Browsing by Subject "computational intelligence"

Now showing 1 - 6 of 6

Access status: Open Access ,
Computational intelligence based design of biomaterials
(Wydawnictwa AGH, 2022) Vinoth, Arulraj; Datta, Shubhabrata
This paper presents an overview of the applications of computational intelligence techniques, viz. artificial neural networks, fuzzy inference systems, and genetic algorithms, for the design of biomaterials with improved performance. These techniques are basically used for developing data-driven models and for optimization. The paper introduces the domain of biomaterials and how they can be designed using computational intelligence techniques. Then a brief description of the tools is made, followed by the applications of the tools in various domains of biomaterials. The applications range in all classes of materials ranging from alloys to composites. There are examples of applications for the surface treatment of biomaterials, materials for drug delivery systems, materials for scaffolds and even in implant design. It is found the tools can be effectively used for designing new and improved biomaterials.
Access status: Open Access ,
Computational intelligence for predicting biological effects of drug absorption in lungs
(Wydawnictwa AGH, 2019) Pacławski, Adam; Szlęk, Jakub; Mendyk, Aleksander
Recently, the lungs have been extensively examined as a route for delivering drugs (active pharmaceutical ingredients, APIs) into the bloodstream, this is mainly due to the possibility of the noninvasive administration of macromolecules such as proteins and peptides. The absorption mechanisms of chemical compounds in the lungs are still not fully understood, which makes pulmonary formulation composition development challenging. This manuscript presents the development of an empirical model capable of predicting the excipients’ influence on the absorption of drugs in the lungs. Due to the complexity of the problem and the not-fully-understood mechanisms of absorption, computational intelligence tools were applied. As a result, a mathematical formula was established and analyzed. The normalized root-mean-squared error (NRMSE) and $R^2$ of the model were 4.57%, and 0.83, respectively. The presented approach is beneficial both practically by developing an in silico predictive model and theoretically by gaining knowledge of the influence of APIs and excipient structure on absorption in the lungs.
Access status: Restricted ,
Metody i techniki sztucznej inteligencji
(Wydawnictwo Naukowe PWN, 2009) Rutkowski, Leszek
Access status: Restricted ,
Metody i techniki sztucznej inteligencji : inteligencja obliczeniowa
(Wydawnictwo Naukowe PWN, 2006) Rutkowski, Leszek
Access status: Open Access ,
Quantum inspired chaotic salp swarm optimization for dynamic optimization
(Wydawnictwa AGH, 2024) Pathak, Sanjai; Mani, Ashish; Sharma, Mayank; Chatterjee, Amlan
Many real-world problems are dynamic optimization problems that are unknown beforehand. In practice, unpredictable events such as the arrival of new jobs, due date changes, and reservation cancellations, changes in parameters or constraints make the search environment dynamic. Many algorithms are designed to deal with stationary optimization problems, but these algorithms do not face dynamic optimization problems or manage them correctly. Although some optimization algorithms are proposed to deal with the changes in dynamic environments differently, there are still areas of improvement in existing algorithms due to limitations or drawbacks, especially in terms of locating and following the previously identified optima. With this in mind, we studied a variant of SSA known as QSSO, which integrates the principles of quantum computing. An attempt is made to improve the overall performance of standard SSA to deal with the dynamic environment effectively by locating and tracking the global optima for DOPs. This work is an extension of the proposed new algorithm QSSO, known as the Quantum-inspired Chaotic Salp Swarm Optimization (QCSSO) Algorithm, which details the various approaches considered while solving DOPs. A chaotic operator is employed with quantum computing to respond to change and guarantee to increase individual searchability by improving population diversity and the speed at which the algorithm converges. We experimented by evaluating QCSSO on a well-known generalized dynamic benchmark problem (GDBG) provided for CEC 2009, followed by a comparative numerical study with well-regarded algorithms. As promised, the introduced QCSSO is discovered as the rival algorithm for DOPs.
Access status: Restricted ,
The usage of computational intelligence and associative systems for recognition and classification of emotional states of interlocutors in the conversation with a chatbot
(Data obrony: 2018-09-26) Szlachta, Alicja
Wydział Elektrotechniki, Automatyki, Informatyki i Inżynierii Biomedycznej