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The tremendous growth in online activity and the Internet of Things (IoT) led to an increase in cyberattacks. Malware infiltrated at least one device in almost every household. Various malware detection methods that use shallow or deep IoT techniques were discovered in recent years. Deep learning models with a visualization method are the most commonly and popularly used strategy in most works. This method has the benefit of automatically extracting features, requiring less technical expertise, and using fewer resources during data processing. Training deep learning models that generalize effectively without overfitting is not feasible or appropriate with large datasets and complex architectures. In this paper, a novel ensemble model, Stacked Ensemble—autoencoder, GRU, and MLP or SE-AGM, composed of three light-weight neural network models—autoencoder, GRU, and MLP—that is trained on the 25 essential and encoded extracted features of the benchmark MalImg dataset for classification was proposed. The GRU model was tested for its suitability in malware detection due to its lesser usage in this domain. The proposed model used a concise set of malware features for training and classifying the malware classes, which reduced the time and resource consumption in comparison to other existing models. The novelty lies in the stacked ensemble method where the output of one intermediate model works as input for the next model, thereby refining the features as compared to the general notion of an ensemble approach. Inspiration was drawn from earlier image-based malware detection works and transfer learning ideas. To extract features from the MalImg dataset, a CNN-based transfer learning model that was trained from scratch on domain data was used. Data augmentation was an important step in the image processing stage to investigate its effect on classifying grayscale malware images in the MalImg dataset. SE-AGM outperformed existing approaches on the benchmark MalImg dataset with an average accuracy of 99.43%, demonstrating that our method was on par with or even surpassed them. Full article

Hydrotropism is the movement or growth of a plant towards water. It is a type of tropism, or directional growth response, that is triggered by water. Plants are able to detect water through various stimuli, including changes in moisture levels and changes in water potential. The purpose of this study is to provide an overview of how root movement towards water and plant water uptake are stabilized. The impact of hydrotropism on plants can be significant. It can help plants to survive in environments where water is scarce, and it can also help them to grow more efficiently by directing their roots towards the most nutrient-rich soil. To make sure that plant growth and water uptake are stabilized, plants must sense water. Flowing down the roots, being absorbed by roots, and evaporating from the leaves are all processes that are governed by plant physiology and soil science. Soil texture and moisture affect water uptake. Hydraulic resistances can impede plants’ water absorption, while loss of water and water movement can change plants’ water potential gradients. Growth causes water potential gradients. Plants respond to gradient changes. Stomata and aquaporins govern water flow and loss. When water is scarce, stomatal closure and hydraulic conductance adjustments prevent water loss. Plants adapt to water stream changes by expanding their roots towards water and refining the architecture of their roots. Our study indicates that water availability, or gradients, are impacted by systemic and local changes in water availability. The amount of water available is reflected in plant turgor. There is still a lot of work to be done regarding the study of how the loss and availability of water affect plant cells, as well as how biophysical signals are transformed in a certain way during their transmission into chemical signals so that pathways such as abscisic acid response or organ development can be fed with information. Full article

Internet of Things (IoT) technology has recently been applied in healthcare systems as an Internet of Medical Things (IoMT) to collect sensor information for the diagnosis and prognosis of heart disease. The main objective of the proposed research is to classify data and predict heart disease using medical data and medical images. The proposed model is a medical data classification and prediction model that operates in two stages. If the result from the first stage is efficient in predicting heart disease, there is no need for stage two. In the first stage, data gathered from medical sensors affixed to the patient’s body were classified; then, in stage two, echocardiogram image classification was performed for heart disease prediction. A hybrid linear discriminant analysis with the modified ant lion optimization (HLDA-MALO) technique was used for sensor data classification, while a hybrid Faster R-CNN with SE-ResNet-101 modelwass used for echocardiogram image classification. Both classification methods were carried out, and the classification findings were consolidated and validated to predict heart disease. The HLDA-MALO method obtained 96.85% accuracy in detecting normal sensor data, and 98.31% accuracy in detecting abnormal sensor data. The proposed hybrid Faster R-CNN with SE-ResNeXt-101 transfer learning model performed better in classifying echocardiogram images, with 98.06% precision, 98.95% recall, 96.32% specificity, a 99.02% F-score, and maximum accuracy of 99.15%. Full article