Extracts of moringa seed and leaf (Moringa oleifera); lemon (Citrus limon) and ginger (Zingiber officinale) were added to fresh sugarcane juice in different combinations and evaluated on storage at 2, 4 and 30C. Combination of moringa seed extract with lemon and ginger showed high antimicrobial activity when compared with sodium benzoate (as chemical preservative), at the permitted level. Lemon lowered the pH of sugarcane juice to 3.01 and inhibited the growth of microorganisms during storage. Further, phytochemical analysis of methanol extract of moringa seeds and leaves revealed polyphenols, flavonoids, tannins and terpenoid compounds. It was revealed that good quality sugarcane juice (100 mL) with satisfactory storage stability at refrigeration could be prepared from heat‐treated juice (72C for 15 sec) before addition of lemon (3 mL) as a combination of flavor, color enhancer and source of citric acid (antioxidant); moringa (10 mL); ginger (0.6 mL) as flavor enhancer. Practical Applications Sugarcane juice is popularly consumed in India. But large‐scale production by mechanizing the manufacturing process is not carried out. Preserving raw sugarcane juice is a challenging problem because it spoils within hours of extraction. The manufacturers rely on the chemical preservatives for extending the shelf‐life of sugarcane juice, which restricts its production. The research work was carried out to remove the chemical‐dependent constraint with an aim to optimize the shelf‐life conditions of sugarcane juice with natural preservatives. A combination of natural preservatives and low temperature storage was found as an effective way of preservation for more than a month with satisfactory sensory qualities. The findings will pave way for the marketability of sugarcane juice.

The COVID-19 emerged at the end of 2019 and has become a global pandemic. There are many methods for COVID-19 prediction using a single modality. However, none of them predicts with 100% accuracy, as each individual exhibits varied symptoms for the disease. To decrease the rate of misdiagnosis, multiple modalities can be used for prediction. Besides, there is also a need for a self-diagnosis system to narrow down the risk of virus spread in testing centres. Therefore, we propose a robust IoT and deep learning-based multi-modal data classification method for the accurate prediction of COVID-19. Generally, highly accurate models require deep architectures. In this work, we introduce two lightweight models, namely CovParaNet for audio (cough, speech, breathing) classification and CovTinyNet for image (X-rays, CT scans) classification. These two models were identified as the best unimodal models after comparative analysis with the existing benchmark models. Finally, the obtained results of the five independently trained unimodal models are integrated by a novel dynamic multimodal Random Forest classifier. The lightweight CovParaNet and CovTinyNet models attain a maximum accuracy of 97.45% and 99.19% respectively even with a small dataset. The proposed dynamic multimodal fusion model predicts the final result with 100% accuracy, precision, and recall, and the online retraining mechanism enables it to extend its support even in a noisy environment. Furthermore, the computational complexity of all the unimodal models is minimized tremendously and the system functions effectively with 100% reliability even in the absence of any one of the input modalities during testing.

Nodes in Mobile Adhoc NETwork (MANET) are suffered by limited bandwidth and frequent changes in the topology due to node mobility. The nodes participating in the network are powered by limited battery resources and the battery depletion can imply network failure. Although establishing correct and efficient routes is an important design issue in MANET, providing energy efficient routes is a more challenging goal because mobile nodes' operation time is the most critical limiting factor. The problem of network exhausting batteries which partitions the entire network can be prevented by considering the energy consumption in MANET. To support real-time communications which require better QoS, metrics like bandwidth, end-to-end delay and energy consumption has to be considered. It is necessary to consider multiple metrics for selecting an efficient path. This multi-metric QoS routing problem can be solved with optimization techniques. An energy efficient genetic algorithm based unicast routing is proposed to provide energy efficient unicast, multipath route by considering multiple QoS parameters such as end-to-end delay, energy consumption , bandwidth and hop count.

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