A Neuro Fuzzy Algorithm to Compute Software Effort Estimation

Software Effort Estimation is highly important and considered to be a primary activity in software project management The accurate estimates are conducted in the development of business case in the earlier stages of project management This accurate prediction helps the investors and customers to identify the total investment and schedule of the project The project developers define process to estimate the effort more accurately with the available mythologies using the attributes of the project The algorithmic estimation models are very simple and reliable but not so accurate The categorical datasets cannot be estimated using the existing techniques Also the attributes of effort estimation are measured in linguistic values which may leads to confusion This paper looks in to the accuracy and reliability of a non-algorithmic approach based on adaptive neuro fuzzy logic in the problem of effort estimation The performance of the proposed method demonstrates that there is a accurate substantiation of the outcomes with the dataset collected from various projects The results were compared for its accuracy using MRE and MMRE as the metrics The research idea in the proposed model for effort estimation is based on project domain and attribute which incorporates the model with more competence in augmenting the crux of neural network to exhibit the advances in software estimatio

Achalasia and hyperthyroidism: A cooccurrence revealing autoimmune connections

Achalasia is a rare esophageal motility disorder characterized by an unknown etiology, myenteric inflammation, and autoimmune markers. We present a case of a 43-year-old female with progressive dysphagia, regurgitation, generalized weakness, and hair loss. Despite normal cranial nerves and normal laboratory values, upper GI endoscopy revealed pangastritis, while manometry indicated type 2 achalasia cardia. The patient underwent laparoscopic Heller's myotomy for achalasia and received treatment for hyperthyroidism. The discussion explores the autoimmune aspects of achalasia and its rare association with hyperthyroidism. Early diagnosis and management are crucial for improving patient outcomes

Comparison of natural farming with organic and conventional farming practices in green gram-paddy cropping system

Natural farming system (NFS) is one of the traditional cultivation methods to cut down production costs as well as dependence on external inputs. Being considered as an agro-ecologically diverse farming practice, it brings a host of ecological and social benefits. In order to know the sustainance of natural farming practice, field experiments were conducted at Zonal Agriculture Research Station (ZARS), V.C. Farm, Mandya, Karnataka, India for consecutive years (2019 to 2022). The experiments were laid out in a randomized complete block design comprised of five replication and four different farming practices as treatments namely, absolute control (AC), organic production system (OPS), Natural farming system (NFS) and recommended package of practice (RPP) of UAS, GKVK, Bengaluru. The pooled data of farming practices indicated significant variation in growth, yield and nutrient uptake, among farming practices significantly higher growth, yield and nutrient uptake were recorded with RPP both in green gram and paddy. The results of four years pooled data indicated that compared to conventional farming practice, natural farming recorded decreased yield of 134 (23.53%) and 3350 kg ha-1 (74.49%) in green gram and paddy, respectively. Also recorded 33.38% and 30.23% weed control efficiency by mulching in green gram and paddy, respectively. Based on this study we found that low nutrient demanding crops such as green gram (Pulses) are more suitable under natural farming compared high nutrient demanding crops viz., Paddy. Yields under natural farming can be enhanced by application of Farm yard manure and other natural sources for plant nutrition

Optimization Of Adsorption Of Congo Red By Corn Cob Powder Using Support Vector Machine

Abstract Escalating environmental information is compelling waste initiators to bear in mind new alternatives like adsorption for the removal of dye in tinted waste water. Owing to outstanding expenses of commercially activated carbon (CAC), inexpensive adsorbent with high adsorption capacity have achieved growing consideration. The current study offers with exploitation of an inexpensive, waste adsorbent material of corn cob powder and enhancing the situations for removing the Congo red dye from an aqueous solution with the help of central composite design (CCD) experiment. UV-Visible Spectrophotometer is applied to establish the concentration of dye within the waste water. The surface uptake capability (SUC) of corn cob powder will increase when the initial concentration of dye, contact time and temperature becomes increased. The SUC decreases with increase in measure of adsorbent and pH level of the medium. Support Vector Machine (SVM) employing central composite design turned into used at the required mixtures of five self determining factors (dye attention, adsorbent dosage, contact time, pH and temperature). By using these subsequent circumstances, dye concentration 80 mg/L, adsorbent dosage 0.05 g/L, contact time 15 min, pH 7.0 and temperature 300C, we have achieved the maximum level of adsorption capacity as 50.0 mg/g. This research procedure will take long time to analyze and it is prolonged manner. But growing pollutants will motive severe harm to the environment. So it’s miles vital to pick out on fastest answer for this problem. In this proposed approach a Support Vector Machine based online solution is achieved for eliminating the Congo red dye from the aqueous solution. The SVM expected SUC is as compared with an experimental result. The accuracy of the proposed SVM Model has been predicted by the simulation result. The study specifies the corn cob powder becomes efficient and also an inexpensive opportunity for removing the Congo red dye.</jats:p