Search

Electric vehicles (EVs) are rising in popularity due to technological developments and the nation’s decarbonization efforts, which are enticing drivers to move away from gasoline-dependent transportation. However, a major challenge in EV charging is optimal day-ahead charging planning under time-varying conditions, such as fluctuating charging prices and traffic conditions. Additionally, these conditions can lead to uncertain energy consumption while driving an EV. Predicting the energy consumption of an electric vehicle is complicated because it is influenced by more factors than a typical battery, such as driving habits and traffic conditions. This paper proposes an optimal EV charging planning strategy using an artificial intelligence-based energy consumption prediction model to address this problem. The energy consumption prediction model, based on long short-term memory (LSTM), was developed and validated against other artificial intelligence models. The LSTM-based model uses real-time training, making it suitable for uncertain scenarios encountered by EVs. The predicted results and traffic information are considered as weight factors in solving the EV charging planning and routing optimization problem. The optimization problem is formulated as mixed integer linear programming. Simulation studies under various driving and traffic conditions validate the proposed optimal charging planning and routing method. Since travel time can vary according to driving characteristics and traffic conditions over the same driving distance, the results confirm that the proposed method can predict energy consumption more accurately than other methods. It also selects routes with lower energy consumption relative to total driving time and provides stable operation.

This study considers the problem of detecting network anomalies caused by computer attacks in the networks of the industrial Internet of things. To detect anomalies, a new method is proposed, built using a hierarchical temporal memory (HTM) computation model based on the neocortex model. An experimental study of the developed method of detecting computer attacks based on the HTM model showed the superiority of the developed solution over the LSTM analog. The developed prototype of the anomaly detection system provides continuous training on unlabeled data sets in real time, takes into account the current network context, and applies the accumulated experience by supporting the memory mechanism. [ABSTRACT FROM AUTHOR]

Fast and accurate malicious domain detection is an essential research theme to prevent cybercrime, and machine learning is an attractive approach for detecting unseen malicious domains in the past decade. In this paper, we present MADMAX (MAchine learning-baseD MAlicious domain eXhauster), a browser-based application leveraging extreme learning machine (ELM) for malicious domain detection. In contrast to the existing work of ELM-based domain detection, MADMAX newly introduces two methods, i.e., selection of optimized features to provide higher accuracy and throughput based on permutation importance and real-time training to retrain a model with an updated malicious dataset for continuous malicious domain detection. We demonstrate that MADMAX fairly outperforms the existing work with respect to accuracy and throughput by virtue of the selection of optimized features. Moreover, we also confirm a model with real-time training stably detects even unseen malicious domains, whereas accuracy of a model without the real-time training decreases due to the unseen domains. The source codes of MADMAX is publicly available via GitHub.

A fast architecture for real-time (i.e., minute-based) training of a traffic predictor is studied, based on the so-called broad learning system (BLS) paradigm. The study uses various traffic datasets by the California Department of Transportation, and employs a variety of standard algorithms (LASSO regression, shallow and deep neural networks, stacked autoencoders, convolutional, and recurrent neural networks) for comparison purposes: all algorithms are implemented in MATLAB on the same computing platform. The study demonstrates a BLS training process two-three orders of magnitude faster (tens of seconds against tens-hundreds of thousands of seconds), allowing unprecedented real-time capabilities. Additional comparisons with the extreme learning machine architecture, a learning algorithm sharing some features with BLS, confirm the fast training of least-square training as compared to gradient training. [ABSTRACT FROM AUTHOR]

Background Acid-fast bacilli  Mycobacterium tuberculosis and Mycobacterium leprae  are the causative organisms behind two major diseases of developing nations, tuberculosis and leprosy, respectively. To efficiently tackle these diseases in developing nations, drugs must be augmented with improved detection modalities. This necessitates the development of enhanced tools that can aid the current detection modalities being used in high-incidence areas. A no-code artificial intelligence model based on image classification is one such tool that can be used in the identification of acid-fast bacilli. This study utilizes three such no-code artificial intelligence models that originate from three different platforms but share identical training, testing, and subsequent evaluation. Thereafter, the study is directed at comparing the three models created and identifying the one that can function as a promising support system for the detection of acid-fast bacilli. Methods To begin with, a total of 1000 images per class, i.e., positive and negative for each disease, were captured from the diagnosed slides of tuberculosis and leprosy, taken from the Department of Pathology. Subsequently, these slides were reviewed again by a pathologist to demarcate them as positive or negative for acid-fast bacilli. Once the required number of images was captured, 600 images of each class were selected as the training set, 300 images as the testing set, and the remaining 100 images as the evaluation set. Data augmentation was then performed using techniques such as rotating, mirroring, cropping, and position shifting. These designated data sets were then used to train the image classification software available on the following three platforms: Lobe (Microsoft Corporation, Redmond, Washington, United States), Create ML (Apple Inc., Cupertino, California, United States), Python-based open-source software (PerceptiLabs, Stockholm, Sweden). The final evaluation was based on different parameters such as sensitivity, specificity, ease of use, learning curve, technological resources required, and feasibility of implementation. All parameters put together served the purpose of comparison to identify the most promising model. Results Out of the three models tested, the one built using Lobe is the most promising in terms of the evaluation parameters considered. For tuberculosis, the sensitivity and specificity values obtained were 96% each, while for leprosy, they were 100% and 96%, respectively. Also, the model built using Lobe had a near-negligible learning curve, in addition to being the most cost-effective and feasible model to implement. Furthermore, it had a unique real-time training feature, which constantly improved the model throughout the testing period, till the final sensitivity and specificity values were achieved. Conclusions In clinical situations where a high number of cases are encountered each day, a no-code artificial intelligence model built using Lobe would get exposed to a huge database, getting trained in real time. Subsequently, such a model would reach considerable levels of sensitivity and specificity and in turn, act as a promising support system for the detection of acid-fast bacilli., Competing Interests: The authors have declared that no competing interests exist., (Copyright © 2024, Arya et al.)

Abstract: A real-time trained system for robust speaker verification is proposed. This system was developed using a relative space of reference speakers, also referred to as anchor models. The real-time training aspect of the system is based on this relative space’s intriguing features and properties. The relative space concept uses relative speaker representation rather than an absolute representation, by comparing the speaker to a set of well-trained reference speakers. The advantage of this approach is that instead of estimating numerous parameters of an absolute model for a speaker, only a few parameters of a model relative to a number of anchor models are estimated. In order to optimize the performance of the proposed system, several techniques were assessed for possible implementation in various blocks of the system. As a result, the best performance was achieved where normalized vector’s mutual angle with the Minimum normalization method was applied to speaker verification in conjunction with an orthogonal relative space of virtual reference speakers. In this case, an Equal Error Rate (EER) of 0.12% on 400 test samples of 100 speakers was obtained. In addition to assessment under normal conditions, the developed speaker verification system was also evaluated under abnormal conditions where noisy or telephonic speech sequence contamination was present. Experiments conducted in this case demonstrated that, in most cases, this system outperforms absolute space based systems even with shortened training speech sequences. Another major contribution of this research is the development of a more complex speaker verification system capable of tackling abnormal conditions more effectively. In this case, other interesting features of the relative space approach were employed. For this purpose, a novel enrichment method was developed to construct a relative space of anchor models trained to tackle noise. The results of the experiments conducted in this part of the research demonstrated an excellent ability of this approach to tackle abnormal conditions. Compared to absolute space based system, applying this method in relative space led to lower error rates of speaker verification in all cases even with low SNR values. [Copyright &y& Elsevier]

Existing literature contains voluminous research indicating that an expatriate''s failure to adapt to the host culture is a leading driver of turnover [e.g. Powers, T. 1992. Managing international hospitality, FIU Hospitality Review, 1(1), 25–34; Stone, R., 1991. Expatriate selection and failure. Human Resource Planning 14, 9–18]. In response to this recurring finding, an increased number of corporations offer cultural training for those beginning (or about to begin) an international assignment. Nevertheless, even in a top-rate training program, only a small fraction of cultural idiosyncrasies can be addressed in advanced training sessions. Therefore, in addition to a priori training, recent literature recommends the use of real-time training [Avril, A., Magnini, V., 2007. A holistic approach to expatriate success. International Journal of Contemporary Hospitality Management 19 (1), 53–63; Magnini, V., Honeycutt, E., 2003. Learning orientation and the hotel expatriate manager experience. International Journal of Hospitality Management 22, 267–380; Mendenhall, M., Stahl, G., 2000. Expatriate training and development: where do we go from here? Human Resources Management 39 (2–3), 251–265]. This research collects exploratory survey results from nineteen current hotel expatriates and finds that the most commonly used source of real-time training is local nationals. In addition, it is found that as the number of previous expatriate assignments increases, the use of CD-Rom real-time training products declines. Further, in terms of culture, expatriates are less likely to turn to repatriates for real-time advice when assigned to an individualistic host nation or when there is a sizable culture distance between home and host country. The implications of these findings are discussed. [Copyright &y& Elsevier]