Improving Students’ Creativity In Mathematic Using SAVI (Somatic Auditory Visual Intellectual) Approach

The low level of student creativity is a reflection of the unsuccessful learning process. Thus, it needs a special treatment to solve it. The study aims to increase student creativity using the SAVI method for 4th semester students of the Mathematic Education Study Program. This research is a classroom action research. Research with a focus on increasing student creativity using the SAVI method was conducted in semester 4 of the Mathematic Education Study Program, with 44 students. This research was conducted in 3 cycles. After observing and evaluating in 3 cycles with the results mentioned above, it can be concluded that the SAVI learning approach can increase the creativity of students of the UMP FKIP Mathematic Education Study Program. It is proven that in cycle I the average response is 33.71%, in cycle II the average response is 49.3%, and in cycle III the average response is 64.9%. With an increase from cycle I to cycle II of 15.59% and from cycle II to cycle III of 15.6%. From the result it can be said that SAVI was effective to increase students creativity in mathematic

Moving object detection via TV-L1 optical flow in fall-down videos

There is a growing demand for surveillance systems that can detect fall-down events because of the increased number of surveillance cameras being installed in many public indoor and outdoor locations. Fall-down event detection has been vigorously and extensively researched for safety purposes, particularly to monitor elderly peoples, patients, and toddlers. This computer vision detector has become more affordable with the development of high-speed computer networks and low-cost video cameras. This paper proposes moving object detection method based on human motion analysis for human fall-down events. The method comprises of three parts, which are preprocessing part to reduce image noises, motion detection part by using TV-L1 optical flow algorithm, and performance measure part. The last part will analyze the results of the object detection part in term of the bounding boxes, which are compared with the given ground truth. The proposed method is tested on Fall Down Detection (FDD) dataset and compared with Gunnar-Farneback optical flow by measuring intersection over union (IoU) of the output with respect to the ground truth bounding box. The experimental results show that the proposed method achieves an average IoU of 0.92524

PEMBINAAN MODEL TRANSISI INSTITUSI BERASASKAN KOMPONEN PENYESUAIAN PELAJAR

Transisi institusi merupakan satu fasa yang kritikal terhadap pelajar. Langkah yang perlu diambil untuk menjadikan fasa ini dapat dilalui dengan baik ialah menyesuaikan diri dengan perubahan yang berlaku. Justeru, satu model transisi institusi berasaskan penyesuaian pelajar bagi memperlihatkan hubung kait antara beberapa pemboleh ubah penyesuaian diperlukan agar dapat menilai perubahan yang berlaku terhadap pelajar. Kajian ini bertujuan membina model transisi institusi berasaskan empat komponen penyesuaian pelajar (TIPP). Teori transisi Schlossberg menjadi asas kepada pembinaan model TIPP. Manakala Student Adaption to College Questionnaire (SACQ) digunakan sebagai instrumen transisi pelajar. Subjek kajian terdiri daripada pelajar Persediaan Program Ijazah Sarjana Muda Perguruan (PPISMP) Pendidikan Matematik di Institut Pendidikan Guru Malaysia (IPGM). Empat komponen penyesuaian (akademik, sosial, peribadi-emosi dan komitmen institusi) menjadi tumpuan kepada kajian ini. Model diuji dan dinilai melalui analisis Model Persamaan Berstruktur Kuasa Dua Terkecil Separa (MPB-KDTS). Kajian dilakukan berjaya mengemukakan model TIPP berdasarkan Teori Transisi Schlossberg dan memperlihatkan hubung kait secara serentak antara komponen-komponen penyesuaian

The profile of students' mathematical representation competence, self-confidence, and habits of mind through problem-based learning models

Mathematics is an essential subject for students. Teachers, therefore, need to provide innovative learning that develops students' mathematical skills. This study was conducted to determine the effect of a problem-based learning (PBL) model on students' mathematical representation competencies, self-confidence, and habits of mind. It used a quantitative methodology and was conducted on eighth-grade students divided into an experimental class with a PBL model and a control class with a direct learning model. The results showed that the mathematical representation competencies of the students in the experimental class were better than those in the control class. Students' self-confidence and habits of mind also influenced their mathematical representation competencies. It shows that the PBL model positively affects students' mathematical representation competency, self-confidence, and habits of mind. Teachers can use the PBL model to develop their students' mathematical representation competencies by paying attention to students' self-confidence and habits of mind

Model orientasi pembelajaran matematik berasaskan penyesuaian pelajar: pendekatan ‘structural equation model-partial least squares’

Kajian ini bertujuan untuk membina model orientasi pembelajaran matematik berasaskan penyesuaian dalam kalangan pelajar di Institut Pendidikan Guru Malaysia. Model Persamaan Berstrukutur-Kuasa Dua Terkecil Separa digunakan untuk menilai kebagusan item-item yang digunakan dari aspek kesahan serta kebolehpercayaan. Seterusnya, model orientasi pembelajaran matematik telah dihasilkan dalam kajian ini. Data diperoleh dengan mengedarkan instrumen kajian kepada 95 orang pelajar Persediaan Program Ijazah Sarjana Muda Perguruan (PPISMP) major pendidikan Matematik di IPGM. Item kajian diterjemah dan diadaptasi daripada ‘Student Adaptation to College Questionnaire’ (SACQ) dan Orientasi Pembelajaran Matematik (OPM). Penilaian kesahan dilakukan berdasarkan kepada kesahan konstruk dan kesahan menumpu item-item pengukuran. Seterusnya, kebolehpercayaan gubahan dinilai melalui ketekalan dalaman berdasarkan nilai alpha cronbach dan kesahan pembeza. Keputusan statistik menunjukkan bahawa nilai varians bagi orientasi pembelajaran matematik dipengaruhi oleh penyesuaian pelajar. Justeru, bagi memperbaiki orientasi pembelajaran matematik, dapatan kajian dan model yang dibina boleh digunakan oleh pihak IPGM, BPG, pensyarah dan seterusnya para pelajar sebagai rujukan

Mapping lung cancer disease in Libya using Standardized Morbidity Ratio, BYM model and mixture model, 2006 to 2011: Bayesian Epidemiological Study

Cancer represents a significant burden on both patients and their families and their societies, especially in developing countries, including Libya. Therefore, the aim of this study was to model the geographical distribution of lung cancer incidence in Libya. The correct choice of a statistical model is a very important step to producing a good map of disease in question. Therefore, in this study will use three models to estimate the relative risk for lung cancer disease, they are initially Standardized Morbidity Ratio, which is the most common statistic used in disease mapping, BYM model, and Mixture model. As an initial step, this study begins by providing a review of all models are proposed, which we then apply to lung cancer data in Libya. In this paper, we show some preliminary results, which are displayed and compared by using maps, tables, graphics and goodness-of-fit, the last measure of displaying the results is common in statistical modelling to compare fitted models. The main general results presented in this study show that the last two models, BYM and Mixture have been demonstrated to overcome the problem of the first model when there no observed lung cancer cases in certain districts. Also, other results show that Mixture model is most robust and gives a better relative risk estimate across compared it with a range of models

A review of automated micro-expression analysis

Micro-expression is a type of facial expression that is manifested for a very short duration. It is difficult to recognize the expression manually because it involves very subtle facial movements. Such expressions often occur unconsciously, and therefore are defined as a basis to help identify the real human emotions. Hence, an automated approach to micro-expression recognition has become a popular research topic of interest recently. Historically, the early researches on automated micro-expression have utilized traditional machine learning methods, while the more recent development has focused on the deep learning approach. Compared to traditional machine learning, which relies on manual feature processing and requires the use of formulated rules, deep learning networks produce more accurate micro-expression recognition performances through an end-to-end methodology, whereby the features of interest were extracted optimally through the training process, utilizing a large set of data. This paper reviews the developments and trends in micro-expression recognition from the earlier studies (hand-crafted approach) to the present studies (deep learning approach). Some of the important topics that will be covered include the detection of micro-expression from short videos, apex frame spotting, micro-expression recognition as well as performance discussion on the reviewed methods. Furthermore, major limitations that hamper the development of automated micro-expression recognition systems are also analyzed, followed by recommendations of possible future research directions

Design of optimal multi-objective-based facts component with proportional-integral-derivative controller using swarm optimization approach

This study proposes a multi-objective-based swarm intelligence method to improve angle stability. An optimization operation with single objective function only improves the performance of one perspective and ignores the other. The combination of two objective functions which derived from real and imaginary components of eigenvalue are able to provide better performance beyond the optimization capabilities of single objective function. Tested using MATLAB, the simulation is performed using a single machine attached to the infinite bus (SMIB) system equipped with static var compensator (SVC) that attached with PID controller (SVC-PID). The objective of this experiment is to explore the excellent parameters in SVC-PID to produce a more stable system. In addition to the comparison of objective functions, this study also compares particle swarm optimization (PSO) capabilities with evolutionary programming (EP) and artificial immune system (AIS) techniques

Mapping Libya’s prostate cancer based on the SMR method: a geographical analysis

Disease mapping has become an important method used in public health research and disease epidemiology. It is a spatial representation of epidemiology data. A very common disease mapping method is called Standardized Morbidity Ratio (SMR). Many researchers used this method to estimate the relative risk of the disease as a preliminary analysis. In this study, the SMR method displays the high and low risk areas of prostate cancer for all districts in Libya. SMR is the ratio of the observed to the expected number of prostate cancer cases and was applied to the observed prostate cancer data from Libya for the years 2010 and 2011. The results were presented in graphs and maps. The highest risk of prostate cancer (all type of cancers) is in the West of Libya probably due to the oil installations in this area such as Mellitah Oil and Gas B.v, the Azawia Oil Refining Company and Bouri Oil Field, as well as the electrical power stations. Susceptible people located in the Eastern part of the country have the lowest risk when compared to the overall population. In conclusion the results show that the use of the SMR method to estimate the relative risk in maps provides high-low risk appearances in maps compared to using the total number of cancer incidence alone. In other words, the SMR method can be considered a basic procedure because it takes into account the total human population for each district

Symmetrically Stacked Long Short-Term Memory Networks for Fall Event Recognition Using Compact Convolutional Neural Networks-Based Tracker

In recent years, the advancement of pattern recognition algorithms, specifically the deep learning-related techniques, have propelled a tremendous amount of researches in fall event recognition systems. It is important to detect a fall incident as early as possible, whereby a slight delay in providing immediate assistance can cause severe unrecoverable injuries. One of the main challenges in fall event recognition is the imbalanced training data between fall and no-fall events, where a real-life fall incident is a sporadic event that occurs infrequently. Most of the recent techniques produce a lot of false alarms, as it is hard to train them to cover a wide range of fall situations. Hence, this paper aims to detect the exact fall frame in a video sequence, as such it will not be dependent on the whole clip of the video sequence. Our proposed approach consists of a two-stage module where the first stage employs a compact convolutional neural network tracker to generate the object trajectory information. Features of interest will be sampled from the generated trajectory paths, which will be fed as the input to the second stage. The next stage network then models the temporal dependencies of the trajectory information using symmetrical Long Short-Term Memory (LSTM) architecture. This two-stage module is a novel approach as most of the techniques rely on the detection module rather than the tracking module. The simulation experiments were tested using Fall Detection Dataset (FDD). The proposed approach obtains an expected average overlap of 0.167, which is the best performance compared to Multi-Domain Network (MDNET) and Tree-structured Convolutional Neural Network (TCNN) trackers. Furthermore, the proposed 3-layers of stacked LSTM architecture also performs the best compared to the vanilla recurrent neural network and single-layer LSTM. This approach can be further improved if the tracker model is firstly pre-tuned in offline mode with respect to a specific type of object of interest, rather than a general object