Abstract: Electrocardiogram (ECG) recordings serve as a cornerstone of cardiac health surveillance, enabling clinicians to identify a broad spectrum of cardiovascular disorders, most notably arrhythmias such as atrial fibrillation (AF). Nevertheless, manual interpretation of these recordings is inherently laborious, technically demanding, and vulnerable to diagnostic inconsistencies owing to the sheer volume and heterogeneity of the data involved. This investigation proposes an innovative framework for fully automated ECG signal categorization, leveraging Long Short-Term Memory (LSTM) neural networks — a specialized class of deep learning architecture well-suited to the analysis of temporally evolving sequential data such as physiological waveforms. The methodology draws upon publicly available, annotated ECG repositories wherein individual signal segments are pre-labeled into four distinct classes: normal cardiac rhythm, atrial fibrillation, other irregular patterns, and noise or signal corruption. Unlike conventional pipelines, the LSTM-based architecture autonomously discovers discriminative temporal features within the raw waveform, rendering manual feature engineering unnecessary. Experimental validation demonstrates that the proposed system achieves a classification accuracy in the range of 95–98%, attesting to its clinical utility. Prospective deployment scenarios encompass wearable biosignal platforms, telehealth assessment environments, and AI-augmented clinical decision-support tools.

Keywords:  ECG, LSTM, Arrhythmia, Atrial Fibrillation, EMG Sensor, Artificial Intelligence, Deep Learning.

Title: ECG Signal Classification Using Long Short-Term Memory (LSTM) Neural Networks

Author: Dr. Jaya Raut, Asmita P., Kalyani P., Yashasvi R., Isha B., Reva K.

International Journal of Novel Research in Electrical and Mechanical Engineering

ISSN 2394-9678

Vol. 13, Issue 1, September 2025 - August 2026

Page No: 16-20

Novelty Journals

Website: www.noveltyjournals.com

Published Date: 20-April-2026

Abstract: The rapid growth in energy demand and the need for efficient power utilization have accelerated the adoption of intelligent energy monitoring systems. Conventional electricity meters lack real-time monitoring, user interaction, and automated control, which often results in inefficient energy usage and billing inaccuracies. To address these challenges, this paper presents the design and implementation of a Smart Prepaid Energy Meter with Automatic Power Cut-Off based on Internet of Things (IoT) technology.

The proposed system employs a Wemos D1 microcontroller integrated with voltage and current sensing modules to continuously monitor electrical parameters and compute power consumption in real time. The measured data is displayed locally on a 16×2 LCD and transmitted to a web-based application through Wi-Fi for remote monitoring and analysis. A prepaid billing mechanism is incorporated, where users recharge their account using a simulated currency for demonstration purposes. The system dynamically deducts the balance according to energy usage, and when the balance reaches zero, the power supply is automatically disconnected using a relay module.

Additionally, the web application provides features such as consumption history, graphical visualization, and intelligent recommendations for energy conservation. The proposed system is cost-effective, scalable, and suitable for industrial and residential applications. It promotes energy awareness, enhances user control, and ensures efficient power management through automation and real-time data accessibility.

Keywords: Smart Meter, IoT, Prepaid Energy Meter, Wemos D1, Power Monitoring, Automatic Power Cut, Energy Management, Web Application.

Title: INDUSTRY LEVEL-SMART METER

Author: Prof. Dilip Kapse, Gauri Ghadge, Janvi Zade, Kumkum Tiwari, Pranoti Mujbaile, Shivani Jaiswal, Shruti Vaidya, Prof. Amol Morankar

International Journal of Novel Research in Electrical and Mechanical Engineering

ISSN 2394-9678

Vol. 13, Issue 1, September 2025 - August 2026

Page No: 8-15

Novelty Journals

Website: www.noveltyjournals.com

Published Date: 20-April-2026

Abstract: The increasing demand for efficient traffic management and sustainable energy utilization has driven the development of intelligent urban infrastructure systems. This paper presents a Smart Street Light and Traffic Control System based on the Arduino Mega 2560, integrating infrared (IR) sensors, RFID technology, and a solar energy module. The system dynamically controls street light intensity based on real-time vehicle detection, thereby reducing energy consumption during low-traffic conditions. Simultaneously, an automated traffic signal mechanism is implemented to regulate vehicle flow at intersections. To enhance emergency response efficiency, RFID-based identification is used to detect emergency vehicles and provide immediate signal priority. In addition, the incorporation of a solar panel enables renewable energy generation and storage, reducing dependency on conventional power sources. The proposed system is cost-effective, scalable, and suitable for smart city applications.

Keywords: Smart Traffic System, Arduino Mega 2560, Infrared Sensors, RFID, Intelligent Street Lighting, Energy Efficiency, Smart Cities, Solar Energy.

Title: SMART STREET LIGHT AND TRAFFIC MANAGEMENT SYSTEM

Author: Prof. Anand Deshkar, Isha Asole, Janhavi Rajurkar, Janvi Deogade, Mamata Vairale, Sneha Gaikwad, Anushree Nimkar

International Journal of Novel Research in Electrical and Mechanical Engineering

ISSN 2394-9678

Vol. 13, Issue 1, September 2025 - August 2026

Page No: 1-7

Novelty Journals

Website: www.noveltyjournals.com

Published Date: 18-April-2026