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The precise forecasting of electricity demand also referred to as load forecasting, is essential for both planning and managing a power system. It is crucial for many tasks, including choosing which power units to commit to, making plans for future power generation capacity, enhancing the power network, and controlling electricity consumption. As Bangladesh is a developing country, the electricity infrastructure is critical for economic growth and employment in this country. Accurate forecasting of electricity demand is crucial for ensuring that this country has a reliable and sustainable electricity supply to meet the needs of its growing population and economy. The complex and nonlinear behavior of such energy systems inhibits the creation of precise algorithms. Within this context, this paper aims to propose a hybrid model of Convolutional Neural Network (CNN) and stacked Bidirectional Long-short Term Memory (BiLSTM) architecture to perform an accurate short-term forecast of the electricity demand of Dhaka city. Short-term forecasting is ordinarily done to anticipate load for the following few hours to a few weeks. Normalization techniques have been also investigated because of the sensitivity of these models towards the input range. The proposed approach produced the best prediction results in comparison to the other benchmark models (LSTM, CNN- BiLSTM and CNN-LSTM) used in the study, with MAPE 1.64%, MSE 0.015, RMSE 0.122 and MAE 0.092. The result of the proposed model also outperformed some of the existing works on load-forecasting.

Eavesdropping from the user's smartphone is a well-known threat to the user's safety and privacy. Existing studies show that loudspeaker reverberation can inject speech into motion sensor readings, leading to speech eavesdropping. While more devastating attacks on ear speakers, which produce much smaller scale vibrations, were believed impossible to eavesdrop with zero-permission motion sensors. In this work, we revisit this important line of reach. We explore recent trends in smartphone manufacturers that include extra/powerful speakers in place of small ear speakers, and demonstrate the feasibility of using motion sensors to capture such tiny speech vibrations. We investigate the impacts of these new ear speakers on built-in motion sensors and examine the potential to elicit private speech information from the minute vibrations. Our designed system EarSpy can successfully detect word regions, time, and frequency domain features and generate a spectrogram for each word region. We train and test the extracted data using classical machine learning algorithms and convolutional neural networks. We found up to 98.66% accuracy in gender detection, 92.6% detection in speaker detection, and 56.42% detection in digit detection (which is 5X more significant than the random selection (10%)). Our result unveils the potential threat of eavesdropping on phone conversations from ear speakers using motion sensors.

Biomimicry has been utilized in many branches of science and engineering to develop devices for enhanced and better performance. The application of nanotechnology has made life easier in modern times. It has offered a way to manipulate matter and systems at the atomic level. As a result, the miniaturization of numerous devices has been possible. Of late, the integration of biomimicry with nanotechnology has shown promising results in the fields of medicine, robotics, sensors, photonics, etc. Biomimicry in nanotechnology has provided eco-friendly and green solutions to the energy problem and in textiles. This is a new research area that needs to be explored more thoroughly. This review illustrates the progress and innovations made in the field of nanotechnology with the integration of biomimicry.

This research presents an examination of categorizing the severity states of patients based on their electronic health records during a certain time range using multiple machine learning and deep learning approaches. The suggested method uses an EHR dataset collected from an open-source platform to categorize severity. Some tools were used in this research, such as openRefine was used to pre-process, RapidMiner was used for implementing three algorithms (Fast Large Margin, Generalized Linear Model, Multi-layer Feed-forward Neural Network) and Tableau was used to visualize the data, for implementation of algorithms we used Google Colab. Here we implemented several supervised and unsupervised algorithms along with semi-supervised and deep learning algorithms. The experimental results reveal that hyperparameter-tuned Random Forest outperformed all the other supervised machine learning algorithms with 76% accuracy as well as Generalized Linear algorithm achieved the highest precision score 78%, whereas the hyperparameter-tuned Hierarchical Clustering with 86% precision score and Gaussian Mixture Model with 61% accuracy outperformed other unsupervised approaches. Dimensionality Reduction improved results a lot for most unsupervised techniques. For implementing Deep Learning we employed a feed-forward neural network (multi-layer) and the Fast Large Margin approach for semi-supervised learning. The Fast Large Margin performed really well with a recall score of 84% and an F1 score of 78%. Finally, the Multi-layer Feed-forward Neural Network performed admirably with 75% accuracy, 75% precision, 87% recall, 81% F1 score., Comment: 32 pages, 13 figures, and 14 tables

Drying is an important agricultural process, particularly for crops, and shriveled products are used all over the world. The performance of drying green chili was also tested in this article, which created an alternate way of drying agricultural products. The goal of this study is to provide a solar biomass hybrid dryer with improved design, construction, and performance testing. During most hours of the trial, the temperature within the solar collector and dryer was sufficiently higher than the ambient temperature, according to the results obtained during the test period. The temperature of the ambient air at the collector intake ranged from 30 to 35 degrees Celsius. The temperature of the air at the collector's outlet ranged from 54 to 64 degrees Celsius, while the temperature of the drying chamber ranged from 51 to 60 degrees Celsius, making it suitable for drying green chili and a variety of other agricultural products. The collector was found to be 46.54 percent efficient. The findings revealed that the alteration of the collector, which produces turbulent air flow and improves chamber wall insulation, affects drying. Based on the results of this study, the created solar biomass hybrid drier is cost-effective for small-scale crop growers in rural areas of developing countries., Comment: 8 Pages, 13 Figures