Enhancing sex determination in forensic anthropology: A comparative analysis of cranial measurements using artificial neural network

Forensic anthropology is a discipline that concerned on postmortem identification from skeletal remains in sex determination. In sex determination, besides empirical techniques such as Discriminant Function Analysis (DFA), Artificial Intelligence techniques such as Artificial Neural Network (ANN) should be considered to get more accurate result. This paper proposes back propagation ANN model for sex determination. By using data and DFA result from previous work, this paper compares the result with the result of ANN model obtained from the experiment. A total sample data of 113 patellae has been generated based on statistics values of previous study. The data is divided into three groups of ages (young, middle, and old) and is measured using three parameters (width, height, and thickness). The ANN model produces average accuracy until 96.1% compared to 92.9% result from DFA technique. This concludes that ANN produces more accurate result in sex determination compared to DFA.KeywordsBackpropagation neural networkForensic anthropologyPatellaSex determination

Engineering Applications of Bio-Inspired Artificial Neural Networks

Artificial Neural Networks (ANN) are increasingly successful in solving tasks long considered hallmarks of cognition in Biological Neural Networks (BNN), such as visual discrimination, playing Go and navigation. While the design of ANNs has been inspired by discoveries in BNNs, it is controversial whether both network types utilize the same fundamental principles and hence if ANNs can serve as models of animal cognition. However, if representations and algorithms are shared between BNNs and ANNs, then the analysis of processing in ANNs could lead to fundamental insights into their biological counterparts. Here, we generated and trained a deep convolutional neural network to solve a heat gradient navigation task using the behavioral repertoire of larval zebrafish. We found that these behavioral constraints led to striking similarities in temperature processing and representation in this ANN with biological circuits and neural dynamics underlying heat avoidance in larval zebrafish. This includes stimulus representation in ON and OFF types as well as ANN units showing adapting and sustained responses. Importantly, ANN performance critically relied on units representing temperature in a fish-like manner while other nodes were dispensable for network function. We next used the accessibility of the ANN to uncover new features of the zebrafish BNN. We 25 identified a novel neuronal response type in the zebrafish brain that was predicted by the ANN but escaped detection in previous brain wide calcium imaging experiments. Finally, our approach generalized since training the same ANN constrained by the C. elegans motor repertoire resulted in distinct neural representations that match closely features observed in the worm. Together, these results emphasize convergence of ANNs and BNNs on canonical representations and that man made ANNs form a powerful tool to understand their biological counterparts.

The search for better climate change adaptation techniques for addressing environmental and economic issues due to changing climate is of paramount interest in the current era. One of the many ways Pacific Island regions and its people get affected is by dry spells and drought events from extreme climates. A drought is simply a prolonged shortage of water supply in an area. The impact of drought varies both temporally and spatially that can be catastrophic for such regions with lack of resources and facilities to mitigate the drought impacts. Therefore, forecasting drought events using predictive models that have practical implications for understanding drought hydrology and water resources management can allow enough time to take appropriate adaption measures. This study investigates the feasibility of the Artificial Neural Network (ANN) algorithms for prediction of a drought index: Standardized Precipitation-Evapotranspiration Index (SPEI). The purpose of the study was to develop an ANN model to predict the index in two selected regions in Queensland, Australia. The first region, is named as the grassland and the second as the temperate region. The monthly gridded meteorological variables (precipitation, maximum and minimum temperature) that acted as input parameters in ANN model were obtained from Australian Water Availability Project (AWAP) for 1915–2013 period. The potential evapotranspiration (PET), calculated using thornthwaite method, was also an input variable, while SPEI was the predictand for the ANN model. The input data were divided into training (80%), validation (10%) and testing (10%) sets. To determine the optimum ANN model, the Levenberg-Marquardt and Broyden-Fletcher-Goldfarb-Shanno quasi-Newton backpropagation algorithms were used for training the ANN network and the tangent sigmoid, logarithmic sigmoid and linear activation algorithms were used for hidden transfer and output functions. The best architecture of input-hidden neuron-output neurons was 4-28-1 and 4-27-1 for grassland and temperate region, respectively. For evaluation and selection of the optimum ANN model, the statistical metrics: Coefficient of Determination (R 2 ), Willmott’s Index of Agreement (d), Nash-Sutcliffe Coefficient of Efficiency (E), Root Mean Squared Error (RMSE) and Mean Absolute Error (MAE) were employed. The R 2 , d, E, RMSE and MAE for optimum ANN models were 0.9839, 0.9909, 0.9838, 0.1338, 0.0882 and 0.9886, 0.9935, 0.9874, 0.1198, 0.0814 for grassland and temperate region, respectively. When prediction errors were analysed, a value of 0.0025 to 0.8224 was obtained for the grassland region, and a value of 0.0113 to 0.6667 was obtained for the temperate region, indicating that the ANN model exhibit a good skill in predicting the monthly SPEI. Based on the evaluation and statistical analysis of the predicted SPEI and its errors in the test period, we conclude that the ANN model can be used as a useful data-driven tool for forecasting drought events. Broadly, the ANN model can be applied for prediction of other climate related variables, and therefore can play a vital role in the development of climate change adaptation and mitigation plans in developed and developing nations, and most importantly, in the Pacific Island Nations where drought events have a detrimental impact on economic development.

In the field of forensic anthropology, researchers aim to identify anonymous human remains and determine the cause and circumstances of death from skeletonized human remains. Sex determination is a fundamental step of this procedure because it influences the estimation of other traits, such as age and stature. Pelvic bones are especially dimorphic, and are thus the most useful bones for sex identification. Sex estimation methods are usually based on morphologic traits, measurements, or landmarks on the bones. However, these methods are time-consuming and can be subject to inter- or intra-observer bias. Sex determination can be done using dry bones or CT scans. Recently, artificial neural networks (ANN) have attracted attention in forensic anthropology. Here we tested a fully automated and data-driven machine learning method for sex estimation using CT-scan reconstructions of coxal bones. We studied 580 CT scans of living individuals. Sex was predicted by two networks trained on an independent sample: a disentangled variational auto-encoder (DVAE) alone, and the same DVAE associated with another classifier (Crecon). The DVAE alone exhibited an accuracy of 97.9%, and the DVAE + Crecon showed an accuracy of 99.8%. Sensibility and precision were also high for both sexes. These results are better than those reported from previous studies. These data-driven algorithms are easy to implement, since the pre-processing step is also entirely automatic. Fully automated methods save time, as it only takes a few minutes to pre-process the images and predict sex, and does not require strong experience in forensic anthropology.

This paper discusses the inter-relations between findings on the physiological neural network (PNN) and artificial neural networks (ANN). It discusses the interaction of progress in both PNN and ANN for the purpose of borrowing from ANN's mathematical understandings to establish pointers for further explorations to better understand the PNN, and also for the reciprocal transferring of knowledge from PNN findings to improve ANN schemes. Such improvements in ANN are essential for better handling the needs of the information technology (IT) explosion in dealing with huge data bases and where data often defy analysis and are incomplete and fuzzy. On the other hand, principles and elements of ANN designs that appear to be important and successful can serve as guides for identifying them in the PNN, to be subsequently confirmed by bioanalytical tests. Hence progress in PNN is obviously essential for progress in ANN, as is progress in ANN helpful in PNN modeling, though its laboratory confirmation is still a far lengthier process. We discuss certain specific ANN schemes with respect to the above inter-relations with PNN. We feel that the progress in both PNN and ANN research provides a major link between the thrust in information technology developments and the thrust in biological science research, which are most probably the two major focus areas of research at the dawn of the 21st century. [Neurol Res 2001; 23: 482-488]

One of the topics covered in forensic anthropology is an investigation of skeletal remains where various properties of the skeleton are to be determined. Typically, the sample found is incomplete, meaning some bone parts are missing or destroyed, and the analysis needs to depend on limited information obtained from what is available. This research focuses on arm, leg, clavicle, and scapula bones, with 8 bone parts in total. Each part is either used independently from the other or considered altogether (aggregate) to test its usability in finding out the owner's identity when facing such a situation. Bone measurements obtained from the database were used as input data for two different classifiers, namely artificial neural networks and supporting vector machines, with two identification targets, namely sex and race. All of the input data came from publicly available Robert J. Terry Anatomical Skeletal Collection Postcranial Osteo-metric database. Accuracies of 86.67% and 70.78% are obtained for those targets using clavicle and aggregate, respectively, showing that using all information possible from the sample rather than focusing on a single bone part is sometimes useful in improving identification accuracy.

In forensic anthropology, age estimation is used to ease the process of identifying the age of a living being or the body of a deceased person. Nonetheless, the specialty of the estimation models is solely suitable to a specific people. Commonly, the models are inter and intra-observer variability as the qualitative set of data is being used which results the estimation of age to rely on forensic experts. This study proposes an age estimation model by using length of bone in left hand of Asian subjects range from newborn up to 18-year-old. One soft computing model, which is Random Forest (RF) is used to develop the estimation model and the results are compared with Artificial Neural Network (ANN) and Support Vector Machine (SVM), developed in the previous case studies. The performance measurement used in this study and the previous case study are R-square and Mean Square Error (MSE) value. Based on the results produced, the RF model shows comparable results with the ANN and SVM model. For male subjects, the performance of the RF model is better than ANN, however less ideal than SVM model. As for female subjects, the RF model overperfoms both ANN and SVM model. Overall, the RF model is the most suitable model in estimating age for female subjects compared to ANN and SVM model, however for male subjects, RF model is the second best model compared to the both models. Yet, the application of this model is restricted only to experimental purpose or forensic practice.

Functional connectivity derived from resting-state functional near infrared spectroscopy has gained attention of recent scholars because of its capability in providing valuable insight into intrinsic networks and various neurological disorders in a human brain. Several progressive methodologies in detecting resting-state functional connectivity patterns in functional near infrared spectroscopy, such as seed-based correlation analysis and independent component analysis as the most widely used methods, were adopted in previous studies. Although these two methods provide complementary information each other, the conventional seed-based method shows degraded performance compared to the independent component analysis-based scheme in terms of the sensitivity and specificity. In this study, artificial neural network and convolutional neural network were utilized in order to overcome the performance degradation of the conventional seed-based method. First of all, the results of artificial neural network- and convolutional neural network-based method illustrated the superior performance in terms of specificity and sensitivity compared to both conventional approaches. Second, artificial neural network, convolutional neural network, and independent component analysis methods showed more robustness compared to seed-based method. Moreover, resting-state functional connectivity patterns derived from artificial neural network- and convolutional neural network-based methods in sensorimotor and motor areas were consistent with the previous findings. The main contribution of the present work is to emphasize that artificial neural network as well as convolutional neural network can be exploited for a high-performance seed-based method to estimate the temporal relation among brain networks during resting state.

Macromorphoscopic trait expression in a cranial sample from Medellín, Colombia

Convergent Temperature Representations in Artificial and Biological Neural Networks

It is key index of cotton yarn quality such as cotton yarn strength and so on. It can well control cotton yarn quality by predicting yarn strength and so on. Generally, it is normal used to predict yarn strength such as Multiple Linear Regression (MLR), Support Vector Regression (SVR) and shallow Artificial Neural Network (ANN). Because the processing of cotton yarn production has time sequence, the paper proposes a new deep neural network, it is artificial Recurrent Neural Network (RNN). It used 1800 sets of data to train RNN, SVR and ANN. It tested RNN, MLR, SVR and ANN with 200 sets of data. Experimental results show that the Recurrent Neural Network (RNN) is the best accuracy among these four algorithms.

Inspired by the control system of voluntary movements developed in the human body based on vision and neural system, this paper presents a new heuristic method merging artificial vision and neural networks used in a sensorless robotic arm position control. This proposal is based on a structure of six artificial neural networks (ANN) of perceptrons, which correct the position of the arm in one of the six predefined directions, four in a the projection plane (forward, backward, right and left) and two in the vertical plane (up and down). The robotic arm displacement is based on the choose performed by the ANN processing the images capture by a camera, thus the chosen of the corresponding direction is derived from knowledge obtained during the supervised learning using similar situations. Finally, experimental results of the ANN learning process and robotic arm positioning tests are presented.

Synthesis Of Artificial Neural Networks By The Means Of Evolutionary Scanning – Preliminary Study

Subsurface engineers pivot on surveillance of reservoir performance for future production rates and plan the optimization strategies at earliest. There are some techniques preferred for unconventional reservoirs such as numerical simulation and decline curve analysis (DCA) for production forecasting, but the uncertainty of uneconomical well test data often occurs in unconventional resources. Moreover, reservoir engineers can also hit a tailback in optimizing and tuning the model. Further, for DCA this approach is only appropriate for well/reservoir that are under boundary dominant flow regime, whereas fracture dominant flow regime is often observed for a longer period in unconventional hydraulically fractured reservoirs. Therefore, to resolve this issue, oil & gas industry (O&G) can adopt AI (Artificial Intelligence) based Algorithms for production forecasting. This paper presents a data-driven algorithm, known as Artificial Neural Networks (ANNs), along with time series forecasting that is a well-known statistical technique. Machine learning model trained by a past well performance data such as tubing head pressure (THP), flowing bottom-hole pressure can predict future production rates. This can be an efficient technique for subsurface engineers to monitor and optimize well performance. Time series neural networks were used for training the model at top and bottom node of the well with variating pressures in the past. After training and validation, the model predicted a target parameter that was gas rate. ANNs are inspired by biological neurons that are present in human brain, a powerful computing tool to make decisions after fueling itself with data. Moreover, prediction (t+1) nonlinear automated regression is preferred for accurate step ahead. Production rates and constraints of unconventional reservoirs were used to train the model. In our results, the NN based model gave the co-efficient of determination (R2) of 0.996 that shows nearly an exact precision. Furthermore, the values generated from NN Model and Arp's decline curve calculations were plotted for validation and it turned out that ANN can accurately predict the parameters. The Neural Network model is a novel approach for production forecasting, of unconventional reservoirs and help engineers in corporate decision making. This approach can mitigate the need of uneconomical well test operations and further provide confidence to production engineers in terms of data and result expectations.