A5044832580- Metaheuristic Optimization Algorithms Research
- Neural Networks and Applications
- Anomaly Detection Techniques and Applications
- Network Security and Intrusion Detection
- Advanced Clustering Algorithms Research
- Machine Learning and ELM
- Face and Expression Recognition
- Artificial Intelligence in Healthcare
- Evolutionary Algorithms and Applications
- Advanced Algorithms and Applications
- COVID-19 diagnosis using AI
- Data Stream Mining Techniques
- Imbalanced Data Classification Techniques
- Data Mining Algorithms and Applications
- Smart Grid Security and Resilience
- Advanced Malware Detection Techniques
- IoT and Edge/Fog Computing
- Advanced Multi-Objective Optimization Algorithms
- Fuzzy Logic and Control Systems
- Internet Traffic Analysis and Secure E-voting
- Complex Network Analysis Techniques
- Software Reliability and Analysis Research
- Smart Agriculture and AI
- Software Engineering Research
- Software System Performance and Reliability
Veer Surendra Sai University of Technology
2015-2024
University of Teramo
2022
Maharaja Sriram Chandra Bhanja Deo University
2022
Jawaharlal Nehru Technological University Anantapur
2020
Siksha O Anusandhan University
2012
The recent outbreak of a novel coronavirus, named COVID-19 by the World Health Organization (WHO) has pushed global economy and humanity into disaster. In their attempt to control this pandemic, governments all countries have imposed nationwide lockdown. Although lockdown may assisted in limiting spread disease, it brutally affected country, unsettling complete value-chains most important industries. impact is devastating on economy. Therefore, study reported about epidemic various...
During the last two decades, a substantial amount of research efforts has been intended for support vector machine at application various data mining tasks.Data Mining is pioneering and attractive area due to its huge areas task primitives.Support Vector Machine (SVM) playing decisive role as it provides techniques those are especially well suited obtain results in an efficient way with good level quality.In this paper, we survey SVM tasks like classification, clustering, prediction,...
Abstract An anomaly exposure system's foremost objective is to categorize the behavior of system into normal and untruthful actions. To estimate possible incidents, administrators smart cities have apply detection engines avert data from being jeopardized by errors or attacks. This article aims propose a novel deep learning‐based framework with dense random neural network approach for distinguishing classifying behaviors based on type attack in Internet Things. Machine learning algorithms...
In this paper, an advanced and optimized Light Gradient Boosting Machine (LGBM) technique is proposed to identify the intrusive activities in Internet of Things (IoT) network. The followings are major contributions: i) An LGBM model has been developed for identification malicious IoT network; ii) efficient evolutionary optimization approach adopted finding optimal set hyper-parameters projected problem. Here, a Genetic Algorithm (GA) with k-way tournament selection uniform crossover...
The applications of both Feed Forward Neural network and Multilayer perceptron are very diverse saturated. But the linear threshold unit feed forward networks causes fast learning with limited capabilities, while due to multilayering, back propagation errors exhibits slow training speed in MLP. So, a higher order can be constructed by correlating between input variables perform nonlinear mapping using single layer units for overcoming above drawbacks. In this paper, Firefly based neural has...
Medical disease classification using machine learning algorithms is a challenging task due to the nature of data, which can contain incomplete, uncertain, and imprecise information. The availability such information in dataset affects performance model. In this paper, Linguistic Neuro-Fuzzy with Feature Extraction (LNF-FE) model utilized for analysis medical data classification. Initially, uses linguistic fuzzification process generate membership values that handle uncertainty problems....
In recent years, Jaya optimization algorithm has been successfully applied in several problems. This paper presents a novel feature selection (FS) approach based on (FSJaya) along with supervised machine learning techniques to select the optimal features. uses search technique find best suitable features by updating worst reduce dimensions of space. improves performance techniques. The effectiveness proposed is evaluated for ten benchmark datasets and compared FS approaches such as using...
This 21st century is notable for experiencing so many disturbances at economic, social, cultural, and political levels in the entire world. The outbreak of novel corona virus 2019 (COVID-19) has been treated as a Public Health crisis global Concern by World Organization (WHO). Various models COVID-19 are being utilized researchers throughout world to get well-versed decisions impose significant control measures. Amid standard methods worldwide epidemic prediction, easy statistical, well...
Diabetes prediction at the early stage is an important issue in healthcare field and helps individual to avoid dangerous situations by initiating treatment. For of diabetes stages, many techniques area machine learning ensemble have been used. In this paper, we propose technique CatBoost which a Gradient Boosting Decision Tree (GBDT) for stages. The experiment conducted comparing performance with other methods such as K-Nearest neighbor, Multi-layer perceptron, Logistic regression, Gaussian...