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The number of road accidents has constantly been increasing recently around the world. As per the national highway traffic safety administration’s investigation, 45% of vehicle crashes are done by a distracted driver right around each. We endeavor to build a precise and robust framework for distinguishing diverted drivers. The existing work of distracted driver detection is concerned with a limited set of distractions (mainly cell phone usage). This paper uses the first publicly accessible dataset that is the state farm distracted driver detection dataset, which contains eight classes: calling, texting, everyday driving, operating on radio, inactiveness, talking to a passenger, looking behind, and drinking performed by 26 subjects to prepare our proposed model. The transfer values of the pertained model EfficientNet are used, as it is the backbone of EfficientDet. In contrast, the EfficientDet model detects the objects involved in these distracting activities and the region of interest of the body parts from the images to make predictions strong and accomplish state-of-art results. Also, in the Efficientdet model, we implement five variants: Efficientdet (D0-D4) for detection purposes and compared the best Efficientdet version with Faster R-CNN and Yolo-V3. Experimental results show that the proposed approach outperforms earlier methods in the literature and conclude that EfficientDet-D3 is the best model for detecting distracted drivers as it achieves Mean Average Precision (MAP) of 99.16% with parameter setting: learning rate of $le-3$ , 50 epoch, batch size of 4, and step size of 250, demonstrating that it can potentially help drivers maintain safe driving habits.

Lyme disease, caused by Borrelia burgdorferi and transmitted via Ixodes ticks, is a common vector-borne illness in the United States, with an estimated 476,000 annual cases. While primarily known for its neurological and rheumatological manifestations, Lyme disease can also involve the cardiac system, known as Lyme carditis, which occurs in about 4% to 10% of cases. This case report details a rare instance of Lyme carditis presenting as ST-segment elevation myocardial infarction (STEMI) in a 31-year-old female with no significant medical history. The patient exhibited symptoms of chest pressure and shortness of breath, with laboratory results showing significantly elevated troponin levels and other indicative markers. Notably, cardiac catheterization revealed no coronary occlusion, suggesting an alternative diagnosis to acute coronary syndrome (ACS). Further testing confirmed Lyme carditis through positive serological tests for Lyme-specific IgM antibodies. The case underscores the importance of considering Lyme myopericarditis in differential diagnoses for STEMI in Lyme-endemic areas and in patients without typical risk factors for coronary artery disease. This report aims to increase clinical awareness of this condition, highlighting the need for thorough investigation in atypical cardiac presentations.