Automated COVID-19 CT Image Classification using Multi-head Channel Attention in Deep CNN

The rapid spread of COVID-19 has necessitated efficient and accurate diagnostic methods. Computed Tomography (CT) scan images have emerged as a valuable tool for detecting the disease. In this article, we present a novel deep learning approach for automated COVID-19 CT scan classification where a modified Xception model is proposed which incorporates a newly designed channel attention mechanism and weighted global average pooling to enhance feature extraction thereby improving classification accuracy. The channel attention module selectively focuses on informative regions within each channel, enabling the model to learn discriminative features for COVID-19 detection. Experiments on a widely used COVID-19 CT scan dataset demonstrate a very good accuracy of 96.99% and show its superiority to other state-of-the-art techniques. This research can contribute to the ongoing efforts in using artificial intelligence to combat current and future pandemics and can offer promising and timely solutions for efficient medical image analysis tasks

Exploring IoT for real-time CO2 monitoring and analysis

As a part of this project, we have developed an IoT-based instrument utilizing the NODE MCU-ESP8266 module, MQ135 gas sensor, and DHT-11 sensor for measuring CO$_2$ levels in parts per million (ppm), temperature, and humidity. The escalating CO$_2$ levels worldwide necessitate constant monitoring and analysis to comprehend the implications for human health, safety, energy efficiency, and environmental well-being. Thus, an efficient and cost-effective solution is imperative to measure and transmit data for statistical analysis and storage. The instrument offers real-time monitoring, enabling a comprehensive understanding of indoor environmental conditions. By providing valuable insights, it facilitates the implementation of measures to ensure health and safety, optimize energy efficiency, and promote effective environmental monitoring. This scientific endeavor aims to contribute to the growing body of knowledge surrounding CO$_2$ levels, temperature, and humidity, fostering sustainable practices and informed decision-makingComment: 9 pages, 7 figure

An Interactive Knowledge-based Multi-objective Evolutionary Algorithm Framework for Practical Optimization Problems

Experienced users often have useful knowledge and intuition in solving real-world optimization problems. User knowledge can be formulated as inter-variable relationships to assist an optimization algorithm in finding good solutions faster. Such inter-variable interactions can also be automatically learned from high-performing solutions discovered at intermediate iterations in an optimization run - a process called innovization. These relations, if vetted by the users, can be enforced among newly generated solutions to steer the optimization algorithm towards practically promising regions in the search space. Challenges arise for large-scale problems where the number of such variable relationships may be high. This paper proposes an interactive knowledge-based evolutionary multi-objective optimization (IK-EMO) framework that extracts hidden variable-wise relationships as knowledge from evolving high-performing solutions, shares them with users to receive feedback, and applies them back to the optimization process to improve its effectiveness. The knowledge extraction process uses a systematic and elegant graph analysis method which scales well with number of variables. The working of the proposed IK-EMO is demonstrated on three large-scale real-world engineering design problems. The simplicity and elegance of the proposed knowledge extraction process and achievement of high-performing solutions quickly indicate the power of the proposed framework. The results presented should motivate further such interaction-based optimization studies for their routine use in practice.Comment: 15 pages, 10 figures in main document; 6 pages, 6 figures in supplementary documen