The present study was conducted to investigate the effect of the residue of Chenopodium murale L. on growth, nodulation and macromolecule content of two legume crops, viz., Cicer arietinum L. (chickpea) and Pisum sativum L. (pea). A significant reduction in root and shoot length as well as dry matter accumulation occurred when both the legumes were grown in the soil amended with 5, 10, 20 and 40 g residue kg -1 soil. In general, a gradual decline in growth was associated with an increasing amount of residues in the soil. There was also a significant reduction in total chlorophyll content and the amounts of protein and carbohydrates (macromolecules) in plants growing in the residueamended soil. The nodulation was completely absent in chickpea and pea when the plants were grown in the soil amended with 10 and 20 g residue kg -1 soil, respectively. At a lower rate of residue amendment (5 g kg -1 soil), a significant decline in nodule number and weight, and leghaemoglobin content was recorded. Root oxidizability, an indirect measure of tissue viability and cellular respiration, was adversely affected in both the legumes under various treatments of residue amendment. The observed growth reduction concomitant with increased proline accumulation indicated the presence of some inhibitory compounds in the residue-amended soil. It was rich in phenolics identified as protocatechuic, ferulic, p-coumaric and syringic acid with 12.8, 30.4, 20.2 and 33.6% relative content, respectively. The results suggest that the residue of C. murale releases phenolic allelochemicals, which deleteriously affect the growth, nodulation and macromolecule content of chickpea and pea.

A study was conducted to determine the potential and nature of root-mediated allelopathic interference of Chenopodium murale on wheat. Early growth of wheat reduced significantly in agar medium where C. murale seedlings were previously growing as well as in rhizosphere soil of C. murale. The reduction in wheat growth was due to the presence of inhibitory metabolites released by roots of C. murale in the growth media. Even the soil incorporation of root residues also reduced the wheat growth in terms of seedling length and seedling dry weight. Only a partial amelioration in growth inhibition occurred upon charcoal supplementation or nitrogen fertilization in these amended soils. Root residues did not reduce the available nutrients in the soil, which was rather nutrient rich. These results indicated the definite role of allelopathy of C. murale roots in retarding wheat growth. Root amended soils contained significantly higher amount of phytotoxic phenolics as the putative allelochemicals, which were ferulic acid, vanillic acid, p-coumaric acid and benzoic acid. The study concluded that C. murale roots and their exudates exerted allelopathic effects on wheat by releasing water-soluble phenolic acids as putative allelochemicals in soil.

Serious concerns regarding vulnerability and security have been raised as a result of the constant growth of computer networks. Intrusion detection systems (IDS) have been adopted by network administrators to provide essential network security. Commercial IDS in the market do not have the capability to identify novel attacks but generate false alarms for legitimate user activities. Neural networks can be applied for the solution of these issues and for providing improved accuracy. Correlation‐based attribute selection ranks the features according to the highest correlation between the attributes and class label. In this article, the authors propose a correlation‐based feature selection integrated with neural network for identifying anomalies. Experimental analysis performed on NSL‐KDD and UNSW‐NB datasets, which are benchmark datasets of intrusion detection with current attacks. The results show that the proposed model is superior in terms of accuracy, sensitivity, and specificity in comparison with some of the state‐of‐the‐art techniques. With the emergence of the Internet of Things Technology, such IDS can be deployed for securing the IoT servers in future. Wireless payment systems can be secured by building and deploying IDS. A secure integrated network management can be achieved which is error‐free and thereby improving performance.

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Agriculture is the primary source of economic development in India. The fertility of soil, weather conditions, and crop economic values make farmers select appropriate crops for every season. To meet the increasing population requirements, agricultural industries look for improved means of food production. Researchers are in search of new technologies that would reduce investment and significantly improve the yields. Precision is a new technology that helps in improving farming techniques. Pest and weed detection and plant leaf disease detection are the noteworthy applications of precision agriculture. The main aim of this paper is to identify the diseased and healthy leaves of distinct plants by extracting features from input images using CNN algorithm. These features extracted help in identifying the most relevant class for images from the datasets. The authors have observed that the proposed system consumes an average time of 3.8 seconds for identifying the image class with more than 94.5% accuracy.