Video streaming is one of the challenging issues in vehicular ad-hoc networks (VANETs) due to their highly dynamic topology and frequent connectivity disruptions. Recent developments in the routing protocol methods used in VANETs have contributed to improvements in the quality of experience (QoE) of the received video. One of these methods is the selection of the next-hop relay vehicle. In this paper, a QoE-aware geographic protocol for video streaming over VANETs is proposed. The selection process of the next relay vehicle is based on a correlated formula of QoE and quality of service (QoS) factors to enhance the users’ QoE. The simulation results show that the proposed GeoQoE-Vanet outperforms both GPSR and GPSR-2P protocols in providing the best end-user QoE of video streaming service.
A four quasiparticle high-K isomer with a meanlife of 45͑2͒ s has been identified at 1451 keV in 180 Ta, populated in the 176 Yb( 11 B,␣3n͒ 180 Ta reaction. The isomer decays into a rotational band which is associated with the two-quasiparticle 9 Ϫ isomer at 75.3 keV. Analysis of the branching ratios within that band and the magnetic moment for the 9 Ϫ isomer, supports the configuration assignment to the 9 Ϫ isomer. The K hindrance for the E2 decay of the 15 Ϫ isomer to the 9 Ϫ band is substantially lower than that for an apparently similar 15 Ϫ isomer in 178 Ta, a difference which can be attributed partly to a change from the 9/2 Ϫ ͓514͔ 3 9/2 ϩ ͓624͔7/2 Ϫ ͓514͔5/2 Ϫ ͓512͔ configuration in 178 Ta to the 3 9/2 Ϫ ͓514͔7/2 ϩ ͓404͔5/2 ϩ ͓402͔9/2 ϩ ͓624͔ configuration in 180 Ta. The reduced hindrance factors for E2 decays from related four-quasiparticle isomers in the isotopes 176,178,180 Ta match the hindrances of the corresponding E2 decays from component 6 ϩ core states in the hafnium isotopes, 174,176,178 Hf .
Arabic is one of the official languages recognized by the United Nations (UN) and is widely used in the middle east, and parts of Asia, Africa, and other countries. Social media activity currently dominates the textual communication on the Internet and potentially represents people’s views about specific issues. Opinion mining is an important task for understanding public opinion polarity towards an issue. Understanding public opinion leads to better decisions in many fields, such as public services and business. Language background plays a vital role in understanding opinion polarity. Variation is not only due to the vocabulary but also cultural background. The sentence is a time series signal; therefore, sequence gives a significant correlation to the meaning of the text. A recurrent neural network (RNN) is a variant of deep learning where the sequence is considered. Long short-term memory (LSTM) is an implementation of RNN with a particular gate to keep or ignore specific word signals during a sequence of inputs. Text is unstructured data, and it cannot be processed further by a machine unless an algorithm transforms the representation into a readable machine learning format as a vector of numerical values. Transformation algorithms range from the Term Frequency–Inverse Document Frequency (TF-IDF) transform to advanced word embedding. Word embedding methods include GloVe, word2vec, BERT, and fastText. This research experimented with those algorithms to perform vector transformation of the Arabic text dataset. This study implements and compares the GloVe and fastText word embedding algorithms and long short-term memory (LSTM) implemented in single-, double-, and triple-layer architectures. Finally, this research compares their accuracy for opinion mining on an Arabic dataset. It evaluates the proposed algorithm with the ASAD dataset of 55,000 annotated tweets in three classes. The dataset was augmented to achieve equal proportions of positive, negative, and neutral classes. According to the evaluation results, the triple-layer LSTM with fastText word embedding achieved the best testing accuracy, at 90.9%, surpassing all other experimental scenarios.
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