Pocket Data Mining (PDM) describes the full process of analysing data streams in mobile ad hoc distributed environments. Advances in mobile devices like smart phones and tablet computers have made it possible for a wide range of applications to run in such an environment. In this paper, we propose the adoption of data stream classification techniques for PDM. Evident by a thorough experimental study, it has been proved that running heterogeneous/different, or homogeneous/similar data stream classification techniques over vertically partitioned data (data partitioned according to the feature space) results in comparable performance to batch and centralised learning techniques.
Where the normal wound healing stages are interrupted or delayed, chronic or non-healing wounds develop. Wound healing complications may arise due to systemic and local factors. Knowledge of likely impediments to normal wound healing allows recognition and prevention of potentially problematic wounds.
Surgical site infections are common in small animal veterinary practice, and can result in increased morbidity and mortality as well as adding to overall healthcare costs. Surgical site infections are nosocomial infections and can be classified as superficial incisional, deep incisional, or organ-space. Biofilm-producing bacteria in surgical site infections have survival advantages compared to sessile bacteria, making diagnosis and treatment more challenging. Treatment of surgical site infections varies and depends on the type of infection, drug susceptibility, patient factors and wound factors. Preoperative, intraoperative, and postoperative measures can be taken to prevent the development of surgical site infections. Surgical materials to reduce the likelihood of biofilm formation have been developed, but strong evidence to support their use is lacking. Further prospective veterinary studies and the development of active veterinary surveillance programmes are warranted.
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