ELECTRONIC STUDENT-TEACHER POLL ATTANDANCE SYSTEM
Our goal is to make roll call systems at schools technological. While Rolling Call system is getting technological capacities some useful outcomes occur as well like; To remove the cost for class books by abolishing the class books used for roll call To eliminate mistakes with the usage of class book .(The numbers written incorrectly) To save again a large amount of waste paper( The short messages will be send to the parents instead of mailing the letters to the their adresses once a week about their students' absance.) To improve communication among school and parents ,by this way to prevent various kinds of problems arising from absenteeism. To make teachers school boards more efficently and motivated besides their affairs will get easier as well. To save time of school managers(They have no Works like saving the absenteeisms into e-school network because everything including all the absenteeism info will be carried into the system automatically) To save time of each lesson the teachers will not loose time for the roll call ,so they will be able to devote their time to their students and training. -Technological polling mechanism will contribute to education, this contribution will make all the students and teachers happy because all the teachers carry concern whether their curriculum will be finished or not through this system recording all the rests of teachers because of illnesses and some national celebrations.
New Screening Method for Early Pediatric Cancer Detection Through Automated Handwriting Analysis
Pediatric cancer has an incidence rate of more than 175,000 per year with a mortality rate of approximately 96,000 per year. One major cause of this problem is late diagnosis. A novel promising way of pediatric cancer screening is handwriting analysis. This method surpasses other methods by detecting pediatric cancer in a very early stage. However, studies are still limited to manual analysis which needs an expert and a long period of time. The aim of this project is to design a computer program to extract handwriting features and build a classification model to classify the user as patient or as control. Dataset was collected from schools and hospitals where all participants could read and write in English. After data cleansing, number of samples was 440 samples. MATLAB (Matrix Laboratory) program was used for extracting geometric features in handwriting. Program was validated using a subset of 50 samples of the dataset. WEKA Package was used to test and build the classifier. Experiments were done using classifiers: Logistic, Multilayer Perceptron, J48, LibSVM, AdaBoostM1 and Naïve Bayes. Best subset of attributes was evaluated and used for each classifier and all calculations were done as the average of cross validation operations of several folds assignments. Best performance was achieved by Logistic classifier with average accuracy of 80.15%, standard deviation of 0.43% and Matthews's correlation coefficient of 0.59. Finally, this project presents a new fast, free, ready, easy and psychologically comfortable method for pediatric cancer detection while keeping suitable accuracy for mass screening.
西爾平斯基船帆與掛毯圖形應用於數位圖形與數位音樂創作
西爾平斯基船帆(Sierpinski Gasket)與西爾平斯基掛毯(Sierpinski Carpet)都屬於碎形(fractals)圖形的一種，可以利用迭代運算系統IFS(Iterated Function Systems)碼來產生，代入迭代運算方程式後，經由多次的運算，可以得到重覆的圖形。本研究中，我將提出一些作法，找出西爾平斯基船帆與掛毯圖形其遞迴關係式，進而討論出其數位圖形之規律性及所涵蓋的內容與性質，著重在推廣西爾平斯基船帆與掛毯圖形的概念，將一段音樂曲取出，把它們看成反覆隨機迭代點，利用程式經由多次的插值運算，計算出各段音符。最後加入基因演算法來解決音符長短的問題，把製造好的音符染色體放置到交配池中，以隨機的方式在交配池中選取其中之一個染色體進行交配的動作，此二音符染色體會交換彼此的基因，產生下一代新的代表音符長短之染色體，應用於數位音樂創作，而衍生的西爾平斯基船帆與掛毯圖形新穎應用與創新的結果，希望能提供數位音樂創作的多樣性，更進而可以找出「好聽的音樂」與數學的直接關聯性。‘Sierpinski Gasket’ and ‘Sierpinski Carpet’ are two graphics that belong to fractals. They can be produced by IFS (Iterated Function Systems). By iterative computation of many times, we can obtain the similar graphics. In my research, there are some methods to generate Sierpinski Gasket, Sierpinski Carpet, and the iterative algorithms. In addition, I would discuss the regularity and the content as well as the properties of those digital patterns. At last, the advanced application of Sierpinski Gasket and Sierpinski Carpet to digital music pieces was presented. The program took a note of several measure of music as the beginning point, and made the IFS calculations for each new note in each measure. But there was no difference in beats if you just make the IFS iteration. So I changed the beats with genetic crossover method. In this research, the expression of the DNA to each beat of note was adopted. The same way, it took a note as a beginning point. And the system obtained the new DNA from the old notes for new ones randomly. That would make a piece of brand new music. What I want to do in this research is improve the multiformity of music and find what the relationship is of ‘good music’ and mathematical algorithms.
IlluminaMed: Developing Novel Artificial Intelligence Techniques for the Use In a Biomedical Image Analysis Toolkit and Personalized Medicine Engine
Despite the multitude of biomedical scans conducted, there is still relatively low accuracy and standardization of diagnoses from these images. In both the fields of computer science and medicine there is very strong interest in developing personalized treatment policies for patients who have variable responses to treatments. The aim of my research was automatic segmentation of brain MRI scans to better analyze patients with tumors, multiple sclerosis, ALS, or Alzheimer’s. In particular, I aim to use this information, along with novel artificial intelligence algorithms, to find an optimal personalized treatment policy which is a non-deterministic function of the patient specific covariate data that maximizes the expected survival time or clinical outcome. The result of the research was IlluminaMed, a biomedical image analysis toolkit that relies on the development of new artificial neural networks and training algorithms and novel research in fuzzy logic. The networks can detect patterns more complex than humans can identify and create patterns over long periods of time. IlluminaMed was trained by a dataset of professionally and manually segmented MRI scans from several prestigious hospitals and universities. I then developed an algorithmic framework to solve multistage decision problem with a varying number of stages that are subject to censoring in which the “rewards” are expected survival times. In specific, I developed a novel Q-learning algorithm that dynamically adjusts for these parameters. Furthermore, I found finite upper bounds on the generalized error of the treatment paths constructed by this algorithm. I have also shown that when the optimal Q-function is an element of the approximation space, the anticipated survival times for the treatment regime constructed by the algorithm will converge to the optimal treatment path. I demonstrated the performance of the proposed algorithmic framework via simulation studies and through the analysis of chronic depression data and a hypothetical clinical trial. IlluminaMed can automatically segment the scans with 98% accuracy, find tumors with 96% accuracy and approximate their volume within a 2% margin of error. It can also find lesions in MS and ALS, distinguishing them from tumors with 94% accuracy. IlluminaMed can, in addition, determine the tendency of a patient to develop Alzheimer’s several months before patients develop symptoms correlating the brain structure and its fluctuations. Lastly, the censored Q-learning algorithm I developed is more effective than the state of the art clinical decision support systems and is able to operate in environments when many covariate parameters may be unobtainable or censored. IlluminaMed is the only fully automatic biomedical image analysis toolkit and personalized medicine engine. The personalized medicine engine runs at a level that is comparable to the best physicians. It is less computationally complex than similar software and is unique in the fact that it can find new patterns in the brain with possible future diagnoses. IlluminaMed’s implications are not only great in terms of the biomedical field, but also in the field of artificial intelligence with new findings in neural networks and the relationships of fuzzy extensional subsets.
Random number generators and their applications in Computer Science with the Monte Carlo Method
Monte Carlo methods are non-parametric algorithms that use random numbers and theorems of probability theory to approximate values that are not random. The purpose of my research was to approximate the surface of different geographical areas that can be easily approximated to polygons (e.g. lakes, glaciers, deserts) with Monte Carlo simulations starting from either Cartesian coordinates or pictures. Computer science would not exist without math, and this research project showed me the importance of a deep understanding of probability theory in the world of simulations and, more generally, the importance of developing new theorems and algorithms. The results of my research could be developed in different ways: it would be interesting to produce software that allows one to approximate areas from pictures taken from a smartphone; as well, the theorem I found has to be proven, and also Monte Carlo methods as a means of random number generation can always be improved. There are still many possibilities.
Self driving car
Autonomous car is a very new concept, being a car without any driver. Several concurrent software process data using Artificial Intelligence to recognize and propose a path which the car should follow. The goal of the project is that a driverless car can reduce the distance between the cars, lowering the degree of road loadings, reducing the number of traffic jams, avoid human errors, and allowing people with disabilities(even blind people) to travel using an autonomous car. Theoretically a car without driver in the future should be much safer, because human reaction speed is higher than 200 ms, and the computing power of the newest computers allows traffic calculations even to 10 ms. The necessary power is provided by three multi-core laptops that process with Artificial Intelligence in order to recognize traffic signs, traffic lanes , traffic car fingerprints, processing the data from a 3D radar, using particle filters to localize car in a GPS map, the management of database with traffic signs, magnetic sensors, acceleration sensors, a distributed software, a supervisory system and the software which drives the stepper motor to turn the steering wheel (acceleration and braking). Currently the software is able to recognize the traffic signs, register them in a database using Google Maps. The fields record the sign and direction of travel from that area. Each car participating in the traffic and using this software will register new signs detected and the will modify the degree of confidence of recognition for other users. Another software component is able to recognize the demarcation lines between lanes, with three cameras to calculate exactly or using probabilities where it is on the road, where the roadsides are and to propose a new direction even in the absence of traffic signs for the next seconds. Another part of the software is trying to use Artificial Intelligence to detect other car fingerprints from webcam images. The calculation was performed on 3 computers, requiring distributed processing. I developed a management information system based on semaphores that allows data processing and supervision from 3 different computers. This project presents a hardware version of a LIDAR – a 3D radar and a software for creating a 3D environment in which the car navigates and using it the car will take decision to avoid obstacles. The LIRDAR contains a total of 16 avalanche photo-detector mounted on a stepper motor that spins at a frequency of 10 Hz. The information provided by my radar is about 576.000 pixels at resolution of 10 bits. The 3D radar helps the entire software system to increase the confidence of decision.
不能說的秘密---網路釣魚防治技術
在數位化的今日，由於網際網路的技術蓬勃發展，網際網路變得更容易使用及具高度的親和性，使得網際網路的使用逐年成長。隨著越來越多人依賴網路進行交易，也衍生了層出不窮的網路詐騙問題。其中，網路釣魚就是一項著名的詐騙技術：詐騙者透過偽裝成知名企業的網站，藉此騙取使用者的個人私密資料。在本研究中，我們提出了一套植基於彩色視覺密碼學原理的網站驗證機制，使用者可以透過此機制，直接利用人類視覺的方式來驗證所連上的網站是否有問題，並在此機制之下，設計出另一套管理使用者密碼的方式，進而方便使用者不必費心的去記憶密碼。 Recently, as networks technology flourishes, Internet becomes easier and friendlier to use, and makes the usage of Internet grow up year after year. With more and more people relying on online transactions, it leads to endless network fraud issues. Among them, phishing is a well-known fraud technology to disguise the famous business website to get user’s private information by cheating. Therefore, in this study, an effective scheme based on color visual cryptography is proposed to test and verify the website. Through the proposed mechanism, users can check whether there is a problem website by using human vision directly. Furthermore, the proposed scheme also provides another way to manage user’s password effectively.