A Comparative Study of Ensemble Classifiers for Paddy Blast Disease Prediction Model

Paddy blast has become most epidemic disease in many rice growing countries. Various statistical methods have been used for the prediction of paddy blast but previously used methods failed in predicting diseases with good accuracy. However the need to develop new model that considers both weather factors and non weather  data called blast disease data that influences paddy disease to grow. Given this point we developed ensemble classifier based paddy disease prediction model taking weather data from January 2013 to December 2019 from Agricultural and Horticulture Research Station Kathalgere Davangere District. For the predictive model we collected 7 kinds of weather data and 7 kinds of disease related data that includes Minimum Temperature, Maximum Temperature, Temperautre Difference,Relative Humidity, Stages of Paddy Cultivation, Varities of seeds, Season of cropping and so on. It is observed and analyzed that Minimum Temperature, Humidity and Rainfall has huge correlation with occurrence of disease. Since some of the variables are non numeric to convert them to numeric data one hot encoding approach is followed and to improve efficiency of ensemble classifiers  4 different filter based features selection methods are used such as Pearson’s correlation, Mutual information, ANNOVA F Value, Chi Square. Three different ensemble classifiers are used as predictive models and classifiers are compared it is observed that Bagging ensemble technique has achieved  accuracy of 98% compared to Adaboost of 97% and Voting classifier of 88%. Other classification metrics are used evaluate different classifiers like precision, recall, F1 Score, ROC and precision recall score. Our proposed ensemble classifers for paddy blast disease prediction has achieved high precision and high recall but when the solutions of model are closely looked bagging classifier is better compared to other ensemble classifers that are proposed in predicting paddy blast disease

A Comparative Study of Ensemble Classifiers for Paddy Blast Disease Prediction Model

Paddy blast has become most epidemic disease in many rice growing countries. Various statistical methods have been used for the prediction of paddy blast but previously used methods failed in predicting diseases with good accuracy. However the need to develop new model that considers both weather factors and non weather  data called blast disease data that influences paddy disease to grow. Given this point we developed ensemble classifer based paddy disease prediction model taking weather data from January 2013 to December 2019 from Agricultural and Horticulture Research Station Kathalgere Davangere District. For the predictive model we collected 7 kinds of weather data and 7 kinds of disease related data that includes Minimum Temperature, Maximum Temperature, Temperautre Difference,Relative Humidity, Stages of Paddy Cultivation, Varities of seeds, Season of cropping and so on. It is observed and analyzed that Minimum Temperature, Humidity and Rainfall has huge correlation with occurrence of disease. Since some of the variables are non numeric to convert them to numeric data one hot encoding approach is followed and to improve efficiency of ensemble classifiers  4 different filter based features selection methods are used such as Pearson’s correlation, Mutual information, ANNOVA F Value, Chi Square. Three different ensemble classifiers are used as predictive models and classifiers are compared it is observed that Bagging ensemble technique has achieved  accuracy of 98% compared to Adaboost of 97% and Voting classifier of 88%. Other classification metrics are used evaluate different classifiers like precision, recall, F1 Score, ROC and precision recall score. Our proposed ensemble classifers for paddy blast disease prediction has achieved high precision and high recall but when the solutions of model are closely looked bagging classifier is better compared to other ensemble classifers that are proposed in predicting paddy blast disease

Study of Lithological Control Over Infiltration Through Water-Level Fluctuation and Phi-Index in Micro-Watersheds of Nagpur District, Maharashtra, India

Source: ICHE Conference Archive - https://mdi-de.baw.de/icheArchiv

Study of underlying particle spectrum during huge X-ray flare of Mkn 421 in April 2013

Context: In April 2013, the nearby (z=0.031) TeV blazar, Mkn 421, showed one of the largest flares in X-rays since the past decade. Aim: To study all multiwavelength data available during MJD 56392 to 56403, with special emphasis on X-ray data, and understand the underlying particle energy distribution. Methods: We study the correlations between the UV and gamma bands with the X-ray band using the z-transformed discrete correlation function. We model the underlying particle spectrum with a single population of electrons emitting synchrotron radiation, and do a statistical fitting of the simultaneous, time-resolved data from the Swift-XRT and the NuSTAR. Results: There was rapid flux variability in the X-ray band, with a minimum doubling timescale of $1.69 \pm 0.13$ hrs. There were no corresponding flares in UV and gamma bands. The variability in UV and gamma rays are relatively modest with $\sim 8 \%$ and $\sim 16 \%$ respectively, and no significant correlation was found with the X-ray light curve. The observed X-ray spectrum shows clear curvature which can be fit by a log parabolic spectral form. This is best explained to originate from a log parabolic electron spectrum. However, a broken power law or a power law with an exponentially falling electron distribution cannot be ruled out either. Moreover, the excellent broadband spectrum from $0.3-79$ keV allows us to make predictions of the UV flux. We find that this prediction is compatible with the observed flux during the low state in X-rays. However, during the X-ray flares, the predicted flux is a factor of $2-50$ smaller than the observed one. This suggests that the X-ray flares are plausibly caused by a separate population which does not contribute significantly to the radiation at lower energies. Alternatively, the underlying particle spectrum can be much more complex than the ones explored in this work.Comment: 11 pages, 7 figures, Accepted in A&

GENETIC DIVERSITY AND RANDOM AMPLIFIED POLYMORPHIC DNA ANALYSIS OF PESTALOTIA SP. ISOLATES OF ENDOPHYTES FROM DIFFERENT HOST

ABSTRACTObjective: The genetic diversity of fungal endophytes medicinally important plant leaves. Isolates fungi were genotypically compared by randomamplified polymorphic DNA (RAPD) techniques. The results indicate that RAPD can be employed for detecting genetic diversity of Pestalotia speciesfrom medicinal plants and for pre-selection of these isolates for bioactive screening program.Methods: Using different instrumental methods for isolation and identification of bioactive compounds and RAPD molecular method for detection oftaxol producing fungi.Results: Our studies also suggest diversity of endophytes as it differs in plant diversity. Pestalotia spp. are of considerable interest to researchers andpharmacists due to their capabilities of synthesizing a wide range of economically important bioactive molecules.Conclusion: RAPD markers did not differentiate and place the isolates into respective host or locations from which they were isolated. The relationshipbetween species isolated from the same or different hosts do not support phylogenetic analysis and also morphologically similar species form closerelationships rather than the isolates of the same host.Keywords: Genetic diversity, Endhophytic fungi, Pestslotia sp., Random amplified polymorphic DNA

EVALUTION OF IN VITRO ANTI-ANGIOGENESIS ACTIVITY ON METHANOLIC EXTRACT OF Clematis buchaniana PLANT

Objective: Angiogenesis plays an important role in embryonic development and various physiological processes. However, excessive angiogenesis is associated with several pathological conditions including cancer. Clematis is a genus of about 300 species within the buttercup family Ranunculaceae. It is native to the India, where has been used as folk medicine for the treatment of various ailments. This work is aimed to evaluate the antiangiogenesis activity in the crude methanolic extract of Clematis buchaniana plant.Methods: The entire aerial parts of C. buchaniana were extracted by soxhelation in methanol. Then, the solvent was evaporated to dryness to yield the dried crude extract of C. buchaniana. Then, the extract was subjected to preliminary phytochemical screening to determine the active constituents for effective pharmacological activity. The in-vitro antiangiogenesis effects were later evaluated using chorioallantoic membrane model carried out by incubation in fresh chicken's eggs.Results: The crude methanolic extract of C. buchaniana was found to have slight ability to inhibit angiogenesis that was evaluated by visualization.Conclusion: C. buchaniana plant extract inhibits angiogenesis by blocking normal vascularization in chick embryo. The ability of inhibiting angiogenic process in eggs by this extract can provide us an herbal anticancer agent in future for further scrutiny.Keywords: Antiangiogenesis, Chorioallantoic membrane, Incubation, Angiogenesis, Clematis buchaniana, Methanolic extract.Â

Pathoblockers or antivirulence drugs as a new option for the treatment of bacterial infections

The rapid development of antimicrobial resistance is threatening mankind to such an extent that the World Health Organization expects more deaths from infections than from cancer in 2050 if current trends continue. To avoid this scenario, new classes of antiinfectives must urgently be developed. Antibiotics with new modes of action are needed, but other concepts are also currently being pursued. Targeting bacterial virulence as a means of blocking pathogenicity is a promising new strategy for disarming pathogens. Furthermore, it is believed that this new approach is less susceptible towards resistance development. In this review, recent examples of anti-infective compounds acting on several types of bacterial targets, e.g., adhesins, toxins and bacterial communication, are described

Corporate Identity: Developing Means for Sustainable Competitive Advantage in Indian Context Towards Model Development

Corporate identity is a dynamic and premeditated asset integral to gaining competitive advantage in the market. It spans both the internal and external functions of the organisations. It is a variable that needs to be studied cohesively. However, the literature on corporate identity has studied the various elements in a non integrated manner. This needs to be replaced by a comprehensive understanding of the processes and nuances of corporate identity, which can help the organisations in developing sustainable competitive advantage. Additionally, this study focuses on digital media and tech savvy individuals from an emerging market: India. In order to achieve this objective, we interviewed 70 respondents from 20 companies and their consumers. Thus, we identified that corporate identity deals with strategic, emotional and social dimensions. This helped us develop a comprehensive model. Further, corporate identity was seen as a benchmark related to corporate challenges and expectations. Lastly, digital media need to work as a bonding platform among the stakeholders to ensure this

EFFECT OF ETHANOLIC EXTRACT OF CALOPHYLLUM INOPHYLLUM LEAVES ON OXIDATIVESTRESS COMPLICATIONS IN MOUSE MODEL

Objectives: To find out the effect of the extract of Calophyllum inophyllum leaves on the streptozotocin-induced oxidative stress complications in mice.Methods: The plant extract was first checked for its radical scavenging activity by the 2,2-diphenyl-2-picrylhydrazyl hydrate (DPPH) method andL-ascorbic acid was used as the standard. DPPH assay activity of the plant extract was found to be close to the standard drug ascorbic acid. Acutetoxicity was conducted as per OECD 425 guidelines. From the results obtained, 250, 300, and 350 mg/kg dose were chosen for further experimentation.After 16 days of drug treatment, glucose, cholesterol, triglyceride and enzymatic antioxidants levels were estimated in serum samples.Results: C. inophyllum leaves extract has significantly reduced the glucose, cholesterol, triglyceride and enzymatic antioxidants levels. Hence, thisproves that the plant has anti-diabetic property. However, vitamin E had no effect on the triglyceride level. Antioxidant activity was monitored bysuperoxide dismutase, catalase, glutathione peroxidase, malondialdehyde assay and it was found that the plant extract has effectively increased theantioxidant activity as the dose increases.Conclusion: C. inophyllum leaves extract have anti-diabetic activity and effective in curbing the oxidative stress complications.Keywords: Calophyllum inophyllum, Oxidative stress complications, Anti-diabetic activity, Free radicals

Skin Cancer Prediction Model Based on Multi-Layer Perceptron Network

Melanoma is acknowledged by the World Health Organization as the most severe type of skin cancer, significantly contributing to skin cancer-related deaths worldwide. This type of cancer manifests through noticeable changes in moles, including their size, shape, colour, or texture. In this study, we introduce an innovative and robust method for detecting and classifying melanoma in various image types, including both basic and clinical dermatological images. Our approach employs the HSV (Hue, Saturation, and Value) colour model, along with mathematical morphology and Gaussian filtering techniques. These methods are used to pinpoint the area of interest in an image and compute four key descriptors crucial for melanoma analysis: symmetry, border irregularity, colour variation, and dimension. Despite the prior usage of these descriptors over an extended period, the manner in which they are calculated in this proposal is a key factor contributing to the improvement of the outcomes. Following this, a multilayer perceptron is utilized for the purpose of categorizing malignant and benign melanoma. The study included three datasets consisting of basic and dermatological photographs that are frequently referenced in academic literature. These datasets were applied to both train and assess the effectiveness of the proposed technique. Based on the results obtained from k-fold cross-validation, it is evident that the proposed model surpasses three existing state-of-the-art approaches. In particular, the model demonstrates remarkable precision, with an accuracy rate of 98.5% for basic images and 98.6% for clinical dermatological images. It exhibits a high level of sensitivity, measuring 96.68% for simple images and 98.05% for dermatological images. Additionally, its specificity stands at 98.15% when analyzing basic images and 98.01% for dermatological images, indicating its effectiveness in both types of image analysis. The findings have demonstrated that the utilization of this gadget as an assistive tool for melanoma diagnosis would enhance levels of reliability in comparison to traditional methods