Stock market forecasting involves predicting fluctuations and trends in the value of financial assets, utilizing statistical and machine learning models to analyze historical market data for insights into future behavior. This practice aids investors, traders, financial institutions, and governments in making informed decisions, managing risks, and assessing economic conditions. Forecasting financial markets is difficult due to the intricate interplay of global economics, politics, and investor sentiment, making it inherently unpredictable. This study introduces a Deep Learning based Expert Framework for Stock Market forecasting (Portfolio prediction) called DLEF-SM. The methodology begins with an improved jellyfish-induced filtering (IJF-F) technique for preprocessing, effectively analyzing raw data and eliminating artifacts. To address imbalanced data and enhance data quality, pre-trained convolutional neural network (CNN) architectures, VGGFace2 and ResNet-50, are used for feature extraction. Additionally, an improved black widow optimization (IBWO) algorithm is designed for feature selection, reducing data dimensionality and preventing under-fitting. For precise stock market predictions, integrate deep reinforcement learning with artificial neural network (DRL-ANN) is proposed. Simulation outcomes reveal that the proposed framework achieves maximum forecasting accuracy, reaching 99.562%, 98.235%, and 98.825% for S&P500-S, S&P500-L, and DAX markets, respectively.

Malware classification is a challenging task due to the constantly evolving nature of malicious software. Traditional signature-based methods and static analysis often fail to detect sophisticated threats, making behavior-based analysis crucial. This study proposes a malware detection model that analyzes the behavior of executable files (.exe) to classify them as malware. The model submits the file to VirusTotal, where it runs in a secure environment to monitor actions such as file modifications, registry changes, or network connections. To enhance detection accuracy, the BERT model is applied to extract key features from these behavior logs. After 100 training epochs, the model achieved 92.25% accuracy and an F1-score of 91.22%, demonstrating strong overall performance. Class-wise evaluation was also conducted, treating each malware family as a distinct class to assess specific detection accuracy. Furthermore, a correlation matrix was analyzed to explore inter-class relationships and identify overlapping behaviors. Experimental results show that SVM achieved the highest F1-Scores for Adware (0.98) and BackDoor (0.91), while Random Forest showed comparable performance. Naïve Bayes, however, performed poorly for FakeAlert (F1-Score: 0.64). These findings confirm the effectiveness of the proposed behavior-based approach using BERT features, with SVM and Random Forest proving to be the most reliable classifiers.

This paper discusses the importance of practical approaches to classifying soil types given the growing interest in precision agriculture. We propose an enhanced deep learning solution for soil type identification using a Convolutional Neural Network (CNN) architecture, EfficientNet-B0, to improve agricultural practices. The proposed method involves feature extraction from input image data using deep learning techniques to classify soil types. We incorporate the EfficientNet-B0 model to classify images representing four soil types—Alluvial, Black, Clay, and Red—using a comprehensive dataset. The model achieves 99.6% training accuracy and an impressive 99% test accuracy, demonstrating its ability to differentiate between various soil types. Further model assessment includes comprehensive metrics, such as confusion matrices, classification reports, and additional visualisation tools like cumulative gains and lift charts. Our findings suggest that the EfficientNet-B0 model is not only suitable for classification but also provides valuable interpretive tools, including feature embeddings and statistical measures. These results suggest that this research has the potential to enhance precision agriculture by effectively classifying soil types, thereby improving soil management and agricultural productivity.