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arXiv 2025年

作者： Zheng, Xiaoya Sun, Geng Li, Jiahui Wang, Jiacheng Wu, Qingqing Niyato, Dusit Jamalipour, Abbas College of Computer Science d Technology Jilin University Changchun130012 China Key Laboratory of Symbolic Computation and Knowledge Engineering of Ministry of Education Jilin University Changchun130012 China College of Computing and Data Science Nanyang Technological University 639798 Singapore College of Computing and ta Science Nanyang Technological University 639798 Singapore Department of Electronic Engineering Shanghai Jiao Tong University Shanghai200240 China School of Electrical and Computer Engiering The University of Sydney SydneyNSW2006 Australia

The low-altitude economy (LAE), as a new economic paradigm, plays an indispensable role in cargo transportation, healthcare, infrastructure inspection, and especially post-disaster communication. Specifically, unmanned aerial vehicles (UAVs), as one of the core technologies of the LAE, can be deployed to provide communication coverage, facilitate data collection, and relay data for trapped users, thereby significantly enhancing the efficiency of post-disaster response efforts. However, conventional UAV self-organizing networks exhibit low reliability in long-range cases due to their limited onboard energy and transmit ability. Therefore, in this paper, we design an efficient and robust UAV-swarm enabled collaborative self-organizing network to facilitate post-disaster communications. Specifically, a ground device transmits data to UAV swarms, which then use collaborative beamforming (CB) technique to form virtual antenna arrays and relay the data to a remote access point (AP) efficiently. Then, we formulate a rescue-oriented post-disaster transmission rate maximization optimization problem (RPTRMOP), aimed at maximizing the transmission rate of the whole network. Given the challenges of solving the formulated RPTRMOP by using traditional algorithms, we propose a two-stage optimization approach to address it. In the first stage, the optimal traffic routing and the theoretical upper bound on the transmission rate of the network are derived. In the second stage, we transform the formulated RPTRMOP into a variant named V-RPTRMOP based on the obtained optimal traffic routing, aimed at rendering the actual transmission rate closely approaches its theoretical upper bound by optimizing the excitation current weight and the placement of each participating UAV via a diffusion model-enabled particle swarm optimization (DM-PSO) algorithm. Simulation results show the effectiveness of the proposed two-stage optimization approach in improving the transmission rate of the construct

关键词： Unmanned aerial vehicles (UAV)

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Cutting-Edge Machine Learning Algorithms for In-Depth Characterization and Efficiency Improvement of Phase Change Heat Transfer Mechanisms

Cutting-Edge Machine Learning Algorithms for In-Depth Charac...

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Emerging Trends in Industry 4.0 (ETI 4.0)

作者： Geetanjli Khambra K Praveena Manjunatha Amit Dutt Irfan Khan Ali Ashoor Issa Department of Computer Applications The Bhopal School of Social Sciences (BSSS) College Bhopal M.P India Institute of Aeronautical Engineering Dundigal Hyderabad Department of Mechanical Engineering New Horizon College of Engineering Bangalore Lovely Professional University Phagwara Knowledge Park II Lloyd Institute of Engineering & Technology Greater Noida Uttar Pradesh College of Technical Engineering National University of Science and Technology Dhi Qar Iraq

ISBN: (数字)9798350373783

ISBN: (纸本)9798350373790

This study introduces a sophisticated and holistic methodology for the enhanced understanding and optimization of phase change heat transfer mechanisms. The proposed framework integrates Deep Learning Neural Networks (DLNN), Random Forest (RF), Long Short-Term Memory (LSTM) Networks, Gaussian Processes (GP), and Reinforcement Learning (RL) in a synergistic manner, emphasizing the iterative and adaptive nature of each algorithm. A detailed flowchart for each algorithm delineates the process, showcasing their roles in refining predictions and optimizing models. An ablation study underscores the crucial contribution of each component, revealing their collective efficacy. Comparative analyses against existing algorithms highlight the proposed method’s consistent superiority in accuracy, precision, recall, mean squared error, and Rsquared. Resource efficiency metrics further affirm its computational effectiveness. Visualizations provide a comprehensive and intuitive representation of the methodology’s strengths. The proposed framework offers a promising avenue for characterizing and optimizing phase change heat transfer, contributing to advancements in thermal sciences and real-world applications.

关键词： Radio frequency Visualization Thermal expansion Machine learning algorithms Accuracy Neural networks Prediction algorithms

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UAV Virtual Antenna Array Deployment for Uplink Interference Mitigation in Data Collection Networks

arXiv

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arXiv 2024年

作者： Li, Hongjuan Kang, Hui Sun, Geng Li, Jiahui Wang, Jiacheng Wang, Xue Niyato, Dusit Leung, Victor C.M. College of Computer Science and Technology Jilin University Changchun130012 China Key Laboratory of Symbolic Computation and Knowledge Engineering of Ministry of Education Jilin University Changchun130012 China College of Computing and Data Science Nanyang Technological University Singapore639798 Singapore College of Computing and Data Science Nanyang Technological University Singapore Department of Communication Engineering Jilin University Changchun130012 China Artificial Intelligence Research Institute Shenzhen MSU-BIT University Shenzhen518115 China College of Computer Science and Software Engineering Shenzhen University Shenzhen518060 China Department of Electrical and Computer Engineering The University of British Columbia VancouverV6T 1Z4 Canada

Unmanned aerial vehicles (UAVs) have gained considerable attention as a platform for establishing aerial wireless networks and communications. However, the line-of-sight dominance in air-to-ground communications often leads to significant interference with terrestrial networks, reducing communication efficiency among terrestrial terminals. This paper explores a novel uplink interference mitigation approach based on the collaborative beamforming (CB) method in multi-UAV network systems. Specifically, the UAV swarm forms a UAV-enabled virtual antenna array (VAA) to achieve the transmissions of gathered data to multiple base stations (BSs) for data backup and distributed processing. However, there is a trade-off between the effectiveness of CB-based interference mitigation and the energy conservation of UAVs. Thus, by jointly optimizing the excitation current weights and hover position of UAVs as well as the sequence of data transmission to various BSs, we formulate an uplink interference mitigation multi-objective optimization problem (MOOP) to decrease interference affection, enhance transmission efficiency, and improve energy efficiency, simultaneously. In response to the computational demands of the formulated problem, we introduce an evolutionary computation method, namely chaotic non-dominated sorting genetic algorithm II (CNSGA-II) with multiple improved operators. The proposed CNSGA-II efficiently addresses the formulated MOOP, outperforming several other comparative algorithms, as evidenced by the outcomes of the simulations. Moreover, the proposed CB-based uplink interference mitigation approach can significantly reduce the interference caused by UAVs to non-receiving BSs. Copyright © 2024, The Authors. All rights reserved.

关键词： Multiobjective optimization

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HUSS: A Heuristic Method for Understanding the Semantic Structure of Spreadsheets

HUSS: A Heuristic Method for Understanding the Semantic Stru...

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IEEE International Conference on Big knowledge (ICBK)

作者： Xindong Wu Hao Chen Chenyang Bu Shengwei Ji Zan Zhang Victor S. Sheng Key Laboratory of Knowledge Engineering with Big Data (the Ministry of Education of China) Hefei University of Technology China School of Computer Science and Information Engineering Hefei University of Technology Hefei China Department of Computer Science Texas Tech University Lubbock TX USA

Spreadsheets contain a lot of valuable data and have many practical applications. The key technology of these practical applications is how to make machines understand the semantic structure of spreadsheets, e.g., identifying cell function types and discovering relationships between cell pairs. Most existing methods for understanding the semantic structure of spreadsheets do not make use of the semantic information of cells. A few studies do, but they ignore the layout structure information of spreadsheets, which affects the performance of cell function classification and the discovery of different relationship types of cell pairs. In this paper, we propose a Heuristic algorithm for Understanding the Semantic Structure of spreadsheets (HUSS). Specifically, based on the existing cell function classification model [11], we propose five types of heuristic rules to extract four different types of cell pairs, based on the cell style and spatial location information. Our experimental results on two real-world datasets demonstrate that the proposed method HUSS can effectively understand the semantic structure of spreadsheets and outperforms corresponding baselines.

关键词： Analytical models Heuristic algorithms Semantics Layout Feature extraction Classification algorithms Data mining

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Adaptive Student Inference Network for Efficient Single Image Super-Resolution

Adaptive Student Inference Network for Efficient Single Imag...

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IEEE International Conference on Data Mining (ICDM)

作者： Kang Miao Zhao Zhang Jiahuan Ren Mingbo Zhao Haijun Zhang Richang Hong School of Computer Science and Information Engineering Hefei University of Technology Hefei China Key Laboratory of Knowledge Engineering with Big Data (Ministry of Education) & Intelligent Interconnected Systems Laboratory of Anhui Province Hefei University of Technology Hefei China Yunnan Key Laboratory of Software Engineering Kunming China School of Information Science and Technology Donghua University Shanghai China Department of Computer Science Harbin Institute of Technology (Shenzhen) Shenzhen China

Recent advances in single image super-resolution (SISR) have achieved remarkable performance through deep learning. However, the high computational cost hinders the deployment of SISR models on edge devices. Instead of proposing new SISR models, a new trend is emerging to improve network efficiency by reducing parameters, FLOPs, and inference time through slight modifications to the original models. However, recent methods usually focus on reducing only one of three metrics, i.e., FLOPs, parameters and inference time, which inevitably increases the other two metrics. In this paper, we propose a novel Adaptive Student Inference Network (ASIN) on popular SISR models, which aims at reducing FLOPs and inference time while maintaining the number of parameters and restoring clearer high-resolution images. Specifically, our ASIN divides a SISR model into three components (head, body and tail) and adopts various strategies for each part. For head and tail parts, to ensure the restored images contain more detailed information, a novel auxiliary Enhanced Teacher Network (ETNet) is designed, which is trained with the ground-truth images to obtain more prior knowledge to guide student network to extract more accurate textures using a new knowledge distillation method. For the body part, owing to the varying difficulties of the reconstructions in different regions, we propose an Adaptive Depth Predicted Module (ADPM) to dynamically shorten average depth of network to reduce the computational cost of overall network. Extensive experiments on two datasets demonstrate the effectiveness and state-of-the-art performance of our ASIN compared to its counterparts.

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A Study on Medical Image Compression Techniques based on Huffman Coding and Discrete Wavelet Transform (DWT) 9

A Study on Medical Image Compression Techniques based on Huf...

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9th IEEE Conference on System, Process and Control, ICSPC 2021

作者： Mydin, Muhammad Akmal Shaik Alkawaz, Mohammed Hazim Ghafoor, Kayhan Zrar Mohammad, Omar Farook Md Johar, Md Gapar Management and Science University School of Graduates Studies Selangor Shah Alam Malaysia Management and Science University Faculty of Information Sciences and Engineering Selangor Shah Alam Malaysia Knowledge University Department of Computer Science Erbil Iraq Al-Hadba University College Computer Technology Engineering Mosul Iraq Information Technology and Innovation Centre Management and Science University Selangor Shah Alam Malaysia

ISBN: (纸本)9781665437639

In emergency situations, different types of medical images must be transferred from one place to another, and larger-sized images are difficult to transfer because they are time-consuming and take up more storage space. The size of the images that belongs to a single patient is very large, and it includes the resolution factor as well as diagnostic images. As a result, effective compression techniques for these medical images are required. Therefore, this paper has studied and analyzed the previous methods for the medical image compression using Huffman Coding Discrete Wavelet Transform (DWT). This paper is also highlighted the strength and the weaknesses of Huffman Coding and Discrete Wavelet Transform (DWT) along with the parameters measurement that used for image compression. © 2021 IEEE.

关键词： Discrete wavelet transforms

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Exploring Cognitive Decline in Hypertension: A Deep Learning Approach to Meningeal Interleukin-17-Producing T Cells in Mice

Exploring Cognitive Decline in Hypertension: A Deep Learning...

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Emerging Trends in Industry 4.0 (ETI 4.0)

作者： Praveen Priyanka Gupta B Rajalakshmi Ginni Nijhawan Neha Kumari Hussein Ali Kadhim Kyhoiesh Knowledge Park II Lloyd Institute of Engineering & Technology Greater Noida Uttar Pradesh Institute of Aeronautical Engineering Dundigal Hyderabad Department of Computer Science New Horizon College of Engineering Bangalore Lovely Professional University Phagwara AP Dept. of CSE St. Martin’s Engineering College Secunderabad Telangana India College of Health and Medical Technology National University of Science and Technology Dhi Qar Iraq

ISBN: (数字)9798350373783

ISBN: (纸本)9798350373790

A deep learning approach that targets meningeal interleukin-17-producing T cells is used to research cognitive loss in high blood pressure patients. A detailed ablation study examined and rated each aspect of our recommended procedure. Including cellular activities was crucial since excluding them reduced accuracy. These elements are crucial to high blood pressure-related cognitive impairment. The deep learning model’s layers worked effectively together to uncover complicated data patterns, as shown by removing some layers. Model complexity and computer time trade-offs were found, which can aid future improvements. In conclusion, our deep learning approach appears to be a promising tool to study how elevated blood pressure affects brain function. The ablation investigation proves the procedure works and reveals key components’ responsibilities. This allows for more modifications and breakthroughs. This research helps us understand brain illnesses, including high blood pressure-related memory loss. It also sets the framework for innovative diagnosis and treatment methods. This study’s deep learning and immunology discoveries might revolutionize high blood pressure-related cognitive impairment research and treatment.

关键词： Deep learning Technological innovation Immunology Computational modeling Cells (biology) Brain modeling Blood pressure

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A review of Communication and Data Networks Algorithms Shaping Modem Connectivity

A review of Communication and Data Networks Algorithms Shapi...

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International Conference on Contemporary Computing and Informatics (IC3I)

作者： Mk Srinath Anitha Gatla Sorabh Lakhanpal Saurabh Sharma B.Ravali Reddy Taqi Mohammed Khattab Al-Rubaye Department of Mechanical Engineering New Horizon College of Engineering Bangalore India Department of Information Technology Institute of Aeronautical Engineering Dundigal Hyderabad India Lovely Professional University Phagwara India Lloyd Law College Plot No. 11 Knowledge Park II Greater Noida Uttar Pradesh Department of Computer Science and Engineering MLR Institute of Technology Hyderabad Telangana India Department of Medical Laboratory Technology College of Medical Technologies The Islamic University Najaf Iraq

ISBN: (数字)9798350350067

ISBN: (纸本)9798350350074

The world of today is highly interconnected and undergoes rapid changes. Algorithms are essential for data networks and communication systems. This study looks at how the complexity of algorithms affects network design. It focusses on the GROA (Global Routing Optimisation Algorithm), LBEA (Load Balancing and Efficient Allocation), and QKD-NS systems in particular. We aim to educate individuals on the impact of various methods on security, route completion time, and resource constraints. Based on the results, GROA creates variable paths, QKD-NS boosts safety with quantum-based innovations, and LBEA efficiently divides up resources based on what the network requires. According to the study, putting these many technologies together could lead to strong partnerships that can handle security breaches, traffic jams, and changes in the network. This study focusses on how advances in technology have led to better computer design, which in turn has made the internet safer and easier to use. The paper elucidates the application of these novel mathematical discoveries to enhance the performance and durability of data networks. These new details help communication networks improve by ensuring that they are safe and can meet today’s link standards.

关键词： Technological innovation Reviews Heuristic algorithms Telecommunication traffic Routing Load management Safety Communication networks Time factors Standards

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An Intelligent System for Cucumber Leaf Disease Diagnosis Based on the Tuned Convolutional Neural Network Algorithm

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Mobile Information Systems 2022年第1期2022卷

作者： Omer, Saman M. Ghafoor, Kayhan Z. Askar, Shavan K. Department of Technical Information System Engineering Erbil Technical Engineering College Erbil Polytechnic University Kurdistan Region Erbil Iraq Department of Computer Science College of Basic Education University of Raparin Kurdistan Region Ranya Iraq Department of Computer Science Knowledge University Kurdistan Region Erbil44001 Iraq Department of Software and Informatics Engineering Salahaddin University-Erbil Kurdistan Region Erbil44001 Iraq

In agriculture farming, pests and other plant diseases are the most imperative factor that causes significant hindrance to cucumber production and its quality. Farmers around the globe are currently facing difficulty in recognizing various cucumber leaf diseases, which is imperative to preventing leaf diseases effectively. Manual techniques to diagnose cucumber diseases are often time-consuming, subjective, and laborious. To address this issue, this paper proposes a tuned convolutional neural network (CNN) algorithm to recognise five cucumber diseases and healthy leaves that comprises image enhancement, feature extraction, and classification. Data augmentation methods were utilized as a preprocessing step to enlarge the datasets, and it was also to decrease the chance of overfitting. Automatically features are extracted by using CNN layers. Finally, five cucumber leaf diseases and one healthy leaf are classified. Furthermore, to overcome the lack of a public dataset, a new dataset of cucumber leaf diseases has been constructed that includes spider, leaf miner, downy mildew, powdery mildew, one viral disease, and healthy class leaves. The dataset has a total of 4868 cucumber leaf images. In order to prove the authenticity of the proposed CNN, comparative experiments were conducted using pretrained models (AlexNet, Inception-V3, and ResNet-50). The proposed CNN achieves a recognition accuracy of 98.19% with the augmented dataset and 100% with the publicly plant disease dataset. The experimental results confirm that the proposed CNN algorithm was efficient for recognizing the cucumber leaf diseases compared with other algorithms. © 2022 Saman M. Omer et al.

关键词： Image enhancement

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Deep Learning for Customer Relationship Management in E-commerce

Deep Learning for Customer Relationship Management in E-comm...

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Human-computer Interaction, Optimization and Robotic Applications (HORA), International Congress on

作者： Saadaldeen Rashid Ahmed Lubna Qassim ALhashmi Ali Jabbar Hussein Ahmed Dheyaa Radhi Ahmed A. Radhi Mohammed Rashid Ahmed Abadal-Salam T. Hussain Jamal Fadhil Tawfeq Computer Science Department Bayan University Erbil Kurdistan Iraq Department of Information and Knowledge Technologies College of Arts Basrah University Basrah Iraq Al Hikma University College Baghdad Iraq College of Pharmacy University of Al-Ameed Karbala Iraq Department of Computer Techniques Engineering Al-Ma’moon University College Al-Washash / Baghdad Iraq College of Technical Engineering National University of Science and Technology Dhi Qar Iraq Department of Medical Instrumentation Techniques Engineering Technical Engineering College Al-Kitab University Kirkuk Iraq Department of Medical Instrumentation Technical Engineering Medical Technical College Al-Farahidi University Baghdad Iraq

ISBN: (数字)9798350394634

ISBN: (纸本)9798350394641

Customer Relationship Management (CRM) plays a pivotal role in the success of e-commerce businesses by fostering stronger connections with customers. De-spite its importance, existing CRM approaches often fall short in effectively leveraging the vast amounts of data available in digital environments. This paper addresses this gap by proposing a novel framework that harnesses the power of deep learning techniques to enhance CRM in e-commerce. Building upon a comprehensive review of previous literature, we identify the need for advanced analytical methods to unlock insights from complex data patterns. Our approach in-volves the development and evaluation of artificial neural networks (ANNs) and deep neural networks (DNNs) tailored to CRM tasks such as customer segmentation, churn prediction, and recommendation systems. Leveraging a meticulously curated dataset encompassing diverse customer attributes and transaction history, we rigorously train and assess our models. Our results demonstrate the efficacy of deep learning in capturing nuanced customer behaviors and improving predictive accuracy in CRM tasks. Our highest achieved accuracy stands at 90%, showcasing the potential of our approach in driving superior CRM performance in e-commerce environments. Through this study, we contribute to bridging the research-practice gap in CRM by offering practical insights and methodologies for leveraging deep learning in e-commerce environments.

关键词： Deep learning Reviews Soft sensors Customer relationship management Artificial neural networks Electronic commerce History

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