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VANET is a category of MANET that aims to provide wireless communication. It increases the safety of roads and passengers. Millions of people lose their precious lives in accidents yearly, millions are injured, and others incur disability daily. Emergency vehicles need clear roads to reach their destination faster to save lives. Video streaming can be more effective as compared to textual messages and warnings. To address this issue, we proposed a methodology to use visual sensors, cameras, and OBU to record emergency videos. Initially, the frames are detected. After re-recording, the frames detection algorithm detects the specific event from the video frames. Blockchain encrypts an emergency or specific event using hashing algorithms in the second layer of our proposed framework. In the third layer of the proposed methodology, encrypted video is broadcast with the help of 5G wireless technology to the connected nodes in the VANET. The dataset used in this research comprises up to 72 video sequences averaging about 120 seconds per video. All videos have different traffic conditions and vehicles. The ResNet-50 model is used for the feature extraction process of extracted frames. The model is trained using Tensorflow and Keras deep learning models. The Elbow method finds the optimal K number for the K Means model. This data is split into training and testing. 70% is reserved for training the support vector machine (SVM) model and test datasets, while 30%. 98% accuracy is achieved with 98% precision and 99% recall as results for the proposed methodology. [ABSTRACT FROM AUTHOR]

Osteoporosis is a global health concern characterized by reduced bone density and compromised bone quality, resulting in an increased risk of fractures, particularly in postmenopausal women. The assessment of bone mineral density (BMD) plays a pivotal role in diagnosing osteoporosis, as it accounts for approximately 70% of overall bone strength. The World Health Organization (WHO) has endorsed BMD measurement as a reliable method for diagnosing this condition. In Pakistan, the incidence of bone fractures is on the rise, largely attributable to an aging population and a range of contributing factors. Understanding the global and local prevalence of osteoporosis, its impact on morbidity and mortality, and the contributing factors is vital for developing effective preventive and therapeutic strategies. The role of adipokines, including chemerin, vaspin, and omentin-1, in bone metabolism is an emerging area of investigation. These adipokines play diverse roles in physiology, ranging from inflammation and metabolic regulation to cardiovascular health. Understanding their potential impact on bone health is a topic of ongoing research. The intricate relationship between bone density, bone quality, and overall bone strength is central to understanding the diagnosis and management of osteoporosis. Current innovation in machine learning and predictive model can bring revolution in the field of bone health and osteoporosis. Early identification of people with osteoporosis or risk of fracture through machine learning can prevent disability and improve the quality of life. [ABSTRACT FROM AUTHOR]

Osteoporosis, also known as low bone mineral density (BMD), is a serious health concern, especially for women who have gone through menopause in community settings. This condition weakens the bones and increases the risk of fractures. Women who have gone through menopause are more susceptible to osteoporosis due to hormonal changes. Therefore, it is crucial to identify the condition early to start preventive treatments and reduce the risk of fractures. To address the challenges of diagnosing low BMD in postmenopausal women in community settings, this study proposes a method that combines machine learning with the AdaBoostM1 algorithm, which has shown promising results. Data acquisition, data preprocessing, data training, model testing, and model prediction and evaluation are integral phases of the operational dynamics of our model in osteoporosis diagnosis. This approach recommends increasing screening initiatives and educating patients as strategies to improve early detection and management of the disease. The analysis method used achieved an impressive accuracy rate of approximately 88.8% on the dataset it was applied to. The area under the curve was 0.87, the true positive rate was 88%, and the F1 measure was 0.88. By using accurate diagnostic techniques and providing proactive community care, the incidence of osteoporotic fractures can be significantly reduced, thus improving the quality of life for this vulnerable population. [ABSTRACT FROM AUTHOR]

The primary objective of this study is to develop an advanced, automated system for the early detection and classification of leaf diseases in potato plants, which are among the most cultivated vegetable crops worldwide. These diseases, notably early and late blight caused by Alternaria solani and Phytophthora infestans, significantly impact the quantity and quality of global potato production. We hypothesize that the integration of Vision Transformer (ViT) and ResNet-50 architectures in a new model, named EfficientRMT-Net, can effectively and accurately identify various potato leaf diseases. This approach aims to overcome the limitations of traditional methods, which are often labor-intensive, time-consuming, and prone to inaccuracies due to the unpredictability of disease presentation. EfficientRMT-Net leverages the CNN model for distinct feature extraction and employs depth-wise convolution (DWC) to reduce computational demands. A stage block structure is also incorporated to improve scalability and sensitive area detection, enhancing transferability across different datasets. The classification tasks are performed using a global average pooling layer and a fully connected layer. The model was trained, validated, and tested on custom datasets specifically curated for potato leaf disease detection. EfficientRMT-Net's performance was compared with other deep learning and transfer learning techniques to establish its efficacy. Preliminary results show that EfficientRMT-Net achieves an accuracy of 97.65% on a general image dataset and 99.12% on a specialized Potato leaf image dataset, outperforming existing methods. The model demonstrates a high level of proficiency in correctly classifying and identifying potato leaf diseases, even in cases of distorted samples. The EfficientRMT-Net model provides an efficient and accurate solution for classifying potato plant leaf diseases, potentially enabling farmers to enhance crop yield while optimizing resource utilization. This study confirms our hypothesis, showcasing the effectiveness of combining ViT and ResNet-50 architectures in addressing complex agricultural challenges. [ABSTRACT FROM AUTHOR]