Because of the rapid growth of mobile technology, social media has become an essential platform for people to express their views and opinions. Understanding public opinion can help businesses and political institutions make strategic decisions. Considering this, sentiment analysis is critical for understanding the polarity of public opinion. Most social media analysis studies divide sentiment into three categories: positive, negative, and neutral. The proposed model is a machine-learning application of a classification problem trained on three datasets. Recently, the BERT model has demonstrated effectiveness in sentiment analysis. However, the accuracy of sentiment analysis still needs to be improved. We propose four deep learning models based on a combination of BERT with Bidirectional Long ShortTerm Memory (BiLSTM) and Bidirectional Gated Recurrent Unit (BiGRU) algorithms. The study is based on pre-trained word embedding vectors that aid in the model fine-tuning process. The proposed methods are trying to enhance accuracy and check the effect of hybridizing layers of BIGRU and BILSTM on both Bert models (DistilBERT, RoBERTa) for no emoji (text sentiment classifier) and also with emoji cases. The proposed methods were compared to two pre-trained BERT models and seven other models built for the same task using classical machine learning. The proposed architectures with BiGRU layers have the best results.
The most common and deadly cancers are lung and colon cancers. More than a quarter of all cancer cases are caused by them. Early detection of the disease, on the other hand, greatly raises the probability of survival. Image enhancement by Double CLAHE stages and modified neural networks are made to improve classification accuracy and use Deep Learning (DL) algorithms to automate cancer detection. A new Artificial Intelligent classification system is presented in this research to recognize five kinds of colon and lung tissues, three malignant and two benign, with three classes for lung cancer and two classes for colon cancer, based on histological images. The results of the study imply that the suggested system can accurately identify tissues of cancer up to 99.5%. The use of this model will aid medical professionals in the development of an automatic and reliable system for detecting different kinds of colon and lung tumors.
Unmanned aerial vehicles are rapidly being utilized in surveillance and traffic monitoring because of their great mobility and capacity to cover regions at various elevations and positions. It is a challenging task to detect vehicles due to their various shapes, textures, and colors. One of the most difficult challenges is correctly detecting and counting aerial view vehicles in real time for traffic monitoring objectives using aerial images and videos. In this research, strategies are presented for improving the detection ability of self-driving vehicles in tough conditions, also for traffic monitoring, vehicle surveillance. We make classification, tracking trajectories, and movement calculation where fog, sandstorm (dust), and snow conditions are challenging. Initially, image enhancement methods are implemented to improve unclear images of roads. The improved images are then subjected to an object detection and classification algorithm to detect vehicles. Finally, new methods were evaluated (Corrected Optical flow/Corrected Kalman filter) to get the least error of trajectories. Also features like vehicle count, type, tracking trajectories by (Optical flow, Kalman Filter, Euclidean Distance) and relative movement calculation are extracted from the coordinates of the observed objects. These techniques aim to improve vehicle detection, tracking, and movement over aerial views of roads especially in bad weather. As a result, for aerial view vehicles in bad weather, our proposed method has an error of less than 5 pixels from the actual value and give the best results. This improves detection and tracking performance for aerial view vehicles in bad weather conditions.
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