Use Of Computer-aided Systems Research Articles

The Impact of a Special Economic Zone Management on the Development of Modern Sectors and Technologies in a Polish Metropolis: The Smart City Context

The development of the high-tech sector has so far been studied within the cities, leaving their functioning within the special economic zones in the context of metropolitan belonging unexplored. The main purpose of the article is to determine whether the economic zone has a positive impact on the economic development of cities, which in the context of this research, has been operationalized with the use of computer-aided systems. The authors purposefully choose a Metropolis GZM, Poland (GZM), and Katowice Special Economic Zone (KSEZ). The importance of the high-tech sector in the GZM depends on the development of business in the region. Innovative specializations of cities located in the KSEZ were examined from the point of view of their perception by users of these new technologies, i.e., city residents. The research was conducted among over 1000 inhabitants of 16 cities in the GZM Metropolis. The results show correlations between the existence of modern businesses in metropolitan cities and their development of useful technologies, perceived from the perspective of residents.

Optimization of Electrocardiogram Classification Using Dipper Throated Algorithm and Differential Evolution

Electrocardiogram (ECG) signal is a measure of the heart’s electrical activity. Recently, ECG detection and classification have benefited from the use of computer-aided systems by cardiologists. The goal of this paper is to improve the accuracy of ECG classification by combining the Dipper Throated Optimization (DTO) and Differential Evolution Algorithm (DEA) into a unified algorithm to optimize the hyperparameters of neural network (NN) for boosting the ECG classification accuracy. In addition, we proposed a new feature selection method for selecting the significant feature that can improve the overall performance. To prove the superiority of the proposed approach, several experiments were conducted to compare the results achieved by the proposed approach and other competing approaches. Moreover, statistical analysis is performed to study the significance and stability of the proposed approach using Wilcoxon and ANOVA tests. Experimental results confirmed the superiority and effectiveness of the proposed approach. The classification accuracy achieved by the proposed approach is (99.98%).

Sanders classification of calcaneal fractures in CT images with deep learning and differential data augmentation techniques

BackgroundClassification of the type of calcaneal fracture on CT images is essential in driving treatment. However, human-based classification can be challenging due to anatomical complexities and CT image constraints. The use of computer-aided classification system in standard practice is additionally hindered by the availability of training images. The aims of this study is to 1) propose a deep learning network combined with data augmentation technique to classify calcaneal fractures on CT images into the Sanders system, and 2) assess the efficiency of such approach with differential training methods. MethodsIn this study, the Principle component analysis (PCA) network was selected for the deep learning neural network architecture for its superior performance. CT calcaneal images were processed through PCA filters, binary hashing, and a block-wise histogram. The Augmentor pipeline including rotation, distortion, and flips was applied to generate artificial calcaneus fractured images. Two types of training approaches and five data sample sizes were investigated to evaluate the performance of the proposed system with and without data augmentation. ResultsCompared to the original performance, use of augmented images during training improved network performance accuracy by almost twofold in classifying Sanders fracture types for all dataset sizes. A fivefold increase in the number of augmented training images improved network classification accuracy by 35%. The proposed deep CNN model achieved 72% accuracy in classifying CT calcaneal images into the four Sanders categories when trained with sufficient augmented artificial images. ConclusionThe proposed deep-learning algorithm coupled with data augmentation provides a feasible and efficient approach to the use of computer-aided system in assisting physicians in evaluating calcaneal fracture types.

Deep Learning Classification for Diabetic Foot Thermograms.

According to the World Health Organization (WHO), Diabetes Mellitus (DM) is one of the most prevalent diseases in the world. It is also associated with a high mortality index. Diabetic foot is one of its main complications, and it comprises the development of plantar ulcers that could result in an amputation. Several works report that thermography is useful to detect changes in the plantar temperature, which could give rise to a higher risk of ulceration. However, the plantar temperature distribution does not follow a particular pattern in diabetic patients, thereby making it difficult to measure the changes. Thus, there is an interest in improving the success of the analysis and classification methods that help to detect abnormal changes in the plantar temperature. All this leads to the use of computer-aided systems, such as those involved in artificial intelligence (AI), which operate with highly complex data structures. This paper compares machine learning-based techniques with Deep Learning (DL) structures. We tested common structures in the mode of transfer learning, including AlexNet and GoogleNet. Moreover, we designed a new DL-structure, which is trained from scratch and is able to reach higher values in terms of accuracy and other quality measures. The main goal of this work is to analyze the use of AI and DL for the classification of diabetic foot thermograms, highlighting their advantages and limitations. To the best of our knowledge, this is the first proposal of DL networks applied to the classification of diabetic foot thermograms. The experiments are conducted over thermograms of DM and control groups. After that, a multi-level classification is performed based on a previously reported thermal change index. The high accuracy obtained shows the usefulness of AI and DL as auxiliary tools to aid during the medical diagnosis.

Preface to the Special Issue on Philosophy in Computer Science

Abstract Fleck’s concept of thought style allows to realize the fact that in contemporary empirical sciences we deal with a computer thought style, as most research works are currently conducted with the use of computer-aided systems. In this article I support the thesis that contemporary research works are dominated by the computer research style. I refer to the findings of Fleck, Bolter, Castells, Crombie and Hacking.

Role of artificial intelligence in the care of patients with nonsmall cell lung cancer.

Lung cancer is the leading cause of cancer death worldwide. In up to 57% of patients, it is diagnosed at an advanced stage and the 5-year survival rate ranges between 10%-16%. There has been a significant amount of research using machine learning to generate tools using patient data to improve outcomes. This narrative review is based on research material obtained from PubMed up to Nov 2017. The search terms include "artificial intelligence," "machine learning," "lung cancer," "Nonsmall Cell Lung Cancer (NSCLC)," "diagnosis" and "treatment." Recent studies support the use of computer-aided systems and the use of radiomic features to help diagnose lung cancer earlier. Other studies have looked at machine learning (ML) methods that offer prognostic tools to doctors and help them in choosing personalized treatment options for their patients based on molecular, genetics and histological features. Combining artificial intelligence approaches into health care may serve as a beneficial tool for patients with NSCLC, and this review outlines these benefits and current shortcomings throughout the continuum of care. We present a review of the various applications of ML methods in NSCLC as it relates to improving diagnosis, treatment and outcomes.

Fuzzy Approach to the Selection of Material Data in Concurrent Engineering Environment

The selection of proper materials for a structural component is critical in engineering design. Existing design procedures may currently be sufficient, especially where experience exists, but fierce industrial competition is spurring the search for improved methods and tools. The main drivers are quality, life-cycle cost, and time-to-market. Improved design efficiency and accuracy may have an enormous impact on the economic viability of the final product. The use of computer-aided systems can assist the designer in selecting the ap- propriate material these may potentially reduce product cost and time-to-market while assisting the concur- rent engineering activities, thereby resolving problems related to materials presented during the initial phase of design. This paper focuses on specific issues such as material selection at early design stage. In this paper material selection algorithm is developed using fuzzy logic technique for selection of proper material from database as per design engineers’ criteria. The information obtained from the selection algorithm is ex- changed through a properly secured web page through pure internet communication to different users in the enterprise so that it can create concurrent engineering environment throughout the product life cycle.