Swag Technique and Dirichlet Distribution to Address Non-Iid Data in Federated Learning

Federated learning deals with the challenge of accessing data from different information sources while preserving their privacy in centralized learning. We can use this paradigm to learn a common global model for multiple clients using model aggregation cycles, without sharing data. Here aggregating the local models is a crucial part of the training. However, the model may experience accuracy and performance loss while aggregating heterogeneous data. We propose a new aggregation method with sampling, FEDSBME, using the Bayesian inference. We sample the local models of the participating clients and build a Bayesian ensemble model to create a powerful aggregation. The sampling of local models is performed using two approaches, SWAG and Dirichlet distribution sampling. Our experimental results prove that our suggested approach can preserve the accuracy and performance of the model when clients’ data are heterogeneous (non-iid) and with deeper neural networks

The use of nanohybrid curcumin to inhibit some types of local yeasts Isolate

The objective of this study was to use nanohybrid Curcumin Cur-ZnO and Cur-MgO as well as free curcumin to detect, isolate, characterize, and inhibit the development of certain yeasts locally isolated from some spoilage foods. Yeasts responsible for spoiling were recovered in 21 different colonies from cheese, cooked cheese, pickles, fig jam, apple jam, dates, rice, canned chickpeas, and dried figs. Nine yeast isolates were chosen for further morphological and biochemical investigation using VITK 2. Six different genera and species of yeast were found, with three of them, all members of the genus Candida (Candida inconspicua/lambica, Candida krusei, and Candida famata), being the most common. It was determined that between one and two isolations belonged to the genus Zygosccharomyces SPP, and that one isolation belonged to each of the races Trichosporon asahii, Malassezia furfur, Kloekera SPP, and Rhodotorula, with probabilities ranging from.87 – 96%. The results showed that the concentration-dependent inhibition was greatest for the Cur-ZnO nanohybrid, next for the Cur-MgO nanohybrid, and finally for the free curcumin

Robust Automatic Modulation Classification Using Convolutional Deep Neural Network Based on Scalogram Information

This study proposed a two-stage method, which combines a convolutional neural network (CNN) with the continuous wavelet transform (CWT) for multiclass modulation classification. The modulation signals’ time-frequency information was first extracted using CWT as a data source. The convolutional neural network was fed input from 2D pictures. The second step included feeding the proposed algorithm the 2D time-frequency information it had obtained in order to classify the different kinds of modulations. Six different types of modulations, including amplitude-shift keying (ASK), phase-shift keying (PSK), frequency-shift keying (FSK), quadrature amplitude-shift keying (QASK), quadrature phase-shift keying (QPSK), and quadrature frequency-shift keying (QFSK), are automatically recognized using a new digital modulation classification model between 0 and 25 dB SNRs. Modulation types are used in satellite communication, underwater communication, and military communication. In comparison with earlier research, the recommended convolutional neural network learning model performs better in the presence of varying noise levels

Robust Automatic Modulation Classification Using Convolutional Deep Neural Network Based on Scalogram Information

This study proposed a two-stage method, which combines a convolutional neural network (CNN) with the continuous wavelet transform (CWT) for multiclass modulation classification. The modulation signals’ time-frequency information was first extracted using CWT as a data source. The convolutional neural network was fed input from 2D pictures. The second step included feeding the proposed algorithm the 2D time-frequency information it had obtained in order to classify the different kinds of modulations. Six different types of modulations, including amplitude-shift keying (ASK), phase-shift keying (PSK), frequency-shift keying (FSK), quadrature amplitude-shift keying (QASK), quadrature phase-shift keying (QPSK), and quadrature frequency-shift keying (QFSK), are automatically recognized using a new digital modulation classification model between 0 and 25 dB SNRs. Modulation types are used in satellite communication, underwater communication, and military communication. In comparison with earlier research, the recommended convolutional neural network learning model performs better in the presence of varying noise levels