A Deep Learning Model for Information Loss Prevention From Multi-Page Digital Documents

Abstract

World Wide Web has redefined almost all the business models in the past twenty-five to thirty years. IoT, Big Data, AI are some of the comparatively recent technologies which brought in a revolution in the digitization and management of data. Along with the revolution arose the need for data security and consumer privacy protection, primarily concerning financial institutions. The data breach of Equifax in 2017 and personal information leaks from Facebook in 2021 led to general skepticism among the customers of large corporations. The GLBA, 1999, also known as the Financial Modernization Act, was implemented by US federal law to enforce the financial institutions to protect their private information. Built upon the GLBA, guidelines are paved by FTC for all financial institutions of the United States of America, including TI companies. In this paper, an ANN-based content classification technique using MLP architecture in combination with n-gram TF-IDF feature descriptor is proposed to detect and protect the customers' sensitive information of a reputed TI company securing its one of the digital image-document stores. The proposed technique is compared with other state-of-the-art strategies. Data samples from the digital document store of the company have been taken into consideration in the study, and the prediction accuracy metrics obtained are found to be substantially better and within the acceptable range defined by the organization's information security monitoring team.

The Effectiveness of the Beritamagelang.id Website as a Public Information Media

“…Research on the Effectiveness of the Beritamagelang.id Website as an Information Media for the People of Magelang Regency obtained data by distributing questionnaires consisting of 30 questions filled in by 400 respondents. This question is based on ten digital literacy competencies compiled by DeLone & McLean (2016) and Guha et al (2021). These factors are divided into ten dimensions: System Quality, Information Quality, Service Quality, Use, User Satisfaction, Net Benefit, Current Need Approach, Everyday Need Approach, Exhaustic Need Approach, and Chatting-up Need Approach.…”

“…Second, Guha et al (2021) describe what must be in the information media. The current need approach is the need for the latest information, which encourages every information user always to be active to get the latest information.…”

“…Without some information, a system will not run smoothly, and eventually, the system will die. According to Guha et al (2021), there are four types of needs expected from information, namely (a) Current Need Approach, an approach to users of information that is up-to-date, namely the need for the latest information which encourages every user of information always to be active in order to get the latest information. Using this approach, every information user interacts with information systems to increase their knowledge.…”

Smart high-yield tomato cultivation: precision irrigation system using the Internet of Things

“…(2019) discussed the proper utilization of water for the growth of tomatoes by using the fuzzy neural network with a genetic algorithm (GA). It predicted the volume of irrigation based on the effect of greenhouse and the growth of crops ( Guha et al., 2021 ). Rodríguez et al ( Rodr´ıguez-Ortega et al., 2019 ).…”

Advances in Sprinkler Irrigation: A Review in the Context of Precision Irrigation for Crop Production

“…Chen [221] recommended the fuzzy neural network with a genetic algorithm (GA) to accomplish precision irrigation for 15-25 better yield and 25% water saving. The same technique was used by Guha [222] to calculate precise amounts of water with reference to greenhouse effects for a significant impact on crop productivity. Perea [223] used a combination of ANN and genetic algorithm (GA) for the prediction of evapotranspiration and crop response under water applications and reported 3-11% better results in terms of system efficiency for crop productivity.…”