Application based technical Approaches of data mining in Pharmaceuticals, and Research approaches in biomedical and Bioinformatics

In the past study shows that flow of direction in the field of pharmaceutical was quit slow and simplest and by the time the process of transformation of information was so complex and the it was out of the reach to the technology, new modern technology could not reach to catch the pharmaceutical field. Then the later on technology becomes the compulsorily part of business and its contributed into business progress and developments. But now a days its get technology enabled and smoothly and easily pharma industries managing their billings and inventories and developing new products and services and now its easy to maintain and merging the drugs detail like its cost ,and usage with the patients records prescribe by the doctors in the hospitals .and data collection methods have improved data manipulation techniques are yet to keep pace with them data mining called and refer with the specific term as pattern analysis on large data sets used like clustering, segmentation and classification for helping better manipulation of the data and hence it helps to the pharma firms and industries this paper describes the vital role of data Mining in the pharma industry and thus data mining improves the quality of decision making services in pharmaceutical fields. This paper also describe a brief overviews of tool kits of Data mining and its various Applications in the field of Biomedical research in terms of relational approaches of data minings with the Emphasis on propositionalisation and relational subgroup discovery, and which is quit helpful to prove to be effective for data analysis in biomedical and its applications and in Bioinformatics as well. DOI: 10.17762/ijritcc2321-8169.15038

Learning Relations: Basing Top-Down Methods on Inverse Resolution

Abstract Top-down algorithms for relational learning specialize general rules until they are consistent, and are guided by heuristics of different kinds. In general, a correct solution is not guaranteed. By contrast, bottom-up methods are well formalized, usually within the framework of inverse resolution. Inverse resolution has also been used as an efficient tool for deductive reasoning, and here we prove that input refutations can be translated into inverse unit refutations. This result allows us to show that top-down learning methods can be also described by means of inverse resolution, yielding a unified theory of relational learning

Virtual Reality as a Diagnostic Tool: A Systematic Review of Studies using Virtual Reality Technology for the Assessment of Mild Cognitive Impairment and Dementia

Dementia, a devastating, progressive disease, is typically assessed using a combination of cognitive testing, physical examination and physical tasks that test the patient’s ability to engage in instrumental activities of daily living (IADL). These tests are designed to evaluate skills such as memory, general motor skills, gaze etc. that enable us to function. Clinical settings have limited access to space and tools that can be used to design said tests. Virtual reality is a cost-effective alternative that can be utilized in such settings to diagnose MCI and dementia. Unfortunately, the research in this area thus far is scarce. This paper aims to assess the feasibility of and strengths and weaknesses associated with using virtual reality simulations as a diagnostic tool to assess neurodegenerative conditions like MCI and dementia. It will involve a systematic look at preexisting literature and offer suggestions for future research based on its findings.Master of Science in Information Scienc

A Comparative Study of Three Neural-Symbolic Approaches to Inductive Logic Programming

An interesting feature that traditional approaches to inductive logic programming are missing is the ability to treat noisy and non-logical data. Neural-symbolic approaches to inductive logic programming have been recently proposed to combine the advantages of inductive logic programming, in terms of interpretability and generalization capability, with the characteristic capacity of deep learning to treat noisy and non-logical data. This paper concisely surveys and briefly compares three promising neural-symbolic approaches to inductive logic programming that have been proposed in the last five years. The considered approaches use Datalog dialects to represent background knowledge, and they are capable of producing reusable logical rules from noisy and non-logical data. Therefore, they provide an effective means to combine logical reasoning with state-of-the-art machine learning

A Comparative Study of Three Neural-Symbolic Approaches to Inductive Logic Programming

An interesting feature that traditional approaches to inductive logic programming are missing is the ability to treat noisy and non-logical data. Neural-symbolic approaches to inductive logic programming have been recently proposed to combine the advantages of inductive logic programming, in terms of interpretability and generalization capability, with the characteristic capacity of deep learning to treat noisy and non-logical data. This paper concisely surveys and briefly compares three promising neural-symbolic approaches to inductive logic programming that have been proposed in the last five years. The considered approaches use Datalog dialects to represent background knowledge, and they are capable of producing reusable logical rules from noisy and non-logical data. Therefore, they provide an effective means to combine logical reasoning with state-of-the-art machine learning

Recent Neural-Symbolic Approaches to ILP Based on Templates

Deep learning has been increasingly successful in the last few years, but its inherent limitations have recently become more evident, especially with respect to explainability and interpretability. Neural-symbolic approaches to inductive logic programming have been recently proposed to synergistically combine the advantages of inductive logic programming in terms of explainability and interpretability with the characteristic capability of deep learning to treat noisy, erroneous, and non-logical data. This paper surveys and briefly compares four relevant neural-symbolic approaches to inductive logic programming that have been proposed in the last five years and that use templates as an effective basis to learn logic programs from data

Recent Neural-Symbolic Approaches to ILP Based on Templates

Deep learning has been increasingly successful in the last few years, but its inherent limitations have recently become more evident, especially with respect to explainability and interpretability. Neural-symbolic approaches to inductive logic programming have been recently proposed to synergistically combine the advantages of inductive logic programming in terms of explainability and interpretability with the characteristic capability of deep learning to treat noisy, erroneous, and non-logical data. This paper surveys and briefly compares four relevant neural-symbolic approaches to inductive logic programming that have been proposed in the last five years and that use templates as an effective basis to learn logic programs from data

Meta-Interpretive Learning Versus Inductive Metalogic Programming : A Comparative Analysis in Inductive Logic Programming

Artificial intelligence and machine learning are fields of research that have become very popular and are getting more attention in the media as our computational power increases and the theories and latest developments of these fields can be put into practice in the real world. The field of machine learning consists of different paradigms, two of which are the symbolic and connectionist paradigms. In 1991 it was pointed out by Minsky that we could benefit from sharing ideas between the paradigms instead of competing for dominance in the field. That is why this thesis is investigating two approaches to inductive logic programming, where the main research goals are to, first: find similarities or differences between the approaches and potential areas where cross-pollination could be beneficial, and secondly: investigate their relative performance to each other based on the results published in the research. The approaches investigated are Meta-Interpretive Learning and Inductive Metalogic Programming, which belong to the symbolic paradigm of machine learning. The research is conducted through a comparative study based on published research papers. The conclusion to the study suggests that at least two aspects of the approaches could potentially be shared between them, namely the reversible aspect of the meta-interpreter and restricting the hypothesis space using the Herbrand base. However, the findings regarding performance were deemed incompatible, in terms of a fair one to one comparison. The results of the study are mainly specific, but could be interpreted as motivation for similar collaboration efforts between different paradigms

Meta-Interpretive Learning Versus Inductive Metalogic Programming : A Comparative Analysis in Inductive Logic Programming

Artificial intelligence and machine learning are fields of research that have become very popular and are getting more attention in the media as our computational power increases and the theories and latest developments of these fields can be put into practice in the real world. The field of machine learning consists of different paradigms, two of which are the symbolic and connectionist paradigms. In 1991 it was pointed out by Minsky that we could benefit from sharing ideas between the paradigms instead of competing for dominance in the field. That is why this thesis is investigating two approaches to inductive logic programming, where the main research goals are to, first: find similarities or differences between the approaches and potential areas where cross-pollination could be beneficial, and secondly: investigate their relative performance to each other based on the results published in the research. The approaches investigated are Meta-Interpretive Learning and Inductive Metalogic Programming, which belong to the symbolic paradigm of machine learning. The research is conducted through a comparative study based on published research papers. The conclusion to the study suggests that at least two aspects of the approaches could potentially be shared between them, namely the reversible aspect of the meta-interpreter and restricting the hypothesis space using the Herbrand base. However, the findings regarding performance were deemed incompatible, in terms of a fair one to one comparison. The results of the study are mainly specific, but could be interpreted as motivation for similar collaboration efforts between different paradigms