Wireless communication for data and a variety of wireless interacted devices have increased dramatically in the past few years. Millimeter wave (mmWave) technology can serve the primary objectives of 5G networks, which include high data throughput and low latency. But mmWave signals for communications lacking substantial diffraction and are consequently more susceptible to obstruction by environmental physical objects, which could cause communication lines to be disrupted and congestion takes place. Wireless data transmission suffers from blockages and path loss, causes high latency as well as reduces the data transmission speed and degrades in quality performance. To overcome the limitations, Rough Set Theory with hypertuned SVM is implemented and designed the congestion prediction model based on the behaviour of network towers for low latency and high-speed data transmission. The data from the different towers is initially collected and created as a dataset. Super MICE is a technique to replace the missing data. Then, the Rough Set Theory is utilized to cluster the data into equivalent classes based on the behaviour of 5G, 4G and 3G wireless network. Hypertuned SVM with a Gazelle optimization algorithm is applied to predict the congestion level by accurately selecting the hyperparameter. By employing performance metrics, the proposed approach is examined and contrasted with existing techniques. The evaluation of performance measurements for the proposed method includes informedness attained as 91%, Adjusted Rand Index obtained value as 0.83, Jaccard as 0.737. Accuracy, precision, sensitivity, error, F1_score, and NPV are also achieved at 93%, 92%, 94%, 7%, 92%, and 90%, respectively. According to this evaluation, the proposed model is superior to perform than the earlier used existing methods.  

Texture image retrieval plays a significant and important role in these days, especially in the era of big-data. The big-data is mainly represented by unstructured data like images, videos and messages etc. Efficient methods of image retrieval that reduces the complexity of the existing methods is need for the big-data era. The present paper proposes a new method of texture retrieval based on local binary pattern (LBP) approach. One of the main disadvantages of LBP is, it generates 256 different patterns on a 3x3 neighborhood and a method based on this for retrieval needs 256 comparisons which is very tedious and complex. The retrieval methods based on uniform LBP's which consists of 59 different patterns of LBP is also complex in nature. To overcome this, the present paper divided LBP into dual LBP's consisting four pixels. The present paper based on this dual LBP derived a 2-dimensional dual uniform LBP matrix (DULBPM) that contains only four entries. The texture image retrieval is performed using these four entries of DULBPM. The proposed method is evaluated on the animal fur, car, leaf and rubber textures.