Detection of Covid-19 and Focusing on Interpretability and Explainability of the Black Box of AI Model. 

Different image processing techniques were used to improve the quality of the image.

Densely Connected Squeeze Convolutional Neural Network (DCSCNN) was proposed to classify the lungs diseases.

Sentiment analysis is used to automatically classify sentiments (positive, negative, neutral) towards topics like products, news, or movies, often leveraging machine learning (ML) techniques for enhanced accuracy.

Social networks like Facebook and Twitter serve as significant sources for sentiment analysis, with wide-ranging applications in understanding user opinions.

Despite its potential, sentiment analysis faces challenges in Natural Language Processing (NLP) that impact its accuracy and efficiency.

This paper proposes a soft voting ensemble (SVE) approach combining five ML classifiers (LR, NB, XGB, RF, MLP) to improve sentiment analysis, particularly for movie reviews.

CNC and MCT machines rely on computer programming for tool manipulation, and early fault detection is vital to reduce maintenance costs and boost productivity.

This study introduces a novel deep learning-based 1D-CNN model for early fault detection using sensor data from CNC/MCT machines, outperforming traditional ML and other deep learning models.

The 1D-CNN model achieved the highest accuracy (91.57%) and superior precision, recall, and F1 scores compared to various classifiers, including RF, MLP, XGBoost, LSTM, and hybrid models.

The approach is particularly effective for small manufacturing companies, enabling automation in fault detection, enhancing productivity, and supporting proactive maintenance and safety.