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All Journal International Journal of Electrical and Computer Engineering Indonesian Journal of Electrical Engineering and Computer Science
Obulesu, Ooruchintala
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Computer Science & IT Electrical & Electronics Engineering

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Secure aware software development life cycle on medical datasets by using firefly optimization and machine learning techniques Obulesu, Ooruchintala; Suneel, Sajja; Jangili, Sudhakar; Ledalla, Sukanya; Swetha, Ballepu; Borra, Subba Reddy
International Journal of Electrical and Computer Engineering (IJECE) Vol 14, No 4: August 2024
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijece.v14i4.pp4195-4203

Abstract

The abstract highlights the critical need for securing sensitive medical data, emphasizing the challenges in the medical domain due to the confidentiality of patient, disease, doctor, and staff information. The proposed study introduces a novel approach using machine learning, specifically integrating the firefly optimization technique with the random forest algorithm, to classify medical data in a secure manner. The significance lies in addressing the security concerns associated with medical datasets, offering a solution that prioritizes confidentiality throughout the software development life cycle (SDLC). The proposed algorithm achieves an impressive accuracy of 96%, showcasing its efficacy in providing a robust and secure framework for the development of applications involving medical data. This research contributes to advancing the field of medical data security, offering a practical solution for safeguarding sensitive information in healthcare applications.
An implementation of GAN analysis for criminal face identification system Sarosh, Ayesha; Komali, Govindu; Battu, Vishnu Vardhan; Kocharla, Laxmaiah; Kopparavuri, Eswaree Devi; Obulesu, Ooruchintala; Mande, Praveen; Mohammad, Amanulla
Indonesian Journal of Electrical Engineering and Computer Science Vol 39, No 2: August 2025
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v39.i2.pp963-972

Abstract

In recent times, the criminal activities are growing at an exponential rate. For the prevention of crime, one of the main issues that are before the police are accurate identification of criminals and on the other hand the availability of police officers are not adequate. The most tedious task is tracking the suspect once a crime was committed. Over the years, several technical solutions have been presented to detect the criminals however most of them were not effective. One of the most significant characteristics for the identification of a person is face. Even identical twins have their own unique faces. Face identification is a challenging topic in computer vision because the human face is a dynamic entity with a high degree of visual variation. In this area, identification accuracy and speed are significant challenges. Hence to solve these issues, an implementation of generative adversarial network (GAN) analysis for criminal face identification system is presented. GAN is used for the identification of criminals. Recall, precision, accuracy, and F1-score are used to assess the performance of the presented technique. Compared to previous models, this model will achieve better performance for criminal face detection.
Co-Authors Battu, Vishnu Vardhan Borra, Subba Reddy Jangili, Sudhakar Kocharla, Laxmaiah Komali, Govindu Kopparavuri, Eswaree Devi Ledalla, Sukanya Mande, Praveen Mohammad, Amanulla Sarosh, Ayesha Suneel, Sajja Swetha, Ballepu