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2023 International Telecommunications Conference, ITC-Egypt 2023

作者： Parchin, Naser Ojaroudi Amar, Ahmed S.I. Fawzy, Diaa E. Allam, A.M.M.A. Eid, Alaa M. Ibrahim, Ahmed Gamal Abdellatif Edinburgh Napier University School of Computing Engineering and the Built Environment Edinburgh United Kingdom Alexandria University Air Defense College Department of Electronics and Communication Alexandria Egypt Izmir University of Economics Faculty of Engineering Izmir Turkey German University in Cairo Faculty of Information and Engineering Technology Cairo Egypt

ISBN: (纸本)9798350326062

This paper introduces a 4-port MIMO slot antenna system with dual notched bands, which is designed for future wireless communications. The antenna structure consists of four modified U-shaped radiation stubs with a shared ground plane, arranged in a planar form. The results demonstrate that the antenna operates effectively across frequencies ranging from 2.1 to 10.7 GHz, covering the entire ultra-wideband (UWB) spectrum. Moreover, two notched filtering characteristics have been incorporated at 3.3 to 4.2 GHz and 5 to 6 GHz, utilizing modified radiation stub resonators. These notches effectively suppress interference from C-band, WLAN, and WiMAX interfaces. The performance of the proposed antenna design has been thoroughly evaluated in terms of its fundamental characteristics. The results show that the antenna achieves satisfactory scattering parameters, 3D radiation patterns, efficiency, and gain levels. The designed UWB antenna system fulfills the requirements for MIMO and diversity applications. © 2023 IEEE.

关键词： Slot antennas

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Large Language Model-Enhanced Symbolic Reasoning for Knowledge Base Completion

arXiv

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arXiv 2025年

作者： He, Qiyuan Yu, Jianfei Wang, Wenya College of Computing and Data Science Nanyang Technological University Singapore School of Computer Science and Engineering Nanjing University of Science and Technology China

Integrating large language models (LLMs) with rule-based reasoning offers a powerful solution for improving the flexibility and reliability of Knowledge Base Completion (KBC). Traditional rule-based KBC methods offer verifiable reasoning yet lack flexibility, while LLMs provide strong semantic understanding yet suffer from hallucinations. With the aim of combining LLMs’ understanding capability with the logical and rigor of rule-based approaches, we propose a novel framework consisting of a Subgraph Extractor, an LLM Proposer, and a Rule Reasoner. The Subgraph Extractor first samples subgraphs from the KB. Then, the LLM uses these subgraphs to propose diverse and meaningful rules that are helpful for inferring missing facts. To effectively avoid hallucination in LLMs’ generations, these proposed rules are further refined by a Rule Reasoner to pinpoint the most significant rules in the KB for Knowledge Base Completion. Our approach offers several key benefits: the utilization of LLMs to enhance the richness and diversity of the proposed rules and the integration with rule-based reasoning to improve reliability. Our method also demonstrates strong performance across diverse KB datasets, highlighting the robustness and generalizability of the proposed framework. © 2025, CC BY.

关键词： Semantics

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Heterogeneous Parallel Key-Insulated Multi-Receiver Signcryption Scheme for IoV

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IEEE Transactions on Information Forensics and Security 2025年 20卷 5123-5137页

作者： Hou, Yingzhe Cao, Yue Xiong, Hu He, Debiao Chi, Chi-Hung Lam, Kwok-Yan Wuhan University School of Cyber Science and Engineering Wuhan430000 China University of Electronic Science and Technology of China School of Information and Software Engineering Chengdu610054 China Nanyang Technological University College of Computing and Data Science Jurong West639798 Singapore

The rapid growth of electric vehicle and autonomous vehicle populations has led to explosive expansion of IoV data being transmitted in the wireless communication infrastructure. Advances in IoV technologies also resulted in more complex and dynamic communication protocols/patterns, which are hard for the underlying wireless network to satisfy. Besides, security considerations of IoV communications require that key management must be stringently prohibit global failure mode of key management, meaning that, if a single IoV node compromises its private key, it will not lead to total security failure of the entire IoV network. To address these issues, in this paper, we propose a heterogeneous parallel key-insulated multi-receiver signcryption scheme for IoV (HPKI-MRSC). Firstly, the proposed scheme can realize one-to-many heterogeneous transmission, in which RSUs are deployed on certificateless cryptography (CLC) system, while vehicles are allocated in identity-based cryptography (IBC) system. In this manner, we observe that message transmission efficiency is improved greatly. Secondly, the parallel key-insulated mechanism can employ two helper keys to update private key periodically, and then solve key disclosure problem. Finally, when the number of receiver n is greater than or equal to 3, the proposed scheme has a lower signcryption overhead than other comparative schemes, and thus it is more suitable for IoV. © 2005-2012 IEEE.

关键词： Receivers Public key Protocols Encryption Polynomials Vehicle dynamics Electronic mail Training Symbols Real-time systems

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Aqua-Trail: IOT Based Smart Water Bottle

Aqua-Trail: IOT Based Smart Water Bottle

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Trends in Quantum computing and Emerging Business Technologies (TQCEBT), International Conference on

作者： Vinay Poddar Vanshaj Barnwal D. Saisanthiya Department of Networking and Communications School of Computing SRM Institute of Science and Technology Chennai Department of Networking and Communications Faculty of Engineering and Technology SRM Institute of Science and Technology Chennai

ISBN: (数字)9798350384277

ISBN: (纸本)9798350384284

The Smart Water Bottle is a cutting-edge technical solution created to address the ever-expanding issue of staying properly hydrated in our daily lives. Dehydration is a serious health problem that can have a range of negative repercussions, including poor physical and cognitive function. A smart water bottle is a type of water bottle that uses sensors and technology to track fluid intake, monitor activity, and provide insights into hydration levels. Smart water bottles can typically connect to smartphones or other devices via Bluetooth or Wi-Fi, allowing users to track their progress towards hydration goals, receive reminders to drink water, and view data on their hydration habits. To measure and optimize users' hydration patterns, this smart water bottle makes use of cutting-edge sensor technology, networking, and data analysis. Numerous sensors, including accelerometers, temperature sensors, and fluid level sensors, are included in this smart water bottle. Together, these sensors keep track of ambient conditions and water intake. The information is then transferred to a special smartphone app through Bluetooth or Wi-Fi so that users can quickly access and understand their current level of hydration. To promote healthier drinking practices, the app offers insights into daily water intake targets, personalized recommendations, and historical data. The Smart Water Bottle may alter messages and reminders based on the user's activity levels, the weather in the area, and individual preferences in addition to monitoring water intake. Additionally, it provides the choice of user-customizable hydration goals, allowing users to adjust the suggested water consumption to meet their own requirements. By encouraging proper hydration, assisting in the prevention of health problems associated with dehydration, and minimizing singleuse plastic bottle waste, this innovation has the potential to significantly improve public health. The Smart Water Bottle is a promising option for a

关键词： Temperature sensors Temperature measurement Fluids Water heating Older adults Intelligent sensors Wireless fidelity

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A Review on Leveraging Language Agnostic Automated Text Data Augmentation with Explainable Semantic Integrity 5

A Review on Leveraging Language Agnostic Automated Text Data...

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5th International Conference on Advanced Research in computing, ICARC 2025

作者： Wijesundera, Sarith Peiris, Suresh Michael University of Westminster Department of Computer Science and Engineering London United Kingdom Informatics Institute of Technology School of Computing Colombo Sri Lanka

ISBN: (纸本)9798331530983

Data Augmentation (DA) is an effective strategy to increase model generalisation. In Natural Language Processing (NLP), DA remains in its early stages, primarily due to the inherent sensitivity of textual data, which complicates the augmentation process. These complexities are further increased in multilingual contexts, as DA techniques effective in one language may not yield comparable results in others. As a result, manually identifying optimal DA techniques that preserve semantic integrity and are language agnostic remains a significant challenge. It emphasises the necessity for automating the text DA process with Explainable Artificial Intelligence (XAI), which offers a promising approach by calculating word importance scores to effectively guide the augmentation. In this paper, a comprehensive review of existing approaches in auto-text DA is presented, beginning with an exploration of the concept of auto-text DA, followed by a discussion on its cross-lingual applications, and concluding with the integration of XAI for semantic preservation. The paper highlights the critical need for further research to enhance the effectiveness and applicability of XAI in auto-text DA, enabling its use across diverse languages. © 2025 IEEE.

关键词： Transfer learning

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Advanced Video-Based Deep Learning Framework for Comprehensive Detection, Diagnosis, and Classification of Dermatological Conditions in Real-Time Datasets 6

Advanced Video-Based Deep Learning Framework for Comprehensi...

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6th International Conference on Futuristic Trends in Networks and computing Technologies, FTNCT 2024

作者： Ahmed, Syed Thouheed Guthur, Amogh S. Rai, Pratyush Kumar Pranava Swaroop, N. School of Computing and Information Technology REVA University Bengaluru India Department of Computer Science and Engineering Sri Venkateshwara College of Engineering VTU Bengaluru India School of Computer Science and Engineering Presidency University Bengaluru India

The advanced acne detection model showcased in this project utilizes deep learning methods to accurately classify skin conditions, including blackheads, dark areas, blemishes, and creases. It employs a YOLOv5 format annotation scheme to analyze spatial and temporal information from video sequences, resulting in exceptional performance in detecting seven distinct classes. The model's resilient performance indicates high accuracy, with a mean Average Precision (mAP) of about 0.85-0.9 at an IoU threshold of 0.5. It also demonstrates generalization and robustness with an mAP of 0.5-0.55 across IoU thresholds from 0.5 to 0.95, making it suitable for real-world dermatological assessments. The proposed method enables early detection and more effective treatment by monitoring skin conditions over time, significantly impacting dermatological image analysis. The goal is to improve patient outcomes and provide personalized skincare recommendations using deep learning techniques, benefiting clinicians and researchers in analyzing and categorizing skin conditions. Additionally, incorporating additional data sources like clinical images or medical histories can enhance the model's diagnostic capabilities and accuracy. Expanding the dataset will enhance the model's generalizability and robustness for new skin conditions. © 2025 The Author(s). Published by Elsevier B.V.

关键词： Deep learning

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Classified Recognition Method for Analyzing User Sentiments Using Social Media Posts 9

Classified Recognition Method for Analyzing User Sentiments ...

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9th International Conference on Science, technology, engineering and Mathematics, ICONSTEM 2024

作者： Ramesh, S. Vishnupriya, K. Anandaraj, A. Satheesh, N.P. Richard, Titus Bohra, Dixit Dutt College of Engineering & Technology Srm Institute of Science And Technology Department of Computing Technologies Tamilnadu Kattankulathur India Karpagam College of Engineering Department of Artificial Intelligence And Data Science Tamilnadu Coimbatore India Sri Eshwar College of Engineering Department of Cse Tamilnadu Coimbatore India Bannari Amman Institute of Technology Department of Ai&ds Tamilnadu Erode India Saveetha School of Engineering Saveetha Institute of Medical And Technical Sciences Saveetha University Department of Biosciences Chennai India Iit Jodhpur Department of Cse Rajasthan 34200 India

ISBN: (纸本)9798350365092

Social media texts and posts are analyzed through modern computing and artificial intelligence systems to detect user sentiments. The severity of the words and their related activities are analyzed to detect the user sentiments. In this paper, a Classified Sentiment Recognition Method (CSRM) is introduced. The proposed method utilizes deep learning to identify and classify sentiments using their associated words. Using the associated labels, deep learning trains the context and occurrence of the texts/ posts to detect precise sentiments. Thus the learning steps are induced with label, context, and occurrence-based training to improve the sentiment detection and type classification. This proposed method is verified using recognition accuracy, classification rate, and recognition error. © 2024 IEEE.

关键词： Social networking (online)

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Research on a New Speech Synthesis Fraud Method

Research on a New Speech Synthesis Fraud Method

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6th International Conference on Intelligent computing and Signal Processing (ICSP)

作者： Hang Zhang Yuhui Li Shijian Kui Zhaoyi Qin Faculty of Computing Harbin Institute of Technology Harbin China School of Automotive Engineering Harbin Institute of Technology Harbin China Faculty of New Energy Harbin Institute of Technology Harbin China School of Information Science and Engineering Harbin Institute of Technology Harbin China

ISBN: (数字)9798350376548

ISBN: (纸本)9798350376555

With the rapid development of deep learning technology, deep counterfeiting has also quietly ascended to the stage of history, and voice disguise fraud, as a new type of attack means, has laid a huge hidden danger for people's privacy security. Based on the research of speech synthesis, this paper designs and implements a speech disguise synthesis system based on the Tacotron2 model, and uses the WaveGlow model as a vocoder to replace the original WaveNet module, which is used to simulate experimental research from the direction of the attack to achieve a better synthesis effect. After this experiment, this paper verifies the feasibility of the program and provides technical references for subsequent security technology development.

关键词： Training Attention mechanisms Vocoders Semantics Syntactics Real-time systems Acoustics Fraud Speech synthesis Security

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Student's Performance Prediction Using Machine Learning Algorithms- A Comparative Study

Student's Performance Prediction Using Machine Learning Algo...

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2024 OPJU International technology Conference on Smart computing for Innovation and Advancement in Industry 4.0, OTCON 2024

作者： Kadu, Varun Mishra, Pawan Kumar Dandhare, Sarthak Patni, Jagdish Chandra Chandel, Palash Pathak, Soham Bahadure, Nilesh Symbiosis Institute of Technology Siu Nagpur Campus Pune India Graphic Era Deemed to Be University Computer Science & Engineering Dehradun India Alliance University Alliance School of Advance Computing Dept. of Cse Karnataka Bengaluru India Nagpur Campus Pune India

ISBN: (纸本)9798350373783

Predicting and enhancing student performance has been a crucial topic of concentration in education amid the quick development of technology and the increasing need for higher-quality instruction. The use of machine learning technology to forecast pupils' academic success is covered in detail in this study report. This research project examined several prediction models that reliably predict students' performance according to a range of extracurricular and academic attributes. An overview of machine learning methods is given in this paper to automate the process of predicting students' academic performance and extracting outcomes. © 2024 IEEE.

关键词： Contrastive Learning

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Enhancing QoS of Network Traffic Based on 5G Wireless Networking Using Machine Learning Approaches 1st

Enhancing QoS of Network Traffic Based on 5G Wireless Networ...

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1st International Conference on Computational Intelligence and Smart Communication, ICCISC 2022

作者： Saini, Shivani Garg, Sharvan Kumar Singh, Pankaj Pratap Ali, Arif Pandey, Akhilesh Subharti Institute of Technology and Engineering Swami Vivekanand Subharti University Meerut India School of Computer Science and Engineering Dev Bhoomi Uttarakhand University Dehradun India Uttaranchal School of Computing Science Uttaranchal University Dehradun India

ISBN: (纸本)9783031229145

5G wireless networks are based on heterogeneous networks. Heterogeneous networks offer a higher quality of service (QoS) and let you better utilize the resources of the network. Control of traffic on a network is complicated when a multiplicity of heterogeneous networks are present. When different protocols and data transmission rates are used, heterogeneous networks face the problem of managing and managing network traffic appropriately. In this paper our objective is to reduce Network Traffic and improve QoS for 5G wireless Network thus we have discuss some Supervised and Unsupervised Algorithm of Machine Learning Approach. So we have implemented K-mean algorithm in this paper will reduce traffic and improve the efficiency of 5G wireless Network. The K-mean is an iterative grouping technique that moves data objects between cluster sets until one desired set is reached. The dataset for K-mean Algorithm divides the traffic into two classes and then weighted mean is calculated for each cluster until the resultant output is identical weighted mean. If there are two clusters have identical weighted mean then there are no changes in cluster of classes. © 2022, The Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： Heterogeneous networks

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