Samed Jukic

International BURCH University · Deparment of Information Technologies

Brain tumors diagnosis in children is a scientific concern due to rapid anatomical, metabolic, and functional changes arising in the brain and non-specific or conflicting imaging results. Pediatric brain tumors diagnosis is typically centralized in clinical practice on the basis of diagnostic clues such as, child age, tumor location and incidence,...

With the exponential growth of the amount of data being generated, stored and processed on a daily basis in the machine learning, data analytics and decision-making systems, the data preprocessing established itself as the key factor for building reliable high-performance machine learning models. One of the roles in preprocessing is variable reduct...

Since the early beginnings of education systems, attendance has always played a crucial role in student success, as well as in the overall interest of the matter. The most productive way of increasing the student attendance rate is to understand why it decreases, try to predict when it is going to happen, and act on causing factors in order to prev...

With the increasing number of users and data on the Internet, especially social media sites, sentiment analysis topic became one of the important and essential fields for most. Collection of people's feelings and sentiment and classifying the data attracted most businesses and companies. Recently, twitter sentiment analysis has attracted much atten...

This research presents the epileptic focus region localization during epileptic seizures by applying different signal processing and ensemble machine learning techniques in intracranial recordings of electroencephalogram (EEG). Multi-scale Principal Component Analysis (MSPCA) is used for denoising EEG signals and the autoregressive (AR) algorithm w...

In this chapter, we present a new method of steganography using decomposition of Catalan numbers. The proposed steganographic method consists of two segments: the first segment refers to the process of embedding data and generation of a complex stego key, whereas the second segment refers to the process of extracting a hidden message based on the g...

Most common pathologic conditions in the alveolar bone derived from necrotic dental pulp are periapical inflammatory lesions (periapical granuloma and periapical cyst). The early diagnosis of lesions of the oral cavity is challenging for clinical practitioners. This research implements a computer-aided diagnostic system for classification of periap...

The usage of handwritten character recognition has been useful for usage from large to common consumer usage. The transitional period of the handwritten to the digital age can be largely improved by focusing on perfecting handwritten character recognition. This paper and work aims to focus on handwritten digit recognition using the decision tree cl...

Localization of epileptogenic foci is an essential phase in surgical treatment planning using the earliest time detection of the seizure onset in the recordings of electroencephalogram (EEG). These recordings are defined as the areas of the brain which can be surgically removed to reach control of seizure. The characteristics of the brain area affe...

In this contribution, a number of Machine Learning Techniques (MLT), such as Rotation Forest, Random Forest, Bagging, Voting, Ada Boost, are compared by terms of epileptic seizure prediction using Hadoop environment. Based on obtained results, considered MLTs are ranked and used to establish patient individualized approach for epileptic seizure pre...

The main aim of the study is to develop a real-time epilepsy prediction approach by using the ensemble machine learning techniques that might predict offline seizure paradigms. The proposed seizure prediction algorithm is patient-specific since generalization showed no satisfactory results in our previous studies. The algorithm is tested on CHB-MIT...

Objective of this study is to parallelize and apply distributed system paradigm to the whole process of EEG signal analysis including the signal segmentation, signal processing, feature extraction, and classification. This study is focused only on time required for execution of every signal processing part within real-time epileptic seizure predict...

In this era, big data applications including biomedical are becoming attractive as the data generation and storage is increased in the last years. The big data processing to extract knowledge becomes challenging since the data mining techniques are not adapted to the new requirements. In this study, we analyse the EEG signals for epileptic seizure...

In this paper, we developed a combining classifier model based on tree-based algorithms for network intrusion detection. The NSL-KDD dataset, a much improved version of the original KDDCUP’99 dataset, was used to evaluate the performance of our detection algorithm. The task of our detection algorithm was to classify whether the incoming network tra...

E-mail still proves to be very popular and an efficient communication tool. Due to its misuse, however, managing e-mails problem for organizations and individuals. Spam, known as unwanted message, is an example of misuse. Specifically, spam is defined as the arrival of unwelcomed bulk email not being requested for by recipients. This paper compares...

Epileptic foci localization is a crucial step in planning surgical treatment of medically intractable epilepsy. The solution to this problem can be determined by the detection of the earliest time of seizure onset in electroencephalographic (EEG) recordings. This study presents the application of support vector machine (SVM) for localization of...