A Review: Effort Estimation Model for Scrum Projects using Supervised Learning

Effort estimation practice in Agile is a critical component of the methodology to help cross-functional teams to plan and prioritize their work. Agile approaches have emerged in recent years as a more adaptable means of creating software projects because they consistently produce a workable end product that is developed progressively, preventing projects from failing entirely. Agile software development enables teams to collaborate directly with clients and swiftly adjust to changing requirements. This produces a result that is distinct, gradual, and targeted. It has been noted that the present Scrum estimate approach heavily relies on historical data from previous projects and expert opinion, while existing agile estimation methods like analogy and planning poker become unpredictable in the absence of historical data and experts. User Stories are used to estimate effort in the Agile approach, which has been adopted by 60–70% of the software businesses. This study's goal is to review a variety of strategies and techniques that will be used to gauge and forecast effort. Additionally, the supervised machine learning method most suited for predictive analysis is reviewed in this paper

MCI Detection using fMRI time series embeddings of Recurrence plots

The human brain can be conceptualized as a dynamical system. Utilizing resting state fMRI time series imaging, we can study the underlying dynamics at ear-marked Regions of Interest (ROIs) to understand structure or lack thereof. This differential behavior could be key to understanding the neurodegeneration and also to classify between healthy and Mild Cognitive Impairment (MCI) subjects. In this study, we consider 6 brain networks spanning over 160 ROIs derived from Dosenbach template, where each network consists of 25-30 ROIs. Recurrence plot, extensively used to understand evolution of time series, is employed. Representative time series at each ROI is converted to its corresponding recurrence plot visualization, which is subsequently condensed to low-dimensional feature embeddings through Autoencoders. The performance of the proposed method is shown on fMRI volumes of 100 subjects (balanced data), taken from publicly available ADNI dataset. Results obtained show peak classification accuracy of 93% among the 6 brain networks, mean accuracy of 89.3% thereby illustrating promise in the proposed approach.Comment: 4 pages, 5 figure

Image complexity based fMRI-BOLD visual network categorization across visual datasets using topological descriptors and deep-hybrid learning

This study proposes a new approach that investigates differences in topological characteristics of visual networks, which are constructed using fMRI BOLD time-series corresponding to visual datasets of COCO, ImageNet, and SUN. A publicly available BOLD5000 dataset is utilized that contains fMRI scans while viewing 5254 images of diverse complexities. The objective of this study is to examine how network topology differs in response to distinct visual stimuli from these visual datasets. To achieve this, 0- and 1-dimensional persistence diagrams are computed for each visual network representing COCO, ImageNet, and SUN. For extracting suitable features from topological persistence diagrams, K-means clustering is executed. The extracted K-means cluster features are fed to a novel deep-hybrid model that yields accuracy in the range of 90%-95% in classifying these visual networks. To understand vision, this type of visual network categorization across visual datasets is important as it captures differences in BOLD signals while perceiving images with different contexts and complexities. Furthermore, distinctive topological patterns of visual network associated with each dataset, as revealed from this study, could potentially lead to the development of future neuroimaging biomarkers for diagnosing visual processing disorders like visual agnosia or prosopagnosia, and tracking changes in visual cognition over time

Ragi Traditional But Nutritional Especially in the Era of COVID-19

Finger millet is the name commonly used to denote the crop Eleusine coracana. It is known as Ragi in many parts of India, which is an important member of the family of cereals. In fact, it is superior to many cereals like wheat and rice in terms of its micronutrient content and bioavailability. Several indigenous processing techniques may be applied to finger millets allowing it to be processed into various value-added products, which may be better in appearance, taste, flavor and acceptability. Development of value-added products that contain Ragi as one of their major components can be beneficial for food and nutrition security of Indians. Ragi may contribute to solving the issue of micronutrient deficiency and nutrition security as it is an important source of micronutrients and can be easily incorporated in various recipes and value-added products. It can therefore be a part of various nutritional programs to enhance the nutritional density of foods

Analyze the Performance of Software by Machine Learning Methods for Fault Prediction Techniques

Trend of using the software in daily life is increasing day by day. Software system development is growing more difficult as these technologies are integrated into daily life. Therefore, creating highly effective software is a significant difficulty. The quality of any software system continues to be the most important element among all the required characteristics. Nearly one-third of the total cost of software development goes toward testing. Therefore, it is always advantageous to find a software bug early in the software development process because if it is not found early, it will drive up the cost of the software development. This type of issue is intended to be resolved via software fault prediction. There is always a need for a better and enhanced prediction model in order to forecast the fault before the real testing and so reduce the flaws in the time and expense of software projects. The various machine learning techniques for classifying software bugs are discussed in this paper

Interpretable simultaneous localization of MRI corpus callosum and classification of atypical Parkinsonian disorders using YOLOv5

Structural MRI(S-MRI) is one of the most versatile imaging modality that revolutionized the anatomical study of brain in past decades. The corpus callosum (CC) is the principal white matter fibre tract, enabling all kinds of inter-hemispheric communication. Thus, subtle changes in CC might be associated with various neurological disorders. The present work proposes the potential of YOLOv5-based CC detection framework to differentiate atypical Parkinsonian disorders (PD) from healthy controls (HC). With 3 rounds of hold-out validation, mean classification accuracy of 92% is obtained using the proposed method on a proprietary dataset consisting of 20 healthy subjects and 20 cases of APDs, with an improvement of 5% over SOTA methods (CC morphometry and visual texture analysis) that used the same dataset. Subsequently, in order to incorporate the explainability of YOLO predictions, Eigen CAM based heatmap is generated for identifying the most important sub-region in CC that leads to the classification. The result of Eigen CAM showed CC mid-body as the most distinguishable sub-region in classifying APDs and HC, which is in-line with SOTA methodologies and the current prevalent understanding in medicine

New and improved method of bamboo cultivation in semi arid areas of Indian Thar desert

Bamboo (Dendrocalamus strictus Roxb.) is widely utilized in construction, pulp and paper, furniture, food and medicine, fuel and handicrafts for a long time. Due to its wider application, a field experiment was carried out to check its cultivation requirements besides its success rate in semi arid area of Indian Thar desert. In the present work, Guggul (Commiphora wightii Arnott.) which is a resident plant of Thar desert has been proved as a potential intercrop in bamboo cultivation. Improved growth was observed in bamboo with plant height (8.92 to 20.74 feet), number of culms (19 to 38), culm diameter (2.2 to 4.3 cm) during intercropping of guggul. Among different methods of irrigation, highest growth was recorded in drip irrigated plants where 50% recommended N:P:K and organic manure were given in combination followed by N:P:K sole. This study indicates that Guggul may play a role in microclimate development in the bamboo cultivation. This is the first report on successful bamboo cultivation in semi arid area of desert using an intercrop.Key words: Dendrocalamus, soil enzymes, fertilization, guggul, nitrogen

Dissecting the morphology of star forming complex S193

We have studied a star-forming complex S193 using near-infrared (NIR) observations and other archival data covering optical to radio wavelengths. We identified stellar clusters in the complex using the NIR photometric data and estimated the membership and distance of the clusters. Using the mid-infrared (MIR) and far-infrared (FIR) images, the distribution of the dust emission around H\,{\sc ii} regions is traced in the complex. The $Herschel$ column density and temperature maps analysis reveal 16 cold dust clumps in the complex. The H$\alpha$ image and 1.4 GHz radio continuum emission map are employed to study the ionised gas distribution and infer the spectral type and the dynamical age of each H\,{\sc ii} region/ionised clump in the complex. The $^{12}$CO(J =3$-$2) and $^{13}$CO(J =1$-$0) molecular line data hint at the presence of two velocity components around [-43,-46] and [-47,-50] km/s, and their spatial distribution reveals two overlapping zones toward the complex. By investigating the immediate surroundings of the central cluster [BDS2003]57 and the pressure calculations, we suggest that the feedback from the massive stars seems responsible for the observed velocity gradient and might have triggered the formation of the central cluster [BDS2003]57.}Comment: Accepted for publication in MNRAS, 20 pages, 15 figure

Post-outburst evolution of bonafide FUor V2493 Cyg: A Spectro-photometric monitoring

We present here the results of eight years of our near-simultaneous optical/near-infrared spectro-photometric monitoring of bonafide FUor candidate `V2493 Cyg' starting from 2013 September to 2021 June. During our optical monitoring period (between October 16, 2015 and December 30, 2019), the V2493 Cyg is slowly dimming with an average dimming rate of $\sim$26.6 $\pm$ 5.6 mmag/yr in V band. Our optical photometric colors show a significant reddening of the source post the second outburst pointing towards a gradual expansion of the emitting region post the second outburst. The mid infra-red colors, on the contrary, exhibits a blueing trend which can be attributed to the brightening of the disc due to the outburst. Our spectroscopic monitoring shows a dramatic variation of the H$\alpha$ line as it transitioned from absorption feature to the emission feature and back. Such transition can possibly be explained by the variation in the wind structure in combination with accretion. Combining our time evolution spectra of the Ca II infra-red triplet lines with the previously published spectra of V2493 Cyg, we find that the accretion region has stabilised compared to the early days of the outburst. The evolution of the O I $\lambda$7773 \AA~ line also points towards the stabilization of the circumstellar disc post the second outburst.Comment: 34 pages, 12 figures, 6 tables, accepted for publication in Ap