Driven by technologies such as mobile edge computing and 5G, recent years have witnessed the rapid development of urban informatics, where a large amount of data is generated. To cope with the growing data, artificial intelligence algorithms have been widely exploited. Federated learning is a promising paradigm for distributed edge computing, which enables edge nodes to train models locally without transmitting their data to a server. However, the security and privacy concerns of federated learning hinder its wide deployment in urban applications such as vehicular networks. In this article, we propose a differentially private asynchronous federated learning scheme for resource sharing in vehicular networks. To build a secure and robust federated learning scheme, we incorporate local differential privacy into federated learning for protecting the privacy of updated local models. We further propose a random distributed update scheme to get rid of the security threats led by a centralized curator. Moreover, we perform the convergence boosting in our proposed scheme by updates verification and weighted aggregation. We evaluate our scheme on three real-world datasets. Numerical results show the high accuracy and efficiency of our proposed scheme, whereas preserve the data privacy.

Differentially Private Asynchronous Federated Learning for Mobile Edge Computing in Urban Informatics

Cervical cancer growth is the fourth maximum of regular diseases in females. It is one of the sicknesses which is compromising ladies' wellbeing everywhere in the world and it is difficult to notice any sign in the beginning phase. But the screening process of cervical cancer sometimes is being hampered due to some social-behavioral factors. There is still a limited number of researches directed in cervical cancer identification dependent on the behavior and machine learning in the area of gynecology and computer science. In this research, we have proposed three machine learning models such as Decision Tree, Random Forest, and XGBoost to predict cervical cancer from behavior and its variables and we got significantly improved outcomes than the current methods with 93.33% accuracy. Moreover, we have shown the top features from the dataset according to the feature important scores to know their impacts on the development of the classification model.

https://link.springer.com/content/pdf/10.1007/s42979-021-00551-6.pdf

Prediction of Cervical Cancer from Behavior Risk Using Machine Learning Techniques

In this paper, high utility itemset mining (HUIM) algorithms driven by Binary Differential Evolution (BDE) and an Adaptive Binary Differential Evolution (ABDE) are proposed separately. These are compared with the HUIM algorithms of (i) Binary Particle Swarm Optimization, (ii) Genetic Algorithm and (iii) a two-phase HUIM found in the literature. The proposed HUIM algorithms are applied on seven datasets, where OnlineRetail dataset is a real-life dataset and the objective there is to segment high value customers based on monetary value. From the results, it is clear that BDE based HUIM outperformed the extant algorithms in literature and also the ABDE HUIM algorithm on all datasets with respect to the maximum number of itemsets mined.

High utility itemset mining using binary differential evolution: An application to customer segmentation

Utility mining has been emerging as an important area in data mining. While existing works on utility mining for retail businesses have primarily focused on the problem of finding high-utility itemsets from transactional databases, they implicitly assume that each item occupies only one slot. Here, the slot size of a given item is the number of (integer) slots occupied by that item on the retail store shelves. However, in many real-world scenarios, the number of slots consumed by different items typically varies. Hence, this paper considers that a given item may physically occupy any fixed (integer) number of slots. Thus, we address the problem of efficiently determining the top-utility itemsets when a given number of slots is specified as input. The key contributions of our work are three fold. First, we present an efficient framework to determine the top-utility itemsets for different user-specified number of slots that need to be filled. Second, we propose a novel flexible and efficient index, designated as Slot Type Utility (STU) index, for facilitating quick retrieval of the top-utility itemsets for a given number of slots. Third, we conducted an extensive performance evaluation using both real and synthetic datasets to demonstrate the overall effectiveness of the STU index in quickly retrieving the top-utility itemsets by considering a placement scheme in terms of execution time and utility (net revenue) as compared to recent existing schemes.

An improved scheme for determining top-revenue itemsets for placement in retail businesses

In addition to maximizing the revenue, retailers also aim at diversifying product offerings for facilitating sustainable revenue generation in the long run. Thus, it becomes a necessity for retailers to place appropriate itemsets in a limited k number of premium slots in retail stores for achieving the goals of revenue maximization and itemset diversification. In this regard, research efforts are being made to extract itemsets with high utility for maximizing the revenue, but they do not consider itemset diversification i.e., there could be duplicate (repetitive) items in the selected top-utility itemsets. Furthermore, given utility and support thresholds, the number of candidate itemsets of all sizes generated by existing utility mining approaches typically explodes. This leads to issues of memory and itemset retrieval times. In this paper, we present a framework and schemes for efficiently retrieving the top-utility itemsets of any given itemset size based on both revenue as well as the degree of diversification. Here, higher degree of diversification implies less duplicate items in the selected top-utility itemsets. The proposed schemes are based on efficiently determining and indexing the top-λ high-utility and diversified itemsets. Experiments with a real dataset show the overall effectiveness and scalability of the proposed schemes in terms of execution time, revenue and degree of diversification w.r.t. a recent existing scheme.

A Diversification-Aware itemset Placement Framework for Long-term Sustainability of Retail Businesses

Utility mining has been emerging as an important area in data mining. While existing works on utility mining have primarily focused on the problem of finding high-utility itemsets from transactional databases, they implicitly assume that each item occupies only one slot. However, in many real-world scenarios, the number of slots consumed by different items typically varies. Hence, this paper considers that a given item may physically occupy any fixed (integer) number of slots. Thus, we address the problem of efficiently determining the top-utility itemsets when a given number of slots is specified as input. The key contributions of our work are three-fold. First, we present an efficient framework to determine the top-utility itemsets for different user-specified number of slots that need to be filled. Second, we propose a novel flexible and efficient index, designated as the STUI index, for facilitating quick retrieval of the top-utility itemsets for a given number of slots. Third, we conducted an extensive performance evaluation using real datasets to demonstrate the overall effectiveness of the proposed indexing scheme in terms of execution time and utility (net revenue) as compared to a recent existing scheme.

A Flexible and Efficient Indexing Scheme for Placement of Top-Utility Itemsets for Different Slot Sizes

Smart Applications and Data Analysis - Third International Conference, SADASC 2020, Proceedings

Urban informatics is emerging as a new discipline for cities and governments to improve the lives of citizens using information technology. In this advanced seminar, we introduce the key challenges and opportunities in urban informatics, discuss topics in mobile computing, Internet of Things (IoT) and big data analytics, to advance the state-of-the-art in urban informatics and provide interesting use cases. This seminar is designed for academicians, researchers, city administrators/planners, application developers, and research students with background in mobile computing and database systems.

Mobile Computing, Internet of Things, and Big Data for Urban Informatics

Systems and related methods for facilitating document banking for one or more account holders are disclosed. The system includes a document banking system and one or more verification partners. The document banking system includes a storage repository to store one or more documents for the one or more account holders. The document banking system further includes a transaction engine facilitating one or more document transactions. The one or more verification partners verify the one or more documents when requested by the document banking system.

Methods and systems for facilitating document banking

A method including authenticating one or more service providers that provide one or more services to users, the one or more service providers being authenticated for sending content to the users via a document banking system. The method further includes receiving one or more templates from the one or more service providers. The method furthermore includes receiving content from a service provider corresponding to a service used by a user, the content is received via secure Application Program Interfaces (APIs). The method includes determining whether the service provider is an authenticated service provider and based on the determination, the method includes identifying a template corresponding to the service for which the content is received. Additionally, the method includes overlaying the received content over the identified template and adding the generated document or the updated document to the document bank account of the user.

Anirban Mondal

Papers

A Flexible and Efficient Indexing Scheme for Placement of Top-Utility Itemsets for Different Slot Sizes

Smart Applications and Data Analysis - Third International Conference, SADASC 2020, Proceedings

Mobile Computing, Internet of Things, and Big Data for Urban Informatics

Methods and systems for facilitating document banking

Methods and systems for handling trusted content from various service providers