Data-Driven Optimization of Public Transit Schedule

no code implementations • 30 Nov 2019 • Sanchita Basak, Fangzhou Sun, Saptarshi Sengupta, Abhishek Dubey

To address these, this paper makes the following contributions to the corpus of studies on transit on-time performance optimization: (a) an unsupervised clustering mechanism is presented which groups months with similar seasonal delay patterns, (b) the problem is formulated as a single-objective optimization task and a greedy algorithm, a genetic algorithm (GA) as well as a particle swarm optimization (PSO) algorithm are employed to solve it, (c) a detailed discussion on empirical results comparing the algorithms are provided and sensitivity analysis on hyper-parameters of the heuristics are presented along with execution times, which will help practitioners looking at similar problems.

Clustering Scheduling

A Review of Deep Learning with Special Emphasis on Architectures, Applications and Recent Trends

no code implementations • 30 May 2019 • Saptarshi Sengupta, Sanchita Basak, Pallabi Saikia, Sayak Paul, Vasilios Tsalavoutis, Frederick Atiah, Vadlamani Ravi, Alan Peters

Where does one start?

Fault Detection Time Series Forecasting

Chaotic Quantum Double Delta Swarm Algorithm using Chebyshev Maps: Theoretical Foundations, Performance Analyses and Convergence Issues

no code implementations • 5 Nov 2018 • Saptarshi Sengupta, Sanchita Basak, Richard Alan Peters II

Quantum Double Delta Swarm (QDDS) Algorithm is a new metaheuristic algorithm inspired by the convergence mechanism to the center of potential generated within a single well of a spatially co-located double-delta well setup.

Mechanisms for Integrated Feature Normalization and Remaining Useful Life Estimation Using LSTMs Applied to Hard-Disks

no code implementations • 21 Oct 2018 • Sanchita Basak, Saptarshi Sengupta, Abhishek Dubey

In this paper we work with components of backend data servers such as hard disks, that are subject to degradation.

Transfer Learning

Particle Swarm Optimization: A survey of historical and recent developments with hybridization perspectives

no code implementations • 15 Apr 2018 • Saptarshi Sengupta, Sanchita Basak, Richard Alan Peters II

Particle Swarm Optimization (PSO) is a metaheuristic global optimization paradigm that has gained prominence in the last two decades due to its ease of application in unsupervised, complex multidimensional problems which cannot be solved using traditional deterministic algorithms.

Data Clustering using a Hybrid of Fuzzy C-Means and Quantum-behaved Particle Swarm Optimization

no code implementations • 15 Dec 2017 • Saptarshi Sengupta, Sanchita Basak, Richard Alan Peters II

Fuzzy clustering has become a widely used data mining technique and plays an important role in grouping, traversing and selectively using data for user specified applications.

Clustering Quantization