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Found 19 papers, 0 papers with code
The State of Lithium-Ion Battery Health Prognostics in the CPS Era
Lithium-ion batteries (Li-ion) have revolutionized energy storage technology, becoming integral to our daily lives by powering a diverse range of devices and applications.
Leveraging External Knowledge Resources to Enable Domain-Specific Comprehension
Machine Reading Comprehension (MRC) has been a long-standing problem in NLP and, with the recent introduction of the BERT family of transformer based language models, it has come a long way to getting solved.
Milestones in Bengali Sentiment Analysis leveraging Transformer-models: Fundamentals, Challenges and Future Directions
Sentiment Analysis (SA) refers to the task of associating a view polarity (usually, positive, negative, or neutral; or even fine-grained such as slightly angry, sad, etc.)
Quality > Quantity: Synthetic Corpora from Foundation Models for Closed-Domain Extractive Question Answering
Domain adaptation, the process of training a model in one domain and applying it to another, has been extensively explored in machine learning.
De-SaTE: Denoising Self-attention Transformer Encoders for Li-ion Battery Health Prognostics
The usage of Lithium-ion (Li-ion) batteries has gained widespread popularity across various industries, from powering portable electronic devices to propelling electric vehicles and supporting energy storage systems.
TFBEST: Dual-Aspect Transformer with Learnable Positional Encoding for Failure Prediction
To overcome these challenges, in this work we propose a novel transformer architecture - a Temporal-fusion Bi-encoder Self-attention Transformer (TFBEST) for predicting failures in hard-drives.
Spatio-temporal Storytelling? Leveraging Generative Models for Semantic Trajectory Analysis
In this paper, we lay out a vision for analysing semantic trajectory traces and generating synthetic semantic trajectory data (SSTs) using generative language model.
Large-scale End-of-Life Prediction of Hard Disks in Distributed Datacenters
Past work suggests using LSTMs as an excellent approach to predicting remaining useful life.
Integration of a machine learning model into a decision support tool to predict absenteeism at work of prospective employees
Originality/value - In this paper, we propose a model that is trained based on attributes that can be collected during the hiring process.
Remaining Useful Life Estimation of Hard Disk Drives using Bidirectional LSTM Networks
Physical and cloud storage services are well-served by functioning and reliable high-volume storage systems.
Data-Driven Optimization of Public Transit Schedule
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.
Duluth at SemEval-2019 Task 4: The Pioquinto Manterola Hyperpartisan News Detector
This paper describes the Pioquinto Manterola Hyperpartisan News Detector, which participated in SemEval-2019 Task 4.
Investigating Antigram Behaviour using Distributional Semantics
The field of computational linguistics constantly presents new challenges and topics for research.
Chaotic Quantum Double Delta Swarm Algorithm using Chebyshev Maps: Theoretical Foundations, Performance Analyses and Convergence Issues
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
In this paper we work with components of backend data servers such as hard disks, that are subject to degradation.
Learning to track on-the-fly using a particle filter with annealed- weighted QPSO modeled after a singular Dirac delta potential
This paper proposes an evolutionary Particle Filter with a memory guided proposal step size update and an improved, fully-connected Quantum-behaved Particle Swarm Optimization (QPSO) resampling scheme for visual tracking applications.
Particle Swarm Optimization: A survey of historical and recent developments with hybridization perspectives
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
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.
