Partial Context Similarity of Gene/Proteins in Leukemia Using Context Rank Based Hierarchical Clustering Algorithm

In this paper we proposed a method which avoids the choice of natural language processing tools such as pos taggers and parsers reduce the processing overhead. Moreover, we suggest a structure to immediately create a large-scale corpus annotated along with disease names, which can be applied to train our probabilistic model. In this proposed work context rank based hierarchical clustering method is applied on different datasets namely colon, Leukemia, MLL medical diseases. Optimal rule filtering algorithm is applied on these datasets to remove unwanted special characters for gene/protein identification. Finally, experimental results show that proposed method outperformed existing methods in terms of time and clusters space

Graph based gene/protein prediction and clustering over uncertain medical databases.

Clustering over protein or gene data is now a popular issue in biomedical databases. In general, large sets of gene tags are clustered using high computation techniques over gene or protein distributed data. Most of the traditional clustering techniques are based on subspace, hierarchical and partitioning feature extraction. Various clustering techniques have been proposed in the literature with different cluster measures, but their performance is limited due to spatial noise and uncertainty. In this paper, an improved graph-based clustering technique is proposed for the generation of efficient gene or protein clusters over uncertain and noisy data. The proposed graph-based visualization can effectively identify different types of genes or proteins along with relational attributes. Experimental results show that the proposed graph model more effectively clusters complex gene or protein data when compared with conventional clustering approaches

Context rank based hierarchical clustering algorithm on medical databases (CRBHCA).

In this paper we propose a method that aims to reduce processing overheads by avoiding the need to choose between natural language processing tools such as part-of-speech taggers and parsers. Moreover, we suggest a structure for the immediate creation of a large-scale, annotated corpus with disease names, which can be applied to train our probabilistic model. In this proposed work, a context rank-based hierarchical clustering method is applied on different datasets relating to colon diseases, leukemia, mixed-lineage leukemia (MLL) and lymphoma medical diseases. An optimal rule-filtering algorithm is applied on these datasets to remove unwanted special characters for gene/protein identification. Finally, experimental results show that our proposed method outperformed existing methods in terms of time and clusters space

A compressive survey on different image processing techniques to identify the brain tumor.

Medical imaging technology has revolutionized health care over the past three decades, allowing doctors to detect, cure and improve patient outcomes. Medicinal imaging involves pictures - of internal organs, parts, tissues and bones - for therapeutic examination and research purposes. X-ray and CT scanners are the two greatest results of progress in imaging methods supplanting 2D procedures. Magnetic resonance imaging (MRI) is an imaging procedure that is utilized in radiology to visualize interior structures of the body and better understand how they work. X-ray provides a 3D image of the body's interior; as well as being critical for tumor discovery, this also enables surgeons to more easily dissect infections or tumors than was possible with older X-beam technology, which provided a 2D image. This paper provides an overview of different systems that can be used for distinguishing and preparing medical images

An efficient face recognition system using local binary pattern.

Facial recognition is a critical and prominent aspect of current research into image processing and computer vision, with particular applications including confront location, confront acknowledgement and outward appearance investigation. A basic advancement towards fruitful facial picture examination is to infer a viable facial portrayal from the first face pictures. Local Binary Patterns (LBP) have recently gained increased attention as an approach for facial depiction. Neighborhood double example (LBP) is a nonparametric descriptor, which proficiently abridges the nearby structures of pictures. In this paper, there will be a complete overview of LBP and an explanation of extentions of that concept. LBP-based facial picture examination is broadly evaluated, while its fruitful expansions (which manage different aspects of facial picture investigation) are additionally featured

Optimizing webpage relevancy using page ranking and content based ranking.

Systems for web information mining can be isolated into a few classifications, depending on the type of target data and the purposes of the activity: Web structure mining; Web utilization mining; and Web Content Mining. This paper proposes another Web Content Mining system for page significance positioning, taking into account the page content investigation. The strategy, Page Content Rank (PCR), consolidates various heuristics that appear to be critical for breaking down the substance of Web pages. The page significance is resolved on the base of the significance of terms that the page contains. The significance of a term is determined concerning a given inquiry "q", and it depends on its measurable and linguistic elements. As a source set of pages for mining, we utilize an arrangement of pages retrieved by a web search tool to the question "q". PCR utilizes a neural system as its inward order structure. We depict a usage of the proposed strategy and an examination of its outcomes with the other existing characterization framework - page rank algorithm

Food survey using exploratory data analysis.

A person's eating habits are the most important aspect of maintaining one's physical wellbeing, which in turn is key to enduring the stresses and emotional hurdles that are so commonplace in our modern lifestyles. Our research shows that, over the past 33 years, the global obesity rate has increased by 27.5%. Moreover, although many people are overweight or obese, most still believe that their eating habits are healthy. This research aimed to further identify which eating habits people consider to be healthy

Speech to text translation enabling multilingualism.

Speech acts as a barrier to communication between two individuals and helps them in expressing their feelings, thoughts, emotions, and ideologies among each other. The process of establishing a communicational interaction between the machine and mankind is known as Natural Language processing. Speech recognition aids in translating the spoken language into text. We have come up with a Speech Recognition model that converts the speech data given by the user as an input into the text format in his desired language. This model is developed by adding Multilingual features to the existent Google Speech Recognition model based on some of the natural language processing principles. The goal of this research is to build a speech recognition model that even facilitates an illiterate person to easily communicate with the computer system in his regional language

Calculating the impact of event using emotion detection.

Regardless of the extraordinary advancements in artificial intelligence, we are still far from having the capacity to normally associate with machines. Feature analysis in emotion recognition is significantly less concentrated than the facial recognition. In events like lectures and meetings, it is common for speakers to request feedback in the form of reviews; however, sometimes people do not have sufficient time to adequately write down all their opinions about the event. We suggest that using an AI system, it would be possible to assess an audience's emotional state over the course of an event without needing to ask them to write down their feedback

Social media survey using decision tree and naive Bayes classification.

Social media - a website or an application that is used to create and share content among a social network - is one of the most important aspects of our day-to-day life. Recent studies claim that an average person spends roughly 142 minutes per day on some form of social media, representing a significant increase over the past few years. The purpose of this study was to learn what types of social media platforms people prefer and to evaluate how secure people feel on each social media platform