BiLSTM-ANN Based Employee Job Satisfaction Analysis from Glassdoor Data Using Web Scraping
- Muhammed Yaseen Morshed Adib,
- Sovon Chakraborty,
- Mashiwat Tabassum Waishy,
- Md Humaion Kabir Mehedi,
- Annajiat Alim Rasel
Job satisfaction among employees is a crucial factor in a burgeoning organization. Satisfied current employees mean more skilled employees will be interested in joining the organization in the future, which will reinforce the prosperity of the ...
Hyperparameter Selection in Reinforcement Learning Using the “Design of Experiments” Method
Artificial Intelligence and Machine Learning is a highly active area of research across numerous subgenres. One such example is Reinforcement Learning, which relies on trial and error based sampling of the environment to train an agent at ...
Prototypical Quadruplet for Few-Shot Class Incremental Learning
The scarcity of data and incremental learning of new tasks pose two major bottlenecks for many modern computer vision algorithms. The phenomenon of catastrophic forgetting, i.e., the model's inability to classify previously learned data after ...
A Novel Deep Multi-head Attentive Vulnerable Line Detector
Detecting and fixing vulnerabilities in software programs before production is crucial in software engineering. Manual vulnerability detection is labor-intensive, especially for large programs, leading to the proposal of machine learning-based ...
HScore: A Target Attention-based Filter Pruning with Visualizing the Area of Feature Map
Neural network pruning plays an important role in the deployment on resource-constrained devices by reducing the scale of the network and the computational complexity. Different from existing pruning methods that only consider the amount of ...
NASiam: Efficient Representation Learning using Neural Architecture Search for Siamese Networks
Siamese networks are one of the most trending methods to achieve self-supervised visual representation learning (SSL). Since hand labeling is costly, SSL can play a crucial part by allowing deep learning to train on large unlabeled datasets. ...
Boosting BERT-Based Knowledge Graph Completion with Contrastive Learning and Hard Sample Training
Knowledge graph (KG), which is often described by a set of triplets (head, relation, tail), has shown to be very useful for many downstream applications, but suffers from the issue of incomplete connections. Knowledge graph completion (KGC) is to ...
Machine Learning Applied to Anomaly Detection on 5G O-RAN Architecture
- Pedro V.A. Alves,
- Mateus A.S.S. Goldbarg,
- Wysterlânya K.P. Barros,
- Iago D. Rego,
- Vinícius J.M.T. Filho,
- Allan M. Martins,
- Vicente A. de Sousa Jr.,
- Ramon dos R. Fontes,
- Eduardo H. da S. Aranha,
- Augusto V. Neto,
- Marcelo A.C. Fernandes
This article presents a study with feasibility and performance analysis of machine learning (ML) techniques using supervised techniques for anomaly detection problems in a 5G communication network. The proposed ML models (Multilayer Perceptron, ...
Investigating Pre-trained Language Models on Cross-Domain Datasets, a Step Closer to General AI
Pre-trained language models have recently emerged as a powerful tool for fine-tuning a variety of language tasks. Ideally, when models are pre-trained on large amount of data, they are expected to gain implicit knowledge. In this paper, we ...
Variational Neural Networks
Bayesian Neural Networks provide a tool to estimate the uncertainty of a neural network by considering a distribution over weights and sampling different models for each input. In this paper, we propose a method for uncertainty estimation in ...
Evaluation of Attention-Based LSTM and Bi-LSTM Networks For Abstract Text Classification in Systematic Literature Review Automation
- Regina Ofori-Boateng,
- Magaly Aceves-Martins,
- Chrisina Jayne,
- Nirmalie Wiratunga,
- Carlos Francisco Moreno-Garcia
Systematic Review (SR) presents the highest form of evidence in research for decision and policy-making. Nonetheless, the structured steps involved in carrying out SRs make it demanding for reviewers. Many studies have projected the abstract ...
icsBERTs: Optimizing Pre-trained Language Models in Intelligent Customer Service
Automatic processing of textual information is a growing application area in intelligent customer service platforms due to the large number of customer requests constantly provided in the form of text. Many pre-trained language models have shown ...
A TransSE-ResNet Deep Learning Model with Multi-Head Attention Mechanism for Covid-19 Chest CT Image Classification
Covid-19 has been spread worldwide for nearly three years and affected the economy and our daily life deeply. Even though Tedros Adhanom Ghebreyesus, director-general of WHO, declared Covid-19 over as a global health emergency. In the coming ...
A Dual-Path Multi-Scale Feature Fusion Decoder for SegFormer
The encoder-decoder structure is the basic structure of most semantic segmentation models and is adopted by a large number of segmentation models. How to effectively extract image features and achieve high-precision mapping through the optimal ...
Distributions-free Martingales Test Distributions-shift
A standard assumption of the theory of machine learning is the data are generated from a fixed but unknown probability distribution. Although this assumption is based on the foundations of the theory of probability, however, for most learning ...
Curriculum Compositional Continual Learning for Neural Machine Translation
Current trends in language modelling leverage large language models pre-trained on a huge corpus of data to achieve state of the art results on several NLP tasks. On the other hand, humans acquire language from small amount of data using cognitive ...
Mining Implicit Behavioral Patterns via Attention Networks for Sequential Recommendation
Sequential recommender systems (SRSs) seek to model users’ dynamic preferences based on their interaction sequences to suggest items they may be interested in. Existing methods suffer from two major flaws in modeling user interest representations. ...
AC-MMOE: A Multi-gate Mixture-of-experts Model Based on Attention and Convolution
Multi-task learning (MTL), an important branch of machine learning, has been successfully applied to many fields, and its effectiveness in practice has been proved. However, at present, the soft parameter sharing model represented by multi-gate ...
LMBNet: Lightweight Multiple Branch Network for Recognition of HER2 Expression Levels
In recent years, more research methods for HER2 automatic evaluation have been presented. However, these methods are complex and expensive. In this paper, we present a lightweight, highly modular network architecture for HER2 classification. The ...
VCOACH: A Virtual Coaching System Based on Visual Streaming
A virtual coaching system is key for monitoring the performance during exercise regimes and rendering timely feedback to avoid potential physical harm. Ideally, the core technologies behind such a system imply exercise recognition/assessment for ...
Efficient Multi-hop Question Generation
Question generation is the parallel task of question answering, where given an input context and optionally, an answer, the goal is to generate a relevant and fluent natural language question. Although recent works on question generation have ...
WF-UNet: Weather Data Fusion using 3D-UNet for Precipitation Nowcasting
Designing early warning systems for harsh weather and its effects, such as urban flooding or landslides, requires accurate short-term forecasts (nowcasts) of precipitation. Nowcasting is a significant task with several environmental applications, ...
Efficient and Lightweight Neuron Morphology Classification Using Gabor Convolutional Networks
Due to the complex and diverse morphology of neurons, achieving accurate classification of neurons is not an easy task. Most existing methods for neuron classification are based on traditional machine learning algorithms, which not only have high ...
Violence Detection in Real-Life Audio Signals Using Lightweight Deep Neural Networks
The automatic detection of violent behavior in acoustic data has become an important research area due to its growing application potential in various surveillance and behavior monitoring tasks where it causes fewer privacy issues than video. In ...
Error-correcting output codes for multi-class classification based on Hadamard matrices and a CNN model
The error-correcting output codes(ECOC) is the ensemble method for the multi-class classification problem. In some applications of ECOC, Hadamard matrices are used because they have good properties to apply ECOC. However, due to the difficulties ...
A Mechanism for Supporting the Peak/Trough Detection in the Concept Drifting Environment
The primary objective of this research endeavor is to tackle the difficult challenge of identifying the elusive aspect peak/trough of the business cycle, which is known to exhibit the tendency of concept drift. To effectively confront this issue, ...
MutFusVAE: Mutational Fusion Variational Autoencoder for Predicting Primary Sites of Cancer
The metastatic propensity of malignant primary tumors is a recurring theme when it comes to the cause of mortality in cancer. Establishing the primary site of a metastatic cancer is a significant but challenging task. There are ∼3% of metastatic ...
Attention Link: An Efficient Attention-Based Low Resource Machine Translation Architecture
Transformers have emerged as a pivotal tool in machine translation. Nonetheless, their effectiveness typically hinges on extensive training with millions of bilingual parallel corpora. This paper presents a novel architecture, termed as Attention ...
