Deep Natural Language Processing Research Articles

AI-Based Phishing Detection and Automated Response: A Multi-Channel Security Framework for Modern Communication Platforms

Phishing attacks have evolved in sophistication over the changing digital communication landscape, taking advantage of several channels like social media, SMS, and email to trick people and businesses. This research offers a multi-channel security architecture for automated phishing response and detection that uses cutting-edge artificial intelligence (AI) technology to counteract this ubiquitous threat. The system utilizes automatic reaction mechanisms to mitigate threats in real time and incorporates state-of-the-art AI algorithms to improve the detection of phishing attempts across various communication channels. This study examines recent developments in artificial intelligence (AI) for cybersecurity, emphasizing the use of deep learning, machine learning, and natural language processing in phishing detection and response. It also looks at how phishing techniques have changed over time, how difficult it is to integrate AI across different platforms, and how dangerous it could be for AI systems to come under hostile attack. The report shows the usefulness and practical use of AI-driven solutions with case studies from social media, financial services, and enterprise communication platforms. It also discusses ethical and regulatory issues, highlighting the necessity of adhering to data protection regulations and using AI responsibly. The technological difficulties, potential avenues for future investigation, and prospects for innovation in AI-based phishing detection are covered in the paper's conclusion. With this methodology, cybersecurity researchers and practitioners can benefit from a thorough approach to improving cybersecurity with artificial intelligence.

EXPLOITING OPTIMAL SELF-ATTENTION DEEP LEARNING-BASED RECOGNITION OF TEXTUAL EMOTIONS FOR DISABLED PERSONS

A disability is a significant issue that has posed and continues to pose a challenge. Disability is a basis of frustration because it can be observed as a mental, constraint, cognitive, and physical handicap that inhibits the individual’s growth and involvement. Consequently, significant effort has been put into removing these kinds of restrictions. These initiatives address the trouble that disabled people encounter. People with disabilities often need to rely on others to meet their requirements. Machine learning (ML) is excelling in producing smart cities and offering a secure environment for disabled individuals. Emotional detection is an important research domain that can expose many appreciated inputs. Emotion is expressed differently through speech and facial expressions, gestures, and written text. Emotion detection in a text document is fundamentally a content-based classification task, utilizing models from deep learning (DL), complex systems and natural language processing (NLP). This paper presents an Optimal Self-Attention DL-based Recognition of Textual Emotions (OSADL-RTE) technique for Disabled Persons. The presented OSADL-RTE technique focuses on identifying distinct types of emotions in the textual data. As a primary preprocessing step, the OSADL-RTE technique comprises different phases to transform the input in a useful way. For word embedding, the bag of words (BoWs) approach is exploited. The OSADL-RTE technique derives self-attention long short-term memory (SA-LSTM) approach to identify emotions. Lastly, the arithmetic fractals optimization algorithm (AOA) approach correctly tunes the hyperparameter selection of the SA-LSTM approach. The experimental study of the OSADL-RTE approach occurs on the emotion database. The investigational outcome of the OSADL-RTE approach portrayed a superior accuracy outcome of 99.59% over existing methods.

Natural Language Processing of Clinical Documentation to Assess Functional Status in Patients With Heart Failure

Serial functional status assessments are critical to heart failure (HF) management but are often described narratively in documentation, limiting their use in quality improvement or patient selection for clinical trials. To develop and validate a deep learning natural language processing (NLP) strategy for extracting functional status assessments from unstructured clinical documentation. This diagnostic study used electronic health record data collected from January 1, 2013, through June 30, 2022, from patients diagnosed with HF seeking outpatient care within 3 large practice networks in Connecticut (Yale New Haven Hospital [YNHH], Northeast Medical Group [NMG], and Greenwich Hospital [GH]). Expert-annotated notes were used for NLP model development and validation. Data were analyzed from February to April 2024. Development and validation of NLP models to detect explicit New York Heart Association (NYHA) classification, HF symptoms during activity or rest, and frequency of functional status assessments. Outcomes of interest were model performance metrics, including area under the receiver operating characteristic curve (AUROC), and frequency of NYHA class documentation and HF symptom descriptions in unannotated notes. This study included 34 070 patients with HF (mean [SD] age 76.1 [12.6] years; 17 728 [52.0]% female). Among 3000 expert-annotated notes (2000 from YNHH and 500 each from NMG and GH), 374 notes (12.4%) mentioned NYHA class and 1190 notes (39.7%) described HF symptoms. The NYHA class detection model achieved a class-weighted AUROC of 0.99 (95% CI, 0.98-1.00) at YNHH, the development site. At the 2 validation sites, NMG and GH, the model achieved class-weighted AUROCs of 0.98 (95% CI, 0.96-1.00) and 0.98 (95% CI, 0.92-1.00), respectively. The model for detecting activity- or rest-related symptoms achieved an AUROC of 0.94 (95% CI, 0.89-0.98) at YNHH, 0.94 (95% CI, 0.91-0.97) at NMG, and 0.95 (95% CI, 0.92-0.99) at GH. Deploying the NYHA model among 182 308 unannotated notes from the 3 sites identified 23 830 (13.1%) notes with NYHA mentions, specifically 10 913 notes (6.0%) with class I, 12 034 notes (6.6%) with classes II or III, and 883 notes (0.5%) with class IV. An additional 19 730 encounters (10.8%) could be classified into functional status groups based on activity- or rest-related symptoms, resulting in a total of 43 560 medical notes (23.9%) categorized by NYHA, an 83% increase compared with explicit mentions alone. In this diagnostic study of 34 070 patients with HF, the NLP approach accurately extracted a patient's NYHA symptom class and activity- or rest-related HF symptoms from clinical notes, enhancing the ability to track optimal care delivery and identify patients eligible for clinical trial participation from unstructured documentation.

Supporting the care to breast cancer patients with unique needs: Evidence from online community members’ responses

BackgroundBreast cancer is the most common cancer diagnosed in women globally. Online cancer communities (OCCs) provide platforms for breast cancer patients to connect, share experiences, and support each other. These communities facilitate discussions on a range of health- and non-health-related topics. However, posts discussing unique topics may receive varying levels of attention and support. This study aims to devise a method for identifying and supporting such posts, enhancing community response and support strategies. MethodsWe propose a Uniqueness Score Extraction Framework to compute health- and non-health-related uniqueness scores for online community posts. The framework utilizes deep learning-based natural language processing models to identify the topics discussed in OCCs and calculates the health- and non-health-related uniqueness scores of a post based on the uniqueness of the topics identified by the BERTopic model. We further employ econometric models to assess how the uniqueness scores of posts affect community members’ responses to those posts. ResultsOur study reveals that posts with a higher concentration of unique health-related topics in OCCs elicit quicker, more frequent, but shorter responses. Conversely, posts containing more unique non-health-related topics in the entire post prompt faster and longer responses, unless these topics become overly dominant, in which case the number of replies decreases, and response times are prolonged. ConclusionOur research develops a framework to identify posts with high uniqueness scores in OCCs, and sheds light on community member responses to these discussions. The findings indicate that while members are supportive, particularly regarding health-related topics, the post-content’s nature and focus greatly affect their engagement. These discoveries could enhance our understanding of community dynamics in OCCs, offering valuable implications for researchers, OCC facilitators, and medical professionals in supporting patients within online platforms.

Efficient labeling of french mammogram reports with MammoBERT

Recent advances in deep learning and natural language processing (NLP) have broadened opportunities for automatic text processing in the medical field. However, the development of models for low-resource languages like French is challenged by limited datasets, often due to legal restrictions. Large-scale training of medical imaging models often requires extracting labels from radiology text reports. Current methods for report labeling primarily rely on sophisticated feature engineering based on medical domain knowledge or manual annotations by radiologists. These methods can be labor-intensive. In this work, we introduce a BERT-based approach for the efficient labeling of French mammogram image reports. Our method leverages both the expansive scale of existing rule-based systems and the precision of radiologist annotations. Our experimental results showcase the superiority of the proposed approach. It was initially fine-tuned on a limited dataset of radiologist annotations. Then, it underwent training on annotations generated by a rule-based labeler. Our findings reveal that our final model, MammoBERT, significantly outperforms the rule-based labeler while simultaneously reducing the necessity for radiologist annotations during training. This research not only advances the state of the art in medical image report labeling but also offers an efficient and effective solution for large-scale medical imaging model development.

AI-Generated Spam Review Detection Framework with Deep Learning Algorithms and Natural Language Processing

Spam reviews pose a significant challenge to the integrity of online platforms, misleading consumers and undermining the credibility of genuine feedback. This paper introduces an innovative AI-generated spam review detection framework that leverages Deep Learning algorithms and Natural Language Processing (NLP) techniques to identify and mitigate spam reviews effectively. Our framework utilizes multiple Deep Learning models, including Convolutional Neural Networks (CNNs), Long Short-Term Memory (LSTM) networks, Gated Recurrent Unit (GRU), and Bidirectional LSTM (BiLSTM), to capture intricate patterns in textual data. The system processes and analyzes large volumes of review content to detect deceptive patterns by utilizing advanced NLP and text embedding techniques such as One-Hot Encoding, Word2Vec, and Term Frequency-Inverse Document Frequency (TF-IDF). By combining three embedding techniques with four Deep Learning algorithms, a total of twelve exhaustive experiments were conducted to detect AI-generated spam reviews. The experimental results demonstrate that our approach outperforms the traditional machine learning models, offering a robust solution for ensuring the authenticity of online reviews. Among the models evaluated, those employing Word2Vec embeddings, particularly the BiLSTM_Word2Vec model, exhibited the strongest performance. The BiLSTM model with Word2Vec achieved the highest performance, with an exceptional accuracy of 98.46%, a precision of 0.98, a recall of 0.97, and an F1-score of 0.98, reflecting a near-perfect balance between precision and recall. Its high F2-score (0.9810) and F0.5-score (0.9857) further highlight its effectiveness in accurately detecting AI-generated spam while minimizing false positives, making it the most reliable option for this task. Similarly, the Word2Vec-based LSTM model also performed exceptionally well, with an accuracy of 97.58%, a precision of 0.97, a recall of 0.96, and an F1-score of 0.97. The CNN model with Word2Vec similarly delivered strong results, achieving an accuracy of 97.61%, a precision of 0.97, a recall of 0.96, and an F1-score of 0.97. This study is unique in its focus on detecting spam reviews specifically generated by AI-based tools rather than solely detecting spam reviews or AI-generated text. This research contributes to the field of spam detection by offering a scalable, efficient, and accurate framework that can be integrated into various online platforms, enhancing user trust and the decision-making processes.

Research on Knowledge Discovery and Sharing in AIGC Virtual Teaching and Research Room Empowered by Big Data Analysis and Natural Language Processing Algorithms

This paper introduces a pioneering framework named Deep Reinforcement Learning based AI-Generated Content for Virtual Teaching (DRL-AIGC-VR), which aims to transform the landscape of online education and research. At the heart of this system is the integration of Deep Reinforcement Learning (DRL) and Natural Language Processing (NLP), making it exceptionally suited for the dynamic and evolving environment of virtual teaching and research rooms. The uniqueness of DRLAIGC-VR lies in its adaptive content curation and presentation capabilities, achieved through a combination of Deep Q-Networks (DQN) with attention mechanisms. This innovative approach allows the system to personalize learning experiences by tailoring them to individual student performance and engagement levels. Simultaneously, it focuses on presenting the most pertinent information, thereby streamlining and optimizing the learning process. One of the most significant features of this system is its ability to handle and analyze large-scale educational data, a vital aspect in today’s big data-driven world. This capability ensures that DRL-AIGCVR offers a highly interactive, responsive, and efficient learning environment, addressing the varied requirements of students and researchers. The implementation of DRL-AIGC-VR in virtual educational settings has shown remarkable improvements in several key areas, including learning outcomes, student engagement, and knowledge retention. These enhancements are indicative of the substantial progress that the framework brings to the domain of virtual education, positioning it as a leading solution in the realm of AI-driven learning platforms. Overall, DRL-AIGC-VR represents a significant step forward in harnessing the power of AI to enrich and elevate the educational experience in virtual settings, paving the way for more advanced, personalized, and effective online learning and research methodologies.

Automated Identification of HeartFailureWith Reduced Ejection Fraction Using Deep Learning-Based Natural Language Processing.

The lack of automated tools for measuring care quality limits the implementation of a national program to assess guideline-directed care in heart failure with reduced ejection fraction (HFrEF). The authors aimed to automate the identification of patients with HFrEF at hospital discharge, an opportunity to evaluate and improve the quality of care. The authors developed a novel deep-learning language model for identifying patients with HFrEF from discharge summaries of hospitalizations with heart failure at Yale New Haven Hospital during 2015 to 2019. HFrEF was defined by left ventricular ejection fraction<40% on antecedent echocardiography. The authors externally validated the model at Northwestern Medicine, community hospitals of Yale, and the MIMIC-III (Medical Information Mart for Intensive Care III) database. A total of 13,251 notes from 5,392 unique individuals (age 73 ± 14 years, 48% women), including 2,487 patients with HFrEF (46.1%), were used for model development (train/held-out: 70%/30%). The model achieved an area under receiver-operating characteristic curve (AUROC) of 0.97 and area under precision recall curve (AUPRC) of 0.97 in detecting HFrEF on the held-out set. The model had high performance in identifying HFrEF with AUROC=0.94 and AUPRC=0.91 on 19,242 notes from Northwestern Medicine, AUROC=0.95 and AUPRC=0.96 on 139 manually abstracted notes from Yale community hospitals, and AUROC=0.91 and AUPRC=0.92 on 146 manually reviewed notes from MIMIC-III. Model-based predictions of HFrEF corresponded to a net reclassification improvement of 60.2 ± 1.9% compared with diagnosis codes (P< 0.001). The authors developed a language model that identifies HFrEF from clinical notes with high precision and accuracy, representing a key element in automating quality assessment for individuals with HFrEF.

A Quantitative Approach for Appraising Quality of Online Education

Abstract : Conventional approaches fall short of managing the current scale of data, underscoring the significance of big data analytics in the field of data science. The study highlights the importance of big data analytics in data science, focusing on sentiment analysis and developing a machine learning framework for identifying sentiments in product reviews and assessing Massive Open Online Courses (MOOCs). Online education proved its value to the academic community during the COVID-19 pandemic, when much of the world was at a standstill. Since the coronavirus outbreak, Massive Open Online Courses (MOOCs) have taken over many public schools and undergraduate degree programs as the new norm. Nowadays, online courses are being incorporated into traditional educational programs. Therefore, it has become crucial to develop a framework that both government and nongovernment organizations could use to assess public opinion before implementing online education programs. This article offers a methodology for utilizing deep learning and natural language processing techniques to examine public sentiments toward online education and courses such as MOOCs.Public reviews from Coursera and Udemy are utilized to assess the quality of online courses. Using sentiment analysis, the proposed methodology employs the Bidirectional Encoder Representations from Transformers (BERT) model to classify public reviews into positive and negative categories. Positive and negative reviews are clustered using the k-means algorithm to evaluate the quality of MOOCs. The study's findings indicate that MOOCs with qualified professors are beneficial for learning basic concepts. But they may lack advanced knowledge, real-world experience, and enough examples. These courses are lengthy, and students request more questions after lessons to comprehend the concept. The proposed framework assists in assessing public opinion before implementing online education and may provide valuable insight to the developers of the courses. Keywords : MOOC, BERT, K-means Algorithm, Clusters, Word Cloud.

Statistical Approaches Used in Studies Evaluating the Reliability of Autonomous Vehicles Based on Disengagements and Reaction Times

The rapid development of autonomous vehicle (AV) technology highlights the critical importance of enhancing the reliability of these vehicles. Due to the need to test the reliability of AVs, since 2014, the California Department of Motor Vehicles has permitted autonomous vehicle manufacturers to establish an AV Testing program, enabling them to test automated systems on the transportation network. With this, studies on the reliability of AVs have increased rapidly. The most emphasized issues regarding the reliability of AVs have been disengagements, accidents, and reaction times. In this study, disengagements and reaction times are categorized and explained in detail according to the data type, company, period, and statistical method. The data used in the studies cover the years 2014-2020. When examining studies on the reliability of AVs, until 2018, inferences were generally made using real data and descriptive statistics, particularly with methods such as correlation analysis and calculation of disengagements per mile, which investigates the relationship between distance traveled and disengagements. However, since 2018, machine learning has gained importance in evaluating AV reliability. It has been observed that regression, classification, and decision trees were frequently used during this period. Techniques such as deep transfer learning, text mining, and natural language processing also stand out. Furthermore, Software Reliability Growth Models are used to measure software reliability, playing an essential role in evaluating, analyzing, and improving the performance and reliability of AVs. This study aims to reveal the development and diversity of the statistical methods used to determine AV reliability. Additionally, this study aims to guide and provide insights to researchers in the field about the statistical approaches they can utilize.

Artificial Intelligence in Head and Neck Cancer: Innovations, Applications, and Future Directions.

Artificial intelligence (AI) is revolutionizing head and neck cancer (HNC) care by providing innovative tools that enhance diagnostic accuracy and personalize treatment strategies. This review highlights the advancements in AI technologies, including deep learning and natural language processing, and their applications in HNC. The integration of AI with imaging techniques, genomics, and electronic health records is explored, emphasizing its role in early detection, biomarker discovery, and treatment planning. Despite noticeable progress, challenges such as data quality, algorithmic bias, and the need for interdisciplinary collaboration remain. Emerging innovations like explainable AI, AI-powered robotics, and real-time monitoring systems are poised to further advance the field. Addressing these challenges and fostering collaboration among AI experts, clinicians, and researchers is crucial for developing equitable and effective AI applications. The future of AI in HNC holds significant promise, offering potential breakthroughs in diagnostics, personalized therapies, and improved patient outcomes.

The potential impact of AI innovations on US occupations.

An occupation is comprised of interconnected tasks, and it is these tasks, not occupations themselves, that are affected by Artificial Intelligence (AI). To evaluate how tasks may be impacted, previous approaches utilized manual annotations or coarse-grained matching. Leveraging recent advancements in machine learning, we replace coarse-grained matching with more precise deep learning approaches. Introducing the AI Impact measure, we employ Deep Learning Natural Language Processing to automatically identify AI patents that may impact various occupational tasks at scale. Our methodology relies on a comprehensive dataset of 17,879 task descriptions and quantifies AI's potential impact through analysis of 24,758 AI patents filed with the United States Patent and Trademark Office between 2015 and 2022. Our results reveal that some occupations will potentially be impacted, and that impact is intricately linked to specific skills. These include not only routine tasks (codified as a series of steps), as previously thought but also nonroutine ones (e.g. diagnosing health conditions, programming computers, and tracking flight routes). However, AI's impact on labor is limited by the fact that some of the occupations affected are augmented rather than replaced (e.g. neurologists, software engineers, air traffic controllers), and the sectors affected are experiencing labor shortages (e.g. IT, Healthcare, Transport).

Graphic association learning: Multimodal feature extraction and fusion of image and text using artificial intelligence techniques

With the advancement of technology in recent years, the application of artificial intelligence in real life has become more extensive. Graphic recognition is a hot spot in the current research of related technologies. It involves machines extracting key information from pictures and combining it with natural language processing for in-depth understanding. Existing methods still have obvious deficiencies in fine-grained recognition and deep understanding of contextual context. Addressing these issues to achieve high-quality image-text recognition is crucial for various application scenarios, such as accessibility technologies, content creation, and virtual assistants. To tackle this challenge, a novel approach is proposed that combines the Mask R-CNN, DCGAN, and ALBERT models. Specifically, the Mask R-CNN specializes in high-precision image recognition and segmentation, the DCGAN captures and generates nuanced features from images, and the ALBERT model is responsible for deep natural language processing and semantic understanding of this visual information. Experimental results clearly validate the superiority of this method. Compared to traditional image-text recognition techniques, the recognition accuracy is improved from 85.3% to 92.5%, and performance in contextual and situational understanding is enhanced. The advancement of this technology has far-reaching implications for research in machine vision and natural language processing and open new possibilities for practical applications.

CMCN: Chinese medical concept normalization using continual learning and knowledge-enhanced

Medical Concept Normalization (MCN) is a crucial process for deep information extraction and natural language processing tasks, which plays a vital role in biomedical research. Although MCN in English has achieved significant research achievements, Chinese medical concept normalization (CMCN) remains insufficiently explored due to its complex syntactic structure and the paucity of Chinese medical semantic and ontology resources. In recent years, deep learning has been extensively applied across numerous natural language processing tasks, owing to its robust learning capabilities, adaptability, and transferability. It has proven to be well suited for intricate and specialized knowledge discovery research in the biomedical field. In this study, we conduct research on CMCN through the lens of deep learning. Specifically, our research introduces a model that leverages polymorphic semantic information and knowledge enhanced through multi-task learning and retain more important medical features through continual learning. As the cornerstone of CMCN, disease names are the main focus of this research. We evaluated various methodologies on Chinese disease dataset built by ourselves, finally achieving 76.12 % on Accuracy@1, 87.20 % on Accuracy@5 and 90.02 % on Accuracy@10 with our best-performing model GCBM-BSCL. This research not only advances the fields of knowledge mining and medical concept normalization but also enhances the integration and application of artificial intelligence in the medical and health field. We have published the source code and results on https://github.com/BearLiX/CMCN.

NLP-based ergonomics MSD risk root cause analysis and risk controls recommendation

An ergonomics assessment of the physical risk factors in the workplace is instrumental in predicting and preventing musculoskeletal disorders (MSDs). Using Artificial Intelligence (AI) has become increasingly popular for ergonomics assessments because of the time savings and improved accuracy. However, most of the effort in this area starts and ends with producing risk scores, without providing guidance to reduce the risk. This paper proposes a holistic job improvement process that performs automatic root cause analysis and control recommendations for reducing MSD risk. We apply deep learning-based Natural Language Processing (NLP) techniques such as Part of Speech (PoS) tagging and dependency parsing on textual descriptions of the physical actions performed in the job (e.g. pushing) along with the object (e.g. cart) being acted upon. The action-object inferences provide the entry point to an expert-based Machine Learning (ML) system that automatically identifies the targeted work-related causes (e.g. cart movement forces are too high, due to caster size too small) of the identified MSD risk (e.g. excessive shoulder forces). The proposed framework utilises the root causes identified to recommend control strategies (e.g. provide larger diameter casters, minimum diameter 8" or 203 mm) most likely to mitigate risk, resulting in a more efficient and effective job improvement process.

ForestAdvisor: A multi-modal forest decision-making system based on carbon emissions

Effectively balancing carbon emission reduction with economic viability through regional forest management is a significant challenge for global ecosystems. This paper introduces an innovative multi-modal forest decision-making system, integrating deep learning and natural language processing technologies, aimed at optimizing forest management strategies. Experimental validation of this system was conducted in three distinct forested regions. Utilizing a deep learning model, the system analyzed and predicted daily carbon emissions data. The experiments demonstrated remarkable accuracy, with the model achieving a coefficient of determination (R2) of up to 0.94, 0.98, and 0.99 across datasets from all three regions, thereby justifying its use for forecasting carbon emission trends over the following months. Subsequently, the system employed natural language processing to assess the importance of various collected forest management strategies. Finally, the system fine-tuned these strategy combinations in response to the predicted carbon emission trends, ensuring flexibility and effectiveness in addressing the complex dynamics of carbon emission fluctuations.

Enhancing Agile Story Point Estimation: Integrating Deep Learning, Machine Learning, and Natural Language Processing with SBERT and Gradient Boosted Trees

Advances in software engineering, particularly in Agile software development (ASD), demand innovative approaches to effort estimation due to the volatility in Agile environments. Recent trends have made the automation of story point (SP) estimation increasingly relevant, with significant potential for enhancing accuracy. This study introduces a novel model for software effort estimation (SEE) utilizing a deep learning (DL)-based sentence-BERT (SBERT) model for feature extraction combined with advanced gradient-boosted tree (GBT) algorithms. A comprehensive evaluation shows that the proposed model outperforms standard SEE and state-of-the-art models, demonstrating a mean absolute error (MAE) of 2.15 and a median absolute error (MdAE) of 1.85, representing a 12% improvement over the baseline model and an 18% improvement over the best-performing state-of-the-art model. The standardized accuracy (SA) is 93%, which is 7% higher than the next best model, across a dataset of 31,960 issues from 26 open-source Agile projects. This study contributes to software engineering by offering a more accurate and reliable decision support system for estimating project efforts.

Advancements in deep learning and natural language processing for effective disaster sentiment analysis: A review

This article comprehensively studies the application of deep learning (DL), natural language processing (NLP), and large language models (LLM) in sentiment analysis in disaster scenarios such as earthquakes and major accidents. The article focuses on the latest developments in these technologies and their role in strengthening disaster management and response. The article explores various methods, including BERT, LSTM, and convolutional neural networks, with a focus on their practicality, challenges, and potential for development. This review aims to provide researchers and relevant practitioners with a comprehensive understanding of this rapidly developing field.

Explainable Detection of Fake News on Social Media Using Pyramidal Co-Attention Network

In today’s world, fake news on social media is a universal trend and has severe consequences. There has been a wide variety of countermeasures developed to offset the effect and propagation of Fake News. The most common are linguistic-based techniques, which mostly use deep learning (DL) and natural language processing (NLP). Even government-sponsored organizations spread fake news as a cyberwar strategy. In literature, computational-based detection of fake news has been investigated to minimize it. The initial results of these studies are good but not significant. However, we argue that the explainability of such detection, particularly why a certain news item is detected as fake, is a vital missing element of the studies. In real-world settings, the explainability of the system’s decisions is just as important as its accuracy. This article explores explainable fake news detection and proposes a sentence-comment-based co-attention sub-network model. The proposed model uses user comments and news contents to mutually apprehend top- <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$k$</tex-math> </inline-formula> explainable check-worthy user comments and sentences for detecting fake news. The experimental result on real-world datasets shows that our proposed model outperforms state-of-the-art techniques by 5.56% in the <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$F$</tex-math> </inline-formula> 1 score. In addition, our model outperforms other baselines by 16.4% in normalized cumulative gain (NDCG) and 22.1% in Precision in identifying top- <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$k$</tex-math> </inline-formula> comments from users, which indicates why a news article can be fake.

Advancing plant biology through deep learning-powered natural language processing.

The application of deep learning methods, specifically the utilization of Large Language Models (LLMs), in the field of plant biology holds significant promise for generating novel knowledge on plant cell systems. The LLM framework exhibits exceptional potential, particularly with the development of Protein Language Models (PLMs), allowing for in-depth analyses of nucleic acid and protein sequences. This analytical capacity facilitates the discernment of intricate patterns and relationships within biological data, encompassing multi-scale information within DNA or protein sequences. The contribution of PLMs extends beyond mere sequence patterns and structure--function recognition; it also supports advancements in genetic improvements for agriculture. The integration of deep learning approaches into the domain of plant sciences offers opportunities for major breakthroughs in basic research across multi-scale plant traits. Consequently, the strategic application of deep learning methodologies, particularly leveraging the potential of LLMs, will undoubtedly play a pivotal role in advancing plant sciences, plant production, plant uses and propelling the trajectory toward sustainable agroecological and agro-food transitions.