Journal of Intelligent Systems and Internet of Things JISIoT 2690-6791 2769-786X 10.54216/JISIoT https://www.americaspg.com/journals/show/2888 2019 2019 Instructor-II / Computing & Software Engineering, U.A. Whitaker College of Engineering, Florida Gulf Coast University,10501 FGCU Blvd. S, Fort Myers, FL 33965 Deepa Deepa Department of CSE, School of Computing, MIT Art, Design and Technology University, Pune, Maharastra, India Rohit pachlor Department of CSE, GITAM University, Rudraram, Hyderabad, Telangana, India Ramesh. M. Department of ECE, Velammal Institute of Technology, Chennai, TN, India G. Shanmugaraj Department of ECE, Alliance College of Engineering and Design, Alliance University, Bengaluru, Karnataka, India Aby K. Thomas Department of Mechanical Engineering, LENDI Institute of Engineering and Technology, Vizianagaram, Andhra Pradesh, India K. Sridhar Deep Learning, or DL for short, is an emerging subfield within the larger discipline of machine learning in today's world. The study being conducted in this area is progressing at an immediate stride, and the discoveries are contributing to the progression of technology. Deep learning (DL) methods were developed with the intention of developing a general-purpose learning method that would enable the gradual learning of characteristics at multiple levels without relying on human-engineered features. This was the goal of deep learning. Because of this, the system is able to acquire intricate purposes and directly map input to output by making use of the data that it has acquired which is based on Internet of things (IoTs). This study places an emphasis on the application of CNN (Convolutional Neural Networks), which are a subcategory of DNN (Deep Neural Networks), and it develops an efficient layered CNN for the classification of ECG arrhythmias. Even while FC-ANNs (Fully Connected Artificial Neural Networks), which are sometimes referred to as Multilayer-Perceptron networks, are effective in categorising ECG arrhythmias, the optimization process for many classification networks takes a significant amount of time in terms of computation. In addition, the features extracted by engineers are what define the accuracy of the categorization of ECG arrhythmias. An improved CNN based filtering, feature abstraction, and classification prototypical is established in order to conduct an accurate analysis of an electrocardiogram (ECG). When measured against ANN, the performance was found to have an accuracy rating of 99.6%. Consequently, the CNN model that was suggested is useful to doctors in arriving at the definitive diagnosis of AFL (atrial flutter), AFIB (atrial fibrillation), VFL (ventricular flutter), and VT (ventricular tachycardia). It includes denoising, feature extraction, and categorization as part of its functionality. 2024 2024 166 176 10.54216/JISIoT.130113 https://www.americaspg.com/articleinfo/18/show/2888