Worldwide, diabetes is a chronic disorder that affects millions of people. One of the main complications of diabetes, diabetic retinopathy, can lead to blindness if left untreated. Diabetic retinopathy must be diagnosed and treated at an early stage in order to protect the eyes from long-term damage. In this paper, we analyze the most recent findings in the study of diabetes diagnosis based on retinopathy. We discuss the many imaging techniques for detecting retinopathy, such as fundus photography, optical coherence tomography, and fluorescein angiography. Furthermore, we explore the application of artificial intelligence and machine learning techniques for the automated diagnosis and classification of diabetic retinopathy.

Automatic speech recognition system are most prominent and important technique for human and computer interaction, controlling devices, extracting information. System user experience completely relies on accuracy, quality of speech notes and on the response time of the system. A system can be monolingual or multilingual, each works differently with different efficiency in accordance with the backend technique used. We have described about various ASR algorithms and feature extraction techniques and their comparison with each other in this paper. Feature extraction techniques like LPC, MFCCs, RASTA filtering, PLDA, LDA, PCA and algorithms like DTW, VQ, SVM, GMM, HMM. IndexTerms SVM, VQ, DTW, GMM, ANN, MFCCs, ASR, RASTA filtering, LPC, PLDA. _______________________________________________________________________________________________________

30Apr 2016 Improved Face Recognition Method usingPCA. Ramesh Kumar Verma , Arun Kumar Deepak Kumar and Lucknesh Kumar. Department of CSE. Assistant Professor, Department of CSE.

SVM and ISVM are the most prominent technique for excellent learning and classification in the field of machine learning such as face detection and recognition, handwriting automatic identification and automatic text categorization, video tracking, Number plate recognition, Traffic Control etc. Nowadays, Face recognition is a challenging task in the field of computer vision. Motive of face recognition is to compare between the Given face database with the input face image and then declare a decision that identifies to whom the input image class belongs to or doesn't belong to the face database.In this analysis we not only study and also compare the sophisticated classification technique i.e. SVM and ISVM for face recognition along with its pros and cons.