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All Journal International Journal of Reconfigurable and Embedded Systems (IJRES) Bulletin of Electrical Engineering and Informatics Indonesian Journal of Electrical Engineering and Computer Science
Meenakshi, Meenakshi
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Economics, Econometrics & Finance Electrical & Electronics Engineering

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Development of internet of vehicles and recurrent neural network enabled intelligent transportation system for smart cities Surve, Jyoti; Bangare, Manoj L.; Bangare, Sunil L.; Pol, Urmila R.; Mali, Manisha; Meenakshi, Meenakshi; Alsalmani, Abdullah; Morsi, Sami A.
International Journal of Reconfigurable and Embedded Systems (IJRES) Vol 14, No 1: March 2025
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijres.v14.i1.pp291-300

Abstract

The number of deaths has increased as a direct result of the increased frequency of traffic accidents, congestion, and other risk factors. Developing countries have prioritised the development of intelligent transport systems in order to reduce pollution, traffic congestion, and wasted time. This article describes an intelligent transport system that leverages the internet of vehicles (IoV) and deep learning to forecast traffic congestion. Data is acquired using a car’s global positioning system (GPS), road and vehicle sensors, traffic cameras, and traffic speed, density, and flow. All acquired data is stored in one location on a cloud server. The cloud server also stores historical traffic, road, and vehicle data. Using particle swarm optimisation, features are improved. The optimised dataset is used to train and test recurrent neural networks (RNNs), support vector machines (SVMs), and multi layer perceptrons (MLPs). A deep learning algorithm can predict traffic congestion and make recommendations to drivers on how fast to travel and which route to take. The experimental effort employs the performance measurement system (PeMS) traffic dataset. RNN has achieved accuracy of 95.1%.
Design of face recognition based effective automated smart attendance system Bangare, Jyoti L.; Chikmurge, Diptee; Kaliyaperumal, Karthikeyan; Meenakshi, Meenakshi; Bangare, Sunil L.; Kasat, Kishori; Rane, Kantilal Pitambar; Veluri, Ravi Kishore; Omarov, Batyrkhan; Jawarneh, Malik; Raghuvanshi, Abhishek
Indonesian Journal of Electrical Engineering and Computer Science Vol 38, No 3: June 2025
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v38.i3.pp2020-2030

Abstract

The issue of automatic attendance marking has been successfully resolved in recent years through the utilization of standard biometric approaches. Although this strategy is automated and forward-thinking, its use is hindered by time constraints. Acquiring a thumb impression requires the individual to form a line, which might lead to inconvenience. The innovative visual system utilizes a computer and camera to detect and record students’ attendance based on their facial features. This article presents a face recognition based automatic attendance system. This system includes- image acquisition, image enhancement using histogram equalization, image segmentation by fuzzy C means clustering technique, building classification model using K-nearest neighbour (KNN), support vector machine (SVM) and AdaBoost technique. For experimental work, 500 images of students of a class are selected at random. Accuracy of KNN algorithm in proposed framework is 98.75%. It is higher than the accuracy of SVM (96.25%) and AdaBoost (86.50%). KNN is also performing better on parameters likesensitivity, specificity, precision and F_measure.
Optimized convolutional neural network enabled technique for sentiment analysis from social media data Veena, Chinta; Sultanpure, Kavita A.; Meenakshi, Meenakshi; Bangare, Sunil L.; Raskar, Punam Sunil; Sadashiv Kulkarni, Shriram; Arcinas, Myla M.; Rane, Kantilal Pitambar
Bulletin of Electrical Engineering and Informatics Vol 14, No 4: August 2025
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/eei.v14i4.7712

Abstract

Sentiment analysis is an area of computational linguistics that studies natural language processing. The most significant subtasks are gathering people's thoughts and organizing them into groups to determine how they feel. The primary purpose of sentiment analysis is to determine whether the individual who created a piece of material has a positive or negative opinion about a subject. It has been claimed that sentiment analysis and social media mining have contributed to the recent success of both private sector and the government. Emotional analysis has applications in practically every aspect of modern life, from individuals to corporations, telecommunications to medical, and economics to politics. This article describes an improved sentiment analysis model based on gray level co-occurrence matrix (GLCM) texture feature extraction and a convolutional neural network (CNN). This model was created using tweets. First, texture characteristics are extracted from the input data set using the GLCM technique. This feature extraction improves categorization accuracy. CNNs are used to classify objects. It outperforms both the support vector machine and the AdaBoost algorithms in terms of accuracy. CNN has achieved an accuracy of 98.5% for sentiment analysis task.
An efficient course recommendation system for higher education students using machine learning techniques M. Arcinas, Myla; Meenakshi, Meenakshi; S. Bahalkar, Pranjali; Bhaturkar, Deepali; Lalar, Sachin; Pitambar Rane, Kantilal; Garg, Shaifali; Omarov, Batyrkhan; Raghuvanshi, Abhishek
Bulletin of Electrical Engineering and Informatics Vol 14, No 2: April 2025
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/eei.v14i2.7711

Abstract

Education institutions and teachers are in desperate need of automated, non-intrusive means of getting student feedback that would allow them to better understand the learning cycle and assess the success of course design. Students would benefit from a framework that intelligently guides their actions and provides exercises or resources to support and enhance their learning. The recommender system framework is a software agent that learns the user's preferences through a variety of channels and then utilizes that knowledge to provide product suggestions. A recommendation engine considers all potential user interests as background information, uses that knowledge to produce convincing recommendations, and then returns those ideas to the user. This article presents a feature selection and machine learning based course recommendation system for higher education students. principal component analysis (PCA) algorithm is used for feature selection. AdaBoost, k nearest neighbour (KNN), and Naïve Bayes algorithms are used to classify and predict student data. It is found that the AdaBoost algorithm is having better accuracy and F1 score for course recommendation to students. PCA AdaBoost is achieving an accuracy of 99.5%.
Co-Authors Alsalmani, Abdullah Arcinas, Myla M. Bangare, Jyoti L. Bangare, Manoj L. Bangare, Sunil L. Bhaturkar, Deepali Chikmurge, Diptee Garg, Shaifali Jawarneh, Malik Kaliyaperumal, Karthikeyan Kasat, Kishori Lalar, Sachin M. Arcinas, Myla Mali, Manisha Morsi, Sami A. Omarov, Batyrkhan Pitambar Rane, Kantilal Pol, Urmila R. Raghuvanshi, Abhishek Rane, Kantilal Pitambar Raskar, Punam Sunil S. Bahalkar, Pranjali Sadashiv Kulkarni, Shriram Sultanpure, Kavita A. Surve, Jyoti Veena, Chinta Veluri, Ravi Kishore