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16th International Conference on Wireless Communications and Signal Processing, WCSP 2024

作者： Huang, Qiulei Song, Zhaohui Xiong, Zehui Xu, Guanjun Zhao, Nan Niyato, Dusit School of Information and Communication Engineering Dalian University of Technology Dalian116024 China Space Information Research Institute Hangzhou Dianzi University Hangzhou310018 China Singapore University of Technology and Design 487372 Singapore College of Computing and Data Science Nanyang Technological University 639798 Singapore

ISBN: (纸本)9798350390643

Simultaneously transmitting and reflecting reconfigurable intelligent surface (STAR-RIS) can be assembled in the air-ground integrated sensing and communication (ISAC) to further enhance the performance. However, the near-field effect should be further considered with higher carrier frequency and increasing number of STAR-RIS elements. In this paper, we propose a STAR-RIS enabled air-ground near-field ISAC scheme, where the unmanned aerial vehicle (UAV) is deployed as a mobile base station and the semi-passive architecture is adopted to alleviate the severe path loss. We maximize the weighted sum rate to guarantee both the communication and sensing by modifying the beamforming vectors, the reflection/transmission matrices and the hovering location of UAV to match the near-field effect. To address this challenge, we first divide it into three subproblems, which are recast into convex ones by the semidefinite relaxation and successive convex approximation. Finally, we develop an alternate algorithm to iteratively solve them. Simulation results are shown to demonstrate the superiority and validity of the proposed scheme. © 2024 IEEE.

关键词： Unmanned aerial vehicles (UAV)

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Real-time Asset False Matching Detection via Attentive Siamese Networks

Real-time Asset False Matching Detection via Attentive Siame...

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Frontiers technology of information and Computer (ICFTIC), IEEE International Conference on

作者： Ziwan Miao Li Zhang School of Computing Beijing Information Science & Technology University Beijing China School of Management Science and Engineering Beijing Information Science & Technology University Beijing China

ISBN: (数字)9798331541750

ISBN: (纸本)9798331541767

Accurate and efficient asset matching is crucial for remote management and diversified investment entities. To address the challenges of low efficiency, high labor costs, and false matching, this paper proposes a real-time asset false matching detection method based on an attentive Siamese network. By integrating the Convolutional Block Attention Module (CBAM) into a Siamese neural network framework, the model enhances feature extraction capabilities, focusing on discriminative regions within asset images. Pretrained weights are transferred using transfer learning, and data augmentation techniques are applied to enhance the model's generalization ability. Experiments on various types of asset data demonstrate that the proposed method achieves an accuracy of 92.42% and an F1 score of 92.48%, significantly improving the precision and efficiency of asset similarity calculations. This research not only advances asset identification and matching but also showcases exceptional performance in intelligent asset inventory, providing reliable support for asset management. Future work will focus on improving model robustness and optimizing the network structure for large-scale and complex environments.

关键词： Accuracy Neural networks Transfer learning Focusing Data augmentation Feature extraction Real-time systems Robustness Data models Investment

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Evaluation of Different Network Coding Algorithm Strategies for Wireless Systems 5th

Evaluation of Different Network Coding Algorithm Strategies ...

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5th International Conference on Data science, Machine Learning and Applications, ICDSMLA 2023

作者： Parameswaran, Murali Savita Kannagi, A. Panwar, Rahul School of Computer Science and Engineering Presidency University Karnataka Bengaluru India Maharishi School of Engineering & Technology Maharishi University of Information Technology Uttar Pradesh Lucknow India Karnataka Bangalore India Department of Electrical Engineering School of Engineering and Computing Dev Bhoomi Uttarakhand University Navgaon Manduwala Dehradun India

ISBN: (纸本)9789819780426

These research objectives are to evaluate and compare distinct community coding algorithms that can be applied in wireless networks to maximize network efficiency, throughput, and facts switch velocity. Special procedures are taken, such as coding in any respect of nodes within the community or sure nodes, which includes area nodes or routers. One-of-a-kind coding algorithm techniques might be tested in this study, including threshold coding and coded pooling. The chosen method will depend on the type of community, the scale, the traffic charge, and the favored performance goals, which include latency or bandwidth. Through leveraging network coding, packets can be transmitted and retransmitted without re-encoding. Studies have been carried out to evaluate the overall performance of diverse network coding algorithms for wireless networks inclusive of flooding, random linear network coding (RLNC), most distance separable codes (MDSC), directional network coding (DANC) and layered community coding (LNC). At the same time, MDSC and directional network coding have been proven more potent in factor-to-factor conversation eventualities. LNC is the most efficient average in many instances because of its ability to modify in real-time to changing link conditions. All of those algorithms provide a couple of advantages for wireless networks. This abstract affords an overview of the unique algorithm techniques, evaluates their effectiveness in numerous scenarios, and highlights their blessings for wireless networks. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Network coding

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Evolving ATM Networks for High Volume Transactions 5th

Evolving ATM Networks for High Volume Transactions

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5th International Conference on Data science, Machine Learning and Applications, ICDSMLA 2023

作者： Impa, B.H. Alam, Intekhab Kavitha, R. Prajapati, Sandhya School of Computer Science and Engineering Presidency University Karnataka Bengaluru India Maharishi School of Engineering and Technology Maharishi University of Information Technology Uttar Pradesh Lucknow India Karnataka Bangalore India Department of Civil Engineering School of Engineering and Computing Dev Bhoomi Uttarakhand University Manduwala Dehradun Navgaon India

ISBN: (纸本)9789819780426

ATM networks are evolving to guide excessive extent transactions and offer new features which includes increased safety, scalability, and cost performance. To lessen value, Banks are utilizing personal cloud environments to host their ATMs, whilst banks are looking for to deploy new offerings that assist their customers provide a higher enjoy. To enhance protection, banks are searching at encrypting data, and leveraging biometric authentication methods. Banks also are seeking to extend their offerings utilization of analytics to better goal purchaser advertising campaigns. Sooner or later, banks are adopting the use of micro services and disbursed architectures to enable building an extra scalable infrastructure that can scale horizontally with minimal effort. A majority of these advances are main to ATM networks which are efficient, comfy and dependable. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Sales

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Leveraging Exponential Smoothing for Time Series Analysis of Wireless Sensor Networks 5th

Leveraging Exponential Smoothing for Time Series Analysis of...

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5th International Conference on Data science, Machine Learning and Applications, ICDSMLA 2023

作者： Alam, Intekhab Ojha, Ananta Verma, Tushar K. Venkatachalam, Amirtha Preeya Maharishi School of Engineering and Technology Maharishi University of Information Technology Uttar Pradesh Lucknow India Karnataka Bangalore India Department of Civil Engineering School of Engineering and Computing Dev Bhoomi Uttarakhand University Manduwala Dehradun Navgaon India School of Computer Science and Engineering Presidency University Karnataka Bengaluru India

ISBN: (纸本)9789819780426

The abstract should summarize the contents of the paper in short terms, i.e. 150–250. This paper explores the applications of exponential smoothing techniques in evaluating time collection records generated using Wireless Sensor Networks (WSNs). Empirical results demonstrate that exponential smoothing outperforms conventional time series models, supplying users with extra accurate analysis and forecasting of WSNs. The essential blessings of this method encompass decreased errors in predicting destiny values, progressed reliability, and stepped forward performance in developing forecasts. This paper affords a comprehensive review of exponential smoothing and its packages in WSNs, including an assessment of the outcomes of applying those techniques and the capability of future research avenues. In addition, it gives hints for practitioners trying to leverage exponential smoothing for time series analysis of WSNs. Exponential smoothing is a critical time series forecasting method that may be applied to evaluate Wi-Fi sensor Networks (WSN). It is miles primarily based on the belief that the fashion in a time collection is a clean continuation of past values. The rules work by calculating an exponentially weighted shifting average, wherein facts points are exponentially weighted and given more importance the closer they are to the present-day time point words. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： exponentially smoothing weighted

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Assessments of Channel Coding Performance in Data Compression and Source Coding 5th

Assessments of Channel Coding Performance in Data Compressio...

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5th International Conference on Data science, Machine Learning and Applications, ICDSMLA 2023

作者： Savita Bargavi, Manju Panwar, Rahul Shyam, Gopal Krishna Maharishi School of Engineering and Technology Maharishi University of Information Technology Uttar Pradesh Lucknow India Karnataka Bangalore India Department of Electrical Engineering School of Engineering and Computing Dev Bhoomi Uttarakhand University Navgaon Manduwala Dehradun India School of Computer Science and Engineering Presidency University Karnataka Bengaluru India

ISBN: (纸本)9789819780426

Channel coding is a technique used to boom the performance and reliability of records transmission. It is able to be used for facts compression and source coding, that are the procedures of reducing the amount of records that desires to be despatched across a *** of channel coding performance can be performed with the aid of analyzing the performance of the records compression and source coding techniques. This can be executed thru diverse measures, such as compression ratio, supply coding charge, mistakes detection and correction, and bit blunders rate. With the intention to appropriately verify the performance of the channel coding approach, all of these measures must be considered. The aim of channel coding is to reap the highest viable fee of data compression with minimal errors detection and correction, and bit blunders rates as little as possible. This guarantees that the records is transmitted securely and efficaciously. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Coding errors

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Attention-Based Recurrent Neural Networks for Medical Image Classification 5th

Attention-Based Recurrent Neural Networks for Medical Image ...

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5th International Conference on Data science, Machine Learning and Applications, ICDSMLA 2023

作者： Manjunath, K.V. Alam, Intekhab Nidhya, M.S. Verma, Tushar K. School of Computer Science and Engineering Presidency University Karnataka Bengaluru India Maharishi School of Engineering and Technology Maharishi University of Information Technology Uttar Pradesh Lucknow India Karnataka Bengaluru India Department of Civil Engineering School of Engineering and Computing Dev Bhoomi Uttarakhand University Navgaon Manduwala Dehradun India

ISBN: (纸本)9789819780426

Attention-primarily based Recurrent Neural Networks (RNNs) are a technique for medical image classification. RNNs can robotically extract temporal and spatial facts from scientific imaging statistics and make sensible decisions. RNNs are especially nicely-perfect for clinical photograph category obligations, cutting-edge their potential to perceive lengthy-term temporal styles, recognize the relationships between distinct medical picture datasets, and enable In this paper, we overview the existing tries to use interest-based RNNs for medical image classification, discuss the contemporary demanding situations and suggest some ability future guidelines for studies. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Recurrent neural networks

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Designing Low-Power Industrial Networks with the Internet of Things 5th

Designing Low-Power Industrial Networks with the Internet of...

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5th International Conference on Data science, Machine Learning and Applications, ICDSMLA 2023

作者： Kannagi, A. Ansari, Tabish Nasurudeen Ahamed, N. Alam, Intekhab Karnataka Bangalore India Department of Civil Engineering School of Engineering and Computing Dev Bhoomi Uttarakhand University Navgaon Manduwala Dehradun India School of Computer Science and Engineering Presidency University Karnataka Bengaluru India Maharishi School of Engineering and Technology Maharishi University of Information Technology Uttar Pradesh Lucknow India

ISBN: (纸本)9789819780426

The Internet of Things (IoT) revolution is evolving conventional commercial networks to fulfill the demands of present-day technological advancements. Low-electricity business networks are becoming increasingly vital to allow a green communique between linked systems and remote monitoring and management of gadgets. Designing and deploying low-electricity networks for business programs brings many demanding situations, which include energy control, conversation latency, and cease-to-give-up safety. This summary will offer a top-level view of the layout issues while implementing IoT and occasional-power business networks, in addition to the capability of high-quality practices for ensuring low-strength, comfortable, and reliable communications among connected devices. Furthermore, IoT techniques for localizing network accessibility, improving facts accuracy, and boosting community performance may be discussed. Subsequently, some of the trendy trends and trends in the industrial networking area could be highlighted. Low-power business networks are getting increasingly important to allow the green conversation of connected structures and remote tracking of devices. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Capability Management Strength

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Marine Predators Algorithm with Deep Learning-Based Leukemia Cancer Classification on Medical Images

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Computer Modeling in Engineering & sciences 2024年第10期141卷 893-916页

作者： Sonali Das Saroja Kumar Rout Sujit Kumar Panda Pradyumna Kumar Mohapatra Abdulaziz S.Almazyad Muhammed Basheer Jasser Guojiang Xiong Ali Wagdy Mohamed Department of Computer Science&Engineering Gandhi Institute for TechnologyBPUTBhubaneswar752054India Department of Information Technology Vardhaman College of Engineering(Autonomous)Hyderabad501218TelanganaIndia Department of Electronic&Communication Engineering Vedang Institute of TechnologyBhubaneswar752010India Department of Computer Engineering College of Computer and Information SciencesKing Saud UniversityRiyadh11543Saudi Arabia Department of Computing and Information Systems School of Engineering and TechnologySunway UniversityJalan UniversitiBandar SunwaySelangor Darul Ehsan47500Malaysia Research Centre for Human-Machine Collaboration(HUMAC) School of Engineering and TechnologySunway UniversityJalan UniversitiBandar SunwaySelangor Darul Ehsan47500Malaysia Guizhou Key Laboratory of Intelligent Technology in Power System College of Electrical EngineeringGuizhou UniversityGuiyang550025China Operations Research Department Faculty of Graduate Studies for Statistical ResearchCairo UniversityGiza12613Egypt Applied Science Research Center Applied Science Private UniversityAmman11931Jordan

In blood or bone marrow,leukemia is a form of cancer.A person with leukemia has an expansion of white blood cells(WBCs).It primarily affects children and rarely affects *** depends on the type of leukemia and the extent to which cancer has established throughout the *** leukemia in the initial stage is vital to providing timely patient *** image-analysis-related approaches grant safer,quicker,and less costly solutions while ignoring the difficulties of these invasive *** can be simple to generalize Computer vision(CV)-based and image-processing techniques and eradicate human *** researchers have implemented computer-aided diagnosticmethods andmachine learning(ML)for laboratory image analysis,hopefully overcoming the limitations of late leukemia detection and determining its *** study establishes a Marine Predators Algorithm with Deep Learning Leukemia Cancer Classification(MPADL-LCC)algorithm onMedical *** projectedMPADL-LCC system uses a bilateral filtering(BF)technique to pre-process medical *** MPADL-LCC system uses Faster SqueezeNet withMarine Predators Algorithm(MPA)as a hyperparameter optimizer for feature ***,the denoising autoencoder(DAE)methodology can be executed to accurately detect and classify leukemia *** hyperparameter tuning process using MPA helps enhance leukemia cancer classification *** results are compared with other recent approaches concerning various measurements and the MPADL-LCC algorithm exhibits the best results over other recent approaches.

关键词： Leukemia cancer medical imaging image classification deep learning marine predators algorithm

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An optimized Yolov5s approach and its application to image detection problems 6

An optimized Yolov5s approach and its application to image d...

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2023 IEEE 6th International Conference on information Systems and Computer Aided Education, ICISCAE 2023

作者： Song, Jiayu Zhu, Zhiquan Qiang, Yufei Jiangsu University School of Computer Science and Communication Engineering Zhenjiang China Southwest Jiaotong University School of Computing and Artificial Intelligence Chengdu China Nanjing University of Information Science and Technology School of Computer Science Nanjing China

ISBN: (纸本)9798350313444

Traditional object detection algorithms cannot cope with motion blur and have poor classification accuracy for small cluster targets, which poses challenges to the development of image classification technology. In order to address these issues, this paper adopts a multi-strategy integrated optimization framework and optimizes the training speed and classification accuracy of the model based on the YOLOv5s network model. Firstly, the CBAM attention mechanism is introduced to mitigate motion blur. Then, the Focal-EIoU loss function is used to enhance the classification of low-accuracy objects. Next, the SiLU activation function is employed to enhance the model's representation capability, and the SGD optimizer is replaced with the Adam optimizer to improve training efficiency. Finally, the improved model underwent 500 rounds of training on a subset of the BDD100K dataset. The results show that the classification accuracy of the model, after multiple strategy optimizations, increased from 46.5% to 74.7%. The YOLOv5s model, optimized by multiple strategies, can effectively solve problems such as motion blur, thereby improving the accuracy and stability of the model. © 2023 IEEE.

关键词： adam optimizer attention mechanism image classification yolov5s

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