Processing Learning 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.

A INTELIGÊNCIA ARTIFICAL COMO FERRAMENTA DE EFICIÊNCIA NA ADMINISTRAÇÃO PÚBLICA

The article “Artificial Intelligence as a Tool for Efficiency in Public Administration” explores the growing role of AI in optimizing government operations and improving the delivery of public services. Focusing on AI’s capabilities to process and analyze large volumes of data, the article discusses how this technology can be used to enhance efficiency, transparency, and responsiveness in government services. The text highlights several key applications of AI in public administration, including large-scale data analysis and processing, automation of bureaucratic processes, and customization of services to citizens. These technologies, equipped with machine learning and natural language processing, enable better resource management, faster citizen service, and more efficient operations less susceptible to human errors. Beyond the benefits, the article also addresses the challenges associated with implementing AI in the public sector, such as technical, ethical, and privacy issues. Concerns about algorithmic bias, data quality, and integration with legacy systems are discussed in detail. The article emphasizes the need for robust policies and adequate training for public employees as essential for successful AI integration. Finally, the discussion concludes with the view that while AI has the potential to significantly transform public administration for the better, a careful and regulated approach is vital. The success of this integration depends not only on technological advancements but also on a commitment to ethical practices and the development of public policies that guide its responsible use.

Mixed Reality-Based Concrete Crack Detection and Skeleton Extraction Using Deep Learning and Image Processing

Advancements in image processing and deep learning offer considerable opportunities for automated defect assessment in civil structures. However, these systems cannot work interactively with human inspectors. Mixed reality (MR) can be adopted to address this by involving inspectors in various stages of the assessment process. This paper integrates You Only Look Once (YOLO) v5n and YOLO v5m with the Canny algorithm for real-time concrete crack detection and skeleton extraction with a Microsoft HoloLens 2 MR device. The YOLO v5n demonstrates a superior mean average precision (mAP) 0.5 and speed, while YOLO v5m achieves the highest mAP 0.5 0.95 among the other YOLO v5 structures. The Canny algorithm also outperforms the Sobel and Prewitt edge detectors with the highest F1 score. The developed MR-based system could not only be employed for real-time defect assessment but also be utilized for the automatic recording of the location and other specifications of the cracks for further analysis and future re-inspections.

NIMG-67. THE CURRENT STATE OF LARGE LANGUAGE MODELS AND NEURO-ONCOLOGY IMAGING

Abstract INTRODUCTION Large language models (LLM), such as chatGPT by OpenAI, exist at the intersection of machine learning and natural language processing, and are poised to transform healthcare with rapid growth in recent years. The ability of these models to process visual data has also advanced in recent years, with GPT-4 being the first model to publicly process visual data, and GPT-4o adding additional capabilities. There has been significant interest in the application of GPT-4o to healthcare image processing. OBJECTIVE This study sought to examine the ability of GPT-4o in determining a final diagnosis based on a single MRI image of an intracranial neoplasm. We then compared the performance to GPT-4, as well as to humans including neurosurgery residents, fellow, and attendings. METHODS GPT-4 and GPT-4o were given a standard prompt and an accompanying image requesting the most likely diagnosis for 20 brain MRI images, including 5 gliomas, 5 meningiomas, 5 pituitary tumors, and 5 sham non-tumor images. These were compared to a survey of neurosurgery attendings, fellows, senior residents, and junior residents. RESULTS GPT-4 supplied correct diagnoses in just 40% of the cases, while GPT-4o correctly identified the pathology in 70% of cases. Neurosurgeons yielded correct responses in 92% of cases, with neurosurgery senior residents faring the best (albeit with a small sample size). CONCLUSION GPT-4 and GPT-4o underperformed neurosurgery residents, fellows, and attendings in correctly identifying CNS malignancies. Still, with no additional training, GPT-4o yielded significantly better results than GPT-4, showing rapid advancement in the LLM over is predecessor in identifying intracranial neoplasms via single-image MRIs.

Personalized Emotion Detection Adapting Models to Individual Emotional Expressions

Emotion recognition from text and speech has become a critical area of research in artificial intelligence (AI), enhancing human-computer interaction across various sectors. This paper explores the methodologies used in emotion recognition, focusing on Natural Language Processing (NLP) for text and acoustic analysis for speech. It reviews key machine learning and deep learning models, including Support Vector Machines (SVM), neural networks, and transformers, and highlights the datasets commonly used in emotion detection studies. The paper also addresses challenges such as multimodal integration, data ambiguity, and ethical considerations like privacy concerns and bias in models. Applications in customer service, healthcare, education, and entertainment are discussed, showcasing the growing importance of emotion recognition in AI- driven systems. Future research directions, including advancements in deep learning, multimodal systems, and real-time processing, are also explored to address existing limitations.

AI-powered language learning: The role of NLP in grammar, spelling, and pronunciation feedback

Abstract. The introduction of artificial intelligence (AI) and natural language processing (NLP) in language learning has transformed the practices of education. This paper analyzes the role of AI-powered tools in English language learning with specific focus on automated essay scoring (AES), speech recognition, and advanced grammar and spelling checkers. By examining the accuracy of the most effective AES systems, it is evident that automated feedback has increased the quality of writing by providing detailed, consistent, and objective feedback on submitted essays. Speech recognition is another tool that is being used to assess pronunciation and fluency of learners. The software is constantly improving, with advances in NLP and machine learning. The technology for fluency tests is becoming more accurate, and is helping to improve learner outcomes significantly. Lastly, we examine advanced grammar and spelling checkers, which are increasingly used for correcting the contextual errors of individual learners. The AI systems adapt to the user and their needs, which make the corrections more effective. This paper offers an overview of how AI is leading to personalized and effective language learning, ultimately enhancing the educational experience of students and embracing future possibilities in education technology.

Integrating AI into Agile Workflows: Opportunities and Challenges

Abstract: Over the past few years, Agile development approaches have become an increasingly popular methodology for software engineering, focused on iterative progress, continuous feedback, and teamwork cooperation. However, the raising difficulties of projects and more demand for faster and more efficient workflows have challenged the situation further. The introduction of Artificial Intelligence into the Agile processes paves the way to optimize decision-making, automate routine tasks, and boost team productivity. This review summarizes the innovations and challenges created by the integration of AI into Agile development practices. Using AI technologies like machine learning, predictive analysis, and natural language processing, Agile teams can enhance sprint planning, resource coordination, and risk management. Also, it presents some risks such as data privacy, workforce skills need, and possible over-dependence of AI. The paper emphasizes on providing a full overview of the innovations and challenges to the application of AI in Agile workflows, providing thoughts for future research and practices.

Enhanced Tuberculosis Detection in Chest X-Rays Using Optimal Feature Selection with Hybrid Binary Particle Swarm Optimization (HBPSO) and a Dual Classifier Framework Combining LSTM and Spiking Neural Networks

Tuberculosis (TB) remains a significant global health challenge, particularly in developing countries, where rapid and accurate diagnosis is crucial for effective treatment. Recent advancements in machine learning and image processing have shown promise in enhancing the detection of TB from chest X-ray images. This research introduces a novel approach that integrates a hybrid feature selection method with a dual classifier framework to improve the accuracy of tuberculosis detection. Feature extraction is conducted using Multi-Scale Local Binary Patterns (MSLBP), Speeded-Up Robust Features (SURF) with Bag of Words (BoW), and Cartoon Texture Features, combining them into a comprehensive feature vector. The Hybrid Binary Particle Swarm Optimization (HBPSO) is employed for optimal feature selection, reducing redundancy and improving classification efficiency. Classification is performed using a dual framework involving a Long Short-Term Memory (LSTM) network and a Spiking Neural Network (SNN), which work in tandem to provide robust predictions. Majority voting is utilized to finalize predictions, ensuring high accuracy and adaptability across varying TB presentations. Extensive evaluation on publicly available datasets shows that this hybrid approach significantly outperforms existing methods, achieving a highest accuracy of 99.73%, along with superior precision, recall, and F1 scores.

Machine Learning and Computer Vision Driving Precision in Robotic Surgery

The utilization of Artificial Intelligence (AI) in robotic surgery represents a significant advancement in healthcare, improving precision, effectiveness, and overall patient outcomes. The research studies the recent improvements in AI-powered robotic surgery, fastening on how artificial intelligence algorithms such as machine learning, computer vision, and natural language processing are transforming surgical processes. AI improves the functioning of robotic systems by employing data-driven algorithms, enabling independent or semi-autonomous tasks, and furnishing surgeons with exceptional delicacy through real-time analytics. AI-supported minimally invasive procedures and stoked reality interfaces have helped to drop complications, rehabilitation times, and surgical failures. The exploration explores the impact of AI on surgical training and simulation, in which AI- AI-grounded virtual surroundings give surgeons realistic practice scripts before performing live operations.

Leveraging artificial intelligence in economics and finance: Enhancing decision-making and market efficiency

Abstract. Artificial Intelligence (AI) has revolutionized the fields of economics and finance by providing advanced tools for decision-making, predictive analysis, and market efficiency. This paper examines the integration of AI technologies such as machine learning and natural language processing with mathematical models, specifically ARIMA for economic forecasting, Black-Scholes for option pricing, and logistic regression for credit risk assessment. By enhancing these models with AI, we demonstrate significant improvements in prediction accuracy and decision-making capabilities. Case studies illustrate a 15% improvement in GDP growth prediction accuracy, a 20% reduction in option pricing errors, and a 20% decrease in credit default rates. The paper also discusses the future prospects of AI, including advancements in quantum computing and ethical considerations like data privacy and algorithmic bias. Implementation challenges such as high costs and data integration issues are addressed, providing a comprehensive roadmap for organizations to effectively leverage AI. This study underscores the transformative potential of AI in shaping the future of economic and financial landscapes, driving innovation and operational efficiency.

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.

Estimating the health of turbine engine based on the relationship between torque margin and density altitude

We present an anomaly detection method developed for the PHM North America 2024 Conference Data Challenge. This competition is aimed at estimating the health of helicopter turbine engines (PHM Society, 2024). The task includes the estimation of the torque margin (regression) and the health state (binary classification) of turbine engines. We developed an estimation model using a hybrid algorithm that combines data-based machine learning and domain knowledge-based processing. Our method achieved scores over 0.99 for both the testing and validation datasets. based on the calculation rules provided by PHM Society. These results were ranked first among all the participating teams.

A New Approach to Assess Sustainable Corporate Reputation with Citizen Comments Using Machine Learning and Natural Language Processing

This study investigates the assessment of sustainable corporate reputation through citizen comments and how it can be measured by sentiment analysis methods based on machine learning and text mining. The research analyses citizen feedback on municipalities in the field of public services and examines their impact on the social reputation of the services provided by municipalities. Support vector machines, one of the machine learning methods, was used for sentiment analysis. In the study, Google Maps comments of the citizens receiving services from the municipality were used. The results of the sentiment analysis reveal that sustainable corporate reputation is directly related to citizen satisfaction and feedback. In this context, municipalities should continuously receive feedback and make strategic improvements based on citizens’ comments to ensure sustainable service quality. Municipalities are especially appreciated by citizens for their fast, effective, and high-quality services. However, some negative comments focus on issues such as the slowness of services, cleaning problems, and staff attitudes, indicating that certain improvements are needed. This feedback emphasises the need for continuous improvement in service quality.

AI based web application for diet planning and recipe generation

Introduces a web-based diet planning system based on machine learning, specifically Random Forests, deep learning and image processing capabilities to offer personalized nutritional input. Based on users' health data, their calorie needs, and food choices, the system is tailored to design diet plans specifically for each user. Finally, the system has functionalities of image processing capabilities: it identifies ingredients that feature in uploaded images of available food and thus comes out with recipes based on whatever is available. The goal of this integration is to make meal planning less painful, especially to individuals who have conditions like diabetes or hypertension, with respect to making healthier diets. Preliminary results demonstrate very good accuracy in recommending diets and identifying ingredients; these findings are positive toward the impact of the entire system in personalized nutrition management.

The Short Video Platform Recommendation Mechanism Based on the Improved Neural Network Algorithm to the Mainstream Media

Currently, the capacity of short videos continues to increase. Video manufacturers hope to enhance user experience and stickiness through recommendation algorithms, while users seek personalized videos to save time and money. Therefore, in order to address the data sparsity and high-dimensional feature extraction, this study proposes a novel short video platform recommendation model. The proposed method utilizes the term frequency inverse document frequency algorithm for text mining, and combines error back propagation neural network for learning to explore the potential connection between users and videos. This method combines natural language processing and image analysis in deep learning to construct accurate user and video models, deeply explore user interests, and improve the accuracy and effectiveness of recommendation systems for user preferences. The research results showed that the recommendation accuracy of this method was 66% and 70% respectively, and the prediction accuracy was 73.50% and 88% respectively. When Num=128, 200 data points were recommended within 0.3678s. The proposed algorithm outperforms the other three methods in terms of recommendation accuracy compared with the ItemCF and UserCF algorithms. This is because the method uses an approach based on image and user vector, combined with relevant features of the video and user. The proposed method can deeply explore the relevant features of videos and users, overcoming the data scarcity in previous collaborative screening, and guiding video recommendation on practical media platforms.

Image-driven prediction system: Automatic extraction of aggregate gradation of pavement core samples integrating deep learning and interactive image processing framework

Aggregate gradation plays a crucial role in determining the asphalt pavement performance, necessitating assessment post-construction. Additionally, efficient gradation measurement is vital for the design and construction of reclaimed pavement. This study proposes a methodology for predicting gradation based on imagery. Initially, a scanning device is devised to capture unfolded side surface images of pavement core samples. Subsequently, an interactive software named PyImageJ is developed for image post-processing and acquiring aggregate feature information. A morphological model is then introduced to determine the mass ratios among various grading levels of coarse aggregates with nominal particle sizes greater than 2.36 mm. Finally, sequence prediction models are established to forecast the mass ratios among different grading levels of fine aggregates with nominal particle sizes smaller than 2.36 mm. Simple conversion of the mass ratios can yield the gradation. Experimental results indicate that the Long Short-Term Memory (LSTM) network offers more accurate predictions for the mass ratios of fine aggregates in stone mastic asphalt (SMA-13), with an R-squared value of 0.8985. Conversely, one-dimensional convolutional neural networks (1D CNN) are better suited for predicting the fine aggregate mass ratios in asphalt concrete (AC-20 and AC-25), with R-squared values of 0.9010 and 0.8824, respectively. Visualization results demonstrate that the predicted complete gradation closely aligns with the actual values (R-squared > 0.99), opening a new avenue for evaluating pavement quality.

AI-Guided Cancer Therapy for Patients with Coexisting Migraines.

Background: Cancer remains a leading cause of death worldwide. Progress in its effective treatment has been hampered by challenges in personalized therapy, particularly in patients with comorbid conditions. The integration of artificial intelligence (AI) into patient profiling offers a promising approach to enhancing individualized anticancer therapy. Objective: This narrative review explores the role of AI in refining anticancer therapy through personalized profiling, with a specific focus on cancer patients with comorbid migraine. Methods: A comprehensive literature search was conducted across multiple databases, including PubMed, Scopus, and Google Scholar. Studies were selected based on their relevance to AI applications in oncology and migraine management, with a focus on personalized medicine and predictive modeling. Key themes were synthesized to provide an overview of recent developments, challenges, and emerging directions. Results: AI technologies, such as machine learning (ML), deep learning (DL), and natural language processing (NLP), have become instrumental in the discovery of genetic and molecular biomarkers of cancer and migraine. These technologies also enable predictive analytics for assessing the impact of migraine on cancer therapy in comorbid cases, predicting outcomes and provide clinical decision support systems (CDSS) for real-time treatment adjustments. Conclusions: AI holds significant potential to improve the precision and effectiveness of the management and therapy of cancer patients with comorbid migraine. Nevertheless, challenges remain over data integration, clinical validation, and ethical consideration, which must be addressed to appreciate the full potential for the approach outlined herein.

Integrating AI in testing automation: Enhancing test coverage and predictive analysis for improved software quality

This research explores the integration of Artificial Intelligence (AI) in testing automation, focusing on its ability to enhance test coverage and enable predictive analysis for improved software quality. As software systems become increasingly complex, traditional testing methods often struggle to meet quality demands. This study evaluates various AI techniques, including machine learning and natural language processing, and their applications in generating test cases, optimizing testing processes, and predicting defects. Through empirical case studies from diverse organizations, we demonstrate significant improvements in test coverage and defect detection rates following AI implementation. The findings highlight the efficiency gains and quality enhancements achieved through AI-driven testing, while also addressing challenges such as data dependency, complexity of implementation, and the need for skilled personnel. This research contributes to the understanding of AI's role in software testing and encourages organizations to adopt these technologies for better quality assurance and faster development cycles.

Understanding AI's Role in Endometriosis Patient Education and Evaluating Its Information and Accuracy: Systematic Review.

Endometriosis is a chronic gynecological condition that affects a significant portion of women of reproductive age, leading to debilitating symptoms such as chronic pelvic pain and infertility. Despite advancements in diagnosis and management, patient education remains a critical challenge. With the rapid growth of digital platforms, artificial intelligence (AI) has emerged as a potential tool to enhance patient education and access to information. This systematic review aims to explore the role of AI in facilitating education and improving information accessibility for individuals with endometriosis. This review followed the Preferred Reporting Items for Systematic reviews and Meta-Analyses (PRISMA) guidelines to ensure rigorous and transparent reporting. We conducted a comprehensive search of PubMed; Embase; the Regional Online Information System for Scientific Journals of Latin America, the Caribbean, Spain and Portugal (LATINDEX); Latin American and Caribbean Literature in Health Sciences (LILACS); Institute of Electrical and Electronics Engineers (IEEE) Xplore, and the Cochrane Central Register of Controlled Trials using the terms "endometriosis" and "artificial intelligence." Studies were selected based on their focus on AI applications in patient education or information dissemination regarding endometriosis. We included studies that evaluated AI-driven tools for assessing patient knowledge and addressed frequently asked questions related to endometriosis. Data extraction and quality assessment were conducted independently by 2 authors, with discrepancies resolved through consensus. Out of 400 initial search results, 11 studies met the inclusion criteria and were fully reviewed. We ultimately included 3 studies, 1 of which was an abstract. The studies examined the use of AI models, such as ChatGPT (OpenAI), machine learning, and natural language processing, in providing educational resources and answering common questions about endometriosis. The findings indicated that AI tools, particularly large language models, offer accurate responses to frequently asked questions with varying degrees of sufficiency across different categories. AI's integration with social media platforms also highlights its potential to identify patients' needs and enhance information dissemination. AI holds promise in advancing patient education and information access for endometriosis, providing accurate and comprehensive answers to common queries, and facilitating a better understanding of the condition. However, challenges remain in ensuring ethical use, equitable access, and maintaining accuracy across diverse patient populations. Future research should focus on developing standardized approaches for evaluating AI's impact on patient education and exploring its integration into clinical practice to enhance support for individuals with endometriosis.

Leveraging openCV and image processing for crime identification through facial recognition

To maintain the safety and security of communities, law enforcement agencies provide vital functions such as crime prevention and identification. Identifying people with a criminal record efficiently and accurately is one of the biggest hurdles in this field. This study delves into a fresh strategy for taking on this problem by making use of state-of-the-art image processing methods and machine learning algorithms. The project's main goal is to identify convicted felons by utilizing OpenCV, a robust open-source computer vision toolkit, and different facial recognition algorithms. By giving them a tool that guarantees a faster reaction rate and improved accuracy in suspect identification, the system is aimed to boost the capacities of law enforcement organizations. This project is based on the tenet of criminal psychology, which states that criminals are prone to committing new crimes. The system's goal is to detect and track repeat criminals both before and after they commit crimes by making use of this information. The method works by taking pictures of people who are known to be criminals and putting them in a database. From there, they can be compared to either live video streams or static photographs taken by surveillance cameras. In order to analyze and identify facial characteristics effectively, algorithms like LBPH and the Haar cascade classifier are also used. The system's robustness and efficiency are guaranteed by integrating these approaches into the OpenCV framework. This research makes a valuable contribution to the field of crime prevention by offering law enforcement authorities a dependable and scalable alternative. This system provides a streamlined interface for storing, retrieving, and matching facial photos. It was built using Python 3.5 and uses SQLite to manage the database of offenders' details. Proactively monitoring persons with a criminal record is made easier and faster with the suggested method. Suspect identification is also made easier. This research showcases the possibility of integrating machine learning and image processing to enhance public safety and crime prevention initiatives. It is an early step towards more complete video surveillance systems.