Speech is one of the most natural and fundamental means of human computer interaction and the state of human emotion is important in various domains. The recognition of human emotion is become essential in real world application, but speed signal is interrupted with various noises from the real world environments and the recognition performance is reduced by these additional signals of noise and emotion. Therefore this paper focuses to develop emotion recognition system for the noisy signal in the real world environment. Minimum Mean Square Error, MMSE is used as the enhancement technique, Mel-frequency Cepstrum Coefficients (MFCC) features are extracted from the speech signals and the state of the arts classifiers used to recognize the emotional state of the signals. To show the robustness of the proposed system, the experimental results are carried out by using the standard speech emotion database, IEMOCAP, under various SNRs level from 0db to 15db of real world background noise. The results are evaluated for seven emotions and the comparisons are prepared and discussed for various classifiers and for various emotions. The results indicate which classifier is the best for which emotion to facilitate in real world environment, especially in noisiest condition like in sport event.

In this paper we go through some very recent imaging techniques that are inspired from space exploration. The advantages of these techniques are to help in searching space.  To explore the effectiveness of these imaging techniques on search spaces, we consider the Particle Swarm Optimization algorithm and extend it using the imaging techniques to train multiple neural networks using several datasets for the purpose of classification. The techniques were used during the population initialization stage and during the main search. The performance of the techniques has been measured based on various experiments, these techniques have been evaluated against each other, and against the particle swarm optimization algorithm alone taking into account the classification accuracy and training runtime. The results show that the use of imaging techniques produces better results.

This paper addresses the problem of identifying certain human behavior such as distraction and also predicting the pattern of it. This paper proposes an artificial emotional intelligent or emotional AI algorithm to detect any change in visual attention for individuals. Simply, this algorithm detects human’s attentive and distracted periods from video stream. The algorithm uses deviation of normal facial alignment to identify any change in attentive and distractive activities, e.g., looking to a different direction, speaking, yawning, sleeping, attention deficit hyperactivity and so on. For detecting facial deviation we use facial landmarks but, not all landmarks are related to any change in human behavior. This paper proposes an attribute model to identify relevant attributes that best defines human’s distraction using necessary facial landmark deviations. Once the change in those attributes is identified, the deviations are evaluated against a threshold based emotional AI model in order to detect any change in the corresponding behavior. These changes are then evaluated using time constraints to detect attention levels. Finally, another threshold model against the attention level is used to recognize inattentiveness. Our proposed algorithm is evaluated using video recording of human classroom learning activity to identify inattentive learners. Experimental results show that this algorithm can successfully identify the change in human attention which can be used as a learner or driver distraction detector. It can also be very useful for human distraction detection, adaptive learning and human computer interaction. This algorithm can also be used for early attention deficit hyperactivity disorder (ADHD) or dyslexia detection among patients.

This paper performs three different contrast testing methods, namely contrast stretching, histogram equalization, and CLAHE using a median filter. Poor quality images will be corrected and performed with a median filter removal filter. STARE dataset images that use images with different contrast values for each image. For this reason, evaluating the results of the three parameters tested are; MSE, PSNR, and SSIM. With the gray level scale image and contrast stretching which stretches the pixel value by stretching the stretchlim technique with the MSE result are 9.15, PSNR is 42.14 dB, and SSIM is 0.88. And the HE method and median filter with the results of the average value of MSE is 18.67, PSNR is 41.33 dB, and SSIM is 0.77. Whereas for CLAHE and median filters the average yield of MSE is 28.42, PSNR is 35.30 dB, and SSIM is 0.86. From the test results, it can be seen that the proposed method has MSE and PSNR values as well as SSIM values. 

With the extensive recent development of communication methods and resulting increase in data surveillance and espionage, the need for reliable data encryption methods is greater than ever. Conventional encryption calculations, for example, DES and RSA, are not beneficial in the field of picture encryption because of some inherent characteristics of pictures such as bulk data size and high redundancy, which are problematic for conventional encryption. Many researchers have proposed different image encryption schemes to overcome image encryption problems. In the last two decades, more and more studies have looked to incorporate conventional encryption methods and the complex behavior of chaotic signals. In this paper, a novel image encryption algorithm is proposed based on pixel chaotic permutation. A chaotic logistic map and Ikeda map are used to design a new pseudo-random bit generator, and a novel permutation scheme is used to modify pixel values. Then, a new permutation algorithm based on a traditional Japanese game called Amidakuji is used for pixel scrambling. Different statistical manners, such as correlation coefficient, NPCR (Number of Pixels Change Rate), UACI (Unified Average Changing Intensity), and entropy, are used to provide analysis of the effectiveness of the proposed encryption methods. Our example reveals that the proposed encryption method can obtain highly secure encrypted images using a novel chaotic permutation method based on Amidakuji.