware Trojan Chip Detector Model Using Arduino Microcontroller

ABDULSALAM

Adebisi²,

DUROJAIYE³

2021

acs

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These days, hardware devices and its associated activities are greatly impacted by threats amidst of various technologies. Hardware trojans are malicious modifications made to the circuitry of an integrated circuit, Exploiting such alterations and accessing the level of damage to devices is considered in this work. These trojans, when present in sensitive hardware system deployment, tends to have potential damage and infection to the system. This research builds a hardware trojan detector using machine learning techniques. The work uses a combination of logic testing and power side-channel analysis (SCA) coupled with machine learning for power traces. The model was trained, validated and tested using the acquired data, for 5 epochs. Preliminary logic tests were conducted on target hardware device as well as power SCA. The designed machine learning model was implemented using Arduino microcontroller and result showed that the hardware trojan detector identifies trojan chips with a reliable accuracy. The power consumption readings of the hardware characteristically start at 1035-1040mW and the power time-series data were simulated using DC power measurements mixed with additive white Gaussian noise (AWGN) with different standard deviations. The model achieves accuracy, precision and accurate recall values. Setting the threshold proba¬bility for the trojan class less than 0.5 however increases the recall, which is the most important metric for overall accuracy acheivement of over 95 percent after several epochs of training.

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Section: Literature Reviewmentioning

confidence: 99%

Implementation of a Hardware Trojan Chip Detector Model Using Arduino Microcontroller

ABDULSALAM

Adebisi²,

DUROJAIYE³

2021

acs

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“…Sequence prediction is a problem that involves using historical sequence information to predict the next sequence or values. Sequences can be symbols such as letters in a sentence or real numbers similar to those in the time series [2]. This is considered one of the most difficult problems to solve in the data science industry.…”

Section: Introductionmentioning

confidence: 99%

“…This is considered one of the most difficult problems to solve in the data science industry. These include many problems; from marketing forecasts to finding patterns in stock market data, from understanding film editing to visualizing your style, from language translation to predicting your next phase on your keypad through neural networks [2].…”

Section: Introductionmentioning

confidence: 99%

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Stock Optimization and Cumulative Hypothesis

2023

IRJMETS

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In this article, we use a recurrent neural network (RNN) with long short-term memory (LSTM) to study stock market prediction problems. The purpose of this work is to evaluate the feasibility and effectiveness of LSTMs in stock market forecasting. We implement the LSTM model by testing different configurations. In other words, multi-layer neural networks are built using a combination of data mining. A neural network is trained on stock quotes using a backpropagation algorithm used to predict the closing price of the stock market. Compare the performance accuracy of neural networks using various sampling performance measures. This article also describes the modeling methods and structures of recurrent neural networks.

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“…Mechine learning technology is one of the mechine lessons that is intended so that a mechine can have the ability to predict and analyze and recognize a pattern [9]. Mechine learning itself has many methods such as K-means, Recurrent Neural Networks, Decision Trees, Artificial Neural Networks, etc [10].…”

Section: Introductionmentioning

confidence: 99%

Climate Prediction Using RNN LSTM to Estimate Agricultural Products Based on Koppen Classification

Andini

Utomo

2021

JISA

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The yield of an agricultural process is very important and influential, where the harvest is used as a support for human life both as food and a source of income. Many factors can influence the success of agriculture, such as the climate that is going on around in the surrounding area. The wrong prediction in determining the future climate will cause crop failure due to incompatibility with the type of plant. In this era, many technologies have been able to predict climate, one of which is technology machine learning that has many types and techniques, which machine learning technology has been widely used in predicting many things. This study aims to predict the climate in an area which is intended to determine crop yields based on the Koppen classification, and also the prediction based on several parameters such as temperature, humidity, duration of sun exposure and rainfall. And the results of this study is have a loss of 0.006 and with the MAPE value as an indicator of the percentage error and as an indicator for determining the accuracy of the prediction results, which is 3.29%, which means that it is included in the very accurate category in predicting climate to estimate agricultural yields.

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Prediction Model Using Recurrent Neural Networks

Cited by 11 publications

References 12 publications

Implementation of a Hardware Trojan Chip Detector Model Using Arduino Microcontroller

Implementation of a Hardware Trojan Chip Detector Model Using Arduino Microcontroller

Stock Optimization and Cumulative Hypothesis

Climate Prediction Using RNN LSTM to Estimate Agricultural Products Based on Koppen Classification

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