Hybridizing Convolutional Neural Network for Classification of Lung diseases

Pulmonary disease is widespread worldwide. There is persistent blockage of the lungs, pneumonia, asthma, TB, etc. It is essential to diagnose the lungs promptly. For this reason, machine learning models were developed. For lung disease prediction, many deep learning technologies, including the CNN, and the capsule network, are used. The fundamental CNN has low rotating, inclined, or other irregular image orientation efficiency. Therefore by integrating the space transformer network (STN) with CNN, we propose a new hybrid deep learning architecture named STNCNN. The new model is implemented on the dataset from the Kaggle repository for an NIH chest X-ray image. STNCNN has an accuracy of 69% in respect of the entire dataset, while the accuracy values of vanilla grey, vanilla RGB, hybrid CNN are 67.8%, 69.5%, and 63.8%, respectively. When the sample data set is applied, STNCNN takes much less time to train at the cost of a slightly less reliable validation. Therefore both specialists and physicians are simplified by the proposed STNCNN System for the diagnosis of lung disease.

A hierarchical RCNN for vehicle and vehicle license plate detection and recognition

<span>Vehicle and vehicle license detection obtained incredible achievements during recent years that are also popularly used in real traffic scenarios, such as intelligent traffic monitoring systems, auto parking systems, and vehicle services. Computer vision attracted much attention in vehicle and vehicle license detection, benefit from image processing and machine learning technologies. However, the existing methods still have some issues with vehicle and vehicle license plate recognition, especially in a complex environment. In this paper, we propose a multivehicle detection and license plate recognition system based on a hierarchical region convolutional neural network (RCNN). Firstly, a higher level of RCNN is employed to extract vehicles from the original images or video frames. Secondly, the regions of the detected vehicles are input to a lower level (smaller) RCNN to detect the license plate. Thirdly, the detected license plate is split into single numbers. Finally, the individual numbers are recognized by an even smaller RCNN. The experiments on the real traffic database validated the proposed method. Compared with the commonly used all-in-one deep learning structure, the proposed hierarchical method deals with the license plate recognition task in multiple levels for sub-tasks, which enables the modification of network size and structure according to the complexity of sub-tasks. Therefore, the computation load is reduced.</span>

Corpus-based Data-driven Learning to Develop Senior Students’ Research Writing Skills: Practical Insights

The article introduces corpus-based DDL (Data-driven Learning) technologies in teaching ESP (English for Specific Purposes). The aim of the author is twofold: to offer English language teachers to design grammatical and lexical activities to develop senior students’ research writing skill and to assist senior students to construct scholarly field-related sentences. For the purpose of the study, the author used a mini manually compiled corpus of robotics, one of the branches of mechanical engineering and demonstrated practical instructions of corpus-based grammatical and lexical insights with DDL technologies. In spite of some limitations and future research, the findings of the study can contribute language teachers to develop senior students’ productive (writing) skills via designing corpus-based data driven materials as well as improve students to construct grammatically and lexically correct sentences for succeeding in their further research and career growth.

Pandemia da covid-19 e os impactos nas atividades acadêmicas no brasil

A pandemia COVID-19 intensificou a diversidade de tecnologias de aprendizagem na educação superior, de maneira impositiva, desmistificou usos remotos, à fronteira da totalidade do ensino à distância a todos cursos superiores, em atendimento aos protocolos sanitários. Todavia, os eixos de aprendizagem (Ensino-Pesquisa-Extensão), tiveram que se adequar ao emprego das tecnologias educacionais. Sob essa abordagem, emergem questões, buscando examinar impactos que os docentes vivenciaram na pandemia para assegurar continuidade de tais eixos. Os dados contaram com 560, respostas válidas, de docentes da educação superior, distribuídos em todas regiões do Brasil. O estudo de abordagem quantitativa contou com o método da estatística não paramétrica, teste qui-quadrado, teste exato de Fischer e U de Mann Whitney. Resultados demonstraram que a adaptação e adoção de novas estratégias aos eixos de aprendizagem foram superadas pelos docentes sem identificações de impactos notórios que gerassem barreiras ou impeditivos. Entretanto, as relações familiares e sociais, assim como a saúde mental e física dos docentes, despertaram como impactos percebidos. Estudos foram sugeridos empregando expectativas diversas, sob contextos que analisem a exclusão digital e a saúde mental como interseccionalidade e enfoque de discussão em período pandêmico e pós-pandemia em países em desenvolvimento. The pandemic COVID-19 intensified the diversity of learning technologies in higher education, in an imposing way, demystified remote uses, to the border of the totality of distance learning to all higher education courses, in attendance to sanitary protocols. However, the learning axes (Teaching-Research-Extension) had to adapt to the use of educational technologies. Under this approach, questions emerge, seeking to examine the impacts that the teachers experienced in the pandemic to ensure the continuity of these axes. The data counted on 560 valid answers from teachers of higher education, distributed in all regions of Brazil. The quantitative approach study relied on the non-parametric statistical method, chi-square test, Fischer's exact test, and Mann Whitney's U test. Results showed that the adaptation and adoption of new strategies to the learning axes were overcome by the teachers without the identification of notorious impacts that would generate barriers or impediments. However, family and social relationships, as well as the mental and physical health of the faculty members aroused as perceived impacts. Studies were suggested employing diverse expectations under contexts that analyze digital exclusion and mental health as intersectionality and focus of discussion in pandemic and post-pandemic period in developing countries.

Evaluation of Automated Measurement of Hair Density Using Deep Neural Networks

Recently, deep learning has been employed in medical image analysis for several clinical imaging methods, such as X-ray, computed tomography, magnetic resonance imaging, and pathological tissue imaging, and excellent performance has been reported. With the development of these methods, deep learning technologies have rapidly evolved in the healthcare industry related to hair loss. Hair density measurement (HDM) is a process used for detecting the severity of hair loss by counting the number of hairs present in the occipital donor region for transplantation. HDM is a typical object detection and classification problem that could benefit from deep learning. This study analyzed the accuracy of HDM by applying deep learning technology for object detection and reports the feasibility of automating HDM. The dataset for training and evaluation comprised 4492 enlarged hair scalp RGB images obtained from male hair-loss patients and the corresponding annotation data that contained the location information of the hair follicles present in the image and follicle-type information according to the number of hairs. EfficientDet, YOLOv4, and DetectoRS were used as object detection algorithms for performance comparison. The experimental results indicated that YOLOv4 had the best performance, with a mean average precision of 58.67.

Artificial Intelligence-Based Approaches to Reflectance Confocal Microscopy Image Analysis in Dermatology

Reflectance confocal microscopy (RCM) is a non-invasive imaging method designed to identify various skin diseases. Confocal based diagnosis may be subjective due to the learning curve of the method, the scarcity of training programs available for RCM, and the lack of clearly defined diagnostic criteria for all skin conditions. Given that in vivo RCM is becoming more widely used in dermatology, numerous deep learning technologies have been developed in recent years to provide a more objective approach to RCM image analysis. Machine learning-based algorithms are used in RCM image quality assessment to reduce the number of artifacts the operator has to view, shorten evaluation times, and decrease the number of patient visits to the clinic. However, the current visual method for identifying the dermal-epidermal junction (DEJ) in RCM images is subjective, and there is a lot of variation. The delineation of DEJ on RCM images could be automated through artificial intelligence, saving time and assisting novice RCM users in studying the key DEJ morphological structure. The purpose of this paper is to supply a current summary of machine learning and artificial intelligence’s impact on the quality control of RCM images, key morphological structures identification, and detection of different skin lesion types on static RCM images.

Predicting variations of the least principal stress with depth: Application to unconventional oil and gas reservoirs using a log-based viscoelastic stress relaxation model

Knowledge of layer-to-layer variations of the least principal stress, S hmin, with depth is essential for optimization of multi-stage hydraulic fracturing in unconventional reservoirs. Utilizing a geomechanical model based on viscoelastic stress relaxation in relatively clay rich rocks, we present a new method for predicting continuous S hmin variations with depth. The method utilizes geophysical log data and S hmin measurements from routine diagnostic fracture injection tests (DFITs) at several depths for calibration. We consider a case study in the Wolfcamp formation in the Midland Basin, where both geophysical logs and values of S hmin from DFITs are available. We compute a continuous stress profile as a function of the well logs that fits all of the DFITs well. We utilized several machine learning technologies, such as bootstrap aggregation (or bagging), to improve the generalization of the model and demonstrate that the excellent fit between predicted and observed stress values is not the result of over-fitting the calibration points. The model is then validated by accurately predicting hold-out stress measurements from four wells within the study area and, without recalibration, accurately predicting stress as a function of depth in an offset pad about 6 miles away.

Sustainability Enhancement Through Information Technology in Higher Education

The quick evolution and development of digital technologies contribute to changes and turning points in every aspect of the modern society. Managing these the digital changes and turning them into long-term sustainable solutions for higher education could be a real challenge. Technology offers immeasurable opportunities to the learning environment and higher education is rapidly integrating information and communication technology (ICT). This paper is discussing the concept of enhanced sustainability in education through learning technologies like applications, electronic resources, and teaching methods for educational development. Technology in higher education is particularly important, therefore it is important to assess the quality that it can provide and the impact on sustainable education. The educational system has been also harshly affected by the latest pandemic situation, which brought significant consequences on the social, economic, and cultural life worldwide. This paper aims to contribute with insights to the literature in the field, by emphasizing that the latest situation around the world could be a great opportunity for further implementation and development of digital technologies for sustainable learning. The methodology used for fulfilling the study goal is a qualitative one, particularly the technique used is data analysis from secondary sources. The results of this analysis lead to the conclusion that there is need and room for improvement of digital sustainable resources in higher education, which includes the challenges that the academic environment face and need to overcome if their goal is to stay relevant on the education market.

Machine learning for emerging infectious disease field responses

Emerging infectious diseases (EIDs), including the latest COVID-19 pandemic, have emerged and raised global public health crises in recent decades. Without existing protective immunity, an EID may spread rapidly and cause mass casualties in a very short time. Therefore, it is imperative to identify cases with risk of disease progression for the optimized allocation of medical resources in case medical facilities are overwhelmed with a flood of patients. This study has aimed to cope with this challenge from the aspect of preventive medicine by exploiting machine learning technologies. The study has been based on 83,227 hospital admissions with influenza-like illness and we analysed the risk effects of 19 comorbidities along with age and gender for severe illness or mortality risk. The experimental results revealed that the decision rules derived from the machine learning based prediction models can provide valuable guidelines for the healthcare policy makers to develop an effective vaccination strategy. Furthermore, in case the healthcare facilities are overwhelmed by patients with EID, which frequently occurred in the recent COVID-19 pandemic, the frontline physicians can incorporate the proposed prediction models to triage patients suffering minor symptoms without laboratory tests, which may become scarce during an EID disaster. In conclusion, our study has demonstrated an effective approach to exploit machine learning technologies to cope with the challenges faced during the outbreak of an EID.

Energy-Saving Methods for Urban Travel and Public Transportation in Smart Cities

In many countries, energy-saving and emissions mitigation for urban travel and public transportation are important for smart city developments. It is essential to understand the impact of smart transportation (ST) in public transportation in the context of energy savings in smart cities. The general strategy and significant ideas in developing ST for smart cities, focusing on deep learning technologies, simulation experiments, and simultaneous formulation, are in progress. This study hence presents simultaneous transportation monitoring and management frameworks (STMF ). STMF has the potential to be extended to the next generation of smart transportation infrastructure. The proposed framework consists of community signal and community traffic, ST platforms and applications, agent-based traffic control, and transportation expertise augmentation. Experimental outcomes exhibit better quality metrics of the proposed STMF technique in energy saving and emissions mitigation for urban travel and public transportation than other conventional approaches. The deployed system improves the accuracy, consistency, and F-1 measure by 27.50%, 28.81%, and 31.12%. It minimizes the error rate by 75.35%.