During extensive osteological study of 150 dry skulls in the Department of Anatomy, Vardhman Mahavir Medical college, an unusual Paramedian Occipital (POC) condyle was detected in the occipital bone of a cadaveric skull. The anatomical details of this unusual occipital condyle were carefully studied and its morphometric measurements taken. A coronal multiplanner reformatted image and a volume rendered image were taken to study radiological details and establish significant clinical correlation. Precise understanding of anatomy of craniovertebral junction and its anomalies have become immensely important for the present day surgeon during orthopaedic and neurosurgical procedures of this region . Technical advancements in imaging modalities such as CT and MRI scans further signify the importance of these anatomical variations ,which are often missed in routine examination. Osteological study combined with radiological details of the paramedian occipital condyle in the present case aims to emphasize the importance of bony anomalies in the craniovertebral region and their role in diagnosis and appropriate treatment of neurovascular compression syndromes of craniovertebral junction. The present study highlights anatomical details, clinical relevance and embryological basis of such a rare unusual paramedian occipital condyle.
The Internet of Things (IoT) is a network that connects various hardware, software, data storage, and applications. These interconnected devices provide services to businesses and can potentially serve as entry points for cyber-attacks. The privacy of IoT devices is increasingly vulnerable, particularly to threats like viruses and illegal software distribution lead to the theft of critical information. Ant Colony-Optimized Artificial Neural-Adaptive Tensorflow (ACO-ANT) technique is proposed to detect malicious software illicitly disseminated through the IoT. To emphasize the significance of each token in source duplicate data, the noise data undergoes processing using tokenization and weighted attribute techniques. Deep learning (DL) methods are then employed to identify source code duplication. Also the Multi-Objective Recurrent Neural Network (M-RNN) is used to identify suspicious activities within an IoT environment. The performance of proposed technique is examined using Loss, accuracy, F measure, precision to identify its efficiency. The experimental outcomes demonstrate that the proposed method ACO-ANT on Malimg dataset provides 12.35%, 14.75%, 11.84% higher precision and 10.95%, 15.78%, 13.89% higher f-measure compared to the existing methods. Further, leveraging block chain for malware detection is a promising direction for future research the fact that could enhance the security of IoT and identify malware threats.