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Artificial Intelligence in Smart Mobile Communication Systems: A Review

Artificial Intelligence in Smart Mobile Communication System...

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IEEE international conference on Emerging & Sustainable Technologies for Power & ICT in a Developing Society (NIGERCON)

作者： Adebajo-Akinpelu Victor O Moses Oluwafemi Onibonoje Ayodeji Olalekan Salau Department of Electrical/Electronics and Computer Engineering Afe Babalola Univerisity Ado-Ekiti Ado-Ekiti Nigeria Dept of Electrical/Electronics & Computer Engineering Afe Babalola University Ado Ekiti Nigeria

ISBN: (数字)9798331542559

ISBN: (纸本)9798331542566

Communication and information service systems are undergoing rapid advancements driven by the evolution of telecommunications and computer technology. the convergence of these fields has led to significant growth in digital networks, which has spurred extensive research and exploration. However, optimizing the use of these networks with minimal additional costs—while integrating features such as video conferencing, email services, and remote database access— remains a substantial technological challenge. this study addresses the limitations of current mobile communication networks by exploring potential enhancements through the application of smart computing solutions, particularly artificial intelligence (AI). the research methodology involves a comprehensive review of existing mobile communication technologies and their integration with AI. this includes examining advancements in AI and machine learning (ML) as applied to mobile networks, with a focus on improving performance, flexibility, and automation. the results of the review indicate that AI and ML have significant potential to enhance network operations. By leveraging vast data generated within 5G systems, these technologies can optimize resource utilization, improve service quality, and support diverse emerging applications. Despite these advancements, challenges such as reliability, speed trade-offs, complexity, privacy, and security persist. the findings highlight the need for further research in AI, including deep neural network acceleration, parallel computing, cloud computing, and distributed deep learning systems. Such research is crucial for enabling mobile communication networks to achieve higher throughput and ultra-low latency. Conclusively, while AI-enabled communication systems offer promising solutions to many challenges, simplifying AI implementation is essential for practical deployment. this will ensure that future communication networks can effectively meet the growing demands and evolving

关键词： Deep learning Privacy Reviews 5G mobile communication parallel processing throughput Complexity theory Security Resource management Artificial intelligence

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Computer Engineering and Intelligent Control (ICCEIC) international conference on

作者： Xun Li Xianfeng Zhu Renmeng Lu Na Long Lijun Pan Information Center of Guizhou Power Grid Co. Ltd Guiyang Guizhou China

ISBN: (数字)9798331507992

ISBN: (纸本)9798331508005

In microservice architecture, due to the fine-grained service splitting, a large number of alarm messages will be generated, which, if not processed, will lead to the operation and maintenance personnel drowning in the sea of alarms, thus missing the real critical problems. therefore, this paper proposes an attribute similarity-based alarm compression and merging method for multiple micro service applications, which aims to reduce redundant alarms and improve the accuracy and processing efficiency of alarms. In this study, the seasonal and trend characteristics of monitoring indicators are eliminated by the time-series decomposition algorithm STL, and the multivariate time-series prediction of the residual series of multiple indicators is performed based on the vector autoregressive model VAR, and whether the microservices are abnormal is judged based on the Euclidean distances of the vectors; according to the characteristics of the ASON alarms of an actual electric power communication network, the communication alarm correlation model based on the hierarchical attribute similarity is established. Finally, a compression and merging method of multiple microservice application alarms based on attribute similarity is used in a real power system communication network. the test results show that by merging similar alarms, the number of alarms is reduced, which helps the operation and maintenance personnel to filter out the real problems that need attention from a large number of alarm messages. Moreover, the alarm compression and merging method based on attribute similarity has significant technical advantages in improving operation and maintenance efficiency, optimizing alarm management, and reducing operation and maintenance costs.

关键词： Costs Merging Microservice architectures Predictive models Load management Vectors Maintenance Resource management Personnel Monitoring

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Elevated Learning based Secured Phishing Website Identification Methodology using Artificial Intelligence Assistance

Elevated Learning based Secured Phishing Website Identificat...

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Electronics and Sustainable Communication Systems (ICESC), 2020 international conference on

作者： G. Ramkumar Department of ECE Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences (SIMATS) Saveetha University Chennai

ISBN: (数字)9798350379945

ISBN: (纸本)9798350379952

Phishing attacks pose a huge threat for online security and therefore need advanced detecting techniques in order to mitigate the damages it causes. this paper introduces a sophisticated model for the detection of phishing websites, named the Blended ResNet-EfficientNet Model (BREM), which unifies the advantages of the ResNet and EfficientNet architectures. To address this challenge, BREM uses the rich hierarchical pattern recognition ability of ResNet-50 and the practical feature extraction capability of EfficientNet-B3 to achieve classification performance in phishing detection. On overall assessment BREM outperforms both traditional machine learning models and standalone deep learning model with an accuracy of 96%, precision at 94%, recall of 95% and FI score of 94.5%. these same authors validate the high specificity (97 %), negative predictive value (95 %) and Matthews correlation coefficient of 0.92 further underlining the robustness and reliability of BREM. this approach does not only improve the accuracy of detection, but also provides much better security against phishing campaigns. In the near future research directions such as real-time deployment, more experiments on different feature sets, adversarial robustness, transfer learning from heterogeneous datasets, model learning models and pattern recognition. they are able to draw from an immense amount of data and address minor deviances, which allows AI's better discrimination of real versus false websites, therefore increasing user cyber protections and preventable breaches [3] [4]. At the heart of securing phishing websites identification is making use of powerful AI capabilities to scan and recognize phishing convincingly. this AI utilizes different methods - natural language processing (NLP), image recognition and behavioral analysis - to keep an eye on things happening to web content and its design. Behavior analysis tracks user-interactions and web behavior to detect for abnormal operations, which co

关键词： Accuracy Phishing Scalability Transfer learning Robustness Natural language processing Real-time systems Pattern recognition Security Protection

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Using Asynchronous Programming in C# - Problems, Practical Tips and Scenarios: A Short Review

Using Asynchronous Programming in C# - Problems, Practical T...

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Communications, Information, Electronic and Energy Systems (CIEES), international conference on

作者： Daniel Damyanov Zlatko Varbanov Dept. of Information Technologies University of Veliko Tarnovo Veliko Tarnovo Bulgaria

ISBN: (数字)9798350352863

ISBN: (纸本)9798350352870

Asynchronous programming in C# plays a crucial role in enhancing the performance and responsiveness of modern applications, especially in contexts involving I/O operations, network communications, and database interactions. However, despite these improvements, developers often encounter various challenges when working with asynchronous code, such as improper task management, error handling difficulties, performance issues, and synchronization concerns. this paper addresses some of the most common problems in asynchronous development, including poor task management, error propagation, and potential performance bottlenecks. It also provides practical tips for effectively applying asynchronous patterns in everyday development, drawing on real-world industry scenarios where asynchronous programming offers significant advantages in terms of scalability and application stability. Special attention is given to preventing common mistakes, such as deadlocks and task overproduction (task explosion), along with best practices for improving code readability and maintainability. By thoroughly examining these issues and offering solutions, this paper aims to help developers not only understand the fundamentals of asynchronous programming but also apply advanced techniques for optimizing performance and security. Additionally, the article explores current challenges and future opportunities in the development of asynchronous systems, with a focus on how they can contribute effectively in multithreaded and high-load environments.

关键词： Performance evaluation Codes Reviews Scalability Operating systems Instruction sets Programming parallel processing System recovery Software

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Cloud Based Voting System Using Blockchain Technology

Cloud Based Voting System Using Blockchain Technology

引用

Advances in Computing, Communication Control and Networking (ICAC3N), international conference on

作者： Harshul Dudeja Jai Kaushik Shalu Department of Computer Science and Technology Manav Rachna University Faridabad India

ISBN: (数字)9798350330861

ISBN: (纸本)9798350330878

Cloud-based voting system employing blockchain technology utilizes the decentralized and encrypted characteristics of blockchain to ensure the legitimacy of election process. Using a cloud-based platform, voters can cast ballots which are then stored on a blockchain network. A cryptographic hash of the prior block and a timestamp are included in each block, rendering the chain resilient. this study examines the prospects for creating a cloud-based voting system using blockchain technology. To improve the security and dependability of electronic voting systems, this manuscript highlighted the perks of blockchain technology, such as decentralization, transparency, and immutability. the study also highlights the difficulties and threats of adopting blockchain technology for voting systems, such as scalability assaults, or reliance on dubious mechanisms, and resistance to the coercion. the research emphasizes the necessity for substantial and in-depth investigations to solve these difficulties and hazards. To address the drawbacks of the current electronic voting systems and guarantee the security, dependability, and integrity of the voting process, deeper analysis and development is required. the emerging world with the advancement of technology is moving towards cloud-based digitization rapidly due to which the need of cloud security is rising dramatically. this research demonstrates the immense potential of blockchain technology for online voting. A safe and transparent voting system can be created by utilizing the advantages of blockchain technology, fostering faith and confidence in the democratic process.

关键词： Resistance Electric potential Electronic voting systems Scalability parallel processing Rendering (computer graphics) Hazards

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Application specific hardware architecture for high-throughput short-length LDPC decoders

Application specific hardware architecture for high-throughp...

引用

IEEE international conference on Intelligent Computer Communication and processing

作者： Bogdan Belean Sergiu Nedevschi Monica Borda National Institute for R&D of Isotopic and Molecular Technologies INCDTIM Computer Science Dept. Technical University of Cluj-Napoca Communication Dept. Technical University of Cluj-Napoca

ISBN: (纸本)9781479914920

LDPC codes have been intensively used in various wireless communication applications, due to their increased BER performance. the present paper summarizes the state of the art applications of short length LDPC codes and proposes FPGA based application specific hardware architectures for short-length LDPC decoders. the decoding algorithms considered for implementation are both belief propagation and min-sum algorithm. Due to the increased BER performances, the proposed architecture make use of parallel computation capabilities offered by FPGA technology in order to implement the belief propagation algorithm. In spite of the iterative nature and increased computational complexity of the LDPC decoding algorithm, the proposed architecture achieves high-throughput, mandatory in real-time application and data transmission. the architecture for the LDPC belief propagation based decoder is based on arctangent hyperbolic function approximation used for check nodes update.

关键词： LDPC codes FPGA Decoding algorithms Hardware implementations parallel computing

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MIMO Multiuser Detection for CDMA Systems

MIMO Multiuser Detection for CDMA Systems

引用

2006 8th international conference on Signal processing

作者： Yang Xiao Moon Ho Lee Institute of Information Science Beijing Jiaotong University Division of Electronics & Information Chonbuk National University Jeonju 561-756Korea

To combine presented MIMO scheme with multiuser detectors for uplink will suffer from the problems of high computation complexity and channenl ***,in this paper we propose a MIMO multiuser detection(MUD) scheme that reduces considerably the system computation complexity. the proposed algorithm adopts inverse channel matrix for MIMO decoding,which is not sensitive to the coherency of *** of the scattering characteristic of the MIMO channel,the inverse channel matrices are always nonsingular, which keeps the receivers can get stable spatial diversity gain. the MUD algorithms can be realized using a parallel modular *** is based on a Minimum Mean Square Error (MMSE) *** results show that our MIMO-MUD performs much better than presented MIMO-MUD for the same order of complexity,though the MIMO CDMA system has only two antennas at each BS and two antennas at each mobile station.

关键词： communication circuits space-time processing MIMO multiuser detection

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qLD: High-performance Computation of Linkage Disequilibrium on CPU and GPU

qLD: High-performance Computation of Linkage Disequilibrium ...

引用

IEEE Symposium on Bioinformatics and Bioengineering (BIBE)

作者： Charalampos theodoris Nikolaos Alachiotis Tze Meng Low Pavlos Pavlidis Technical University of Crete Greece University of Twente The Netherlands Carnegie Mellon University USA Foundation for Reseach and Technology-Hellas Greece

ISBN: (数字)9781728195742

ISBN: (纸本)9781728195759

Linkage disequilibrium (LD) is the non-random association between alleles at different loci. Assessing LD in thousands of genomes and/or millions of single-nucleotide poly-morphisms (SNPs) exhibits excessive time and memory requirements that can potentially hinder future large-scale genomic analyses. To this end, we introduce qLD (quickLD) (https//***/StrayLamb2lqLD), a highly optimized open-source software that assesses LD based on Pearson's correlation coefficient. qLD exploits the fact that the computational kernel for calculating LD can be cast in terms of dense linear algebra operations. In addition, the software employs memory-aware techniques to lower memory requirements, and parallel GPU architectures to further shorten analysis times. qLD delivers up to 5x faster processing than the current state-of-the-art software implementation when run on the same CPU, and up to 29x when computation is offloaded to a GPU. Furthermore, the software is designed to quantity allele associations between arbitrarily distant loci in a time-and memory-efficient way, thereby facilitating the evaluation of long-range LD and the detection of co-evolved genes. We showcase qLD on the analysis of 22,554 complete SARS-CoV-2 genomes.

关键词： Couplings Memory management Graphics processing units Genomics Central processing Unit Kernel Bioinformatics

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A novel parallel hybrid model for forecasting the stock price 22

A novel parallel hybrid model for forecasting the stock pric...

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Proceedings of the 2022 5th international conference on Machine Learning and Natural Language processing

作者： Chengdong Zhu Zhenyang Xu Lianfeng Han China University of mining and technology China

ISBN: (纸本)9781450399067

In recent years, in addition to the growth in the number of investors in the stock market, there has been a growing interest in predicting stock prices. Accurate stock prices can effectively improve investment returns on the premise of reducing investment risks for stock investors. therefore, this study presents a hybrid Bo-LSTM-SVR model to predict the next day's stock closing price. Firstly, the hyper-parameters of LSTM and SVR as well as the length of their respective sliding windows are optimized by the Bayesian optimization method, so as to obtain more accurate predicted values of the single models. the genetic algorithm is then adopted every day to decide the weight of the two single models, and finally, the combined predicted values are obtained. In order to ensure that the prediction of the proposed model is more accurate, this model and the other six models are applied to predict the closing prices of the Shanghai Composite Index on the next trading day. the results reveal that the model proposed in this study is the most accurate, with the smallest MAE and RMSE as well as the largest R^2. Compared with other models, the proposed model is more suitable for stock price prediction, which provides a dependable tool for investors to make stock investment decisions.

关键词： hybrid model SVR LSTM Bayesian optimization stock price prediction

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GNNear: Accelerating Full-Batch Training of Graph Neural Networks with near-Memory processing 22

GNNear: Accelerating Full-Batch Training of Graph Neural Net...

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Proceedings of the international conference on parallel architectures and Compilation Techniques

作者： Zhe Zhou Cong Li Xuechao Wei Xiaoyang Wang Guangyu Sun Peking University Beijing China Peking University and Alibaba Group Beijing China

ISBN: (纸本)9781450398688

Recently, Graph Neural Networks (GNNs) have become state-of-the-art algorithms for analyzing non-euclidean graph data. However, to realize efficient GNN training is challenging, especially on large graphs. the reasons are many-folded: 1) GNN training incurs a substantial memory footprint. Full-batch training on large graphs even requires hundreds to thousands of gigabytes of memory. 2) GNN training involves both memory-intensive and computation-intensive operations, challenging current CPU/GPU platforms. 3) the irregularity of graphs can result in severe resource under-utilization and load-imbalance *** paper presents a GNNear accelerator to tackle these challenges. GNNear adopts a DIMM-based memory system to provide sufficient memory capacity. To match the heterogeneous nature of GNN training, we offload the memory-intensive Reduce operations to in-DIMM Near-Memory-Engines (NMEs), making full use of the high aggregated local bandwidth. We adopt a Centralized-Acceleration-Engine (CAE) to process the computation-intensive Update operations. We further propose several optimization strategies to deal with the irregularity of input graphs and improve GNNear's performance. Comprehensive evaluations on 16 GNN training tasks demonstrate that GNNear achieves 30.8× / 2.5× geomean speedup and 79.6× / 7.3× (geomean) higher energy efficiency compared to Xeon E5-2698-v4 CPU and NVIDIA V100 GPU.

关键词： machine learning near-memory processing graph neural networks domain-specific accelerator

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