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All Journal International Journal of Electrical and Computer Engineering Indonesian Journal of Electrical Engineering and Computer Science
Vamsikrishna, Mangalapalli
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Computer Science & IT Electrical & Electronics Engineering

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Cloud computing environment based hierarchical anomaly intrusion detection system using artificial neural network Vamsikrishna, Mangalapalli; Latha, Garapati Swarna; Babu, Gajjala Venkata Ramesh; Giridhar, Koppisetti; Alluri, Lakshmeelavanya; Somasekhar, Giddaluru; Sagar, Bhimunipadu Jestadi Job Karuna; Dondapati, Naresh
International Journal of Electrical and Computer Engineering (IJECE) Vol 15, No 1: February 2025
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijece.v15i1.pp1209-1217

Abstract

Nowadays, computer technology is essential to everyday life, including banking, education, entertainment, and communication. Network security is essential in the digital era, and detecting intrusion threats is the most difficult problem. As a result, the network is monitored for unusual activity using this hierarchical anomaly intrusion detection system, and when these actions are detected, an alert is generated. This hierarchical anomaly intrusion detection system, which uses artificial neural network (ANN) and is implemented on a cloud computing environment, analyzes data even in the high levels of traffic and protects computer networks and data from malicious activity. As a result, this system shows better detection, accuracy, and precision rates.
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.
Co-Authors Alluri, Lakshmeelavanya Babu, Gajjala Venkata Ramesh Bagade, Jayashri Bhimanpallewar, Ratnmala Bodapati, Jagadeesh Bugge, Bhagya Prasad Deepthi, Natha Dondapati, Naresh Giridhar, Koppisetti Gupta, Manu Komali, Govindu Kumar, Asileti Suneel Latha, Garapati Swarna Mande, Praveen Sagar, Bhimunipadu Jestadi Job Karuna Shelke, Priya Somasekhar, Giddaluru Subrahmanyam, K.B.V.S.R. Sudhakar, Oggi Thankachan, Blessy