Technology evaluation in the electronics field leads to the great development of Wireless Sensor Networks (WSN) for a variety of applications. The sensor nodes are deployed in hazardous environments, and they are operated by isolated battery sources. Network connectivity is purely based on power availability, which impacts the network lifetime. Hence, power must be used wisely to prolong the network lifetime. The sensor nodes that fail due to power have to detect quickly to maintain the network. In a WSN, classifiers are used to detect the faults for checking the data generated by the sensor nodes. In this paper, six classifiers such as Support Vector Machine, Convolutional Neural Network, Multilayer Perceptron, Stochastic Gradient Descent, Random Forest and Probabilistic Neural Network have been taken for analysis. Six different faults (Offset fault, Gain fault, Stuck-at fault, Out of Bounds, Spike fault and Data loss) are injected in the data generated by the sensor nodes. The faulty data are checked by the classifiers. The simulation results show that the Random Forest detected more faults and it also outperformed all other classifiers in that category.

Text mining, also known as text analysis, is the process of converting unstructured text data into meaningful and functional information. Text mining uses different AI technologies to automate data and generate valuable insights, allowing enterprises to make data-based decisions. Text mining enables the user to extract important content from text data sets. Text analysis encourages machine learning ability for research areas such as medical and pharmaceutical innovation fields. Apart from this, text analysis converts inaccessible data into a structured format, which can be used for further analysis. Text analysis emphasizes facts and relationships from large data sets. This information is extracted and converted into structured data for visualization, analysis, and integration as structured data and refines the information using machine-learning methods. Like most things related to Natural Language Processing, text mining can seem like a difficult concept to understand. But the fact is, it does not have to be. This research article will go through the basics of text mining, clarify its different methods and techniques, and make it easier to understand how it works. We implemented Latent Dirichlet Allocation techniques for mining the data from the data set; it works properly and will be in future development data mining techniques.

Perumal I, et al. Clin Exp Vaccine Res. 2025 Jan;14(1):1-9. https://doi.org/10.7774/cevr.2025.14.e5

The fact that small creatures can be detected in a photograph of wildlife is important in monitoring an ecosystem, assessing the diversity of species or in formulating strategies to protect these creatures. Nevertheless, small creatures are typically a failure case of deep learning techniques since images are not detailed, animals are mixed, or the environment is too chaotic to make any detection reliable. The paper presents a more robust configuration that relies on Linear Contrast Stretching (LCS), YOLOv8, and Slicing Aided Hyper Inference (SAHI) to enhance the performance of small animal detection on drone shots. LCS then changes how the light and contrast of the images are perceived before processing to make animals easier to distinguish between them and their environment. SAHI, conversely, enhances the recognition of small objects by dividing huge images into intersecting parts. This aids in facilitating the process of seeing far away or partly concealed animals. Wildlife image datasets are tested with experiments demonstrating that the methods are great in comparison with a typical YOLOv8 model. Rather than uninteresting YOLOv8, the new LCS-YOLOv8-SAHI configuration achieved an average Precision (mAP) of 0.502 at 0.50 - 65 times higher than the 0.304 of YOLOv8. To make things worse, mAP 0.50:0.95 increased by 54 points, going between 0.14 and 0.216. These figures support the strength and competence of the mixed model in identification of small animals in photographs. With clever application of computer vision and deep learning gimmicks, such an approach provides a legitimate chance to enhance automatic wildlife tracking, to expand conservation efforts, and to advance ecology research.