Michał Maj

Lecturer

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Poland

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Michał Maj is a Lecturer at Wyzsza Szkola Ekonomii i Innowacji w Lublinie, Poland. His research focuses on the optimization of customer support systems, the application of advanced machine learning techniques in logistics and project management, as well as emotion analysis through deep learning. Recent publications include studies on intelligent support systems and the automation of optical quality control.

Michał Maj

Research Interests

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Articles (18)

Advanced emotion analysis: harnessing facial image processing and speech recognition through deep learning

Optimizing delivery time with an intelligent forecasting model: leveraging ai and machine learning for efficient logistics

An intelligent support system and emotional state tests for people who are sick or recovering

A Real-Time Autonomous Machine Learning System for Face Recognition Using Pre-Trained Convolutional Neural Networks

Enhancing Customer Service in Shopping Malls with an Advanced Chatbot-Integrated Concierge Device

Comparison of CIE CAM and CIE LAB color space models for better automation of optical quality control,Porównanie modeli przestrzeni barw CIE CAM i CIE LAB w celu lepszej automatyzacji optycznej kontroli jakości

Optimizing Customer Support Using Text2SQL to Query Natural Language Databases

The Effect of the Context Length of Large Language Models on the Quality of Extracted Information in Polish in the Process of Project Management

Autonomous Face Classification Online Self-Training System Using Pretrained ResNet50 and Multinomial Naïve Bayes

Automation of optical quality control in the example of the furniture industry

Comparison of CNN and LSTM algorithms for solving the EIT inverse problem

A stereo vision system on Jetson device using deep learning,System widzenia stereo na urządzeniu Jetson wykorzystujący głębokie uczenie)

3D lung segmentation of the CT series based on 2D Chan-Vese

UNet model in image reconstruction for electrical impedance tomography,Model UNet w rekonstrukcji obrazu do elektrycznej tomografii impedancyjnej

Comparison of Machine Learning Methods for Image Reconstruction Using the LSTM Classifier in Industrial Electrical Tomography

Methods of process mining and prediction using deep learning

The Use of Transfer Learning with Very Deep Convolutional Neural Network in Quality Management

Image reconstruction by solving the inverse problem in ultrasonic transmission tomography system

Collaborators (4)

Magdalena Rzemieniak

Tomasz Rymarczyk

Grzegorz Kłosowski

Tomasz Cieplak

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