Race classification using gaussian-based weight K-nn algorithm for face recognition

One of the greatest challenges in facial recognition systems is to recognize faces around different race and illuminations. Chromaticity is an essential factor in facial recognition and shows the intensity of the color in a pixel, it can greatly vary depending on the lighting conditions. The race classification scheme proposed which is Gaussian based-weighted K-Nearest Neighbor classifier in this paper, has very sensitive to illumination intensity. The main idea is first to identify the minority class instances in the training data and then generalize them to Gaussian function as concept for the minority class. By using combination of K-NN algorithm with Gaussian formula for race classification. In this paper, image processing is divided into two phases. The first is preprocessing phase. There are three preprocessing comprises of auto contrast balance, noise reduction and auto-color balancing. The second phase is face processing which contains six steps; face detection, illumination normalization, feature extraction, skin segmentation, race classification and face recognition. There are two type of dataset are being used; first FERET dataset where images inside this dataset involve of illumination variations. The second is Caltech dataset which images side this dataset contains noises

Mazdak technique for PSNR estimation in audio steganography

A novel method to estimate PSNR of the resu lt of audio steganography before embedding is presented. Estimated PSNR by proposed linear interpolation formula was tested and the result was almost the same with the obtained PSNR in practical way

Machine learning based lightweight interference mitigation scheme for wireless sensor network

The interference issue is most vibrant on low-powered networks like wireless sensor network (WSN). In some cases, the heavy interference on WSN from different technologies and devices result in life threatening situations. In this paper, a machine learning (ML) based lightweight interference mitigation scheme for WSN is proposed. The scheme detects and identifies heterogeneous interference like Wifi, bluetooth and microwave oven using a lightweight feature extraction method and ML lightweight decision tree. It also provides WSN an adaptive interference mitigation solution by helping to choose packet scheduling, Acknowledgement (ACK)-retransmission or channel switching as the best countermeasure. The scheme is simulated with test data to evaluate the accuracy performance and the memory consumption. Evaluation of the proposed scheme’s memory profile shows a 14% memory saving compared to a fast fourier transform (FFT) based periodicity estimation technique and 3% less memory compared to logistic regression-based ML model, hence proving the scheme is lightweight. The validation test shows the scheme has a high accuracy at 95.24%. It shows a precision of 100% in detecting WiFi and microwave oven interference while a 90% precision in detecting bluetooth interference

MPEG-4 video transmission using distributed TDMA MAC protocol over IEEE 802.15.4 wireless technology

The issues of green technology nowadays give an inspiration to the researcher to make all the future design to be energy efficient. Medium Access Control (MAC) layer is the most effective layer to provide energy efficient due to its ability to control the physical radio directly. One of the important applications in the future is a video transmission that can be transmitted with low-cost and low power consumption. MPEG-4 is one of the international standards for moving video. MPEG-4 provide better compression and primarily design at low bit rate communication. In order to achieve good quality for video application, the design at MAC layer must be strong. Therefore, to increase the performance of the MPEG-4 in IEEE 802.15.4, in this paper we propose a cross layer design between MAC layer and Application layer. A priority queue will be implemented at MAC scheduling depends on the level of frame important in MPEG-4 format frame. A distributed Time division Multiple Access (TDMA) will be used for MAC protocol to provide reliable data transmission for high priority frame

Threshold Based Skin Color Classification

In this paper, we presented a new formula for skin classification. The proposed formula can overcome sensitivity to noise. Our approach was based multi-skin color Hue, Saturation, and Value color space and multi-level segmentation. Skin regions were extracted using three skin color classes, namely the Caucasoid, Mongolid and Nigroud. Moreover, in this formula, we adopted Gaussian-based weight k-NN algorithm for skin classification. The experiment result shows that the best result was achieved for Caucasoid class with 84.29 percent fmeasure

A genetic-algorithm-based approach for audio steganography

In this paper, we present a novel, principled approach to resolve the remained problems of substitution technique of audio steganography. Using the proposed genetic algorithm, message bits are embedded into multiple, vague and higher LSB layers, resulting in increased robustness. The robustness specially would be increased against those intentional attacks which try to reveal the hidden message and also some unintentional attacks like noise addition as well

Water Thermocline Confirms Susceptibility of Tilapia Cultured in Lakes to Streptococcus agalactiae

A study was conducted on water quality profiling to confirm susceptibility of tilapia cultured in lakes to Streptococcus agalactiae infection. A total of 1,010 and 719 tilapias of different sizes were collected from two lakes; the Kenyir and Pedu lakes, respectively. They were randomly sampled for a period of 24 months. Swabs of brain, eye and kidney were streaked directly onto blood agar before S. agalactiae was identified by the API 20 STREP kit, Slidex Strepto-kit and PCR technique. The water temperature (thermocline) and dissolved oxygen profiling were determined at 1 m intervals for up to 20 m deep. Water clarity and flow rate were also recorded using Secchi disk and a current meter. S. agalactiae was successfully isolated from both lakes throughout the year, ranging between 2 and 78%. Isolation was more frequent during the hot and dry months of both years. During this period, the mean water temperature was >29 degrees C for up to 8 m deep due to the significantly (p12 m deep. This and the slow water flow kept the water temperature at 4 m deep where tilapias under the cage culture system were kept to remain high causing stress to tilapia and increases susceptibility to S. agalactiae. Dissolved oxygen profiling, however remained high at >5 mg L-1 for up to 8 m deep and did not give adverse effects to susceptibility of tilapia to S. agalactiae