Intelligent load forecasting analysis with machine learning algorithms to improve efficiency

Document Type : Primary Research paper


1 Assistant Professor , PG & Research Department of Computer Science, Muthurangam Govt. Arts College (Autonomous), Otteri Road , Vellore - 632002. Vellore Dist, Tamil Nadu , India.

2 Professor, Department of Computer Science and Engineering, K.S.K College of Engineering and Technology, Kumbakonam, Tamilnadu-612702, India.

3 Assistant professor (Sl.G), Department of EEE, KPR Institute of Engineering and Technology,Coimbatore-641407.

4 Associate Professor, Department of Computer Science and Engineering,Presidency University, Bangalore-64.

5 Associate Professor, Department of Computer Science and Engineering, Velammal Institute of Technology, Velammal Gardens, Chennai – 601204.

6 Associate Professor ,Department of Computer Science & Engineering,Koneru Lakshmaiah Education Foundation (KLEF),Greenfields, Vaddeswaram, Guntur-522502.


Computer networks that are connected to the Internet of Things (IoT) have a significant issue with information technology security, the checking of PC dangers. The examination proposes a blend of machine learning methods and equal information handling to determine this issue. The architecture for IoT network attacks, as well as a new method of merging key classifiers, are currently being developed. In the issue categorization statement, the consistency proportion to preparing time is a significant proportion of adequacy. We recommend that you take utilization of Spark's information handling and multi-strung mode to assist the readiness and evaluation measures. An elective strategy for getting ready informational collections is shown, which leads in a critical diminishing of the length of the example as a result of the procedure. As indicated by an exploratory assessment of the proposed strategy, the accuracy of IoT network assault recognition is 100%, and the handling pace of information gathering increments with the quantity of equal strings.