A large-scale sentiment analysis using political tweets

Twitter has become a key element of political discourse in candidates’ campaigns. The political polarization on Twitter is vital to politicians as it is a popular public medium to analyze and predict public opinion concerning political events. The analysis of the sentiment of political tweet contents mainly depends on the quality of sentiment lexicons. Therefore, it is crucial to create sentiment lexicons of the highest quality. In the proposed system, the domain-specific of the political lexicon is constructed by using the supervised approach to extract extreme political opinions words, and features in tweets. Political multi-class sentiment analysis (PMSA) system on the big data platform is developed to predict the inclination of tweets to infer the results of the elections by conducting the analysis on different political datasets: including the Trump election dataset and the BBC News politics. The comparative analysis is the experimental results which are better political text classification by using the three different models (multinomial naïve Bayes (MNB), decision tree (DT), linear support vector classification (SVC)). In the comparison of three different models, linear SVC has the better performance than the other two techniques. The analytical evaluation results show that the proposed system can be performed with 98% accuracy in linear SVC

MODELLING OF DYNAMIC SPEED LIMITS USING THE MODEL PREDICTIVE CONTROL

The article considers the issues of traffic management using intelligent system “Car-Road” (IVHS), which consist of interacting intelligent vehicles (IV) and intelligent roadside controllers. Vehicles are organized in convoy with small distances between them. All vehicles are assumed to be fully automated (throttle control, braking, steering). Proposed approaches for determining speed limits for traffic cars on the motorway using a model predictive control (MPC). The article proposes an approach to dynamic speed limit to minimize the downtime of vehicles in traffic

ALGORITHMS FOR TRAFFIC MANAGEMENT IN THE INTELLIGENT TRANSPORT SYSTEMS

Traffic jams interfere with the drivers and cost billions of dollars per year and lead to a substantial increase in fuel consumption. In order to avoid such problems the paper describes the algorithms for traffic management in intelligent transportation system, which collects traffic information in real time and is able to detect and manage congestion on the basis of this information. The results show that the proposed algorithms reduce the average travel time, emissions and fuel consumption. In particular, travel time has decreased by about 23%, the average fuel consumption of 9%, and the average emission of 10%

Effect of leucaena forage and silage substitution in concentrates on digestibility, nitrogen utilization and milk yield in dairy cows

This experiment was conducted to determine the effect of feeding leucaena forage and silage substitution in concentrate on the performances of dairy cows. Nine cross-bred Holstein Friesian cows (410±12kg) in the 12th week of lactation were randomly allocated to one of three treatment groups with three replicates/treatments in a completely randomized design. The three treatments were control diet without substitution of leucaena forage and silage (DLFS0), diet with substitution of leucaena forage 10% (DLF10) and diet with substitution of leucaena silage 10% (DLS10). Cows were fed treatments for 60 days. Although nutrient intakes were not significantly different (p>0.05) each other, digestibility of DLFS0 was significantly higher (p<0.05) than others. Conversely, nitrogen utilization and average milk yield of cows offered DLFS0 were significantly lower (p<0.05) than those of cows fed on DLF10 and DLS10. The highest feed cost (p<0.05) per kg of milk was found in DLFS0 and the lowest cost was observed in DLF10. Therefore, although the leucana forage and silage could be substitute up to 10% of concentrates without adverse effects on the performances of dairy cows, the substitution of leucaena forage gave the better performances than that of leucaena silage

Implementation of Neural Network based Illicit Object Detection for Enhanced Security

This paper presents the development technique of an automated illicit object detection system in aviation security using X-ray screening where classification phase is being implemented by artificial neural network. Neural network is investigated for the purpose of identifying illicit material in hand luggage. We have proposed in intelligent security system that impacts on airlines and airport operations. The proposed system uses appropriate image processing techniques, feature extraction and neural network for detecting objects. For programming and simulation of this system MATLAB software is applied. In this paper, a neural network trained with the back propagation algorithm (trainscg) is classified in conjunction with feature extraction for various object images

Consideration of Influential Factors on Bioleaching of Gold Ore Using Iodide-Oxidizing Bacteria

Iodide-oxidizing bacteria (IOB) oxidize iodide into iodine and triiodide which can be utilized for gold dissolution. IOB can be therefore useful for gold leaching. This study examined the impact of incubation conditions such as concentration of the nutrient and iodide, initial bacterial cell number, incubation temperature, and shaking condition on the performance of the gold dissolution through the experiments incubating IOB in the culture medium containing the marine broth, potassium iodide and gold ore. The minimum necessary concentration of marine broth and potassium iodide for the complete gold dissolution were determined to be 18.7 g/L and 10.9 g/L respectively. The initial bacterial cell number had no effect on gold dissolution when it was 1 × 104 cells/mL or higher. Gold leaching with IOB should be operated under a temperature range of 30−35 °C, which was the optimal temperature range for IOB. The bacterial growth rate under shaking conditions was three times faster than that under static conditions. Shaking incubation effectively shortened the contact time compared to the static incubation. According to the pH and redox potential of the culture solution, the stable gold complex in the culture solution of this study could be designated as gold (I) diiodide

Surface ruptures of the Mw 6.8 March 2011 Tarlay earthquake, Eastern Myanmar

Field observations indicate the Mw 6.8 Tarlay, Myanmar, earthquake of 24 March 2011 resulted from the rupture of a short section of the left‐lateral Nam Ma fault. We document coseismic left‐lateral offsets that range from approximately 10 cm to about 2 m along a 19 km long section of the fault, based upon both field investigations and the interpretation of high‐resolution satellite imagery. Comparison of these early field and later remote measurements suggests that most of the paddy‐field offsets became obscured within a year or two. Our measurements indicate the magnitude of sinistral offset decreases gradually eastward from the epicentral area to the rupture terminus within the Tarlay basin, along the southern edge of a 2–3 km wide transtensional stepover. Our measurements confirm that the earthquake resulted from rupture of a structurally distinct segment of the Nam Ma fault, bounded on the west by the fault’s terminus and on the east by the Tarlay basin stepover. If the 215 km Nam Ma fault were to rupture entirely in a single event, the magnitude of the resulting earthquake would likely be about Mw 7.7