Emotion recognition and analysis of netizens based on micro-blog during covid-19 epidemic

The research is about emotion recognition and analysis based on Micro-blog short text. Emotion recognition is an important field of text classification in Natural Language Processing. The data of this research comes from Micro-blog 100K record related to COVID-19 theme collected by Data fountain platform, the data are manually labeled, and the emotional tendencies of the text are negative, positive and neutral. The empirical part adopts dictionary emotion recognition method and machine learning emotion recognition respectively. The algorithms used include support vector machine and naive Bayes based on TFIDF, support vector machine and LSTM based on wod2vec. The five results are compared. Combined with statistical analysis methods, the emotions of netizens in the early stage of the epidemic are analyzed for public opinion. This research uses machine learning algorithm combined with statistical analysis to analyze current events in real time. It will be of great significance for the introduction and implementation of national policies

Classification of Fatigue Damaging Segments Using Artificial Neural Network / M. F. M. Yunoh ...[et al.]

This paper focuses on the classification of the fatigue damaging segments datasets associated with the measurement of Variable Amplitude Loadings of strain signals from the coil springs of an automobile during road tests. The wavelet transform was used to extract high damaging segments of the fatigue strain signals. The parameters of the kurtosis, wavelet-based coefficients, and fatigue damage were then calculated for every segment. All the parameters were used as input for the classification analysis using artificial neural networks. Using the back-propagation trained artificial neural network, the corresponding fatigue damages were classified. It was observed that the classification method was able to give 100% accuracy on the classifications based on the damaging segments that were extracted from the training and the validation datasets. From this approach, it classified the level of fatigue damage for coils spring

Determining Damaging Fatigue Cycles under Influence of Random Loadings using the Root-Mean-Square Level / M. Mahmud ...[et al.]

The aim of this paper is to investigate the damaging fatigue cycles criterion using the root-mean-square level under the influence of random loads for coil spring. Fatigue life cycle analysis especially in signal processing involves high computational effort because it deals with large quantity of data from vibratory loads obtained from the coil spring. The captured data of frequent low amplitude cycles generally consist of noise or vibrations which are meaningless and not significant for analysis. Therefore, a criterion using the root-mean-square level is proposed in assessing fatigue life of the captured strain signal from the coil spring. Four strain signals were analysed statistically using global statistics and distribution fitting. Fatigue damage was determined using the Morrow model and control charts were used in the classification of predefined damaging cycles. For evaluating the contribution of these cycles to fatigue damage, cycle elimination process was performed. The results showed a significant reduction of 48%−62% in damage values with damage probability ranging from 0.9362 to 0.9999. Hence, the criterion is useful and has potential to be extended in determining damaging cycles in fatigue analysis in indicating the damaging effects for coil spring

Use of a Combination of MRSS-ANP for Making an Innovative Landfill Siting Decision Model

Landfill siting is a complex, multicriteria decision-making problem that needs an extensive evaluation of environmental, social, land use, and operational criteria. Integration of a median ranked sample set (MRSS) and an analytic network process (ANP) has been implemented to rank the associated criteria and select a suitable landfill site. It minimizes the uncertainty and the subjectivity of human judgments. Four groups of experts with different backgrounds participated in this study, and each group contained four experts. The respondent preferences were ranked in a 4-by-4 matrix to obtain the judgment sets for the MRSS. These sets were subsequently analyzed using ANP to obtain the priorities in the landfill siting criteria. The results show that land topology and distance from surface water are the most influential factors, with priorities of 0.18 and 0.17, respectively. The proposed integrated model may become a promising tool for the environmental planners and decision makers

EVALUATING THE RELIABILITY OF PRE-TEST DIFFERENTIAL EQUATIONS QUESTIONS USING RASCH MEASUREMENT MODEL

A good exam questions should be able to gauge student’s understanding and achievement related to Course Outcome (CO), Bloom’s Taxonomy level and Programme Outcome (PO). To achieve this, a set of pre-test questions were prepared to evaluate the pre achievement level among the students related to CO, PO and the Bloom’s Taxonomy level. In this study, a pre-test for Differential Equations (KKKQ2123) was given to 100 second year students from the department of Electrical, Electronic and Systems Engineering. The level of Bloom’s Taxonomy measured consists of level 1 (knowledge) to level 6 (creation). Rasch Measurement Model was applied to analyse the reliability of the pre-test questions. The analysis revealed that all the pre-test questions were reliable and no questions were found unsuitable. Prior assessment (pre-test) is important in the preparation of final exam questions as it would indicate the level of student’s understanding in a particular topic that relates to the CO and PO of the programme

Fatigue feature classification for automotive strain data

Fatigue strain signal were analysed using data segmentation and data clustering. For data segmentation, value of fatigue damage and global statistical signal analysis such as kurtosis was obtained using specific software. Data clustering were carried out using K-Mean clustering approaches. The objective function was calculated in order to determine the best numbers of groups. This method is used to calculate the average distance of each data in the group from its centroid. Finally, the fatigue failure indexes of metallic components were generated from the best number of group that has been acquired. Based on four data collect from two different roads which are D1, D2, the index value generated is not the same for all of data because due to K-Mean clustering, the best group is different for each of the data used. The maximum indexes generated are different for two types of road and namely the index 4 for D1 and index 5 for D2. Due to the road surface condition, higher distributions of the best groups give higher values of index and reflect to higher fatigue damage experienced by the system