Firefly algorithm approach for rational bézier border reconstruction of skin lesions from macroscopic medical images

Image segmentation is a fundamental step for image processing of medical images. One of the most important tasks in this step is border reconstruction, which consists of constructing a border curve separating the organ or tissue of interest from the image background. This problem can be formulated as an optimization problem, where the border curve is computed through data fitting procedures from a collection of data points assumed to lie on the boundary of the object under analysis. However, standard mathematical optimization techniques do not provide satisfactory solutions to this problem. Some recent papers have applied evolutionary computation techniques to tackle this issue. Such works are only focused on the polynomial case, ignoring the more powerful (but also more difficult) case of rational curves. In this paper, we address this problem with rational Bézier curves by applying the firefly algorithm, a popular bio-inspired swarm intelligence technique for optimization. Experimental results on medical images of melanomas show that this method performs well and can be successfully applied to this problem

Microstructure Alterations of Ti-6Al-4V ELI During Turning by Using Tungsten Carbide Inserts Under Dry Cutting Condition

Titanium alloys possesses a hexagonal close packed(h.c.p) structure, called phase to ambient temperature. Thisstructure changes to body center cubic (b.c.c), called phase tothe temperature of 882 C. Machining process that generates hightemperature during machining can affect on microstructures ofmachined surface, which represents as a quality of components.The turning parameters evaluated are cutting speed (55 - 95m/min), feed rate (0.15 - 0.35 mm/rev), depth of cut (0.10 - 0.20mm) and tool grade (uncoated, CVD and PVD). The aims of thispaper are to investigate the effects of machining process onmicrostructures of machined surface and chip were machinedusing tungsten carbide inserts under dry cutting condition. Theresults show that machining at high cutting speed (95 m/min)affected on the microstructure significantly at the end ofmachining. The temperature is the most significant factoraffected on microstructure of the machined surface and chip atshear zone. The changes of microstructure were also affected bythe tool pressure during cutting

Application of ANFIS in predicting TiAlN coatings flank wear

In this paper, a new approach in predicting the flank wear of Titanium Aluminum Nitrite (TiAlN) coatings using Adaptive Network Based Fuzzy Inference System (ANFIS) is implemented. TiAlN coated cutting tool is widely used in machining due to its excellent resistance to wear. The TiAlN coatings were formed using Physical Vapor Deposition (PVD) magnetron sputtering process. The substrate sputtering power, bias voltage and temperature were selected as the input parameters and the flank wear as an output of the process. A statistical design of experiment called Response Surface Methodology (RSM) was used in collecting optimized data. The ANFIS model was trained using the limited experimental data. The triangular, trapezoidal, bell and Gaussian shapes of membership functions were used for inputs as well as output. The results of ANFIS model were validated with the testing data and compared with fuzzy rule-based and RSM flank wear models in terms of the root mean square error (RMSE), coefficient determination (R2) and model accuracy (A). The result indicated that the ANFIS model using three bell shapes membership function obtained better result compared to the fuzzy and RSM flank wear models. The result also indicated that the ANFIS model could predict the output response in high prediction accuracy even using limited training data

Plant-Derived Protectants in Combating Soil-Borne Fungal Infections in Tomato and Chilli

Fungal infections transmitted through the soil continue to pose a threat to a variety of horticultural and agricultural products, including tomato and chilli. The indiscriminate use of synthetic pesticides has resulted in a slew of unintended consequences for the surrounding ecosystem. To achieve sustainable productivity, experts have turned their attention to natural alternatives. Due to their biodegradability, varied mode of action, and minimal toxicity to non-target organisms, plant-derived protectants (PDPs) are being hailed as a superior replacement for plant pesticides. This review outlines PDPs’ critical functions (including formulations) in regulating soil-borne fungal diseases, keeping tomato and chilli pathogens in the spotlight. An in-depth examination of the impact of PDPs on pathogen activity will be a priority. Additionally, this review emphasises the advantages of the in silico approach over conventional approaches for screening plants’ secondary metabolites with target-specific fungicidal activity. Despite the recent advances in our understanding of the fungicidal capabilities of various PDPs, it is taking much longer for that information to be applied to commercially available pesticides. The restrictions to solving this issue can be lifted by breakthroughs in formulation technology, governmental support, and a willingness to pursue green alternatives among farmers and industries

Plant-Derived Protectants in Combating Soil-Borne Fungal Infections in Tomato and Chilli

Fungal infections transmitted through the soil continue to pose a threat to a variety of horticultural and agricultural products, including tomato and chilli. The indiscriminate use of synthetic pesticides has resulted in a slew of unintended consequences for the surrounding ecosystem. To achieve sustainable productivity, experts have turned their attention to natural alternatives. Due to their biodegradability, varied mode of action, and minimal toxicity to non-target organisms, plant-derived protectants (PDPs) are being hailed as a superior replacement for plant pesticides. This review outlines PDPs' critical functions (including formulations) in regulating soil-borne fungal diseases, keeping tomato and chilli pathogens in the spotlight. An in-depth examination of the impact of PDPs on pathogen activity will be a priority. Additionally, this review emphasises the advantages of the in silico approach over conventional approaches for screening plants' secondary metabolites with target-specific fungicidal activity. Despite the recent advances in our understanding of the fungicidal capabilities of various PDPs, it is taking much longer for that information to be applied to commercially available pesticides. The restrictions to solving this issue can be lifted by breakthroughs in formulation technology, governmental support, and a willingness to pursue green alternatives among farmers and industries.Peer reviewe

Hybrid RSM-fuzzy modeling for hardness prediction of TiAlN coatings

In this paper, a new approach in predicting the hardness of Titanium Aluminum Nitrite (TiAlN) coatings using hybrid RSM-fuzzy model is implemented. TiAlN coatings are usually used in high-speed machining due to its excellent surface hardness and wear resistance. The TiAlN coatings were produced using Physical Vapor Deposition (PVD) magnetron sputtering process. A statistical design of experiment called Response Surface Methodology (RSM) was used in collecting optimized data. The fuzzy rules were constructed using actual experimental data. Meanwhile, the hardness values were generated using the RSM hardness model. Triangular shape of membership functions were used for inputs as well as output. The substrate sputtering power, bias voltage and temperature were selected as the input parameters and the coating hardness as an output of the process. The results of hybrid RSM-fuzzy model were compared against the experimental result and fuzzy single model based on the percentage error, mean square error (MSE), co-efficient determination (R2) and model accuracy. The result indicated that the hybrid RSM-fuzzy model obtained the better result compared to the fuzzy single model. The hybrid model with seven triangular membership functions gave an excellent result with respective average percentage error, MSE, R2 and model accuracy were 11.5%, 1.09, 0.989 and 88.49%. The good performance of the hybrid model showed that the RSM hardness model could be embedded in fuzzy rule-based model to assist in generating more fuzzy rules in order to obtain better prediction result

Plant-Derived Protectants in Combating Soil-Borne Fungal Infections in Tomato and Chilli

Fungal infections transmitted through the soil continue to pose a threat to a variety of horticultural and agricultural products, including tomato and chilli. The indiscriminate use of synthetic pesticides has resulted in a slew of unintended consequences for the surrounding ecosystem. To achieve sustainable productivity, experts have turned their attention to natural alternatives. Due to their biodegradability, varied mode of action, and minimal toxicity to non-target organisms, plant-derived protectants (PDPs) are being hailed as a superior replacement for plant pesticides. This review outlines PDPs’ critical functions (including formulations) in regulating soil-borne fungal diseases, keeping tomato and chilli pathogens in the spotlight. An in-depth examination of the impact of PDPs on pathogen activity will be a priority. Additionally, this review emphasises the advantages of the in silico approach over conventional approaches for screening plants’ secondary metabolites with target-specific fungicidal activity. Despite the recent advances in our understanding of the fungicidal capabilities of various PDPs, it is taking much longer for that information to be applied to commercially available pesticides. The restrictions to solving this issue can be lifted by breakthroughs in formulation technology, governmental support, and a willingness to pursue green alternatives among farmers and industries

Exterior noise due to interaction of tyre-thermoplastic transverse rumble strips

Transverse rumble strips (TRS) are a common choice to reduce vehicle speed and increase driver alertness on roadways. However, there is a potential trade-off using them on rural roadway due to the noise problem created when vehicles go over the strips. The present study investigated the noise level, spectral analysis, and the possible noise generation mechanism when the TRS is hit by a vehicle. Tenraised- rumbler (RR) and three-layer-overlapped (TLO) TRS were selected in this study as they have received complaints from the public. Results showed that RR generated a relatively higher noise and impulse at a low speed, and increased sound level in each octave band. Based on these results, RR may irritate human ears even when the vehicle travels at a low speed. It was found that RR increased all noise generation mechanisms of tyre-pavement interaction whilst TLO increased structural resonance, sidewall and surface texture vibration

The prediction of suspended solids of river in forested catchment using artificial neural network

This study presents an artificial neural network (ANN) model that is able to predict suspended solids concentrations in forested catchment namely Berring River, Kelantan, Malaysia.The network was trained using data collected during a period of 13 days in April 2001. The sampling location was established in the middle section of the river for collecting water samples. The study was carried out for a duration of two weeks in April 2001. The water sample was collected at 60% of the total depth from the river bed for every two hours starting from 6:00 am to 12:00 midnight for the whole duration of the study period. In this study five parameters were selected as input parameter for the network which are turbidity, flow velocity, depth, width, and weather condition of during the sampling period, while suspended solids as desire output. The data fed to the neural network were divided into two set: a training set and testing set. 116 of the data were used in training set and 24 remained as testing set. A network of the model was detected automatically by the network to give good predictions for both training and testing data set. A partitioning method of the connection weights of the network was used to study the relative percentage contribution of each of the input variables. It was found that turbidity and river width gives 73.03% and 24.73% each. The performance of the neural network model was measured by computing the correlation coefficient which gives the value of 0.93. It’s shown that the neural network gives superior predictions. Based on the results of this study, ANN modeling appears to be a promising technique for the prediction of suspended solids. Dynamic Metadata(s