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The combination of Artificial Intelligence and the Internet of Things (AIoT) is enabling the next economic revolution in which data and immediacy are at the key players. Agriculture is one of the sectors that can benefit most from the use of AIoT to optimise resources and reduce its environmental footprint. However, this convergence requires computational resources that enable the execution of AI workloads, and in the context of agriculture, ensuring autonomous operation and low energy consumption. In this work, we evaluate TinyML and edge computing platforms to predict the indoor temperature of an operational greenhouse in situ. In particular, the computational/energy trade-off of these platforms is assessed to analyse whether their use in this context is feasible. Two artificial neural networks are adapted to these platforms to predict the indoor temperature of the greenhouse. Our results show that the microcontroller-based devices can offer a competitive and energy-efficient computational alternative to more traditional edge computing approaches for lightweight ML workloads. [ABSTRACT FROM AUTHOR]

Society's support upon chemicals over the last few decades has led to their increased production, application and discharge into the environment. Wastewater treatment plants (WWTPs) contain a multitude of these chemicals such us; pharmaceutical compounds (PCs). Often, their biodegradability by activated sludge microorganisms is significant for their elimination during wastewater treatment. In this paper the focus is laid on two PCs carbamazepine (CBZ) and diclofenac (DCF) and their main transformation products (TPs). Laboratory degradation tests with these two pharmaceuticals using activated sludge as inoculum under aerobic conditions were performed and microbial metabolites were analyzed by liquid chromatography-mass spectrometry (LC/MS-MS). In two different Mixed liquid Suspended Solids (MLSS) concentrations the biodegradability by activated sludge of CBZ and DCF were evaluated. Also, this article proposes a decision support system to optimize the prediction process of this type of pharmacological compounds. A study and analysis of the techniques of Support Vector Machine, Random Forest, Decision Trees and Multilayer Perceptron Network is carried out to select the most reliable and accurate predictor for the decision system. There are not significant differences in the removal of DCF with 30 mg MLSS/L and 60 mg MLSS/L. DCF was better removed than CBZ in all experiments studied. The TP detected in the samples were mainly 4-OH-DCF for DCF and 10, 11 EPOXICBZ for CBZ. The results show that the best models are obtained with Random Forest and Multilayer Perceptron Network techniques, with a model fit of more than 95% for both carbamazepine and diclofenac metabolites. Obtaining a root means square errors of 0.80 µg/L for the metabolite 4-OH-DCF for DCF with the technique Random Forest and a root means square errors of 1.13 µg/L for the metabolite 10, 11 EPOXICBZ for CBZ with the Multilayer Perceptron Network technique. [ABSTRACT FROM AUTHOR]

Introduction: Ovarian tumors are the most common diagnostic challenge for gynecologists and ultrasound examination has become the main technique for assessment of ovarian pathology and for preoperative distinction between malignant and benign ovarian tumors. However, ultrasonography is highly examiner-dependent and there may be an important variability between two different specialists when examining the same case. The objective of this work is the evaluation of different well-known Machine Learning (ML) systems to perform the automatic categorization of ovarian tumors from ultrasound images. Methods: We have used a real patient database whose input features have been extracted from 348 images, from the IOTA tumor images database, holding together with the class labels of the images. For each patient case and ultrasound image, its input features have been previously extracted using Fourier descriptors computed on the Region Of Interest (ROI). Then, four ML techniques are considered for performing the classification stage: K-Nearest Neighbors (KNN), Linear Discriminant (LD), Support Vector Machine (SVM) and Extreme Learning Machine (ELM). Results: According to our obtained results, the KNN classifier provides inaccurate predictions (less than 60% of accuracy) independently of the size of the local approximation, whereas the classifiers based on LD, SVM and ELM are robust in this biomedical classification (more than 85% of accuracy). Conclusions: ML methods can be efficiently used for developing the classification stage in computer-aided diagnosis systems of ovarian tumor from ultrasound images. These approaches are able to provide automatic classification with a high rate of accuracy. Future work should aim at enhancing the classifier design using ensemble techniques. Another ongoing work is to exploit different kind of features extracted from ultrasound images. [ABSTRACT FROM AUTHOR]

Portable mid-infrared (MIR) technology is well suited for the provision of detailed and inexpensive information on key soil properties for optimum soil management. This technology requires prior complex multivariate modelling. In this manuscript, we propose an intelligent system approach based on portable MIR spectroscopy data modelled by machine learning techniques to predict total carbon (TC), nitrogen (TN), cation exchange capacity (CEC), clay, silt and exchangeable sodium (Na+) in 458 representative soil samples from Australia. To find the best performing algorithm to be incorporated into the intelligent system, we evaluated the performance of a number of machine learning techniques, including Gaussian process regression (GPR), Random Forest (RF), M5 rules, and Bagging and Decision Trees, which were compared with traditional Partial Least Squares Regression (PLSR). Generally, all the models showed a satisfactory performance. However, GPR stood out (according to statistical tests), especially for the predictions of clay, Na+ and CEC, obtaining root mean squared error differences between the GPR and PLSR techniques of −0.27%, −0.40% and −0.63 cmol+ kg−1, respectively. This study evidenced the feasibility of using the GPR technique to model data provided by a portable MIR instrument for the prediction of soil properties, providing the best, most stable and reliable results. Highlights • Different machine learning techniques have been tested to predict soil properties. • The predicted soil properties are TC, TN, CEC, clay, silt and Na+. • The best predictive machine learning technique has been the Gaussian Process. • The Gaussian process is better compared to the traditional PLSR technique. • The Gaussian Process is the candidate for the development of intelligent system. [ABSTRACT FROM AUTHOR]

Abstract: Selection of the optimal neural architecture to solve a pattern classification problem entails to choose the relevant input units, the number of hidden neurons and its corresponding interconnection weights. This problem has been widely studied in many research works but their solutions usually involve excessive computational cost in most of the problems and they do not provide a unique solution. This paper proposes a new technique to efficiently design the MultiLayer Perceptron (MLP) architecture for classification using the Extreme Learning Machine (ELM) algorithm. The proposed method provides a high generalization capability and a unique solution for the architecture design. Moreover, the selected final network only retains those input connections that are relevant for the classification task. Experimental results show these advantages. [Copyright &y& Elsevier]

Abstract: In Multi-Task Learning (MTL), when several related tasks are learned at the same time considering one of them as the main task and the others as secondary ones, there is a transfer of positive information that improves the performance of the main one. However, not only does the difficulty of finding the relationship among different tasks pose a problem in real applications, but also knowing the kind of relationship among them. This paper presents a new method to generate artificial hints (subsets from the original data set) that helps the learning of the main task when all of them are learned simultaneously (as in a MTL scheme). Thus, although these hints cannot be strictly considered as secondary tasks, they will act as guides for the main one. The results obtained with toy and real problems show the advantages of the proposed method. In particular, a faster convergence, a very good performance, and a reduction in the likelihood of being trapped in a local minimum are achieved. [Copyright &y& Elsevier]

• Proposed a deep learning model to detect cervical cancer. • Proposed a cell merger model to classify in a similar way to real smear examination. • Classification of squamous cells without prior image preprocessing. • Classification of squamous cells in HSIL, LSIL, ASCUS and normal. • Detection of cervical squamous atypiae with 88.8% accuracy in classification. Early detection of cancer is important to improve survival and reduce associated morbility. Nowadays, there is no automatic classification process with enough accuracy to be recommended to its use in population cervical cancer screening. In most automatic medical image classifications, these images are clean in background and without overlap between elements, which means that these images do not reflect reality and the model cannot be applied to directly obtained images from medical samples. The objectives of this study are to design and implement a Cell Merger Approach to improve the efficiency and realism of the PAP-smears classification model, by allowing overlapping and folding of different cells, to design and implement a Convolutional Neural Network for PAP-smears image classification, and to optimize and integrate the cell fusion approach with the neural network building a feasible, reliable and highly accurate system for cervical smears classification. The carried out experiments have validated both the CNN and the proposed Cell Merger Approach with very interesting results. The most outstanding results show that the Convolutional Neural Network models together with the Cell Merger Approach have a classification accuracy of 88.8% with a standard deviation of 1%, obtaining a sensitivity and specificity of 0.92 and 0.83 respectively. This classification level depicts a robust and accurate model that is comparable to an expert pathologist competencies. [ABSTRACT FROM AUTHOR]

Motivation Molecular docking methods are extensively used to predict the interaction between protein–ligand systems in terms of structure and binding affinity, through the optimization of a physics-based scoring function. However, the computational requirements of these simulations grow exponentially with: (i) the global optimization procedure, (ii) the number and degrees of freedom of molecular conformations generated and (iii) the mathematical complexity of the scoring function. Results In this work, we introduce a novel molecular docking method named METADOCK 2, which incorporates several novel features, such as (i) a ligand-dependent blind docking approach that exhaustively scans the whole protein surface to detect novel allosteric sites, (ii) an optimization method to enable the use of a wide branch of metaheuristics and (iii) a heterogeneous implementation based on multicore CPUs and multiple graphics processing units. Two representative scoring functions implemented in METADOCK 2 are extensively evaluated in terms of computational performance and accuracy using several benchmarks (such as the well-known DUD) against AutoDock 4.2 and AutoDock Vina. Results place METADOCK 2 as an efficient and accurate docking methodology able to deal with complex systems where computational demands are staggering and which outperforms both AutoDock Vina and AutoDock 4. Availability and implementation https://Baldoimbernon@bitbucket.org/Baldoimbernon/metadock%5f2.git. Supplementary information Supplementary data are available at Bioinformatics online. [ABSTRACT FROM AUTHOR]

The availability of water resources is limited, and rising consumption has increased pressure on natural resources. Therefore, reclaimed water represents an alternative option for use in urban areas, industry and, in particular, agriculture. Recent research has shown that some pharmaceutical compounds are not fully removed by wastewater treatment plants (WWTPs) and may eventually be released into agricultural systems through the application of wastewater-based resources (sludge and effluent). The present study develops an intelligent expert system (based on a feedforward neural network trained via an extreme learning machine algorithm) for predicting the carbamazepine (CBZ) and diclofenac (DCF) content in lettuce tissues irrigated with reclaimed water from WWTPs. This reduces laboratory costs, mitigates the negative impacts on the environment and leads to more effective, safer decisions on the use of reclaimed water in agriculture. The results obtained, which were validated through statistical testing, demonstrate that the intelligent expert system is well calibrated and reliable. Finally, this system was used to predict maps of CBZ and DCF accumulation in lettuce crops if they were watered with effluent from 10 WWTPs located in the Region of Murcia (Spain). In conclusion, our system provides highly accurate predictions of the amount of CBZ and DCF contained in different lettuce tissues (roots and leaves) and the predicted concentrations do not present any health risk. Highlights • Expert system predicts pharmaceutical absorption in lettuce by analysing effluent. • Predictions for accumulation in lettuces irrigated from 10 treatment plants in Murcia. • Predicted concentrations in lettuce do not constitute a potential risk to health. [ABSTRACT FROM AUTHOR]

A correct estimation of the instantaneous peak flow (IPF) is crucial to reducing the consequences of flash floods. An approach to estimate the IPF, obtained by combining Soil and Water Assessment Tool (SWAT) simulation and machine-learning models, was proposed and then verified by comparison with observation-based results in the Ladra river basin, northwest Spain. The SWAT model has been used to estimate the maximum mean daily flow (MMDF), and machine-learning models have been used to estimate the IPF based on MMDF. Four nonlinear time-series intelligence models, artificial neural network (ANN), adaptive neuro-fuzzy inference system (ANFIS), support vector machine (SVM) and extreme learning machine (ELM) were applied, and their results were compared. The Modified Nash-Sutcliffe efficiency coefficient (MNSE) and the index of agreement (d) were used to evaluate SWAT performance while simulating MMDF, and the coefficient of determination (R2) and the root mean square error (RMSE) were employed to evaluate the performance of these intelligent systems. According to the results, the SWAT hydrological model is a useful tool to simulate MMDF. Validation analyses resulted in values of statistical indexes (MNSE = 0.64 and d = 0.95). Regarding intelligent systems, the results show that they can be successfully used in predicting IPF, but ELM has demonstrated a superior ability to estimate IPF from the MMDF (R2 = 0.86 and RMSE = 48.59). The results of this study can contribute to predicting IPF in areas where sub-daily observational data are scarce, thereby reducing uncertainties associated with IPF estimations. Graphical abstract Highlights • SWAT model has been used to estimate maximum mean daily flows. • Intelligent Systems estimate instantaneous peak flow based on the daily flows. • Extreme Learning and Support Vector Machine were optimal to estimate peak flow. • Predictions correlated with experimental data with coefficients of 0.86–0.88. • Results assist predicting peak flow with limited sub-daily observational data. [ABSTRACT FROM AUTHOR]

Various methods are used to make the partition of data sets for QSAR development and model validation. In this work we used a fuzzy minimals partitioning and we compare this methodology with another rational partition methods like k -means clustering (KMS) and Minimal Test Set Dissimilarity (MTSD). For the development of QSAR models Ordinary Least Squares (OLS) and Extreme Learning Machine (ELM) methods were used. The generated QSAR equations were validated by the coefficient of determination of the internal leave one out (LOO) cross validation method Q LOO 2 and then the coefficient of the external test set Q ext 2 was compared between partition methods. The results of this comparison showed that using fuzzy minimal for big and structurally diverse data sets gave an applicability domain similar to KMS and a better predictability models than both methods, KMS and MTSD. [ABSTRACT FROM AUTHOR]

We are witnessing the dramatic consequences of the COVID-19 pandemic which, unfortunately, go beyond the impact on the health system. Until herd immunity is achieved with vaccines, the only available mechanisms for controlling the pandemic are quarantines, perimeter closures and social distancing with the aim of reducing mobility. Governments only apply these measures for a reduced period, since they involve the closure of economic activities such as tourism, cultural activities, or nightlife. The main criterion for establishing these measures and planning socioeconomic subsidies is the evolution of infections. However, the collapse of the health system and the unpredictability of human behavior, among others, make it difficult to predict this evolution in the short to medium term. This article evaluates different models for the early prediction of the evolution of the COVID-19 pandemic to create a decision support system for policy-makers. We consider a wide branch of models including artificial neural networks such as LSTM and GRU and statistically based models such as autoregressive (AR) or ARIMA. Moreover, several consensus strategies to ensemble all models into one system are proposed to obtain better results in this uncertain environment. Finally, a multivariate model that includes mobility data provided by Google is proposed to better forecast trend changes in the 14-day CI. A real case study in Spain is evaluated, providing very accurate results for the prediction of 14-day CI in scenarios with and without trend changes, reaching 0.93 R 2 , 4.16 RMSE and 1.08 MAE. [ABSTRACT FROM AUTHOR]

Precision agriculture is a growing sector that improves traditional agricultural processes through the use of new technologies. In southeast Spain, farmers are continuously fighting against harsh conditions caused by the effects of climate change. Among these problems, the great variability of temperatures (up to 20 ° C in the same day) stands out. This causes the stone fruit trees to flower prematurely and the low winter temperatures freeze the flower causing the loss of the crop. Farmers use anti-freeze techniques to prevent crop loss and the most widely used techniques are those that use water irrigation as they are cheaper than other techniques. However, these techniques waste too much water and it is a scarce resource, especially in this area. In this article, we propose a novel intelligent Internet of Things (IoT) monitoring system to optimize the use of water in these anti-frost techniques while minimizing crop loss. The intelligent component of the IoT system is designed using an approach based on a multivariate Long Short-Term Memory (LSTM) model, designed to predict low temperatures. We compare the proposed approach of multivariate model with the univariate counterpart version to figure out which model obtains better accuracy to predict low temperatures. An accurate prediction of low temperatures would translate into significant water savings, as anti-frost techniques would not be activated without being necessary. Our experimental results show that the proposed multivariate LSTM approach improves the univariate counterpart version, obtaining an average quadratic error no greater than 0.65 ° C and a coefficient of determination R 2 greater than 0.97. The proposed system has been deployed and is currently operating in a real environment obtained satisfactory performance. [ABSTRACT FROM AUTHOR]

The Internet of Things (IoT) is driving the digital revolution. AlSome palliative measures aremost all economic sectors are becoming "Smart" thanks to the analysis of data generated by IoT. This analysis is carried out by advance artificial intelligence (AI) techniques that provide insights never before imagined. The combination of both IoT and AI is giving rise to an emerging trend, called AIoT, which is opening up new paths to bring digitization into the new era. However, there is still a big gap between AI and IoT, which is basically in the computational power required by the former and the lack of computational resources offered by the latter. This is particularly true in rural IoT environments where the lack of connectivity (or low-bandwidth connections) and power supply forces the search for "efficient" alternatives to provide computational resources to IoT infrastructures without increasing power consumption. In this paper, we explore edge computing as a solution for bridging the gaps between AI and IoT in rural environment. We evaluate the training and inference stages of a deep-learning-based precision agriculture application for frost prediction in modern Nvidia Jetson AGX Xavier in terms of performance and power consumption. Our experimental results reveal that cloud approaches are still a long way off in terms of performance, but the inclusion of GPUs in edge devices offers new opportunities for those scenarios where connectivity is still a challenge. [ABSTRACT FROM AUTHOR]

Wireless acoustic sensor networks are nowadays an essential tool for noise pollution monitoring and managing in cities. The increased computing capacity of the nodes that create the network is allowing the addition of processing algorithms and artificial intelligence that provide more information about the sound sources and environment, e.g., detect sound events or calculate loudness. Several models to predict sound pressure levels in cities are available, mainly road, railway and aerial traffic noise. However, these models are mostly based in auxiliary data, e.g., vehicles flow or street geometry, and predict equivalent levels for a temporal long-term. Therefore, forecasting of temporal short-term sound levels could be a helpful tool for urban planners and managers. In this work, a Long Short-Term Memory (LSTM) deep neural network technique is proposed to model temporal behavior of sound levels at a certain location, both sound pressure level and loudness level, in order to predict near-time future values. The proposed technique can be trained for and integrated in every node of a sensor network to provide novel functionalities, e.g., a method of early warning against noise pollution and of backup in case of node or network malfunction. To validate this approach, one-minute period equivalent sound levels, captured in a two-month measurement campaign by a node of a deployed network of acoustic sensors, have been used to train it and to obtain different forecasting models. Assessments of the developed LSTM models and Auto regressive integrated moving average models were performed to predict sound levels for several time periods, from 1 to 60 min. Comparison of the results show that the LSTM models outperform the statistics-based models. In general, the LSTM models achieve a prediction of values with a mean square error less than 4.3 dB for sound pressure level and less than 2 phons for loudness. Moreover, the goodness of fit of the LSTM models and the behavior pattern of the data in terms of prediction of sound levels are satisfactory. [ABSTRACT FROM AUTHOR]

According to the World Health Organization, the world's leading cause of death is heart disease, with nearly two million deaths per year. Although some factors are not possible to change, there are some keys that help to prevent heart diseases. One of the most important keys is to keep an active daily life, with moderate exercise. However, deciding what a moderate exercise is or when a slightly abnormal heart rate value is a risk depends on the person and the activity. In this paper we propose a context-aware system that is able to determine the activity the person is performing in an unobtrusive way. Then, we have defined ontology to represent the available knowledge about the person (biometric data, fitness status, medical information, etc.) and her current activity (level of intensity, heart rate recommended for that activity, etc.). With such knowledge, a set of expert rules based on this ontology are involved in a reasoning process to infer levels of alerts or suggestions for the users when the intensity of the activity is detected as dangerous for her health. We show how this approach can be accomplished by using only everyday devices such as a smart phone and a smart watch. [ABSTRACT FROM AUTHOR]

Artificial Intelligence is providing astonishing results, with medicine being one of its favourite playgrounds. Machine Learning and, in particular, Deep Neural Networks are behind this revolution. Among the most challenging targets of interest in medicine are cancer diagnosis and therapies but, to start this revolution, software tools need to be adapted to cover the new requirements. In this sense, learning tools are becoming a commodity but, to be able to assist doctors on a daily basis, it is essential to fully understand how models can be interpreted. In this survey, we analyse current machine learning models and other in-silico tools as applied to medicine—specifically, to cancer research—and we discuss their interpretability, performance and the input data they are fed with. Artificial neural networks (ANN), logistic regression (LR) and support vector machines (SVM) have been observed to be the preferred models. In addition, convolutional neural networks (CNNs), supported by the rapid development of graphic processing units (GPUs) and high-performance computing (HPC) infrastructures, are gaining importance when image processing is feasible. However, the interpretability of machine learning predictions so that doctors can understand them, trust them and gain useful insights for the clinical practice is still rarely considered, which is a factor that needs to be improved to enhance doctors' predictive capacity and achieve individualised therapies in the near future. [ABSTRACT FROM AUTHOR]