Milan Gnjatović's research while affiliated with University of Criminal Investigation and Police Studies and other places

... The production of effective vision frameworks for programmed reconnaissance innovation is a troublesome and obliged issue [2]. Biometrics investigates how to recognize or match individuals in light of at least one biometric quality, like voice, unique mark, or face picture [3]. Free access and few usage restrictions are characteristics of semi-public areas. ...

... Some applications with nonlinear process demonstrate the different neural network-based models [6]. Some heuristic procedures can reduce computational complexity with respect to optimal multiple sequence alignment and thus outperform humans in the post-processing of recognition hypotheses [7]. Adaptive network enhancement method for the best least squares approximation using two-layer Rectified-Linear-Unit (ReLU) neural networks is introduce in [8]. ...

... Clustering Approach [1] traffic load analysis improved k-means clustering algorithm [2] traffic congestion analysis self-organizing maps neural network [3] traffic state classification k-medoids algorithm [4] road network level identification k-means algorithm [5] traffic congestion analysis grey relational clustering model [6] traffic accidents and pattern extraction ROCK algorithm [7] traffic accident pattern identification COOLCAT algorithm [8] traffic accident factor analysis k-means algorithm [9] road traffic accident modeling a comparative study of machine learning classifiers [10] traffic accident black spots identification HDBSCAN algorithm [11] traffic congestion analysis k-means algorithm [12] driving behavior risk analysis k-means algorithm [13] optimal path routing a modified K-medoids algorithm [14] analysis of pedestrian crash fatalities and severe injuries KDE method [15] traffic-management system DBSCAN agorithm [16] severity of traffic accident analysis DBSCAN algorithm [17] highway safety assessment k-means algorithm [18] pedestrian crash severity analysis KDE method [19] detection of road segments of spatially prolonged and high traffic accident risk a clustering algorithm based on the Gestalt principle of proximity This paper goes along the second research line. It introduces a parameter-free approach to clustering critical traffic road segments in urban areas, i.e., road segments of spatially prolonged and high traffic accident risk. ...

... The most representative tool for VMI is Drakvuf [11], capable of tracing Kernel and user-level malware by observing process execution, file operations, and system calls as kernel functions at the hypervisor level. Drakvuf injection technique allows it to monitor the instructions written into the VM memory at the location of interest and, therefore, can detect kernel rootkits and limits malware capability to perform evasive techniques. ...

... Sequences of strings are configured, obtained from symbol-based modeling of spatiotemporal signals using IMU data. Sequences of strings can be understood as weighted graph vertices, whereby the weight of an edge presents a modified Levenshtein distance [10] between its vertices. ...

... [1]. In recent years, with the rapid development of modern signal processing technology, many domestic and foreign scholars have proposed a variety of methods to improve the accuracy and speed of residual current detection, such as wavelet analysis, Empirical Mode Decomposition (EMD), neural networks [2,3] and other algorithms have been applied to residual current detection, combining these emerging technologies with RCD to further improve the reliability of RCD. For example, X. Xiao et al. proposed the adaptive filtering detection method [4], which achieves adaptive current detection by noise reduction and residual current separation of the measured current, but the N-LMS model has the disadvantages of too slow convergence and low detection accuracy; H. Yan et al. proposed the EMD detection method [5], which uses the characteristics of the time-frequency domain change of residual current as the basis for determining the occurrence of electrocution faults, which is better improved accuracy, but the detection is prone to problems such as modal mixing and endpoint effects; C. Li et al. performed the detection of residual current by combining VMD with LSTM [6], which has certain advantages due to LSTM in sequence modelling problems with long-time memory. ...

... Other studies include the determination of dialogue boundaries [43], a method for estimating utterance topics for conference support [44], and bilingual topic modeling [45]. However, their approach differs from ours, which is based on an originally constructed interview dialogue corpus. ...

... The replicable experimental findings on three publicly available datasets were provided in this study, demonstrating outperforming outcomes and validating the utility of the suggested technique. Liu et al. [36] utilize Gaussian, triangular fuzzy average, and median smoothing filters to pre-process the picture by fuzzifying the region outside the border to increase the signal-to-noise ratios. They used augmented photos to train deep learning systems using fuzzy operations, which sped up the convergence process and improved identification accuracy. ...

... Although machine learning may produce practical and advanced applications, more is needed to replace the human capacity for domain expertise. Gnjatović et al. [34] focused on reintroducing humans into the learning loop. For instance, false, inaccurate or incomplete information floated online in news, social media and on the Web causes societal harm. ...

... For example, Figure 1 shows a set of image frame segments derived from a video stream captured by a mobile phone application. Each frame is processed separately by the external number recognition system introduced in [1,6]. In Table 1, for each frame, a recognition hypothesis is provided. ...