Prakash Balasubramanian

Deep Learning (DL) drives academics to create models for cancer diagnosis using medical image processing because of its innate ability to recognize difficult-to-detect patterns in complex, noisy, and massive data. The use of deep learning algorithms for real-time cancer diagnosis is explored in depth in this work. Real-time medical diagnosis determines the illness or condition that accounts for a patient’s symptoms and outward physical manifestations within a predetermined time frame. With a waiting period of anywhere between 5 days and 30 days, there are currently several ways, including screening tests, biopsies, and other prospective methods, that can assist in discovering a problem, particularly cancer. This article conducts a thorough literature review to understand how DL affects the length of this waiting period. In addition, the accuracy and turnaround time of different imaging modalities is evaluated with DL-based cancer diagnosis. Convolutional neural networks are critical for real-time cancer diagnosis, with models achieving up to 99.3% accuracy. The effectiveness and cost of the infrastructure required for real-time image-based medical diagnostics are evaluated. According to the report, generalization problems, data variability, and explainable DL are some of the most significant barriers to using DL in clinical trials. Making DL applicable for cancer diagnosis will be made possible by explainable DL.

Prediction of the stock market is a challenging and time-consuming process. In recent times, various research analysts and organizations have used different tools and techniques to analyze and predict stock price movements. During the early days, investors mainly depend on technical indicators and fundamental parameters for short-term and long-term predictions, whereas nowadays many researchers started adopting artificial intelligence-based methodologies to predict stock price movements. In this article, an exhaustive literature study has been carried out to understand multiple techniques employed for prediction in the field of the financial market. As part of this study, more than hundreds of research articles focused on global indices and stock prices were collected and analyzed from multiple sources. Further, this study helps the researchers and investors to make a collective decision and choose the appropriate model for better profit and investment based on local and global market conditions.

Software projects mostly exceeds budget, delivered late and does not meet with the customer’s satisfaction for years. In the past, many traditional development models like waterfall, spiral, iterative, and prototyping methods are used to build the software systems. In recent years, agile models are widely used in developing the software products. The major reasons are – simplicity, incorporating the requirement changes at any time, light-weight approach and delivering the working product early and in short duration. Whatever the development model used, it still remains a challenge for software engineer’s to accurately estimate the size, effort and the time required for developing the software system. This survey focuses on the existing estimation models used in traditional as well in agile software development.

Frequent pattern mining algorithms as the name says, mines sets frequent patterns form given datasets. These algorithms provide immensely helpful results which have a wide scope of application starting from simple decision problems to complex business intelligence aspects. This paper attempts to apply the same concept of frequent pattern mining to solve the location management problem of GSM networks. In GSM networks the task of keeping track of a mobile user (MU) and relaying an incoming call, is called location management. It basically includes two processes, location update and paging. Location update deals with managing the current location of the MUs. There are many approaches to do this like time based, movement based, distance based etc. In this paper the location update procedure relies on the collected data of user movements form one network cell to another cell. This data has a definite pattern, as in day to day life a person mostly has a fixed route of travelling e.g. home to office in the morning and office to home in the evening. During this movement he crosses a specific set of network cells which remains same throughout the week. Thus, frequent pattern mining algorithm can be applied on the user’s mobility log and try to find out the most probable location where the mobile user could be found. Using the results, a dynamic location area for individual user’s current location can be created. Thus minimizing cost related to location update which otherwise involves communication between the mobile handset and the base station, and calculations related to keeping track of the location of mobile users.