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All Journal IAES International Journal of Artificial Intelligence (IJ-AI) International Journal of Reconfigurable and Embedded Systems (IJRES)
Ramachandran, Harikrishnan
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Computer Science & IT Economics, Econometrics & Finance Engineering

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Artificial intelligence-enabled profiling of overlapping retinal disease distribution for ocular diagnosis Sundararajan, Sridhevi; Ramachandran, Harikrishnan; Gupta, Harshitha
IAES International Journal of Artificial Intelligence (IJ-AI) Vol 13, No 3: September 2024
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijai.v13.i3.pp2713-2724

Abstract

Eyesight, an invaluable gift profoundly impacts our daily lives. In a rapidly evolving healthcare landscape, the preservation and enhancement of ocular health stand as critical objectives. This research endeavors to analyze the two retinal fundus multi-disease image datasets (RFMiD) one containing 3200 images and the other containing 860 fundus images. The primary objective of this study is to scrutinize these datasets, discern variations in the frequency of labeled diseases within and across them, and explore common combinations of labels. These findings hold important implications for the field of retinal image analysis, as they provide valuable insights into the distribution and co-occurrence of defects.
Remote surveillance of enclosed and open architectures using unmanned vehicle with advanced security Khetan, Ayushman; Sarkar, Abhirup; Sabunwala, Hussain; Gupta, Eshan; Ramachandran, Harikrishnan; Shahane, Priti
International Journal of Reconfigurable and Embedded Systems (IJRES) Vol 13, No 1: March 2024
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijres.v13.i1.pp126-132

Abstract

Monitoring behavior, numerous actions, or any such information is considered as surveillance and is done for information gathering, influencing, managing, or directing purposes. Citizens employ surveillance to safeguard their communities. Governments do this for the purposes of intelligence collection, including espionage, crime prevention, the defense of a method, a person, a group, or an item; or the investigation of criminal activity. Using an internet of things (IoT) rover, the area will be secured with better secrecy and efficiency instead of humans, will provide an additional safety step. In this paper, there is a discussion about an IoT rover for remote surveillance based around a Raspberry Pi microprocessor which will be able to monitor a closed/open space. This rover will allow safer survey operations and would help to reduce the risks involved with it.
Predicting enhanced diagnostic models: deep learning for multi-label retinal disease classification Sundararajan, Sridhevi; Ramachandran, Harikrishnan; Gupta, Harshita; Patil, Yashraj
IAES International Journal of Artificial Intelligence (IJ-AI) Vol 14, No 1: February 2025
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijai.v14.i1.pp54-61

Abstract

In this study, we assess three convolutional neural network (CNN) architectures—VGG16, ResNet50, and InceptionV3 for multi classification of fundus images in the retinal fundus multi-disease image dataset (RFMID2), comprising of 860 images. Focusing on diabetic retinopathy, exudation, and hemorrhagic retinopathy, we preprocessed the dataset for uniformity and balance. Using transfer learning, the models were adapted for feature extraction and fine-tuned to our multi-label classification task. Their performance was measured by subset accuracy, precision, recall, F1-score, hamming loss, and Jaccard score. VGG16 emerged as the top performer, with the highest subset accuracy (84.81%) and macro precision (95.83%), indicating its superior class distinction capabilities. ResNet50 showed commendable accuracy (79.75%) and precision (86.70%), whereas InceptionV3 lagged with lower accuracy (66.67%) and precision (81.21%). These findings suggest VGG16’s depth offers advantages in multi-label classification, highlighting InceptionV3’s limitations in complex scenarios. This analysis helps optimize CNN architecture selection for specific tasks, suggesting future exploration of dataset variability, ensemble methods, and hybrid models for improved performance.
A novel approach to transparent and accurate fuel dispensing for consumer protection Phade, Gayatri; Ohatkar, Sharada Narsingrao; Pushpavalli, Murugan; Chitre, Vidya; Pawar, Vijaya; Vaidya, Omkar Suresh; Ramachandran, Harikrishnan
International Journal of Reconfigurable and Embedded Systems (IJRES) Vol 14, No 2: July 2025
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijres.v14.i2.pp353-364

Abstract

Consumer rights are exploited around the world and it is necessary for to protect consumer rights by means of safeguarding consumers from various unfair trade practices. Those most vulnerable to such exploitation must be shielded, and this is achieved through consumer protection measures. One such example of unethical behavior is fuel stealing at fuel stations. To overcome this critical issue, a low-cost fuel quantity sensing and monitoring system is proposed in this paper. A fuel detection system will ensure the exact quantity of fuel filled in fuel tank and will detect fuel theft, if any, at fuel pumps. An embedded system is developed for this purpose, consisting of sensors, display devices, communication devices and microcontroller. The quantity of fuel filled in the tank is transmitted to mobile phone of the consumer to avoid fuel theft. Performance of the system is validated by comparing the displayed amount of fuel dispensed and actual filled in the tank and achieve 99.95% accuracy. With this consumer right to get the value for amount paid for the petrol will be protected. This novel feature can be added in the fuel tank of the smart vehicle development and design as a future scope.
Co-Authors Chitre, Vidya Gupta, Eshan Gupta, Harshita Gupta, Harshitha Khetan, Ayushman Ohatkar, Sharada Narsingrao Patil, Yashraj Pawar, Vijaya Phade, Gayatri Pushpavalli, Murugan Sabunwala, Hussain Sarkar, Abhirup Shahane, Priti Sundararajan, Sridhevi Vaidya, Omkar Suresh