Healthcare As a Service (HAAS): CNN-based cloud computing model for ubiquitous access to lung cancer diagnosis.

The field of automated lung cancer diagnosis using Computed Tomography (CT) scans has been significantly advanced by the precise predictions offered by Convolutional Neural Network (CNN)-based classifiers. Critical areas of study include improving image quality, optimizing learning algorithms, and enhancing diagnostic accuracy. To facilitate a seamless transition from research laboratories to real-world applications, it is crucial to improve the technology's usability-a factor often neglected in current state-of-the-art research. Yet, current state-of-the-art research in this field frequently overlooks the need for expediting this process. This paper introduces Healthcare-As-A-Service (HAAS), an innovative concept inspired by Software-As-A-Service (SAAS) within the cloud computing paradigm. As a comprehensive lung cancer diagnosis service system, HAAS has the potential to reduce lung cancer mortality rates by providing early diagnosis opportunities to everyone. We present HAASNet, a cloud-compatible CNN that boasts an accuracy rate of 96.07%. By integrating HAASNet predictions with physio-symptomatic data from the Internet of Medical Things (IoMT), the proposed HAAS model generates accurate and reliable lung cancer diagnosis reports. Leveraging IoMT and cloud technology, the proposed service is globally accessible via the Internet, transcending geographic boundaries. This groundbreaking lung cancer diagnosis service achieves average precision, recall, and F1-scores of 96.47%, 95.39%, and 94.81%, respectively.

Antibacterial Activity of Polyaniline Coated Silver Nanoparticles Synthesized from Piper Betle Leaves Extract.

Plants or natural resources have been found to be a good alternative method for nanoparticles synthesis. In this study, polyaniline coated silver nanoparticles (AgNPs) synthesized from Piper betle leaves extract were investigated for their antibacterial activity. Silver nanoparticles were prepared from the reduction of silver nitrate and NaBH4 was used as reducing agent. Silver nanoparticles and extracts were mixed thoroughly and then coated by polyaniline. Prepared nanoparticles were characterized by Visual inspection, Ultraviolet-visible spectroscopy (UV), Fourier transform infrared Spectroscopy (FT-IR), Transmission Electron Microscopy (TEM) techniques. Antibacterial activities of the synthesized silver nanoparticles were tested against Staphylococcus aureus ATCC 25923, Salmonella typhi ATCC 14028, Escherichia coli ATCC 25922 and Pseudomonas aeruginosa ATCC 27853. UV-Vis spectrum of reaction mixture showed strong absorption peak with centering at 400 nm. The FT-IR results imply that Ag-NPs were successfully synthesized and capped with bio-compounds present in P. betle. TEM image showed that Ag-NPs formed were well dispersed with a spherical structures and particle size ranging from 10 to 30 nm. The result revealed that Ag-Extract NPs showed 32.78±0.64 mm zone of inhibition against S. aureus, whereas norfloxacin (positive control) showed maximum 32.15±0.40 mm zone of inhibition for S. aureus. Again, maximum zone of inhibition 29.55±0.45 mm was found for S. typhi, 27.12±0.38 mm for E. coli and 21.95±0.45 mm for P. aeruginosa. The results obtained by this study can't be directly extrapolated to human; so further studies should be undertaken to established the strong antimicrobial activity of Ag-Extract NPs for drug development program.