No abstract

No abstract

Artificial neural networks (ANNs) are applied in engineering and certain medical fields. ANN has immense potential and is rarely been used in breast lesions. In this present study, we attempted to build up a complete robust back propagation ANN model based on cytomorphological data, morphometric data, nuclear densitometric data, and gray level co-occurrence matrix (GLCM) of ductal carcinoma and fibroadenomas of breast cases diagnosed on fine-needle aspiration cytology (FNAC). We selected 52 cases of fibroadenomas and 60 cases of infiltrating ductal carcinoma of breast diagnosed on FNAC by two cytologists. Essential cytological data was quantitated by two independent cytologists (SRM, PD). With the help of Image J software, nuclear morphomeric, densitometric, and GLCM features were measured in all the cases on hematoxylin and eosin-stained smears. With the available data, an ANN model was built up with the help of Neurointelligence software. The network was designed as 41-20-1 (41 input nodes, 20 hidden nodes, 1 output node). The network was trained by the online back propagation algorithm and 500 iterations were done. Learning was adjusted after every iteration. ANN model correctly identified all cases of fibroadenomas and infiltrating carcinomas in the test set. This is one of the first successful composite ANN models of breast carcinomas. This basic model can be used to diagnose the gray zone area of the breast lesions on FNAC. We assume that this model may have far-reaching implications in future.

Cumin is one of the important spice crops grown in arid and semi arid regions of India and is being adopted to cure some of the dreaded diseases. Determination of optimum water requirement which is function of soil crop and atmosphere is needed for achieving more profit and higher productivity per unit of water. Keeping in view, a field experiment was undertaken to access the conjugate impact of three irrigation regimes (0.6IW/ETc, 0.8IW/ETc and1.0IW/ETc) and three lateral spacing (0.60m, 0.70m and 0.80m) on productivity of cumin. Split plot design with three treatment replications was adopted. Drip irrigation with 0.8 IW/ETc resulted higher seed yield, plant height and dry matter of 1344.17 kg/ha, 36.42 cm and 2365 kg/ha respectively at 0.8 IW/ETc with lateral spacing 0.6 m as compared to other treatments. Highest water use efficiency (5.58 kg/ha.mm) was observed at 0.6 IW/ETc with 0.60 m lateral spacing. Highest B:C ratio (2.27) observed at 0.8 IW/ETcwith lateral spacing 0.6m as compared to other treatments.

The process of evapotranspiration (ET) is a vital part of the water cycle. Exact estimation of the value of ET is necessary for designing irrigation systems and water resources management. Accurate estimation of ET is essential in agriculture, its over-estimation leads to cause the waste of valuable water resources and its underestimation leads to the plant moisture stress and decrease in the crop yield. The well known Penman-Monteith (PM) equation always performs the highest accuracy results of estimating reference Evapotranspiration (ET0) among the existing methods is without any discussion. However, the equation requires climatic data that are not always available particularly for a developing country. ET0 is a complex process which is depending on a number of interacting meteorological factors, such as temperature, humidity, wind speed, and radiation. The lack of physical understanding of ET0 process and unavailability of all appropriate data results in imprecise estimation of ET0. Over the past two decades, artificial neural networks (ANNs) have been increasingly applied in modeling of hydrological processes because of their ability in mapping the input–output relationship without any understanding of physical process. This paper investigates for the first time in the semiarid environment of Junagadh, the potential of an artificial neural network (ANN) for estimating ET0 with limited climatic data set.