One of the most challenging tasks in the education sector in India is to predict student's academic performance due to a huge volume of student data. In the Indian context, we don't have any existing system by which analyzing and monitoring can be done to check the progress and performance of the student mostly in Higher education system. Every institution has their own criteria for analyzing the performance of the students. The reason for this happing is due to the lack of study on existing prediction techniques and hence to find the best prediction methodology for predicting the student academics progress and performance. Another important reason is the lack in investigating the suitable factors which affect the academic performance and achievement of the student in particular course. So to deeply understand the problem, a detail literature survey on predicting student's performance using data mining techniques is proposed. The main objective of this article is to provide a great knowledge and understanding of different data mining techniques which have been used to predict the student progress and performance and hence how these prediction techniques help to find the most important student attribute for prediction. Actually, we want to improve the performance of the student in academic by using best data mining techniques. At last, it could also provide some benefits for faculties, students, educators and management of the institution.

A shout, scream or yell is a loud vocalization in which air is passed through the vocal folds with greater force than is used in regular or close-distance vocalization. This process can be performed by any creature possessing lungs, including humans. A scream is a natural action, with a strong emotional aspect, like fear, pain, surprise, joy, anger, and others. But there is a difference between the scream in fear or pain and joy or surprise. This research is based on the study of such parameters those separates a scream of distress from a hoot in the joy of a person specifically women, having objective to design and develop security devices based on women emotions to provide them security. The present study has used high-reliability audio recordings to capture the acoustic features of both high pitch signals of women. Results indicated that perceptually categorized both the signals have distinct acoustic features. Distress scream reflect a slightly higher level of intensity than joyful scream. Moreover, the duration of the signal plays a vital role.

In today's network-based cloud computing era, software applications are playing big role. The security of these software applications is paramount to the successful use of these applications. These applications utilize cryptographic algorithms to secure the data over the network through encryption and decryption processes. The use of parallel processors is now common in both mobile and cloud computing scenarios. Cryptographic algorithms are compute intensive and can significantly benefit from parallelism. This paper introduces a parallel approach to symmetric stream cipher security algorithm known as RC4. The algorithm is very popular and used in many key applications such the Wi-Fi connections, Skype, and Bit Torrent protocol systems. We present an efficient parallel implementation to the compute intensive PRGA that is pseudo-random generation algorithm portion of the RC4 algorithm and the resulted algorithm will be named as PARC4. The algorithm divides a message into fix sized large blocks and encrypts these blocks concurrently on multi core machine.

Computed tomography, a noninvasive medical procedure for high‐resolution detailed cross‐sectional images of the internal body organs exposes a patient to hazardous health risks like cancer due to high radiation over time, making it a growing concern. Low‐dose computed tomography (LDCT) is a safe alternative with reduced radiation exposure but the generated CT images are corrupted with noise and artifacts that critically degrade the image quality, impacting the diagnostic accuracy and interpretation. Despite extensive research in LDCT denoising, it is still a challenging and an open area of active investigation that aims to strike an enhanced tradeoff between noise reduction and CT image quality. This paper introduces a hybrid deep learning framework in which the power of convolutional neural network (CNN) U‐Net is complemented by a bottleneck encompassing a recurrent gated block to capture interslice correlations and an attention block that focuses on multidomain features to learn global anatomical context. To prioritize pixel‐level correctness and realism in the denoised CT image, the network is trained with least absolute deviations and adversarial loss. The LDCT image denoising capability of the proposed model is experimentally evaluated on the Mayo Clinic TCIA (Public Dataset) and a methodically and strategically self‐collected private dataset. The model achieves 40.0 db for peak‐signal‐to‐noise ratio (PSNR), 0.95 for Structural Similarity Index (SSIM), and 0.0133 for root mean square error (RMSE) and generates visually coherent images outperforming the performance of the benchmark denoising networks with potential for effective LDCT denoising in public healthcare.