An Improved Object Detection and Trajectory Prediction Method for Traffic Conflicts Analysis

Although computer vision-based methods have seen broad utilisation in evaluating traffic situations, there is a lack of research on the assessment and prediction of near misses in traffic. In addition, most object detection algorithms are not very good at detecting small targets. This study proposes a combination of object detection and tracking algorithms, Inverse Perspective Mapping (IPM), and trajectory prediction mechanisms to assess near-miss events. First, an instance segmentation head was proposed to improve the accuracy of the object frame box detection phase. Secondly, IPM was applied to all detection results. The relationship between them is then explored based on their distance to determine whether there is a near-miss event. In this process, the moving speed of the target was considered as a parameter. Finally, the Kalman filter is used to predict the object\u27s trajectory to determine whether there will be a near-miss in the next few seconds. Experiments on Closed-Circuit Television (CCTV) datasets showed results of 0.94 mAP compared to other state-of-the-art methods. In addition to improved detection accuracy, the advantages of instance segmentation fused object detection for small target detection are validated. Therefore, the results will be used to analyse near misses more accurately

Cryptocurrency Returns Over a Decade: Breaks, Trend Breaks, and Outliers

This study finds breaks, trend breaks, and outliers in the last decade returns of five cryptocurrencies Bitcoin, Ethereum, Litecoin, Tether USD, and Ripple that experienced frequent changes. The study uses the indicator saturation (IS) approach to simultaneously identify breaks, trend breaks, and outliers in these returns to gain a deeper understanding in their dynamics. The study found that monthly, weekly and daily breaks existed in these returns as well as trend breaks, and outliers mostly during the market peaks in 2017, 2018, 2020, and 2021 that can be attributed to a number of things, such as the global Covid-19 pandemic in 2020, the 2021 crypto crackdown in China, the 2020 price halving of Bitcoin, and the 2017–2018 initial coin offering (ICO) boom. These returns also have common break segments and outliers. The application of IS technique to cryptocurrencies and simultaneous detection of market breaks, trend breaks, and outliers makes this study unique. This study is limited to considering only returns of five digital coins. These results may help traders, investors, and financial analysts modify their tactics and risk-management techniques to deal with the complexity of the cryptocurrency market

Seaweed modeling for drying and the efficiency as heavy metal removal in Kappaphycus Striatum variety Sacol using Solar Dryer

The solar drying experiment of seaweed using Green V-Roof Hybrid Solar Drier (GVRHSD) was conducted in Semporna, Sabah under the metrological condition in Malaysia. Drying of sample seaweed in GVRHSD reduced the moisture content from about 92.68% to 32.06% in 4 days at average solar radiation of about 600W/m 2 and mass flow rate about 0.5 kg/s. The drying kinetics was fitted with six published exponential model thin layer drying models. The models were fitted using the coefficient of determination (R 2), and root mean square error (RMSE). The result showed modified page was the best model for describe the drying behavior. In addition, the dried seaweed was used to show biosorptions of cadminium, lead, zinc and copper. Batch mode experiments were performed to determine experimental parameters affecting sorption process such as pH, initial metal ion concentration, shaking rate and biomass dosage. The Pb(II) showed Int. J. Environ. Bioener. 2013, 8(1): 42 highest sorption on pH 4, shaking rate on 250 rpm with 24.18% removal rate; at initial concentration of 100 ppm and adsorbent dosage at 4g/l the removal percentage is 28.30%. Overall, this report indicates that Kappaphycus Striatum Variety Sacol is an effective and economical sorbent for removal of heavy metals from wastewaters

Sauna technique, drying kinetic modelling and effectiveness compared with direct drying in drying process of Kappaphycus Striatum in Selakan Island Malaysia.

A sauna drying technique—the solar drier was designed and imposed, constructed and tested for drying of seaweed. The seaweed moisture content was decreased around 50% in 2-day sau-na. Kinetic curves of drying of seaweed were known to be used in this system. The non-linear re-gression procedure was used to fit three different drying models. The models were compared with experimental data of red seaweed being dried on the daily average of air temperature about 40°C. The fit quality of the models was evaluated using the coefficient of determination (R2), Mean Bias Error (MBE) and Root Mean Square Error (RMSE). The highest values of R2 (0.99027), the lowest MBE (0.00044) and RMSE (0.03039) indicated that the Page model was the best mathematical model to describe the drying behavior of sauna dried seaweed. The percentage of the saved time using this technique was calculated at 57.9% on the average solar radiation of about 500 W/m2 and air flow rate of 0.056 kg/s

The effectiveness of sauna technique on the drying period and kinetics of seaweed Kappaphycus Alvarezii using solar drier

A sauna drying technique was designed and imposed. A V-Groove Hybrid Solar Drier (VGHSD) was constructed and tested for drying of seaweeds. Drying of sample seaweed in VGHSD reduced the moisture content from about 90.50% to 38% in 4 days at average solar radiation of about 600W/m2 and mass flow rate about 0.056 kg/s. The seaweed moisture content was decreased around 50% within 2 days in the sauna. Kinetic curves of drying of seaweeds are known to use this system. The non-linear regression procedure was used to fit three different drying models. The models were compared with experimental data of red seaweed drying at the daily air temperature average of about 40oC. The fit quality of the models was evaluated using the coefficient of determination (R2), Mean Bias Error (MBE) and Root Mean Square Error (RMSE). The highest values of R2 (0.9348), the lowest MBE (0.00131) and RMSE (0.01325) indicated that the Page model is the best mathematical model to describe the drying behavior of sauna dried seaweed. The saving time using this technique was calculated of 57.9% at the average solar radiation of about 500 W/m2 and air flow rate of 0.056 kg/s

Hybridised Network of Fuzzy Logic and a Genetic Algorithm in Solving 3-Satisfiability Hopfield Neural Networks

This work proposed a new hybridised network of 3-Satisfiability structures that widens the search space and improves the effectiveness of the Hopfield network by utilising fuzzy logic and a metaheuristic algorithm. The proposed method effectively overcomes the downside of the current 3-Satisfiability structure, which uses Boolean logic by creating diversity in the search space. First, we included fuzzy logic into the system to make the bipolar structure change to continuous while keeping its logic structure. Then, a Genetic Algorithm is employed to optimise the solution. Finally, we return the answer to its initial bipolar form by casting it into the framework of the hybrid function between the two procedures. The suggested network’s performance was trained and validated using Matlab 2020b. The hybrid techniques significantly obtain better results in terms of error analysis, efficiency evaluation, energy analysis, similarity index, and computational time. The outcomes validate the significance of the results, and this comes from the fact that the proposed model has a positive impact. The information and concepts will be used to develop an efficient method of information gathering for the subsequent investigation. This new development of the Hopfield network with the 3-Satisfiability logic presents a viable strategy for logic mining applications in future

Effectiveness the drying time and kinetic of seaweed kappaphycus alvarezii var. Tambalang in green v-roof hybrid solar drier

The solar drying experiment of seaweed using Green V-Roof Hybrid Solar Drier (GVRHSD) was conducted in Semporna, Sabah under the metrological condition in Malaysia. Drying of sample seaweed in GVRHSD reduced the moisture content from about 90.50% to 38% in 4 days at average solar radiation of about 600W/m2 and mass flow rate about 0.05 kg/s. The drying kinetics were fitted with ten published exponential model thin layer drying models. The models were fitted using the coefficient of determination (R2), and root mean square error (RMSE).The modeling of models using raw data be tested with the possible of exponential drying method. The result showed that the model from modified Page was found to the best model for describe the drying behavior. The R2 and RSME values for the best model was 0.9989 and 0.0497 respectively

Study of transmission of tuberculosis by SIR model using Runge-Kutta method

This project is conducted to see the prediction of the transmission of the tuberculosis disease's trend with demography and without demography. It is carried out by the SIR model with the Runge-Kutta fourth-order technique using mathematical modelling to analyse Tuberculosis transmission. Furthermore, this project examines the Tuberculosis disease prediction performance of the two SIR models by comparing the data and also to predict the future trend of Tuberculosis transmission in Malaysia in the year 2021 by calculating its incidence rate for each 100 thousand people. We discovered that combining the SIR Model with demography improves the prediction of Tuberculosis disease spread. We also discovered that the higher the transmission rate, the lower the incidence rate per 100 thousand people, and the higher the incidence rate per 100 thousand people, the lower the recovery rate. As a result, it is acceptable to argue that these variables play a significant impact in determining epidemic growth rates

Complexity Reduction Approach for Solving Second Kind of Fredholm Integral Equations

Initially, the concept of the complexity reduction approach was applied to solve symmetry algebraic systems that were generated from the discretization of the partial differential equations. Consequently, in this paper, the effectiveness of a complexity reduction approach based on half- and quarter-sweep iteration concepts for solving linear Fredholm integral equations of the second kind is investigated. Half- and quarter-sweep iterative methods are applied to solve dense linear systems generated from the discretization of the second kind of linear Fredholm integral equations using a repeated modified trapezoidal (RMT) scheme. The formulation and implementation of the proposed methods are presented. In addition, computational complexity analysis and numerical results of test examples are also included to verify the performance of the proposed methods