Rice is a staple and most important security food crop consumed by almost half of the world's population. More rice production is needed due to the rapid population growth in the world. Rice blast caused by the fungus, Magnaporthe oryzae is one of the most destructive diseases of this crop in different part of the world. Breakdown of blast resistance is the major cause of yield instability in several rice growing areas. There is a need to develop strategies providing long-lasting disease resistance against a broad spectrum of pathogens, giving protection for a long time over a broad geographic area, promising for sustainable rice production in the future. So far, molecular breeding approaches involving DNA markers, such as QTL mapping, marker-aided selection, gene pyramiding, allele mining and genetic transformation have been used to develop new resistant rice cultivars. Such techniques now are used as a low-cost, high-throughput alternative to conventional methods allowing rapid introgression of disease resistance genes into susceptible varieties as well as the incorporation of multiple genes into individual lines for more durable blast resistance. The paper briefly reviewed the progress of studies on this aspect to provide the interest information for rice disease resistance breeding. This review includes examples of how advanced molecular method have been used in breeding programs for improving blast resistance. New information and knowledge gained from previous research on the recent strategy and challenges towards improvement of blast disease such as pyramiding disease resistance gene for creating new rice varieties with high resistance against multiple diseases will undoubtedly provide new insights into the rice disease control.

/pdf/molecular-breeding-strategy-and-challenges-towards-29qmhi0j9k.pdf

Molecular Breeding Strategy and Challenges Towards Improvement of Blast Disease Resistance in Rice Crop

Artificial intelligence (AI) models and optimization algorithms (OA) are broadly employed in different fields of technology and science and have recently been applied to improve different stages of plant tissue culture. The usefulness of the application of AI-OA has been demonstrated in the prediction and optimization of length and number of microshoots or roots, biomass in plant cell cultures or hairy root culture, and optimization of environmental conditions to achieve maximum productivity and efficiency, as well as classification of microshoots and somatic embryos. Despite its potential, the use of AI and OA in this field has been limited due to complex definition terms and computational algorithms. Therefore, a systematic review to unravel modeling and optimizing methods is important for plant researchers and has been acknowledged in this study. First, the main steps for AI-OA development (from data selection to evaluation of prediction and classification models), as well as several AI models such as artificial neural networks (ANNs), neurofuzzy logic, support vector machines (SVMs), decision trees, random forest (FR), and genetic algorithms (GA), have been represented. Then, the application of AI-OA models in different steps of plant tissue culture has been discussed and highlighted. This review also points out limitations in the application of AI-OA in different plant tissue culture processes and provides a new view for future study objectives. KEY POINTS: • Artificial intelligence models and optimization algorithms can be considered a novel and reliable computational method in plant tissue culture. • This review provides the main steps and concepts for model development. • The application of machine learning algorithms in different steps of plant tissue culture has been discussed and highlighted.

Application of artificial intelligence models and optimization algorithms in plant cell and tissue culture

The domain of Internet of Things (IoT) has witnessed immense adaptability over the last few years by drastically transforming human lives to automate their ordinary daily tasks. This is achieved by interconnecting heterogeneous physical devices with different functionalities. Consequently, the rate of cyber threats has also been raised with the expansion of IoT networks which puts data integrity and stability on stake. In order to secure data from misuse and unusual attempts, several intrusion detection systems (IDSs) have been proposed to detect the malicious activities on the basis of predefined attack patterns. The rapid increase in such kind of attacks requires improvements in the existing IDS. Machine learning has become the key solution to improve intrusion detection systems. In this study, an ensemble-based intrusion detection model has been proposed. In the proposed model, logistic regression, naive Bayes, and decision tree have been deployed with voting classifier after analyzing model’s performance with some prominent existing state-of-the-art techniques. Moreover, the effectiveness of the proposed model has been analyzed using CICIDS2017 dataset. The results illustrate significant improvement in terms of accuracy as compared to existing models in terms of both binary and multi-class classification scenarios.

/pdf/a-new-ensemble-based-intrusion-detection-system-for-internet-2w0cjxmocg.pdf

A New Ensemble-Based Intrusion Detection System for Internet of Things

Combining hyper-resolution land surface modeling with SMAP brightness temperatures to obtain 30-m soil moisture estimates

Accurate and reliable prediction of streamflow is vital to the optimization of water resources management, reservoir flood operations, catchment, and urban water management. In this research, support vector regression (SVR) was optimized by six meta-heuristic algorithms, namely, Ant Lion Optimization (SVR-ALO), Multi-Verse Optimizer (SVR-MVO), Spotted Hyena Optimizer (SVR-SHO), Harris Hawks Optimization (SVR-HHO), Particle Swarm Optimization (SVR-PSO), and Bayesian Optimization (SVR-BO) to predict daily streamflow in Naula watershed, State of Uttarakhand, India. The significant inputs and parameter combinations for hybrid SVR models were extracted through Gamma Test before processing. The results obtained by hybrid SVR models during calibration (training) and validation (testing) periods, which were compared against observed streamflow using performance indicators of root mean square error (RMSE), scatter index (SI), coefficient of correlation (COC), Willmott index (WI), and by visual inspection (time-series plot, scatter plot and Taylor diagram). The results of comparison demonstrated that SVR-HHO during calibration/validation periods (RMSE = 92.038/181.306 m3/s, SI = 0.401/0.715, COC = 0.881/0.717, and WI = 0.928/0.777) had superior performance to the SVR-ALO, SVR-MVO, SVR-SHO, SVR-PSO, and SVR-BO models in predicting daily streamflow in the study basin. In addition, the new HHO algorithm outperformed the other meta-heuristic algorithms in terms of prediction accuracy.

Support vector regression optimized by meta-heuristic algorithms for daily streamflow prediction

Our ultimate objective in data mining is to identify any hidden patterns or relationships between our data elements, and in one sense, machine learning provides us with a set of techniques to do just that: techniques that allow us to learn the patterns without any outside influence (unsupervised learning). However, just as is the case with anything, that power comes at a price, but the results can be very interesting and very significant. In other cases, we have some sense on what the results should be and so can guide the learning techniques through an initial “training” phase, directing our system and honing the results (supervised learning). These two broad classifications of machine-learning methods will ground us as we discuss a broad range of techniques and where they are currently being applied in life sciences research, expanding our toolkit and enabling us to take a very different path in our analysis efforts: using an artificial intelligence discipline and letting the data tell us what it contains. As datasets grow, these techniques become more important.

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