Error correction codes are majorly important to detect and correct occurred errors because of various noise sources. When the technology is scaling down, the effect of noise sources is high. The coupling capacitance is one of the main constraints to affect the performance of on-chip interconnects. Because of coupling capacitance, the crosstalk is introduced at on-chip interconnecting wires. To control the single or multiple errors, an efficient error correction code is required. By combining crosstalk avoidance with error control code, the reliable intercommunication is obtained in network-on-chip (NoC)-based system on chip (SoC). To reduce the power consumption of error control codes, the bus invert-based low-power code is integrated to network interface of NoC. The advanced work is designed and implemented with Xilinx 14.7; thereby the performance of improved NoC is evaluated and compared with existing work. The 8 × 8 mesh-based NoC is simulated at various traffic patterns to analyze the energy dissipation and average data packet latency.

In this article, a bio-inspired AlexNet-DrpXLm architype is proposed for an effective brain stroke lesion detection and classification within a short period. Here, the input CT image datasets are collected from Himalayan Institute of Medical Sciences, then the images are preprocessed to take away the noises and also enhance the quality of the images. After that, the input images are trained and the features are extracted with the help of AlexNet model, and then classified as the brain images of normal and abnormal. The last three layers of the AlexNet model are replaced with the dropout extreme learning machine (DrpXLM) classifier to classify the images in efficient manner. The DrpXLM weight parameters are tuned by improved dolphin swarm optimization algorithm. The proposed method attains higher accuracy 99.54%, high precision 90.43%, high F-score 89.40%, higher specificity 93.56%, higher sensitivity 93.56%, lower computational time 0.02 s, and the proposed method is compared to the existing methods, like random decision forest with gravitational search algorithm, hybrid native Bayes and sample weighted with random forest classification algorithm, and random forest with fractional-order Darwinian particle swarm optimization.

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