The economy of Pakistan mainly relies upon agriculture alongside other vital industries. Fungal blast is one of the significant plant diseases found in rice crops, leading to reduction of agricultural products and hindrance in the country's economic development. Plant disease detection is an initial step towards improving the yield and quality of agricultural products. Manual Analyzation of plant health is tiresome, time taking and costly. Machine learning offers an alternate inspection method providing benefits of automated inspection, ease of availability, and cost reduction. The visual patterns on the rice plants are processed using the machine learning classifiers such as support vector machine (SVM), logistic regression, decision tree, Naïve Bayes, random forest, linear discriminant analysis (LDA), principal component analysis (PCA), and based on classification results plants are recognized as healthy or unhealthy. For this process, a dataset containing 1000 images of rice seed crop is collected from different fields of Kashmore, and whole analysis of image acquisition, pre-processing, and feature extraction is done on the rice seed only. The dataset is annotated with healthy and unhealthy samples with the help of a plant disease expert. The algorithms used for processing data are evaluated in terms of F1-score and testing accuracy. This paper contains results from traditional classifiers, and alongside these classifiers, transfer learning has been used to compare the results. Finally, a comparative analysis is done between the results of traditional classifiers and deep learning networks.

Malware, short for malicious software, is any software program designed to cause harm to a computer or computer network. Malware can take many forms, such as viruses, worms, Trojan horses, and ransomware. Because malware can cause significant damage to a computer or network, it is important to avoid its installation to prevent any potential harm. This paper proposes a machine learning-based malware detection method called MalwD&C to allow the secure installation of Programmable Executable (PE) files. The proposed method uses machine learning classifiers to analyze the PE files and classify them as benign or malware. The proposed MalwD&C scheme was evaluated on a publicly available dataset by applying several machine learning classifiers in two settings: two-class classification (malware detection) and multi-class classification (malware categorization). The results showed that the Random Forest (RF) classifier outperformed all other chosen classifiers, achieving as high as 99.56% and 97.69% accuracies in the two-class and multi-class settings, respectively. We believe that MalwD&C will be widely accepted in academia and industry due to its speed in decision making and higher accuracy.

A microsleep is a brief and involuntary sleep-related loss of consciousness of up to 15 s. We investigated the performances of seven pairwise inter-channel relationships – covariance, Pearson’s correlation coefficient, wavelet cross-spectral power, wavelet coherence, joint entropy, mutual information, and phase synchronization index – in continuous prediction of microsleep states from EEG. These relationships were used as feature sets of a linear discriminant analysis (LDA) and a linear support vector machine (LSVM) classifiers. Priors for both classifiers were incorporated to address the class imbalance in the training data sets. Each feature set was extracted from a 5-s window of EEG with the step of 0.25 s and was demeaned with respect to the mean of first 2 min. The sequential forward selection (SFS) method, based on a serial combination of the correlation coefficient, Fisher score-based filter, and an LDA-based wrapper, was used to select features from each training set. The comparison was based on 16-channel EEG data from 8 subjects who had performed a 1-D visuomotor task for two 1-h sessions. The prediction performances were evaluated using leave-one-subject-out cross-validation. For both classifiers, non-normalized feature sets were found to perform better than normalized feature sets. Furthermore, demeaning the non-normalized features considerably improved the prediction performance. Overall, the LDA classifier with joint entropy features resulted in the best average prediction performances (phi, AUCPR, AUCROC) of (0.47, 0.50, 0.95). Joint entropy between O1-O2 from theta frequency band was the most informative feature.

This paper presents WideConvNet, a novel convolutional neural network (ConvNet or CNN) that outperforms three established end-to-end models – EEGNet, Shallow Convolutional Neural Network (ShallowConvNet), and Deep Convolutional Neural Network (DeepConvNet) – in classifying event-related potentials (ERPs) from electroencephalogram (EEG) signals. At the core of WideConvNet is an adapted inception module for 1D data, utilizing varying size 1D filters inspired by the wavelet scattering transform, which are learned during the training phase to capture temporal patterns across different scales within the EEG signals. The performance of WideConvNet was evaluated on multichannel EEG data from 20 male subjects with use disorder (AUD) and 20 healthy male controls, each providing 16 1.0-s ERPs. Using twice repeated 10-fold leave-subjects-out stratified cross-validation, WideConvNet largely outperformed both EEGNet and ShallowConvNet in terms of area under the curve of the receiver operating characteristic (AUC-ROC, $p=0.00003, 0.0001$ ). Whereas, in terms of accuracy it largely outperformed EEGNet and moderately outperformed ShallowConvNet ( $p=0.00029, 0.00024$ ). Despite comparable performances to DeepConvNet, WideConvNet required 28% fewer parameters and was more interpretable. The Shapley additive explanation (SHAP) values revealed parietal and occipital regions as the key contributors to both WideConvNet and DeepConvNet predictions. The findings suggest that WideConvNet, specifically on short-duration EEG signals, offers a balanced solution, combining robust classification performance with improved computational efficiency, making it suitable for clinical diagnosis and real-time brain-computer interface (BCI) applications.