Globally, pests and plant diseases severely threaten forestry and agriculture. Plant protection could be substantially enhanced by using non-contact, extremely effective, and reasonably priced techniques for identifying and tracking pests and plant diseases across large geographic areas. Precision agriculture is the study of using other technologies, such as hyperspectral remote sensing (RS), to increase cultivation instead of traditional agricultural methods with less negative environmental effects. In this work, we proposed a novel deep-learning architecture and optimization algorithm for crop leaf disease recognition. In the initial step, a multilevel contrast enhancement technique is proposed for a better visual of the disease on the leaves of cotton and wheat. After that, we proposed three novel residual block and self-attention mechanisms named 3-RBNet Self, 5-RBNet Self, and 9-RBNet Self. After that, the proposed models are trained on enhanced images and later extracted deep features from the self-attention layer. The 5-RBNET Self and 9-RBNET Self performed well in terms of accuracy and precision rate; therefore, we did not consider the 3-RBNET Self for the next process. The dragonfly optimization algorithm is proposed for the best feature selection and applied to the self-attention features of 5-RBNET Self and 9-RBNET Self models to improve the classification performance further and reduce the computational cost. The proposed method is evaluated on two publically available crop disease images, such as the Cotton, Wheat, and EuroSAT datasets. For both crops, the proposed method obtained a maximum accuracy of 98.60 and 93.90%, respectively, whereas for the EuroSAT, the proposed method obtained an accuracy of 83.10%. Compared to the results with recent techniques, the proposed method shows improved accuracy and precision rate.

Textual data are among the most popular data types that provide useful knowledge today. The need to extract insights from text has initiated and overseen the progression of research in the text mining domain. The challenge of addressing textual data comes from the complexity of text processing. The nature of text presents challenges that demand sophisticated processing methods. Subsequently, the degree of complexity involved in this field drives researchers to continuously improve the existing feature selection and extraction techniques, highlighting the essential work that is necessary to propel the field forward. Unlike the existing reviews that focus on individual components such as preprocessing, feature selection, or feature extraction in isolation, this study offers a unified perspective by integrating all three aspects. This review explores the feature selection and extraction methods advances achieved in text mining over the last decade. The focus of this investigation is the development of novel approaches that have been designed to address the complexities that accompany textual data. The comprehensive examination includes identifying the current trends and significant breakthroughs that characterize this dynamic field, thus providing a comprehensive view of the ongoing efforts to refine and optimize these methodologies. The presented explanation of the details associated with feature selection and extraction not only keeps researchers up to date on the latest developments but also contributes significantly to the ongoing refinement and growth exhibited by text mining techniques.

In today’s world, stress is a major factor for various diseases in modern societies which affects the day-to-day activities of human beings. The measurement of stress is a contributing factor for governments and societies that impacts the quality of daily lives. The strategy of stress monitoring systems requires an accurate stress classification technique which is identified via the reactions of the body to regulate itself to changes within the environment through mental and emotional responses. Therefore, this research proposed a novel deep learning approach for the stress classification system. In this paper, we presented an Enhanced Long Short-Term Memory(E-LSTM) based on the feature attention mechanism that focuses on determining and categorizing the stress polarity using sequential modeling and word-feature seizing. The proposed approach integrates pre-feature attention in E-LSTM to identify the complicated relationship and extract the keywords through an attention layer for stress classification. This research has been evaluated using a selected dataset accessed from the sixth Korea National Health and Nutrition Examination Survey conducted from 2013 to 2015 (KNHANES VI) to analyze health-related stress data. Statistical performance of the developed approach was analyzed based on the nine features of stress detection, and we compared the effectiveness of the developed approach with other different stress classification approaches. The experimental results shown that the developed approach obtained accuracy, precision, recall and a F1-score of 75.54%, 74.26%, 72.99% and 74.58%, respectively. The feature attention mechanism-based E-LSTM approach demonstrated superior performance in stress detection classification when compared to other classification methods including naïve Bayesian, SVM, deep belief network, and standard LSTM. The results of this study demonstrated the efficiency of the proposed approach in accurately classifying stress detection, particularly in stress monitoring systems where it is expected to be effective for stress prediction.

In recent years, disease attacks have posed continuous threats to agriculture and caused substantial losses in the economy. Thus, early detection and classification could minimize the spread of disease and help to improve yield. Meanwhile, deep learning has emerged as the significant approach to detecting and classifying images. The classification performed using the deep learning approach mainly relies on large datasets to prevent overfitting problems. The Automatic Segmentation and Hyper Parameter Optimization Artificial Rabbits Algorithm (AS-HPOARA) is developed to overcome the above-stated issues. It aims to improve plant leaf disease classification. The Plant Village dataset is used to assess the proposed AS-HPOARA approach. Z-score normalization is performed to normalize the images using the dataset’s mean and standard deviation. Three augmentation techniques are used in this work to balance the training images: rotation, scaling, and translation. Before classification, image augmentation reduces overfitting problems and improves the classification accuracy. Modified UNet employs a more significant number of fully connected layers to better represent deeply buried characteristics; it is considered for segmentation. To convert the images from one domain to another in a paired manner, the classification is performed by HPO-based ARA, where the training data get increased and the statistical bias is eliminated to improve the classification accuracy. The model complexity is minimized by tuning the hyperparameters that reduce the overfitting issue. Accuracy, precision, recall, and F1 score are utilized to analyze AS-HPOARA’s performance. Compared to the existing CGAN-DenseNet121 and RAHC_GAN, the reported results show that the accuracy of AS-HPOARA for ten classes is high at 99.7%.

Fog computing extends mobile cloud computing facilities at the network edge, yielding low-latency application execution. To supplement cloud services, computationally intensive applications can be distributed on resource-constrained mobile devices by leveraging underutilized nearby resources to meet the latency and bandwidth requirements of application execution. Building upon this premise, it is necessary to investigate idle or underutilized resources that are present at the edge of the network. The utilization of a microservice architecture in IoT application development, with its increased granularity in service breakdown, provides opportunities for improved scalability, maintainability, and extensibility. In this research, the proposed schedule tackles the latency requirements of applications by identifying suitable upward migration of microservices within a multi-tiered fog computing infrastructure. This approach enables optimal utilization of network edge resources. Experimental validation is performed using the iFogSim2 simulator and the results are compared with existing baselines. The results demonstrate that compared to the edgewards approach, our proposed technique significantly improves the latency requirements of application execution, network usage, and energy consumption by 66.92%, 69.83%, and 4.16%, respectively.

Breast cancer is a leading cause of mortality worldwide, emphasizing the need for accurate and timely prediction of risk factors. This study proposes a novel approach using deep learning algorithms to predict breast cancer. The self-adaptive sea lion optimization-based recurrent neural network (SA-SLnO-RNN) is developed to select optimal features that enhance prediction accuracy. Evaluated on the breast cancer Wisconsin diagnostic (BCWD) dataset, the SA-SLnO-RNN achieves superior performance compared to existing methods, with accuracy, precision, recall, specificity, and F1-score values of 98.82%, 98.78%, 98.71%, 99.06%, and 98.75%, respectively. The proposed model’s self-adaptive nature prevents overfitting, while the RNN with optimization algorithm enables quick convergence and captures complex temporal dependencies, ensuring early and accurate breast cancer prediction.