Medical Student’s Attitudes and Perceptions Toward Artificial Intelligence Applications

To evaluate medical students' perceptions in radiology and medical applications of artificial intelligence (AI). Students at 10 prestigious medical schools were issued an online survey that was created using Survey Monkey. It was divided into many parts with the goal of assessing the students' past understanding of AI in radiology and beyond as well as their attitudes about AI in medicine more generally. Anonymity of the respondents was protected. A total of 263 students—166 female and 94 male—with a median age of 23—replied to the survey. Concerning 52 percent of respondents were aware of the current debate about AI in radiology, while 68 percent said they were ignorant of the underlying technology. abnormalities in radiological scans, but they believed that AI would not be able to provide a definitive diagnosis (56 percent). In contrast to claims that human radiologists would be displaced, the majority (77 percent and 86 percent) believed that AI would revolutionize and enhance radiology (83 percent). Over two-thirds of respondents felt that medical education must include AI (71 percent). Male and tech-savvy respondents had higher levels of confidence in the advantages of AI and lower levels of fear of these technologies in sub-group analyses. In conclusion, Contrary to what has been mentioned in the media, medical students are aware of the possible applications and effects of AI on radiology and medicine and do not worry that it will replace human radiologists. The situations in which artificial intelligence has reportedly substituted human radiologists are known to medical students. Since it is their duty, the field of radiology must take the initiative in teaching students about these freshly developed tools

Medical student’s attitudes and perceptions toward artificial intelligence applications

To evaluate medical students' perceptions in radiology and medical applications of artificial intelligence (AI). Students at 10 prestigious medical schools were issued an online survey that was created using Survey Monkey. It was divided into many parts with the goal of assessing the students' past understanding of AI in radiology and beyond as well as their attitudes about AI in medicine more generally. Anonymity of the respondents was protected. A total of 263 students—166 female and 94 male—with a median age of 23—replied to the survey. Concerning 52 percent of respondents were aware of the current debate about AI in radiology, while 68 percent said they were ignorant of the underlying technology. abnormalities in radiological scans, but they believed that AI would not be able to provide a definitive diagnosis (56 percent). In contrast to claims that human radiologists would be displaced, the majority (77 percent and 86 percent) believed that AI would revolutionize and enhance radiology (83 percent). Over two-thirds of respondents felt that medical education must include AI (71 percent). Male and tech-savvy respondents had higher levels of confidence in the advantages of AI and lower levels of fear of these technologies in sub-group analyses. In conclusion, Contrary to what has been mentioned in the media, medical students are aware of the possible applications and effects of AI on radiology and medicine and do not worry that it will replace human radiologists. The situations in which artificial intelligence has reportedly substituted human radiologists are known to medical students. Since it is their duty, the field of radiology must take the initiative in teaching students about these freshly developed tools

A powerful comparison of deep learning frameworks for Arabic sentiment analysis

Deep learning (DL) is a machine learning (ML) subdomain that involves algorithms taken from the brain function named artificial neural networks (ANNs). Recently, DL approaches have gained major accomplishments across various Arabic natural language processing (ANLP) tasks, especially in the domain of Arabic sentiment analysis (ASA). For working on Arabic SA, researchers can use various DL libraries in their projects, but without justifying their choice or they choose a group of libraries relying on their particular programming language familiarity. We are basing in this work on Java and Python programming languages because they have a large set of deep learning libraries that are very useful in the ASA domain. This paper focuses on a comparative analysis of different valuable Python and Java libraries to conclude the most relevant and robust DL libraries for ASA. Throw this comparative analysis, and we find that: TensorFlow, Theano, and Keras Python frameworks are very popular and very used in this research domain

Deep Learning based Cryptanalysis of Stream Ciphers

Conventional cryptanalysis techniques necessitate an extensive analysis of non-linear functions defining the relationship of plain data, key, and corresponding cipher data. These functions have very high degree terms and make cryptanalysis work extremely difficult. The advent of deep learning algorithms along with the better and efficient computing resources has brought new opportunities to analyze cipher data in its raw form. The basic principle of designing a cipher is to introduce randomness into it, which means the absence of any patterns in cipher data. Due to this fact, the analysis of cipher data in its raw form becomes essential. Deep learning algorithms are different from conventional machine learning algorithms as the former directly work on raw data without any formal requirement of feature selection or feature extraction steps. With these facts and the assumption of the suitability of employing deep learning algorithms for cipher data, authors introduced a deep learning based method for finding biases in stream ciphers in the black-box analysis model. The proposed method has the objective to predict the occurrence of an output bit/byte at a specific location in the stream cipher generated keystream. The authors validate their method on stream cipher RC4 and its improved variant RC4A and discuss the results in detail. Further, the authors apply the method on two more stream ciphers namely Trivium and TRIAD. The proposed method can find bias in RC4 and shows the absence of this bias in its improved variant and other two ciphers. Focusing on RC4, the authors present a comparative analysis with some existing methods in terms of approach and observations and showed that their process is more straightforward and less complicated than the existing ones

Bioinformatics Solutions for Image Data Processing

In recent years, the increasing use of medical devices has led to the generation of large amounts of data, including image data. Bioinformatics solutions provide an effective approach for image data processing in order to retrieve information of interest and to integrate several data sources for knowledge extraction; furthermore, images processing techniques support scientists and physicians in diagnosis and therapies. In addition, bioinformatics image analysis may be extended to support several scenarios, for instance, in cyber-security the biometric recognition systems are applied to unlock devices and restricted areas, as well as to access sensitive data. In medicine, computational platforms generate high amount of data from medical devices such as Computed Tomography (CT), and Magnetic Resonance Imaging (MRI); this chapter will survey on bioinformatics solutions and toolkits for medical imaging in order to suggest an overview of techniques and methods that can be applied for the imaging analysis in medicine

Image compression approach for improving deep learning applications

In deep learning, dataset plays a main role in training and getting accurate results of detection and recognition objects in an image. Any training model needs a large size of dataset to be more accurate, where improving the dataset size is one of the most research problems that needs enhancement. In this paper, an image compression approach was developed to reduce the dataset size and improve classification accuracy for the trained model using a convolutional neural network (CNN), and speeds up the machine learning process, while maintaining image quality. The results revealed that the best scenario for deep learning models that provided good and acceptable classification accuracy was one that had the following parameters: 80×80 image size, 10 epochs, 64 batch size, 40 images dataset quality (images compressed 60%), and gray image mode. For this scenario a Dog vs Cat dataset is used, and the training time was 48 minutes, classification accuracy was 86%, and images dataset size was 317 MB on storage device. This size makes up 58% of the size of the original image’s dataset, saves 42% of the storage space and reduces the processing resources consumption

Software engineering for deep learning applications: usage of SWEng and MLops tools in GitHub repositories

The rising popularity of deep learning (DL) methods and techniques has invigorated interest in the topic of SE4DL, the application of software engineering (SE) practices on deep learning software. Despite the novel engineering challenges brought on by the data-driven and non-deterministic paradigm of DL software, little work has been invested into developing AI-targeted SE tools. On the other hand, tools tackling more general engineering issues in DL are actively used and referred to under the umbrella term of ``MLOps tools''. Furthermore, the available literature supports the utility of conventional SE tooling in DL software development. Building upon previous MSR research on tool usage in open-source software works, we identify conventional and MLOps tools adopted in popular applied DL projects that use Python as the main programming language. About 70% of the GitHub repositories mined contained at least one conventional SE tool. Software configuration management tools are the most adopted, while the opposite applies to maintenance tools. Substantially fewer MLOps tools were in use, with only 9 tools out of a sample of 80 used in at least one repository. The majority of them were open-source rather than proprietary. One of these tools, TensorBoard, was found to be adopted in about half of the repositories in our study. Consequently, the use of conventional SE tooling demonstrates its relevance to DL software. Further research is recommended on the adoption of MLOps tooling by open-source projects, focusing on the relevance of particular tool types, the development of required tools, as well as ways to promote the use of already available tools