Wyniki wyszukiwania - Biblioteka Nauki

Ten serwis zostanie wyłączony 2025-02-11.

Nowa wersja platformy, zawierająca wyłącznie zasoby pełnotekstowe, jest już dostępna.
Przejdź na https://bibliotekanauki.pl

Ograniczanie wyników

Znaleziono wyników: 3

Liczba wyników na stronie

Wyniki wyszukiwania

Sortuj według:

Ogranicz wyniki do:

Geoinformatyczne narzędzie do badania zasięgu lokalnych stacji nadawczych radiofonii cyfrowej DAB+

100%

Falkowski-Gilski P. , Zieliński R. , Kulawiak M. , Kulawiak M.

tom nr 7-8

328--331, CD

Kwestia zapewnienia odpowiedniego pokrycia sygnałem multipleksu jest kluczowym aspektem zarówno dla nadawców publicznych jak i prywatnych, a w szczególności lokalnych stacji zainteresowanych procesem cyfryzacji. W pracy przedstawiono geoinformatyczne narzędzie, umożliwiające badanie zasięgu lokalnych stacji nadawczych radiofonii cyfrowej DAB+. Analizę przeprowadzono dla pionierskiej w Polsce stacji LocalDAB we Wrocławiu. Wyniki opisanych prac mogą być pomocne dla naukowców i profesjonalistów działających we wspomnianej dziedzinie.

The subject of providing high coverage of the digital multiplex is an important factor to both public and private broadcasters, especially local stations interested in the digitalization process. This work describes a geoinformatic tool, dedicated to coverage analysis of local DAB+ broadcasting stations. The analysis involved the Polish pioneer LocalDAB station in Wrocław. Results of carried out research may be of interest to scientists and professionals active in the aforementioned field.

Development of an AI-based audiogram classification method for patient referral

100%

Kassjański M. , Kulawiak M. , Przewoźny T.

Annals of Computer Science and Information Systems

2022

tom Vol. 30

163--168

Hearing loss is one of the most significant sensory disabilities. It can have various negative effects on a person's quality of life, ranging from impeded school and academic performance to total social isolation in severe cases. It is therefore vital that early symptoms of hearing loss are diagnosed quickly and accurately. Audiology tests are commonly performed with the use of tonal audiometry, which measures a patient's hearing threshold both in air and bone conduction at different frequencies. The graphic result of this test is represented on an audiogram, which is a diagram depicting the values of the patient's measured hearing thresholds. In the course of the presented work several different artificial neural network models, including MLP, CNN and RNN, have been developed and tested for classification of audiograms into two classes - normal and pathological represented hearing loss. The networks have been trained on a set of 2400 audiograms analysed and classified by professional audiologists. The best classification performance was achieved by the RNN architecture (represented by simple RNN, GRU and LSTM), with the highest out-of-training accuracy being 98% for LSTM. In clinical application, the developed classifier can significantly reduce the workload of audiology specialists by enabling the transfer of tasks related to analysis of hearing test results towards general practitioners. The proposed solution should also noticeably reduce the patient's average wait time between taking the hearing test and receiving a diagnosis. Further work will concentrate on automating the process of audiogram interpretation for the purpose of diagnosing different types of hearing loss.

Detecting type of hearing loss with different AI classification methods: a performance review

63%

Kassjański M. , Kulawiak M. , Przewoźny T. , Tretiakow D. , Kuryłowicz J. , Molisz A. , Koźmiński K. , Kwaśniewska A. , Mierzwińska-Dolny P. , Grono M.

Annals of Computer Science and Information Systems

2023

tom Vol. 35

1017--1022

Hearing is one of the most crucial senses for all humans. It allows people to hear and connect with the environment, the people they can meet and the knowledge they need to live their lives to the fullest. Hearing loss can have a detrimental impact on a person's quality of life in a variety of ways, ranging from fewer educational and job opportunities due to impaired communication to social withdrawal in severe situations. Early diagnosis and treatment can prevent most hearing loss. Pure tone audiometry, which measures air and bone conduction hearing thresholds at various frequencies, is widely used to assess hearing loss. A shortage of audiologists might delay diagnosis since they must analyze an audiogram, a graphic representation of pure tone audiometry test results, to determine hearing loss type and treatment. In the presented work, several AI-based models were used to classify audiograms into three types of hearing loss: mixed, conductive, and sensorineural. These models included Logistic Regression, Support Vector Machines, Stochastic Gradient Descent, Decision Trees, RandomForest, Feedforward Neural Network (FNN), Convolutional Neural Network (CNN), Graph Neural Network (GNN), and Recurrent Neural Network (RNN). The models were trained using 4007 audiograms classified by experienced audiologists. The RNN architecture achieved the best classification performance, with an out-of-training accuracy of 94.46%. Further research will focus on increasing the dataset and enhancing the accuracy of RNN models.