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2024 Asia Pacific Conference on Innovation in Technology, APCIT 2024

作者： Vijaya Vardan Reddy, S.P. Juliet, A. Hency Jayadurga, R. Sethu, S. Pavithra, K.N. Samuthira Pandi, V. R.M.K. Engineering College Department of Electronics and Communication Engineering Tamil Nadu Chennai India Saveetha Institute of Medical and Technical Sciences Department of Data Analytics Saveetha College of Liberal Arts and Sciences Saveetha University Tamil Nadu Chennai India Prince Shri Venkateshwara Padmavathy Engineering College Department of Master of Business Administration Tamil Nadu Chennai India Department of Computer Science and Engineering Tamilnadu Chennai India R. M. D. Engineering College Department of Science and Humanities Tamil Nadu Chennai India Chennai Institute of Technology Centre for Advanced Wireless Integrated Technology Tamil Nadu Chennai India

ISBN: (纸本)9798350361537

The significance of communication networks is growing in tandem with the proliferation of communication technologies. Present methods for maintaining 5G Wireless Sensor networks are still restricted to routine maintenance and post-maintenance tasks. They lack a comprehensive function for monitoring the network's status, are unable to assess the network's health, and are difficult to maintain before the 5G-based WSNs seriously degrade. 5G Wireless Sensor Network faults can only be resolved by highly trained technicians due to low maintenance efficiency. As a result, errors cannot be detected or located promptly or accurately, leading to forced repairs that incur the expense of new network cables. First, the article lays forth the basics of network fault analysis. Then, it uses deep learning to simulate communication network problem diagnosis. Lastly, the experimental section compares various methodologies and analyses the findings of fault location. The results of the simulation demonstrate that the suggested approach mitigates the created model's flaws to a certain degree while simultaneously enhancing the network fault detection model's accuracy, universality, and robustness. A novel approach to autonomous placement, the Fault Node Recovery Protocol is described in this study. It is implemented in 5G mobile communications to detect faulty data according to wireless sensor network standards, and it is made to fix the problems with conventional techniques, such poor positioning precision and lengthy running time. The automated localization model for 5G mobile communication fault data, constructed using the suggested FNRP approach, is presented in this work. By comparing it to the standard Adhoc On-Demand Distance Vector Routing protocol, we can see how well the suggested system performs. © 2024 IEEE.

关键词： 5G mobile communication systems

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Enhance Multi-Domain Sentiment Analysis of Review Texts Through Prompting Strategies

Enhance Multi-Domain Sentiment Analysis of Review Texts Thro...

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High Performance Big data and Intelligent Systems (HPBD&IS)

作者： Yajing Wang Zongwei Luo Department of computer science Faculty of science and technology Hong Kong Baptist University BNU-HKBU United International College Hongkong Zhuhai China BNU-UIC Institute of AI and Future Networks Artificial Intelligence and Data Science Research Hub Beijing Normal University BNU-HKBU United International College Zhuhai China

Large Language Models (LLMs) have made significant strides in both scientific research and practical applications. Existing studies have demonstrated the state-of-the-art (SOTA) performance of LLMs in various natural language processing tasks. However, the question of how to further enhance LLMs' performance in specific task using prompting strategies remains a pivotal concern. This paper explores the enhancement of LLMs' performance in sentiment analysis through the application of prompting strategies. We formulate the process of prompting for sentiment analysis tasks and introduce two novel strategies tailored for sentiment analysis: RolePlaying (RP) prompting and Chain-of-thought (CoT) prompting. Specifically, we also propose the RP-CoT prompting strategy which is a combination of RP prompting and CoT prompting. We conduct comparative experiments on three distinct domain datasets to evaluate the effectiveness of the proposed sentiment analysis strategies. The results demonstrate that the adoption of the proposed prompting strategies leads to a increasing enhancement in sentiment analysis accuracy. Further, the CoT prompting strategy exhibits a notable impact on implicit sentiment analysis, with the RP-CoT prompting strategy delivering the most superior performance among all strategies.

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Enhance Multi-domain Sentiment Analysis of Review Texts through Prompting Strategies

arXiv

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arXiv 2023年

作者： Wang, Yajing Luo, Zongwei Hong Kong Baptist University BNU-HKBU United International College Faculty of Science and Technology Department of computer science Hongkong Zhuhai China BNU-UIC Institute of AI and Future Networks Artificial Intelligence and Data Science Research Hub Beijing Normal University BNU-HKBU United International College Zhuhai China

—Large Language Models (LLMs) have made significant strides in both scientific research and practical applications. Existing studies have demonstrated the state-of-the-art (SOTA) performance of LLMs in various natural language processing tasks. However, the question of how to further enhance LLMs’ performance in specific task using prompting strategies remains a pivotal concern. This paper explores the enhancement of LLMs’ performance in sentiment analysis through the application of prompting strategies. We formulate the process of prompting for sentiment analysis tasks and introduce two novel strategies tailored for sentiment analysis: RolePlaying (RP) prompting and Chain-of-thought (CoT) prompting. Specifically, we also propose the RP-CoT prompting strategy which is a combination of RP prompting and CoT prompting. We conduct comparative experiments on three distinct domain datasets to evaluate the effectiveness of the proposed sentiment analysis strategies. The results demonstrate that the adoption of the proposed prompting strategies leads to a increasing enhancement in sentiment analysis accuracy. Further, the CoT prompting strategy exhibits a notable impact on implicit sentiment analysis, with the RP-CoT prompting strategy delivering the most superior performance among all strategies. Copyright © 2023, The Authors. All rights reserved.

关键词： Sentiment analysis

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Bankruptcy Prediction using Emperor Penguin Optimizer with Deep Learning Model on Qualitative dataset 5

Bankruptcy Prediction using Emperor Penguin Optimizer with D...

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5th International Conference on Smart Systems and Inventive Technology, ICSSIT 2023

作者： Abrol, Vijay Singh, Priyanka Subhashini, R. Kumar, A. Sasi Singh, Kamlesh Mannar, B. Raja Uttam Group of Institutions Department of Computer Science Uttar Pradesh Agra India Sgsj Government Polytechnic Department of Technical Education Uttar Pradesh Bulandshahr India Sona College of Technology Department of Information Technology Tamil Nadu Salem India Mentor-IT - Inurture Education Solutions Pvt Ltd Bangalore Department of Cloud Technology and Data Science Institute of Engineering and Technology Srinivas University Srinivas Nagar Mukka Surathkal Dakshina Kannada District Karnataka State Mangalore574146 India Graphic Era Hill University Dehradun Campus Department of Computer Science and Engineering Dehradun India Sri Venkateswara University Department of Commerce Andhra Pradesh Tirupati India

ISBN: (纸本)9781665474672

Bankruptcy prediction and credit risk study are amongstthe most important problems in the domain of financial and accounting decision making. Evolving an effectual Classification Rule Induction (CRD) structure for bankruptcy prediction and credit risk examination at suitable time is vital to avoid the business communities being bankrupt. Classical statistical approaches and Artificial Intelligence (AD) play an important role in bankruptcy prediction and credit risks. Most previous research works can be executed on quantitative approaches but some studies are presented on qualitative approaches for improvising the efficiency of bankruptcy prediction techniques. This study focuses on the development of Bankruptcy Prediction using Emperor Penguin optimizer with Deep Learning (BP-EPODL) model on Qualitative dataset. The projected BP-EPODL technique identifies the existence of bankruptcy or not. Here, the BP-EPODL technique exploits EPO algorithm for the choice of optimum features. For bankruptcy prediction, the BP-EPODL technique applies Long Short Term Memory (LS TM) model. At last, Adam optimizer is applied as a hyperparameter tuning technique for the IS TM model. The experimental validation of the BP-EPODL technique is tested on benchmark qualitative dataset. The obtained values inferred that the BP-EPODLtechnique has shown effectual results over other models. © 2023 IEEE.

关键词： Long short-term memory

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Multimodal Knowledge Graph Vision-Language Models for Precision Retrieval and Inference in Embedded Systems

Multimodal Knowledge Graph Vision-Language Models for Precis...

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IEEE International Conference on Robotics and Biomimetics

作者： Mingyi Li Jiayin Li Hui Liu Lijin Han Dengting Liao Zekun Zhang Zong Gao Baoshuai Liu Zhongfeng Jin School of Mechanical Engineering Beijing Institute of Technology Beijing China School of Computing and Data Science Xiamen University Malaysia Selangor Malaysia Shanghai Zhangjiang Institute of Mathematicas Zhangjiang Institute of Shanghai Shanghai China General Technology Department China North Vehicle Research Institute China GNational Computer Network Emergency Response Technical Team Coordination Center of China China

ISBN: (数字)9798331509644

ISBN: (纸本)9798331509651

In light of the challenges associated with enhancing multimodal knowledge graph retrieval and improving embodied intelligence in dynamic environments, we propose the Multimodal Knowledge Graph Vision-Language System (MKGVL). This system is designed to enhance the reasoning and feedback capabilities of embodied intelligent systems by continuously updating the knowledge graph through real-time feedback from the Vision-Language Model (VLM). By integrating visual encoders, language models, and knowledge graph networks, MKGVL constructs a unified multimodal representation that enables adaptive decision-making and responsiveness to environmental changes. Experimental results show that MKGVL outperforms existing models in fine-grained retrieval tasks, achieving an 11.5% improvement in Rank1 accuracy and a mean Average Precision (mAP) of 97.49%. Further evaluations conducted on datasets such as ARKitScenes, MultiScan, and 3RScan highlight the model's robustness and adaptability. Additionally, MKGVL's deployment on embedded platforms like Jetson Orin demonstrates its efficiency in real-time multimodal tasks, particularly in resource-constrained environments. These findings underscore MKGVL's ability to deliver accurate and efficient multimodal processing, making it a strong solution for adaptive knowledge-based systems in complex settings.

关键词： Adaptation models Solid modeling Visualization Accuracy Three-dimensional displays Adaptive systems Knowledge graphs Real-time systems Cognition Intelligent systems

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Channel Estimation for 29GHz mm Wave MIMO Systems in Tropical Region

Channel Estimation for 29GHz mm Wave MIMO Systems in Tropica...

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IEEE International Symposium on Telecommunication Technologies (ISTT)

作者： Li Lu Aduwati Sali School of Microelectronics and Data Science Anhui University of Technology Maanshan China Department of Computer and Communication Systems Engineering Wireless and Photonics Networks Research Centre of Excellence (WiPNET) Faculty of Engineering Universiti Putra Malaysia Serdang Selangor Malaysia Institute for Mathematical Research (INSPEM) Universiti Putra Malaysia Serdang Selangor Malaysia

ISBN: (数字)9798350392074

ISBN: (纸本)9798350392081

This paper focuses on the 29GHz mmWave MIMO communication system in tropical environments, considering the impact of rain attenuation and providing a detailed analysis of channel estimation and system performance. Experimental results indicate that as the signal-to-noise ratio (SNR) increases, the mean square error (MSE) of channel estimation decreases. Under the same SNR conditions, MIMO systems with different antenna configurations exhibit distinct channel estimation error characteristics. Notably, in the 128x128 antenna configuration, the channel estimation error is most pronounced. Furthermore, increasing the number of training signals significantly reduces channel estimation errors, especially in the 128x128 antenna configuration. This study offers a performance evaluation of the 29GHz mmWave MIMO communication system in tropical regions, providing valuable insights for further research and optimization in this field.

关键词： Training Rain System performance Channel estimation Mean square error methods Attenuation Millimeter wave communication Antennas MIMO communication Signal to noise ratio

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Health Information Broadcast Distributed Pattern Association based on Estimated Volume

Health Information Broadcast Distributed Pattern Association...

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Artificial Intelligence and Smart Systems (ICAIS), International Conference on

作者： R. Dhanalakshmi A.V. Kalpana J. Umamageswaran B. Praveen Kumar Department of Computer Science and Engineering Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences Chennai Tamil Nadu India Department of Data Science and Business Systems SRM Institute of Science and Technology Kattankulathur Chennai Tamil Nadu India Department of Information Technology R.M.K Engineering College Chennai Tamil Nadu India Department of Computer Science and Engineering K L University Andhra Pradesh India

Any study on dataset and inadequate representation are featured in the conventional data mining process. Consequently, data mining findings reveal high chances of challenges and huge root-mean-square approximation errors besides long utilization. The proposed algorithm for associating distribution patterns in streams of medical data is based on density calculations. The distance approach will be utilized to identify any orphaned or questionable data within the data stream, and the gathering of medical data will be used to achieve the aim. data stream duplicates are compiled by sector match quality, and both the out-of-the-ordinary and the redundant data are eventually eliminated. The density of the information stream is then calculated based on histogram calculations. This investigates the flow of medical information from the viewpoints of concentration and dispersion, as well as the physical characteristics of information distribution, in combination with the results of data density estimate. The model is then built upon a neural network composite, the data distribution parameters in the clustering layer of the model are entered, and in-depth testing is carried out via the neural B.P. network (Back Propagation) on the mining layer of the model. Temporarily, all laws under the grouping of the hidden layer operation value besides the resulting output value are derived, and all rules under the grouping of the hidden layer value besides the conforming contribution value in the medical information stream pattern. The new findings show a curve of contour contiguous to the true curve of the likelihood density, a fair range of medical data dispersion and high medical data consistency, a lower probability of data redundancy, a low RMSEA for mining, a lower time for data recovery.

关键词： Histograms Neural networks Redundancy Approximation algorithms data models Classification algorithms data mining

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Hiding Scrambling Text Message in Speech Signal Based on Lightwight Hyperchaotic Map and Conditional Lsb Mechanisms

SSRN

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SSRN 2023年

作者： Al Sibahee, Mustafa A. Abduljaleel, Iman Qays Luo, Chengwen Zhang, Jin Huang, Yijing Abduljabbar, Zaid Ameen Ma, Junchao National Engineering Laboratory for Big Data System Computing Technology Shenzhen Technology University 518060 China Department of Computer Science College of Computer Science and Information Technology University of Basrah 61004 Iraq College of Computer Science and Software Engineering Shenzhen University 518060 China Department of Computer Science College of Education for Pure Sciences University of Basrah Basrah61004 Iraq Technical Computer Engineering Department Al-kunooze University College Basrah61001 Iraq Huazhong University of Science and Technology Shenzhen Institute Shenzhen518118 China College of Big Data and Internet Shenzhen Technology University Shenzhen518118 China

This study proposed a lightweight and secure audio steganography system for hiding text messages during transmission over the Internet to address the computational cost exaggeration, and Insufficient levels of security in earlier studies. The paper proposes a two-phase functioning mechanism. Text characters are transformed into ASCII code and then stored in a vector, which is then divided into three sub-vectors. The sub-vectors are scrambled using a two low complexity operation, namely a forwardbackward reading technique and an odd-even index. Two scrambling circuits can be performed: the first on tiny sub-vectors and another on the vector as a whole. In the hiding phase, the speech signal samples were divided into 256 blocks using only 200 values per block, and low complexity Quadratic and the Hénon maps to hide the speech signal in a random manner. This method utilizes the conditional LSB as low complexity algorithm to identify hidden bits and a special hyperchaotic map algorithm for randomly choosing locations. The proposed approach provides good security for a scrambled text message with high SNR and PSNR, small MSE error and PESQ, SSIM near one, BER close to zero, and MOS near five as well as the lowest computational hiding cost. © 2023, The Authors. All rights reserved.

关键词： Steganography

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Evaluation of Cloud Recovery System for Financial Applications Based on Hadoop

Evaluation of Cloud Recovery System for Financial Applicatio...

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IEEE International Symposium on Telecommunication Technologies (ISTT)

作者： LU Li LU Tong Aduwati Sali School of Microelectronics and Data Science Anhui University of Technology Maanshan China Department of Computer and Communication Systems Engineering Faculty of Engineering Wireless and Photonics Networks Research Centre of Excellence (WiPNET) Universiti Putra Malaysia Serdang Selangor Malaysia Institute for Mathematical Research (INSPEM) Universiti Putra Malaysia Serdang Selangor Malaysia

ISBN: (数字)9798350392074

ISBN: (纸本)9798350392081

The rise of cloud-based disaster recovery systems has reshaped disaster recovery practices. Integrated into the Hadoop framework with core components like Hadoop Distributed File System (HDFS), MapReduce, and Yet Another Resource Negotiator (YARN), these systems ensure data integrity and business continuity during crises while enhancing data reliability and operational efficiency, surpassing traditional recovery systems. This study presents a comprehensive comparative analysis of traditional and Hadoop-integrated cloud recovery systems, focusing on hardware architecture and performance. Architecturally, it highlights the stark differences between conventional models and Hadoop-based innovations. The results show that the Hadoop-backed recovery system exhibits remarkable improvement, reducing Recovery Time Objective (RTO) from over 18 seconds to under 7 seconds while preserving data integrity as per Recovery Point Objective (RPO) standards. This finding underscores the academic importance of evaluating cloud-based disaster recovery within the Hadoop context, offering insights into its transformative potential for disaster recovery strategies.

关键词： Technological innovation Disasters data integrity Switches Attenuation Hardware Business continuity Yarn Millimeter wave communication Standards

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Generative Federated Learning with Small and Large Models In Consumer Electronics for Privacy preserving data Fusion in Healthcare Internet of Things

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IEEE Transactions on Consumer Electronics 2025年

作者： Ghazal, Taher M. Islam, Shayla Hasan, Mohammad Kamrul Abu-Shareha, Ahmad A. Mokhtar, Umi A. Khan, M. Attique Baili, Jamel Saeed, Ali Q. Bhattt, Mohammed Wasim Ahmad, Munir Department of Networks and Cybersecurity Hourani Center for Applied Scientific Research Amman Jordan UCSI University Institute of Computer Science and Digital Innovation Kuala Lumpur56000 Malaysia Center for Cyber Security Faculty of Information Science and Technology Malaysia Al-Ahliyya Amman University Hourani Center for Applied Scientific Research Department of Data Science and Artificial Intelligence Amman Jordan Prince Mohammad bin Fahd University Saudi Arabia King Khalid University College of Computer Science Department of Computer Engineering Abha61413 Saudi Arabia Northern Technical University Computer Center Ninevah Iraq Korea University College of Informatics Seoul02841 Korea Republic of

Healthcare Internet of Things (HIoT) requires large-scale privacy features to ensure maximum security in sharing sensitive physiological data in consumer electronics. Recent approaches utilize the fusion concept to provide maximum privacy in health data sharing. Embedded signing data fusion with the health observed data ensures privacy preserved sharing across heterogeneous medical consumer devices for diagnosis. This article proposes a Dependency-correlated data Fusion Scheme (DcDFS) to maximize the privacy of the health data-sharing process. The proposed scheme prepares separate key signing procedures using triple-DES (data encryption standard) to embed with the accumulated health data. The fusion process is carried out by defining key headers and integrity footers for authentication and verification. Therefore, the fusion generates a combined sequence of linear authentication and validation procedures enclosing the health data. In this scheme, the role of federated learning is to prevent permuted sequences for the same health data. This research integrates Small Language Model (SLM) and Large Language Model (LLM) into the federated learning module to support secure, scalable, and intelligent healthcare data sharing. Their collaboration enhances context-aware training while preserving privacy across decentralized, encrypted environments. A similar sequence mapped between the header and footer is responsible for discarding unauthorized data handling. The learning process verifies the permutation for many-to-one header to footer and vice versa. Therefore, the proposed fusion scheme generates a linear dependency between the actual and security-related data for maximum privacy. The proposed scheme achieves the following: the computation time is confined by 12.424%, the privacy leakage by 12.923%, and the computation efficiency is improved by 11.46%, as observed under the maximum sequences. © 1975-2011 IEEE.

关键词： Sensitive data

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