Time-shifted Pilot-based Scheduling with Adaptive Optimization for Pilot Contamination Reduction in Massive MIMO, Journal of Telecommunications and Information Technology, 2020, nr 4

Massive multiple-input multiple-output (MIMO) is considered to be an emerging technique in wireless communication systems, as it offers the ability to boost channel capacity and spectral efficiency. However, a massive MIMO system requires huge base station (BS) antennas to handle users and suffers from inter-cell interference that leads to pilot contamination. To cope with this, time-shifted pilots are devised for avoiding interference between cells, by rearranging the order of transmitting pilots in different cells. In this paper, an adaptive-elephant-based spider monkey optimization (adaptive ESMO) mechanism is employed for time-shifted optimal pilot scheduling in a massive MIMO system. Here, user grouping is performed with the sparse fuzzy c-means (Sparse FCM) algorithm, grouping users based on such parameters as large-scale fading factor, SINR, and user distance. Here, the user grouping approach prevents inappropriate grouping of users, thus enabling effective grouping, even under the worst conditions in which the channel operates. Finally, optimal time-shifted scheduling of the pilot is performed using the proposed adaptive ESMO concept designed by incorporating adaptive tuning parameters. The efficiency of the adaptive ESMO approach is evaluated and reveals superior performance with the highest achievable uplink rate of 43.084 bps/Hz, the highest SINR of 132.9 dB, and maximum throughput of 2.633 Mbp

Taylor-Bird Swarm Optimization-Based Deep Belief Network For Medical Data Classification

Heart disease classification is considered a challenging and complex task in the field of medical informatics. Various medical data classification methods are developed in the existing research works, but achieving higher classification accuracy is a great challenge in the medical sector due to the presence of noisy, and high-dimensional data. Fuzzy clustering-based filtering methods are introduced for essential feature selection. From the selected features, deep learning has become an important stage for disease diagnosis. However, finding the most appropriate deep learning algorithm for a medical classification problem along with its optimal parameters becomes a difficult task. Deep Belief Network (DBN) is a sophisticated learning system that requires a high level of approach and executes well. The major contribution of this research is to introduce a Taylor-Bird Swarm optimization-based Deep Belief Network (Taylor-BSA-based DBN) for medical data classification. Firstly, the pre-processing of medical data is done using log-transformation that converts the data to its uniform value range. Then, the feature selection process is performed using sparse fuzzy-c-means (FCM) for selecting significant features to classify medical data. Incorporating sparse FCM for the feature selection process provides more benefits for interpreting the models, as this sparse technique provides important features for detection and can be utilized for handling high-dimensional data. Then, the selected features are given as input to the DBN classifier which is trained using the Taylor-based bird swarm algorithm (Taylor-BSA). Taylor-BSA is designed by combining the Taylor series and bird swarm algorithm (BSA)

Medical Informatics and Data Analysis

During recent years, the use of advanced data analysis methods has increased in clinical and epidemiological research. This book emphasizes the practical aspects of new data analysis methods, and provides insight into new challenges in biostatistics, epidemiology, health sciences, dentistry, and clinical medicine. This book provides a readable text, giving advice on the reporting of new data analytical methods and data presentation. The book consists of 13 articles. Each article is self-contained and may be read independently according to the needs of the reader. The book is essential reading for postgraduate students as well as researchers from medicine and other sciences where statistical data analysis plays a central role

Computer Aided Diagnostic System for Blood Cells in Smear Images Using Texture Features and Supervised Machine Learning

Identification and diagnosis of leukemia earlier is a contentious issue in therapeutic diagnostics for reducing the rate of death among people with Acute Lymphoblastic Leukemia (ALL). The investigation of White Blood Cells (WBCs) is essential for the detection of ALL-leukaemia cells, for which blood smear images were being used. This study created an intelligent framework for identifying healthy blood cells from leukemic blood cells in blood smear images. The framework combines the features extracted by Center Symmetric Local Binary Pattern (CSLBP), Gabor Wavelet Transform (GWT), and Local Gradient Increasing Pattern (LGIP), the data was then fed into machine learning classifiers including Decision Tree (DT), Ensemble, K-Nearest Neighbor (KNN), Naïve Bayes (NB), and Random Forest (RF)).  As the training set, the ALL-IDB2 database was utilized to create a balanced database with 260 blood smear images. Consequently, to generate the optimum feature set, a recommended model was established by using numerous individual and combined feature extraction methodologies. The investigational consequences demonstrate that the developed feature fusion strategy surpassed previous existing techniques, with an overall accuracy of 97.49 ± 1.02% utilizing Ensemble classifier

Нейромережева модель розпізнавання вирв від бомбардування за супутниковими даними

Магістерська дисертація містить 109 сторінок, 20 ілюстрацію, і 181 джерел літератури. Наразі задача розпізнавання вирв від бомбардувань стає все гострішою. Після повномасштабної військової агресії російської федерації, чимало фондів намагаються оцінити збитки, які були нанесені об’єктам інфраструктури, цивільним будівлям, тощо. Нейромережева модель розпізнавання вирв від бомбардувань за супутниковими даними дасть змогу комплексно та всеціло оцінити масштаб руйнувань, який в подальшому може бути використаний для підрахунку збитків. Для досягнення мети було використано: нейромережеву модель U-Net ; Google Collaboratory; бібліотеки pytorch, torchvision, matplotlib, Pillow, imutils, scikit-learn, tqdm, gdal, numpy.The master's thesis contains 109 pages, 20 illustrations, and 181 references. Nowadays the task of recognizing explosions from bombings is becoming more and more acute. After the full-scale military aggression of the Russian Federation, many funds are trying to assess the damage that was caused to infrastructure objects, civilian buildings, etc. A neural network model for recognizing bombardment eruptions based on satellite data will enable a comprehensive and comprehensive assessment of the scale of destruction, which can later be used to estimate damages. To achieve the goal, was used the following: U-Net neural network model; Google Collaboratory; Libraries pytorch, torchvision, matplotlib, Pillow, imutils, scikit-learn, tqdm, gdal, numpy

Optimization Driven MapReduce Framework for Indexing and Retrieval of Big Data

With the technical advances, the amount of big data is increasing day-by-day such that the traditional software tools face a burden in handling them. Additionally, the presence of the imbalance data in big data is a massive concern to the research industry. In order to assure the effective management of big data and to deal with the imbalanced data, this paper proposes a new indexing algorithm for retrieving big data in the MapReduce framework. In mappers, the data clustering is done based on the Sparse Fuzzy-c-means (Sparse FCM) algorithm. The reducer combines the clusters generated by the mapper and again performs data clustering with the Sparse FCM algorithm. The two-level query matching is performed for determining the requested data. The first level query matching is performed for determining the cluster, and the second level query matching is done for accessing the requested data. The ranking of data is performed using the proposed Monarch chaotic whale optimization algorithm (M-CWOA), which is designed by combining Monarch butterfly optimization (MBO) [22] and chaotic whale optimization algorithm (CWOA) [21]. Here, the Parametric Enabled-Similarity Measure (PESM) is adapted for matching the similarities between two datasets. The proposed M-CWOA outperformed other methods with maximal precision of 0.9237, recall of 0.9371, F1-score of 0.9223, respectively

Taylor Bird Swarm Algorithm Based on Deep Belief Network for Heart Disease Diagnosis

Contemporary medicine depends on a huge amount of information contained in medical databases. Thus, the extraction of valuable knowledge, and making scientific decisions for the treatment of disease, has progressively become necessary to attain effective diagnosis. The obtainability of a large amount of medical data leads to the requirement of effective data analysis tools for extracting constructive knowledge. This paper proposes a novel method for heart disease diagnosis. Here, the pre-processing of medical data is done using log-transformation that converts the data to its uniform value range. Then, the feature selection process is performed using sparse fuzzy-c-means (FCM) for selecting significant features to classify medical data. Incorporating sparse FCM for the feature selection process provides more benefits for interpreting the models, as this sparse technique provides important features for detection, and can be utilized for handling high dimensional data. Then, the selected features are given to the deep belief network (DBN), which is trained using the proposed Taylor-based bird swarm algorithm (Taylor-BSA) for detection. Here, the proposed Taylor-BSA is designed by combining the Taylor series and bird swarm algorithm (BSA). The proposed Taylor-BSA–DBN outperformed other methods, with maximal accuracy of 93.4%, maximal sensitivity of 95%, and maximal specificity of 90.3%, respectively