This document discusses biologically-inspired computing (BIC) and proposes that BIC efforts can be viewed cohesively within the framework of information technology paradigms using a concept called "digital ecology". It suggests digital ecology provides a unified cyberspace where various approaches of BIC, like neural networks, genetic algorithms, and artificial life, can be modeled as interacting "digital species" within a complex, open computational environment. The document explores how key aspects of digital ecology, such as species, environment, and self-organization, can be applied to conceptualize BIC as a complex adaptive system.
Molecules of Knowledge: Self-Organisation in Knowledge-Intensive EnvironmentsStefano Mariani
Molecules of Knowledge (MoK) is a coordination model supporting self-organisation of knowledge in Knowledge Intensive Environments (KIE). Usual approaches to knowledge management in KIE consider data as a passive, "dead" entity and rely on "brute force" approaches assuming an ever-increasing computational power and storage capacity (e.g. big data). This won't scale forever, thus alternative approaches should be explored. MoK promotes the vision of data as a "live" thing, continuously and spontaneously interacting and evolving---self-organising. Accordingly, MoK relies on features such as locality, probability and situatedness to tackle KIE challenges such as scale, openness and unpredictability. In this seminar, the MoK model is motivated and introduced, then some early "evaluation" described.
1. The document summarizes a 1997 paper by Dr. Tefko Saracevic discussing the history and goals of information science and information retrieval. It describes how information science aims to organize knowledge and make relevant information accessible to users.
2. Saracevic outlines two approaches to information retrieval - a systems-centered approach focused on algorithms and a human-centered approach prioritizing user studies. He also discusses the "natural limits" of perfectly satisfying all user information needs.
3. The document reviews the origins and specialties within information science, from Vannevar Bush's 1945 proposal of an early information management system to the different educational models of Shera and Salton regarding integrating IR into library science or computer
This document discusses a call for proposals on self-awareness in autonomic systems. The call aims to create systems that can optimize performance and resource usage in response to changing conditions through awareness of both internal changes and external context. Key areas of research include developing awareness at the node level and enabling systems to dynamically adapt abstraction levels using self-awareness. The background discusses challenges around managing increasingly complex systems and the potential for awareness, autonomy, distribution and learning to address these challenges.
This document summarizes the 5th IEEE International Conference on Cognitive Infocommunications (CogInfoCom 2014) which was held in Vietri sul Mare, Italy from November 5-7, 2014. It provides information on the chairs, institutions, statistics on authors from various countries, definitions of cognitive infocommunications and topics welcome at the conference. Emerging topics in cognitive infocommunications discussed at the conference included socio-cognitive ICT, cognitive biases, mathability, speechability, cognitive control theory, and cognitive infocommunications aided industrial applications. The document concludes by mentioning the next generation of CogInfoCom conferences including 3D augmented conferences and the VirCA association for cognitive infoc
The document summarizes details about the CogInfoCom 2014 conference held in Vietri sul Mare, Italy from November 5-7, 2014. It lists the chairpersons and supporting institutions. It provides statistics that 287 authors attended from various countries in Europe, Asia, North and South America, and the Middle East. The document defines cognitive infocommunications and lists topics that were welcome at the conference, including cognitive sciences, human-computer interaction, and emerging topics like socio-cognitive ICT and cognitive biases in CogInfoCom systems.
Artificial neural networks are fundamental means for providing an attempt at modelling the information
processing capabilities of artificial nervous system which plays an important role in the field of cognitive
science. This paper focuses the features of artificial neural networks studied by reviewing the existing research
works, these features were then assessed and evaluated and comparative analysis. The study and literature
survey metrics such as functional capabilities of neurons, learning capabilities, style of computation, processing
elements, processing speed, connections, strength, information storage, information transmission,
communication media selection, signal transduction and fault tolerance were used as basis for comparison. A
major finding in this paper showed that artificial neural networks served as the platform for neuron computing
technology in the field of cognitive science.
Molecules Of Knowledge: Self-Organisation In Knowledge-Intensive EnvironmentsStefano Mariani
This talk discusses the paper “Molecules of Knowledge: Self-Organisation in Knowledge-Intensive Environments”, presented at the 6th International Symposium on Intelligent Distributed Computing (IDC 2012).
Molecules of Knowledge: Self-Organisation in Knowledge-Intensive EnvironmentsAndrea Omicini
We propose a novel self-organising knowledge-oriented model based on biochemical tuple spaces, called Molecules of Knowledge (MoK). We introduce MoK basic entities, define its computational model, and discuss its mapping on the TuCSoN coordination model for its implementation.
[IDC 2012, Calabria, Italy, 24/9/2012
Molecules of Knowledge: Self-Organisation in Knowledge-Intensive EnvironmentsStefano Mariani
Molecules of Knowledge (MoK) is a coordination model supporting self-organisation of knowledge in Knowledge Intensive Environments (KIE). Usual approaches to knowledge management in KIE consider data as a passive, "dead" entity and rely on "brute force" approaches assuming an ever-increasing computational power and storage capacity (e.g. big data). This won't scale forever, thus alternative approaches should be explored. MoK promotes the vision of data as a "live" thing, continuously and spontaneously interacting and evolving---self-organising. Accordingly, MoK relies on features such as locality, probability and situatedness to tackle KIE challenges such as scale, openness and unpredictability. In this seminar, the MoK model is motivated and introduced, then some early "evaluation" described.
1. The document summarizes a 1997 paper by Dr. Tefko Saracevic discussing the history and goals of information science and information retrieval. It describes how information science aims to organize knowledge and make relevant information accessible to users.
2. Saracevic outlines two approaches to information retrieval - a systems-centered approach focused on algorithms and a human-centered approach prioritizing user studies. He also discusses the "natural limits" of perfectly satisfying all user information needs.
3. The document reviews the origins and specialties within information science, from Vannevar Bush's 1945 proposal of an early information management system to the different educational models of Shera and Salton regarding integrating IR into library science or computer
This document discusses a call for proposals on self-awareness in autonomic systems. The call aims to create systems that can optimize performance and resource usage in response to changing conditions through awareness of both internal changes and external context. Key areas of research include developing awareness at the node level and enabling systems to dynamically adapt abstraction levels using self-awareness. The background discusses challenges around managing increasingly complex systems and the potential for awareness, autonomy, distribution and learning to address these challenges.
This document summarizes the 5th IEEE International Conference on Cognitive Infocommunications (CogInfoCom 2014) which was held in Vietri sul Mare, Italy from November 5-7, 2014. It provides information on the chairs, institutions, statistics on authors from various countries, definitions of cognitive infocommunications and topics welcome at the conference. Emerging topics in cognitive infocommunications discussed at the conference included socio-cognitive ICT, cognitive biases, mathability, speechability, cognitive control theory, and cognitive infocommunications aided industrial applications. The document concludes by mentioning the next generation of CogInfoCom conferences including 3D augmented conferences and the VirCA association for cognitive infoc
The document summarizes details about the CogInfoCom 2014 conference held in Vietri sul Mare, Italy from November 5-7, 2014. It lists the chairpersons and supporting institutions. It provides statistics that 287 authors attended from various countries in Europe, Asia, North and South America, and the Middle East. The document defines cognitive infocommunications and lists topics that were welcome at the conference, including cognitive sciences, human-computer interaction, and emerging topics like socio-cognitive ICT and cognitive biases in CogInfoCom systems.
Artificial neural networks are fundamental means for providing an attempt at modelling the information
processing capabilities of artificial nervous system which plays an important role in the field of cognitive
science. This paper focuses the features of artificial neural networks studied by reviewing the existing research
works, these features were then assessed and evaluated and comparative analysis. The study and literature
survey metrics such as functional capabilities of neurons, learning capabilities, style of computation, processing
elements, processing speed, connections, strength, information storage, information transmission,
communication media selection, signal transduction and fault tolerance were used as basis for comparison. A
major finding in this paper showed that artificial neural networks served as the platform for neuron computing
technology in the field of cognitive science.
Molecules Of Knowledge: Self-Organisation In Knowledge-Intensive EnvironmentsStefano Mariani
This talk discusses the paper “Molecules of Knowledge: Self-Organisation in Knowledge-Intensive Environments”, presented at the 6th International Symposium on Intelligent Distributed Computing (IDC 2012).
Molecules of Knowledge: Self-Organisation in Knowledge-Intensive EnvironmentsAndrea Omicini
We propose a novel self-organising knowledge-oriented model based on biochemical tuple spaces, called Molecules of Knowledge (MoK). We introduce MoK basic entities, define its computational model, and discuss its mapping on the TuCSoN coordination model for its implementation.
[IDC 2012, Calabria, Italy, 24/9/2012
This document discusses how natural computation techniques can be applied to web usage mining. It begins by introducing web usage mining and its importance. It then provides an overview of various natural computation approaches, including artificial neural networks, evolutionary algorithms, swarm intelligence, artificial immune systems, bacterial foraging, DNA computation, and hybrid approaches. The document explains how each of these natural computation techniques can inspire computational methods for analyzing web usage data.
Bioinformatics is an interdisciplinary field that combines computer science, statistics, mathematics and engineering to study and process biological data, such as DNA sequences, in order to better understand biology. It involves developing methods and software tools to analyze large amounts of biological data, including sequencing genomes to understand what makes different organisms function. As data sets have grown enormously in size, bioinformatics relies on high-performance computing to make sense of it all and gain insights into normal cellular processes and how they are altered in disease states.
Bioinformatics is an interdisciplinary field that uses tools from biology, computer science, and mathematics to analyze and interpret large biological data sets. It involves developing algorithms to discover relationships in data, analyzing sequences and structures, and building tools to access and manage information. A key goal is enabling new biological insights by facilitating large-scale data mining and integration.
The evolution to network and computational paradigm has gone through a amazing phase of
expansion and development. The growth curve was indeed very steep in many major domains. The
advent of Cloud computing & Machine learning has enhanced the implementation in application area like
Bioinformatics. With huge application-domain scope Cloud computing has emerged as a special area of
interest for many bioinformatics researchers. Research is being done on different aspects of Cloud
computing with bioinformatics for identifying areas of improvement and their respective remedies for
living beings. Specially the cloud computing are acting very helpful for identifying H1N1 virus in human.
H1N1 is an infectious virus which, when spread affects a large volume of the population. It
spreads very easily and has a high death rate. Similarly cloud computing doing good job for detection of
Hypertension, Diabetics, Cancer and Heart patient with software as a service, so the development of
healthcare support systems using cloud computing is emerging as an effective solution with the
benefits of better quality of service, reduced costs. This paper, provide an effective review towards cloud
computing important effort in a field of bioinformatics.
Looking into the Crystal Ball: From Transistors to the Smart EarthThe Innovation Group
This paper is based on a keynote talk presented by Prof. Sangiovanni-Vincentelli at the 50th DAC. It discusses the evolution of cyber-physical and bio-cyber systems leading us to a smarter planet, and it predicts how EDA and embedded systems have to expand into this new field.
Biocomputing is an interdisciplinary research area which combines biology, computer science, and engineering. It is the process of building computers that use biological materials. It uses systems of biologically derived molecules, such as proteins and DNA, to perform computational calculations. This paper provides a brief introduction to biocomputing. Matthew N. O. Sadiku | Nana K. Ampah | Sarhan M. Musa "Biocomputing" Published in International Journal of Trend in Scientific Research and Development (ijtsrd), ISSN: 2456-6470, Volume-2 | Issue-6 , October 2018, URL: http://www.ijtsrd.com/papers/ijtsrd18825.pdf
An analysis of recent advancements in computational biology and Bioinformatic...Pubrica
Scientific and medical research papers are produced by the team of researchers and writers at Pubrica, and they may be invaluable sources for authors and practitioners. Pubrica medical writers help you create and modify the introduction by using the reader to alert them to the gaps in the selected study subject. Our experts know the sequence in which the topic where the hypothesis is given is followed by the broad subject, the issue, and the backdrop.
https://pubrica.com/academy/systematic-review/an-analysis-of-recent-advancements-in-computational-biology-and-bioinformatics/
The document presents BioInfoMark, a benchmark suite of 14 bioinformatics tools that cover major areas of computational biology like sequence comparison, phylogenetic analysis, protein structure analysis, and molecular dynamics simulation. The suite includes tools like BLAST, FASTA, HMMER, and Glimmer for sequence analysis, tools for phylogenetic analysis and protein structure analysis, and molecular dynamics simulation tools. It provides the source code, input datasets, compilation instructions, and pre-compiled binaries for the benchmarks to facilitate computer architecture research on emerging bioinformatics workloads.
This document provides a review of computational intelligence paradigms in wireless sensor networks. It begins with an introduction to computational intelligence and its characteristics such as adaptation, high computational speed, versatility, robustness, self-organization, and self-learning. Various applications of computational intelligence are discussed including autonomous delivery robots, diagnostic assistants, and infobots. Key computational intelligence paradigms like artificial neural networks, genetic algorithms, fuzzy logic, swarm intelligence, and artificial immune systems are described and compared. The document concludes with a table comparing the state variables and number of search points used in different computational intelligence algorithms.
Comparative Analysis of Computational Intelligence Paradigms in WSN: Reviewiosrjce
Computational Intelligence is the study of the design of intelligent agents. An agent is something that
react according to an environment—it does something. Agents includes worms, dogs, thermostats, airplanes,
humans, and society. The purpose of computational intelligence is to understand the principles that make
intelligent behavior possible, in real or artificial systems. Techniques of Computational Intelligence are
designed to model the aspects of biological intelligence. These paradigms include that exhibit an ability to
learn or adapt to new situations,to generalize, abstract, learn and associate. This paper gives review of
comparison between computational intelligence paradigms in Wireless Sensor Network and Finally,a short
conclusion is provided.
The document provides information about an introductory bioinformatics course taught by Prof. Dr. Nizamettin Aydin. It includes details about the course such as the course code, name, credits, and instructors. It also outlines the assessment breakdown and rules of conduct for the course. Recommended textbooks for the course are listed at the end.
This proposal outlines a novel genetic circuit that could be inserted into E. coli to detect safe and harmful concentrations of lead in liquid samples. The circuit would utilize existing lead-binding proteins and promoters, as well as common metabolic signals, fluorescent reporters, and terminator sequences. It is composed of three modules: a concentration detector, memory unit, and signal amplifying fluorescent reporter. While the actual circuit cannot be constructed yet, computer simulations show it could function as intended given the appropriate biological parts. The proposal provides detailed specifications and simulations of each module and the complete circuit.
This paper explores the complex field of synthetic biology, including its historical roots, guiding ideas, contemporary uses, and moral dilemmas raised by its groundbreaking discoveries.
The National Resource for Network Biology aims to provide freely available, open-source software tools to enable researchers to assemble biological data into networks and pathways and use these networks to better understand biological systems and disease; it pursues this mission through technology research and development projects, driving biological projects, collaboration and service projects, training, and dissemination; key components include the Cytoscape software platform, supercomputing infrastructure, and partnerships with over 30 external research groups.
Towards the Intelligent Internet of EverythingRECAP Project
In this presentation, Prof. Theo Lynn (DCU) was talking about observations on Multi-disciplinary Challenges in Intelligent Systems Research, at the RECAP consortium meeting in Dublin, Ireland on 06 November 2018.
Journal of Applied Bioinformatics & Computational Biology (JABCB) promotes rigorous research that makes a significant contribution in advancing knowledge in the fields of Biology & Bioinformatics.
BioinformaticsPurpose Bioinformatics is the combination of comp.docxrichardnorman90310
Bioinformatics
Purpose: Bioinformatics is the combination of computer science and biology which used various methods of storing and retrieving the biological data which have pros and cons, scientists are able to discover new information on various diseases, its mutation, it helps in differentiating one organism from another by analyzing their genetic data, biological development and will stop various crimes, disadvantages and develops the algorithm that helps in measuring the sequence similarity.
1. Introduction: Bioinformatics is a field which include molecular biology, statistics, issues, computer problems, and extensive mathematics complex problem. It has two stages deliberately gather various insights from the natural information and to make a computational model. It can be found in the study area of precision and preventive medicine.
0. Background info on of bioinformaticsComment by R Daniel Creider: A, B, C and D are not a part of the introduction. The outline is not organized correctly
0. How to approach bioinformatics?
1. Goals of Bioinformatics
0. Development of efficient algorithms
0. Extension of experimental data by predictions
1. Advantages of bioinformatics
1. World is getting information on new discovery and crimes are prevented
1. Discover new information on various diseases
1. How organisms mutate
1. How it analyses data to differentiate one organism from another
1. Disadvantages of bioinformatics
2. Data manipulation, complexity, lack of well-trained manpower to use the software
2. Misuse of the information
0. Problems behinds it
0. Data about the genetic information lack proper analyzed
0. Importance of Bioinformatics
3. Genetic research
0. Genomics and proteomics
1.
Solution
of the problem
1. Use software wisely
1. Decrease its complexity
1. Future of the bioinformatics
2. Bioinformatics is the present and future of biotechnology
0. Use for research and exchange information for comparison, storage and analysis
BIOINFORMATICS: A Technical Report
Texas A&M University-Commerce
Bishow KunwarComment by R Daniel Creider: Your name comes before the name of the University.
Abstract
The main aim of Bioinformatics is to improve the various methods of storing, retrieving and organizing the biological data by critically evaluating the data. The effectiveness of bi informatics in the field of genetics and genomics is playing its part in a way that particularly in textual mining of biological development. Bioinformatics is the application which is the mix of two fields (software engineering and science). It is a field that includes different things like sub-atomic science, measurement issues, software engineering issues, and broad arithmetic complex issues.
Keywords; Bioinformatics, Genetic, Genomic, Biological Development
Introduction:
Bioinformatics is the application which is the combination of two fields (computer science and biology). It is a field that involves multiple things like molecular .
BioinformaticsPurpose Bioinformatics is the combination of comp.docxjasoninnes20
Bioinformatics
Purpose: Bioinformatics is the combination of computer science and biology which used various methods of storing and retrieving the biological data which have pros and cons, scientists are able to discover new information on various diseases, its mutation, it helps in differentiating one organism from another by analyzing their genetic data, biological development and will stop various crimes, disadvantages and develops the algorithm that helps in measuring the sequence similarity.
1. Introduction: Bioinformatics is a field which include molecular biology, statistics, issues, computer problems, and extensive mathematics complex problem. It has two stages deliberately gather various insights from the natural information and to make a computational model. It can be found in the study area of precision and preventive medicine.
0. Background info on of bioinformaticsComment by R Daniel Creider: A, B, C and D are not a part of the introduction. The outline is not organized correctly
0. How to approach bioinformatics?
1. Goals of Bioinformatics
0. Development of efficient algorithms
0. Extension of experimental data by predictions
1. Advantages of bioinformatics
1. World is getting information on new discovery and crimes are prevented
1. Discover new information on various diseases
1. How organisms mutate
1. How it analyses data to differentiate one organism from another
1. Disadvantages of bioinformatics
2. Data manipulation, complexity, lack of well-trained manpower to use the software
2. Misuse of the information
0. Problems behinds it
0. Data about the genetic information lack proper analyzed
0. Importance of Bioinformatics
3. Genetic research
0. Genomics and proteomics
1.
Solution
of the problem
1. Use software wisely
1. Decrease its complexity
1. Future of the bioinformatics
2. Bioinformatics is the present and future of biotechnology
0. Use for research and exchange information for comparison, storage and analysis
BIOINFORMATICS: A Technical Report
Texas A&M University-Commerce
Bishow KunwarComment by R Daniel Creider: Your name comes before the name of the University.
Abstract
The main aim of Bioinformatics is to improve the various methods of storing, retrieving and organizing the biological data by critically evaluating the data. The effectiveness of bi informatics in the field of genetics and genomics is playing its part in a way that particularly in textual mining of biological development. Bioinformatics is the application which is the mix of two fields (software engineering and science). It is a field that includes different things like sub-atomic science, measurement issues, software engineering issues, and broad arithmetic complex issues.
Keywords; Bioinformatics, Genetic, Genomic, Biological Development
Introduction:
Bioinformatics is the application which is the combination of two fields (computer science and biology). It is a field that involves multiple things like molecular ...
Computational methods to analyze biological data. It is a way to introduce some of the many resources available for analyzing sequence data with bioinformatics software. This paper will cover the theoretical approaches to data resources and we will get knowledge about some sequential alignments with its databases. As an interdisciplinary field of science, bioinformatics combines biology, computer science, information engineering, mathematics, and statistics to analyze and interpret biological data. Bioinformatics has been used for in silico analyses of biological queries using mathematical and statistical techniques. Databases are essential for bioinformatics research and applications. Many databases exist, covering various information types for example, DNA and protein sequences, molecular structures, phenotypes, and biodiversity. Databases may contain empirical data. Conceptualizing biology in terms of molecules and then applying informatics techniques from math, computer science, and statistics to understand and organize the information associated with these molecules on a large scale. In this materialistic world, People are studying bioinformatics in different ways. Some people are devoted to developing new computational tools, both from software and hardware viewpoints, for the better handling and processing of biological data. They develop new models and new algorithms for existing questions and propose and tackle new questions when new experimental techniques bring in new data. Other people take the study of bioinformatics as the study of biology with the viewpoint of informatics and systems. Durgesh Raghuvanshi | Vivek Solanki | Neha Arora | Faiz Hashmi "Computational of Bioinformatics" Published in International Journal of Trend in Scientific Research and Development (ijtsrd), ISSN: 2456-6470, Volume-4 | Issue-4 , June 2020, URL: https://www.ijtsrd.com/papers/ijtsrd30891.pdf Paper Url :https://www.ijtsrd.com/engineering/computer-engineering/30891/computational-of-bioinformatics/durgesh-raghuvanshi
The document discusses several emerging disruptive technologies including biochips, digital twins, carbon nanotubes, smart workspaces, brain computer interfaces, 4D printing, and smart homes. Biochips can perform many biochemical reactions simultaneously and are used for disease diagnosis and identification. Digital twins are digital representations of physical systems that mirror and interact with the physical world. Carbon nanotubes have unique electrical and mechanical properties and potential applications in electronics, sensors, and medicine. Smart workspaces use connectivity, flexibility, and technology to improve employee productivity and experience. Brain computer interfaces allow direct communication between the brain and external devices for research, assistance, and repair of cognitive functions. 4D printing produces objects that can change shape over time in response to environmental
This document discusses how natural computation techniques can be applied to web usage mining. It begins by introducing web usage mining and its importance. It then provides an overview of various natural computation approaches, including artificial neural networks, evolutionary algorithms, swarm intelligence, artificial immune systems, bacterial foraging, DNA computation, and hybrid approaches. The document explains how each of these natural computation techniques can inspire computational methods for analyzing web usage data.
Bioinformatics is an interdisciplinary field that combines computer science, statistics, mathematics and engineering to study and process biological data, such as DNA sequences, in order to better understand biology. It involves developing methods and software tools to analyze large amounts of biological data, including sequencing genomes to understand what makes different organisms function. As data sets have grown enormously in size, bioinformatics relies on high-performance computing to make sense of it all and gain insights into normal cellular processes and how they are altered in disease states.
Bioinformatics is an interdisciplinary field that uses tools from biology, computer science, and mathematics to analyze and interpret large biological data sets. It involves developing algorithms to discover relationships in data, analyzing sequences and structures, and building tools to access and manage information. A key goal is enabling new biological insights by facilitating large-scale data mining and integration.
The evolution to network and computational paradigm has gone through a amazing phase of
expansion and development. The growth curve was indeed very steep in many major domains. The
advent of Cloud computing & Machine learning has enhanced the implementation in application area like
Bioinformatics. With huge application-domain scope Cloud computing has emerged as a special area of
interest for many bioinformatics researchers. Research is being done on different aspects of Cloud
computing with bioinformatics for identifying areas of improvement and their respective remedies for
living beings. Specially the cloud computing are acting very helpful for identifying H1N1 virus in human.
H1N1 is an infectious virus which, when spread affects a large volume of the population. It
spreads very easily and has a high death rate. Similarly cloud computing doing good job for detection of
Hypertension, Diabetics, Cancer and Heart patient with software as a service, so the development of
healthcare support systems using cloud computing is emerging as an effective solution with the
benefits of better quality of service, reduced costs. This paper, provide an effective review towards cloud
computing important effort in a field of bioinformatics.
Looking into the Crystal Ball: From Transistors to the Smart EarthThe Innovation Group
This paper is based on a keynote talk presented by Prof. Sangiovanni-Vincentelli at the 50th DAC. It discusses the evolution of cyber-physical and bio-cyber systems leading us to a smarter planet, and it predicts how EDA and embedded systems have to expand into this new field.
Biocomputing is an interdisciplinary research area which combines biology, computer science, and engineering. It is the process of building computers that use biological materials. It uses systems of biologically derived molecules, such as proteins and DNA, to perform computational calculations. This paper provides a brief introduction to biocomputing. Matthew N. O. Sadiku | Nana K. Ampah | Sarhan M. Musa "Biocomputing" Published in International Journal of Trend in Scientific Research and Development (ijtsrd), ISSN: 2456-6470, Volume-2 | Issue-6 , October 2018, URL: http://www.ijtsrd.com/papers/ijtsrd18825.pdf
An analysis of recent advancements in computational biology and Bioinformatic...Pubrica
Scientific and medical research papers are produced by the team of researchers and writers at Pubrica, and they may be invaluable sources for authors and practitioners. Pubrica medical writers help you create and modify the introduction by using the reader to alert them to the gaps in the selected study subject. Our experts know the sequence in which the topic where the hypothesis is given is followed by the broad subject, the issue, and the backdrop.
https://pubrica.com/academy/systematic-review/an-analysis-of-recent-advancements-in-computational-biology-and-bioinformatics/
The document presents BioInfoMark, a benchmark suite of 14 bioinformatics tools that cover major areas of computational biology like sequence comparison, phylogenetic analysis, protein structure analysis, and molecular dynamics simulation. The suite includes tools like BLAST, FASTA, HMMER, and Glimmer for sequence analysis, tools for phylogenetic analysis and protein structure analysis, and molecular dynamics simulation tools. It provides the source code, input datasets, compilation instructions, and pre-compiled binaries for the benchmarks to facilitate computer architecture research on emerging bioinformatics workloads.
This document provides a review of computational intelligence paradigms in wireless sensor networks. It begins with an introduction to computational intelligence and its characteristics such as adaptation, high computational speed, versatility, robustness, self-organization, and self-learning. Various applications of computational intelligence are discussed including autonomous delivery robots, diagnostic assistants, and infobots. Key computational intelligence paradigms like artificial neural networks, genetic algorithms, fuzzy logic, swarm intelligence, and artificial immune systems are described and compared. The document concludes with a table comparing the state variables and number of search points used in different computational intelligence algorithms.
Comparative Analysis of Computational Intelligence Paradigms in WSN: Reviewiosrjce
Computational Intelligence is the study of the design of intelligent agents. An agent is something that
react according to an environment—it does something. Agents includes worms, dogs, thermostats, airplanes,
humans, and society. The purpose of computational intelligence is to understand the principles that make
intelligent behavior possible, in real or artificial systems. Techniques of Computational Intelligence are
designed to model the aspects of biological intelligence. These paradigms include that exhibit an ability to
learn or adapt to new situations,to generalize, abstract, learn and associate. This paper gives review of
comparison between computational intelligence paradigms in Wireless Sensor Network and Finally,a short
conclusion is provided.
The document provides information about an introductory bioinformatics course taught by Prof. Dr. Nizamettin Aydin. It includes details about the course such as the course code, name, credits, and instructors. It also outlines the assessment breakdown and rules of conduct for the course. Recommended textbooks for the course are listed at the end.
This proposal outlines a novel genetic circuit that could be inserted into E. coli to detect safe and harmful concentrations of lead in liquid samples. The circuit would utilize existing lead-binding proteins and promoters, as well as common metabolic signals, fluorescent reporters, and terminator sequences. It is composed of three modules: a concentration detector, memory unit, and signal amplifying fluorescent reporter. While the actual circuit cannot be constructed yet, computer simulations show it could function as intended given the appropriate biological parts. The proposal provides detailed specifications and simulations of each module and the complete circuit.
This paper explores the complex field of synthetic biology, including its historical roots, guiding ideas, contemporary uses, and moral dilemmas raised by its groundbreaking discoveries.
The National Resource for Network Biology aims to provide freely available, open-source software tools to enable researchers to assemble biological data into networks and pathways and use these networks to better understand biological systems and disease; it pursues this mission through technology research and development projects, driving biological projects, collaboration and service projects, training, and dissemination; key components include the Cytoscape software platform, supercomputing infrastructure, and partnerships with over 30 external research groups.
Towards the Intelligent Internet of EverythingRECAP Project
In this presentation, Prof. Theo Lynn (DCU) was talking about observations on Multi-disciplinary Challenges in Intelligent Systems Research, at the RECAP consortium meeting in Dublin, Ireland on 06 November 2018.
Journal of Applied Bioinformatics & Computational Biology (JABCB) promotes rigorous research that makes a significant contribution in advancing knowledge in the fields of Biology & Bioinformatics.
BioinformaticsPurpose Bioinformatics is the combination of comp.docxrichardnorman90310
Bioinformatics
Purpose: Bioinformatics is the combination of computer science and biology which used various methods of storing and retrieving the biological data which have pros and cons, scientists are able to discover new information on various diseases, its mutation, it helps in differentiating one organism from another by analyzing their genetic data, biological development and will stop various crimes, disadvantages and develops the algorithm that helps in measuring the sequence similarity.
1. Introduction: Bioinformatics is a field which include molecular biology, statistics, issues, computer problems, and extensive mathematics complex problem. It has two stages deliberately gather various insights from the natural information and to make a computational model. It can be found in the study area of precision and preventive medicine.
0. Background info on of bioinformaticsComment by R Daniel Creider: A, B, C and D are not a part of the introduction. The outline is not organized correctly
0. How to approach bioinformatics?
1. Goals of Bioinformatics
0. Development of efficient algorithms
0. Extension of experimental data by predictions
1. Advantages of bioinformatics
1. World is getting information on new discovery and crimes are prevented
1. Discover new information on various diseases
1. How organisms mutate
1. How it analyses data to differentiate one organism from another
1. Disadvantages of bioinformatics
2. Data manipulation, complexity, lack of well-trained manpower to use the software
2. Misuse of the information
0. Problems behinds it
0. Data about the genetic information lack proper analyzed
0. Importance of Bioinformatics
3. Genetic research
0. Genomics and proteomics
1.
Solution
of the problem
1. Use software wisely
1. Decrease its complexity
1. Future of the bioinformatics
2. Bioinformatics is the present and future of biotechnology
0. Use for research and exchange information for comparison, storage and analysis
BIOINFORMATICS: A Technical Report
Texas A&M University-Commerce
Bishow KunwarComment by R Daniel Creider: Your name comes before the name of the University.
Abstract
The main aim of Bioinformatics is to improve the various methods of storing, retrieving and organizing the biological data by critically evaluating the data. The effectiveness of bi informatics in the field of genetics and genomics is playing its part in a way that particularly in textual mining of biological development. Bioinformatics is the application which is the mix of two fields (software engineering and science). It is a field that includes different things like sub-atomic science, measurement issues, software engineering issues, and broad arithmetic complex issues.
Keywords; Bioinformatics, Genetic, Genomic, Biological Development
Introduction:
Bioinformatics is the application which is the combination of two fields (computer science and biology). It is a field that involves multiple things like molecular .
BioinformaticsPurpose Bioinformatics is the combination of comp.docxjasoninnes20
Bioinformatics
Purpose: Bioinformatics is the combination of computer science and biology which used various methods of storing and retrieving the biological data which have pros and cons, scientists are able to discover new information on various diseases, its mutation, it helps in differentiating one organism from another by analyzing their genetic data, biological development and will stop various crimes, disadvantages and develops the algorithm that helps in measuring the sequence similarity.
1. Introduction: Bioinformatics is a field which include molecular biology, statistics, issues, computer problems, and extensive mathematics complex problem. It has two stages deliberately gather various insights from the natural information and to make a computational model. It can be found in the study area of precision and preventive medicine.
0. Background info on of bioinformaticsComment by R Daniel Creider: A, B, C and D are not a part of the introduction. The outline is not organized correctly
0. How to approach bioinformatics?
1. Goals of Bioinformatics
0. Development of efficient algorithms
0. Extension of experimental data by predictions
1. Advantages of bioinformatics
1. World is getting information on new discovery and crimes are prevented
1. Discover new information on various diseases
1. How organisms mutate
1. How it analyses data to differentiate one organism from another
1. Disadvantages of bioinformatics
2. Data manipulation, complexity, lack of well-trained manpower to use the software
2. Misuse of the information
0. Problems behinds it
0. Data about the genetic information lack proper analyzed
0. Importance of Bioinformatics
3. Genetic research
0. Genomics and proteomics
1.
Solution
of the problem
1. Use software wisely
1. Decrease its complexity
1. Future of the bioinformatics
2. Bioinformatics is the present and future of biotechnology
0. Use for research and exchange information for comparison, storage and analysis
BIOINFORMATICS: A Technical Report
Texas A&M University-Commerce
Bishow KunwarComment by R Daniel Creider: Your name comes before the name of the University.
Abstract
The main aim of Bioinformatics is to improve the various methods of storing, retrieving and organizing the biological data by critically evaluating the data. The effectiveness of bi informatics in the field of genetics and genomics is playing its part in a way that particularly in textual mining of biological development. Bioinformatics is the application which is the mix of two fields (software engineering and science). It is a field that includes different things like sub-atomic science, measurement issues, software engineering issues, and broad arithmetic complex issues.
Keywords; Bioinformatics, Genetic, Genomic, Biological Development
Introduction:
Bioinformatics is the application which is the combination of two fields (computer science and biology). It is a field that involves multiple things like molecular ...
Computational methods to analyze biological data. It is a way to introduce some of the many resources available for analyzing sequence data with bioinformatics software. This paper will cover the theoretical approaches to data resources and we will get knowledge about some sequential alignments with its databases. As an interdisciplinary field of science, bioinformatics combines biology, computer science, information engineering, mathematics, and statistics to analyze and interpret biological data. Bioinformatics has been used for in silico analyses of biological queries using mathematical and statistical techniques. Databases are essential for bioinformatics research and applications. Many databases exist, covering various information types for example, DNA and protein sequences, molecular structures, phenotypes, and biodiversity. Databases may contain empirical data. Conceptualizing biology in terms of molecules and then applying informatics techniques from math, computer science, and statistics to understand and organize the information associated with these molecules on a large scale. In this materialistic world, People are studying bioinformatics in different ways. Some people are devoted to developing new computational tools, both from software and hardware viewpoints, for the better handling and processing of biological data. They develop new models and new algorithms for existing questions and propose and tackle new questions when new experimental techniques bring in new data. Other people take the study of bioinformatics as the study of biology with the viewpoint of informatics and systems. Durgesh Raghuvanshi | Vivek Solanki | Neha Arora | Faiz Hashmi "Computational of Bioinformatics" Published in International Journal of Trend in Scientific Research and Development (ijtsrd), ISSN: 2456-6470, Volume-4 | Issue-4 , June 2020, URL: https://www.ijtsrd.com/papers/ijtsrd30891.pdf Paper Url :https://www.ijtsrd.com/engineering/computer-engineering/30891/computational-of-bioinformatics/durgesh-raghuvanshi
The document discusses several emerging disruptive technologies including biochips, digital twins, carbon nanotubes, smart workspaces, brain computer interfaces, 4D printing, and smart homes. Biochips can perform many biochemical reactions simultaneously and are used for disease diagnosis and identification. Digital twins are digital representations of physical systems that mirror and interact with the physical world. Carbon nanotubes have unique electrical and mechanical properties and potential applications in electronics, sensors, and medicine. Smart workspaces use connectivity, flexibility, and technology to improve employee productivity and experience. Brain computer interfaces allow direct communication between the brain and external devices for research, assistance, and repair of cognitive functions. 4D printing produces objects that can change shape over time in response to environmental
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In search of a cyberspace …to launch Biologically-Inspired Advanced Computing Strategies: A Digital Ecology Solution
1. In search of a cyberspace … to launch 1
biologically-inspired advanced computing strategies:
a digital ecology solution
Dr. Perambur S. Neelakanta, Ph.D., C. Eng., Fellow IEE
Professor
Department of Electrical Engineering
College of Engineering and Computer Science
Florida Atlantic University
Boca Raton, Florida 33431, USA
neelakan@fau.edu
Invited Lecture
International Conference on Advanced Computing (ICAC 2009),
August 7-8, 2009, Tiruchirappalli, Tamil Nadu, India
1
2. 2
Biologically-inspired computing (BIC)…?
Simply, known as bio-inspired computing (or just bio-computing),
BIC denotes…
“a field of study that loosely knits together subfields
related to the topics of connectionism, social behavior and
emergence.
It is often closely related to the field of artificial
intelligence, as many of its pursuits can be linked to machine
learning.
It relies heavily on the fields of biology, computer
science and mathematics…”.
In nut-shell, BIC is the use of computers to model nature, and
simultaneously the study of nature to improve the usage of
computers. It is, therefore a major subset of natural computation.
2
3. In search of a cyberspace … 3
…to launch biologically-inspired advanced computing
strategies….
Whether the strategies of BIC comes within the purview of
information technology (IT)-oriented considerations is still
unclear and remains as an open-question.
This paper heuristically searches for a cyberspace wherein
BIC efforts can be viewed cohesively in the broader sense of
IT-paradigms.
Hence, attempted here is an exploration to
cast comprehensively the universe of BIC in
the domain of so-called…
“digital ecology” (DE)
Now what is “digital ecology”? 3
4. 4
Now what is “digital ecology”?
Digital ecology (DE) is a neoteric terminology mostly
applied to the evolution of social and civic ecosystem
commensurate with modern IT perspectives
Its usage in modern context includes the plethora of (i)
entertainment media ecology, (ii) the entirety of computing
ambient and (iii) the environment of communication
networks.
In each of this gamut, the transfer of information (or
informatics) negotiates a sizable cardinality of
stochastically interacting stochastically interacting subsets
that structuralize a complex open-source network and
computational environment.
4
5. 5
Now what is “digital ecology”? … Continued
In short, DE refers to an environment, which is:
- open in visibly portraying the interactions involved;
- loosely-coupled in mediating the open relationships
between species;
- domain-clustered in creating a field of balanced
common interest;
- demand-driven in conglomerating the species as
interest groups;
- self-organizing in autonomous decision-making; and,
- agent-based in rendering an ambient of synergism
between human and machines where each agent
participates proactively in the computational endeavors
as well as in the information transfers akin to the
species of biological ecosystem.
5
6. 6
“Digital ecology” …a cyberspace to launch
biologically-inspired advanced computing strategies
Digital ecology enables a unified presentation of
computational tools and algorithmic endeavors modern
and advanced computing schemes) in an IT-specific
domain. So attempted here in an ambient of BIC efforts
towards…
… constructing a DE platform to support
BIC concepts
As an illustrative example, the strategy of artificial
neural networks (ANN) mapped in terms of relevant
ontological norms of digital ecology is presented.
6
7. 7
Biologically-inspired computing (BIC)…More
BIC bears the perspectives of cybernetics in the
computational efforts involving …
simulated annealing
artificial neural networks
genetic algorithms
DNA and molecular computing
biological ecology etc.
Thus, the field of BIC is highly multidisciplinary,
attracting a host of disciplines…
- …computer science, molecular biology, genetics,
engineering, mathematics, physics, chemistry
and others.
7
8. Biologically-inspired computing (BIC)……potential 8
applications in:
DNA computation
nanofabrication
storage devices
sensing
healthcare
basic scientific research – for example …
…providing biologists with an IT-oriented
paradigm to look at how cells “compute” or process the
information
…helping computer scientists and engineers to
construct algorithms based on natural systems, such as
evolutionary and genetic algorithms
8
9. 9
Biologically-inspired computing (BIC)…
BIC… its scope
Enabling new themes of computing technologies
and fresh areas of computer science using biology
or biological processes as metaphor/inspiration
Expanding information science concepts and
tools to explore biology from a different theoretical
perspective.
BIC as such, however, does not include in its scope
the framework of, (i) the general use of computers;
(ii) the strategies of computational analyses,
and/or (iii) data management in biology - for
example, bioinformatics or computational biology.
9
10. 10
Biologically-inspired computing (BIC)…
… BIC and its cousins: Areas of emphasis
Genetic algorithms (GAs) ↔ Follows natural evolution with
the rules of selection, recombination, reproduction, mutation
and more recently transposition. Such simple rules of evolution
in complex organisms are observed and adopted in GAs
constituting BIC approach.
Artificial Intelligence (AI) ↔ Traditional AI is the intelligence
of machines towards the design of intelligent agents.
The way in which BIC differs from traditional AI is in how it
takes a more evolutionary approach to learning, as opposed to
what could be described as 'creationist' methods used in
traditional AI. In this perspective AI inclines towards BIC.
10
11. 11
BIC and its cousins: Areas of emphasis… continued
Biodegradability prediction ↔ Accurate sequence
details and genetic information vis-à-vis biodegradation are
essential for assessing molecular basis of enzyme specificity,
their catalytic mechanism, the evolutionary origin of
related metabolism and proliferation of such activities in
the environment.
(Although some basic formalization toward useful
tools as a predictor of chemical/biodegradability is
feasible, the absence of information at the sequence level
of proteins etc. are imminently required for systematic
studies of biodegradation. This is facilitated via
biocomputing).
11
12. 12
BIC and its cousins: Areas of emphasis… continued
Cellular automata ↔ Cellular automaton is a discrete
model of a regular grid of cells, each in one of a finite
number of states.
Relevant evolutionary computation programs with cellular
arrays in decentralized platforms (where the information
processing occurs in the form of global and local pattern
dynamics) lead to emergent computation (expressed in
terms of GAs) and adopted to evolve patterns in cellular
automata in the perspectives of BIC.
12
13. 13
BIC and its cousins: Areas of emphasis… continued
Emergent systems ↔ The way complex systems and
patterns arise out of a multiplicity of relatively simple
interactions as in biological systems is specified by
“emergence”.
It has been the holy grail of BIC. Emergence is
something like a macro phenomenon that appears as a
by-product of a (generally but not always large)
collection of micro phenomena.
13
14. 14
BIC and its cousins: Areas of emphasis… continued
Neural networks ↔ Biological neural networks are made up of
real biological neurons that are connected or functionally related
in the peripheral nervous system or the central nervous system.
Artificial neural networks (ANNs) are composed of
simulated neuron units made “in the image of real neurons”. By
interconnecting “artificial neurons” – a programming strategy is
set up that constructs a massively parallel connectivity, mimicing
the biological neurons.
ANN with its interconnected structure of artificial
neuron uses a paradigm of mathematical or computational model
for information processing based on a connectionist approach to
computation adaptively to changes in external or internal
information via biological-inspiration.
14
15. 15
BIC and its cousins: Areas of emphasis… continued
Artificial life ↔ Commonly known as Alife or alife, it
depicts a field of study and an associated art form
which examine systems related to life, its processes, and
its evolution through simulations using computer
models, robotics, and biochemistry.
There are three major versions of alife, based on their
approaches: soft- from software; hard- from hardware;
and wet- from biochemistry. Artificial life imitates
traditional biology in recreating biological phenomena.
Essentially, the term "artificial life" is often used to
specifically refer to soft alife.
15
16. 16
BIC and its cousins: Areas of emphasis… continued
Artificial immune systems (AIS) ↔ Abstracting and
mapping the structure and function of an immune system
to a computational set of frameworks so as to investigate
the application of such systems towards solving
computational problems with the aid of mathematics,
engineering, and information technology.
AIS is a sub-field of computational intelligence, BIC,
and natural computation, with a focus on machine
learning. It can be said to belong the broader field of AI.
Further, AIS are adaptive systems, inspired by theoretical
immunology and observed immune functions, principles
and models, which are applied to problem solving.
16
17. 17
BIC and its cousins: Areas of emphasis… continued
Rendering (computer graphics) ↔ a process of generating
an image from a model (description of 3D objects in a
strictly defined language or data structure) using
computer programs.
It contains features of geometry, viewpoint, texture,
lighting, and shading information in digital image or a
raster graphics image format.
The term rendering in computing context is an
analogy of an "artist's rendering" of a scene. (In biological
context, rendering simply refers to patterning and
rendering of animal skins, bird feathers, mollusk shells
and bacterial colonies)
17
18. 18
BIC and its cousins: Areas of emphasis… continued
Lindenmeyer systems ↔ Computing self-organization in
the context of environmentally sensitive growth and/or
development modeling behavior and visualization of cells
of plants/plant structures:
- Mathematical, spatial models that treat plant geometry
as a continuum or as discrete components in space.
- Developmental models that describe form as a result of
growth in terms of growth influencing variables
- Simulations produce numerical output, which can be
complemented by rendered images and animations for the
purpose of easy comprehension
18
19. 19
BIC and its cousins: Areas of emphasis… continued
Communication networks and protocols ↔ Analogy
between viral dynamics in humans and in computers is
useful in assessing infectious disease epidemiology on
human social networks versus communication in
wireless networks
Epidemiology as a metaphor may hold insights into
communication networks.
New paradigms of mathematics and methodologies
sought towards linking epidemiology and the spread of
disease are generalized biological-inspirations seen
toward modeling modern communication systems.
19
20. 20
BIC and its cousins: Areas of emphasis… continued
Membrane computers ↔ The membrane computing is
an effort to replicate organic structures of the brain
and the intra-membrane molecular processes in the
living cells onto silicone.
This is to create indeterminate outcome machines
that are capable of learning through external stimuli.
Such membrane computers will be a interesting
technology when it is finally developed, say in creating
artificial brains and teaching machines… a dream
sought in BIC.
20
21. 21
BIC and its cousins: Areas of emphasis… continued
Excitable media ↔ An excitable medium is a nonlinear
dynamical system that has the capacity to propagate a wave of
some description, experiencing an elapsed time (refractory
time). A forest is an example of an excitable medium: That is,
when a wildfire burns through the forest, no fire can return to
a burnt spot until the vegetation has gone through its
refractory period and re-grown. BIC implications are related
to…
Pathological activities in the heart and brain can also be
modeled as excitable media.
In cellular automata the state of a particular cell in the
next time step depends on the state of the cells around it--its
neighbors-at the current time.
21
22. 22
BIC and its cousins: Areas of emphasis… continued
Sensor networks ↔ Sensor networks are a sensing,
computing and communication infrastructure that
allows to instrument, observe, and respond to
phenomena in the natural environment, and in the
physical as well as cyber infrastructure.
Akin to biological systems that present remarkable
adaptation, reliability, and robustness in various
environments, even under hostility (in a distributed and
self-organized way), they provide useful resources for
designing the dynamical and adaptive routing schemes
of wireless mobile sensor networks.
22
23. 23
BIC and its cousins: Areas of emphasis… continued
DNA computing ↔ a computing strategy that uses
interdisciplinary aspects of DNA, biochemistry and
molecular biology, instead of the traditional silicon-based
computer technologies. It is a molecular computing stategy
similar to parallel computing and employs many different
molecules of DNA to try many different information
processing at once. Mostly, DNA computers are faster and
smaller than any other computer built so far. However,
unlike quantum computing, in DNA machines to solve
extremely large EXPSPACE problems, the amount of DNA
required is too large to be practical.
23
24. 24
BIC: CAN IT BE COMPREHENDED IN A UNIFIED
CYBERSPACE?
Biologically-inspired computing will be “wonderful tools,
(and) will eventually lead the way to a “molecular
revolution,” which ultimately will have a very dramatic
effect on the world”. As such biocomputing, in general has
the potential to be a very powerful tool.
BIC shouldering the marvels of computation per
se is not the traditional “computing with silicon-chips”, but
in essence, it. It relies on information-science (technology?)
and borrows the metaphors from biological sciences.
The query that lingers is whether the various
avenues of BIC can be comprehended in a unified
cyberspace. If so, how?
24
25. 25
BIC: CAN IT BE COMPREHENDED IN A UNIFIED
CYBERSPACE? …Continued
In modern perspective, in sheltering the BIC within the
scope of IT-oriented considerations is still unclear and
remains as an open-question.
Suppose BIC-related computational tools and algorithmic
endeavors are to be viewed in an IT-specific cyberspace.
It is then necessary to seek a platform that permits a
cohesive activity of a complex system where biological
evolutionary principles are invoked in terms of interacting
species having self-organizing features. Further overlaid
thereon are feasible aspects of informatics and paradigms
of computation.
25
26. 26
BIC: CAN IT BE COMPREHENDED IN A UNIFIED
CYBERSPACE? …Continued
Can the underlying abstract of a unified cyberspace of BIC
be specified in the so-called digital ecology (DE) platform
towards a compatible solution?
DE is “the medley of digital code and
environmentalism” that prescribes information ecosystems
constituted by information flows being processed through
various mediating species across biological ecology. In this
perspective, considering the intersecting aspects of a
complex system and ecological prescriptions, models of
BIC can be projected in the realm of digital ecosystem
ontology.
26
27. 27
BIC: CAN IT BE COMPREHENDED IN A UNIFIED
CYBERSPACE? …Continued
Digital ecosystems have been conceived in “the image of” complex
biological ecology expressed in terms of "digital environment"
ontology and is populated by "digital species" that mediate
massive information exchange.
Compared with natural ecosystems where species may
follow adaptation to local conditions, in digital ecosystem, new
digital species continuously emerge and they help cleanse the
ecosystem (for example supplanting older scheme of computation
with an advanced one).
Digital ecosystems thus capture the essence of classical,
complex ecological environment in nature, where organisms
cohesively constitute a dynamic, self-organizing and interrelated
complex ecosystem conserving and utilizing the environment of its
resources.
27
28. 28
BIC: A COMPLEX SYSTEM THAT FOLLOWS
A DIGITAL ECOSYSTEM ONTOLOGY...
… a possible suite for modeling the complex system
profile of BIC is to apply DE considerations identified
in terms of certain DE ontology nomenclature:
{Species} ⇔ {Domain, Task, Profit, Rule, Role,
Supplier, Requester, Available
Service, Requested Service}
{Environment} ⇔ {Technology, Service, (Species),
Open-environment, Loosely-coupled
environment, Demand-driven
environment, Domain-clustered
environment}
28
29. 29
BIC: SPELT IN THE ONTOLOGY OF DIGITAL
ECOLOGY – AN EXAMPLE…ANN
{Species} ⇒ {Domain, Task, Profit, Rule, Role,
Supplier, Requester, Available
Service, Requested Service}
{Environment} ⇒ {Open, loosely-coupled, demand-
driven; domain-clustered}
⇑ ⇓
{Interacting neurons, layered ANN architecture, massively
parallel computation, output/goal-realization, nonlinear
processing of collective information, supervised learning;
output validation via teacher value, input ambient, user
(programmer), convergence of the output against learned
pattern, testing an input set against learned pattern}
29
30. 30
BIC: SPELT IN THE ONTOLOGY OF DIGITAL
ECOLOGY – AN ANN EXAMPLE…continued
Teacher
Input Hidden
Input
layer layers
Weights Weights Ouput Ti
zi layer
+
Σ –
Inputs Oi
yi = f(xi) Σ
Oi = KΣzi
A
neuronal Weight vector εi = (Oi, Ti)
unit adjustments Error
30
31. BIC: SPELT IN THE ONTOLOGY OF DIGITAL ECOLOGY 31
– AN ANN EXAMPLE…continued
(A) Subfunction PeudoCode I on:
DEFINE_(ANN-DE)_SPECIES & ENVIRONMENT–ONTOLOGY: Initialize
⇒ FOR Complex ANN system: Neurons/neuronal units
CALL: DEFINE_ENVIRONMENT: ANN
DEFINE_SPECIES: Neuronal units ⇒
comesFrom -domain ANN architecture
DEFINE_DOMAIN: ⇒ common field for all species
DEFINE_TASK carriesOut goal-oriented tasks
Goal: converged ANN output
DEFINE_PROFIT relatesTo task
- computational advantage
isDrivenBy species: neurons
DEFINE_RULE:-follows nonlinear norms regulating
species collectively
DEFINE_ROLE- role of interaction with other
Species (neurons) defineBy weight-modification,
inter-play of input data at the hidden layer(s)
CALL: DEFINE_SUPPLIER
CALL: DEFINE_REQUESTER
31
32. BIC: SPELT IN THE ONTOLOGY OF DIGITAL ECOLOGY 32
– AN ANN EXAMPLE…continued
(B) Subfunction Code II on: ENVIRONMENT ontology -
Initialize:
Inputs: Training and prediction sets:
DEFINE_DIGITAL_ECOSYSTEM: ANN
DEFINE_ENVIRONMENT
⇒ architecture items of SPECIES
DEFINE_TECHNOLOGY of the Environment isSupportedBy INPUTS
and Teacher values
Connectivity isProvidedBy SPECIES
GOTO: SPECIES
DEFINE_SERVICES
Error feedback –backpropagation etc.
Weighting is rendered on
SPECIES/Interconnected
DEFINE_ENVIRONMENT set:{open, demand-driven, agent-based,
self-
organizing, domain-clustered, loosely-coupled}-ANN
architecture
32
33. BIC: SPELT IN THE ONTOLOGY OF DIGITAL ECOLOGY 33
– AN ANN EXAMPLE…continued
Computation of: ANN Output
Inputs to: { Species and Environment}:
←DOMAIN data set {details on neurons, layers, logistic function,
momentum function, learning
coefficient}
← ENVIRONMENT data set {Training data set to visible neurons,
teacher values}
← TASK data set {Defining error, type of feedback etc.}
← RULE data set {Stop criterion on iterations, tuning the
weighting coefficients}
← ROLE data set {Adjusting the nonlinearity, momentum and
learning towards convergence}
← REQUESTER data set {Input data to visible neurons, teacher set}}
← SUPPLIER data set {ANN user}
Compute I: Related subfunctions towards output Oi(t)
← REQUESTER observation at the output node
Compute II: IF computed error is too high,
← THEN do iteration
← OR ELSE, GOTO Compute I
END
33
34. BIC: SPELT IN THE ONTOLOGY OF DIGITAL ECOLOGY 34
– AN ANN EXAMPLE…continued
Subfunction Codes on: SUPPLIER and REQUESTER
Subfunction Code IIA Subfunction Code IIB on:
on: REQUESTER
SUPPLIER suite of suite of SPECIES ontology
SPECIES ontology DEFINE_ROLE
Convergence toward
DEFINE_ROLE objective function
DEFINE_SUPPLIER DEFINE_REQUESTER
-ANN user ⇒ ANN output
DEFINE_REQUESTED_
DEFINE_AVAILABLE_ SERVICE
SERVICE ⇒ Convergence
- ANN capability towards the
goal sought
34
35. 35A
REFERENCES
[1] N. Forbes, Biologically inspired computing, Computing in Science and Engineering,
November/December 2000, vol. 2(6), 84-87
[2] H. Boley and E. Chang, “Digital ecosystem: Principles and semantics,” in 2007 Inaugural
IEEE International Conference in Digital Ecosystems and Technologies (IEEE DEST 2007),
2007, 1-4244-047003/07.
[3] H. Dong, F. K. Hussain, and E. Chong, “Ontology-based digital ecosystem conceptual
representation,” in Proceedings of the Third International Conference on Automatic and
Autonomous Systems (ICAS’07), 2007, 0-7695-2859-5/07
[4] P. S. Neelakanta and R. C. Tourinho, Modeling an It-centric complex system via digital
ecology concepts, Presented in Third IEEE International Conference on Digital Ecosystems
and Technologies (IEEE-DEST 2009), Istanbul, Turkey, 31 May 2009 – 3 June 2009)
[5] G. W. Flake: The Computational Beauty of Nature, MIT Press. Boston, MA: 2000
[6] P.S. Neelakanta and D. De Groff, Neural Network Modeling: Statistical Mechanics and
Cybernetic Perspectives, CRC Press, Boca Raton, FL, 1994.
[7] P.S. Neelakanta, “Dynamics of neural learning in the information theoretic plane,” Chapter
5, Information-Theoretic Aspects of Neural Networks (Editor: P.S. Neelakanta), CRC Press,
Boca Raton, FL, 1999.
[8] L. M. Adleman, Computing with DNA, Scientific American, August 1998, 54-61
35
36. 35B
In search of a cyberspace … to launch BIC…
Conclusions
This study attempts to portray biologically-motivated
computing considerations…
… in the framework of a complex digital ecosystem.
… the ANN is chosen as an example and characterized in
the domain of interest.
… Relevant details on ANN describe the relational
aspects of Species and Environment vis-à-vis the BIC
in terms of the ontological details of [3].
36
THANK YOU!!!