47 research outputs found
Gaze-Based Biometrics: Some Recent Research
No full textGaze-based biometrics analyzes the ocular behavior of individuals to obtain information about their identity or distinctive features. In recent years, studies in this field have grown considerably, also thanks to the availability of low-cost eye trackers. After a presentation of the essential characteristics
of biometric techniques based on eye tracking, the paper provides a short overview of this research area, especially focusing on some projects recently developed at the University of Pavia
WRITING WITH THE MEDITERRANEAN IN THE TWENTIETH CENTURY: THE ADVENT OF THE FISHERMAN OF HALICARNASSUS
No full textThis article introduces the Turkish humanist writer, Cevat Sakir Kabaagacli (1890-1973), who adopted the penname of "Halikarnas Balikcisi" (the Fisherman of Halicarnassus), as a tribute to the ancient historian, Herodotus of Halicarnassus. He contributed to the genesis and spread of Turkish Mediterraneanism at a time when Fernand Braudel's influence among Turkish historians was significant yet fell short of producing a similar effect. After a biographical sketch of the author, this article explains how Cevat Sakir's Mediterranean journeys affected his life, so that the Mediterranean is seen as a formative influence. Finally, the article summarizes his multifaceted contributions to Mediterranean studies
Artificial neural network model for prediction of tool tip temperature and analysis
No full textTechnological improvements put computer systems in the center of our life and various scientific disciplines. These can range from controlling a device in our home to public institutions and the industry. One of these disciplines is a sub-area in mechanical engineering called machining is concerned with not only mechanical systems but also computer aided systems. Artificial Neural Networks -an area of artificial intelligence- which is concerned with learning and decision making of computers is a field that scientists are very interested in. In this study, an Artificial Neural Network system was designed for predicting the temperature at the tool tip in the machining process. In the metal cutting process, tool tip temperature is one of the conditions that must be identified, analyzed and monitored. For this purpose, an ANN model was developed to determine the tool tip temperature in the turning process. In the designed ANN model, parameters consisting of three inputs and one output were used. The three input variables were rake angle (?-o), approaching angle (-o), feedrate (fmm/rev) respectively. The output parameter was the tool tip temperature (T-0C). The most appropriate model was determined according to Mean Squared Error ratio. In the test phase of the Artificial Neural Network, the smallest Mean Squared Error was obtained with the Artificial Neural Network topology formed as 3-4-1. In this Artificial Neural Network model, calculations were Mean Squared Error0.00144, R20.9956 (absolute fraction of variance) in the training phase and Mean Squared Error0.00231, R20.9954 in the test phase. The results show that the designed Artificial Neural Network model can be used for predicting and analyzing tool tip temperatur
Fuzzy expert system approach for determination of alfa-linolenic acid content of eggs obtained from hens by dietary flaxseed
No full textThis paper presents the development of a fuzzy expert system (FES) for determination of -linolenic acid content of eggs, obtained from hens fed dietary flaxseed. Based on experimental values FES models were designed using MATLAB 6.5 fuzzy logic toolbox in Windows XP running on Intel 1.9 Gh environment. It was used time and flaxseed ratio as input parameters and linolenic acid content as output. There was a good correlation (R20.9983) between experimental values and FES (P0.05,t-test)
GERMAN CREDIT RISKS CLASSIFICATION USING SUPPORT VECTOR MACHINES
No full textSupport Vector Machines (SVM) is one of the most popular classification algorithms. SVM penalty parameter and the kernel parameters have high impact over the classification performance and the complexity of the algorithm. So, this brings the problem of choosing the suitable values for SVM parameters. This problem can be solved using meta-heuristic optimization algorithms. Salp Swarm Algorithm (SSA) and Crow Search Algorithm (CSA) are new meta-heuristic algorithms. SSA is a swarm algorithm that is inspired from a mechanism salps forming in deep ocean called salp chain. CSA algorithm is inspired by the intelligent behavior of crows. In this paper, SVM parameter optimization is done using SSA and CSA. German Credit dataset from the UCI data repository is used for the experiments. All experiments results are gathered from a 10-fold cross validation block. Evaluation criteria determined as accuracy, sensitivity, specificity and AUC. SSA and CSA gave accuracy results of 0.72±4.62 and 0.71±3.53 respectively. Also, ROC curves and box plots of the algorithms are given. CSA algorithm draws better graphs
A Steganalysis System Utilizing Temporal Pixel Correlation Of HEVC Video
No full textHigh Efficiency Video Coding (HEVC) is the most recent video codec coming after currently most popular H.264/MPEG4 codecs and has promising compression capabilities. It is conjectured that it will be a substitute for current video compression standards. However, to the best knowledge of the authors, none of the current video steganalysis methods designed or tested with HEVC video. In this paper, pixel domain steganography applied on HEVC video is targeted for the first time. Also, its the first paper that employs accordion unfolding transformation, which merges temporal and spatial correlation, in pixel domain video steganalysis. With help of the transformation, temporal correlation is incorporated into the system. Its demonstrated for three different feature sets that integrating temporal dependency substantially increased the detection accuracy
Spatio-Temporal Rich Model For Motion Vector Steganalysis
No full textWe propose a spatio-temporal rich model of motion vector planes as a part of a full steganalytic system against motion vector based steganography. Superior detection accuracy of the rich model over the previous methods has been lately demonstrated for digital images in both spatial and DCT domain. It has not been heretofore used for detection of motion vector steganography. We also introduced a transformation so as to extend the feature set with temporal residuals. We carried out the tests along with most recent motion vector steganalysis and steganography methods. Test results show that the proposed model delivers an outstanding performance compared to the previous methods
