BIOENGINEERING AND BIOMEDICAL ENGINEERING CONFERENCE


Bioengineering and Biomedical Engineering Conference is one of the leading research topics in the international research conference domain. Bioengineering and Biomedical Engineering is a conference track under the Biomedical and Biological Engineering Conference which aims to bring together leading academic scientists, researchers and research scholars to exchange and share their experiences and research results on all aspects of Biomedical and Biological Engineering.

internationalscience.net provides a premier interdisciplinary platform for researchers, practitioners and educators to present and discuss the most recent innovations, trends, and concerns as well as practical challenges encountered and solutions adopted in the fields of (Biomedical and Biological Engineering).

Bioengineering and Biomedical Engineering is not just a call for academic papers on the topic; it can also include a conference, event, symposium, scientific meeting, academic, or workshop.

You are welcome to SUBMIT your research paper or manuscript to Bioengineering and Biomedical Engineering Conference Track will be held at “Biomedical and Biological Engineering Conference in Paris, France in November 2019” - “Biomedical and Biological Engineering Conference in London, United Kingdom in January 2020” - “Biomedical and Biological Engineering Conference in Tokyo, Japan in March 2020” - “Biomedical and Biological Engineering Conference in Amsterdam, Netherlands in May 2020” - “Biomedical and Biological Engineering Conference in Istanbul, Turkey in June 2020” - “Biomedical and Biological Engineering Conference in Stockholm, Sweden in July 2020” - “Biomedical and Biological Engineering Conference in Zürich, Switzerland in September 2020” - “Biomedical and Biological Engineering Conference in New York, United States in November 2020” .

Bioengineering and Biomedical Engineering is also a leading research topic on Google Scholar, Semantic Scholar, Zenedo, OpenAIRE, BASE, WorldCAT, Sherpa/RoMEO, Elsevier, Scopus, Web of Science.

INTERNATIONAL BIOMEDICAL AND BIOLOGICAL ENGINEERING CONFERENCE

NOVEMBER 21 - 22, 2019
PARIS, FRANCE

  • Abstracts/Full-Text Paper Submission Deadline March 14, 2019
  • Notification of Acceptance/Rejection Deadline March 28, 2019
  • Final Paper and Early Bird Registration Deadline October 21, 2019
  • CONFERENCE CODE: 18BBE11FR
  • One Time Submission Deadline Reminder

INTERNATIONAL BIOMEDICAL AND BIOLOGICAL ENGINEERING CONFERENCE

JANUARY 21 - 22, 2020
LONDON, UNITED KINGDOM

  • Abstracts/Full-Text Paper Submission Deadline March 14, 2019
  • Notification of Acceptance/Rejection Deadline March 28, 2019
  • Final Paper and Early Bird Registration Deadline December 19, 2019
  • CONFERENCE CODE: 20BBE01GB
  • One Time Submission Deadline Reminder

INTERNATIONAL BIOMEDICAL AND BIOLOGICAL ENGINEERING CONFERENCE

MARCH 26 - 27, 2020
TOKYO, JAPAN

  • Abstracts/Full-Text Paper Submission Deadline March 14, 2019
  • Notification of Acceptance/Rejection Deadline March 28, 2019
  • Final Paper and Early Bird Registration Deadline February 27, 2020
  • CONFERENCE CODE: 20BBE03JP
  • One Time Submission Deadline Reminder

INTERNATIONAL BIOMEDICAL AND BIOLOGICAL ENGINEERING CONFERENCE

MAY 13 - 14, 2020
AMSTERDAM, NETHERLANDS

  • Abstracts/Full-Text Paper Submission Deadline March 14, 2019
  • Notification of Acceptance/Rejection Deadline March 28, 2019
  • Final Paper and Early Bird Registration Deadline April 14, 2020
  • CONFERENCE CODE: 20BBE05NL
  • One Time Submission Deadline Reminder

INTERNATIONAL BIOMEDICAL AND BIOLOGICAL ENGINEERING CONFERENCE

JUNE 25 - 26, 2020
ISTANBUL, TURKEY

  • Abstracts/Full-Text Paper Submission Deadline March 14, 2019
  • Notification of Acceptance/Rejection Deadline March 28, 2019
  • Final Paper and Early Bird Registration Deadline May 26, 2020
  • CONFERENCE CODE: 20BBE06TR
  • One Time Submission Deadline Reminder

INTERNATIONAL BIOMEDICAL AND BIOLOGICAL ENGINEERING CONFERENCE

JULY 14 - 15, 2020
STOCKHOLM, SWEDEN

  • Abstracts/Full-Text Paper Submission Deadline March 14, 2019
  • Notification of Acceptance/Rejection Deadline March 28, 2019
  • Final Paper and Early Bird Registration Deadline June 11, 2020
  • CONFERENCE CODE: 20BBE07SE
  • One Time Submission Deadline Reminder

INTERNATIONAL BIOMEDICAL AND BIOLOGICAL ENGINEERING CONFERENCE

SEPTEMBER 15 - 16, 2020
ZÜRICH, SWITZERLAND

  • Abstracts/Full-Text Paper Submission Deadline March 14, 2019
  • Notification of Acceptance/Rejection Deadline March 28, 2019
  • Final Paper and Early Bird Registration Deadline August 13, 2020
  • CONFERENCE CODE: 20BBE09CH
  • One Time Submission Deadline Reminder

INTERNATIONAL BIOMEDICAL AND BIOLOGICAL ENGINEERING CONFERENCE

NOVEMBER 05 - 06, 2020
NEW YORK, UNITED STATES

  • Abstracts/Full-Text Paper Submission Deadline March 14, 2019
  • Notification of Acceptance/Rejection Deadline March 28, 2019
  • Final Paper and Early Bird Registration Deadline October 05, 2020
  • CONFERENCE CODE: 20BBE11US
  • One Time Submission Deadline Reminder

Biomedical and Biological Engineering Conference Call For Papers are listed below:

Previously Published Papers on "Bioengineering and Biomedical Engineering Conference"

  • Problems and Prospects of Agricultural Biotechnology in Nigeria’s Developing Economy
    Authors: Samson Abayomi Olasoju, Olufemi Adekunle, Titilope Edun, Johnson Owoseni, Keywords: Biosafety, biotechnology, food security, genetic engineering, genetic modification. DOI:10.5281/zenodo.2021611 Abstract: Science offers opportunities for revolutionizing human activities, enriched by input from scientific research and technology. Biotechnology is a major force for development in developing countries such as Nigeria. It is found to contribute to solving human problems like water and food insecurity that impede national development and threaten peace wherever it is applied. This review identified the problems of agricultural biotechnology in Nigeria. On the part of rural farmers, there is a lack of adequate knowledge or awareness of biotechnology despite the fact that they constitute the bulk of Nigerian farmers. On part of the government, the problems include: lack of adequate implementation of government policy on bio-safety and genetically modified products, inadequate funding of education as well as research and development of products related to biotechnology. Other problems include: inadequate infrastructures (including laboratory), poor funding and lack of national strategies needed for development and running of agricultural biotechnology. In spite of all the challenges associated with agricultural biotechnology, its prospects still remain great if Nigeria is to meet with the food needs of the country’s ever increasing population. The introduction of genetically engineered products will lead to the high productivity needed for commercialization and food security. Insect, virus and other related diseases resistant crops and livestock are another viable area of contribution of biotechnology to agricultural production. In conclusion, agricultural biotechnology will not only ensure food security, but, in addition, will ensure that the local farmers utilize appropriate technology needed for large production, leading to the prosperity of the farmers and national economic growth, provided government plays its role of adequate funding and good policy implementation.
  • Method of Cluster Based Cross-Domain Knowledge Acquisition for Biologically Inspired Design
    Authors: Shen Jian, Hu Jie, Ma Jin, Peng Ying Hong, Fang Yi, Liu Wen Hai, Keywords: Knowledge based engineering, biologically inspired design, knowledge cell, knowledge clustering, knowledge acquisition. DOI:10.5281/zenodo.1314576 Abstract: Biologically inspired design inspires inventions and new technologies in the field of engineering by mimicking functions, principles, and structures in the biological domain. To deal with the obstacles of cross-domain knowledge acquisition in the existing biologically inspired design process, functional semantic clustering based on functional feature semantic correlation and environmental constraint clustering composition based on environmental characteristic constraining adaptability are proposed. A knowledge cell clustering algorithm and the corresponding prototype system is developed. Finally, the effectiveness of the method is verified by the visual prosthetic device design.
  • Reverse Engineering of Agricultural Machinery: A Key to Food Sufficiency in Nigeria
    Authors: Williams S. Ebhota, Virginia Chika Ebhota, Samuel A. Ilupeju, Keywords: Agricultural machinery, domestic manufacturing, forward engineering, production reverse engineering, technology. DOI:10.5281/zenodo.1130057 Abstract: Agriculture employs about three-quarter of Nigeria's workforce and yet food sufficiency is a challenge in the country. This is largely due to poor and outdated pre-harvest and post-harvest farming practices. The land fallow system is still been practised as fertiliser production in the country is grossly inadequate and expensive. The few available post-harvest processing facilities are faced with ageing and are inefficient. Also, use of modern processing equipment is limited by farmers' lack of fund, adequate capacity to operate and maintain modern farming equipment. This paper, therefore, examines key barriers to agricultural products processing equipment in the country. These barriers include over-dependence on foreign technologies and expertise; poor and inadequate manufacturing infrastructure; and lack of political will by political leaders; lack of funds; and lack of adequate technical skills. This paper, however, sees the increase in the domestic manufacturing of pre-harvest and post-harvest machinery and equipment through reverse engineering approach as a key to food production sufficiency in Nigeria.
  • Fuzzy Optimization in Metabolic Systems
    Authors: Feng-Sheng Wang, Wu-Hsiung Wu, Kai-Cheng Hsu, Keywords: Fuzzy multi-objective optimization problem, kinetic model, metabolic engineering. DOI:10.5281/zenodo.1093680 Abstract: The optimization of biological systems, which is a branch of metabolic engineering, has generated a lot of industrial and academic interest for a long time. In the last decade, metabolic engineering approaches based on mathematical optimizations have been used extensively for the analysis and manipulation of metabolic networks. In practical optimization of metabolic reaction networks, designers have to manage the nature of uncertainty resulting from qualitative characters of metabolic reactions, e.g., the possibility of enzyme effects. A deterministic approach does not give an adequate representation for metabolic reaction networks with uncertain characters. Fuzzy optimization formulations can be applied to cope with this problem. A fuzzy multi-objective optimization problem can be introduced for finding the optimal engineering interventions on metabolic network systems considering the resilience phenomenon and cell viability constraints. The accuracy of optimization results depends heavily on the development of essential kinetic models of metabolic networks. Kinetic models can quantitatively capture the experimentally observed regulation data of metabolic systems and are often used to find the optimal manipulation of external inputs. To address the issues of optimizing the regulatory structure of metabolic networks, it is necessary to consider qualitative effects, e.g., the resilience phenomena and cell viability constraints. Combining the qualitative and quantitative descriptions for metabolic networks makes it possible to design a viable strain and accurately predict the maximum possible flux rates of desired products. Considering the resilience phenomena in metabolic networks can improve the predictions of gene intervention and maximum synthesis rates in metabolic engineering. Two case studies will present in the conference to illustrate the phenomena.
  • Odor Discrimination Using Neural Decoding of Olfactory Bulbs in Rats
    Authors: K.-J. You, H.J. Lee, Y. Lang, C. Im, C.S. Koh, H.-C. Shin, Keywords: biomedical signal processing, neural engineering, olfactory,neural decoding, BMI DOI:10.5281/zenodo.1084131 Abstract: This paper presents a novel method for inferring the odor based on neural activities observed from rats- main olfactory bulbs. Multi-channel extra-cellular single unit recordings were done by micro-wire electrodes (tungsten, 50μm, 32 channels) implanted in the mitral/tufted cell layers of the main olfactory bulb of anesthetized rats to obtain neural responses to various odors. Neural response as a key feature was measured by substraction of neural firing rate before stimulus from after. For odor inference, we have developed a decoding method based on the maximum likelihood (ML) estimation. The results have shown that the average decoding accuracy is about 100.0%, 96.0%, 84.0%, and 100.0% with four rats, respectively. This work has profound implications for a novel brain-machine interface system for odor inference.
  • Full-genomic Network Inference for Non-model organisms: A Case Study for the Fungal Pathogen Candida albicans
    Authors: Jörg Linde, Ekaterina Buyko, Robert Altwasser, Udo Hahn, Reinhard Guthke, Keywords: Pathogen, network inference, text-mining, Candida albicans, LASSO, mutual information, reverse engineering, linear regression, modelling. DOI:10.5281/zenodo.1070029 Abstract: Reverse engineering of full-genomic interaction networks based on compendia of expression data has been successfully applied for a number of model organisms. This study adapts these approaches for an important non-model organism: The major human fungal pathogen Candida albicans. During the infection process, the pathogen can adapt to a wide range of environmental niches and reversibly changes its growth form. Given the importance of these processes, it is important to know how they are regulated. This study presents a reverse engineering strategy able to infer fullgenomic interaction networks for C. albicans based on a linear regression, utilizing the sparseness criterion (LASSO). To overcome the limited amount of expression data and small number of known interactions, we utilize different prior-knowledge sources guiding the network inference to a knowledge driven solution. Since, no database of known interactions for C. albicans exists, we use a textmining system which utilizes full-text research papers to identify known regulatory interactions. By comparing with these known regulatory interactions, we find an optimal value for global modelling parameters weighting the influence of the sparseness criterion and the prior-knowledge. Furthermore, we show that soft integration of prior-knowledge additionally improves the performance. Finally, we compare the performance of our approach to state of the art network inference approaches.
  • Pragati Node Popularity (PNP) Approach to Identify Congestion Hot Spots in MPLS
    Authors: E. Ramaraj, A. Padmapriya, Keywords: Conditional Probability Distribution, Congestion hotspots, Operational Networks, Traffic Engineering. DOI:10.5281/zenodo.1331637 Abstract: In large Internet backbones, Service Providers typically have to explicitly manage the traffic flows in order to optimize the use of network resources. This process is often referred to as Traffic Engineering (TE). Common objectives of traffic engineering include balance traffic distribution across the network and avoiding congestion hot spots. Raj P H and SVK Raja designed the Bayesian network approach to identify congestion hors pots in MPLS. In this approach for every node in the network the Conditional Probability Distribution (CPD) is specified. Based on the CPD the congestion hot spots are identified. Then the traffic can be distributed so that no link in the network is either over utilized or under utilized. Although the Bayesian network approach has been implemented in operational networks, it has a number of well known scaling issues. This paper proposes a new approach, which we call the Pragati (means Progress) Node Popularity (PNP) approach to identify the congestion hot spots with the network topology alone. In the new Pragati Node Popularity approach, IP routing runs natively over the physical topology rather than depending on the CPD of each node as in Bayesian network. We first illustrate our approach with a simple network, then present a formal analysis of the Pragati Node Popularity approach. Our PNP approach shows that for any given network of Bayesian approach, it exactly identifies the same result with minimum efforts. We further extend the result to a more generic one: for any network topology and even though the network is loopy. A theoretical insight of our result is that the optimal routing is always shortest path routing with respect to some considerations of hot spots in the networks.