Deep Supramolecular Language Processing for Co‐Crystal Prediction

CONTEXT: Self-efficacy is a concept researched in various areas of knowledge that impacts various factors such as performance, satisfaction, and motivation. In Software Engineering, it has mainly been studied in the academic context, presenting results similar to other areas of knowledge. However, it is also important to understand its impact in the industrial context. OBJECTIVE: Therefore, this study aims to understand the impact on the software development context with a focus on understanding the behavioral signs of self-efficacy in software engineers and how self-efficacy can impact the work-day of software engineers. METHOD: A qualitative research was conducted using semi-structured questionnaires with 31 interviewees from a software development company located in Brazil. The interviewees participated in a Bootcamp and were later assigned to software development teams. Thematic analysis was used to analyze the data. RESULTS: In the perception of the interviewees, 21 signs were found that are related to people with high and low self-efficacy. These signs were divided into two dimensions: social and cognitive. Also, 18 situations were found that can lead to an increase or decrease of self-efficacy of software engineers. Finally, 12 factors were mentioned that can impact software development teams. CONCLUSION: This work evidences a set of behavioral signs that can help team leaders to better perceive the self-efficacy of their members. It also presents a set of situations that both leaders and individuals can use to improve their self-efficacy in the development context, and finally, factors that can be impacted by self-efficacy in the software development context are also presented. Finally, this work emphasizes the importance of understanding self-efficacy in the industrial context.

Non-negative Matrix Factorization (NMF) and Singular Value Decomposition (SVD) are widely recognized as pivotal dimensionality reduction techniques in the literature, particularly for deep learning applications involving large and high-dimensional datasets like SEM images. This study systematically evaluates the impact of SVD and NMF on the performance, efficiency, and energy consumption of four deep learning architectures: GoogleNet, AlexNet, ResNet, and SqueezeNet. By applying these techniques to reduce dataset dimensions, we observed that SVD excelled in computational efficiency, achieving up to 35% faster processing times compared to raw datasets. NMF, on the other hand, provided superior feature interpretability, which proved beneficial for tasks requiring meaningful pattern extraction. Energy consumption analysis revealed that SVD led to a 28% reduction in computational energy cost on average, making it a practical choice for resource-constrained environments. Among the evaluated models, ResNet consistently delivered the highest classification accuracy after dimensionality reduction, showing an improvement of 4-6% over models trained on non-reduced data. These findings underscore the critical role of dimensionality reduction in enhancing the scalability, energy efficiency, and classification accuracy of deep learning models, offering valuable insights for optimizing high-dimensional data applications in both academic and industrial contexts.

AI has rapidly changed how web applications are created and delivered in recent years. Using AI brings the user benefits of better automated processes through intelligent self-service, personalization of user experiences/customers, predictive analysis, and now includes real-time decision making. Before AI, web applications were mostly static or rules-based with limited or no change in response to how users interact with them. The introduction of advanced learning technologies such as Machine Learning (ML), Deep Learning (DL), and Natural Language Processing (NLP) has enabled web applications to become "intelligent" platforms that can receive input from a large volume of data and make adjustments in performance to continuously improve Keywords:Artificial Intelligence (AI), Web Applications, Machine Learning (ML), Deep Learning, Natural Language Processing (NLP), Recommendation Systems, Chatbots, Predictive Analytics, Data Mining.

Automatic assessment of pain based on deep learning methods: A systematic review

Breast cancer is the most prevalent cancer globally and presents a significant threat to human health. The low survival rate for advanced stages highlights the importance of early and accurate diagnosis. Pathology images, providing insights into cancer's cellular aggressiveness, are crucial for breast cancer diagnosis. Advances in deep learning have transformed automated medical image diagnosis. However, traditional deep learning methods, reliant on large labeled datasets, face challenges with the typically small size of medical image datasets. To overcome this, our study combines deep transfer learning (DTL) with convolutional neural networks(CNN). We utilize and enhance a ResNet50 model pretrained on a large dataset. Additionally, we apply four different deep transfer learning methods to feature extraction and classification of the BreakHis dataset, aiming to determine if breast cancer is benign or malignant. Ultimately, our system achieved the best average accuracy rates under four different magnifications: 98.57%, 96.83%, 97.76%, and 97.16%. These results demonstrate the accuracy and potential of deep transfer learning in classifying breast cancer histopathological images and its promising application in tumor prediction.

Agriculture is a vital industry that adds significantly to the global economy. Researchers are currently starting to investigate the prospect of integrating deep learning techniques and machine learning into agriculture, due to recent developments in technologies for deep learning. The paper examines several deep neural network designs and machine learning techniques used in agriculture, including irrigation, weeding, pattern recognition, and crop disease identification. The primary goal of this study is to determine multiple uses of deep learning in agriculture and to summarise existing state-of-the-art approaches. The review addresses the particular deep learning algorithms utilized, the sources of data used, study achievement, the equipment used, and the possibility for immediate application as well as integration with autonomous mechanical platforms. According to the results of the chapter, the use of deep learning research outperforms typical machine learning techniques in terms of reliability. In general, the study indicates the enormous potential of deep learning and machine learning in agriculture and the necessity for additional study in this field. It may be able to improve agricultural efficiency, decrease waste, and raise the yields of crops by utilizing the potential of these methods, ultimately enhancing the worldwide availability of food

Advancing radar-based precipitation nowcasting : an open benchmark and the potential of deep learning

This research aims to (1) assess current and future digital industry demands to align academic learning with industry-required competencies, (2) classify and analyze computer science curriculum components based on Thailand’s Professional Qualification Standards, (3) evaluate digital software-related professional competencies and their alignment with academic courses, (4) develop a decision-support model for career selection based on program learning outcomes (PLOs), (5) implement a web-based platform to support decision-making, and (6) evaluate the predictive accuracy of the model. Findings reveal seven key components of higher education improvement in computer science (mean = 4.92), five key course learning outcomes (mean = 4.87), and seventeen PLO elements (mean = 4.81), all rated at excellent levels. The study integrated the Thai Professional Qualification Institute (TPQI) framework into curriculum development. Data from 200 graduates were analyzed and mapped with current job roles and industry expectations. A predictive model using deep learning was developed and deployed on a browser-based platform. Accuracy evaluation shows that the system aids students in aligning career paths with qualifications and helps industries identify suitable talent. User satisfaction among students was high, indicating practical value in academic and industrial contexts.

To facilitate the management, Internet of Things (IoT) vendors usually apply remote ways such as HTTP services to uniformly manage IoT devices, leading to traditional web application vulnerabilities that also endanger the cloud interfaces of IoT, such as cross‐site scripting (XSS), code injection, and Remote Command/Code Execute (RCE). XSS is one of the most common web application attacks, which allows the attacker to obtain private user information or attack IoT devices and IoT cloud platforms. Most of the existing XSS payload detection models are based on machine learning or deep learning, which usually require a lot of external resources, such as pretrained word vectors, to achieve a better performance on unknown samples. But in the field of XSS payload detection, high‐quality vector representations of samples are often difficult to obtain. In addition, existing models all perform substantially worse when the distribution of XSS payloads and benign samples in the test dataset is extremely unbalanced (e.g., XSS payloads: benign samples = 1: 20). While in the real XSS attack scenario against IoT, an XSS payload is often hidden in a massive amount of normal user requests, indicating that these models are not practical. In response to the above issues, we propose an XSS payload detection model based on inductive graph neural networks, IGXSS (XSS payload detection model based on inductive GCN), to detect XSS payloads targeting IoT. Firstly, we treat the samples and words obtained from segmenting the samples as nodes and attach lines between them in order to form a graph. Then, we obtain the feature matrix of nodes and edges utilizing information between nodes only (instead of external resources such as pretrained word vectors). Finally, we feed the obtained feature matrix into a two‐layer GCN for training and validate the performance of models in several datasets with different sample distributions. Extensive experiments on the real datasets show that IGXSS performs better compared to other models under various sample distributions. In particular, when the sample distribution is extremely unbalanced, the recall and F1 score of IGXSS still reach 1.000 and 0.846, demonstrating that IGXSS is more robust and more suitable for practical scenarios.

Deep learning has become a significant tool in the healthcare industry with the potential to change the way care is provided and enhance patient outcomes. With a focus on personalised medicine, ethical issues and problems, future directions and opportunities, real-world case studies, and data privacy and security, this review article investigates the existing and potential applications of deep learning in healthcare. Deep learning in personalised medicine holds enormous promise for improving patient care by enabling more precise diagnoses and individualised treatment approaches. But it's important to take into account ethical issues like data privacy and the possibility of bias in algorithms. Deep learning in healthcare will likely be used more in the future to manage population health, prevent disease, and improve access to care for underprivileged groups of people. Case studies give specific examples of how deep learning is already changing the healthcare industry, from discovering rare diseases to forecasting patient outcomes. To fully realize the potential of deep learning in healthcare, however, issues including data quality, interpretability, and legal barriers must be resolved. Remote monitoring and telemedicine are two promising areas where deep learning is lowering healthcare expenses and enhancing access to care. Deep learning algorithms can be used to analyse patient data in real-time, warning medical professionals of possible problems before they worsen and allowing for online discussions with experts. Finally, when applying deep learning to healthcare, the importance of data security and privacy cannot be understated. To preserve patient data and guarantee its responsible usage, the appropriate safeguards and rules must be implemented. Deep learning has the ability to transform the healthcare industry by delivering more individualised, practical, and efficient care. However, in order to fully realize its promise, ethical issues, difficulties, and regulatory barriers must be solved. Deep learning has the potential to significantly contribute to enhancing patient outcomes and lowering healthcare costs with the right safeguards and ongoing innovation

This paper introduces a novel deep learning framework for highly accurate COVID-19 detection using chest X-ray images. The proposed model tackles the challenge by combining stacked Convolutional Neural Network models for superior feature extraction to potentially enhance interpretability. The proposed model achieved a high accuracy in distinguishing COVID-19 from healthy cases. The study demonstrates the potential of deep hybrid learning for accurate COVID-19 detection, paving the way for its application in real-world settings. Future research directions could explore methods to further refine the model's capabilities. Overall, this work contributes significantly to the development of robust deep-learning methods for COVID-19 detection with the potential for broader use in medical image analysis.

This study analyses the implementation of Google Docs in supporting deep learning among students using the SOLO Taxonomy in the Development of Evaluation Systems and PAI Learning Outcomes course. Twelve students participated in this qualitative case study. Data collection was conducted through observation, interviews, and documentation. The results of the study show that Google Docs enables real-time collaboration that facilitates student development from unistructural and multistructural levels to relational levels. However, the digital divide and unequal participation limit progress to higher extended abstract levels. This study confirms that digital collaborative tools aligned with the SOLO Taxonomy can support cognitive development when supported by appropriate infrastructure and teaching. To realise the full potential of deep and equitable learning, educational practices must prioritise digital learning policies, SOLO Taxonomy support, and a culture of sustainable collaboration. Highlights: Real-time document collaboration supported structured cognitive progression within group learning activities. Most students demonstrated integrated conceptual understanding at relational cognitive levels. Infrastructure limitations and uneven participation constrained higher-order knowledge generalisation. Keywords: SOLO Taxonomy, Deep Learning, Collaborative Learning, Cognitive Development, Islamic Education

Deep Learning and Beyond: AI’s New Horizons” is an expansive monograph that delves into the dynamic and rapidly evolving field of deep learning, a key area of artificial intelligence. This comprehensive work provides an insightful exploration of the revolutionary developments, foundational concepts, and future directions of deep learning, positioning itself as a crucial resource for understanding this transformative technology. The monograph commences with an introductory chapter that lays the groundwork by elucidating the background of machine learning and chronicling the ascent of deep learning. This foundational perspective is vital for comprehending the underlying principles and methodologies that have propelled deep learning to the forefront of technological innovation. Subsequent chapters delve into the “Historical Evolution” of AI, tracing the origins of neural networks from their early inception to the Renaissance period of deep learning. This historical analysis is pivotal in understanding the field’s trajectory and significant milestones. The “Foundations of Deep Learning” chapter presents an in-depth analysis of neural networks, covering their basic architecture, activation functions, layers, and the critical process of backpropagation and gradient descent. This is followed by examining “Advanced Neural Network Architectures,” where the focus shifts to state-of-the-art models such as convolutional neural networks, recurrent neural networks, and groundbreaking transformers and attention mechanisms. In addressing the “Training Deep Models,” the monograph explores regularization techniques, optimization strategies, and the increasingly significant role of transfer learning and pre-trained models. The application-oriented chapter, “Applications of Deep Learning,” showcases real-world implementations in domains like image recognition, natural language viii processing, and autonomous systems, illustrating deep learning technologies’ vast potential and versatility. The monograph also tackles the “Challenges in Deep Learning,” including overfitting, interpretability, and the ethical considerations surrounding AI deployment. It then ventures into the “Future of Deep Learning,” contemplating emerging trends such as integrating quantum computing, neural architecture search, and the convergence of neuroscience and deep learning. “Case Studies” provide practical insights and empirical evidence of the impact of deep learning. At the same time, the concluding chapter synthesizes the current state of deep learning, offers predictions for the future, and provides reflective thoughts on this transformative field. “Deep Learning and Beyond: AI’s New Horizons” educates and informs readers about the vast potential and future possibilities of deep learning and artificial intelligence. Keywords: Deep Learning, Artificial Intelligence, Neural Networks, Machine Learning, Convolutional Neural Networks, Recurrent Neural Networks, Transformers, Quantum Computing, Ethical AI, Neural Architecture Search, Natural Language Processing, Autonomous Systems, AI in Healthcare, Future of AI, AI Applications, AI Challenges.

Based on OWASP, code injection is one of the top lists of security risks. Structured Query Language (SQL) Injection is one of these types of attacks. SQL injection attack is an attack by spoofing the server to execute malicious code. The main object of this paper is to identify relevant works about deep learning methods to detect SQL-Injection on web applications. To achieve that, we conduct a survey review of the literature. In this study, we provide a review of 14 studies using deep learning algorithms to detect SQL Injection on web applications and it provides a comparison between them. Deep learning has great potential in threat intelligence detection.

A new virus SARS COV 2 spillover event has developed as a global pandemic that is impacting people’s health. Later it was named Covid-19. It is essential to screen the affected patients in a timely and cost-effective manner in order to fight this disease. The standard method for detecting COVID-19 is the Reverse transcription-polymerase chain reaction (RT-PCR) test. But the availability of RT-PCR tests is in short supply. As a result of this, the early detection of the disease is difficult. This paper presents the prediction of COVID19 with Chest X-Ray images in which detection can be quickly compared to RT-PCR and the implementation of an image processing system operated using deep learning and neural networks. In this paper, a Deep Learning, and Convolutional Neural Network-based approach for predicting Covid-19 positive and normal patients using Chest X-Ray pictures is proposed. In this study, deep learning tools such as TensorFlow were used for building and training neural nets. Scikit-learn was used for deep learning from end to end. Various deep learning features are used, such as Conv2D, Dense Net, Dropout, and Maxpooling2D for creating the model. The proposed approach had a classification accuracy of 98 percent after training and testing the X-Ray pictures. Finally, a web application has been developed using flask for general users, which will detect chest x-ray images either as covid, normal or viral pneumonia also gives covid-19 graph representation to display death, conformed, and recovery cases in Mangalore regions.