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Mande, Praveen
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An ensemble approach for detection of diabetes using SVM and DT Vamsikrishna, Mangalapalli; Gupta, Manu; Bagade, Jayashri; Bhimanpallewar, Ratnmala; Shelke, Priya; Bodapati, Jagadeesh; Komali, Govindu; Mande, Praveen
Indonesian Journal of Electrical Engineering and Computer Science Vol 38, No 1: April 2025
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v38.i1.pp689-698

Abstract

As diabetes affects the health of the entire population, it is a chronic disease that is still an important worldwide health issue. Diabetes increases the possibility of long-term complications, such as kidney failure and heart disease. If this disease is discovered early, people may live longer and in better health. In order to detect and prevent particular diseases, machine learning (ML) has become essential. An ensemble approach for detection of diabetes using support vector machine (SVM) and decision tree (DT) presents in this paper. In this case, to identify diabetes, two ML techniques are DT and SVM have been combined with an ensemble classifier. They obtain the information, they require from the Public Health Institute’s statistics area. There are 270 records, or instances, in the collection. This dataset includes the following attributes: age, a body mass index (BMI) glucose, and insulin. The development of a system that predictions a patient’s risk of diabetes is the goal of this analysis. Several performance metrics, including F1-score, recall, accuracy, and precision, were used to achieve this. From overall results, 96% of precision, 97% of accuracy, 96% of F1-score, and 97% of recall values are the results achieved for the ensemble model (SVM+DT) which is more effective than other individual ML models as DT and SVM.
Region based lossless compression for digital images using entropy coding Vamsikrishna, Mangalapalli; Sudhakar, Oggi; Bugge, Bhagya Prasad; Kumar, Asileti Suneel; Thankachan, Blessy; Subrahmanyam, K.B.V.S.R.; Deepthi, Natha; Mande, Praveen
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.pp1870-1879

Abstract

Image compression is a method for reducing video and image storage space. Moreover, enhancing the performance of the transmission and storage processes is important. The region based coding technique is important for compressing and sending medical images. In the medical field, lossless compression can help telemedicine applications achieve high efficiency. It affects image quality and takes a long time to encode. As a result, this study proposes region-based lossless compression for digital images using entropy coding. The best performance is achieved by segmenting these areas. In this case, an integer wavelet transform (IWT) is utilized after the ROI of the image was manually generated. The IWT compression method is helpful for reversibly reconstructing the original image to the required quality. For enhancing the quality of compression, entropy coding is utilized. By passing images of varying sizes and formats, various quantitative metrics can be determined. The simulation results demonstrate that the region based lossless compression technique utilizing range blocks and iterations resulted in reduced encoding time and improved quality.
An efficient implementation of credit card fraud detection using CatBoost algorithm Suryanarayana, Vadhri; Maddileti, Kuruva; Satyanarayana, Dune; Jyothi, R Leela; Sreekanth, Kavuri; Mande, Praveen; Miriyala, Raghava Naidu; Sudhakar, Oggi
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.pp1914-1923

Abstract

Transaction fraud has grown to be an important issue in worldwide, banking and commerce security is easier access to trade information. Every day, there are more and more incidents of transaction fraud, which causes large financial losses for both consumers and financial professionals. The ability to identify transaction fraud is getting closer to reality due to improvements in computer science's machine learning (ML) and data mining areas. So, one of them that is becoming dangerous is credit card fraud (CCF). Millions of people are experiencing financial loss and identity theft as a result of these malicious operations. The CCF of many illegal activities that fraudsters are always using new methods to carry out. One major problem facing financial services sector is CCF. To overcome this, categorical boosting (CatBoost) algorithm is explained as a solution to these problems. Fraud or fraudulent transactions are identified using this effective CatBoost algorithm implementation for identification of CCF. Thus, in terms of accuracy, precision, and detection rate this method gives better performance.
Internet of things based autonomous robot system architecture for home automation and healthcare services Karuna Sagar, Bhimunipadu Jestadi Job; Latha, Garapati Swarna; Bolla, Sreenivasulu; Nanajkar, Jyotsna Amit; Patnala, Pattabhirama Mohan; Mande, Praveen; Kharde, Mukund Ramdas; Narasimharao, Jonnadula
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.pp1624-1633

Abstract

The internet of things (IoT) is playing a major role in the development of the health industry by enabling more accessible and affordable virtual and distant patient contacts through applications that are easy to use. The IoT and automated homes are becoming more popular in recent days. A network of connected devices, including hardware, equipment, and technical support, is known as the IoT. Their purpose is to allow data exchange with other systems through the internet. This paper presents, internet of things based autonomous robot system architecture (IoT-ARSA) for home automation and healthcare services. The primary goal of this secure home automation system is to help the elderly and disabled people by allowing them to operate home appliances. Additionally, the system uses a cloud server to predict the health conditions of patients and the elderly people, providing information to a guardian. The patient's health condition is determined using sensors like temperature, pulse, blood pressure, and oxygen level. Ultrasonic sensor and face detection are used for home automation. Each sensor will interact with the Raspberry Pi 4 to record data, which will then be processed and stored in the cloud. From results it is clear that described (IoT-ARSA) for home automation and healthcare services model is very efficient with high accuracy and high security. Health monitoring is achieved with this model continuously with great efficiency.
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 Bagade, Jayashri Battu, Vishnu Vardhan Bhimanpallewar, Ratnmala Bodapati, Jagadeesh Bolla, Sreenivasulu Bugge, Bhagya Prasad Deepthi, Natha Gupta, Manu Jyothi, R Leela Karuna Sagar, Bhimunipadu Jestadi Job Kharde, Mukund Ramdas Kocharla, Laxmaiah Komali, Govindu Kopparavuri, Eswaree Devi Kumar, Asileti Suneel Latha, Garapati Swarna Maddileti, Kuruva Miriyala, Raghava Naidu Mohammad, Amanulla Nanajkar, Jyotsna Amit Narasimharao, Jonnadula Obulesu, Ooruchintala Patnala, Pattabhirama Mohan Sarosh, Ayesha Satyanarayana, Dune Shelke, Priya Sreekanth, Kavuri Subrahmanyam, K.B.V.S.R. Sudhakar, Oggi Suryanarayana, Vadhri Thankachan, Blessy Vamsikrishna, Mangalapalli