The paper suggests a hybrid multimodal sentiment analysis (MSA) model that would enhance the accuracy of sentiment prediction through the combination of textual, auditory, and visual information. In most cases, the traditional sentiment analysis models have been challenged because of numerous overlapping features and poor fusion methods when using ...

By Jayaprakash Vattikundala, M. Siva Ganga Prasad

To solve the problems of traditional Apriori algorithm in power marketing big data processing, such as candidate item set redundancy, low single-machine computing efficiency, and difficulty in adapting to multi-dimensional time series data, this study proposes an improved Apriori algorithm that integrates Resilient Distributed Dataset (RDD) distrib...

By Fan Pan, Lingen Zhou, Lu Gan, Wei Kang, Xiaolei Li

The stability of smart grids (SG) plays a critical role in improving the stability of power supply, particularly when system failures or sensor breakdowns could occur and result in a lack of input data. This paper provides a new method of prediction of smart grid consistency by using a Gradient Policy prediction model, which is based on reinforceme...

By S. Mahendran, B. Gomathy

It presents a dynamic model of inventory management of the deteriorating items with time-sensitive demand and variable holding costs. The model is used to solve the problem in industries where demand is seasonal or because of other external conditions, like weather or market conditions. A two-tier inventory model is implemented as a way of minimizi...

By Prashant Sharma, Birendra Kumar Chauhan, Gajraj Singh

Rapid and accurate assessment of civic infrastructure following a natural or artificial disaster is essential to planning emergency response and recovery. This paper introduces a control system based on deep reinforcement learning (DRL) to coordinate unmanned aerial vehicle (UAV) swarms and methodically approach the post-disaster infrastructure ins...

By Moti Ranjan Tandi, Archana Mishra

The innovation of Machine Learning (ML) techniques is evolving from basic techniques to optimized techniques, considerably improving the performance of prediction models. In the proposed work, the study primarily explores fundamental ML classification methods to classify banking customers based on their credit information. The classification of cus...

By Sufaira Shamsudeen, K. Ranjith Singh

Women's crimes in India are a serious social issue, and new-age solutions to their detection and prevention are essential. The article introduces an analytical model using modern data and geography tools to identify and map cases of violence against women in India. It uses several different types of sources such as police reports, social media and ...

By Aby Rose Varghese, Dr. R. Gunasundari

Purpose- The main objective of the proposed paper is to create and implement a real-time wearable health monitoring system based on IoT, i.e., Oxy Sense-Wear, that will enable the constant control of the main physiological parameters, such as ECG, EMG, SpO2, body temperature, and physical activity. The system is aimed at long-term surveillance of t...

By M.N. Vimal Kumar, M. Pravin Kumar, Baskar Duraisamy, A.K. Jaithunbi, P. Samson Peter, V.M. Thejashri

High-precision crop type mapping is fundamental for agricultural monitoring, food security assessment, and sustainable land management. Recent breakthroughs in Earth observation and machine learning (ML) have greatly enhanced the potential for satellite data to capture crop phenology, spatial variability, and temporal variations. This paper conduct...

By Benazir Meerasha, K. Martin Sagayam, P. Malin Bruntha, Jasmine David, Vasu Koduri

The sophistication of the contemporary code has augmented defect prediction (SDP) with vital concerns like severe imbalance in classes, high redundancy of features and failure of conventional techniques to gain the rich semantic and structural context of a source code. The model suggested within the current paper is HDA-SE-GFF that has Semantic-Enr...

By P. Bhavani, N. Danapaquiame