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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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Cloud Computing Approach in Healthcare Technology and Application: A Review 5

Cloud Computing Approach in Healthcare Technology and Applic...

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5th IEEE International Conference on Electro-Computing Technologies for Humanity, NIGERCON 2024

作者： Kanna, R. Kishore Anitha, R. Sundaramoorthy, K. Salau, Ayodeji Olalekan Ray, Satendar Swain, Jhum Vel Tech Rangarajan Dr.Sagunthala R&d Institute of Science and Technology Department of Biomedical Engineering Chennai India Jerusalem College of Engineering Department of Biomedical Engineering Chennai India Jerusalem College of Engineering Department of Information Technology Chennai India Afe Babalola University Department of Electrical/ Electronics and Computer Engineering Ado-Ekiti Nigeria Saveetha Institute of Medical and Technical Sciences Saveetha School of Engineering Tamil Nadu Chennai India Itm University School of Nursing Science MP Gwalior India Swami Vivekananda Institute of Technology Department of Cse - Data Science Mahbub campus Telangana India

ISBN: (纸本)9798331542559

Cloud computing is transforming the healthcare industry by providing scalable, and cost-effective solutions for storage, analysis, and access to health information and services. This comprehensive review examines the roles, benefits, challenges, and the future prospects of cloud computing in healthcare. The paper highlights the ways in which cloud computing improves data management and collaboration, enables telemedicine, lowers costs, and supports research and development. To fully realize the promise of cloud computing, hurdles such as data security, compliance and stability of the system must be overcome. The integration of new technologies such as blockchain, IoT with cloud computing show the possibility of future developments and shows that the application of cloud computing in healthcare will have a significant impact on healthcare delivery, enabling healthcare solutions that are personalized, flexible and global. © 2024 IEEE.

关键词： Cloud Computing Healthcare

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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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Mixture-of-Experts for Distributed Edge Computing with Channel-Aware Gating Function

arXiv

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arXiv 2025年

作者： Song, Qiuchen Jing, Shusen Zhang, Shuai Zhang, Songyang Huang, Chuan Shenzhen Future Network of Intelligence Institute School of Science and Engineering Guangdong Provincial Key Laboratory of Future Networks of Intelligence The Chinese University of Hong Kong Shenzhen518172 China Department of Radiation Oncology University of California San FranciscoCA94118 United States Department of Data Science New Jersey Institute of Technology NewarkNJ07102 United States Department of Electrical and Computer Engineering University of Louisiana at Lafayette LafayetteLA70504 United States School of Science and Engineering Shenzhen Future Network of Intelligence Institute Guangdong Provincial Key Laboratory of Future Networks of Intelligence The Chinese University of Hong Kong Shenzhen518172 China

In a distributed mixture-of-experts (MoE) system, a server collaborates with multiple specialized expert clients to perform inference. The server extracts features from input data and dynamically selects experts based on their areas of specialization to produce the final output. Although MoE models are widely valued for their flexibility and performance benefits, adapting distributed MoEs to operate effectively in wireless networks has remained unexplored. In this work, we introduce a novel channel-aware gating function for wireless distributed MoE, which incorporates channel conditions into the MoE gating mechanism. To train the channel-aware gating, we simulate various signal-to-noise ratios (SNRs) for each expert’s communication channel and add noise to the features distributed to the experts based on these SNRs. The gating function then utilizes both features and SNRs to optimize expert selection. Unlike conventional MoE models which solely consider the alignment of features with the specializations of experts, our approach additionally considers the impact of channel conditions on expert performance. Experimental results demonstrate that the proposed channel-aware gating scheme outperforms traditional MoE models. Copyright © 2025, The Authors. All rights reserved.

关键词： Edge computing

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