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The people in the world rely on social media for gathering news, and it is mainly because of the development of technology. The approaches employed in natural language processing are still deficient in judgement factors, and these techniques frequently rely upon political or social circumstances. Numerous low‐level communities in the area are curious after experiencing the negative effects caused by the spread of false information in different sectors. Low‐resource languages are still distracted, because these techniques are extensively employed in the English language. This work aims to provide an analysis of regional language fake news and develop a referral system with advanced techniques to identify fake news in Hindi and Tamil. This proposed model includes (a) Regional Language Text Collection; (b) Text preprocessing; (c) Feature Extraction; (d) Weighted Stacked Feature Fusion; and (e) Fake News Detection. The text data is collected from the standard datasets. The collected text data is preprocessed and given into the feature extraction, which is done by using bidirectional encoder representations from transformers (BERT), transformer networks, and seq2seq network for extracting the three sets of language text features. These extracted feature sets are inserted into the weighted stacked feature fusion model, where the three sets of extracted features are integrated with the optimized weights that are acquired through the enhanced osprey optimization algorithm (EOOA). Finally, these resultant features are given to multi‐scale atrous convolution‐based one‐dimensional convolutional neural network with dilated long short‐term memory (MACNN‐DLSTM) for detecting the fake news. Throughout the result analysis, the experimentation is conducted based on the standard Tamil and Hindi datasets. Moreover, the developed model shows 92% for Hindi datasets and 96% for Tamil datasets which shows effective performance regarding accuracy measures. The experimental analysis is carried out by comparing with the conventional algorithms and detection techniques to showcase the efficiency of the developed regional language‐based fake news detection model. [ABSTRACT FROM AUTHOR]

Soil salinity is a major abiotic stress that limits the growth and productivity of horticultural crops. Most fruit crops are glycophytes, and their production is adversely affected by salt stress. The key findings indicate that salinity reduces several plant growth parameters such as shoot and root length, number of leaves, plant biomass and relative water content as well as biochemical parameters such as photosynthetic pigments and proline. Physiological results show decreased water absorption, nutrient imbalances such as changes in ion concentrations and hormone levels and increased oxidative stress. Several mitigation strategies are recommended, such as the use of salt-tolerant rootstocks, application of growth regulators and soil amendments to enhance salt tolerance in tropical fruit crops. This review concludes that integrating physiological and agronomic strategies is essential for mitigating the adverse effects of salinity on tropical fruits. [ABSTRACT FROM AUTHOR]

Wireless sensor networks (WSNs) and the Internet of Things (IoT) are increasingly making an impact in a wide range of domain-specific applications. In IoT-integrated WSNs, nodes generally function with limited battery units and, hence, energy efficiency is considered as the main design challenge. For homogeneous WSNs, several routing techniques based on clusters are available, but only a few of them are focused on energy-efficient heterogeneous WSNs (HWSNs). However, security provisioning in end-to-end communication is the main design challenge in HWSNs. This research work presents an energy optimizing secure routing scheme for IoT application in heterogeneous WSNs. In our proposed scheme, secure routing is established for confidential data of the IoT through sensor nodes with heterogeneous energy using the multipath link routing protocol (MLRP). After establishing the secure routing, the energy and network lifetime is improved using the hybrid-based TEEN (H-TEEN) protocol, which also has load balancing capacity. Furthermore, the data storage capacity is improved using the ubiquitous data storage protocol (U-DSP). This routing protocol has been implemented and compared with two other existing routing protocols, and it shows an improvement in performance parameters such as throughput, energy efficiency, end-to-end delay, network lifetime and data storage capacity.

Since, the Network-on-Chip (NoC) has been emerging as a very promising paradigm for scalable on-chip communication architectures in many fields. To achieve excellent results in Network on Chip (NoC) Systems application the load balanced in the routing as to be achieved. Normally the routing is based on the probability function using the data of the past and current, which means the route may exist or may not, due to which the size of the routing table also increases. To overcome this drawback in the NoC systems, we propose K-Step Look Ahead in Load Balanced Adaptive Routing, where all the details about the routing the routing table is maintained by the every router are achieved by ACO-based Cascaded Adaptive Routing (ACO-CAR). These routing tables are formed based on the probability of pheromones left behind by the ant packets in the past. Then we implement a k-Step Look Ahead (KSLA) algorithm which finds the exact number of steps for the ant packet to reach the destination using the information of the ACO-CAR routing tables and updates the routing table by eliminating the route information which does not exists. [ABSTRACT FROM AUTHOR]

The dry rind of the pomegranate (Punica granatum) has been used as dye stuff since earlier times. In the present study the dye extracts were obtained from the rinds of the P. granatum and used for dyeing the cotton fabrics. The fabrics were assessed for antimicrobial, dyeability and fastness properties. The color strength (K/S) of dyed fabric was assessed through dyeability test. The antibacterial assessment is performed qualitatively by disc diffusion method and parallel streak method (AATCC 147) and quantitatively by percentage reduction test (AATCC 100) against the test organisms Escherichia coli and Staphylococcus aureus. The fabrics were also assessed for fastness properties such as wash fastness, light fastness and rubbing fastness as per the AATCC standards. The fabrics were finally subjected to wash durability tests. The dyeability results showed that the fabrics dyed with the P. granatum rind extracts have high K/S value when compared with the untreated control fabric. The natural extract dyed fabric has prominent antimicrobial activity which was evidenced by clear zone of bacterial inhibition in qualitative tests and bacterial reduction in quantitative tests whereas the control fabric has no antibacterial activity. Wash durability test showed that the antimicrobial activity of the fabrics was durable up to 10 wash cycles. [ABSTRACT FROM AUTHOR]