The Sentimental Analysis approach is typically used for analyzing a user's ideas, sentiments, and text subjectivity, all of which are expressed through text. Sentimental analysis, also known as "opinion mining," is a type of data mining that follows the concept of emotional analysis presented by people in a thoughtful manner. Based on historical evidence, websites are the most effective venue for soliciting customer feedback. Existing methodologies based on sentimental analysis are ineffective. As a result, a novel hybrid framework based on three classifiers, including SVM, logistic regression, and random forest, is proposed in this paper. Based on user feedback or historical data, the hybrid model serves as an effective classifier, assisting in the development of more accurate classification results. Furthermore, the proposed model has worked well and has been compared to other methods based on several performance metrics, such as accuracy, precision, recall, and recall.
The microstrip patch antenna is extensively used in the wireless network because the Microstrip patch Antenna can be easily manufactured. Bandwidth Improvement of the Microstrip patch antenna is shown in this paper. The Antenna is made at the 8.5GHz frequency. The material used for the substrate is FR4-epoxy. For this material, the dielectric loss tangent is 0.0018. This antenna observes the Bandwidth to 660MHz, gain 5.4028, Directivity 5.5068, return loss -37.0048, and VSWR 1.0629. In this design there is an Improvement of bandwidth to 660 MHz which is suitable for wireless network applications and the software which is being used for the simulation is the High-Frequency Structure Simulator which is also known as HFSS.
In a Wireless Sensor Network (WSN), Numerous cost-effective and energy-constrained sensor nodes are typically used. In a typical Wireless Sensor Network, a single Base Station (BS) gathers information from the whole network, which contributes to concerns including latency, network failure, and congestion. The overwhelming proportion of energy consumption, as well as the energy hole limitation, significantly degrades the overall system performance and network lifetime, which is owing to the sensor nodes that are near the BS consuming more energy. To tackle this problem, it’s essential to determine the perfect spot for mobile sink nodes, which minimizes the power consumed and so increases the network's lifespan. In this work, an effective strategy is designed and developed to detect the location of a mobile sink considering factors such as distance, estimated energy, and fairness, using Deep learning-based energy prediction with an adjacency cell score model. In addition, the predicted energy is determined by employing the Deep Maxout Network (DMN). However, a Minimum distance of 137.364, maximal residual energy of 30.903, maximum standardized fairness of 64.426, maximum network duration of 60, and maximum standardized throughput of 60.613 was obtained using the proposed adjacency-based cell score + Deep Maxout Network.
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