Blindness is one of the serious issues in the present medical world scenario mainly caused by Diabetic Retinopathy (DR). It is a diabetes complication, that is produced due to the problems in retina blood vessel. For clinical treatment, it will be extremely helpful, if diabetic retinopathy is detected in early stages. In recent years, the manual detection of DR consumes more time and moreover, the detection of DR in early stages is still a challenging task. In order to avoid these issues, this research work focus on an automated as well as effective solution for detecting DR symptoms from retinal images and requires less time for accurate detection. A Novel histogram equalization technique is used for performing contrast enhancement and equalization in initial pre-processing stage. Then, from these pre-processed images, image patches are extracted regularly. Improved Discrete Curvelet Transform based Grey Level Co-occurrence Matrix (IDCT-GLCM) is used in second stage for extracting features. Then, extracted features are given to Classifier. At last, an Improved Alexnet model-based CNN (IAM-CNN) classification approach is used for diagnosing DR from digital fundus images. In terms of accuracy, specificity and sensitivity, effectiveness and efficiency of proposed method is shown by extensive simulation results.

Now a days due to de-monetization everyone had started using credit cards for different types of transactions. So there will be a more chances for occurring fraud. Banks have many and enormous databases. Important business information can be extracted from these data stores. Fraud is an issue with far reaching consequences in the backing industry, government, corporate sectors and for ordinary consumers. Increasing dependence on new technologies such as cloud and mobile computing in recent years has encountered the problem. Physical detections are not only time consuming they are costly and they don't give accurate results. Not surprisingly economic institutions have turned to automated process using numerical and computational methods. Traditional approaches relied on manual techniques such as auditing, which are inefficient and unreliable due to the difficulty of the problem. Data miningbased approaches have been shown to be useful because of their ability to identify small anomalies in large data sets. So we have used some of the supervised algorithms to detect the fraud which gives accurate results. There are many different types of fraud, as well as a variety of data mining methods, and research is continually being undertaken to find the best approach for each case. Financial fraud is a term with various potential meanings, but for our purposes it can be defined as the on purpose use of illegal methods or practices for the purpose of obtaining financial gain. Fraud has a large negative impact on business and society credit card fraud alone accounts for billions of dollars of lost revenue each year.

Attention Deficit Hyperactivity Disorder (ADHD) is one of the major mental-health disorders worldwide. ADHD is typically characterized by impaired executive function, impulsivity, hyperactivity and with respect to these behavioral symptoms, diagnosis of ADHD is performed. These symptoms are obviously seen at in early stage. Serious impairments and substantial burdens are induced for society as well as to families. However, for ADHD, there is no diagnostic laboratory in current scenario. Psychological tests like Brown Attention Deficit Disorder Scale (BADDS), Conners Parent Rating Scale and ADHD Rating Scale (ADHD-RS) are carried out for ADHD diagnosis. Tedious and complex clinical analysis are needed in this testing and this makes low efficiency of the diagnostic process. A traditional diagnosis technique of ADHD produces degraded results. So, enhanced extreme learning machine is incorporated with existing techniques for avoiding the issues of performance degradation. There is a need to enhance the classifier performance further and there is a chance for unwanted noise in input samples, which may degrade the performance of classifier. For avoiding these issues, an enhanced and automated ADHS diagnosis technique is proposed. First stage is pre-processing, and it is carried out based on min max normalization and feature extraction is a next stage, which is carried out through Fast Independent Component Analysis and third stage is a Deep Extreme Learning Machine (DELM) based ADHD identification and classification. Extreme Learning Machine with Kernel (KELM) and Multilayer Extreme Learning Machine (MLELM) algorithm are combined in this method and it is termed as deep extreme learning machine (DELM). Collection of neuro images are used for quantitative and qualitative analysis and with respect to f-measure, recall, precision and accuracy, robustness of proposed technique is demonstrated.

No abstract

No abstract