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2nd International Conference on Smart Technologies and Systems for Next Generation Computing, ICSTSN 2023

作者： Liu, Zihao Li, Han Gao, Bo Xiong, Ke Fan, Pingyi Beijing Jiaotong University School of Computer and Information Technology Beijing China Engineering Research Center of Network Management Technology for High Speed Railway Ministry of Education School of Computer and Information Technology Beijing Jiaotong University Beijing China Tsinghua University Department of Electronic Engineering Beijing China

ISBN: (纸本)9798350348002

This paper focuses on how to optimize the deployment location of the anchor Wi-Fi access points (APs) in order to enhance the indoor positioning performance of the finetime measurement (FTM) protocol. Compared to conventional Wi-Fi positioning, FTM protocol is able to achieve relatively good indoor positioning performance since it estimates distance between a sender and its receiver with the flight duration of Wi-Fi signals. However, the FTM-based positioning accuracy in NLoS environments is still not high, which restricts its application in indoor scenarios. To this end, we formulate an optimization problem to maximize the total number of LoS paths by optimizing the locations of the anchor APs. To solve the problem, we propose a Deep Q-network (DQN)-based AP deployment optimization method (DQ-WiAPDoM), which utilizes DQN to optimize the locations of APs such that the total number of LoS paths over all covered area is maximized. Experimental results show that DQ-WiAPDoM converges well and is able to reduce the average positioning error efficiently. © 2023 IEEE.

关键词： Wi-Fi

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Joint User Cooperation and Scheduling for Federated Learning in CFmMIMO networks 2

Joint User Cooperation and Scheduling for Federated Learning...

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2nd International Conference on Smart Technologies and Systems for Next Generation Computing, ICSTSN 2023

作者： Bao, Han Gao, Bo Xiong, Ke Fan, Pingyi Beijing Jiaotong University School of Computer and Information Technology Beijing China Beijing Jiaotong University Engineering Research Center of Network Management Technology for High Speed Railway Ministry of Education School of Computer and Information Technology Beijing China Tsinghua University Department of Electronic Engineering Beijing China

ISBN: (纸本)9798350348002

This paper proposes a joint user cooperation and scheduling (JUCS) method to minimize the maximum transmission time among users in the synchronous federated learning (FL) architecture, since the user with the worst quality link limits the training performance of FL. To this end, an optimization problem is formulated to find the optimal user cooperation pairing and the power control coefficients. Meanwhile, as the links among users operate on the same frequency bands, the interface between them is taken into account in this paper. As the problem is non-convex, we decouple it into two sub-problems. For the first sub-problem, we propose a fair scheduling (FS)-based algorithm to schedule the helping users to pair with the ones who are required to be assisted. For the second sub-problem, an algorithm based on deterministic policy gradient (DDPG) is proposed to find the optimized power control coefficients. Experiments show that the proposed JUCS method shortens about 34.48% transmission time of the worst user in FL compared to the traditional one without JUCS. © 2023 IEEE.

关键词： Scheduling algorithms

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Leveraging Machine Learning to Identify Synergistic Drug Combinations for Effective Cancer Treatment 7

Leveraging Machine Learning to Identify Synergistic Drug Com...

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7th International Conference on Image Information Processing, ICIIP 2023

作者： Sujatha, P. Saravanan, K. Sohail, Mohammed Ali Basi Reddy, A. Dixit, Rohit R. Krishnaiah, Nallam Amrita Vishwa Vidyapeetham Department of AI/ML Guntur Amaravati India R.M.K Engineering College Department of Information Technology Tamil Nadu Tiruvallur District India Jazan University College of Computer Science & Information Technology Department of Computer & Network Engineering Jazan Saudi Arabia Mohan Babu University School of Computing Department of Computer Science And Engineering Andhra Pradesh Tirupati India Siemens Healthineers NH03755 United States St.Martin's Engineering College Department of Ai&ml Secunderabad Telangana Dhulapally500100 India

ISBN: (纸本)9798350371406

The potential of drug combinations to treat and overcome medication resistance complex genetic diseases is evident. Synergistic drug combinations offer a promising way to enhance drug therapy efficacy and reduce the required medication dosage. However, developing effective combination medication therapies with synergistic effects has been challenging, despite numerous ongoing clinical investigations. Current models and approaches to detect medication synergy outlined in the literature lack the expected consistency in outcomes. to better comprehend the impact of particular medication combinations, it is essential to be familiar with the vocabulary used to describe synergy. In this study, a combinational drug screen is utilized to identify useful features for locating synergistic or efficient drug combinations. The feature selection algorithm (Boruta) helps select the most relevant features, and machine learning models are then trained using the selected feature dataset. Performance assessment metrics like sensitivity, accuracy, and specificity are used to compare the trained models, and the Random Forest model stands out for its significantly better performance compared to other models. © 2023 IEEE.

关键词： Drug dosage

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Multi-Representation Spatial-Temporal Graph Convolutional networks for network Traffic Prediction

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IEEE Internet of Things Journal 2025年

作者： Yang, Yang He, Yechen Zhao, Binnan Wu, Celimuge Gao, Zhipeng Rui, Lanlan Beijing University of Posts and Telecommunications School of Computer Science Beijing100876 China University of Electro-Communications Department of Computer and Network Engineering Tokyo1828585 Japan

With the rapid proliferation of the Internet of Things, network traffic prediction has become crucial for intelligent network management, enabling more reliable and flexible services for a vast array of IoT devices and applications. The heterogeneous and dynamic nature of IoT networks introduces complex spatial relations and underlying periodic dependencies in spatial-temporal graphs that existing methods struggle to model effectively. In this paper, we propose Multi-Representation Spatial-Temporal Graph Convolutional networks (MRSTGCN), a novel unified framework specifically designed to address these challenges. MRSTGCN integrates a Multi-Representation Graph Convolutional network (MRGCN) module to model node heterogeneity and complex traffic propagation, and two complementary embedding modules - Historical Embedding and Temporal Embedding - to capture and fuse periodic dependencies across different fine-grained temporal cycles. Extensive experiments are conducted on two network traffic datasets, and the results demonstrate that MRSTGCN achieves state-of-the-art performance with obvious improvements in MAE, RMSE and MAPE on three prediction horizons. © 2025 IEEE.

关键词： Convolutional neural networks

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Improving Machine Learning based ASD Diagnosis with Effective Feature Selection

Improving Machine Learning based ASD Diagnosis with Effectiv...

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International Symposium on Artificial Intelligence and Signal Processing (AISP)

作者： Zhino Safahi Ehsan Azimipour Shima Saedi Sadegh Sulaimany Department of Computer Engineering Social & Biological Network Laboratory (SBNA) University of Kurdistan Sanandaj Iran

Autism spectrum disorder (ASD) poses major public health challenges due to its increasing prevalence and the critical need for early intervention to improve outcomes. This study investigates machine learning approaches for automated screening and diagnosis of ASD, leveraging questionnaire and demographic data from large cohorts of toddlers (n=1,054) and adults (n=704). After data cleaning and preprocessing, an efficient sequential feature selection technique identifies subsets of the most predictive features. Several classification algorithms, including neural networks, random forests, support vector machines, and logistic regression, are developed using these refined datasets and rigorously evaluated through 10-fold cross-validation. The models demonstrate excellent predictive performance, achieving 100% accuracy in identifying ASD in both toddlers and adults. This signifies that the selected feature sets are highly relevant for capturing autistic traits across age groups, although manifestations vary developmentally. Compared to existing methods, the models show substantially improved accuracy, highlighting machine learning’s potential for accessible and scalable assessment of ASD. Additional validation on bigger real-world datasets would further establish generalizability. Overall, this study demonstrates that machine learning can assist evidence-based clinical decision-making for ASD diagnosis. Promising future work should focus on enhancing model interpretability, translating these technologies responsibly to bolster early intervention, and evaluating impact on patient outcomes through rigorous trials. Machine learning shows immense promise in aiding the identification and timely treatment of this import yet often underdiagnosed public health issue.

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Community Detection to Improve Machine Learning Based Heart Disease Prediction

Community Detection to Improve Machine Learning Based Heart ...

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International Symposium on Artificial Intelligence and Signal Processing (AISP)

作者： Abolfazl Dibaji Sadegh Sulaimany Department of Computer Engineering Social & Biological Network Laboratory (SBNA) University of Kurdistan Sanandaj Iran

This research article introduces a novel approach to enhance the accuracy of heart disease classification through the integration of graph-based techniques and community detection with machine learning. The paper explores the potential of capturing complex relationships within heart disease datasets using community detection and encode the membership of a node as a feature set, using on hot encoding, in order to enrich the current available features as the input of machine learning classifiers. The study utilizes a comprehensive dataset amalgamated from multiple sources and employs graph conversion, community detection, and machine learning algorithms to analyze the data. Results indicate that the proposed approach surpasses traditional machine learning models, achieving an accuracy of 94%. This study highlights the potential of graph-based methods to improve heart disease classification. It provides insights into opportunities, limitations, and areas for future research in this field. While there are certain limitations around sample size and challenges in real-world deployment, the research presents strong evidence supporting the effectiveness of community detection techniques for heart disease classification. The findings have implications for diagnosis and treatment.

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Multi-View Graph Embedding Learning for Image Co-Segmentation and Co-Localization

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IEEE Transactions on Circuits and Systems for Video Technology 2024年第6期34卷 4942-4956页

作者： Huang, Aiping Li, Lijian Zhang, Le Niu, Yuzhen Zhao, Tiesong Lin, Chia-Wen Fuzhou University Fujian Key Laboratory for Intelligent Processing and Wireless Transmission of Media Information College of Physics and Information Engineering Fuzhou350108 China Fuzhou University Fujian Key Laboratory of Network Computing and Intelligent Information Processing College of Computer and Data Science Fuzhou350108 China University of Electronic Science and Technology of China School of Information and Communication Engineering Chengdu611731 China Fuzhou University Fujian Key Laboratory for Intelligent Processing and Wireless Transmission of Media Information College of Physics and Information Engineering The Fujian Science and Technology Innovation Laboratory for Optoelectronic Information Fuzhou350108 China Institute of Communications Engineering National Tsing Hua University Department of Electrical Engineering Hsinchu30013 Taiwan

Image co-segmentation and co-localization exploit inter-image information to identify and extract foreground objects with a batch mode. However, they remain challenging when confronted with large object variations or complex backgrounds. This paper proposes a multi-view graph embedding (MV-Gem) learning scheme which integrates diversity, robustness and discernibility of object features to alleviate this phenomenon. To encourage the diversity, the deep co-information containing both low-layer general representations and high-layer semantic information is generated to form a multi-view feature pool for comprehensive co-object description. To enhance the robustness, a multi-view adaptive weighted learning is formulated to fuse the deep co-information for feature complementation. To ensure the discernibility, the graph embedding and sparse constraint are embedded into the fusion formulation for feature selection. The former aims to inherit important structures from multiple views, and the latter further selects important features to restrain irrelevant backgrounds. With these techniques, MV-Gem gradually recovers all co-objects through optimization iterations. Extensive experimental results on real-world datasets demonstrate that MV-Gem is capable of locating and delineating co-objects in an image group. © 1991-2012 IEEE.

关键词： Semantics

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On the MDS Condition of Generalized Expanded-Blaum-Roth Codes 18

On the MDS Condition of Generalized Expanded-Blaum-Roth Code...

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18th International Symposium on Problems of Redundancy in Information and Control Systems, REDUNDANCY 2023

作者： Hou, Hanxu Song, Linqi Dongguan University of Technology Dongguan Key Lab of Artificial Information Network School of Electrical Engineering & Intelligentization China City University of Hong Kong Department of Computer Science Hong Kong

ISBN: (纸本)9798350381528

Generalized Expanded-Blaum-Roth (GEBR) codes [You et al. 2020] are designed for large-scale distributed storage systems that have larger recoverability for single-symbol failures, multi-column failures and multi-row failures, compared with locally recoverable codes (LRC). GEBR codes encode an α × k information array into a pτ ×(k + r) array such that lines of slope i with 0 ≤ i ≤ r -1 have even parity and each column contains pτ-α local parity symbols, where p is an odd prime and k +r ≤ pτ. Necessary and sufficient conditions for GEBR codes to be (n, k) recoverable (i.e., any k out of n = k+r columns can retrieve all information symbols) are given in [Hou et al. 2023] for α = (p -1)τ. However, the (n, k) recoverable condition of GEBR codes is unknown when α © 2023 IEEE.

关键词： Multiprocessing systems

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An Intelligent Hybrid Mutual Authentication Scheme for Industrial Internet of Thing networks

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computers, Materials & Continua 2021年第7期68卷 447-470页

作者： Muhammad Adil Jehad Ali Muhammad Sajjad Khan Junsu Kim Ryan Alturki Mohammad Zakarya Mukhtaj Khan Rahim Khan Su Min Kim Department of Computer Science Virtual University of PakistanLahore54000Pakistan Department of Computer Engineering Department of AI Convergence NetworkAjou UniversitySuwon16499South Korea Department of Electronics Engineering Korea Polytechnic UniversitySlheungSouth Korea Department of Computer Science Abdul Wali Khan University Mardan23200Pakistan

Internet of Things(IoT)network used for industrial management is vulnerable to different security threats due to its unstructured deployment,and dynamic communication *** literature various mechanisms addressed the security issue of Industrial IoT networks,but proper maintenance of the performance reliability is among the common *** this paper,we proposed an intelligent mutual authentication scheme leveraging authentication aware node(AAN)and base station(BS)to identify routing attacks in Industrial IoT *** AAN and BS uses the communication parameter such as a route request(RREQ),node-ID,received signal strength(RSS),and round-trip time(RTT)information to identify malicious devices and routes in the deployed *** feasibility of the proposed model is validated in the simulation environment,where OMNeT++was used as a simulation *** compare the results of the proposed model with existing field-proven schemes in terms of routing attacks detection,communication cost,latency,computational cost,and *** results show that our proposed scheme surpasses the previous schemes regarding these performance parameters with the attack detection rate of 97.7%.

关键词： Security industrial Internet of Things routing attacks routing protocols base station authentication aware nodes

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Futuristic E-booking for Parking Lot with Tracking Amenity 4

Futuristic E-booking for Parking Lot with Tracking Amenity

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4th International Conference on Recent Trends in computer Science and Technology, ICRTCST 2021

作者： Khan, Haneef Alam, Md Imran Siddiqui, Shams Tabrez Khan, Mohammad Rafeek Alam, Naved Salim, Abu Jazan University College Of Cs & It Department Of Computer And Network Engineering Jazan Saudi Arabia Jazan University College Of Computer Science And Information Technology Department Of Computer Science Jazan Saudi Arabia Jamia Millia Islamia Department Of Electrical Engineering New Delhi110025 India

ISBN: (纸本)9781665466332

The number of automobiles on the road is increasing all of the time, especially in metropolitan regions. Finding parking may be a time-consuming and challenging chore for drivers, and the amount of fuel used in the parking lot has a detrimental influence on the environment as a consequence. The result is the need for an efficient method of finding parking spots in a short period of time so that drivers may save time while also ensuring that our cities remain clean and orderly. This project makes use of the Internet of Things (IoT) and other technologies in order to provide a framework for delivering parking for vehicles. Furthermore, microcontrollers such as the ATmega328P, as well as Internet-connected devices and web-based applications, are planned to be used in the project. Additionally, a number of other payment options are being explored, which will aid in the reduction of parking lot congestion and congestion. © 2022 IEEE.

关键词： Microcontrollers

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