Volume 9 | Issue 2 |

 Abstract

Stock market investment is one of the most complex and sophisticated way to do business. Stock market is very uncertain as the prices of stocks keep fluctuating because of several factors that makes prediction of stocks a difficult and extremely complicated task. Nowadays investors need fast and accurate information to make effective decisions and highly interested in the research area with exponentially growing technological advances of stock price prediction. Understanding the pattern of stock price of a particular company by predicting their future development and financial growth will be highly beneficial. This paper focuses on the usage of a type of recurrent neural network (RNN) based Machine learning which is known as Long Short Term Memory (LSTM) to predict stock values.

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Creative Commons Attribution 4.0 and The Open Definition

 Keywords

Long Short Term Memory, Recurrent Neural Network, Machine learning, Stock Price Prediction

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Creative Commons Attribution 4.0 and The Open Definition

 Abstract

For any complex digital systems, design and analysis through state machine approach is preferred. In this paper, sequence detector using Mealy finite state machine is designed with reversible logic circuits. Reducing power dissipation is the main requirement for low power VLSI design. Reversible logic circuits have received more attention in the recent years because of their ability to reduce the power dissipation. The behavior of these circuits are described in Verilog HDL, Simulated and Synthesized in Xilinx ISE environment. In this work, sequence detectors with and without overlap sequence are considered

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Creative Commons Attribution 4.0 and The Open Definition

 Keywords

Finite state machine, Sequence detector, Verilog HDL, Xilinx, Reversible logic

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Creative Commons Attribution 4.0 and The Open Definition

 Abstract

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Creative Commons Attribution 4.0 and The Open Definition

 Keywords

Suppressed emotions, catharsis, intense emotions, repressed.

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Creative Commons Attribution 4.0 and The Open Definition

 Abstract

In more recent times, there has been an increase in the number of people using computers, as a result of which there is a widespread use of the Internet. The use of the Internet enables hackers to access computers using new, more sophisticated, and more complex forms of attacks, to protect computers from them Intrusion Detection System (IDS) is used, which is trained with few machine learning algorithms along with datasets. The datasets used are collected over a period of time in some networks and usually contain up-to-date data. Furthermore, they are unbalanced and incapable of storing adequate data for all forms of attacks. These uneven and outdated datasets reduce the effectiveness of current IDSs, especially for attacks that are rarely encountered. In this paper, Using Random Forest, Linear Discriminant Analysis, K-Nearest Neighbor, Decision-Tree, Adaboost, and Gradient Boosting algorithms, we suggest six machine-based IDSs. To make IDS more logical, an up-to-date security database, CSE-CIC-IDS2018, is being used in place of older and more widely used datasets. The selected database is also not balanced. As a result, the rate of inequality in the dataset is reduced using a data model called the Synthetic Minority Oversampling Technique (SMOTE) to improve the reliability of the system based on the types of attacks and to eliminate unreliable access and false alarms. Data processing is done for small classes, and their numbers increase to medium data size in this way. Experimental results have shown that the proposed method significantly increases the acquisition rate of the attacks that are rarely encountered.

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Creative Commons Attribution 4.0 and The Open Definition

 Keywords

machine-learning, intrusion detection, IDS, SMOTE, CSE-CIC-IDS2018 dataset.

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Creative Commons Attribution 4.0 and The Open Definition

 Abstract

Licence: creative commons attribution 4.0

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Creative Commons Attribution 4.0 and The Open Definition

 Keywords

Heartbeat sensor,Event recorder, Holter Monitor; RaspberryPi and IoT.

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Creative Commons Attribution 4.0 and The Open Definition

 Abstract

In this paper, analysis of two dimensional data hiding technique in noisy environment has been presented. Data hiding methods are generally used for protection of multimedia data. The work deals with implementation of third level data hiding technique using wavelet. The proposed work embed the secret data into mid frequency band. After embedding, proposed technique is tested under various attacks such as: noise addition, resizing filtering etc. and simulation results demonstrated that this technique is robust against attacks. All simulation results have been performed on MATLAB platform and displayed.

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Creative Commons Attribution 4.0 and The Open Definition

 Keywords

Data Hiding, DWT, Attacks, PSNR, NCC.

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Creative Commons Attribution 4.0 and The Open Definition

 Abstract

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Creative Commons Attribution 4.0 and The Open Definition

 Keywords

plant spread, number of primary branches, days taken for 50% flowering, individual flower weight, weight of flowers per plant, weight of flowers per plot

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Creative Commons Attribution 4.0 and The Open Definition

 Abstract

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Creative Commons Attribution 4.0 and The Open Definition

 Keywords

Motherhood, Practicing, Rural

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Creative Commons Attribution 4.0 and The Open Definition

 Abstract

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Creative Commons Attribution 4.0 and The Open Definition

 Keywords

Diabetic Neuropathy, diabetic Foot, Amputation, Autonomous, Neurosensory, alpha lipoic Acid, Micro vascular vessels.

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Creative Commons Attribution 4.0 and The Open Definition

 Abstract

Licence: creative commons attribution 4.0

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Creative Commons Attribution 4.0 and The Open Definition

 Keywords

COVID 19 Obstruction, Effect on Pharmaceutical Industry

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Creative Commons Attribution 4.0 and The Open Definition