http://hdl.handle.net/10366/143230 2026-05-06T13:43:17Z 2026-05-06T13:43:17Z Adcaij, Editorial Team http://hdl.handle.net/10366/143317 2025-06-05T12:36:20Z 2019-09-30T00:00:00Z 2019-09-30T00:00:00Z Abdullayeva, Gulchin Alizade, Ulker http://hdl.handle.net/10366/143316 2025-06-05T12:36:20Z 2019-12-18T00:00:00Z

An approach to objective assessment of ultrasound examination is presented. To this end, modern information technologies and a set of mathematical methods in the form of a package are proposed. In this paper, diagnosis is viewed as a three-step process, and closed sub-objects are investigated using complex images, which pertains to the earliest diagnostic stage. For this purpose, three new features related to the disclosure of a growth are included in the paper. A system that performs the detection of the growth and finds the coordinates, area, gravity center and color palette of the obtained image is developed. By means of the created software package, the image is cleared from noise, filtering operations are performed, boundaries are defined more clearly and recognition by the mathematical morphology method is completed using selected classifiers. The main purpose is to direct doctor's attention to the presence of the pre-indicator of a non-specific symptom and to control the future development of the growth. The accuracy of the system is confirmed by the detection and identification of closed growths in the images taken in an ultrasound examination of internal organs of the human body. The system's operability has been tested directly on the ultrasound images (138 cases investigated), with the result of 98.8% at the diagnostic stage, 92, 03% at the early diagnostic stage; 2 cases have been recorded at the earliest diagnostic stage in 2018 and the frequency of monitoring has been determined.

2019-12-18T00:00:00Z Ullah, Rafi Khan, Ayaz H. Emaduddin, S.m. http://hdl.handle.net/10366/143315 2025-06-05T12:36:20Z 2019-08-14T00:00:00Z

k-Nearest Neighbor (k-NN) is a non-parametric algorithm widely used for the estimation and classification of data points especially when the dataset is distributed in several classes. It is considered to be a lazy machine learning algorithm as most of the computations are done during the testing phase instead of performing this task during the training of data. Hence it is practically inefficient, infeasible and inapplicable while processing huge datasets i.e. Big Data. On the other hand, clustering techniques (unsupervised learning) greatly affect results if you do normalization or standardization techniques, difficult to determine "k" Value. In this paper, some novel techniques are proposed to be used as pre-state mechanism of state-of-the-art k-NN Classification Algorithm. Our proposed mechanism uses unsupervised clustering algorithm on large dataset before applying k-NN algorithm on different clusters that might running on single machine, multiple machines or different nodes of a cluster in distributed environment. Initially dataset, possibly having multi dimensions, is pass through clustering technique (K-Means) at master node or controller to find the number of clusters equal to the number of nodes in distributed systems or number of cores in system, and then each cluster will be assigned to exactly one node or one core and then applies k-NN locally, each core or node in clusters sends their best result and the selector choose best and nearest possible class from all options. We will be using one of the gold standard distributed framework. We believe that our proposed mechanism could be applied on big data. We also believe that the architecture can also be implemented on multi GPUs or FPGA to take flavor of k-NN on large or huge datasets where traditional k-NN is very slow.

2019-08-14T00:00:00Z Facchini, Eduardo Dias, Eduardo Mario http://hdl.handle.net/10366/143314 2025-06-05T12:36:20Z 2019-09-14T00:00:00Z

This article aims to discuss the importance of the development of methodologies and control processes by the public power for the proper management of the urban bus system. It reflects on the idea of management control from the perspective of an operational, strategic and innovative perspective. It proposes a new look at the solution of negative externalities that arise in the urban environment of cities such as collisions, roadkill, works that will obstruct the roadbed and various events disrupting local traffic and directly impacting the mobility of public transport on tires. The theme is current, as it addresses the most problematic urban problem in the contemporary world, which is mobility. Thus, the article tells the experience of São Paulo and discusses a new control methodology for urban bus service./n 

2019-09-14T00:00:00Z Zafary, Farzaneh http://hdl.handle.net/10366/143313 2025-06-05T12:36:20Z 2019-09-14T00:00:00Z

Whenever an organization decides to implement an ERP, it must assess its readiness to implement these complex systems. Therefore, the present study aims at considering the pre-implementation phase of ERP and the factors affecting the readiness of the organization for successful implementation of these systems. In this research, the SWOT matrix was used to classify the identified factors. Moreover, fuzzy-dimensional network analysis was used to evaluate decision options due to the weakness of SWOT technique. Since the factors involved in SWOT analysis are not only disjointed, but sometimes there are relationships among some of its factors. Therefore, internal and external factors of the organization are evaluated and prioritized in the research. In addition, finding the strategic position of the organization and identifying the appropriate strategies and prioritizing them to improve the organization's readiness for ERP implementation were other tasks in this research. After analyzing the data, 25 organizational factors are prioritized. The numerical results indicated that the organization is in the offensive position therefore, four strategies are designed and prioritized according to its position.

2019-09-14T00:00:00Z Pekel Özmen, Ebru Pekel, Engin http://hdl.handle.net/10366/143312 2025-06-05T12:36:20Z 2019-09-14T00:00:00Z

The number of flight (NF) is one of the key factors for the administration of the airport to evaluate the apron capacity and airline companies to fix the size of the flight. This paper aims to estimate the monthly NF by performing particle swarm optimization (PSO) and artificial neural network (ANN). Performed PSO-ANN algorithm aims to minimize the proposed evaluation criterion in the training stage. PSO-ANN based on the proposed evaluation criterion offers satisfying fitness values with respect to correlation coefficient and mean absolute percentage error in the training and testing stage.

2019-09-14T00:00:00Z Patil, Mahesh S Chickerur, Satyadhyan Meti, Anand Nabapure, Priyanka M Mahindrakar, Sunaina Naik, Sonali Kanyal, Soumya http://hdl.handle.net/10366/143311 2025-06-05T12:36:20Z 2019-09-14T00:00:00Z

Speech Communication in a noisy environment is a difficult and challenging task. Many professionals work in noisy environments like aviation, constructions, or manufacturing, and find it difficult to communicate orally. Such noisy environments need an automated lip-reading system that could be helpful in communicating some instructions and commands. This paper proposes a novel lip-reading solution, which extracts the geometrical shape of lip movement from the video and predicts the words/sentences spoken. An Indian specific language data set is developed which consists of lip movement information captured from 50 persons. This includes students in the age group of 18 to 20 years and faculty in the age group of 25 to 40 years . All have spoken a paragraph of 58 words within 10 sentences in Hindi (Devanagari, spoken in India) language which was recorded under various conditions. The implementation consists of facial parts detection, along with Long short term memory’s. The proposed solution is able to predict the words spoken with 77% and 35% accuracy for data set of 3 and 10 words respectively. The sentences are predicted with 20% accuracy, which is encouraging.

2019-09-14T00:00:00Z Kabakus, Abdullah Talha http://hdl.handle.net/10366/143310 2025-06-05T12:36:20Z 2019-09-14T00:00:00Z

Face detection is the task of detecting faces on photos, videos as well as the streaming data such as a webcam. Face detection, which is a specific type of general-purpose object detection, is a key prerequisite for many other artificial intelligence tasks such as face verification, face tagging and retrieval, and face tracking. In addition to that, nowadays, face detection is commonly used in daily routines such as social media, and camera software of smartphones. As a result of this necessity, several face detection tools have been proposed. In this study, an experimental performance comparison of well-known face detection tools in terms of (1) accuracy, and (2) elapsed time of detection, which has become even more critical criteria especially when the face detection mechanism is utilized for a real-time system, is proposed. As a result of this experimental study, it is aimed that shed light on the much-concerned query “which face detection tool provides the best performance?”. In addition to that, this study succeeds in showing that convolutional neural networks achieve great accuracy for face detection.

2019-09-14T00:00:00Z Adcaij, Editorial Team http://hdl.handle.net/10366/143309 2025-06-05T12:36:20Z 2019-09-30T00:00:00Z 2019-09-30T00:00:00Z