Background: Vitiligo is a pathology that causes the appearance of achromic macules on the skin that can spread on to other areas of the body. It is estimated that it affects 1. 2% of the world population and can disrupt the mental state of people in whom this disease has developed, generating negative feelings that can become suicidal in the worst of cases. The present work focuses on the development of a support tool that allows to objectively quantifying the repigmentation of the skin. Methods: We propose a novel method based on artificial neural networks that use characteristics of the interaction of light with the skin to determine areas of healthy skin and skin with vitiligo. We used photographs of specific areas of skin containing vitiligo. We select as independent variables: the type of skin, the amount of skin with vitiligo and the amount of repigmented skin. Considering these variables, the experiments were organized in an orthogonal table. We analyzed the result of the method based on three parameters (sensitivity, specificity, and F1‑ Score) and finally, its results were compared with other methods proposed in similar research. Results: The proposed method demonstrated the best performance of the three methods, and it also showed its capability to detect healthy skin and skin with vitiligo in areas up to 1 × 1 pixels. Conclusion: The results show that the proposed method has the potential to be used in clinical applications. It should be noted that the performance could be significantly improved by increasing the training patterns.

This paper proposes a novel approach to analyzing and managing electricity consumption using a clustering algorithm and a high-accuracy classifier for smart meter data. The proposed method utilizes a multilayer perceptron neural network classifier optimized by an Imperialist Competitive Algorithm (ICA) called ICA-optimized MLP, and a CD Index based on Fuzzy c-means to optimally determine representative load curves. A case study involving a real dataset of residential smart meters is conducted to validate the effectiveness of the proposed method, and the results demonstrate that the ICA-optimized MLP method achieves an accuracy of 98.62%, outperforming other classification methods. This approach has the potential to improve energy efficiency and reduce costs in the power system, making it a promising solution for analyzing and managing electricity consumption.

Emotion recognition from speech has noticeable applications within the speechprocessing systems. The goal of this paper is to permit a totally natural interaction among human and system. In this paper, an attempt is made to design and implement a system to determine and detect emotions of anger and happiness in the Persian speech signals. Research on recognizing some emotions has been done in most languages, but due to the difficulty of creating a speech database, so far little research has been done to identify emotions in Persian speech. In this article, because of the dearth of a suitable database in Persian to detect feelings, before everything, a database for moods of happiness and anger and neutral (with no emotion) in Persian, including 720 sentences was set up. Then the frequency features of speech signals obtained from Fourier transform such as maximum, minimum, median and mean as well as LPC coefficients were extracted. Then, the MLP neural network was used to detect emotions of happiness and anger. Results show that our algorithm performs 87. 74% accurately.

In recent years, the artificial neural networks (ANNs) are used as an alternative to the conventional type curve matching techniques for the determination of aquifer parameters. In this paper two multilayer perceptron networks (MLPNs) are developed for the determination of leaky confined aquifers parameters. Leakage into the aquifer takes place from either the upper aquifer through the confining aquitard or the storage in the confining aquitard. The first and second networks are trained for the well functions of leaky aquifers (a) without and (b) with storage in the confining aquitard, respectively. By applying the principal component analysis (PCA) on the adopted training data sets the topology of both networks are reduced and their efficiency increased considerably. In contrast to the existing networks the topology of developed networks is fixed to (2´10´2) regardless of number of records in the pumping test data. The networks generate the match point coordinates for any individual pumping test data set. The match point coordinates are incorporated with Hantush-Jacob (1955) and Hantush (1960) analytical solutions and the aquifer parameter values are determined. The performance of the MLPNs is evaluated by three sets of real field data and their accuracy is compared with that of type curve matching techniques. The proposed MLPNs are recommended as simple and reliable alternatives to previous ANN methods and the type-curve matching techniques.

Extended Abstract Introduction Rockfalls are a type of slope movement process that does not require a transporting medium (e.g., water) and predominantly occur under the influence of gravity (Dikau, 2006). The detachment of a single rock block or a volume of blocks from steep slopes is followed by motions mainly through the air. The fall trajectory is strongly controlled by the mean slope gradient, enabling different motion modes such as free-falling, bouncing, and rolling. Consequently, rockfalls present an unpredictable threat, especially along highways and railways, where they can cause damage to infrastructure or endanger human lives (Bostjančić et al., 2020). In high mountain regions, rockfall activity is thought to be changing due to accelerated climate warming and permafrost degradation, potentially resulting in increased activity and larger volumes involved in individual falls (Stoffel et al., 2024). Therefore, it is crucial to assess areas prone to rockfalls in mountainous regions. The aim of this research is to zone the risk of rockfalls long the Khalkhal–Shahroud road using a Multi-layer perceptron algorithm. Material and Methods The Multi-layer perceptron algorithm is a modern machine learning model capable of solving complex problems. In this study, the necessary data were obtained from topographic maps at a scale of 1:25,000, geological maps at a scale of 1:100,000, a digital elevation model (DEM) with 12.5-meter resolution from the ALOS-PALSAR satellite, Sentinel data (spatial resolution of 10 meters), Google Earth satellite images, and field studies. In rockfall hazard zoning using GIS, the most critical component is the preparation of a rockfall distribution map or rockfall inventory. Fieldwork was conducted to identify rockfalls and prepare the inventory. Results and Discussion To identify the factors contributing to rockfall occurrence, field studies identified eight key factors: elevation, vegetation, slope aspect, distance from faults, distance from roads, geology, land use, and slope gradient. After pre-processing, all layers were entered into SPSS Modeler software, and the model was designed with 8 input neurons, 8 intermediate neurons, and 1 output neuron. The results revealed that, in the Multi-layer perceptron algorithm, the geological layer had the highest weight value (0.20), followed by the land use layer (0.14) and distance from roads (0.12). In the model validation phase, the results demonstrated an AUC value of 0.9810 in the training phase and 0.9876 in the testing phase, indicating high model validity in both phases. ConclusionThis research aimed to identify areas at risk of rockfall along the Khalkhal–Shahroud road using a Multi-layer perceptron algorithm. The results highlight the significant influence of geological conditions on rockfall occurrences, emphasizing the need to consider slope instability in all spatial planning efforts in this region. It is recommended that future studies explore other machine learning models, such as support vector machines, to further evaluate rockfalls and related slope movements.

Introduction: Soil compaction is one of the most destructive effects of machine traffic. Compaction increases soil mechanical strength and reduces its porosity, plant rooting and ultimately the yield. Nowadays, agricultural machinery has the maximum share on soil compaction in modern agriculture. The soil destruction may be as surface deformation or as subsurface compaction. Any way machine traffic destructs soil structure and as result has unfavorable effect on the yield. Hence, soil compaction recognition and its management are very important. In general, soil compaction is the most destructive effect of machine traffic. Modeling of ecological systems by conventional modeling methods due to the multitude effective parameters has always been challenging. Artificial intelligence and soft computing methods due to their simplicity, high precision in simulation of complex and nonlinear processes are highly regarded. The purpose of this research was the modeling of soil compaction system affected by soil moisture content, the tractor forward velocity and soil depth by multilayer perceptron neural network. Materials and Methods: In order to carry out the field experiments, a tractor MF285 which was equipped with a three-tilt moldboard plough was used. Experiments were conducted at the Agricultural research field of University of Mohaghegh Ardabili in five levels of moisture content of 11, 14, 16, 19 and 22%, forward velocity of 1, 2, 3, 4 and 5 km/h, and soil depths of 20, 25, 30, 35 and 40 cm as a randomized complete block design with three replications. In this study, perceptron neural network with five neurons in the hidden layer with sigmoid transfer function and linear transfer function for the output neuron was designed and trained. Results and Discussion: Field experiments showed three main factors were significant on the bulk density (P<0. 01). The mutual effect of moisture on depth and mutual binary effect of moisture on velocity and depth on velocity were significant (P<0. 01). Mutual triplet effect of moisture on velocity on depth was significant (P<0. 05). Maximum bulk density of 1362 kg/m3 was obtained at the highest moisture of 22% and the lowest forward velocity of 1 km/h at the depth of 20 cm. Whilst the minimum value of 1234. 5 kg/m3 was related to the moisture, forward velocity and depth of 11%, 5 km/h and depth of 40 cm, respectively. Compaction increased as soil moisture content increased up to 22% which was critical moisture. In contrast, soil compaction decreased as the tractor velocity and soil depth increased. A comparison of neural network output and experimental results indicated a high determination coefficient of R2 = 0. 99 between them. Also, the mean square error of the model was 0. 174, in addition, mean absolute percentage error of the system (MAPE) was equal to %0. 29 which showed high accuracy of neural network to model soil compaction. Conclusions: It was concluded that soil compaction increased as soil moisture content increased up to a critical value. Increasing soil moisture act as lubricant and soil layers compacted together. Hence knowledge of soil moisture can help us to manage soil compaction during agricultural operations. Increasing the tractor forward velocity reduced soil compaction. However, agricultural operations should be conducted at certain speeds to carry out the duty properly and increasing speed more that value decreases the efficiency of work. Neural network of MLP with 5 neurons in hidden layer and sigmoid function in middle layer and one neuron with linear transfer function was found the most accurate and precise in prediction of the soil bulk density. A high determination coefficient of R2 = 0. 99 was found between measured and predicted values.

INTRODUCTION: Temperature is one of the most important ecological factors affecting the life of the plants. For each species, there is a specific temperature below which the plant growth would be halted. That is, it represents the minimum growth temperature of a plant (Mohammadneya et al, 2010). Thus, if the climate elements are not sufficiently considered in the agricultural planning, the results will not be much promising, as it has been proved that the low efficiency of agricultural crops is mainly due to the inability to maintain moderate climate conditions. Temperature drop and frost at different stages of growth and reproduction of agricultural crops could be dangerous, limiting the ultimate production of plants.

Introduction: Acute appendicitis is the most common cause of admittance of patients with abdominal pain to hospital and appendectomy is the most commonly performed emergency surgery. Despite significant advances in the field of diagnosis, a significant number of negative appendectomies are reported. In this study, the design and evaluation of artificial neural networks to help diagnose acute appendicitis was investigated.Methods: In this descriptive study, variables affecting the diagnosis were identified through literature review. Then, these variables were categorized in the form of a checklist, and evaluated and prioritized by general surgery specialists. The sample size was determined as 181 cases to train and evaluate the performance of neural networks. The database was created using records of patients who had undergone appendectomy during 2015 in Modarres Hospital, Tehran, Iran. Then, different architectures of artificial multilayer perceptron (MLP) neural network were implemented and compared in MATLAB environment to determine the optimal diagnostic performance. Parameters such as specificity, sensitivity, and accuracy were used for network assessment.Results: Comparison of the optimal output of the MLP with pathological results showed that the sensitivity, specificity, and accuracy of the diagnosis network were 68.8%, 82%, and 78.5%, respectively. Based on the existing standards and the general surgeons’ opinions, the MLP network improved diagnostic accuracy for acute appendicitis.Conclusion: The designed MLP can model the performance of an expert with acceptable accuracy. The use of this MLP in clinical decision support systems can be useful in the reduction of negative references to medical centers, timely diagnosis, prevention of negative appendectomy, reduction of the duration of hospitalization, and reduction of medical expenses.

Stock market forecasting is a challenging task for investors and researchers in the financial market due to highly noisy, nonparametric, volatile, complex, non-linear, dynamic and chaotic nature of stock price time series. With the development of computationally intelligent method, it is possible to predict stock price time series more accurately. Artificial neural networks (ANNs) are one of the most promising biologically inspired techniques. ANNs have been widely used to make predictions in various research. The performance of ANNs is very dependent on the learning technique utilized to train the weight and bias vectors. The proposed study aims to predict daily Tehran Exchange Dividend Price Index (TEDPIX) via the hybrid multilayer perceptron (MLP) neural networks and metaheuristic algorithms which consist of genetic algorithm (GA), particle swarm optimization (PSO), black hole (BH), grasshopper optimization algorithm (GOA) and grey wolf optimization (GWO). We have extracted 18 technical indicators based on the daily TEDPIX as input parameters. Therefore, the experimental result shows that grey wolf optimization has superior performance to train MLPs for predicting the stock market in metaheuristic-based.

Among the important factors in urban planning and management, particularly in line with the achievement of the sustainable development in the urban areas as well as regarding the optimal use of the land, is on-time access to the data of land cover conditions in these regions. The remote sensing data has a high potential for the preparation of the update urban land cover maps. In order to present on-time and digital satellite data, a variety of shapes and possibility of processing during land cover maps are of high significance. In order to use the satellite photos Landsat/ETM+ and two algorithm of supervised classification including the maximum likelihood and the artificial neural network, land cover maps were prepared. During classification, the neural network algorithm of a perceptron network with a hidden layer and 7 input neurons, nine middle neurons and 4 output neurons were used. The input neurons are the same in number as the bands of the Landsat photos and the number of output neurons are the same as land cover map classes. Eventually, land cover map of the region has been classified into four classes of residential areas, barren lands, plant coverage, and roads. In order to evaluate the correctness of the classification results, many photos have been taken using GPS. Using overall accuracy and Kappa Coefficient the precision evaluation results of these two methods indicate that perceptron neural network has an overall accuracy of 98.24 and Kappa Coefficient 97.03 compared to the algorithm of maximum likelihood with an overall accuracy of 94.23 and Kappa Coefficient 90.34 is of higher precision. The findings of this study also show that the classification method for multilayer perceptron neural network as compared with the maximum likelihood method is of higher separation and capability for preparing the land cover map in the urban regions.