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This article reviews rotational seismology, considering different areas of interest, as well as measuring devices used for rotational events investigations. After a short theoretical description defining the fundamental parameters, the authors summarized data published in the literature in areas such as the indirect numerical investigation of rotational effects, rotation measured during earthquakes, teleseismic wave investigation, rotation induced by artificial explosions, and mining activity. The fundamental data on the measured rotation parameters and devices used for the recording are summarized and compared for the above areas. In the section on recording the rotational effects associated with artificial explosions and mining activities, the authors included results recorded by a rotational seismograph of their construction—FOSREM (fibre-optic system for rotational events and phenomena monitoring). FOSREM has a broad range of capabilities to measure rotation rates, from several dozen nrad/s to 10 rad/. It can be controlled remotely and operated autonomously for a long time. It is a useful tool for systematic seismological investigations in various places. The report concludes with a short discussion of the importance of rotational seismology and the great need to obtain experimental data in this field.

The discrimination between earthquakes and artificial explosions is a significant issue in seismic analysis to efficiently prevent and respond to seismic events. However, the discrimination of seismic events is challenging due to the low incidence rate. Moreover, the similarity between earthquakes and artificial explosions with a local magnitude derives a nonlinear data distribution. To improve the discrimination accuracy, this paper proposes machine-learning-based seismic discrimination methods—support vector machine, naive Bayes, and logistic regression. Furthermore, to overcome the nonlinear separation problem, the kernel functions and regularized logistic regression are applied to design seismic classifiers. To efficiently design the classifier, P- and S-wave amplitude ratios on the time domain and spectral ratios on the frequency domain, which is converted by fast Fourier transform and short-time Fourier transform are selected as feature vectors. Furthermore, an adaptive synthetic sampling algorithm is adopted to enhance the classifier performance against the seismic data imbalance issue caused by the non-equivalent number of occurrences. The comparisons among classifiers are evaluated by the binary classification performance analysis methods.

Machine learning addresses the question of how to build computers that improve automatically through experience. It is one of today's most rapidly growing technical fields, lying at the intersection of computer science and statistics, and at the core of artificial intelligence and data science. Recent progress in machine learning has been driven both by the development of new learning algorithms and theory and by the ongoing explosion in the availability of online data and low-cost computation. The adoption of data-intensive machine-learning methods can be found throughout science, technology and commerce, leading to more evidence-based decision-making across many walks of life, including health care, manufacturing, education, financial modeling, policing, and marketing.

This paper proposes a novel meta-heuristic optimization method, namely “Chernobyl Disaster Optimizer (CDO)”. The underlying concepts and principles behind the proposed approach is inspired by the nuclear reactor core explosion of Chernobyl. In CDO, radioactivity happened because of nuclear instability, which different types of radiations are emitted from nuclei. The most common kinds of these radiations are called gamma, beta, and alpha particles. These particles fly away from the explosion point (high pressure point) to the low pressure point (the human standing point), which are harmful to the humans. The CDO mimics the process of nuclear radiation while attaching human after the nuclear explosion. The main steps of nuclear explosion and attaching human are implemented in which gamma, beta, and alpha particles are involved in this process. The CDO is evaluated with optimizing “Congress on Evolutionary Computation (CEC 2017)” test bed suites. In addition, it is compared against well-known optimization methods, such as “Sperm Swarm Optimization” and “Gravitational Search Algorithm”. The experimental results prove its efficiency, which can be considered as viable alternative.

The recent explosion of ‘big data’ has ushered in a new era of artificial intelligence (AI) algorithms in every sphere of technological activity, including medicine, and in particular radiology. However, the recent success of AI in certain flagship applications has, to some extent, masked decades-long advances in computational technology development for medical image analysis. In this article, we provide an overview of the history of AI methods for radiological image analysis in order to provide a context for the latest developments. We review the functioning, strengths and limitations of more classical methods as well as of the more recent deep learning techniques. We discuss the unique characteristics of medical data and medical science that set medicine apart from other technological domains in order to highlight not only the potential of AI in radiology but also the very real and often overlooked constraints that may limit the applicability of certain AI methods. Finally, we provide a comprehensive perspective on the potential impact of AI on radiology and on how to evaluate it not only from a technical point of view but also from a clinical one, so that patients can ultimately benefit from it.Key Points• Artificial intelligence (AI) research in medical imaging has a long history• The functioning, strengths and limitations of more classical AI methods is reviewed, together with that of more recent deep learning methods.• A perspective is provided on the potential impact of AI on radiology and on its evaluation from both technical and clinical points of view.

Using P‐wave seismograms, we trained a seismic source classifier using a Convolutional Neural Network. We trained for three classes: earthquake P‐wave, underground nuclear explosion (UNE) P‐wave, and noise. With the current absence of nuclear testing by countries that have signed the Comprehensive Test Ban Treaty, high quality seismic data from UNEs is limited. Even with limited training data, our model can accurately characterize most events recorded at regional and teleseismic distances, finding over 95% signals in the validation set. We applied the model on holdout datasets of the North Korean test explosions to evaluate the performance on unique region and station‐source pairs, with promising results. Additionally, we tested on the Source Physics Experiment events to investigate the potential for chemical explosions to act as a surrogate for nuclear explosions. We anticipate that machine‐learning models like our classifier system can have broad application for other seismic signals including volcanic and non‐volcanic tremor, anomalous earthquakes, ice‐quakes or landslide‐quakes. Plain Language Summary We train a global seismic event classifier using machine learning on underground nuclear test explosion seismic data. Our classifier model can successfully discriminate (with over 95% accuracy) between underground nuclear explosion, earthquake, and noise signals from stations both regionally and far‐field. Since this model was trained on a relatively small data set (for machine learning applications) we expect that similar methods can be applied to event or discrimination of other unique seismic sources like those from volcanoes, landslides, or glaciers. Key Points We successfully discriminate underground nuclear explosions with a Convolutional Neural network (CNN) trained on P‐wave seismograms Robust global seismic event discrimination is possible with machine learning trained on regional and teleseismic data A CNN trained with historical nuclear explosion data can be applied with high accuracy to other regions, like the six Democratic People's Republic of Korea's test explosions

Success in the quest for artificial intelligence has the potential to bring unprecedented benefits to humanity, and it is therefore worthwhile to investigate how to maximize these benefits while avoiding potential pitfalls. This article gives numerous examples (which should by no means be construed as an exhaustive list) of such worthwhile research aimed at ensuring that AI remains robust and beneficial.

This is an opinion paper about the strengths and weaknesses of Deep Nets for vision. They are at the heart of the enormous recent progress in artificial intelligence and are of growing importance in cognitive science and neuroscience. They have had many successes but also have several limitations and there is limited understanding of their inner workings. At present Deep Nets perform very well on specific visual tasks with benchmark datasets but they are much less general purpose, flexible, and adaptive than the human visual system. We argue that Deep Nets in their current form are unlikely to be able to overcome the fundamental problem of computer vision, namely how to deal with the combinatorial explosion, caused by the enormous complexity of natural images, and obtain the rich understanding of visual scenes that the human visual achieves. We argue that this combinatorial explosion takes us into a regime where “big data is not enough” and where we need to rethink our methods for benchmarking performance and evaluating vision algorithms. We stress that, as vision algorithms are increasingly used in real world applications, that performance evaluation is not merely an academic exercise but has important consequences in the real world. It is impractical to review the entire Deep Net literature so we restrict ourselves to a limited range of topics and references which are intended as entry points into the literature. The views expressed in this paper are our own and do not necessarily represent those of anybody else in the computer vision community.

Model-Based Control (MBC) techniques have dominated flight controller designs for Unmanned Aerial Vehicles (UAVs). Despite their success, MBC-based designs rely heavily on the accuracy of the mathematical model of the real plant and they suffer from the explosion of complexity problem. These two challenges may be mitigated by Artificial Neural Networks (ANNs) that have been widely studied due to their unique features and advantages in system identification and controller design. Viewed from this perspective, this survey provides a comprehensive literature review on combined MBC-ANN techniques that are suitable for UAV flight control, i.e., low-level control. The objective is to pave the way and establish a foundation for efficient controller designs with performance guarantees. A reference template is used throughout the survey as a common basis for comparative studies to fairly determine capabilities and limitations of existing research. The end-result offers supported information for advantages, disadvantages and applicability of a family of relevant controllers to UAV prototypes.

The rice leaves related diseases often pose threats to the sustainable production of rice affecting many farmers around the world. Early diagnosis and appropriate remedy of the rice leaf infection is crucial in facilitating healthy growth of the rice plants to ensure adequate supply and food security to the rapidly increasing population. Therefore, machine-driven disease diagnosis systems could mitigate the limitations of the conventional methods for leaf disease diagnosis techniques that is often time-consuming, inaccurate, and expensive. Nowadays, computer-assisted rice leaf disease diagnosis systems are becoming very popular. However, several limitations ranging from strong image backgrounds, vague symptoms’ edge, dissimilarity in the image capturing weather, lack of real field rice leaf image data, variation in symptoms from the same infection, multiple infections producing similar symptoms, and lack of efficient real-time system mar the efficacy of the system and its usage. To mitigate the aforesaid problems, a faster region-based convolutional neural network (Faster R-CNN) was employed for the real-time detection of rice leaf diseases in the present research. The Faster R-CNN algorithm introduces advanced RPN architecture that addresses the object location very precisely to generate candidate regions. The robustness of the Faster R-CNN model is enhanced by training the model with publicly available online and own real-field rice leaf datasets. The proposed deep-learning-based approach was observed to be effective in the automatic diagnosis of three discriminative rice leaf diseases including rice blast, brown spot, and hispa with an accuracy of 98.09%, 98.85%, and 99.17% respectively. Moreover, the model was able to identify a healthy rice leaf with an accuracy of 99.25%. The results obtained herein demonstrated that the Faster R-CNN model offers a high-performing rice leaf infection identification system that could diagnose the most common rice diseases more precisely in real-time.