The Implementation of Neural Network On Determining the Determinant Factors Towards Students’ Stress Resistance

Stress is a condition that commonly felt by almost everyone, including college student. Naturally, human beings have a stress resistance in various levels. On previous research, an artificial neural network with backpropagation algorithm has been built to predict stress resistance level among college student. The level of stress resistance was predicted using four determinant factors i.e. frustration tolerance, conflict tolerance, anxiety tolerance, and tolerance to perceive changes as a challenge. On that research, the artificial neural network can predict stress resistance among college student correctly with an accuracy reach 75% after being trained up to 10334 epochs. On this research, dimensional reduction method will be applied on the determinant factors of stress resistance to eliminate disturbance factor and increase the accuracy of artificial neural networks in predicting stress resistance among college student. After the network was trained without disturbance factor i.e. anxiety tolerance, better network obtained. Experimental result showed that artificial neural network not only has better accuracy up to 81.5% but also faster training process which is only take 5000 epochs. Based on these results, the determinant factors of stress resistance among college student are: frustration tolerance, conflict tolerance, and tolerance to perceive changes as a challenge

An adaptive neuro-fuzzy inference system for the physiological presentation of seizure disorder

Seizure is the clinical manifestation of an excessive, hypersynchronous discharge of a population of cortical neurons accompanied by indescribable "pins- and needles-like” bodily sensations, smells or sounds, fear or depression, hallucinations, momentary jerks or head nods, staring with loss of awareness, and convulsive movements (i.e., involuntary muscle contractions) lasting for some seconds to a few minutes. In this work, an attempt is made to promote a better understanding of seizure disorder by proposing an adaptive neuro-fuzzy simulation model as a tool for capturing the physiological presentation of the disorder. Decision making was performed in two stages, namely the feature extractions using Microsoft Excel for corresponding digital value of the waveform of the EEG recordings of a seizure and those of a non-seizure patient directly from the EEG machine, and the transient features are accurately captured and localized in both time and amplitude. This extracted data were used for our Adaptive Neuro-Fuzzy Inference System (ANFIS) training and the ANFIS was trained with the backpropagation gradient descent method in combination with the least squares method to establish the validity of our ANFIS. The result shows an accuracy of 90.7% of predictions as the number of epochs increase.Keywords: Adaptive Neuro-Fuzzy Inference System, Electroencephalogram, Seizure Disorde

A hybrid unsupervised approach toward EEG epileptic spikes detection

Epileptic spikes are complementary sources of information in EEG to diagnose and localize the origin of epilepsy. However, not only is visual inspection of EEG labor intensive, time consuming, and prone to human error, but it also needs long-term training to acquire the level of skill required for identifying epileptic discharges. Therefore, computer-aided approaches were employed for the purpose of saving time and increasing the detection and source localization accuracy. One of the most important artifacts that may be confused as an epileptic spike, due to morphological resemblance, is eye blink. Only a few studies consider removal of this artifact prior to detection, and most of them used either visual inspection or computer-aided approaches, which need expert supervision. Consequently, in this paper, an unsupervised and EEG-based system with embedded eye blink artifact remover is developed to detect epileptic spikes. The proposed system includes three stages: eye blink artifact removal, feature extraction, and classification. Wavelet transform was employed for both artifact removal and feature extraction steps, and adaptive neuro-fuzzy inference system for classification purpose. The proposed method is verified using a publicly available EEG dataset. The results show the efficiency of this algorithm in detecting epileptic spikes using low-resolution EEG with least computational complexity, highest sensitivity, and lesser human interaction compared to similar studies. Moreover, since epileptic spike detection is a vital component of epilepsy source localization, therefore this algorithm can be utilized for EEG-based pre-surgical evaluation of epilepsy

Four Classifiers Used in Data Mining and Knowledge Discovery for Petroleum Exploration and Development

The application of data mining and knowledge discovery in databases for petroleum exploration and development (PE&D) is becoming promising, though still at an early stage. Up to now, the data mining tools usually used in PE&D are four classifiers: multiple regression analysis (MRA), Bayesian discrimination (BAYD), back-propagation neural network (BPNN), and support vector machine (SVM). Each of the four classifiers has its advantages and disadvantages. A question, however, has been raised in applications is: which classifier is the most applicable to a specified application? This paper has given an answer to the question through two case studies: 1) trap quality evaluation of the Northern Kuqa Depression of the Tarim Basin in western China, and 2) oil identification of the Xiefengqiao anticlinal structure of the Jianghan Basin in central China. Case 1 shows that the results of BAYD, BPNN and SVM are same and can have zero residuals, while MRA has unallowable residuals; but Case 2 shows that the results of only SVM have zero residuals, while BAYD, BPNN and MRA have unallowable residuals. The reasons are: a) since the two cases are nonlinear problems, the linear MRA is not applicable; b) since the nonlinearity of Case 1 is weak, the nonlinear BAYD, BPNN and SVM are applicable; and c) since the nonlinearity of Case 2 is strong, only nonlinear SVM is applicable. Therefore, it is proposed that: we can adopt MRA when a problem is linear; adopt BAYD, BPNN, or SVM when a problem is weakly nonlinear; and adopt only SVM when a problem is strongly nonlinear. In addition, the predictions of the applicable classifiers coincide with real exploration results, and a commercial gas trap was discovered after the forecast in Case 1 and SVM can correct some erroneous well-log interpretations in Case 2.Key words: Multiple regression analysis; Bayesian discrimination; Back-propagation neural network; Support vector machine; Trap quality evaluation; Oil identificatio

Imaging Sensors and Applications

In past decades, various sensor technologies have been used in all areas of our lives, thus improving our quality of life. In particular, imaging sensors have been widely applied in the development of various imaging approaches such as optical imaging, ultrasound imaging, X-ray imaging, and nuclear imaging, and contributed to achieve high sensitivity, miniaturization, and real-time imaging. These advanced image sensing technologies play an important role not only in the medical field but also in the industrial field. This Special Issue covers broad topics on imaging sensors and applications. The scope range of imaging sensors can be extended to novel imaging sensors and diverse imaging systems, including hardware and software advancements. Additionally, biomedical and nondestructive sensing applications are welcome

An artificially-intelligent biomeasurement system for total hip arthroplasty patient rehabilitation.

This study concerned the development and validation of a hardware and software biomeasurement system, which was designed to be used by physiotherapists, general practitioners and other healthcare professionals. The purpose of the system is to detect and assess gait deviation in the form of reduced post-operative range of movement (ROM) of the replacement hip joint in total hip arthroplasty (THA) patients. In so doing, the following original work is presented: Production of a wearable, microcontroller-equipped system which was able to wirelessly relay accelerometer sensor data of the subjects key hip-position parameters to a host computer, which logs the data for later analysis. Development of an artificial neural network is also reported, which was produced to process the sensor data and output assessment of the subjects hip ROM in the flexion/extension and abduction/adduction rotations (forward and backward swing and outward and inward movement of the hip respectively). The review of literature in the area of biomeasurement devices is also presented. A major data collection was carried out using twenty-one THA patients, where the device output was compared to the output of a Vicon motion analysis system which is considered the gold standard in clinical gait analysis. The Vicon system was used to show that the device developed did not itself affect the patients hip, knee or ankle gait cycle parameters when in use, and produced measurement of hip flexion/extension and abduction/adduction closely approximating those of the Vicon system. In patients who had gait deviations manifesting in reduced ROM of these hip parameters, it was demonstrated that the device was able to detect and assess the severity of these excursions accurately. The results of the study substantiate that the system developed could be used as an aid for healthcare professionals in the following ways: 1) To objectively assess gait deviation in the form of reduced flexion/extension and abduction/adduction in the human hip, after replacement; 2) Monitoring of patient hip ROM post-operatively; 3) Assist in the planning of gait rehabilitation strategies related to these hip parameters

XXIV congreso anual de la sociedad española de ingeniería biomédica (CASEIB2016)

En la presente edición, más de 150 trabajos de alto nivel científico van a ser presentados en 18 sesiones paralelas y 3 sesiones de póster, que se centrarán en áreas relevantes de la Ingeniería Biomédica. Entre las sesiones paralelas se pueden destacar la sesión plenaria Premio José María Ferrero Corral y la sesión de Competición de alumnos de Grado en Ingeniería Biomédica, con la participación de 16 alumnos de los Grados en Ingeniería Biomédica a nivel nacional. El programa científico se complementa con dos ponencias invitadas de científicos reconocidos internacionalmente, dos mesas redondas con una importante participación de sociedades científicas médicas y de profesionales de la industria de tecnología médica, y dos actos sociales que permitirán a los participantes acercarse a la historia y cultura valenciana. Por primera vez, en colaboración con FENIN, seJane Campos, R. (2017). XXIV congreso anual de la sociedad española de ingeniería biomédica (CASEIB2016). Editorial Universitat Politècnica de València. http://hdl.handle.net/10251/79277EDITORIA