A novel fine-tuned deep-learning-based multi-class classifier for severity of paddy leaf diseases.

Introduction: Paddy leaf diseases have a catastrophic influence on the quality and quantity of paddy grain production. The detection and identification of the intensity of various paddy infections are critical for high-quality crop production. Methods: In this paper, infections in paddy leaves are considered for the identification of illness severity. The dataset contains both primary and secondary data. The four online repositories used for secondary data resources are Mendeley, GitHub, Kaggle and UCI. The size of the dataset is 4,068 images. The dataset is first pre-processed using ImageDataGenerator. Then, a generative adversarial network (GAN) is used to increase the dataset size exponentially. The disease severity calculation for the infected leaf is performed using a number of segmentation methods. To determine paddy infection, a deep learning-based hybrid approach is proposed that combines the capabilities of a convolutional neural network (CNN) and support vector machine (SVM). The severity levels are determined with the assistance of a domain expert. Four degrees of disease severity (mild, moderate, severe, and profound) are considered. Results: Three infections are considered in the categorization of paddy leaf diseases: bacterial blight, blast, and leaf smut. The model predicted the paddy disease type and intensity with a 98.43% correctness rate. The loss rate is 41.25%. Discussion: The findings show that the proposed method is reliable and effective for identifying the four levels of severity of bacterial blight, blast, and leaf smut infections in paddy crops. The proposed model performed better than the existing CNN and SVM classification models.

Algorithmic Approach to Virtual Machine Migration in Cloud Computing with Updated SESA Algorithm.

Cloud computing plays an important role in every IT sector. Many tech giants such as Google, Microsoft, and Facebook as deploying their data centres around the world to provide computation and storage services. The customers either submit their job directly or they take the help of the brokers for the submission of the jobs to the cloud centres. The preliminary aim is to reduce the overall power consumption which was ignored in the early days of cloud development. This was due to the performance expectations from cloud servers as they were supposed to provide all the services through their services layers IaaS, PaaS, and SaaS. As time passed and researchers came up with new terminologies and algorithmic architecture for the reduction of power consumption and sustainability, other algorithmic anarchies were also introduced, such as statistical oriented learning and bioinspired algorithms. In this paper, an indepth focus has been done on multiple approaches for migration among virtual machines and find out various issues among existing approaches. The proposed work utilizes elastic scheduling inspired by the smart elastic scheduling algorithm (SESA) to develop a more energy-efficient VM allocation and migration algorithm. The proposed work uses cosine similarity and bandwidth utilization as additional utilities to improve the current performance in terms of QoS. The proposed work is evaluated for overall power consumption and service level agreement violation (SLA-V) and is compared with related state of art techniques. A proposed algorithm is also presented in order to solve problems found during the survey.

Purification and characterization of an extracellular, thermo-alkali-stable, metal tolerant laccase from Bacillus tequilensis SN4.

A novel extracellular thermo-alkali-stable laccase from Bacillus tequilensis SN4 (SN4LAC) was purified to homogeneity. The laccase was a monomeric protein of molecular weight 32 KDa. UV-visible spectrum and peptide mass fingerprinting results showed that SN4LAC is a multicopper oxidase. Laccase was active in broad range of phenolic and non-phenolic substrates. Catalytic efficiency (kcat/Km) showed that 2, 6-dimethoxyphenol was most efficiently oxidized by the enzyme. The enzyme was inhibited by conventional inhibitors of laccase like sodium azide, cysteine, dithiothreitol and ß-mercaptoethanol. SN4LAC was found to be highly thermostable, having temperature optimum at 85°C and could retain more than 80% activity at 70°C for 24 h. The optimum pH of activity for 2, 6-dimethoxyphenol, 2, 2'-azino bis[3-ethylbenzthiazoline-6-sulfonate], syringaldazine and guaiacol was 8.0, 5.5, 6.5 and 8.0 respectively. Enzyme was alkali-stable as it retained more than 75% activity at pH 9.0 for 24 h. Activity of the enzyme was significantly enhanced by Cu2+, Co2+, SDS and CTAB, while it was stable in the presence of halides, most of the other metal ions and surfactants. The extracellular nature and stability of SN4LAC in extreme conditions such as high temperature, pH, heavy metals, halides and detergents makes it a highly suitable candidate for biotechnological and industrial applications.