The identification of breast cancer stages plays a vital role for understanding the aggressiveness of cancer disease and the patient survival as an outcome. The main parameter of breast cancer staging is counting the mitotic cells in biopsy samples of breast cancer tissues. In the present scenario the manually counting of the mitotic cells in histopathology image slides of the tissue examined by the expert under clinical microscope is 10X, 20X ,40X ,100X,400X magnification of the sample. The manual process is laborious, inaccurate, erroneous and tedious, hence the traditional method demands the computerized approach to recognize and identify the cancer stages for the expert to come up with robust decision. In this work we proposed a novel approach for automatic recognition and identification through computer aided diagnosis systems (CAD). In this CAD proposed model the work is divided into five stages. In the first stage histopathological image are preprocessed to enhance the contrast of the mitotic cells and non mitotic cells using image adjustment technique. In second stage the foreground and background is segmented using Otsu segmentation algorithm. In the third stage the Bit plane slicing is applied to separate the mitotic and non mitotic cells. In the fourth stage the number of mitotic cells is counted in the samples. In the fifth stage of the work, based on the number of mitotic cells the cancer stages are determined. In this work, ICPR 2012 database images are adopted for the experimentation. The diagnosis of the stage of the cancer will help the oncologist to take proper decision and also reduces the burden of the work.

In this paper, we use three machine learning techniques: Linear Discriminant Analysis (LDA) along different Eigen vectors of an image, Fuzzy Inference System (FIS) and Fuzzy c-mean clustering (FCM) to recognize objects and human face. Again, Fuzzy c-mean clustering is combined with multiple linear regression (MLR) to reduce the four-dimensional variable into two dimensional variables to get the influence of all variables on the scatterplot. To keep the outlier within narrow range, the MLR is again applied in logistic regression. Individual method is found suitable for particular type of object recognition but does not reveal standard range of recognition for all types of objects. For example, LDA along Eigen vector provides high accuracy of detection for human face recognition but very poor performance is found against discrete objects like chair, butterfly etc. The FCM and FIS are found to provide moderate result in all kinds of object detection but combination of three methods of the paper provide expected result with low process time compared to deep leaning neural network.  

Face Recognition plays a major role in the new modern information technology era for security purposes in biometric modalities and has still various challenges in many applications of computer vision systems. Consequently, it is a hot topic research area for both industrial and academic environments and was developed with many innovative ideas to improve accuracy and robustness. Therefore, this paper proposes a recognition system for facial images by using Deep learning strategies to detect a face, extract features, and recognize. The standard facial dataset, FEI is used to prove the effectiveness of the proposed system and compare it with the other previous research works, and the experiments are carried out for different detection methods. The results show that the improved accuracy and reduce time complexity can provide from this system, which is the advantage of the Convolution Neural Network (CNN) than other some of the previous works.

Ability to locate sound source in human acoustic system is a prime factor. The source of sound has various spectral, temporal and strength characteristics depending on where it is located. To identify the sound location, the listeners analyze these characteristics arising from various directions on the horizontal and the vertical surfaces. In noisy background, it is very difficult to understand the speech for individuals with sensorineural hearing loss. In order to reliably distinguish various sound sources and increase speech intelligibility in noisy conditions, binaural hearing is adopted. Diffraction induced by the pinnae, head, shoulders and torso changes the pressure waveform when sound waves travel from the audio source to the listener's eardrum. Two transfer functions that specify the relation between the sound pressures at the listener's right and left ear drums will catch these propagation effects. These spectral changes are recorded by Head Related Transfer Functions (HRTFs). Different hearing aid algorithms are to be studied to measure their effectiveness in improving speech perception through series of subjective evaluations involving subjects with sensorineural hearing loss with different types of loss characteristics under different listening conditions. We investigated the various proposed approaches, weighed in on their benefits and drawbacks and most importantly, examined whether and how the resulting HRTFs perceptual validity is evaluated. This paper brings out current research efforts on sound source localization ability in hearing aids, which includes use of Head Related Transfer Functions (HRTFs) for generating spatial sounds in elevation and azimuth plane, evaluating the effect of monaural and binaural hearing aid algorithms on source localization under different listening conditions on subjects with different hearing losses and also to assess the effectiveness of localization with type of hearing aids.

Soft biometrics is not a unique trait in itself, but it is valuable in enhancing the performance of unique traits used in biometric recognition systems. In this paper, we perform a comparative analysis of soft biometric traits and fusion schemes for improving face recognition systems. Specifically, we present an analysis of the performance of such systems as a function of the fusion strategy used and the soft biometric feature employed. We outline the strengths and weaknesses of the biometric feature employed in fused face and soft biometric systems. The analysis presented in this work is significantly important and different from existing works as the performance profiles of a wider variety of soft biometric traits are compared over major metrics of permanence, ease of collection and distinctiveness.