A5047384926- Advanced Control Systems Optimization
- Control Systems and Identification
- Artificial Intelligence in Healthcare
- Fault Detection and Control Systems
- Big Data and Business Intelligence
- Building Energy and Comfort Optimization
- Neural Networks and Applications
- Energy Efficiency and Management
- Gaussian Processes and Bayesian Inference
- Solar Radiation and Photovoltaics
- Air Quality Monitoring and Forecasting
- Energy Load and Power Forecasting
- Advanced Malware Detection Techniques
- Smart Parking Systems Research
- Smart Grid Energy Management
- Advanced Algorithms and Applications
- Anomaly Detection Techniques and Applications
- Advanced Multi-Objective Optimization Algorithms
- Network Security and Intrusion Detection
- Advanced Sensor and Control Systems
Turin Polytechnic University
2022-2024
Tashkent University of Information Technology
2019-2024
IMT School for Advanced Studies Lucca
2024
Koneru Lakshmaiah Education Foundation
2024
Westminster International University in Tashkent
2024
The goal of the present research is to better understand need accurate and ongoing monitoring in complicated chronic metabolic disease known as diabetes. With integration an intelligent system utilising a hybrid adaptive machine learning classifier, suggested method presents novel way tracking individuals with uses cutting edge technologies like (ML) improve efficacy accuracy diabetes patient monitoring. Integrating smart gadgets, sensors, telephones key locations gather full body dimension...
The Internet of Things (IoT) connects schemes, programs, data management, and operations, as they continuously assist in the corporation, may be a fresh entryway for cyber-attacks. Presently, illegal downloading virus attacks pose significant threats to IoT security. These risks acquire confidential material, causing reputational financial harm. In this paper hybrid optimization mechanism deep learning,a frame is used detect attack prevention IoT. To develop cybersecurity warning system huge...
Contemporary firms rely heavily on the effectiveness of their supply chain management. Modern chains are complicated and unpredictable, traditional methods frequently find it difficult to adjust these factors. Increasing efficiency through improved supplier performance, demand prediction, inventory optimisation, streamlined logistics processes may be achieved by utilising sophisticated data analytics machine learning approaches. In order improve management efficiency, this study suggests a...
This paper gives a clear understanding of how system can be modeled in non-parametric and probabilistic way using machine learning based on historical data with taking into account uncertain noise added to the output system. We explained key concepts step by end up unique algorithm. Moreover, we demonstrated an illustrative example proposed algorithm case nonlinear dynamic
This paper gives a clear understanding of how system can be modeled in non-parametric and probabilistic way using machine learning based on historical data with taking into account uncertain noise added to the output system. We explained key concepts step by end up unique algorithm. Moreover, we demonstrated an illustrative example proposed algorithm case nonlinear dynamic
Despite recent advances in computing hardware and optimization algorithms, solving model predictive control (MPC) problems real time still poses some technical challenges when long prediction horizons are used, due to the presence of several variables constraints. In this paper, we propose reduce computational burden by shortening horizon a single step while preserving good closed-loop performance. This is achieved using machine learning techniques construct tailored quadratic convex...
To overcome environmental impacts of a manufacturing factory over its life cycle, the role sustainable energy effectiveness is vital.For this reason, implementing conservation technologies to empower efficiency has become an important business for majority plants.Data driven control set ups seem be novel idea handle such complex systems, while machine learning becoming well-known in system engineering community.In paper, new approach together with optimal application considered open...
Abstract A classic way to design a nonlinear model predictive control (NMPC) scheme with guaranteed stability is incorporate terminal cost and constraint into the problem formulation. While long prediction horizon often desirable obtain large domain of attraction good closed‐loop performance, related computational burden can hinder its real‐time deployment. In this article, we propose an NMPC no drastically decrease numerical complexity without significantly impacting performance. This...
The increasing prevalence of smart building architectures, driven by the integration Internet Things (IoT) devices and automation systems, has led to a surge in energy consumption. This research explores application swarm intelligence techniques as an innovative approach optimize neural networks, aiming strike balance between maintaining desired performance levels minimizing study investigates swarm-based optimization algorithms, such Particle Swarm Optimization (PSO) into training operation...
High blood glucose levels cause diabetes, and it is characterized as a chronic disease that will disrupt fat protein metabolism. The rise because cannot be burned in the cells due to shortage of insulin secretion by pancreas, or produced cell insufficient. If exact early detection possible, hazard prevalence diabetes can decreased considerably. With this, application technology has been an essential part providing accurate acceptable results prevention illness. This research implements...
High blood glucose levels cause diabetes, and it is characterized as a chronic disease that will disrupt fat protein metabolism. The rise because cannot be burned in the cells due to shortage of insulin secretion by pancreas, or produced cell insufficient. If exact early detection possible, hazard prevalence diabetes can decreased considerably. With this, application technology has been an essential part providing accurate acceptable results prevention illness. This research implements...
For the last few decades, thermal comfort has been considered an aspect of sustainable building evaluation methods and tools. However, estimating indoor air temperature buildings is a complicated task due to nonlinear behaviour heating, ventilation conditioning systems combined with complex dynamics characterized by time-varying environment disturbances. This issue can be alleviated modelling using Gaussian processes since it also measures uncertainty bounds. The main focus this paper...
High blood glucose levels cause diabetes, and it is characterized as a chronic disease that will disrupt fat protein metabolism. The rise because cannot be burned in the cells due to shortage of insulin secretion by pancreas, or produced cell insufficient. If exact early detection possible, hazard prevalence diabetes can decreased considerably. With this, application technology has been an essential part providing accurate acceptable results prevention illness. This research implements...
For the past few decades, control and building engineering communities have been focusing on thermal comfort as a key factor in designing sustainable evaluation methods tools. However, estimating indoor air temperature of buildings is complicated task due to nonlinear complex dynamics characterised by time-varying environment with disturbances. The primary focus this paper predictive probabilistic room model using Gaussian processes (GPs) incorporating it into (MPC) minimise energy...