Estimation of Cost and Efforts in Agile Methodologies - A Review

The concept of Traditional software Development slowly started converting into a new methodology called as AGILE Methodology. Where in agile methodology the aim is to satisfy the customer, faster development times with less defects. Where as in traditional software development the effort and cost estimation methods are more when compared with Agile Methodology even though agile process is itself a software development process it has its own limitations generally used techniques or methods. In this paper we explain all the existing techniques which we discuss along with newly introduced methods. Keywords - Agile methodology, FPA, COCOMO, E

Knowledge-Based Analysis of Genomic Expression Data by Using Different Machine Learning Algorithms for the Purpose of Diagnostic, Prognostic or Therapeutic Application

With more and more biological information generated, the most pressing task of bioinformatics has become to analyze and interpret various types of data, including nucleotide and amino acid sequences, protein structures, gene expression profiling and so on. In this dissertation, we apply the data mining techniques of feature generation, feature selection, and feature integration with learning algorithms to tackle the problems of disease phenotype classification, clinical outcome and patient survival prediction from gene expression profiles. We analyzed the effect of batch noise in microarray data on the performance of classification. Batchmatch, a batch adjusting algorithm based on double scaling method is advantageous over Combat, another batch correcting algorithm based on the empirical bayes frame work. In order to identify genes associated with disease phenotype classification or patient survival prediction from gene expression data, we compared and analyzed the performance of five feature selection algorithms. Our observations from these studies indicated that Gainratio algorithm performs better and more consistently over the other algorithms studied. When it comes to performance metric to choose the best classifiers, MCC gives unbiased performance results over accuracy in some endpoints, where class imbalance is more. In the aspect of classification algorithms, no single algorithm is absolutely superior to all others, though SVM achieved fairly good results in most endpoints. Naive bayes algorithm also performed well in some endpoints. Overall, from the total 65 models we reported (5 top models for 13 end points) SVM and SMO (a variant of SVM) dominate mostly, also the linear kernel performed well over RBF in our binary classifications

Process improvement of Crane Compliance Using Agile Methodology

XYZ Corp has business operations spread all over world in Australia and North America. It owns nearly 20,500 cranes and the number been increasing daily resulting in difficulty in their maintenance and compliance check. This need to be handled and the process need to be automated for handling cranes compliance checks. Self-service should be enabled by implementing SSAS Tabular model

A DATA DRIVEN APPROACH TO IDENTIFY JOURNALISTIC 5WS FROM TEXT DOCUMENTS

Textual understanding is the process of automatically extracting accurate high-quality information from text. The amount of textual data available from different sources such as news, blogs and social media is growing exponentially. These data encode significant latent information which if extracted accurately can be valuable in a variety of applications such as medical report analyses, news understanding and societal studies. Natural language processing techniques are often employed to develop customized algorithms to extract such latent information from text. Journalistic 5Ws refer to the basic information in news articles that describes an event and include where, when, who, what and why. Extracting them accurately may facilitate better understanding of many social processes including social unrest, human rights violations, propaganda spread, and population migration. Furthermore, the 5Ws information can be combined with socio-economic and demographic data to analyze state and trajectory of these processes. In this thesis, a data driven pipeline has been developed to extract the 5Ws from text using syntactic and semantic cues in the text. First, a classifier is developed to identify articles specifically related to social unrest. The classifier has been trained with a dataset of over 80K news articles. We then use NLP algorithms to generate a set of candidates for the 5Ws. Then, a series of algorithms to extract the 5Ws are developed. These algorithms based on heuristics leverage specific words and parts-of-speech customized for individual Ws to compute their scores. The heuristics are based on the syntactic structure of the document as well as syntactic and semantic representations of individual words and sentences. These scores are then combined and ranked to obtain the best answers to Journalistic 5Ws. The classification accuracy of the algorithms is validated using a manually annotated dataset of news articles

Comparative Degradation of LDPE, HDPE and HMHDPE under Different Soil Conditions

The Present work includes, degradation of polyethylene under different environmental conditions to know the effect of physical, chemical and biological factors prevailing in those conditions on degradation of polyethylene. Plastic films viz., Low density polyethylene (LDPE), High density polyethylene (HDPE) and High molecular weight high density polyethylene (HMHDPE) each were incubated in three different conditions viz., black soil, sandy soil and red soil for a period of 3 months. The changes in the properties of plastic films after incubation was studied by change in the weight of the plastic film and mechanical parameters like tensile strength, breaking load and percentage of elongation. Among all the plastic films HDPE was found to be highly susceptible with 33% weight loss and 40% reduction in percentage of elongation compared to LDPE with 26% weight loss and 34 % reduction in percentage of elongation in black soil. Whereas HMHDPE found to be highly resistant in all the soils with no significant weight loss and percentage of elongation (15%). None of the plastic films had shown degradation in sandy soil even after incubation for 3 months. FTIR spectroscopy results showed that HDPE film incubated in black soil had undergone extensive degradation when compared with un incubated HDPE film

The Influence of Friction Time on the Joint Interface and Mechanical Properties in Dissimilar Friction Welds

The welding of dissimilar materials is one of the challenging issues in thefabrication industry to obtain required quality welds using fusion weldingmethods. However, some processes recently improved interface bondingwith low joint strength. Unfortunately, the major intermetallic compoundscould not alleviate from the joint interface. Alternatively, solid-statewelding methods revealed fewer intermetallics at the joint interface fordissimilar material welds. Among them, friction welding was chosen to joinincompatible materials with the necessary properties successfully. Frictiontime is a critical parameter for obtaining strong welds through frictionwelding, apart from friction pressure, forging pressure, forging time, androtational speed. Variability of friction time can change the strength offriction by changing mechanical properties such as tensile strength. Thischange of tensile strength is typically influenced by the intermixing region,dependent on friction time. In this experiment, carbon steel and stainlesssteel have been friction welded to test the impact of friction time on thejoint interface where the substrate’s faying surface meets. This interfaceconsists of the intermixing region of the two materials on which the frictionwelding is performed. The results showed an interesting variation in tensilestrength, with varying friction time. The width of the intermixing zoneincreased gradually with friction time until and decreased with the furtherincreasing. The strength of the welds obtained was the highest of 730 MPaat a friction time of 4 s and fell as friction time’s increased value after 4 s

A -Comprehensive Review of Integrating Medicinal Plant Constituents into Neurological Disorder Therapeutics

Neurological diseases are disorders of the nervous system that gradually destroy the structure and function of the central or peripheral nervous system, affecting various parts of the brain. It affects a great number of individuals globally and is one of the main causes of death. Many disorders, particularly neurological diseases that have resisted conventional medical treatment have been treated with plant-based medications. Alzheimer's disease (AD) and Parkinson's disease (PD) are two of the most common neurological disorders (NDDs) and have a significant socioeconomic impact. Over many ages, people have looked to natural herbal remedies for a solution for non-divertic disorders. Numerous therapeutic plants and their secondary metabolites have been shown to be able to reduce the symptoms of NDDs. The main mechanisms that have been found to underpin phytochemicals' neuroprotective effects and potential to maintain neurological health as we age include inhibition of acetylcholinesterase and monoamine oxidase, antioxidant, anti-inflammatory, antithrombotic, and antiapoptotic properties, as well as neurotrophic activities. This article examines the molecular targets of some of the main herbal products that have the potential to cure non-communicable diseases (NDDs). Numerous investigations proved that plant extracts or their bioactive components were effective against non-disease-causing bacteria. Millions of individuals worldwide suffer from age-related NDDs, and herbal products may provide new avenues for therapy

Impact of COVID 19 on Steel Industry – A case Study of RINL, Visakhapatnam Steel Plant

Steel is by far, the most widely used and is environment friendly as it can be recycled 100%. Steel has wide variety of applications in day to day life. Indeed, steel is the backbone and support of the global economy and infrastructure. Steel has got many forward linkages with manufacturing industries. Status of global steel industry before the onset of COVID 19 and the effect of pandemic on industry in both global and Indian contexts were discussed. The status of Indian economy was briefly explained and projected outlook for steel market after breakout of the pandemic. The effect of COVID on RINL was discussed in detail and various strategies adopted by the industry during the period were explained. Handling of such future eventualities were addressed. After the outbreak, the recovery and outlook were explained in detail. Interventions and initiatives sought from the Government were presented in this paper