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\"In Language and Chronology, Toner and Han apply innovative Machine Learning techniques to the problem of the dating of literary texts. Many ancient and medieval literatures lack reliable chronologies which could aid scholars in locating texts in their historical context. The new machine-learning method presented here uses chronological information gleaned from annalistic records to date a wide range of texts. The method is also applied to multi-layered texts to aid the identification of different chronological strata within single copies While the algorithm is here applied to medieval Irish material of the period c.700-c.1700, it can be extended to written texts in any language or alphabet. The authors' approach presents a step change in Digital Humanities, moving us beyond simple querying of electronic texts towards the production of a sophisticated tool for literary and historical studies\"-- Provided by publisher.

Electricity theft is one of the challenging problems in smart grids. The power utilities around the globe face huge economic loss due to ET. The traditional electricity theft detection (ETD) models confront several challenges, such as highly imbalance distribution of electricity consumption data, curse of dimensionality and inevitable effects of non-malicious factors. To cope with the aforementioned concerns, this paper presents a novel ETD strategy for smart grids based on theft attacks, long short-term memory (LSTM) and gated recurrent unit (GRU) called TLGRU. It includes three subunits: (1) synthetic theft attacks based data balancing, (2) LSTM based feature extraction, and (3) GRU based theft classification. GRU is used for drift identification. It stores and extracts the long-term dependency in the power consumption data. It is beneficial for drift identification. In this way, a minimum false positive rate (FPR) is obtained. Moreover, dropout regularization and Adam optimizer are added in GRU for tackling overfitting and trapping model in the local minima, respectively. The proposed TLGRU model uses the realistic EC profiles of the Chinese power utility state grid corporation of China for analysis and to solve the ETD problem. From the simulation results, it is exhibited that 1% FPR, 97.96% precision, 91.56% accuracy, and 91.68% area under curve for ETD are obtained by the proposed model. The proposed model outperforms the existing models in terms of ETD.

The effect of bad air quality on human health is a well-known risk. Annual health costs have significantly been increased in many countries due to adverse air quality. Therefore, forecasting air quality-measuring parameters in highly impacted areas is essential to enhance the quality of life. Though this forecasting is usual in many countries, Sri Lanka is far behind the state-of-the-art. The country has increasingly reported adverse air quality levels with ongoing industrialization in urban areas. Therefore, this research study, for the first time, mainly focuses on forecasting the PM10 values of the air quality for the two urbanized areas of Sri Lanka, Battaramulla (an urban area in Colombo), and Kandy. Twelve air quality parameters were used with five models, including extreme gradient boosting (XGBoost), CatBoost, light gradient-boosting machine (LightBGM), long short-term memory (LSTM), and gated recurrent unit (GRU) to forecast the PM10 levels. Several performance indices, including the coefficient of determination (R2), root mean squared error (RMSE), mean absolute error (MAE), mean squared error (MSE), mean absolute relative error (MARE), and the Nash–Sutcliffe efficiency (NSE), were used to test the forecasting models. It was identified that the LightBGM algorithm performed better in forecasting PM10 in Kandy (R2=0.99, MSE =0.02, MAE=0.002, RMSE =0.1225, MARE =1.0, and NSE=0.99). In contrast, the LightBGM achieved a higher performance (R2=0.99, MSE =0.002, MAE =0.012 , RMSE =1.051, MARE =0.00, and NSE=0.99) for the forecasting PM10 for the Battaramulla region. As per the results, it can be concluded that there is a necessity to develop forecasting models for different land areas. Moreover, it was concluded that the PM10 in Kandy and Battaramulla increased slightly with existing seasonal changes.

Despite the accuracy of machine learning (ML) techniques in predicting invalid bug reports, as shown in earlier research, and the importance of early identification of invalid bug reports in software maintenance, the adoption of ML techniques for this task in industrial practice is yet to be investigated. In this study, we used a technology transfer model to guide the adoption of an ML technique at a company for the early identification of invalid bug reports. In the process, we also identify necessary conditions for adopting such techniques in practice. We followed a case study research approach with various design and analysis iterations for technology transfer activities. We collected data from bug repositories, through focus groups, a questionnaire, and a presentation and feedback session with an expert. As expected, we found that an ML technique can identify invalid bug reports with acceptable accuracy at an early stage. However, the technique’s accuracy drops over time in its operational use due to changes in the product, the used technologies, or the development organization. Such changes may require retraining the ML model. During validation, practitioners highlighted the need to understand the ML technique’s predictions to trust the predictions. We found that a visual (using a state-of-the-art ML interpretation framework) and descriptive explanation of the prediction increases the trustability of the technique compared to just presenting the results of the validity predictions. We conclude that trustability, integration with the existing toolchain, and maintaining the techniques’ accuracy over time are critical for increasing the likelihood of adoption.

This edited book comprises chapters that describe the IoT, machine learning, and blockchain technologies for renewable energy and modern hybrid power systems with simulation examples and case studies. After reading this book, users will understand recent technologies such as IoT, machine learning techniques, and blockchain technologies and the application of these technologies to renewable energy resources and modern hybrid power systems through simulation examples and case studies. This edited book comprises chapters that describe the IoT, machine learning, and blockchain technologies for renewable energy and modern hybrid power systems with simulation examples and case studies.

Testing is considered as one of the expensive activities in software development process. Fixing the defects during testing process can increase the cost as well as the completion time of the project. Cost of testing process can be reduced by identifying the defective modules during the development (before testing) stage. This process is known as “Software Defect Prediction”, which has been widely focused by many researchers in the last two decades. This research proposes a classification framework for the prediction of defective modules using variant based ensemble learning and feature selection techniques. Variant selection activity identifies the best optimized versions of classification techniques so that their ensemble can achieve high performance whereas feature selection is performed to get rid of such features which do not participate in classification and become the cause of lower performance. The proposed framework is implemented on four cleaned NASA datasets from MDP repository and evaluated by using three performance measures, including: F-measure, Accuracy, and MCC. According to results, the proposed framework outperformed 10 widely used supervised classification techniques, including: “Naïve Bayes (NB), Multi-Layer Perceptron (MLP), Radial Basis Function (RBF), Support Vector Machine (SVM), K Nearest Neighbor (KNN), kStar (K*), One Rule (OneR), PART, Decision Tree (DT), and Random Forest (RF)”.

Accurately estimating the carbon budgets in terrestrial ecosystems ranging from flux towers to regional or global scales is particularly crucial for diagnosing past and future climate change. This research investigated the feasibility of two comparatively advanced machine learning approaches, namely adaptive neuro-fuzzy inference system (ANFIS) and extreme learning machine (ELM), for reproducing terrestrial carbon fluxes in five different types of ecosystems. Traditional artificial neural network (ANN) and support vector machine (SVM) models were also utilized as reliable benchmarks to measure the generalization ability of these models according to the following statistical metrics: coefficient of determination (R2), index of agreement (IA), root mean square error (RMSE), and mean absolute error (MAE). In addition, we attempted to explore the responses of all methods to their corresponding intrinsic parameters in terms of the generalization performance. It was found that both the newly proposed ELM and ANFIS models achieved highly satisfactory estimates and were comparable to the ANN and SVM models. The modeling ability of each approach depended upon their respective internal parameters. For example, the SVM model with the radial basis kernel function produced the most accurate estimates and performed substantially better than the SVM models with the polynomial and sigmoid functions. Furthermore, a remarkable difference was found in the estimated accuracy among different carbon fluxes. Specifically, in the forest ecosystem (CA-Obs site), the optimal ANN model obtained slightly higher performance for gross primary productivity, with R2 = 0.9622, IA = 0.9836, RMSE = 0.6548 g C m−2 day−1, and MAE = 0.4220 g C m−2 day−1, compared with, respectively, 0.9554, 0.9845, 0.4280 g C m−2 day−1, and 0.2944 g C m−2 day−1 for ecosystem respiration and 0.8292, 0.9306, 0.6165 g C m−2 day−1, and 0.4407 g C m−2 day−1 for net ecosystem exchange. According to the findings in this study, we concluded that the proposed ELM and ANFIS models can be effectively employed for estimating terrestrial carbon fluxes.

Manufacturing simulation is an encouraging field in every manufacturing industry. The manufacturing simulation facilitate to virtually analysis the performance of the product before manufacturing. So for most of the manufacturing activities are simulated effective and researchers have developed adequate tool for the simulation of various activities of manufacturing. Heat exchanger is one of the important devices used for the purposes including medical, food processing, air conditioning system, etc. Performance of these heat exchangers also important for achieving better performance in those fields. So simulation of heat exchanger gives more beneficial to the engineers to analysis its performance before manufacturing. Hence in this paper, a machine learning approach for the modelling and simulation of heat exchanger is proposed. The proposed technique uses support vector machine technique for the prediction of performance of the heat exchanger. The performance of the proposed technique is validated in terms of prediction accuracy. Ultimately the analysis proves that the proposed technique is more beneficial for the modelling of heat exchanger.

Bacteria are single-celled organisms with a propensity to survive in a wide range of environments. Most of these species can be found both in soil and in oceans whereas some of them are also present in the human body. Majority of bacterial species are harmful to humans, producing a wide range of infectious diseases like cholera, strep throat, tuberculosis, etc. Only a small minority of bacterial species are beneficial to humans. Thus, the study of bacteria is extremely important to analyze, identify benefits and to get rid of their negative effects. Microbiologist uses slide culturing process for bacteria identification, which involve microscope for examination of various species of bacteria. As a result, the shapes of the various samples differ, and to distinguish one sample from another, several characteristics, such as differences in cellular structure and cell-component divergence, are seen. This process is time-consuming and labor-intensive and significantly dependent on expensive machinery and human expertise. A variety of defects and problems can be easily remedied with the development and widespread use of machine learning-based computer assisted solutions in this area. The model developed with the help of machine learning tools and technologies is particularly effective in this field of image analysis and has shown an extraordinarily high rate of improvement in clinical microbiology research by recognizing different bacterial species. For more precise and better outcomes in the categorization of bacteria, feature extraction from digital images is crucial and incredibly vital. The method of feature extraction helps to eliminate extraneous data from a data collection, which speeds up learning and generalization throughout the entire machine learning process. In this paper, we try to attempt various machine learning methods to build an ensembled feature extractor and selector for better classification of microscopic bacterial Images. In this paper, we attempt to compare different feature extraction algorithms with various machine learning classifiers. The experiments have been performed on a novel dataset comprising microscopic images of four bacteria species i.e. Acetobacter aceti , Micrococcus luteus , Bacillus anthracis and Thermus sp. For feature extraction HOG (Histogram of Oriented Gradients), LBP (Local Binary Pattern), ResNet50 and VGG16 techniques have been employed. Using these features performance of five classification algorithms i.e., SVM (Support Vector Machine), RF (Random Forest), Naïve Bayes, Decision Tree and KNN (K-Nearest Neighbor) has been compared. Moreover, comparison has also been performed among feature extraction techniques also. The experimental results show that the combination of SVM and the deep features extracted using VGG16 outperformed other techniques in terms of different classification performance measures, and achieved an accuracy of 99.89%.

Predicting the penetration rate is a complex and challenging task due to the interaction between the tunnel boring machine (TBM) and the rock mass. Many studies highlight the use of empirical and theoretical techniques in predicting TBM performance. However, reliable performance prediction of TBM is of crucial importance to mining and civil projects as it can minimize the risks associated with capital costs. This study presents new applications of supervised machine learning techniques, i.e., k-nearest neighbor (KNN), chi-squared automatic interaction detection (CHAID), support vector machine (SVM), classification and regression trees (CART) and neural network (NN) in predicting the penetration rate (PR) of a TBM. To achieve this aim, an experimental database was set up, based on field observations and laboratory tests for a tunneling project in Malaysia. In the database, uniaxial compressive strength, Brazilian tensile strength, rock quality designation, weathering zone, thrust force, and revolution per minute were utilized as inputs to predict PR of TBM. Then, KNN, CHAID, SVM, CART, and NN predictive models were developed to select the best one. A simple ranking technique, as well as some performance indices, were calculated for each developed model. According to the obtained results, KNN received the highest-ranking value among all five predictive models and was selected as the best predictive model of this study. It can be concluded that KNN is able to provide high-performance capacity in predicting TBM PR. KNN model identified uniaxial compressive strength (0.2) as the most important and revolution per minutes (0.14) as the least important factor for predicting the TBM penetration rate.