Advanced handwriting identification: Triboelectric sensor array integrating with deep learning toward high information security

Transfer learning can solve some problems that the lack of data and labeled data is unavailable in deep learning. The domain adaptation method in transfer learning can solve the problem of domain-shift, which is the main direction of current research. The generative adversarial network is popular in the generative model now, So the combination of them will obtain more respectable performance. While previous approaches only use it as a data augmentation tool. Even though the overall distribution is aligned, some samples will still be misclassified, and the corresponding classifier is trained on the source samples and cannot perfectly generalize to the target. We propose a novel generative discriminative domain adaptation method based on the reverse gradient by combining the generative adversarial method to reverse gradient. The method uses unlabeled target domain data to make the target and source domains distributions get closer in the feature space. At the same time, the auxiliary class's generation adversarial network is used to generate consistent-class representation, and the corresponding class's distribution is aligned across domains. Besides, we use circle loss as the classification function. Circle loss proposed a unified formula for deep feature learning features. It is useful for our classification task in our method. Our proposed method achieves better performance than others in most experimental settings and has shown to work well across different datasets such as digits and Office.

Statement of the problem. Mastering vocabulary in foreign language classes is a prerequisite for the development of basic speech skills (reading, listening, writing, speaking). According to modern requirements for teaching vocabulary, various visualization tools are successfully used for effective work with lexical material. This article considers Word cloud and Mind map as visualization tools. The purpose of this article is to describe the practice of using Word cloud and Mind map tools when teaching vocabulary in foreign language classes to students majoring in linguistics. Methodology (materials and methods). The methodology is based on the experience of Russian and foreign researchers who have dealt with the methods of working with vocabulary, including the use of technical and visualization tools, such as Word cloud and Mind map. Also, as a research method, an experiment with the participation of students of the department of foreign languages is used. As part of the experiment, students were divided into two groups and assigned to present new vocabulary in the Word cloud or Mind map format after initial familiarization with the vocabulary and the rules of designing a Word cloud and a Mind map. At the next stage, students were asked to complete a written task using the vocabulary they had learned. Research results. As a result of the study, the analysis of students’ written works was carried out, the advantages and disadvantages of using both tools were identified, the prospects for using Word cloud and Mind map when mastering vocabulary in foreign language classes were outlined. Conclusion. The research showed the effectiveness of using the Mind map and Word cloud tools in foreign language classes to improve students’ professional and personal skills. Obviously, the involvement of these tools in working with vocabulary contributes to the coverage of a larger amount of material, more effective memorization and high-quality reproduction of new vocabulary in students’ speaking practice.

Deep learning is a fast flattering highest development in the machine learning technique leading to augment performance in diverse medical applications. The tributary artificial intelligence is rapidly becoming metamorphic in the domain of Healthcare Technology rendering facility to analyze the medical data with high rapidity and exactness. Due to the overwhelming development of artificial intelligence (AI), the contemporary encroachments of deep learning employing advanced and futuristic deep learning methods has become a vivacious research domain for processing medical data in the health care industries. This paper presents a complete analysis about the deep structured learning based methods used in medical image analysis and also elucidate about the advancement of convolutional neural network which is a major class of deep neural networks used in medical image processing. It also gives insight about the fundamentals of deep learning methods and reviews in image interpretation. It also explains about the implementation of an integrated framework for medical image synthesis combining Speckle GAN and Di-Conv-AE-Net.

This paper describes the background of smart information infrastructure and the needs for smart grid information security. It introduces the conceptual analysis to the methodology with the application of hermeneutic circle and information security functional requirement identification. Information security for the grid market cover matters includes automation and communications industry that affects the operation of electric power systems and the functioning of the utilities that manage them and its awareness of this information infrastructure has become critical to the reliability of the power system. Community benefits from of cost savings, flexibility and deployment along with the establishment of wireless communications. However, concern revolves around the security protections for easily accessible devices such as the smart meter and the related communications hardware. On the other hand, the changing points between traditional versus smart grid networking trend and the information security importance on the communication field reflects the criticality of grid information security functional requirement identification. The goal of this paper is to identify the functional requirement and relate its significance addresses to the consumer requirement of an information security of a smart grid. Vulnerabilities may bring forth possibility for an attacker to penetrate a network, make headway admission to control software, alter it to load conditions that destabilize the grid in unpredictable ways. Focusing on the grid information security functional requirement is stepping ahead in developing consumer trust and satisfaction toward smart grid completeness.

Introduction. The significance of machine learning under the conditions of digital transformation of industry, and methods of implementing deep learning to provide the performance of trust management systems are considered. The necessity of using convolutional artificial neural networks for deep machine learning is determined. Various technologies and architectures for the implementation of artificial neural networks are briefly considered; a comparative analysis of their performance is carried out. The work objective is to study the need to develop new approaches to the architecture of computing machines for solving problems of deep machine learning in the trust management system implementation.Materials and Methods. In the context of digital transformation, the use of artificial intelligence reaches a new level. The technical implementation of artificial neural systems with deep machine learning is based on the use of one of three basic technologies: high performance computing (HPC) with parallel data processing, neuromorphic computing (NC), and quantum computing (QC).Results. Implementation models for deep machine learning, basic technologies and architecture of computing machines, as well as requirements for trust assurance in control systems using deep machine learning are analyzed. The problem of shortage of computation power for solving such problems is identified. None of the currently existing technologies can solve the full range of learning and impedance problems. The current level of technology does not provide information security and reliability of neural networks. The practical implementation of trust management systems with deep machine learning based on existing technologies for a significant part of the tasks does not provide a sufficient level of performance.Discussion and Conclusions. The study made it possible to identify the challenge of the computation power shortage for solving problems of deep machine learning. Through the analysis of the requirements for trust management systems, the external challenges of their implementation on the basis of existing technologies, and the need to develop new approaches to the computer architecture are determined.

Background: Classification of brain tumors is an integral aspect of medical imaging studies as different tumor features require multiclassification which aids in making the diagnosis. However, this task is of great difficulty owing to the nature of the brain MRI images. Recent deep learning (DL) models have made it possible to have a brain tumor classification with high accuracy, which is very useful to neurologists. Based on these new capabilities, this study intends to enhance brain tumor detection with the help of a hybrid transfer learning approach. Methods: This study developed a brain tumor diagnosis system using five advanced DL architectures: Xception, ResNet164, DenseNet121, DenseNet201, and Inception-ResNetV2. A hybrid DL model was created by integrating a deep-dense block and a softmax activation function in the final layers of these architectures. The proposed approach enhances classification accuracy and precision. Two experiments were conducted: Three-class classification, involving images from patients with gliomas, meningiomas, and pituitary tumors. Four-class classification, which included gliomas, meningiomas, pituitary tumors, and normal brain images. Results: The Xception architecture emerged as the most effective model in detecting brain tumors when implemented with the proposed hybrid DL modifications. The system achieved an accuracy of 99.71% on the three-class dataset and 96.17% on the four-class dataset, outperforming state-ofthe- art methods in brain tumor classification. Conclusion: This research shows that hybrid DL models can enhance the capabilities of brain tumor classification systems in the context of complex MRI datasets. The optimization of activation functions and the inclusion of deep-dense blocks increase both accuracy and precision, providing adequate solutions for neurologists. These results lead to the recognition of the Xception-based hybrid model as a robust and efficient model for detecting brain tumors.

With the development of data science and technology, information security has been further attention. In order to solve privacy problems such as personal privacy being peeped and copyright being infringed, information hiding technology has been developed. Image information hiding is to make use of the redundancy of the cover image to hide secret information in it. Ensure that the stego image cannot be distinguished from the cover image, and send secret information to receiver through the transmission of the stego image. At present, the model based on deep learning is also widely applied to the field of information hiding. This paper makes a conclusion on image information hiding based on deep learning. It is divided into four parts of steganography algorithms, watermarking embedding algorithms, coverless information hiding algorithms and steganalysis algorithms based on deep learning. From these four aspects, the state-of-theart information hiding technologies based on deep learning are illustrated and analyzed.

In the rapidly evolving landscape of information security, traditional defence mechanisms often fall short against sophisticated cyber threats. Artificial Intelligence (AI) has emerged as a pivotal technology in enhancing the resilience of information systems against such attacks. This research article explores various AI techniques employed to fortify information security, including machine learning, deep learning, natural language processing, and anomaly detection. By leveraging these advanced methodologies, organizations can proactively identify, prevent, and respond to security breaches with greater efficiency and accuracy. The study conducts a comprehensive review of current AI-driven security solutions, analysing their effectiveness in mitigating different types of cyber threats such as malware, phishing, and insider attacks. Furthermore, the research examines the integration challenges of AI technologies within existing security frameworks and assesses the ethical implications associated with AI-driven decision-making in cybersecurity. Through a mixed-methods approach, including case studies and empirical data analysis, this paper highlights the strengths and limitations of AI techniques in information security. The findings suggest that while AI significantly enhances threat detection and response capabilities, it also introduces new vulnerabilities and requires continuous monitoring and updating to remain effective. This research contributes to the ongoing discourse on leveraging AI for robust information security strategies, providing actionable insights for practitioners and policymakers aiming to safeguard digital assets in an increasingly interconnected world.

Deep learning will be one of the key technologies to promote learning in the next five years. With the rapid advancement of the information era, significant changes in students’ learning and thinking patterns have occurred. How to further encourage the development of students’ professional skills and innovative capacity has become the focus of society under the influence of the notion of deep learning. With the application of information technology in education, blended learning, as the only key trend mentioned in the new media alliance report for five consecutive years, has injected fresh vitality into the reform of traditional classrooms and laid a foundation for better promoting in-depth learning. Therefore, how to effectively use blended learning to change these phenomena has become an urgent problem to be solved. The goal of this research is to encourage pupils to learn in depth. This study specifies the design idea of a hybrid teaching mode supported by an information environment based on the promotion of high-order thinking capacity. Firstly, this study uses the literature research method to sort out the relevant literature on deep learning and hybrid teaching, which provides a theoretical basis for the later construction. Second, a questionnaire is utilized to assess the existing state of in-depth learning as well as the need for blended teaching. The mixed teaching mode has effectively promoted the development of students’ high-level thinking abilities such as autonomous learning, problem-solving, and application innovation; played a positive role in cultivating students’ in-depth learning; and finally won the unanimous recognition of students.

This study will investigate the security problems that are linked with the Hadoop ecosystem, Big Data, Map Reduce, and cloud computing. The objective of this research is to analyze these concerns. Specifically, the primary emphasis is placed on the security concerns that are linked with big data in cloud computing. Big data applications have a lot to offer organizations, companies, and a broad range of industries, no matter how big or little. There have been issues with Hadoop and cloud computing security, therefore we also look at several possible solutions to these difficulties. The field of cloud computing security is experiencing fast growth in several areas, including computer security, network security, information security, and data privacy. Data, apps, and the infrastructure related to them are greatly helped by cloud computing's use of rules, technologies, controls, and big data tools to keep everything safe. Researchers also anticipate that cloud computing, big data, and their associated benefits will represent the most exciting new areas of study.

There are legitimate worries about gaps between scientific evidence of brain function and legal criteria for determining criminal culpability. Behavioral evidence (such as arranging a getaway car) appears easier for judges and juries to use for purposes of determining criminal liability because it triggers the application of commonsense psychological (CSP) concepts that guide responsibility assessments. In contrast, scientific evidence of neurological processes will not generally lead a judge or jury to make direct inferences regarding criminal culpability. In these cases, an expert witness will be required to indicate to the fact-finder what scientific evidence means with regard to mental capacity; and then another inference must be made from this possible lack of capacity to the legal criteria for guilt. In this chapter I argue that formulating the relevant mental capacities as executive functions within the brain can provide a reliable link between neuroscience and assessments of criminal culpability.

Injury to the spinal accessory nerve during lymph node biopsy in the lateral cervical triangle is a dreaded complication. It is disproportionately frequently the basis for medico-legal debates even though an evidence base is lacking. The scientific clarification of meaningful and mandatory measures during the procedure is essential. A legal database query from 1970 to 2011 was carried out using related keywords. Judgements were examined for expert witnesses, and a literature search regarding expert witnesses was done. The arguments found were verified with respect to evidence. From 1970 to 2011, 18 verdicts were found with 11 claims upheld and seven rejected. Expert witnesses regularly asked for clear preparation of the nerve as well as the requirement of specialist standards, and often used the prima facie argument to show surgeon errors. In contrast, analyses of the literature showed a significant risk of injury during nerve preparation. The need for specialist standards remains, however, with significantly lower demands upon the expertise of the surgeon as described by expert witnesses. There was a lack of scientific evidence for special manoeuvers during surgical procedures in the lateral cervical triangle. This prompted experts to ask for scientifically unproved manoeuvers during the procedure. "Eminence-based" expert witnesses with a teaching aptitude still have considerable influence on judicial decisions but are an unnecessary burden regarding the provision of medical treatment.

Xia Xu Doctor Xia Xu from Beihang University in China, talks to Electronics Letters about the paper ‘Tropical Cyclone Intensity Prediction Based on Recurrent Neural Networks', page 413. My research field is deep learning, and I am applying deep learning methods for marine disaster (such as typhoons) forecasting. In short, my aim is to predict the expected intensity and path of typhoons. Generally, the mechanisms which govern typhoon behaviour are not clear or well specified, which makes typhoon forecasting a “black box” problem. Deep learning is a new technique uniquely skilled at addressing such “black box” problems and is the method our group chose to apply. Researchers usually try to clarify the mechanisms of typhoons to improve their models and provide more accurate forecasts. However, as is mentioned above, although there are many works about typhoon mechanisms, there is no consensus on an optimal model, and this complicates the use and effectiveness of traditional methods. Typhoon intensity forecasting is a well-recognised international problem. Compared with conventional methods, this work is an entirely different approach, which provides a unique and promising perspective on the problem. In this paper, we reported a new prediction model for the typhoon intensity prediction problem. Our method, deep learning, is not novel in and of itself. However, we have created a new network structure which is able to handle the typhoon intensity prediction task. Thus, the novelty in the presented work mainly exists in the idea and its application, as opposed to the specific technology. We think that this exploratory work will provide inspiration for subsequent research efforts. If this idea is expanded and improved to its fullest extent, it may become a new way to handle the typhoon intensity prediction problem. What was previously considered a traditional challenge will disappear. Furthermore, once deep learning is verified as effective in dealing with typhoon intensity prediction problems, it can then be extended to other similar fields, such as storm surge prediction. These tasks can share the same techniques with only relatively minor model alterations. However, we must face many challenges before reaching this point. Deep learning has had great success in image classification, audio recognition, and other engineering fields, but how can deep learning be geared toward our task? This was the most serious challenge addressed in this Letter. This work has validated the feasibility of using deep learning for typhoon intensity prediction and supplied the drive to continue efforts to further improve the accuracy of this approach. In the short term, more advanced deep learning models and more powerful training tricks may appear to handle this problem. However, in a long term, it is unclear whether our idea will outperform traditional methods. Therefore, with our idea as a first attempt at this approach, future works will explore its effectiveness and validate its eventual success. This initial work is an exploratory one, where a simple RNN model is used to test the feasibility of the approach. It can be easily extended by changing to more powerful models, adding new structures and developing new tricks. In this paper, our accuracy is comparable to traditional methods. In the future, we will work to improve the accuracy, and demonstrate any emerging advantages of the prediction model. While the work presented in our Letter will not, objectively speaking, have an immediate impact in the field it sets an important precedent in the application of deep learning to typhoon forecasting. This work is not in the current mainstream of typhoon intensity prediction as there are not many researchers dedicated to using deep learning methods in this manner. However, I am confident this will change with the continued growth and relevance of deep learning in the coming years. It is a powerful tool, and future research will surely demonstrate its potential and validate its use in our chosen application.

Chest X-ray imaging plays a vital role in the evaluation of thoracic organs, serving as a primary diagnostic tool for various pulmonary conditions. Interpretations of chest X-ray reports can sometimes differ from a physician’s clinical judgment, leading to diagnostic inconsistencies and potential delays in treatment. This study explores whether the application of advanced deep transfer learning techniques can improve the accuracy of chest X-ray interpretation. Deep Learning presents powerful capabilities for processing and interpreting complex imaging data. Convolutional Neural Networks (CNNs) are widely used in deep learning to perform image classification through hierarchical feature extraction. training deep models with large annotated datasets often demands significant computational resources. In this research, MobileNet and Inception V3 architectures are employed to detect diseases such as lung cancer, pneumonia, and tuberculosis from chest X-ray images. Conducted a detailed evaluation of these models and compare their performance with traditional diagnostic approaches. The results demonstrate notable improvements in accuracy and sensitivity, confirming that deep transfer learning techniques significantly enhance diagnostic outcomes. This suggests that MobileNet and Inception V3 can serve as reliable, efficient tools to support radiologists in early detection and decision-making, and contributing to more timely and accurate medical diagnoses.

Deep learning is an advanced representation learning method and can automatically discover hidden features from raw data. Researchers have attempted to adopt it for ECG data analysis in the past few years. However, traditional deep learning algorithms usually require great efforts and experience to fine-tune the neural networks during their training processes. Moreover, these algorithms may suffer from a sharply declined accuracy when a well-trained model is directly applied to analyze the data from another group of patients. To address these issues, we propose a deep multi-task learning scheme for ECG data analysis which only requires limited efforts to fine-tune the network and can enable the trained model to be well applied to other datasets. Specifically, we first convert the ECG data analysis problem into a multi-task learning problem by dividing the ECG data analysis into multiple tasks. We then construct the multiple datasets for each task. Finally, we design a deep parameter-sharing network which inserts parameter-sharing neural layers in traditional neural networks. We conduct experiments by using the MIT-BIH database to validate the performance of our proposed scheme. Results illustrate that our proposed scheme can improve the accuracy of ECG data analysis by up to about 5.1%.