LEAF DISEASE DETECTION AND IDENTIFICATION USING HYBRID MULTICLASS SVM (HM-SVM)

The agricultural industry is critical to long-term economic growth & food security. Crop diseases, on the other hand, can pose a significant threat to achieving this expansion. Early diagnosis and categorization of plant diseases are essential for a good outcome. This opened up a slew of new options for study in this field. A lot of effort is being done now to use neural networks to better identify and categorise plant diseases. A Hybrid Multiclass SVM (HM-SVM) model strategy towards leaf disease detection is presented in this research. To distinguish healthy and sick leaves, the researchers developed an HM-SVM for automated feature extraction and classification. Experiment results indicate that the proposed technique is capable of reaching high accuracy. Disease detection as well as identification in large fields using automated techniques is beneficial since it minimises people or farmers' labour, as well as time and money spent on observation and study of illness signs. This study explains how to use Hybrid multiclass SVM to identify and detect leaf diseases. Hybrid Multiclass SVM classifier is used to classify illnesses, and thus the detection accuracy is increased by maximising the information exploitation. We are applying image processing algorithms to classify diseases in this suggested system, and diagnosis may be done fast according to the disease. Crop productivity will be increased as a result of this strategy. Acquisition, image pre-processing, segmentation, and feature extraction are some of the procedures involve

Surgical importance of distance from mandibular condyle to carotid canal and foramen spinosum: an anatomical study

Background: The objective of this study was to compare the distance from mandibular condyle to internal carotid artery and middle meningeal artery.Methods: In this study 20 skulls obtained from the Department of Anatomy were utilized for the study. The following two parameters were measured using Vernier Caliper (digital). 1. Distance from Mandibular condyle to carotid canal 2. Distance from medial margin of Mandibular condyle to Foramen spinosum. All the measurements were taken thrice to minimize errors. Photograph of the skull base showing the measurements done was captured.Results: A total of 40 sides, 20 right and 20 left sides were studied. The mean distance between medial margin of mandibular condyle to carotid canal was 11.2 mm±0.6 on right side and 11.6mm±0.8 on left side. The mean distance from the medial margin of mandibular condyle to Foramen spinosum (middle meningeal artery) was 9.3 mm±1.1 on right side and 9.8mm±0.9 on left side. Conclusions: The distance between mandibular condyle to Middle meningeal artery is less compared to the distance between Mandibular condyle to carotid artery. The current study concludes that MMA is comparatively at high risk for damage compared to internal carotid artery

Impact of demand response management on chargeability of electric vehicles

AbstractLarge-scale penetration of electric vehicles (EVs) would significantly increase the load requirements of buildings in highly urbanized cities. EVs exhibit higher degree of charging flexibility when compared to other interruptible loads in buildings. Hence, EVs can be assigned lower priority and interrupted before interrupting any other loads. Any temporary interruption will have minimum impact on EV owner's satisfaction/comfort. However, it should be ensured that the EVs could be charged to the owner's required state of charge (SOC) by the time of departure. The scheduling algorithms that are used to manage the EV charging process ensure that the charging requirements are fulfilled even when there are temporary interruptions. The capability of the scheduling algorithms to manage mismatches decreases with the decrease in time available for charging. In this paper, the impact of demand response management (DRM) on the chargeability of the EVs while using different priority criteria is examined. Subsequently, the proportion of interruption for each EV with different priority criteria and the need for determining the chargeability of EVs before shedding them are studied. A scheduling driven algorithm is proposed which can be used for determining the chargeability of EVs and can be used in combination with DRM

Effect of pulse sprout spray as a foliar nutrition to enhance seed yield and quality in barnyard millet (Echinochloa frumentacea l.)

Millets are rich in valuable nutrients such as carbohydrates, proteins, dietary fibre, minerals and vitamins. The uninterrupted and disproportionate use of chemical fertilizers over a longer period has resulted in deterioration of soil health and reduced yield.  Foliar spray is a very easy way to supply valuable nutrients to plants. With this background, an experiment was conducted to see the effect of pulse sprout extract spray as a foliar spray on the seed crop Barnyard millet (Echinochloa frumentacea L.). The seed crop given foliar treatment with 2% horse gram pulse sprout extract spray recorded higher growth attributes namely plant height (172.8 cm), total chlorophyll content (1.560 mg/g) and yield attributes viz., seed yield per plant (26.5 g), seed yield per plot (2.54 kg), seed yield per hectare (2506 kg), 1000 seed weight (3.28 g), quality parameters viz., germination (89%), vigour index (2461) and biochemical parameters of resultant seeds in both kharif and rabi seasons. The crop given with foliar nutrition of 2% horse gram sprout extract spray showed a low number of days to flower initiation (45 days) and 50% flowering (54 days) when compared to control followed by 2% cowpea sprout extract. Hence it was hypothesized that application of the nutrient extract from the sprouted pulses in the form of foliar spray would enable better crop growth and productivity of Barnyard millet

PRODUCTION OF AMYLASE FROM CUCUMIS MELO USING ASPERGILLUS NIGER BY LIQUID FERMENTATION

Submerged fermentation was carried out using muskmelon shell as a substrate for the production of amylase using Aspergillus niger. It was observed that the activity started to peak at 60 hrs as 102.6 µg/ml/min, reached maximum at 118.56µg/ml/min at the 84th hrs and then went on decreasing at 108 hrs to111.72 µg/ml/min, respectively. The results show that the amylase activity was decreasing after the 3rd day of incubation in the same optimal conditions. The optimum temperature maintained for amylase activity, was 30°C at pH 8.The process parameters influencing the production of α-amylase were optimized. Key words: Cucumis melo, Aspergillus niger, α-Amylase, Submerged Fermentatio

A comprehensive dataset from a smart grid testbed for machine learning based CPS security research

Data-sets play a crucial role in advancing the research. However, getting access to real-world data becomes difficult when it comes to critical infrastructures and more so if that data is being acquired for security research. In this work, a comprehensive dataset from a real-world smart electric grid testbed is collected and shared with the research community. A few of the unique features of the dataset and testbed are highlighted

A Novel System-Theoretic Matrix-Based Approach to Analysing Safety and Security of Cyber-Physical Systems

Cyber-Physical Systems (CPSs) are getting increasingly complex and interconnected. Consequently, their inherent safety risks and security risks are so intertwined that the conventional analysis approaches which address them separately may be rendered inadequate. STPA (Systems-Theoretic Process Analysis) is a top-down hazard analysis technique that has been incorporated into several recently proposed integrated Safety and Security (S&S) analysis methods. This paper presents a novel methodology that leverages not only STPA, but also custom matrices to ensure a more comprehensive S&S analysis. The proposed methodology is demonstrated using a case study of particular commercial cloud-based monitoring and control system for residential energy storage systems

NITROGEN, PHOSPHORUS, AND POTASSIUM RANGE OF VERMICOMPOST USING EISENIA FETIDA AND PERIONYX EXCAVATUS

The aim of the presentation is to produce vermicomposting from organic kitchen solid wastes using two types of earthworms such as Eisenia fetida and Perionyx excavatus and check the nitrogen, phosphorus, and potassium level between E. fetida and P. excavatus. This study examines the potential of the E. fetida and P. excavatus in the vermicompost of kitchen waste. As kitchen waste is rich in organic material. Physical and biochemical parameters were analyzed during the period of 60 days. Pre-decomposition is 15 days and subsequent vermicomposting is 60 days indicates, the rule of these species of vermitechnology increase was found in all the parameters such as total nitrogen (%), available phosphorus (%), and exchangeable potassium (%) while a decrease was found in pH and carbon-to-nitrogen ratio in E. fetida as the timing of vermicomposting increased from 0 days to 60 days