Accepted Papers

  • Computer based diagnosis system for tumor detection & classification: a hybrid approach
    Virupakshappa,Department of CSE, Appa IET,India,Dr. Basavaraj Amarapur,Department of E & E E, P D A C E,India

    Tumor detection and separating the tumor region from MRI is image most important in medical tumor disease analysis. It needs clinical experts to meet the standard level of accuracy. This limitation is overcome by the application of computer aided technology in medical field for tumor identification and segmentation. In this paper we proposed an efficient method consisting of tumor segmentation model by using multiple feature extraction and artificial neural network classifier. The system performance is examined with 40 trained images with 60 tested MRI scanned medical dataset. The system performance examined term of accuracy with respect to the confusion matrix is presented in result section. From the result section we proved that we meet required system accuracy level up to 92.5%.

  • Life style modification by wearable actigraphic sensors with peer monitoring function
    Emi Yuda,Yutaka Yoshida,Junichiro Hayano,Nagoya City University Graduate School of Medical Sciences, Nagoya, Japan,Akira Kurata,Kenz Product Department, Suzuken Co., Ltd., Nagoya, Japan

    We investigated whether peer monitoring function of wearable actigraphic sensors will help to improve daily activities among a group of workplace colleagues. Peer monitoring of actigraphic data showed no significant effect on exercise energy consumption or step count. It rather reduced the time of physical activities >3 Mets and increased heart rate during working hours. Peer monitoring of actigraphic among workplace colleagues may cause complicated psychologic effects and may not improve physical activities.

  • Speech Assistive Communication System Using Eog
    Anson Bastos and Sidharth Alhat,Department of Electrical Engineering, VJTI, Mumbai, India

    This paper gives complete guidelines for authors submitting papers for the AIRCC Journals.This paper presents a wearable single-channel electrooculography (EOG) based human-computer interface (HCI) for the physically handicapped speech impaired persons. In the developed system, EOG signals for control are generated from eye blinks or eye movements. Also signals from wrinkling of the forehead have been used. These signals are conditioned by a hardware system and processed by software using a DFT based algorithm. The system then transfer the digital data wirelessly to a phone or a tablet. The HCI provides two modes of operation: auto scroll mode and manual scroll mode. Text to speech has been used on the phone or tablet for the user to speak the typed phrase. The designed system is highly user friendly and provides a low power, cost effective, ergonomic and a reliable solution to enable efficient communication for the people with motor neuron diseases.