Computer vision (CV) has shown to have tremendous capabilities of providing motor functions assessment in rehabilitation. Nevertheless, the current research is still disjointed, and the particular uses and aims of CV-based rehabilitation evaluation approaches have not been fully revealed. To address this gap, the study will use the method of an exploratory review to give a summary of the current research on the topic of CV based rehabilitation motor function assessment. The findings of the 15 articles used show that CV technology use in the rehabilitation assessment is mostly revealed in estimating the posture and measuring joint kinematics, gait and evaluating spatiotemporal parameters, motion recognition and continuous functional movement, and motion data synthesis and further development. The applications are designed to enhance the scaleability and availability of assessments, attain objective and automated assessments, enable longitudinal follow-ups, as well as feedback and exploration system building. The paper highlights the importance of using more goal-focused and clinically-based CV technologies in the rehabilitation assessment and offers an overall viewpoint to inform further studies and system design.

Unwanted messages in electronic communication, such as spam and phishing attempts, continue to pose significant risks, including information theft, malware distribution, and data loss. Traditional rule-based and keyword-based filtering methods have become less effective due to the evolving tactics used by malicious actors. This study presents a comprehensive machine learning framework for detecting unwanted messages, which combines both textual features—such as term frequency-inverse document frequency (TF-IDF)—and structural features including message length, word count, frequency of special characters, and ratios of uppercase letters. The framework is evaluated using three widely adopted classification algorithms: logistic regression, linear support vector machine, and random forest, applied to a large, publicly available Kaggle dataset of electronic messages. Experimental results demonstrate that the random forest model outperforms the other methods, achieving a precision-recall score of 0.986 and an area under the ROC curve of 0.998. These findings highlight the advantage of integrating diverse engineered features with classical machine learning techniques for effective and interpretable spam detection. Further analysis of feature importance and classification errors provides additional insight into model behavior and error patterns. Future research will focus on incorporating advanced deep learning and explainability methods to further improve detection accuracy and transparency in real-world communication systems.

This comprehensive research synthesizes the foundational principles of Natural Language Processing (NLP) and their realization within ChatGPT, examining the ways deep learning architectures internalize profound linguistic complexity. Structural interplay is investigated. A dual-track empirical framework is systematically employed. By contrasting traditional Long Short-Term Memory (LSTM) networks with the Transformer architecture, the first track effectively demonstrates how parallelized self-attention maintains deep semantic coherence. High-order representational accuracy is achieved. The Qwen2.5-1.5B series is analyzed. By systematically comparing "Base" and "Instruct" models to decouple intelligence origins, the second track reveals that while massive scaling creates an expansive "Cognitive Reservoir" of knowledge, Reinforcement Learning from Human Feedback (RLHF) provides the essential "Functional Bridge" for precise, intent-driven execution. Aligned utility is realized. Ultimately, modern AI is viewed as the synergistic integration of structural efficiency, volumetric growth, and intentional refinement.