Summary
The accuracy of brain tumor diagnosis based on medical images is greatly affected by the segmentation process. The segmentation determines the tumor shape, location, size, and texture. In ...this study, we proposed a new segmentation approach for brain tissues using MR images. The method includes three computer vision fiction strategies which are enhancing images, segmenting images, and filtering out non ROI based on the texture and HOG features. A fully automatic model‐based trainable segmentation and classification approach for MRI brain tumour using artificial neural networks to precisely identifying the location of the ROI. Therefore, the filtering out non ROI process have used in view of histogram investigation to avert the non ROI and select the correct object in brain MRI. However, identification the tumor kind utilizing the texture features. A total of 200 MRI cases are utilized for the comparing between automatic and manual segmentation procedure. The outcomes analysis shows that the fully automatic model‐based trainable segmentation over performs the manual method and the brain identification utilizing the ROI texture features. The recorded identification precision is 92.14%, with 89 sensitivity and 94 specificity.
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FZAB, GIS, IJS, KILJ, NLZOH, NUK, OILJ, SAZU, SBCE, SBMB, UL, UM, UPUK
In this study, slinky (the slinky-loop configuration is also known as the coiled loop or spiral loop of flexible plastic pipe)type ground heat exchanger (GHE) was established for a solar-assisted ...ground source heat pump system. System modelling is performed with the data obtained from the experiment. Artificial neural network (ANN) and adaptive neuro-fuzzy inference system (ANFIS) are used in modelling. The slinky pipes have been laid horizontally and vertically in a ditch. The system coefficient of performance (COP
sys
) and the heat pump coefficient of performance (COP
hp
) have been calculated as 2.88 and 3.55, respectively, at horizontal slinky-type GHE, while COP
sys
and COP
hp
were calculated as 2.34 and 2.91, respectively, at vertical slinky-type GHE. The obtained results showed that the ANFIS is more successful than that of ANN for forecasting performance of a solar ground source heat pump system.
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BFBNIB, DOBA, GIS, IJS, IZUM, KILJ, KISLJ, NUK, PILJ, PNG, SAZU, UILJ, UKNU, UL, UM, UPUK
In this research, five hybrid novel machine learning approaches, artificial neural network (ANN)-embedded grey wolf optimizer (ANN-GWO), multi-verse optimizer (ANN-MVO), particle swarm optimizer ...(ANN-PSO), whale optimization algorithm (ANN-WOA) and ant lion optimizer (ANN-ALO), were applied for modelling monthly reference evapotranspiration (ET
o
) at Ranichauri (India) and Dar El Beida (Algeria) stations. The estimates yielded by hybrid machine learning models were compared against three models, Valiantzas-1, 2 and 3 based on root mean square error (RMSE), Nash-Sutcliffe efficiency (NSE), Pearson correlation coefficient (PCC) and Willmott index (WI). The results of comparison show that the ANN-GWO-1 model with five input variables (T
min
, T
max
, RH, U
s
, R
s
) provides better estimates at both study stations (RMSE = 0.0592/0.0808, NSE = 0.9972/0.9956, PCC = 0.9986/0.9978, and WI = 0.9993/0.9989). Also, the adopted modelling strategy can build a truthful expert intelligent system for estimating the monthly ET
o
at study stations.
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BFBNIB, GIS, IJS, IZUM, KILJ, KISLJ, NUK, PILJ, PNG, SAZU, UL, UM, UPUK
Smarty is a fully-reconfigurable on-chip feed-forward artificial neural network (ANN) with ten integrated time-to-digital converters (TDCs) designed in a 16 nm FinFET CMOS technology node. The ...integration of TDCs together with an ANN aims to reduce system complexity and minimize data throughput requirements in positron emission tomography (PET) applications. The TDCs have an average LSB of 53.5 ps. The ANN is fully reconfigurable, the user being able to change its topology as desired within a set of constraints. The chip can execute 363 MOPS with a maximum power consumption of 1.9 mW, for an efficiency of 190 GOPS/W. The system performance was tested in a coincidence measurement setup interfacing Smarty with two groups of five 4 mm × 4 mm analog silicon photomultipliers (A-SiPMs) used as inputs for the TDCs. The ANN succesfully distinguished between six different positions of a radioactive source placed between the two photodetector arrays by solely using the TDC timestamps.
In response to the stringent emission regulations, artificial neural network (ANN) coupled with genetic algorithm (GA) is employed to optimize a novel internal combustion engine strategy named direct ...dual fuel stratification (DDFS). An enhanced ANN model is introduced to improve the accuracy and stability of predictions. Compared to the conventional computational fluid dynamics (CFD)-GA optimization method, the ANN-GA method can identify a better solution to achieve higher fuel efficiency and lower nitrogen oxide (NOx) emissions with the lower computational time. This is attributed to the lower cost of ANN calculation, which allows ANN-GA to introduce larger population to seek optimal solutions. Through combining the required new training data with the previous ones, the original ANN model can be updated to adapt to a wider parameter range. Thus, ANN-GA can readily deal with the optimization problems with variable parameters and objectives. When more re-optimizations are required, ANN-GA can save the computational time over 75% than CFD-GA owing to the data-driven nature of ANN-GA by fully utilizing the available data. Overall, the ANN-GA method shows the superiority in accuracy, efficiency, expansibility, and flexibility for DDFS strategy optimization. It is promising to integrate ANN with optimization algorithm for further improvements of engine performance.
•Machine learning with genetic algorithm is used to enhance engine optimization.•An enhanced ANN model is proposed to improve accuracy and stability of prediction.•ANN-GA can adapt to engine optimization with variable parameters and objectives.•Higher engine efficiency is achieved by ANN-GA method without other penalties.•ANN-GA method shows merit in accuracy, efficiency, expansibility, and flexibility.
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GEOZS, IJS, IMTLJ, KILJ, KISLJ, NLZOH, NUK, OILJ, PNG, SAZU, SBCE, SBJE, UILJ, UL, UM, UPCLJ, UPUK, ZAGLJ, ZRSKP
Complementary features of batteries and supercapacitors can be effectively used in a hybrid energy storage system (HESS). The utilization of the HESS in electric vehicles (EVs) offers many ...advantages, such as efficient regenerative braking, battery safety, and improved vehicle acceleration. In this paper, a new regenerative braking system (RBS) is proposed for EVs with HESS and driven by brushless DC (BLDC) motor. During regenerative braking, the BLDC acts as a generator. Hence, by using an appropriate switching algorithm, the dc-link voltage is boosted and the energy is transferred to the supercapacitor or the battery through the inverter. The harvested energy can be utilized to improve the vehicle acceleration and/or keep the battery pack from deep discharging while driving uphill. To provide a reliable and smooth brake, braking force distribution is realized through an artificial neural network. Simultaneously, the braking current is adjusted by a PI controller for constant torque braking. To evaluate the performance of the proposed RBS, different simulations and experiments are carried out. The results confirm high capability of the proposed RBS.
Accurate PM2.5 concentrations predicting is critical for public health and wellness as well as pollution control. However, traditional methods are difficult to accurately predict PM2.5. An adaptive ...model coupled with artificial neural network (ANN) and wavelet analysis (WANN) is utilized to predict daily PM2.5 concentrations with remote sensing and surface observation data. The four evaluation metrics, namely Pearson correlation coefficient (R), mean absolute percentage error (MAPE), root mean square error (RMSE), and mean absolute error (MAE), are utilized to evaluate the performances of the artificial neural network (ANN) and WANN methods. From the predicting results, The WANN model has a higher R (R = 0.9990) during the testing period compared with R (R = 0.6844) based on the ANN model. Similarly, the WANN model has a lower MAPE (3.6988%), RMSE (1.0145 μg/m3), MAE (1.3864 μg/m3), compared with MAPE (80.0086%), RMSE (16.5838 μg/m3), MAE (12.2420 μg/m3) of the ANN. In addition, comparing the outcomes of the proposed WANN method with the ANN method, it was observed that the error during the training and verification period has decreased significantly. Furthermore, the statistical methods are used to analyze WANN and ANN, showing that WANN has higher training accuracy and better stability. Therefore, it is feasible to establish WANN to predict PM2.5 concentrations (1 day in advance) by using remote sensing and surface observation data.
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•Models for predicting PM2.5 were developed.•The important information (CA2, CD2, and CD1) is extracted by DWT.•Properties of the WANN models are super to those of the ANN models.•WANN model with trainbr achieved the best performance with R of 0.9990.
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GEOZS, IJS, IMTLJ, KILJ, KISLJ, NLZOH, NUK, OILJ, PNG, SAZU, SBCE, SBJE, UILJ, UL, UM, UPCLJ, UPUK, ZAGLJ, ZRSKP
The ionization defects in bipolar junction transistors (BJTs) are sensitive to ionization dose rate and molecular hydrogen in oxides. The bimolecular reaction model, a physical model with ...self-coincidence forms, is established in earlier works to describe formations of ionization defects in BJTs. This work proposes a deep-learning model with artificial neural network (ANN) framework to calculate the densities of ionization defects. The ANN model can be trained using data prepared by the physical model in conjunction with numerical calculations. Case-studies with a dataset of ~6.0×10 5 points suggest an optimized ANN model which is composed of four hidden layers and sixteen ELU-activated (Exponential Linear Unit) neurons per hidden layer. This model provides excellent fits to the data from physical model for the dose-, dose-rate- and hydrogen-dependence of ionization defects in gate-controlled p-n-p BJTs. Our encouraging results show that the deep-learning modeling methodology may find its effective application in dynamics of ionization defects.
Geotechnical engineering deals with soils and rocks and their use in engineering constructions. By their nature, soils and rocks exhibit complex behaviours and a high level of uncertainty in material ...modelling. Artificial intelligence (AI) methods have been developed and used by an increasing number of researchers in the field of geotechnical engineering in the last three decades. These methods have been considered successful due to their ability to predict complex nonlinear relationships. Based on more than one thousand (i.e. 1235) published literatures, this paper presents a detailed review of the performance of AI methods and algorithms used in geotechnical engineering. Nine key areas where the application of AI methods is prominent were identified: frozen soils and soil thermal properties, rock mechanics, subgrade soil and pavements, landslide and soil liquefaction, slope stability, shallow and piles foundations, tunnelling and tunnel boring machine, dams, and unsaturated soils. Artificial Neural Network (ANN) emerged as the most widely used and preferred AI method with 52% of studies relying on it. Other methods that were used to a lesser extent were FIS, ANFIS, SVM, LSTM, CNN, ResNet and GAN. The analysis shows that the success and accuracy of AI applications depends on the number and type of datasets and selection of input parameters. The paper also provides statistical information on research incorporating AI methods and discusses the opportunities and challenges for future research and practical applications in geotechnical engineering.
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GEOZS, IJS, IMTLJ, KILJ, KISLJ, NLZOH, NUK, OILJ, PNG, SAZU, SBCE, SBJE, UILJ, UL, UM, UPCLJ, UPUK, ZAGLJ, ZRSKP
System design and deployment of millimeter-wave (mmWave) wireless communication systems for outdoor coverage need sophisticated channel models to describe the wave interaction with illuminated ...physical objects. However, the conventional physical-statistical model cannot accurately predict the site-specific mmWave channel characteristics when involving complex system configurations and geometric information of transceiver relative locations. A framework of machine learning assisted channel modeling approach is proposed, in which the statistical models are leveraged for inter-cluster level channel characterization and the propagation properties within each kind of clusters are predicted using a hybrid physics-based and data-driven approach. In particular, with a focus on the mmWave through-vegetation scattering effect, a set of dedicated directional channel measurements and ray-tracing simulations is performed in an identical vegetated street canyon environment at 28 GHz for the performance evaluation of the proposed approach. Moreover, the training results and model validation in different environments show that comparing with the physical-statistical model, the proposed hybrid model, which adds the environment features to the artificial neural network as inputs, has higher prediction accuracy and greater generalization ability in terms of the site-specific through-vegetation cluster parameters, such as vegetation attenuation, delay spread, and angular spread.
