Introduction: Colon cancer (CC) image classification plays a key role in the diagnostic process in clinical contexts, especially as computational medical solutions become the trend for future radiology and pathology practices. This study presents a novel lightweight Convolutional Neural Network (CNN) model designed with effective data cleaning strategy for the classification and visualization of histopathology images of various colon cancer tissues.Methods: Addressing the critical need for efficient diagnostic tools in colon cancer detection, the proposed model leverages a non-pretrained architecture optimized for performance in resource-constrained environments. Utilizing the NCT-CRC-HE-100K and CRC-VAL-HE-7K datasets, this model employs a parametric Gaussian distribution-based data cleaning approach to enhance data quality by removing outliers.Results: With a total of 4,414,217 parameters and a total size of 16.9 megabytes, the model achieves a test accuracy of 0.990 ± 0.003 with 95% level of confidence, which demonstrates high precision, recall, specificity, and F1 scores across various tissue classes.Discussion: Comparative analysis with benchmark studies underscores the model’s effectiveness, while discussions on underfitting and overfitting provide insights into potential fine-tuning strategies. This research presents a robust, lightweight solution for colon cancer histopathology image classification, offering a foundation for future advancements in colon cancer diagnostics with result visualization.

INTRODUCTION: The fact of our lives is that we will be reaching to elderly life stage sooner or later. Our elderly community has been left far behind of new technology updates. Technology advances just as age is catching up on us. To many who are in their twilight years, using cutting edge technologies may be a hurdle in their activities of daily living (ADLs).OBJECTIVES: Therefore, this study aims to identify and measure the ADL challenges that the elderly encounter and improve their quality of life (QoL).METHODS: This research embarked on semi-structured interviews at 9 geriatric care centres in Malaysia to investigate the ADL challenges by the elderly residents. The thematic analysis approach was employed for data analysis and further discussion.RESULTS: The research findings suggested that the QoL of the elderly is limited by the challenges of geriatric issues, poor living conditions, and technology acceptance barriers.CONCLUSION: In conclusion, the current research provides an overview of the ADL challenges faced by the elderly with recommendations of user-centred Internet of Things (IoT) devices for elderly to use in an ambient assisted living (AAL) environment in Society 5.0.

A concept of the smart contract powered by Ethereum has raised the attention of investors in the crypto-currency market in the light of the distributed account