MACHINE LEARNING IN BIOMEDICAL APPLICATIONS CONFERENCE


Machine Learning in Biomedical Applications Conference is one of the leading research topics in the international research conference domain. Machine Learning in Biomedical Applications 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).

Machine Learning in Biomedical Applications 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 Machine Learning in Biomedical Applications 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” .

Machine Learning in Biomedical Applications 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 "Machine Learning in Biomedical Applications Conference"

  • 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.
  • Investigations of Protein Aggregation Using Sequence and Structure Based Features
    Authors: M. Michael Gromiha, A. Mary Thangakani, Sandeep Kumar, D. Velmurugan, Keywords: Aggregation prone regions, amyloids, thermophilic proteins, amino acid residues, machine learning. DOI:10.5281/zenodo.1123851 Abstract: The main cause of several neurodegenerative diseases such as Alzhemier, Parkinson and spongiform encephalopathies is formation of amyloid fibrils and plaques in proteins. We have analyzed different sets of proteins and peptides to understand the influence of sequence based features on protein aggregation process. The comparison of 373 pairs of homologous mesophilic and thermophilic proteins showed that aggregation prone regions (APRs) are present in both. But, the thermophilic protein monomers show greater ability to ‘stow away’ the APRs in their hydrophobic cores and protect them from solvent exposure. The comparison of amyloid forming and amorphous b-aggregating hexapeptides suggested distinct preferences for specific residues at the six positions as well as all possible combinations of nine residue pairs. The compositions of residues at different positions and residue pairs have been converted into energy potentials and utilized for distinguishing between amyloid forming and amorphous b-aggregating peptides. Our method could correctly identify the amyloid forming peptides at an accuracy of 95-100% in different datasets of peptides.
  • Contribution for Rural Development through Training in Organic Farming
    Authors: Raquel P. F. Guiné, Daniela V. T. A. Costa, Paula M. R. Correia, Moisés Castro, Luis T. Guerra, Cristina A. Costa, Keywords: Mobile-learning, organic farming, rural development, survey. DOI:10.5281/zenodo.1108468 Abstract: The aim of this work was to characterize a potential target group of people interested in participating into a training program in organic farming in the context of mobile-learning. The information sought addressed in particular, but not exclusively, possible contents, formats and forms of evaluation that will contribute to define the course objectives and curriculum, as well as to ensure that the course meets the needs of the learners and their preferences. The sample was selected among different European countries. The questionnaires were delivered electronically for answering on-line and in the end 135 consented valid questionnaires were obtained. The results allowed characterizing the target group and identifying their training needs and preferences towards m-learning formats, giving valuable tools to design the training offer.
  • Methods for Distinction of Cattle Using Supervised Learning
    Authors: Radoslav Židek, Veronika Šidlová, Radovan Kasarda, Birgit Fuerst-Waltl, Keywords: Genetic data, Pinzgau cattle, supervised learning. DOI:10.5281/zenodo.1093044 Abstract: Machine learning represents a set of topics dealing with the creation and evaluation of algorithms that facilitate pattern recognition, classification, and prediction, based on models derived from existing data. The data can present identification patterns which are used to classify into groups. The result of the analysis is the pattern which can be used for identification of data set without the need to obtain input data used for creation of this pattern. An important requirement in this process is careful data preparation validation of model used and its suitable interpretation. For breeders, it is important to know the origin of animals from the point of the genetic diversity. In case of missing pedigree information, other methods can be used for traceability of animal´s origin. Genetic diversity written in genetic data is holding relatively useful information to identify animals originated from individual countries. We can conclude that the application of data mining for molecular genetic data using supervised learning is an appropriate tool for hypothesis testing and identifying an individual.
  • Evaluating and Measuring the Performance Parameters of Agricultural Wheels
    Authors: Ali Roozbahani, Aref Mardani, Roohollah Jokar, Hamid Taghavifar, Keywords: Slip, single wheel-tester, soil bin, soil–machine, speed, traction. DOI:10.5281/zenodo.1326793 Abstract: Evaluating and measuring the performance parameters of wheels and tillage equipments under controlled conditions obligates the use of soil bin facility. In this research designing, constructing and evaluating a single-wheel tester has been studied inside a soil bin. The tested wheel was directly driven by the electric motor. Vertical load was applied by a power bolt on wheel. This tester can measure required draft force, the depth of tire sinkage, contact area between wheel and soil, and soil stress at different depths and in the both alongside and perpendicular to the direction of traversing. In order to evaluate the system preparation, traction force was measured by the connected S-shaped load cell as arms between the wheel-tester and carriage. Treatments of forward speed, slip, and vertical load at a constant pressure were investigated in a complete randomized block design. The results indicated that the traction force increased at constant wheel load. The results revealed that the maximum traction force was observed within the %15 of slip.
  • Changes in Behavior and Learning Ability of Rats Intoxicated with Lead
    Authors: Amira, A. Goma, U. E. Mahrous, Keywords: Lead toxicity, rats, learning ability, behavior. DOI:10.5281/zenodo.1089062 Abstract: Measuring the effect of perinatal lead exposure on learning ability of offspring is considered as a sensitive and selective index for providing an early marker for central nervous system damage produced by this toxic metal. A total of 35 Sprague-Dawley adult rats were used to investigate the effect of lead acetate toxicity on behavioral patterns of adult female rats and learning ability of offspring. Rats were allotted into 4 groups, group one received 1g/l lead acetate (n=10), group two received 1.5g/l lead acetate (n=10), group three received 2g/l lead acetate in drinking water (n=10) and control group did not receive lead acetate (n=5) from 8th day of pregnancy till weaning of pups. The obtained results revealed a dose dependent increase in the feeding time, drinking frequency, licking frequency, scratching frequency, licking litters, nest building and retrieving frequencies, while standing time increased significantly in rats treated with 1.5g/l lead acetate than other treated groups and control, on contrary lying time decreased gradually in a dose dependent manner. Moreover, movement activities were higher in rats treated with 1g/l lead acetate than other treated groups and control. Furthermore, time spent in closed arms was significantly lower in rats given 2g/l lead acetate than other treated groups, while, they spent significantly much time spent in open arms than other treated groups which could be attributed to occurrence of adaptation. Furthermore, number of entries in open arms was dose dependent. However, the ratio between open/closed arms revealed a significant decrease in rats treated with 2g/l lead acetate than control group.
  • 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.
  • Implementation of Response Surface Methodology using in Small Brown Rice Peeling Machine: Part I
    Authors: S. Bangphan, P. Bangphan, T.Boonkang, Keywords: Brown rice, Response surface methodology(RSM), Rubber clearance, Round per minute (RPM), Peeling machine. DOI:10.5281/zenodo.1080786 Abstract: Implementation of response surface methodology (RSM) was employed to study the effects of two factor (rubber clearance and round per minute) in brown rice peeling machine of The optimal BROKENS yield (19.02, average of three repeats),.The optimized composition derived from RSM regression was analyzed using Regression analysis and Analysis of Variance (ANOVA). At a significant level α = 0.05, the values of Regression coefficient, R 2 (adj)were 97.35 % and standard deviation were 1.09513. The independent variables are initial rubber clearance, and round per minute parameters namely. The investigating responses are final rubber clearance, and round per minute (RPM). The restriction of the optimization is the designated.
  • Programmable Logic Controller for Cassava Centrifugal Machine
    Authors: R. Oonsivilai, M. Oonsivilai, J. Sanguemrum, N. Thumsirirat, A. Oonsivilai, Keywords: Control system, Machinery, Measurement, Potato starch DOI:10.5281/zenodo.1077703 Abstract: Chaiyaphum Starch Co. Ltd. is one of many starch manufacturers that has introduced machinery to aid in manufacturing. Even though machinery has replaced many elements and is now a significant part in manufacturing processes, problems that must be solved with respect to current process flow to increase efficiency still exist. The paper-s aim is to increase productivity while maintaining desired quality of starch, by redesigning the flipping machine-s mechanical control system which has grossly low functional lifetime. Such problems stem from the mechanical control system-s bearings, as fluids and humidity can access into said bearing directly, in tandem with vibrations from the machine-s function itself. The wheel which is used to sense starch thickness occasionally falls from its shaft, due to high speed rotation during operation, while the shaft may bend from impact when processing dried bread. Redesigning its mechanical control system has increased its efficiency, allowing quality thickness measurement while increasing functional lifetime an additional 62 days.
  • A Comparison of SVM-based Criteria in Evolutionary Method for Gene Selection and Classification of Microarray Data
    Authors: Rameswar Debnath, Haruhisa Takahashi, Keywords: support vector machine, generalization error-bound,feature selection, evolutionary algorithm, microarray data DOI:10.5281/zenodo.1074916 Abstract: An evolutionary method whose selection and recombination operations are based on generalization error-bounds of support vector machine (SVM) can select a subset of potentially informative genes for SVM classifier very efficiently [7]. In this paper, we will use the derivative of error-bound (first-order criteria) to select and recombine gene features in the evolutionary process, and compare the performance of the derivative of error-bound with the error-bound itself (zero-order) in the evolutionary process. We also investigate several error-bounds and their derivatives to compare the performance, and find the best criteria for gene selection and classification. We use 7 cancer-related human gene expression datasets to evaluate the performance of the zero-order and first-order criteria of error-bounds. Though both criteria have the same strategy in theoretically, experimental results demonstrate the best criterion for microarray gene expression data.