Методи оптимізації алгоритмів розпізнавання обличчя

The paper examines the main drawbacks of modern face recognition algorithms: low processing speed, high sensitivity to image quality and face positioning. A division into three approaches to face recognition algorithms optimization is proposed: optimization of feature weights, algorithm hyperparameters, and constructing an optimal distributed system architecture. Examples of the application of Particle Swarm Optimization, Cuckoo Search, Simulated Annealing, and genetic algorithms to overcome the mentioned limitations in existing algorithms are provided. The study demonstrates the advantages and disadvantages of these optimization methods and identifies promising directions for further research in face identification methods optimization using genetic algorithms.Prombles in programming 2025; 1: 74-81У роботі розглянуто основні недоліки сучасних алгоритмів розпізнавання обличчя: низьку швидкість роботи, високу чутливість до якості зображень та розташування обличчя. Запропоновано поділ на три основні підходи до оптимізації алгоритмів розпізнавання обличчя: оптимізація ваги ознак, гіперпараме трів алгоритмів та побудова оптимальної розподіленої системи; наведено приклади застосування алго ритмів Particle Swarm Optimization, Cuckoo Search, методу імітації відпалу, генетичних алгоритмів для усунення згаданих обмежень у наявних алгоритмах. У дослідженні продемонстровано переваги та недо ліки вказаних методів оптимізації, визначено перспективні напрями подальших досліджень у галузі оп тимізації методів ідентифікації обличчя за допомогою генетичних алгоритмів.Prombles in programming 2025; 1: 74-8

Optimization methods for face recognition algorithmes

The paper examines the main drawbacks of modern face recognition algorithms: low processing speed, high sensitivity to image quality and face positioning. A division into three approaches to face recognition algorithms optimization is proposed: optimization of feature weights, algorithm hyperparameters, and constructing an optimal distributed system architecture. Examples of the application of Particle Swarm Optimization, Cuckoo Search, Simulated Annealing, and genetic algorithms to overcome the mentioned limitations in existing algorithms are provided. The study demonstrates the advantages and disadvantages of these optimization methods and identifies promising directions for further research in face identification methods optimization using genetic algorithms.Prombles in programming 2025; 1: 74-8

Label-Free Peptide-Based Biosensor for Express Detection of Protein Markers of Acute Cardiovascular Conditions in Biological Fluids

Acute cardiovascular conditions require prompt assistance, which depends on a timely and accurate diagnosis. This could be achieved by using biosensor systems based on peptide aptamers capable of selectively binding protein markers of diseases. In this work, a label-free biosensor system based on fluorometric registration of the formation of a “peptide aptamer—target protein” complex is considered. It comprises a microfluidic subsystem integrated with arrays of sites with immobilized peptide aptamers, coupled with an optical detection system. The clinical sample of the whole blood is loaded into the inlet basin, where the cells are separated and plasma flows into the microfluidic channel for analysis. Peptide aptamers were created using the molecular complement search technique based on the search for systems of conjugated ion-hydrogen bonds in the three-dimensional structures of target proteins. The technology for manufacturing a microfluidic chip is a combination of thick-film and photolithography technologies based on the SU-8 photoresist, for which the relief and surface morphology have been studied. The composition of the biochip layers is selected in such a way that ultraviolet light with a wavelength of 280 nm passes through an inlet window, excites fluorescence inside the channel, which passes through the glass window, which absorbs UV-light. This wavelength accounts for the maximum absorption of aromatic amino acids—tyrosine and tryptophan. In this case, one of the last layers is a luminophore layer for re-emission of protein fluorescence as a visible light. The reading platform includes a 280 nm LED, a video sensor, 3D-printed PLA tooling, and software for processing and analyzing the received signal

Toward Development of a Label-Free Detection Technique for Microfluidic Fluorometric Peptide-Based Biosensor Systems

The problems of chronic or noncommunicable diseases (NCD) that now kill around 40 million people each year require multiparametric combinatorial diagnostics for the selection of effective treatment tactics. This could be implemented using the biosensor principle based on peptide aptamers for spatial recognition of corresponding protein markers of diseases in biological fluids. In this paper, a low-cost label-free principle of biomarker detection using a biosensor system based on fluorometric registration of the target proteins bound to peptide aptamers was investigated. The main detection principle considered includes the re-emission of the natural fluorescence of selectively bound protein markers into a longer-wavelength radiation easily detectable by common charge-coupled devices (CCD) using a specific luminophore. Implementation of this type of detection system demands the reduction of all types of stray light and background fluorescence of construction materials and aptamers. The latter was achieved by careful selection of materials and design of peptide aptamers with substituted aromatic amino acid residues and considering troponin T, troponin I, and bovine serum albumin as an example. The peptide aptamers for troponin T were designed in silico using the «Protein 3D» (SPB ETU, St. Petersburg, Russia) software. The luminophore was selected from the line of ZnS-based solid-state compounds. The test microfluidic system was arranged as a flow through a massive of four working chambers for immobilization of peptide aptamers, coupled with the optical detection system, based on thick film technology. The planar optical setup of the biosensor registration system was arranged as an excitation-emission cascade including 280 nm ultraviolet (UV) light-emitting diode (LED), polypropylene (PP) UV transparent film, proteins layer, glass filter, luminophore layer, and CCD sensor. A laboratory sample has been created

Hybrid Impedimetric Biosensors for Express Protein Markers Detection

Impedimetric biosensors represent a powerful and promising tool for studying and monitoring biological processes associated with proteins and can contribute to the development of new approaches in the diagnosis and treatment of diseases. The basic principles, analytical methods, and applications of hybrid impedimetric biosensors for express protein detection in biological fluids are described. The advantages of this type of biosensors, such as simplicity and speed of operation, sensitivity and selectivity of analysis, cost-effectiveness, and an ability to be integrated into hybrid microfluidic systems, are demonstrated. Current challenges and development prospects in this area are analyzed. They include (a) the selection of materials for electrodes and formation of nanostructures on their surface; (b) the development of efficient methods for biorecognition elements’ deposition on the electrodes’ surface, providing the specificity and sensitivity of biosensing; (c) the reducing of nonspecific binding and interference, which could affect specificity; (d) adapting biosensors to real samples and conditions of operation; (e) expanding the range of detected proteins; and, finally, (f) the development of biosensor integration into large microanalytical system technologies. This review could be useful for researchers working in the field of impedimetric biosensors for protein detection, as well as for those interested in the application of this type of biosensor in biomedical diagnostics