IoT Ecosystems Enable Smart Communication Solutions: A Case Study

The Internet of Things (IoT) is a platform for innovation, allowing people to invest in and use IoT to improve life, business, and society. It will be applicable to all or any industry sectors, verticals, people, machines, and everything. This creates difficult requirements in terms of higher system capacity, extremely low latency, such as for the tactile Internet, extremely high throughput values, a wide range of services, such as IoT and M2M, and a more uninterrupted experience. As a symbiotic confluence of up to date and existing technologies, the IOT architecture will use Hetnet RAN, Cloud enhanced RAN, and SW defined data centres to combine novel and legacy technologies. As a result, IOT will combine next-generation largearea extensible service experiences anytime and anywhere, with ultra-dense installations, nearzero latency, and GB experiences–when and where it matters. Collaboration on research, standardisation, and spectrum sharing with the IT/Internet world, industry verticals, policymakers, and academia is a significant success element. Trillions of dollars in smart ecosystems prospects covering secure connections, digital service enablement, applications and repair provisioning, and a wide range of internet of things and consumer applications are available to communications service providers and enterprises

Combination of Reduction Detection Using TOPSIS for Gene Expression Data Analysis

In high-dimensional data analysis, Feature Selection (FS) is one of the most fundamental issues in machine learning and requires the attention of researchers. These datasets are characterized by huge space due to a high number of features, out of which only a few are significant for analysis. Thus, significant feature extraction is crucial. There are various techniques available for feature selection; among them, the filter techniques are significant in this community, as they can be used with any type of learning algorithm and drastically lower the running time of optimization algorithms and improve the performance of the model. Furthermore, the application of a filter approach depends on the characteristics of the dataset as well as on the machine learning model. Thus, to avoid these issues in this research, a combination of feature reduction (CFR) is considered designing a pipeline of filter approaches for high-dimensional microarray data classification. Considering four filter approaches, sixteen combinations of pipelines are generated. The feature subset is reduced in different levels, and ultimately, the significant feature set is evaluated. The pipelined filter techniques are Correlation-Based Feature Selection (CBFS), Chi-Square Test (CST), Information Gain (InG), and Relief Feature Selection (RFS), and the classification techniques are Decision Tree (DT), Logistic Regression (LR), Random Forest (RF), and k-Nearest Neighbor (k-NN). The performance of CFR depends highly on the datasets as well as on the classifiers. Thereafter, the Technique for Order of Preference by Similarity to Ideal Solution (TOPSIS) method is used for ranking all reduction combinations and evaluating the superior filter combination among all