Machine Learning Approach to Forecast Average Weather Temperature of Bangladesh

Weather prediction is gaining popularity very rapidly in the current era of Artificial Intelligence and Technologies It is essential to predict the temperature of the weather for some time In this research paper we tried to find out the pattern of the average temperature of Bangladesh per year as well as the average temperature per season We used different machine learning algorithms to predict the future temperature of the Bangladesh region In the experiment we used machine learning algorithms such as Linear Regression Polynomial Regression Isotonic Regression and Support Vector Regressor Isotonic Regression algorithm predicts the training dataset most accurately but Polynomial Regressor and Support Vector Regressor predicts the future average temperature most accuratel

Obfuscated memory malware detection in resource-constrained iot devices for smart city applications

Obfuscated Memory Malware (OMM) presents significant threats to interconnected systems, including smart city applications, for its ability to evade detection through concealment tactics. Existing OMM detection methods primarily focus on binary detection. Their multiclass versions consider a few families only and, thereby, fail to detect much existing and emerging malware. Moreover, their large memory size makes them unsuitable to be executed in resource-constrained embedded/IoT devices. To address this problem, in this paper, we propose a multiclass but lightweight malware detection method capable of identifying recent malware and is suitable to execute in embedded devices. For this, the method considers a hybrid model by combining the feature-learning capabilities of convolutional neural networks with the temporal modeling advantage of bidirectional long short-term memory. The proposed architecture exhibits compact size and fast processing speed, making it suitable for deployment in IoT devices that constitute the major components of smart city systems. Extensive experiments with the recent CIC-Malmem-2022 OMM dataset demonstrate that our method outperforms other machine learning-based models proposed in the literature in both detecting OMM and identifying specific attack types. Our proposed method thus offers a robust yet compact model executable in IoT devices for defending against obfuscated malware

Performance Analysis of Parametric Channel Estimation for 3D Massive MIMO/FD-MIMO OFDM Systems.

With the promise of meeting future capacity demands for mobile broadband communications, 3D massive-MIMO/Full Dimension MIMO (FD-MIMO) systems have gained much interest among the researchers in recent years. Apart from the huge spectral efficiency gain offered by the system, the reason for this great interest can also be attributed to significant reduction of latency, simplified multiple access layer, and robustness to interference. However, in order to completely extract the benefits of massive-MIMO systems, accurate channel state information is very critical. In this paper, a channel estimation method based on direction of arrival (DoA) estimation is presented for massive-MIMO OFDM systems. To be specific, the DoA is estimated using Estimation of Signal Parameter via Rotational Invariance Technique (ESPRIT) method, and the root mean square error (RMSE) of the DoA estimation is analytically characterized for the corresponding MIMO-OFDM system