An API in JAVA Which Render Ease at Programming for Developers

– Based on the concept of Application programming interface (API).This project comprises of a package named "algokit" which contains several algorithms based on the category of searching, sorting, dynamic programming, tree traversals and swapping. Keeping in mind that different algorithms from the same category have its own benefit in time and space complexity, This project covers almost all the algorithms known and available from each category. This would give the user several options to choose the right algorithm for its code.An user just requires to import the package named AlgoKit and call the functions inside it for a smooth programming experience. One of the prime objectives of this project is to build a kit that serves the purpose of reducing the number of lines of code and also reduce the time taken to run the same code elsewhere. It is platform independent and can be used in any open source Java development environment.

A Study of Machine Learning Techniques in Cryptography for Cybersecurity

The importance of cybersecurity is on the rise as we have become more technologically dependent on the internet than ever before. Cybersecurity implies the process of protecting and recovering computer systems, networks, devices, and programs from any cyber attack. Cyber attacks are an increasingly sophisticated and evolving danger to our sensitive data, as attackers employ new methods to circumvent traditional security controls. Cryptanalysis is mainly used to crack cryptographic security systems and gain access to the contents of the encrypted messages, even if the key is unknown. It focuses on deciphering the encrypted data as it works with ciphertext, ciphers, and cryptosystems to understand how they work and find techniques for weakening them. For classical cryptanalysis, the recovery of ciphertext is difficult as the time complexity is exponential. The traditional cryptanalysis requires a significant amount of time, known plaintexts, and memory. Machine learning may reduce the computational complexity in cryptanalysis. Machine learning techniques have recently been applied in cryptanalysis, steganography, and other data-securityrelated applications. Deep learning is an advanced field of machine learning which mainly uses deep neural network architecture. Nowadays, deep learning techniques are usually explored extensively to solve many challenging problems of artificial intelligence. But not much work has been done on deep learning-based cryptanalysis. This paper attempts to summarize various machine learning based approaches for cryptanalysis along with discussions on the scope of application of deep learning techniques in cryptography.

Disease Prediction from Drug Information using Machine Learning

Drug reviews play a very important role in providing crucial medical care information for both healthcare professionals and consumers. Also, in the absence of an actual practicing healthcare professional, a consumer can look for an online review of drugs before making a purchase. But these reviews are generally unstructured in nature and often do not provide concise information on the disease/nature of the disease, the drugs are prescribed for. In this scenario, a learning model that can be trained to predict the disease/type of disease, when provided with a drug name and its corresponding review, becomes very important. To mitigate the above-mentioned issue, we present and compare various machine learning-based prediction models. Also, the performance of each of the models has been quantified using metrics such as precision, recall, F1-Score, and accuracy.