Diagnosis of Brain tumor at an early stage has became an important topic of research in recent time. Detection of tumor at an early stage for primary treatment increases the patient’s survival rate. Processing of Magnetic resonance image (MRI) for an early tumor detection face the challenge of high processing overhead due to large volume of image input to the processing system. This result to large delay and decrease in system efficiency. Hence, the need of an enhanced detection system for accurate segmentation and representation for a faster and accurate processing has evolved in recent past. Development of new approaches based on improved learning and processing for brain tumor detection has been proposed in recent literatures. This paper outlines a brief review on the developments made in the area of MRI processing for an early diagnosis and detection of brain tumor for segmentation, representation and applying new machine learning (ML) methods in decision making. The learning ability and fine processing of Machine learning algorithms has shown an improvement in the current automation systems for faster and more accurate processing for brain tumor detection. The current trends in the automation of brain tumor detection, advantages, limitations and the future perspective of existing methods for computer aided diagnosis in brain tumor detection is outlined.

Speech is the most important communication among humans. Processing of speech signal has many strategies including speech coding, speaker recognition, speaker verification, etc. Speaker diarization is the pre-processing stage for many applications of speaker recognition systems. Speaker Diarization is the mission of determining “who Spoke when” for any audio recording that carries an unknown quantity of records and an unknown variety of audio systems. Speaker diarization has come to be achief era for many tasks like navigation, retrieval, or higher-level interference on audio data. It mainly performs three operations feature extraction, voice activity detection, and classification. In this paper, we’ve reviewed the few speaker diarization Techniques. The trendy speaker diarization structures finished nice outcomes. In this paper, few speaker diarization device performances are evaluated for Diarization mistakes, Tracking time, and False alarm.