The agricultural sector plays a key role in supplying quality food and makes the greatest contribution to growing economies and populations. Plant disease may cause significant losses in food production and eradicate diversity in species. Early diagnosis of plant diseases using accurate or automatic detection techniques can enhance the quality of food production and minimize economic losses. In recent years, deep learning has brought tremendous improvements in the recognition accuracy of image classification and object detection systems. Hence, in this paper, we utilized convolutional neural network (CNN)-based pre-trained models for efficient plant disease identification. We focused on fine tuning the hyperparameters of popular pre-trained models, such as DenseNet-121, ResNet-50, VGG-16, and Inception V4. The experiments were carried out using the popular PlantVillage dataset, which has 54,305 image samples of different plant disease species in 38 classes. The performance of the model was evaluated through classification accuracy, sensitivity, specificity, and F1 score. A comparative analysis was also performed with similar state-of-the-art studies. The experiments proved that DenseNet-121 achieved 99.81% higher classification accuracy, which was superior to state-of-the-art models.

The application of electroencephalogram (EEG)-based emotion recognition (ER) to the brain–computer interface (BCI) has become increasingly popular over the past decade. Emotion recognition systems involve pre-processing and feature extraction, followed by classification. Deep learning has recently been used to classify emotions in BCI systems, and the results have been improved when compared to classic classification approaches. The main objective of this study is to classify the emotions from electroencephalogram signals using variant recurrent neural network architectures. Three architectures are used in this work for the recognition of emotions using EEG signals: RNN (recurrent neural network), LSTM (long short-term memory network), and GRU (gated recurrent unit). The efficiency of these networks, in terms of performance measures was confirmed by experimental data. The experiment was conducted by using the EEG Brain Wave Dataset: Feeling Emotions, and achieved an average accuracy of 95% for RNN, 97% for LSTM, and 96% for GRU for emotion detection problems.

As technology advances, electronic circuits are more vulnerable to errors. Soft errors are one among them that causes the degradation of a circuit’s reliability. In many applications, protecting critical modules is of main concern. One such module is Fast Fourier Transform (FFT). Real FFT (RFFT) is a memory-based FFT architecture. RFFT architecture can be optimized by its processing element through employing several types of adder and multipliers and an optimized memory usage. It has been seen that various blocks operate simultaneously in many applications. For the protection of parallel FFTs using conventional Error Correction Codes (ECCs), algorithmic-based fault tolerance (ABFT) techniques like Parseval checks and its combination are seen. In this brief, the protection schemes are applied to the single RAM-based parallel RFFTs and dual RAM-based parallel RFFTs. This work is implemented on platforms such as field programmable gate arrays (FPGAs) using Verilog HDL and on application-specific integrated circuit (ASIC) using a cadence encounter digital IC implementation tool. The synthesis results, including LUTs, slices registers, LUT–Flip-Flop pairs, and the frequency of two types of protected parallel RFFTs, are analyzed, along with the existing FFTs. The two proposed architectures with the combined protection scheme Parity-SOS-ECC present an 88% and 33% reduction in area overhead when compared to the existing parallel RFFTs. The performance metrics like area, power, delay, and power delay product (PDP) in an ASIC of 45 nm and 90 nm technology are evaluated, and the proposed single RAM-based parallel RFFTs architecture presents a 62.93% and 57.56% improvement of PDP in 45 nm technology and a 67.20% and 60.31% improvement of PDP in 90 nm technology compared to the dual RAM-based parallel RFFTs and the existing architecture, respectively.

Finding sarcastic statements has recently drawn a lot of curiosity in social media, mainly because sarcastic tweets may include favorable phrases that fill in unattractive or undesirable attributes. As the internet becomes increasingly ingrained in our daily lives, many multimedia information is being produced online. Much of the information recorded mostly on the internet is textual data. It is crucial to comprehend people's sentiments. However, sarcastic content will hinder the effectiveness of sentiment analysis systems. Correctly identifying sarcasm and correctly predicting people's motives are extremely important. Sarcasm is particularly hard to recognize, both by humans and by machines. We employ the deep bi-directional long-short memory (Bi-LSTM) and a hybrid architecture of the convolution neural network+Bi-LSTM (CNN+Bi-LSTM) with attention networks for identifying sarcastic remarks in a corpus. Using the SarcasmV2 dataset, we test the efficacy of deep learning methods BiLSTM, and CNN+BiLSTM with attention network) for the task of identifying text sarcasm. The suggested approach incorporating deep networks is consistent with various recent and advanced techniques for sarcasm detection. With attention processes, the improved CNN+Bi-LSTM model achieved an accuracy rate of 91.76%, which is a notable increase over earlier research.