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Komali, Govindu
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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.
Internet of things based smart agriculture using K-nearest neighbor for enhancing the crop yield Dasari, Kalyankumar; Kharde, Mukund Ramdas; Maddileti, Kuruva; Pasupuleti, Venkat Rao; Ram, Mylavarapu Kalyan; Sujana, Challapalli; Komali, Govindu; Fariddin, Shaik Baba
Indonesian Journal of Electrical Engineering and Computer Science Vol 39, No 1: July 2025
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v39.i1.pp436-445

Abstract

Agriculture is one of the major occupations in India and is one of the significant contributors to the economy of India. The agriculture plays a vital role in country gross domestic product (GDP) and is also part of civilization. The production of crop influences the economies of countries. However, still the agriculture filed stands technologically backward. In addition, the lack of favourable weather conditions might result loss of crops yields. The farmers need awareness about their soils, timely weather updates and techniques to improve their soil for growing healthy crops. Hence it is essential to develop a system which can technologically support the farmers for suggesting the crop and improving crop yields. With the development of electronics, researchers have been developed many applications and micro controllerbased systems to do agricultural operations. The internet of things (IoT) has opened many opportunities to design and implements a smart agriculture system and machine learning (ML) algorithm can help to obtain accurate performance. Hence, in this analysis, IoT based smart agriculture using K-nearest neighbor (KNN) for enhancing the crop yields is presented. With the combination of IoT and ML algorithm this system is designed which integrates primary agriculture operations such as recommendation of crops, automated watering and fertilizers recommendation.
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 Dasari, Kalyankumar Fariddin, Shaik Baba Gupta, Manu Kharde, Mukund Ramdas Kocharla, Laxmaiah Kopparavuri, Eswaree Devi Maddileti, Kuruva Mande, Praveen Mohammad, Amanulla Obulesu, Ooruchintala Pasupuleti, Venkat Rao Ram, Mylavarapu Kalyan Sarosh, Ayesha Shelke, Priya Sujana, Challapalli Vamsikrishna, Mangalapalli