ABSTRACT
The development of Medium Access Control (MAC) protocols for Internet of Things should consider various aspects such as energy saving, scalability for a wide number of nodes, and grouping awareness. Although numerous protocols consider these aspects in the limited view of handling the medium access, the proposed Grouping MAC (GMAC) exploits prior knowledge of geographic node distribution in the environment and their priority levels. Such awareness enables GMAC to significantly reduce the number of collisions and prolong the network lifetime. GMAC is developed on the basis of five cycles that manage data transmission between sensors and cluster head and between cluster head and sink. These two stages of communication increase the efficiency of energy consumption for transmitting packets. In addition, GMAC contains slot decomposition and assignment based on node priority, and, therefore, is a grouping-aware protocol. Compared with standard benchmarks IEEE 802.15.4 and industrial automation standard 100.11a and user-defined grouping, GMAC protocols generate a Packet Delivery Ratio (PDR) higher than 90%, whereas the PDR of benchmark is as low as 75% in some scenarios and 30% in others. In addition, the GMAC accomplishes lower end-to-end (e2e) delay than the least e2e delay of IEEE with a difference of 3 s. Regarding energy consumption, the consumed energy is 28.1 W/h for GMAC-IEEE Energy Aware (EA) and GMAC-IEEE, which is less than that for IEEE 802.15.4 (578 W/h) in certain scenarios.
ABSTRACT
A crucial biological process called angiogenesis plays a vital role in migration, growth, and wound healing of endothelial cells and other processes that are controlled by chemical signals. Angiogenesis is the process that controls the growth of blood vessels within tissues while angiogenesis proteins play a significant role in the proper working of this process. The balancing of these signals is necessary for the proper working of angiogenesis. Unbalancing of these signals increases blood vessel formation, which causes abnormal growth or several diseases including cancer. The proposed work focuses on developing a two-layered prediction model using different classifiers like random forest (RF), neural network, and support vector machine. The first level performs in silico identification of angiogenesis proteins based on the primary structure. In the case the protein is an angiogenesis protein, then the second level predicts whether the protein is linked with tumor angiogenesis or not. The performance of the model is evaluated through various validation techniques. The model was evaluated using k-fold cross-validation, independent, self-consistency, and jackknife testing. The overall accuracy using an RF classifier for angiogenesis at the first level was 97.8% and for tumor angiogenesis at the second level was 99.5%, ANN showed 94.1% accuracy for angiogenesis and 79.9% for tumor angiogenesis, and the accuracy of SVM for angiogenesis was 78.8% and for tumor angiogenesis was 65.19%.
