Cancer, including the highly dangerous melanoma, is marked by uncontrolled cell growth and the possibility of spreading to other parts of the body. However, the conventional approach to machine learning relies on centralized training data, posing challenges for data privacy in healthcare systems driven by artificial intelligence. The collection of data from diverse sensors leads to increased computing costs, while privacy restrictions make it challenging to employ traditional machine learning methods. Researchers are currently confronted with the formidable task of developing a skin cancer prediction technique that takes privacy concerns into account while simultaneously improving accuracy. In this work, we aimed to propose a decentralized privacy-aware learning mechanism to accurately predict melanoma skin cancer. In this research we analyzed federated learning from the skin cancer database. The results from the study showed that 92% accuracy was achieved by the proposed method, which was higher than baseline algorithms.
Corrosion of mild steel in acidic environments is a major industrial challenge, driving the synthesis of efficient and tunable organic inhibitors. Herein, twelve halogenated 4-aminoantipyrine amide derivatives (M1-M12) were synthesized and characterized by FT-IR and NMR (1H-NMR, 13C-NMR, COSY, and HSQC) spectroscopy and HRMS. Their ability for the corrosion inhibition of mild steel in 1 M HCl was evaluated by weight loss, PDP, EIS, and SEM analyses and DFT calculations. At 1 mM, M5, M12 and M7 exhibited inhibition efficiencies (IE) of 96.46 ± 1.9%, 95.58 ± 1.9% and 94.69 ± 1.8%, respectively; M5 also exhibited the highest electrochemical efficiencies at 0.5 mM (PDP 96.51 ± 0.9% and EIS 95.70 ± 1.1%). Efficiency increased with concentration and decreased with temperature. Negative ΔG° values (-15.53 to -10.86 kJ mol-1) and the Langmuir fit indicate spontaneous, predominantly physical adsorption, possibly with specific donor-acceptor contributions. PDP showed mixed-type behaviour, and EIS showed increased charge-transfer resistance. Halogen type, substituent position and alkyl-chain length strongly influenced performance.