Privacy-Diffusion: Privacy-Preserving Stable Diffusion Without FHE and Differential Privacy by Po-Chu Hsu, Ziying Yu, Shuhei Mise, Hideaki Miyaji

“…Text-to-image generation is trending in the generative artificial intelligence (GenAI) field. Among open-sourced image generation projects, Stable Diffusion is the state-of-the-art. …”
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Knowledge Graph Completion With Pattern-Based Methods by Maryam Sabet, Mohammadreza Pajoohan, Mohammad Reza Moosavi

“…In this paper, we propose a Pattern-based Knowledge Graph Completion (PKGC) method that consists of three phases. The first phase utilizes multi-source information and expands the KG using entity description as external information with efficient Natural Language Processing (NLP) techniques. …”
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Optimizing the Production of LNG and NGL from Arab Crudes and Wet Gases by Adel M. Hemeida, Mohammed S. Al-Blehed, Saad El-Din Desouky

“…The developed model should be utilized as a useful tool to help the design of an efficient processing of natural gases. …”
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A Novel Method for the Estimation of Sea Surface Wind Speed from SAR Imagery by Zahra Jafari, Pradeep Bobby, Ebrahim Karami, Rocky Taylor

“…Wind is one of the important environmental factors influencing marine target detection as it is the source of sea clutter and also affects target motion and drift. …”
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An Integrated Model for Dam Break Flood Including Reservoir Area, Breach Evolution, and Downstream Flood Propagation by Huiwen Liu, Zhongxiang Wang, Dawei Zhang, Liyun Xiang

“…The reasonable and efficient prediction of dam failure events is of great significance to the emergency rescue operations and the reduction in dam failure losses. …”
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Assessment of Collaboration-Based and Non-Collaboration-Based Logistics Risks with Plithogenic SWARA Method by Alptekin Ulutaş, Ieva Meidute-Kavaliauskiene, Ayse Topal, Ezgi Demir

“…<i>Conclusions</i>: The proposed technique will help logistics professionals make informed decisions and manage and analyze risks more efficiently. This study will also contribute to the literature as it is the first time that logistical risks have been addressed by utilizing the Plithogenic SWARA technique.…”
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MULTI-MODEL STACK ENSEMBLE DEEP LEARNING APPROACH FOR MULTI-DISEASE PREDICTION IN HEALTHCARE APPLICATION by Bhaskar Adepu, T. Archana

“…This paper introduces an innovative method for disease prediction utilizing deep learning, particularly focusing on an ensemblebased multi-disease prediction model. …”
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Biometric and IoT Integration for Secure and Remote Door Access Control Using Fingerprint Recognition and GSM Technology by S Kanagamalliga, S Rajalingam, A Kannan, M Karthikeyan

“…The reliability and flexibility of Arduino, coupled with the GSM capabilities of SIM900, are utilized to create a robust and efficient solution. …”
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CHAM-CLAS: A Certificateless Aggregate Signature Scheme with Chameleon Hashing-Based Identity Authentication for VANETs by Ahmad Kabil, Heba Aslan, Marianne A. Azer, Mohamed Rasslan

“…Due to the critical time constraints in VANETs, efficient batch verification techniques are crucial. …”
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Enhancing SR-Tree for Nearest Neighbor Search by Kayumov Abduaziz, Shincheol Lee, Ji Sun Shin

“…While dynamic index structures have been extensively utilized for indexing dynamic data, their performance in nearest neighbor search can further be improved. …”
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An Intelligent Technique for Android Malware Identification Using Fuzzy Rank-Based Fusion by Altyeb Taha, Ahmed Hamza Osman, Yakubu Suleiman Baguda

“…Android’s open-source nature, combined with its large market share, has made it a primary target for malware developers. …”
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Advanced computational methods for news classification: A study in neural networks and CNN integrated with GPT by Fahim Sufi

“…This research offers substantial practical contributions, providing detailed insights into news source contributions, effective anomaly detection, and predictive trend analysis using neural networks. …”
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Model of the malicious traffic classification based on hypergraph neural network by Wenbo ZHAO, Zitong MA, Zhe YANG

“…As the use and reliance on networks continue to grow, the prevalence of malicious network traffic poses a significant challenge in the field of network security.Cyber attackers constantly seek new ways to infiltrate systems, steal data, and disrupt network services.To address this ongoing threat, it is crucial to develop more effective intrusion detection systems that can promptly detect and counteract malicious network traffic, thereby minimizing the resulting losses.However, current methods for classifying malicious traffic have limitations, particularly in terms of excessive reliance on data feature selection.To improve the accuracy of malicious traffic classification, a novel malicious traffic classification model based on Hypergraph Neural Networks (HGNN) was proposed.The traffic data was represented as hypergraph structures and HGNN was utilized to capture the spatial features of the traffic.By considering the interrelations among traffic data, HGNN provided a more accurate representation of the characteristics of malicious traffic.Additionally, to handle the temporal features of traffic data, Recurrent Neural Networks (RNN) was introduced to further enhance the model’s classification performance.The extracted spatiotemporal features were then used for the classification of malicious traffic, aiding in the detection of potential threats within the network.Through a series of ablative experiments, the effectiveness of the HGNN+RNN method was verified.These experiments demonstrate the model’s ability to efficiently extract spatiotemporal features from traffic, resulting in improved classification performance for malicious traffic.The model achieved outstanding classification accuracy across three widely-used open-source datasets: NSL-KDD (94% accuracy), UNSW-NB15 (95.6% accuracy), and CIC-IDS-2017 (99.08% accuracy).These results underscore the potential significance of the malicious traffic classification model based on hypergraph neural networks in enhancing network security and its capacity to better address the evolving landscape of network threats within the domain of network security.…”
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AI-Powered Mobile App for Nuclear Cataract Detection by Alicja Anna Ignatowicz, Tomasz Marciniak, Elżbieta Marciniak

“…The proposed solution utilizes a multi-stage classification approach to analyze ocular images acquired with a slit lamp, sourced from the Nuclear Cataract Database for Biomedical and Machine Learning Applications. …”
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Evaluation of Photo-Fenton and Photo-Fenton-Like Methods for Removing Direct Orange 118 Dye from Wastewater by Tamara Kawther Hussein, Nidaa Adil Jasim

“… Wastewater treatment has used efficient technology methods that can remove any pollutant. …”
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User Preferences of Airbnb Accommodation Spatially in The Special Region of Yogyakarta, Indonesia by Azis Musthofa, Arief Wicaksono, Raden Rijanta

“…Airbnb's accommodation concept is highly efficient in utilizing unused assets, aligning with SDG Goal 11. …”
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THE IMPACT OF CORPORATE SOCIAL RESPONSIBILITY ON BANK PERFORMANCE IN NIGERIA by Ibrahim Yinka Agbeyinka

“…Larger firms generally enjoy higher returns on equity and are more efficient in utilizing their assets to generate returns. …”
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High-throughput discovery of perturbation-induced topological magnons by Mohammed J. Karaki, Ahmed E. Fahmy, Archibald J. Williams, Sara Haravifard, Joshua E. Goldberger, Yuan-Ming Lu

“…In this work, we significantly advance the symmetry-based approach for identifying topological magnons through developing a fully automated algorithm, utilizing the theory of symmetry indicators, that enables a highly efficient and large-scale search for candidate materials hosting perturbation-driven topological magnons. …”
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Optimized Extraction of Polyphenols from Kiwifruit Peels and Their Biological Activities by Batoul Shkeir, Nada El Darra, Bilal Azakir, Salma Khazaal, Elie Salem Sokhn, Mohamed Koubaa, Richard G. Maroun, Nicolas Louka, Espérance Debs

“…This indicates that IR was more efficient in extracting phenolics from kiwi peels. …”
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Carbon footprint assessment of livestock farms under tropical conditions: first approximation by S. A. Guamán-Rivera, F. M. Carrillo Riofrío, M. E. Jativa-Brito, L. E. Chuqui-Puma, G. M. Soldado Soldado, L. D. Cabezas Andrade, H. R. Sanchez Quispe, A. A. Casierra Cardenas, J. M. Mira Naranjo, J. P. Santillán Aguirre, C. D. Congo-Yépez

“…Abstract This study evaluates the carbon footprint (CF) of livestock farms in the Orellana Province of the Ecuadorian Amazon, focusing on emissions from enteric fermentation, manure management, and energy use. Utilizing Tier I and Tier II methodologies outlined by the IPCC (2006), the research quantifies greenhouse gas (GHG) emissions across three distinct typologies of Agricultural Productive Units (APUs): Conventional Systems (Type 1), Efficient Resource Use (Type 2), and Subsistence and Dependency Systems (Type 3). …”
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