This study develops a 7-layer Long Short-Term Memory (LSTM) model to enhance early diabetes detection in Oman, aligning with the theme of 'Artificial Intelligence in Healthcare'. The model focuses on addressing the increasing prevalence of Type 2 diabetes, projected to impact 23.8% of Oman's population by 2050. It employs LSTM neural networks to manage factors contributing to this rise, including obesity and genetic predispositions, and aims to bridge the gap in public health awareness and prevention. The model's performance is evaluated through various metrics. It achieves an accuracy of 99.40%, specificity and sensitivity of 100% for positive cases, a recall of 99.34% for negative cases, an F1 score of 96.24%, and an AUC score of 94.51%. These metrics indicate the model's capability in diabetes detection. The implementation of this LSTM model in Oman's healthcare system is proposed to enhance early detection and prevention of diabetes. This approach reflects an application of AI in addressing a significant health concern, with potential implications for similar healthcare challenges globally.st diagnostic capabilities, representing a significant leap forward in healthcare technology in Oman.

Abstract: The surge of diabetes poses a significant global health challenge, particularly in Oman and the Middle East. Early detection of diabetes is crucial for proactive intervention and improved patient outcomes. This research leverages the power of machine learning, specifically, Convolutional Neural Networks (CNNs), to develop an innovative 4D CNN model dedicated to early diabetes prediction. A region-specific dataset from Oman is utilized to enhance health outcomes for individuals at risk of developing diabetes. The proposed model showcases remarkable accuracy, achieving an average accuracy of 98.49% to 99.17% across various epochs. Additionally, it demonstrates excellent F1 score, recall, and sensitivity, highlighting its ability to identify true positive cases. The findings contribute to the ongoing effort to combat diabetes and pave the way for future research in using deep learning for early disease detection and proactive healthcare.

This study explores the use of remote ultrasound vibration to enhance welding strength. It examines the effects of 20 kHz, and 40 kHz High Power Ultrasound Transducers (HPUT) in contact and contactless modes during ultrasonic assistance laser welding. Employing shear tests and microscopy for assessment, the research evaluates ultrasound's impact on welding strength and identifies the optimal transducer. Theoretical and numerical analyses investigate vibration transmission into the welding area, particularly examining the vibration distribution and cavitation initiation within the molten pool area. Results indicate that ultrasound-assisted welding increases pull test performance, including the resistance force and strain, and improves microscopic structural integrity. Numerical simulations suggest that narrow-band vibration intensifies pressure in specific welding zones. Contactless ultrasound application also shows enhanced welding quality, offering a promising alternative to current methods. This work contributes to electric car technology by suggesting improvements in welding techniques for power battery systems.

This paper investigates the use of contactless power ultrasonic excitation to decrease the electrical impedance of the weld in laser welding. The literature extensively documents the impact of em-ploying contact power ultrasonic excitation on the microstructure morphology and refinement of grain in the weld. This study involves characterizing an industrial High Power Ultrasound Transducer (HPUT) by determining the optimal distance and angle for contactless excitation of the fusion zone in the weld, aiming to achieve the maximum amplitude. Subsequently, the transducer is integrated into the laser welding system, resulting in the creation of an ultrasonic-assisted welding system. To find the improvement due to the contactless vibration assistance, the welding area was char-acterised by an impedance ohmmeter device. The results indicate an approximately 6 % im-provement in the welding quality in terms of the impedance value, an important parameter for battery pack welding. In response to the issue of overheating in the industrial transducer during prolonged welding operations, an alternative transducer was proposed to overcome this challenge. Further investigations are carried out by the alternative transducer to find the effect of different wave types, namely, shear and compressional waves, on the welding quality. The contact vibration can excite the plate approximately 50 times higher in acceleration amplitude than contactless ex-citation. Nevertheless, enhancements of 10% and 6% are observed in the impedance value when utilising compressional and shear waves, respectively, as compared to the results obtained with contactless vibration.