A study on user mobility in device to device (D2D) networks through distrubted catching

Self Cite

The name “cryptography” comes from a Greek word that refers to the art of securing data by organizing it in a disorganized and unintelligible way. It combines software engineering with math. The explosive expansion of the Internet has led to a greater awareness of intriguing uncertainty concerns. Although security is the biggest concern when it comes to the internet, many apps have been developed and designed without taking confidentiality, authentication, and protection—the three essential components of data security—into account. Knowing these kinds of security problems and challenges is going to be more important as our daily activities rely more and more on data networks. Cryptography is necessary to prevent some unwanted customers or persons from gaining access to the data. This study presents a novel hybrid security cipher that combines the three most significant ciphers, such as the Caesar, Rail Fence and Vigenère ciphers. When compared to traditional ciphers, this hybrid encryption cipher offers more security.

Section: Literature Surveymentioning

confidence: 99%

Cipher Craft: Design and Analysis of Advanced Cryptographic Techniques for Secure Communication Systems

Subbarayudu,

Vijendar Reddy,

Shankar

et al. 2024

Self Cite

“…By dividing the source set into subsets according to an attribute value test, a tree can be "taught." Recursive partitioning is the process of repeating this procedure on every derived subset [6][7][8][9]. When every member of the subset at a node has the same value for the target variable, or when splitting no longer improves the predictions, the recursion is said to be finished.…”

Section: Literature Surveymentioning

confidence: 99%

Predicting Students’ Failure Risk Education Through Machine Learning

Subbarayudu,

Vijendar Reddy,

Raj Kumar

et al. 2024

Self Cite

In the evolving landscape of education, the paradigm shift towards online learning has become even more pronounced in response to the prevailing global conditions. This transition, while offering unprecedented accessibility and flexibility, brings forth unique challenges, particularly in the realm of assessing and monitoring student performance. The absence of traditional face-to-face interactions and direct observations necessitates innovative solutions to gauge and predict student success effectively. Recognizing the imperative need for proactive intervention, this project endeavours to harness the power of machine learning algorithms to develop a robust predictive model. By leveraging comprehensive datasets encompassing student activities, grades, interactions with educators and peers, and other pertinent information derived from learning management systems, the aim is to construct a reliable framework capable of forecasting the likelihood of a student encountering academic challenges or failure. The predictive model proposed in this study is poised to revolutionize the educational landscape by enabling early identification of students at risk. Through the analysis of diverse parameters, the model seeks to provide educators with actionable insights, empowering them to take timely and targeted measures to support struggling students. The overarching goal is not only to predict potential academic setbacks but to also equip tutors with the tools necessary to implement tailored strategies that mitigate these risks, ultimately contributing to a substantial reduction in the overall failure rate within educational institutions. In essence, this project embodies a forward-looking approach to education, where data-driven insights pave the way for personalized interventions, fostering a supportive and inclusive learning environment. As we navigate the digital era of education, the development of such predictive models becomes indispensable in ensuring the success and well-being of students, ushering in a new era of educational excellence and adaptability.

“…The radiologist can also make quick decisions with the aid of these predictions. The suggested work applies a self-defined Artificial Neural Network (ANN) and a convolution neural network (CNN) to detect brain tumors and analyses their performance [16][17][18][19].…”

Section: Related Workmentioning

confidence: 99%

The Evaluation of 2D and EfficientB0 Convolution Networks for detecting Brain tumor based on MRI images

Subbarayudu,

Vijendar Reddy,

Keerthi

et al. 2024

Self Cite

Brain tumors represent a significant healthcare challenge, affecting both children and adults with potentially aggressive consequences. Accounting for a substantial percentage of all primary Central Nervous System (CNS) tumors, brain tumors pose a substantial burden, with approximately 11,700 new diagnoses annually. The classification of brain tumors into benign, malignant, pituitary, and other types necessitates precise diagnostic techniques and treatment planning to enhance patient life expectancy. Traditionally, the detection of brain tumors relied on the expertise of specialists analyzing Magnetic Resonance Images (MRI) without the aid of advanced technology. MRI remains the gold standard for brain tumor detection, generating vast amounts of image data for radiologists to interpret. Manual examinations, however, carry a risk of errors due to the intricacies and diverse properties of brain tumors, potentially leading to delayed treatment and, tragically, loss of lives. In this context, the application of automated classification techniques using Machine Learning (ML) and Deep Learning (DL) has emerged as a promising solution. These techniques, primarily employing Deep Learning Algorithms such as Convolutional Neural Networks (CNN) like 2D-convolutions and Deep Learning Models like ResNet50 and EfficientNetB0, in addition to traditional Machine Learning algorithms like Support Vector Machines (SVM), have consistently demonstrated superior accuracy in brain tumor detection compared to manual prediction. These automated methods have consistently exhibited superior accuracy in the detection and classification of brain tumors compared to manual approaches. This research proposes a robust system for the early detection and accurate classification of brain tumors, leveraging the power of Deep Learning and Machine Learning. By incorporating state-of-the-art techniques, this system aims to empower medical professionals worldwide in identifying brain tumors at their earliest stages, ultimately leading to more timely and effective treatments. Such advancements hold great promise in reducing the human suffering associated with brain tumors and improving patient outcomes.