Disease Predictive Diagnostics Using Machine Learning

Big Data is collecting large amounts of data. That's big. What is Uncontrollable with the Conventional Method It is difficult to process this large amount of data in a conventional way. So there are many techniques to handle and analyze this huge amount of data. The challenge we face when storing this huge amount of data is analysis, sharing, storage, etc. Big data is difficult to master with the traditional approach, so there are different methods. Clustering and classification have played a significant role in countless applications such as cognitive services, image recognition and processing, business and law, text and speech, medicine, weather forecasting, genetics, bioinformatics and so on.    Some as of late settled machine learning approaches are introduced here, with the point of passing on vital ideas to order and grouping specialists.For this purpose, record the hospital data of a particular region. For missing data, use a latent factor model to obtain the incomplete data.The previous work on disease prediction uses the CNN-UDRP (Convolutional Neural Network Based Unimodel Disease Prediction) algorithm.The prediction of the CNN-MDRP algorithm is more accurate than in the previous prediction algorithm.

Design and development of three-row improved pull-type rice transplanter for small farmers

A pull-type transplanter was developed by improvising the functional components of existing six-row manual IRRI rice transplanter. The improved transplanter is capable of planting seedlings in three rows at 250 mm row spacing. Picker mechanism was designed in such a way to perform the planting operation simultaneously as the equipment is being pulled with handle. Thus the push-pull mechanism adopted in the existing transplanter has been eliminated in this improved model, reducing drudgery of operation. While evaluating the unit in different puddling conditions as well as textural conditions, the field capacity was observed as 0.058 ha/h with field efficiency of 85.4% at the draft of 261.7 N. Percentage of missing hills was found as 9.61 with the optimized growing density of seedling of 60 g/mat. The ground wheel diameter was optimized as 500 mm based on intra-row spacing as well as force requirement for pulling the unit. The picker-finger width of 3 mm was optimized as there was high mortality rate observed with other fingers. For the optimized picker-finger width, single seedling was observed in 12.1% hills, double seedlings in 30.4% hills, triple seedlings in 31.7% hills, and 25.8% hills are planted with more than three seedlings. By employing ergonomic consideration in seedling picking mechanism, the energy requirement with improved transplanter was reduced to 19.84 kJ/min from 26.41 as in existing transplanter, and hence there was 24.9% reduction in drudgery. Although energy cost with improved transplanter is graded as ‘heavy’, the unit can be operated effectively with the recommended rest-pause of 12.5 min for every 45 min. Cost of transplanting with the equipment worked out ` 1 150 per ha with a cost-saving of 80.8% in addition to time-saving of 91.3% compared to hand transplanting. The improved transplanter has good scope for introduction in marginal farms

Design and development of power-operated continuous-run potting machine for seedling-nursery

Potting is still a manual operation in plantation, forestry, and 11 other horticultural nurseries in India and the preparation of pot-mixture and filling in polybags are crucial tasks and are time consuming. Therefore, a poweroperated continuous-run machine was specially designed, fabricated, and tested to master seedling-nursery management capable of mixing, pulverizing, sieving, and filling of pot ingredients in polybags. The machine is a vertical freestanding unit mounted on four legs and consists of 3-hp motor, feed-hopper, pulverizing chamber with 8-numbers of paddles, sieving compartment operated by a slider-crank mechanism, vending instrumentation, and outlet. Ingredients like soil, sand, granite power, farmyard manure, and compost are fed from the top and the pot-mixture is collected at the bottom. Electronic vending is the novelty of the machine, which permits filling pot-mixture at set quantity at set time-gap. Aggregate analysis, degree of pulverization, and other physical parameters of machine-made mixture are at recommended level as well as on par with manually-made pot-mixture. More proportion (81.8%) of desirable level of aggregate was achieved with machine compared in manual method (79.5%) resulted in improved quality of the mixture for seedling establishment. Bagging through machine worked out 71.4% cost-saving and 80.2% time-saving. The machine is recommended for nursery-holders around the nation since the machine can provide pot-mixture for development of saplings of 30 000 numbers per month in a commercial nursery

Effect of turmeric and Spatoglossum asperum on shelf life extension of marine finfish Sillago sihama in chilled storage condition

829-838The effect of turmeric and seaweed powder (Spatoglossum asperum) on shelf life extension of Sillago sihama in chilled storage condition was determined by sensory, pH, biochemical and bacteriological analysis. The experimental setup was divided into six groups, undeveined, deveined, undeveined coated with 5 % S. asperum powder, deveined coated with 5 % S. asperum powder, undeveined coated with 5 % turmeric and deveined coated with 5 % turmeric, all the group of fishes were stored in chilled conditions with 1:1 (fish:ice) ratio. Deveined S. sihama coated with 5 % turmeric demonstrated a longer shelf life of 14 days and between the groups significant differences (P < 0.05) were found in the sensorial, pH, biochemical and bacteriological values. Nevertheless, the validity of group one and two were found to be acceptable up to 8 and 10 days, respectively. In conclusion, deveined S. sihama coated with 5 % turmeric and stored in chilled conditions retain the shelf-life up to 14 days

Ionospheric TEC Forecast Using LSTM during High-Intensity Solar Flares Occurred during the Year 2024 and Validation with IRI-2017

Satellite communication and navigation systems are increasingly essential in modern society, making it crucial to understand the impact of solar activity on these technologies. Total electron content (TEC) significantly influences satellite performance, necessitating accurate forecasting to maintain operational reliability. This research focuses on predicting TEC during eleven distinct X-class solar flares that occurred in February, March, May, June, and August 2024, utilizing a long short-term memory (LSTM) model. The study employs a comprehensive dataset of TEC data sourced from the IONOLAB database, alongside important solar and geomagnetic parameters such as Kp, Ap, SSN, and F10.7 obtained from NASA OmniWeb. The model’s predictive performance was validated against the IRI-2017 model. Results demonstrate that the LSTM model effectively captures TEC variations during periods of extreme solar activity, consistently outperforming the IRI-2017 model. For instance, during significant solar events, the LSTM model achieved notable performance metrics, indicating its capability to provide precise TEC forecasts. This research contributes to the advancement of space weather forecasting models, enhancing the reliability of satellite-dependent systems critical for global communication and navigation.</p

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Not AvailableSmall cardamom (Elettaria cardamomum Maton) is a major spice crop cultivated for its economic, culinary and medicinal values. Rhizome/clump rot, caused by Pythium vexans, Fusarium oxysporum and Rhizoctonia solani, is one of the destructive fungal diseases accounting to 30 per cent crop loss. Deployment of beneficial microbes possessing growth promotion activity and antagonistic potential against pathogens could be a viable and sustainable approach to nullify the deleterious effects of synthetic molecules on nature and to control the disease effectively. In this study, an effort was made to isolate the endosymbiotic fungi associated with allied genera of cardamom and evaluating their antagonistic efficacy under in vitro conditions against the rhizome rot pathogens. Among the endophytic fungi isolated, maximum inhibition of P. vexans was noticed in AsuL4 with 72.4 per cent, followed by HcoL1 with 60.3 per cent, while AmeR2 recorded maximum inhibition 65.3 per cent over control against R. solani followed by HcoL1 with 55.1 per cent inhibition. Among the 17 isolates tested against F. oxysporum, endophytes isolated from Amomum subulatum, AsuLV3 recorded maximum inhibition of 73.8 per cent followed by AsuL4 with 69.9 per cent. The shortlisted efficacious isolates need to be further evaluated under glasshouse and field conditions to confirm their efficacy and could be employed as integral components in cardamom production system to manage rhizome-root rot efficiently, economically and eco-friendly in a sustainable mannerNot Availabl