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In today’s world, people study and evaluate trading stocks to make informed decisions, based on available financial data and market information. Previous researchers relied on trend identification before making any decision to buy or sell stocks but fail to make accurate decisions due to complex systems. Some studies showed analysis to apply to stop loss on every stock transaction that got wrong levels due to limited features scaling that relied on single indicators without checking the performance metrics such as mean, standard deviation, and value at risk. Some existing models are based on theoretical implementation and they possess inaccurate success in real-time stock market transactions. Earlier risk management techniques were based on fundamental statistics of the company performance based on specific quarters that propose the future expects in the positive direction that is not every true which results in huge financial loss. Previous researchers failed to consider dynamic risk management parameters to ensure minimum loss for decision-making in fast-moving stock variations. Machine learning simply refers to learning about computers and making predictions from data. Identifying and analyzing the risk factors in the stock market are the major and crucial stage for predicting the company stock values at the national and international levels. In existing research, all risk management-related factors are analyzed based on fundamental statistics of the company performance which are measured as quarterly results, which will not give long-term true predictions and will not provide positive directions to invest in further stocks. This research majorly focused on risk management for national stock companies using the machine learning methodology and algorithms. The objective is to determine if stock market indicators are suitable decision-aid tools within the context of intraday risk management. The review of the literature revealed that while there are many studies looking to foresee changes in the stock market, there are few studies looking to improve stock market risk management methods using machine learning algorithms. The goal of this study was to fill this gap by utilizing the body of existing research on stock index forecasting combined with machine learning techniques for both short- and long-term risk managements. It has described the association between machine learning models and implicated the data with respect to discrete models based on supportive, dependable, nondependable parameters along with the name and type of the stock. This research has integrated a few crucial dependable parameters such as oil prices, on-hand projects, and future projects. It has integrated with the simple, multiple linear regression models to generate a signal for SPY growth. The proposed ML-based model has been evaluated by comparing two states of training and testing and achieved 96.3% of accuracy. The parameters used for evaluation are closing price, price differences, and daily return. The performance range of the proposed multiple regression model lies along the maximum drawn down which is 0.04411 for test cases and 1.2533 for training cases. Compare the performance of the proposed approach with that existing models with respect to the number of keys and methods associated with training and testing the data.

Seed grading is an important practice for better crop and also useful in separation of quality seeds in a seed lot. The aim of grading is to improve the uniformity of the seed lot by removing seeds of the same species with low quality. The present study was carried out to find out the optimum sieve size for size grading of seeds of barnyard millet (Echinochloa frumentacea L.) MDU1. The effect of seed size on physiological parameters were evaluated using BSS 10×10, BSS12×12 and BSS14×14 wire mesh sieves along with control. The barnyard millet seeds graded with BSS12×12 recorded higher seed recovery (76.67%) than other sieves used with required germination (92%), 1000 seed weight (3.9g), root length (14.1cm), shoot length (8.45cm), dry matter production (0.027g/10 seedlings) and vigour index (1983). Hence, seeds of barnyard millet MDU1 could be size graded using BSS12×12 sieve for more seed recovery with required seed quality standards as compared to BSS 10×10, BSS14×14 and ungraded seeds.

Background: Seed maturation is genetically controlled process involves a series of morphological and physiological changes extending from fertilization to independence from the mother plant. Horse gram (Macrotyloma uniflorum Lam.) is an under exploited legume and it is an inexpensive source of protein, rich in minerals and vitamins. However, information on harvesting time of horse gram seeds are still limited. Therefore, this study was carried out in horse gram to determine the physiological maturity in obtaining good quality of seeds for better planting value.Methods: The laboratory experiment was carried out at Department of Seed Science and Technology, Agricultural College and Research Institute, Tamil Nadu Agricultural University, Madurai, India to determine the appropriate time of harvesting horse gram var. Paiyur 2 seeds. The crop was raised as bulk in the field during rabi season of 2019 and the pods were harvested in interval of seven days from 7 days after anthesis (DAA) to 56 DAA and subjected for determinations of pod and seed characteristics.Result: The result revealed that, pod length and pod fresh weight of seeds showed a steady increase upto 35 DAA showed maximum pod length (4.76 cm) and fresh weight of pods (6.903 g/10 pods). The seeds attained maximum germination (94%), dry weight (0.859 g/25 seeds) and vigour parameters viz., root length (17.7 cm), shoot length (8.8 cm), dry matter production (0.161 g/10 seedlings), vigour index I (2491) and vigour index II (15) on 49 DAA.

Decades of documented successful pieces of evidence in agritech have shown us a clear picture of the importance of biotechnology in crop improvement. The production in agriculture should have steady growth and to achieve these objective conventional methods should go on parallel with biotechnological approaches. Genetic engineering which has revolutionized the path of crop improvement involves the identification and transfer of novel genes into the existing elite cultivars. Different methods of transferring the gene into plant cells have been developed and continuous efforts have been made to increase its efficiency. Both direct and indirect method of gene transfer has its own merits and demerits. Efforts have been made continuously to eliminate drawbacks and to develop an easy, elite and eco-friendly method to transfer genes. The transformation method which is a base of genetic engineering is vital and Agrobacterium-mediated gene transfer and gene gun have shown to be doing well in recent years. As a whole the methodology involved, merits and demerits of different methods have been briefly discussed.

Background: Ocular surface squamous neoplasia (OSSN) in a broader terminology recently introduced in the field of ophthalmology that includes conjunctival malignancies which ranges from mild epithelial dysplasia to invasive squamous cell carcinoma. The routinely used topical chemotherapy for OSSN is mitomycin C (MMC), 5-fluorouracil, interferon-alpha, and cidofovir, and among these, MMC is usually preferred by most of the ophthalmologists because of its cost-effectiveness and lesser side effects. Aim: The aim of study was to evaluate the role of MMC as an adjuvant therapy intraoperatively and postoperatively in the management of OSSN. Methodology: Patients in the age group of 40 years and above with a diagnosis of OSSN were included as our study subjects. The diagnosis of OSSN was made based on the clinical presentation and the cytological picture. A total of 22 patients with OSSN were taken as our study subjects. The tumor was surgically removed in toto along with 3–4 mm of uninvolved conjunctiva. Further, 0.4 mg/ml of MMC was applied over the excised site. Postoperatively, two cycles of 0.04% MMC eye drops were given for 2 weeks with a dosage of 4 times/day. It is given in two cycles, each cycle lasting for a week with an interval of 1 week in between. Results: Postoperatively, only one patient (4.5%) had recurrence which had developed after 6 months. Patients aged more than 60 years and who had the initial size of the lesion as > 8 mm were more prone to recurrence. Other postoperative complications that had occurred were one patient had allergic conjunctivitis and the other patient had punctate erosion. Conclusion: The use of MMC eye drop in the concentration of 0.04% has shown good clinical results without any serious side effects and with a very less recurrence rate during the follow-up period of 1 year when used as alternate 7-day courses.

Albeit the advent of fast computing facilities, digital image classification of remotely sensed data is still remain the topic of research. This might be due to the reason that the ancillary information such as texture and topography is absent in image classification. Since two decades, texture is widely applied in image classification but there is no explicit icon in most popularly used remote sensing software. Hence the aim of this study is to classify the Landsat ETM+ captured in 2000 using spectral information, topographic information and texture information. This study helps to throw light into statistical texture analysis i.e., the effect window size i.e., 3 × 3 to 9 × 9, on image classification. The ability of Grey Run Length Matrix (GRLM), which is computationally complex compared to industrially well-known Grey Level Co-occurrence Matrix (GLCM) but encompasses greater potential to discriminate between two classes, is explored. Eight spectral bands, 11 texture parameters extracted from Landsat ETM+ data and elevation, slope, aspect extracted from DEM data are classified individually using Artificial Neural Network (ANN) and the individually classified information is integrated using endorsement theory. Validations of classified results are performed using Google Maps and Landmap services updated in 2009. The results are compared with Maximum Likelihood classification (MLC) and hence all the evidence (spectral, texture and topography) with 5 × 5 texture window provided maximum classification accuracy of 70.44 %.

Investigations on the effect of seed pelleting and foliar spraying on physiology and productivity in sesame were done at the Department of Crop Physiology, Tamil Nadu Agricultural University, Coimbatore, Tamil Nadu during the year 2016–17. The study was conducted to understand the physiological basis for yield improvement in sesame. Sesame seeds were pelleted using TNAU pelleting mixture. Foliar treatments included urea 1 per cent, diammonium phosphate (DAP) spray 1 per cent, NPK (19: 19: 19) 1 per cent, manganese sulphate (MnSO4) 0.5 per cent, monoammonium phosphate (MAP) 0.5 per cent, nutrient consortium 2 per cent and salicylic acid 100 ppm. Foliar sprays were given on 30 and 45 DAS. In general pelleted seeds gave better seedling characters such as germination percentage and vigour. Plants that received two sprays of nutrient consortium were taller at physiological maturity followed by foliar spray of NPK (19: 19: 19). Nutrient consortium and NPK (19: 19: 19) treatments also recorded a higher CGR and TDM content. The plants that received nutrient consortium 2 per cent on 30 and 45 DAS showed superiority in recording higher seed yield per plant followed by NPK (19: 19: 19) in both the factors (non-pelleted and pelleted seeds). Control plants recorded least seed yield compared to other treatments in both the factors.