Showing 1–12 of 134 results

  • EMG-Based Interface Using Machine Learning


    Abstract— This paper presents an EMG (electromyogram)-based input interface using machine learning for people with physical disabilities of the extremities. We have developed a virtual hand that can be operated in virtual environment using EMG signals. In this paper, we performed a lifting object task and box and block test task with the virtual hand. From the experimental results of the lifting object tasks, it was confirmed that six wrist joint movements were classified, and that an experimental subject appropriately lifted objects with the virtual hand in the virtual space. In the box and block tests task, it was confirmed that he moved block(s) to the opposite side of the box 9 times within 60 sec.
    Keywords—EMG Signal; Machine Learning; Myoelectric Prosthesis

  • A Study on Smart Home Voice Control Terminal


    Abstract—With the development of the smart home, people are not only satisfied to control the home appliances and lights remotely by pressing the button. If people can make full use of voice as the most effective way to communicate information, it will make the smart home more convenient in control.
    This paper describes the ARM microprocessor, speech recognition chip, voice broadcast module, and NRF24L01 wireless transceiver module. The voice control system of smart home, which is composed of sensor detection and other main modules, is different from the mainstream smart home control products in the market, such as Xiaomi Intelligent Audio. Its input device is portable wearable. When it is used, what you do is only to touch the button to start the recognition mode. Most importantly, it includes the function of voice broadcast so that it can let users achieve simple interaction.
    Keywords—Arm microcontroller; Speech recognition; Wireless transceiver; Voice broadcast

  • Decision Making Using Fuzzy Cognitive Maps in Post-Triage of Non-Critical Elderly Patients


    Abstract:  For patients arriving in the Emergency Departments (EDs) of hospitals a key aspect is to classify patients and identify high-risk patients since they have the potential for rapid deterioration during the waiting time. Triage is a widely applied and well-known process of evaluating and categorizing patients’ condition, in EDs. On the other hand, EDs are frequently overcrowded, which  makes triage an extremely challenging and demanding process in order to ensure that patients stepping into the ED are given the appropriate medical attention in time. This paper discusses the introduction of a general decision making procedure based on Fuzzy Cognitive Maps so that to create a Medical Decision Support System for Post-Triage decisions. The case of non-emergent and non-urgent elderly patients is examined and the corresponding model is developed.

    Keywords: Soft Computing; Medical Decision Support; Triage Assessment; Fuzzy Cognitive Maps

  • Comparing Two Feature Selection Methods for Influenza-A Antivral Resistance Determination.


    Abstract:  The paper thoroughly analyzes the use of Principal Component Analysis (PCA) in comparison to Information Gain (IG) as a feature selection method for improving the classification of Influenza-A antiviral resistance. Neural networks were used as the classification method of choice with PCA, while decision trees were the classification of choice with IG. The experiment was conducted on cDNA viral segments of Influenza-A belonging to the H1N1 strain. The 7 Infleunza-A segments generating the best results were used for comparison. Sequences from each segment were further divided into Adamantane-resistant, & non-Adamantane-resistant. Accuracy, sensitivity, specificity precision & time were used as performance measures. Using PCA for feature selection increased preprocessing speeds from an average processing time of 1.5 hours to 5 minutes, as opposed to IG. IG had higher accuracy. The best accuracy generated by PCA & NNs on the M1/M2 was 96.5%, while that of IG & DTs was 98.2% Using PCA features & DTs also generated a comparable accuracy to that of IG features & DT at 97.6% on the M1/M2 segment. There was a 88%  increase in feature selection processing speed when using PCA compared to IG on the M1/M2 segment alone

    Keywords: Influenza-A, Principle component analysis (PCA), Machine learning, Information gain, DNA classification, decision trees, neural networks.

  • ICIIBMS 2021 Call for paper


    The conference will be held on Nov.25-27. Please join us

  • The Impact of Trait Anxiety under a Painful Stimulus on the Chaotic Synchronization of Respiration and Pulse Waves


    Abstract: Thus far, attention has been paid to the relation between pain and anxiety, which has been studied. On the other hands, the earlier studies have hinted at the importance of considering mental and physical health from a holistic perspective, while taking into consideration the principles that prescribe the chaotic behavior of living organisms. Therefore, the purpose of this study is to use the respiration and pulse waves to examine the nature of the chaotic connection between biosignals involved in people’s mental and physical conditions, particularly those involved in the psychological trait of anxiety under a painful stimulus in this case. The results showed that with the high anxiety group, the extent of the synchronicity between their respiration and pulse waves under a painful stimulus increased, while this decreased for the low anxiety group. In other words, chaos dynamics for living systems are expressed in synchronous phenomena for the LLE for respiration and pulse waves. It also implied that these dynamics are prescribed by trait anxiety under a painful stimulus. This has opened up the possibility that, in the future, the cross-correlation function for LLE in pulse waves and respiration will make contributions to treating and assessing chronic pain in the field of clinical medicine.

    Keywords: Pain; Anxiety; Chaos; Synchronization

  • User Experience Evaluation on the Cryptocurrency Website by Trust Aspect


    Abstract:  A portal into the public ledger cryptocurrency makes a competition of the best web sites and easily trusted by the public. These are believed to constitute a measurable dimensions of User Experience (UX). This study aims to evaluate the user experience of the use of the three web sites most frequently accessed cryptocurrency from Indonesia. The evaluation conducted aimed at knowing the factors that influence user trust through the design of the interface and can be installed on a new design. Methods include Performance Metrics, Post-Task Rating, Post-Session Rating, and Experiential Overview and eye-tracking device. Based on the results of research, in the overall evaluation of the web site, the web site is the most superior of Indodax. The results of the evaluation are then applied on a new design using the software Invision and examined again to see a comparison of the respondent at the time of first use. The result of the research is the assessment, recommendations, and design the look of the web site cryptocurrency are trustworthy based on user experience

    Keywords: Cognitive Ergonomics, Human-Computer Interaction, User Experience, Cryptocurrency, Website, Emotional Design, Online Design

  • Placeholder

    Formation of efficient and inefficient social convention driven by conformity bias

    Abstract: Social conventions govern social behavior in many ways, ranging from left- and right-hand traffic to greetings. We sometimes find that inefficient social conventions, such as bullying in a class, are spontaneously formed, where almost all the people in the group are at a disadvantage. Although such a convention can be disadvantageous for all the people in the group, why are those conventions formed and continue to be maintained? A conformity bias, the behavioral tendency with which people take an action that a majority of the group take, can be one of the key ingredients of this phenomenon. In this study, we investigated the impact of conformity bias and an individual’s trial-and-error learning on the spontaneous formation of social conventions. Analyzing the stationary states of the dynamics and the tipping points at which the dynamics are divided into one converging to the efficient and the other converging to the inefficient convention, we found that by increasing the extent of conformity bias, an inefficient convention tends to be formed. On the other hand, an individual’s trial-and-error learning can suppress conformity bias and promote the formation of efficient conventions.


    Keywords: Social convention; Conformity bias; Positive externality; Reinforcement learning model; Tipping points

  • Terahertz Spectrum Recognition of Pathogens Based on PCA-Siamese Neural Network


    Abstract: In the terahertz time-domain spectroscopy technique, 16 common pathogens were experimentally studied and their characteristics absorption spectra in the frequency range of 0.1 to 2.2THz were obtained. The terahertz absorption spectra of 16 common pathogens were trained and identified by Siamese neural network method. First, the terahertz absorption spectra of the 16 pathogens were reduced by PCA to construct training data. Then, the constructed Siamese neural network model was trained by back propagation. Finally, the pathogen measured at different times was used as the target spectrum to evaluate the model, after comparing with the training data, the matching absorption spectrum was obtained, and the recognition rate reached 97.34%. The recognition results fully indicate that the identification of different kinds of pathogens can be recognized by Siamese neural network, which provides an effective method of the detection and identification of pathogens by terahertz spectroscopy.

    Keywords: THz spectroscopy; machine learning; Siamese neural network; similarity learning

  • Application of vertical switching signal prediction based on ship networking in heterogeneous networks


    Abstract: With the rise of ship networking, in view of the coexistence of many heterogeneous networks, the vertical switching technology of heterogeneous networks is studied, and a vertical signal prediction algorithm is proposed by using grey theory. Through the simulation analysis of MATLAB test, it is verified that the algorithm can effectively improve the accuracy of the prediction signal and is suitable for ship terminals.

    Keywords: Ship Networking, Heterogeneous Network, Vertical Switching, Prediction Algorithm.

  • Suspicious Bank Card Transaction Recognition Based on K-means Clustering and Random Forest Algorithm


    Abstract: Suspicious transactions are hidden in thousands of massive transaction data, causing incalculable losses and risks, but the detection is very difficult. In terms of how to effectively explore and identify suspicious transactions from massive transaction data accurately and quickly, this paper adopts the method based on the combination of k-means algorithm and random forest algorithm to solve the problem of data imbalance in the identification of suspicious transactions in bank accounts, and proposes an effective suspicious transaction detection model. At the same time, the AUC(Area Under Curve) and Recall indicators such as the unbalanced data classification standard of performance evaluation, and finally to Kaggle data platform for the bank account of suspicious transactions data set, the results show that the proposed detection model of performance evaluation index AUC increased by 5%, F1-measure increases by 1%, show that the method has some reference value to the suspicious transactions recognition, limited information utilization rate is higher, which makes all kinds of Banks prediction speed and accuracy of suspicious transactions events get improved, can to some extent, reduce the operating cost and risk of the banking sector.

    Keywords: Big data, K-means Algorithm, Random Forest, Suspicious Identification, Unbalanced Data

  • Association Analysis of primary Liver Cancer based on Apriori Algorithm


    Abstract: This paper briefly describes the Apriori algorithm for primary liver cancer data, and then performs data preprocessing based on the characteristics of primary liver cancer patients, including data import and extraction, and embedding the algorithm into primary liver cancer. The implementation of clinical warning. After that, the Apriori algorithm is used to realize the association of the data association rules of primary liver cancer, and the internal valuable association rules are obtained, which provides suggestions for improving the doctor’s remediation and prevention, so as to prevent the occurrence and reduction of primary liver cancer. The incidence of primary liver cancer has important practical significance.

    Keywords: Apriori algorithm; primary liver cancer; Association rules; association analysis