Session-based recommendation aims to predict the anonymous user’s next click action based on his/her latest click sequence. Most state-of-the-art approaches use graph neural networks to model sessions as graphs to ca...
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The rapid development of short video platforms poses new challenges for traditional recommendation *** systems typically depend on two types of user behavior feedback to construct user interest profiles:explicit feedb...
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The rapid development of short video platforms poses new challenges for traditional recommendation *** systems typically depend on two types of user behavior feedback to construct user interest profiles:explicit feedback(interactive behavior),which significantly influences users’short-term interests,and implicit feedback(viewing time),which substantially affects their long-term ***,the previous model fails to distinguish between these two feedback methods,leading it to predict only the overall preferences of users based on extensive historical behavior ***,it cannot differentiate between users’long-term and shortterm interests,resulting in low accuracy in describing users’interest states and predicting the evolution of their *** paper introduces a video recommendationmodel calledCAT-MFRec(CrossAttention Transformer-Mixed Feedback Recommendation)designed to differentiate between explicit and implicit user feedback within the DIEN(Deep Interest Evolution Network)*** study emphasizes the separate learning of the two types of behavioral feedback,effectively integrating them through the cross-attention ***,it leverages the long sequence dependence capabilities of Transformer technology to accurately construct user interest profiles and predict the evolution of user *** results indicate that CAT-MF Rec significantly outperforms existing recommendation methods across various performance *** advancement offers new theoretical and practical insights for the development of video recommendations,particularly in addressing complex and dynamic user behavior patterns.
In this paper, we describe an artificial intelligence (AI) method that recognizes valid power grid physics data in the memory dump of an industrial computer, otherwise known as a protective relay. Protective relays ar...
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ISBN:
(纸本)9798400706295
In this paper, we describe an artificial intelligence (AI) method that recognizes valid power grid physics data in the memory dump of an industrial computer, otherwise known as a protective relay. Protective relays are special-purpose computers that run algorithms specialized in monitoring and controlling power grid equipment such as power transformers. We designed a backtracking search algorithm based on the IEC 61850 specification, which seeks to validate the physics meaning of analyzed data. These data are assigned to the domains of power quantities, which are then structured into a constraint hypergraph. Our approach performs node and arc consistency checks and revisions on the constraint hypergraph, generates power quantities for data assignments during the search, and then generates a search tree while exploring assignments of data to power quantities that are physics compliant. If a complete assignment is reached, the analyzed data are deemed physics compliant. If otherwise a solution is not possible, the analyzed data are deemed to include malicious code and *** rationale behind our work is that the bytes of shellcode or memory addresses, which are commonly injected by exploits and malware, fail to show a relation with the physics of power grid. We implemented our constraint satisfaction modeling and backtracking search in Python, and hence tested the complete AI approach against a large repository of publicly available malicious code.
This survey provides a comprehensive overview of recent emerging technologies in artificial intelligence (AI) applied to the Internet of Things (IoT), highlighting their significance and applications across various do...
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The emergence of AI-driven tools has significantly transformed various domains, including travel planning. This project introduces an intelligent travel itinerary planner designed to enhance the trip-planning experien...
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ISBN:
(数字)9798331525439
ISBN:
(纸本)9798331525446
The emergence of AI-driven tools has significantly transformed various domains, including travel planning. This project introduces an intelligent travel itinerary planner designed to enhance the trip-planning experience through automation, adaptability, and interactive natural language processing. Built with Python and FastAPI, the system enables users to generate personalized travel plans with minimal manual effort, ensuring accuracy and convenience. It incorporates secure authentication, scalable data management, and cloud-based deployment on AWS, ensuring high reliability and data security. The planner dynamically updates itineraries in response to real-time factors such as flight changes, weather conditions, and availability, providing a seamless and responsive travel experience. Its modular architecture allows smooth integration with existing travel management platforms, making it a valuable asset for both individuals and businesses. By streamlining complex planning tasks and offering AI-driven recommendations, this tool redefines how travelers organize, modify, and optimize their schedules. Designed with scalability in mind, it remains adaptable to emerging trends in travel technology, ensuring long-term efficiency and continuous improvement in user experiences.
Accurate body composition assessment is essential for evaluating health and diagnosing conditions like sar copenia and cardiovascular disease. Approaches for accurately measuring body composition, such as Dual Energy ...
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Suicide prevention remains a critical challenge, and the detection of suicidal intentions through social media content has emerged as a promising method to identify individuals at risk. This study investigates the use...
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ISBN:
(数字)9798350357509
ISBN:
(纸本)9798350357516
Suicide prevention remains a critical challenge, and the detection of suicidal intentions through social media content has emerged as a promising method to identify individuals at risk. This study investigates the use of advanced machine learning, deep learning, and transformer-based models to detect signs of suicidal ideation in bilingual social media posts. A carefully curated dataset, consisting of Bengali and English posts which were collected from various social media like Facebook, Twitter and Youtube, was annotated with expert verification to ensure accuracy. The models were evaluated on their ability to capture the complex linguistic and cultural nuances inherent in both languages. Among the models tested, MultilingualBERT achieved the best performance with a F1 score of 0.90, demonstrating its effectiveness in handling the intricacies of bilingual data. The results emphasize the effectiveness of advanced models in practical applications for mental health monitoring and suicide prevention, offering a powerful tool for early detection and intervention.
Six-dimensional movable antenna (6DMA) is an innovative and transformative technology to improve wireless network capacity by adjusting the 3D positions and rotations of antennas/antenna surfaces based on the channel ...
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Cyberbullying has emerged as a significant societal issue in the digital age, causing emotional and psychological harm to individuals on social media platforms. The anonymity and reach of these platforms amplify harmf...
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ISBN:
(数字)9798350357509
ISBN:
(纸本)9798350357516
Cyberbullying has emerged as a significant societal issue in the digital age, causing emotional and psychological harm to individuals on social media platforms. The anonymity and reach of these platforms amplify harmful behaviors, necessitating effective interventions. Traditional moderation techniques, such as manual review or user reports, are insufficient to handle the growing scale and complexity of online content. This study introduces a comprehensive framework for cyberbullying classification, leveraging advanced machine learning (ML) techniques and natural language processing (NLP). It highlights the innovative application of SMOTETS to mitigate class imbalance and improve model robustness. A variety of classification models, including ensemble methods and deep learning approaches, were evaluated, with the highest accuracy reaching 92%. Emphasis is placed on preprocessing strategies like tokenization, stemming, and word embeddings to capture nuanced linguistic patterns, such as sarcasm and slang, commonly used in harmful content. By demonstrating the effectiveness of ensemble learning techniques, this work contributes to the development of scalable solutions for cyberbullying detection. The findings aim to advance research in automated abuse detection and promote safer digital environments for all users.
Concerns about security and privacy were heightened by the increasing dependence on interconnected networks, underscoring the vital role intrusion detection systems (IDS) play in protecting private data. As essential ...
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