Performance of using Mel-Frequency Cepstrum Based Features in Nonlinear Classifiers for Phonocardiography Recordings

Cardiovascular system diseases can be identified by using a specialized diagnostic process utilizing a digital stethoscope. Digital stethoscopes provide phonocardiography (PCG) recordings for further inspection, besides filtering and amplification of heart sounds. In this paper, a framework that is useful to develop feature extraction and classification of PCG recordings is presented. This framework is built upon a previously proposed segmentation algorithm that processes a feature vector produced by the agglutinate application of Mel-frequency cepstrum and discrete wavelet transform (DWT). The performance of the segmentation algorithm is also tested on a new data set and compared to the previously reported results. After identifying the fundamental heart sounds and segmenting the PCG recordings, five principal features are extracted from the time domain signal and Mel-Frequency cepstral coefficients (MFCC) of each cardiac cycle. Classification outcomes are reported for three nonlinear models: k nearest neighbor (k-NN), support vector machine (SVM), and multilayer perceptrons (MLP) classifiers in comparison with a linear approach, namely Mahalanobis distance linear classifier. The results underline that although neural networks and linear classifier show compatible performance in basic classification problems, with the increase in the nonlinearity of the classification problem their performance significantly vary.Comment: in 2023 31st European Signal Processing Conference (EUSIPCO

Multilabel Text Classification Menggunakan SVM dan Doc2Vec Classification Pada Dokumen Berita Bahasa Indonesia

Seiring dengan berkembangnya informasi yang ada di sekitar dengan pesat, maka jenis informasi yang ada pun menjadi sangat bervariasi dan sangat banyak jumlahnya, dan akan semakin terus bertambah. Dengan kondisi tersebut, kita akan mengalami kesulitan untuk mengenali jenis dari informasi tersebut satu persatu. Oleh karena itu dengan adanya proses klasifikasi teks dan dokumen sangatlah membantu untuk memilah dan mengenali informasi-informasi apa saja yang ada, baik informasi yang lama maupun informasi yang baru dan belum pernah ditemui sebelumnya. Bertujuan untuk dapat mengidentifikasi dan mengklasifikasikan dokumen-dokumen berita dalam bahasa Indonesia ke dalam beberapa kategori sekaligus, maka dibuatlah sebuah penelitian berupa sistem untuk menangani klasifikasi dokumen teks dalam bahasa Indonesia. Sistem tersebut akan memproses berita-berita yang diberikan, dan kemudian akan memberikan 2 kategori yang paling mendekati terhadap isi dari berita tersebut. Sistem dibuat dengan menggunakan Python, memanfaatkan Doc2Vec untuk mengambil fitur dataset, dan SVM untuk melakukan klasifikasi terhadap banyak kelas. Dataset yang digunakan adalah kumpulan dokumen berupa berita-berita yang diperoleh dari CNN Indonesia tahun 2016-2017, dan terbagi dalam 5 kategori berita utama, yaitu: Politik, Ekonomi, Teknologi, Olahraga, dan Hiburan. Dikarenakan sedikitnya literatur untuk klasifikasi text dalam bahasa Indonesia, maka pada penelitian ini hanya menargetkan akurasi sebesar 70% saja. Namun dari hasil ujicoba, akurasi yang diperoleh melebihi 90%. Hasil prediksi untuk kelas dokumen pun memiliki tingkat keberhasilan yang tinggi. Dengan penggunaan dataset dan penanganan preprocessing yang tepat untuk dokumen bahasa Indonesia, maka hasil yang dicapai bisa lebih bagus dan akurat

Mermer türlerinin makine öğrenmesi teknikleri kullanılarak sınıflandırılması

Doğal taşlar, insanların barınmadan silaha kadar vazgeçilmez unsurlarından bir tanesidir. Bu taş türleri içerisinde mermerler ve mermer türevli ürünler banyodan mutfağa, bahçe tasarımından küçük dekoratif ev süslerine kadar insanların sürekli tercih ettiği objelerdendir. Mermerler çıkarıldıkları bölgelere göre isimlendirilirken bu alanda uzman olarak nitelendirilen kişiler tarafından gözleme dayalı olarak türleri ve kaliteleri sınıflandırılmaktadır. Uzman kişilerin gözleme dayalı yaptığı bu sınıflandırma ekonomik anlamda risk taşımakta, iş yükünü arttırmakta ve hata oranı yüksek olabilen zorlu bir süreçtir. Bu süreçlerin hızlı, kolay ve doğruluk oranı yüksek bir dijital dönüşüme ihtiyacı bulunmaktadır. Bu çalışmada mermerlerin tür sınıflandırmasında derin öğrenme kullanılarak özellik çıkarımı yapılmıştır. Çıkarılan özellikler makine öğrenme teknikleri kullanılarak sınıflandırma uygulaması gerçekleştirilmiştir. 28 ayrı türe ait 3703 mermer ve mermer türevli doğal taş imgesinden oluşan veri seti ile yapılan uygulamanın test sonucunda DenseNet derin öğrenme modeli ve K-En Yakın Komşu metodu ile %99,7’lik sınıflandırma başarımı elde edilmiştir.Natural stones are one of the indispensable elements of people from shelter to weapons. Among these stone types, marbles and marble-derived products are among the objects that people always prefer, from bathroom to kitchen, from garden design to small decorative home decorations. While the marbles are named according to the regions where they are extracted, their types and qualities are classified based on observation by people who are qualified as experts in this field. This classification, which is made by experts based on observation, carries risks in economic terms, increases the workload and is a difficult process with a high error rate. These processes need a fast, easy and highly accurate digital transformation. In this study, feature extraction was done by using deep learning in the species classification of marbles. The extracted features were classified using machine learning techniques. As a result of the application made with the data set consisting of 3703 marble and marble-derived natural stone images belonging to 28 different species, a classification success of 99.7% was obtained with the DenseNet deep learning model and the K-Nearest Neighbor method

A bayesian allocation model based approach to mixed membership stochastic blockmodels

Although detecting communities in networks has attracted considerable recent attention, estimating the number of communities is still an open problem. In this paper, we propose a model, which replicates the generative process of the mixed-membership stochastic block model (MMSB) within the generic allocation framework of Bayesian allocation model (BAM) and BAM-MMSB. In contrast to traditional blockmodels, BAM-MMSB considers the observations as Poisson counts generated by a base Poisson process and marks according to the generative process of MMSB. Moreover, the optimal number of communities for BAM-MMSB is estimated by computing the variational approximations of the marginal likelihood for each model order. Experiments on synthetic and real data sets show that the proposed approach promises a generalized model selection solution that can choose not only the model size but also the most appropriate decomposition.WOS:000750893600001Scopus - Affiliation ID: 60105072Science Citation Index ExpandedQ3-Q4Article; Early AccessUluslararası işbirliği ile yapılmayan - HAYIRŞubat2022YÖK - 2021-22YÖK - 2021-2

A Framework to Detect Presentation Attacks

Biometric-based authentication systems are becoming the preferred choice to replace password-based authentication systems. Among several variations of biometrics (e.g., face, eye, fingerprint), iris-based authentication is commonly used in every day applications. In iris-based authentication systems, iris images from legitimate users are captured and certain features are extracted to be used for matching during the authentication process. Literature works suggest that iris-based authentication systems can be subject to presentation attacks where an attacker obtains printed copy of the victim’s eye image and displays it in front of an authentication system to gain unauthorized access. Such attacks can be performed by displaying static eye images on mobile devices or iPad (known as screen attacks). As iris features are not changed, once an iris feature is compromised, it is hard to avoid this type of attack. Existing approaches relying on static features of the iris are not suitable to prevent presentation attacks. Feature from live Iris (or liveness detection) is a promising approach. Further, additional layer of security from iris feature can enable hardening the security of authentication system that existing works do not address. To address these limitations, this thesis proposed iris signature generation based on the area between the pupil and the cornea . Our approach relies on capturing iris images using near infrared light. We train two classifiers to capture the area between the pupil and the cornea. The image of iris is then stored in the database. This approach generates a QR code from the iris. The code acts as a password (additional layer of security) and a user is iii required to provide it during authentication. The approach has been tested using samples obtained from publicly available iris database. The initial results show that the proposed approach has lower false positive and false negative rates

Ambient Backscatter Communication Based Cooperative Relaying for Heterogeneous Cognitive Radio Networks

In this paper, a new network model is proposed to improve the performance of the secondary channel in cognitive radio networks (CRNs) based ambient backscatter communication systems. This model is considered as a cooperative system with multi-secondary transmitter (ST) and multi-relay. The ST backscatters data to both the secondary receiver (SR) and relay. Also it harvests energy from the signal emitted by the primary transmitter (PT) during the busy period. The relay activated by the ST user forwards the information from ST to SR. During the idle period, the PT broadcast is interrupted and ST also performs active data transmission using the energy it has harvested. We aim to maximize the number of data transmitted to the SR. Therefore, how long the ST will perform backscattering, energy harvesting and active data transmission is a problem to be solved. In such cooperative systems with multiple users, the solution of the problem becomes more complex. Therefore, the system model has been mathematically modeled and transformed into an optimization problem, considering that users are transmitting data using time division multiple access (TDMA) and non-orthogonal multiple access (NOMA) techniques. Numerical results showed that higher data rates were achieved in NOMA. Additionally, It has been seen that the proposed model performs better when compared to the existing approaches in the literature, where the ST can only harvest energy and transmit data actively or only transmit data with ambient backscatter communication

A Hybrid Multi-Domain Index Modulation for Covert Communication

In this letter, a hybrid multi-domain index modulation scheme based on spreading codes domain and beam domain is proposed. In our design, the information bit stream is divided into two parts: one for covert transmission using the index of spreading code and the other using the directional modulation to improve bit error ratio (BER) performance in the desired direction and to prevent eavesdropping. Then, based on joint boundaries and statistical theory, the average error probabilities of legitimate users and eavesdroppers are derived. Moreover, we analyze the average BER and validate through simulation results that the proposed hybrid multi-domain scheme is capable of achieving better BER performance compared to conventional coded wireless communication systems

Tooth Position Determination by Automatic Cutting and Marking of Dental Panoramic X-ray Film in Medical Image Processing

This paper presents a novel method for automatic segmentation of dental X-ray images into single tooth sections and for placing every segmented tooth onto a precise corresponding position table. Moreover, the proposed method automatically determines the tooth’s position in a panoramic X-ray film. The image-processing step incorporates a variety of image-enhancement techniques, including sharpening, histogram equalization, and flat-field correction. Moreover, image processing was implemented iteratively to achieve higher pixel value contrast between the teeth and cavity. The next image-enhancement step is aimed at detecting the teeth cavity and involves determining the segment and points separating the upper and lower jaw, using the difference in pixel values to cut the image into several equal sections and then connecting each cavity feature point to extend a curve that completes the description of the separated jaw. The curve is shifted up and down to look for the gap between the teeth, to identify and address missing teeth and overlapping. Under FDI World Dental Federation notation, the left and right sides receive eight-code sequences to mark each tooth, which provides improved convenience in clinical use. According to the literature, X-ray film cannot be marked correctly when a tooth is missing. This paper utilizes artificial center positioning and sets the teeth gap feature points to have the same count. Then, the gap feature points are connected as a curve with the curve of the jaw to illustrate the dental segmentation. In addition, we incorporate different image-processing methods to sequentially strengthen the X-ray film. The proposed procedure had an 89.95% accuracy rate for tooth positioning. As for the tooth cutting, where the edge of the cutting box is used to determine the position of each tooth number, the accuracy of the tooth positioning method in this proposed study is 92.78%