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Amyotrophic Lateral Sclerosis (ALS) is a progressive neurodegenerative disorder that presents significant diagnostic challenges due to its heterogeneous clinical manifestations and symptom overlap with other neurological conditions. Early and accurate diagnosis is critical for initiating timely interventions and improving patient outcomes. Traditional diagnostic approaches rely heavily on clinical expertise and manual interpretation of neuroimaging data, such as structural MRI, Diffusion Tensor Imaging (DTI), and functional MRI (fMRI), which are inherently time-consuming and prone to interobserver variability. Recent advances in Artificial Intelligence (AI) and Deep Learning (DL) have demonstrated potential for automating neuroimaging analysis, yet existing models often suffer from limited generalizability across modalities and datasets. To address these limitations, we propose a Transformer-augmented deep learning ensemble framework for automated ALS diagnosis using multi-modal neuroimaging data. The proposed architecture integrates Convolutional Neural Networks (CNNs), Recurrent Neural Networks (RNNs), and Vision Transformers (ViTs) to leverage the complementary strengths of spatial, temporal, and global contextual feature representations. An adaptive weighting-based fusion mechanism dynamically integrates modality-specific outputs, enhancing the robustness and reliability of the final diagnosis. Comprehensive preprocessing steps, including intensity normalization, motion correction, and modality-specific data augmentation, are employed to ensure cross-modality consistency. Evaluation using 5-fold cross-validation on a curated multi-modal ALS neuroimaging dataset demon-strates the superior performance of the proposed model, achieving a mean classification accuracy of 94.5% ± 0.7%, precision of 93.9% ± 0.8%, recall of 92.9% ± 0.9%, F1-score of 93.4% ± 0.7%, spec-ificity of 97.4% ± 0.6%, and AUC-ROC of 0.968 ± 0.004. These results significantly outperform baseline CNN models and highlight the potential of transformer-augmented ensembles in complex neurodiagnostic applications. This framework offers a promising tool for clinicians, supporting early and precise ALS detection and enabling more personalized and effective patient management strategies.

Prophecies about the coming of the Messiah are recorded in the Old Testament and have been fulfilled in the New Testament. The prophecy delivered by the prophets Isaiah, Jeremiah, and Ezekiel is a prophecy of the coming of the Messiah to redeem mankind from sin and He will come as King for all nations. The arrival of the Messiah in the Old Testament is associated with the fall of the Kingdom of David and He comes as a liberator in suffering and He comes as a strengthener of the Kingdom of David. The Messiah came as a leader who brought His people into prosperity and peace. In the New Testament the Messiah came as a bringer of salvation for mankind. He came to free humans from sin and humans live in peace.

The vision of passing from death to life is a glorious awakening. It is something that nature completely ignores, totally contrary to how it works (une privatione ad habitum non datur regressus-no way without ownership). Interestingly, the book of Ezekiel shows an unusual hope. In a valley full of human bones, God asked, "Can these bones rise again?" " Impossible! However, Ezekiel knows that nothing is impossible in God's hands. This paper uses qualitative research methods with a literary approach to present problems as well as analyze and collect data. With this in mind, the writer wants to discuss a number of things related to the text of Ezekiel 37 :1-14 itself. The main problem being considered is how to interpret the passage Ezekiel. 37:1-14. The conclusion is that through the text of Ezekiel 37:1-14, God actually wants to convey promises and messages of hope through Ezekiel. Text it mentions that Ezekiel saw a vision of dry bones in the valley, which was interpreted as a promise to save the Israelites.