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A Novel Access Point Deployment Framework for mmWave Cell-Free Massive MIMO Networks

IEEE Transactions on Wireless Communications 2025年第6期24卷 4581-4597页

作者： Topal, Ozan Alp Demir, Ozlem Tugfe Bjornson, Emil Cavdar, Cicek School of Electrical Engineering and Computer Science KTH Royal Institute of Technology Stockholm Sweden Department of Electrical and Electronics Engineering TOBB University of Economics and Technology Ankara Turkey

Millimeter-wave network deployment is an essential and ongoing problem due to the limited coverage and expensive network infrastructure. In this work, we solve a joint network deployment and resource allocation optimization problem for a mmWave cell-free massive MIMO network considering indoor environments. The objective is to minimize the number of deployed access points (APs) for a given environment, bandwidth, AP cooperation, and precoding scheme while guaranteeing the rate requirements of the user equipments (UEs). Considering coherent joint transmission (C-JT) and non-coherent joint transmission (NC-JT), we solve the problem of AP placement, UE-AP association, and power allocation among the UEs and resource blocks jointly. For numerical analysis, we model a mid-sized airplane cabin in ray-tracing as an exemplary case for IDS. Results demonstrate that a minimum data rate of 1 Gbps can be guaranteed with less than 10 APs with C-JT. From a holistic network design perspective, we analyze the trade-off between the required fronthaul capacity and the processing capacity per AP, under different network functional split options. We observe an above 600 Gbps fronthaul rate requirement, once all network operations are centralized, which can be reduced to 200 Gbps under physical layer functional splits. 2002-2012 IEEE.

关键词： Integer programming

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Bridging the gap in biomedical information retrieval: Harnessing machine learning for enhanced search results and query semantics

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Journal of Intelligent and Fuzzy Systems 2024年第4期46卷 9491-9510页

作者： Madhubala, P. Ghanimi, Hayder M.A. Sengan, Sudhakar Abhishek, Kumar Department of Computer Science and Engineering Bharathiyar Institute of Engineering for Women Tamil Nadu Salem India Information Technology Department College of Science University of Warith Al-Anbiyaa Karbala Iraq Computer Science Department College of Computer Science and Information Technology University of Kerbala Karbala Iraq Department of Computer Science and Engineering PSN College of Engineering and Technology Tamil Nadu Tirunelveli India Department of Mathematics VIT Bhopal Bhopal India

The medical domain faces unique challenges in Information Retrieval (IR) due to the complexity of medical language and terminology discrepancies between user queries and documents. While traditional Keyword-Based Methods (KBM) have limitations, the integration of semantic knowledge bases and concept mapping techniques enhances data organization and retrieval. Addressing the growing demands in the biomedical field, a novel medical Information Retrieval System (IRS) is proposed that employs Deep Learning (DL) and KBM. This system comprises five core steps: pre-processing of texts, document indexing using DL (ELMo) and KBM, advanced query processing, a BiLSTM-based retrieval network for contextual representation, and a KR-R re-ranking algorithm to refine document relevance. The purpose of the system is to give users improved biomedical search results through the integration of all of these techniques into a method that takes into consideration the semantic problems of medical records. An in-depth examination of the TREC-PM track samples from 2017 to 2019 observed an impressive leading MRR score of 0.605 in 2017 and a best-in-class rPrec score of 0.350 in 2019, proving how well able the system is to detect and rank relevant medical records accurately. © 2024 – IOS Press. All rights reserved.

关键词： Information retrieval

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Classifying chest x-rays for COVID-19 through transfer learning: a systematic review

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Multimedia Tools and Applications 2025年第2期84卷 689-748页

作者： Mallick, Devanshi Singh, Arshdeep Ng, Eddie Yin-Kwee Arora, Vinay Thapar Institute of Engineering & Technology Punjab Patiala India Wipro Limited Gurugram India School of Mechanical and Aerospace Engineering Nanyang Technological University Singapore Singapore Computer Science Engineering Department Thapar Institute of Engineering & Technology Punjab Patiala India

This study makes a comprehensive assessment of the predominant Transfer Learning (TL) techniques employed for the classification of COVID-19 cases in Chest X-rays (CXR) images. The methodologies have been selected on the basis of their merits and demerits, suitability, and possible impact on the development of the region being studied. The study examines the various methods of TL employed in the classification of COVID-19 cases with the objective to gain a deeper understanding about all the aspects of these methodologies. It can be of great significance for the researchers and medical professionals in making well-informed decisions about the implementation of these techniques to improve the precision and effectiveness of COVID-19 diagnosis. The practical consequences of these techniques help in early identification of such cases for having a suitable intervention. As many as 48 studies conducted during the period 2020–2023 have been included in the current research work for having an assessment about the problem under investigation. The study has specifically focused on transfer learning-based models utilized for the identification of COVID-19 through CXR pictures. It highlights the challenges posed by dataset dynamics, methodological variations, and performance metrics of different models. © The Author(s), under exclusive licence to Springer science+Business Media, LLC, part of Springer Nature 2024.

关键词： COVID-19

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EEG Emotion Prediction Using Artificial Intelligence

EEG Emotion Prediction Using Artificial Intelligence

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2024 International Conference on Signal Processing, Computation, Electronics, Power and Telecommunication, IConSCEPT 2024

作者： Singh, Samvardhan Chaudhary, Kshitij Devi, Durga SRM institute of science and technology Electronics and computer engineering Chennai India SRM institute of science and technology Computer science engineering Chennai India

ISBN: (纸本)9798331540685

Emotions are fundamental for human beings and play an important role in human cognition. Emotion is commonly associated with logical decision making, perception, human interaction, and to a certain extent, human intelligence itself. Emotion detection discussed in this paper is a technique that uses electroencephalography (EEG) to measure brain activity and identify different emotional states, it is a non-invasive method that measures electrical activity in the brain through electrodes placed on the scalp. The electrical activity of the brain is related to different cognitive and emotional processes, and electroencephalography can be used to measure changes in brain activity that occur in response to different emotions. The emotion prediction model can be used for early detection and of mental health disorders and to tailor personalised treatment plans for such patients, to help researchers gain a better understanding of the emotional aspects of various conditions and even in stress management, wellness program and more. © 2024 IEEE.

关键词： Electroencephalography

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A Secure Framework for WSN-IoT Using Deep Learning for Enhanced Intrusion Detection

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computers, Materials & Continua 2024年第10期81卷 471-501页

作者： Chandraumakantham Om Kumar Sudhakaran Gajendran Suguna Marappan Mohammed Zakariah Abdulaziz S.Almazyad School of Computer Science&Engineering Vellore Institute of TechnologyChennai CampusChennai600127India School of Electronics&Engineering Vellore Institute of TechnologyChennai CampusChennai600127India Department of Computer Sciences and Engineering College of Applied ScienceKing Saud UniversityRiyadh11543Saudi Arabia Department of Computer Engineering College of Computer and Information SciencesKing Saud UniversityRiyadh11543Saudi Arabia

The security of the wireless sensor network-Internet of Things(WSN-IoT)network is more challenging due to its randomness and self-organized *** detection is one of the key methodologies utilized to ensure the security of the *** intrusion detection mechanisms have issues such as higher misclassification rates,increased model complexity,insignificant feature extraction,increased training time,increased run time complexity,computation overhead,failure to identify new attacks,increased energy consumption,and a variety of other factors that limit the performance of the intrusion system *** this research a security framework for WSN-IoT,through a deep learning technique is introduced using Modified Fuzzy-Adaptive DenseNet(MF_AdaDenseNet)and is benchmarked with datasets like NSL-KDD,UNSWNB15,CIDDS-001,Edge IIoT,Bot *** this,the optimal feature selection using Capturing Dingo Optimization(CDO)is devised to acquire relevant features by removing redundant *** proposed MF_AdaDenseNet intrusion detection model offers significant benefits by utilizing optimal feature selection with the CDO *** results in enhanced Detection Capacity with minimal computation complexity,as well as a reduction in False Alarm Rate(FAR)due to the consideration of classification error in the fitness *** a result,the combined CDO-based feature selection and MF_AdaDenseNet intrusion detection mechanism outperform other state-of-the-art techniques,achieving maximal Detection Capacity,precision,recall,and F-Measure of 99.46%,99.54%,99.91%,and 99.68%,respectively,along with minimal FAR and Mean Absolute Error(MAE)of 0.9%and 0.11.

关键词： Deep learning intrusion detection fuzzy rules feature selection false alarm rate accuracy wireless sensor networks

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A Novel and Optimized Collaborative Diversity-Driven Routing Mechanism in MANETs

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SN computer science 2024年第1期5卷 22页

作者： Malyadri, Neelam Ramakrishna, M. Department of Computer Science and Engineering Vemana Institute of Technology VTU Belagavi India School of Computer Science and Engineering REVA University Karnataka Bengaluru India Department of Computer Science and Engineering Vemana Institute of Technology Karnataka Bengaluru India

Wireless networks such as MANETs present unique challenges due to their dynamic and decentralized nature. Efficient routing protocols are essential for achieving reliable and robust communication in such networks. In this research, we propose a novel routing mechanism called collaborative diversity-driven routing (CollabNet) that integrates collaborative diversity to enhance the routing process in ad hoc networks. CollabNet utilizes a collaborative approach to address the challenges posed by dynamic network environments. CollabNet incorporates collaborative power control, adaptive power allocation, and energy-efficient routing to automatically adjust the dynamic power of nodes. By promoting collaborative behavior and optimizing power levels. It fosters collaborative behavior among nodes by establishing cooperation groups and leveraging multiple relay nodes. Through collaborative multipath diversity CollabNet facilitates the transmission of packets toward the destination via multiple paths within the transmission range. This approach increases the probability of successful packet delivery by establishing redundant connections. By promoting collaboration among nodes, CollabNet improves the reliability and connectivity of wireless ad hoc networks. The mechanism leverages the strength of collaborative diversity to enhance the overall performance of routing protocols. Extensive simulations demonstrate that CollabNet outperforms traditional routing protocols in terms of network connectivity, reliability, and efficiency. The proposed mechanism offers a promising solution for efficient routing in MANETs contributing to the advancement of wireless communication in dynamic ad hoc network environments. © 2023, The Author(s), under exclusive licence to Springer Nature Singapore Pte Ltd.

关键词： Collaborative diversity Collaborative diversity-driven routing MANETs Routing protocols Wireless ad hoc networks

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Performance analysis of free space optical system incorporating circular polarization shift keying and mode division multiplexing

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Journal of Optical Communications 2024年第2期45卷 347-354页

作者： Meenu Sharma, Anshu Malhotra, Rahul Department of Electronics and Communication Engineering CT Institute of Engineering Research and Technology Greater Kailash Maqsudan Punjab Jalandhar144008 India Computer Science and Engineering CT Institute of Technology & Research Greater Kailash Maqsudan Punjab Jalandhar144008 India Computer Science and Engineering CT Institute of Technology and Management Shahpur Near Lambra Punjab Jalandhar144020 India

In this work, a free-space optical (FSO) communication system with the integration of mode division multiplexing and circular polarization shift keying (CpolSK) is proposed at 2 × 40 Gbps using LG00 and LG01 modes. Effects of diverse weather conditions such as clear weather, light rain, moderate rain, heavy rain, thin fog, thick fog, and heavy fog are studied on system performance. Further, a detailed comparison of CpolSK and polarization shift keying (PolSK) is performed at different FSO lengths in terms of log bit error rate. For implementation, analysis, and comparison, Optiwave Optisystem software is used and results show that CpolSK covers 100 km link distance and PolSK limits to 90 km only. Also, LG00 mode performs better than LG01 mode under all weather instabilities in the proposed system. © 2024 Walter de Gruyter GmbH. All rights reserved.

关键词： Fog

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Classifying Hematoxylin and Eosin Images Using a Super-Resolution Segmentor and a Deep Ensemble Classifier

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Intelligent Automation & Soft Computing 2023年第8期37卷 1983-2000页

作者： P.Sabitha G.Meeragandhi Department of Computer Science and Engineering Sathyabama Institute of Science and TechnologyChennaiTamilnaduIndia

Developing an automatic and credible diagnostic system to analyze the type,stage,and level of the liver cancer from Hematoxylin and Eosin(H&E)images is a very challenging and time-consuming endeavor,even for experienced pathologists,due to the non-uniform illumination and *** several Machine Learning(ML)and Deep Learning(DL)approaches are employed to increase the performance of automatic liver cancer diagnostic systems,the classi-ﬁcation accuracy of these systems still needs signiﬁcant improvement to satisfy the real-time requirement of the diagnostic *** this work,we present a new Ensemble Classiﬁer(hereafter called ECNet)to classify the H&E stained liver histopathology images *** proposed model employs a Dropout Extreme Learning Machine(DrpXLM)and the Enhanced Convolutional Block Attention Modules(ECBAM)based residual *** applies Voting Mechanism(VM)to integrate the decisions of individual classiﬁers using the average of probabilities ***,the nuclei regions in the H&E stain are seg-mented through Super-resolution Convolutional Networks(SrCN),and then these regions are fed into the ensemble DL network for classiﬁ*** effectiveness of the proposed model is carefully studied on real-world *** results of our meticulous experiments on the Kasturba Medical College(KMC)liver dataset reveal that the proposed ECNet signiﬁcantly outperforms other existing classiﬁca-tion networks with better accuracy,sensitivity,speciﬁcity,precision,and Jaccard Similarity Score(JSS)of 96.5%,99.4%,89.7%,95.7%,and 95.2%,*** obtain similar results from ECNet when applied to The Cancer Genome Atlas Liver Hepatocellular Carcinoma(TCGA-LIHC)dataset regarding accuracy(96.3%),sensitivity(97.5%),speciﬁcity(93.2%),precision(97.5%),and JSS(95.1%).More importantly,the proposed ECNet system consumes only 12.22 s for training and 1.24 s for ***,we carry out the Wilcoxon statistical test to determine whether the ECN

关键词： Convolutional block attention modules dropout ELM ensemble classiﬁer liver cancer segmentation voting mechanism

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Early detection of stroke disease using patients previous medical data instil with deep learning

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Multimedia Tools and Applications 2025年第16期84卷 16853-16881页

作者： Diwan, Tausif Gajbhiye, Saurav M. Goydani, Purva R. Gannarpwar, Vedant R. Khandait, Harshal R. Tembhurne, Jitendra V. Sahare, Parul Department of Computer Science and Engineering Indian Institute of Information Technology Maharashtra Nagpur441108 India Department of Electronics and Communication Engineering Indian Institute of Information Technology Maharashtra Nagpur441108 India

Early detection of any disease and starting its treatment in this early stage are the most important steps in case of any life-threatening disease. Stroke is not an exception in this regard which is one of the leading causes of death and disability worldwide. We develop a simple but efficient deep neural network for the stroke prediction that accurately evaluates the probability of occurrence of stroke disease by treating this as a binary classification problem on one of the standard datasets named as Stroke Prediction Dataset available on Kaggle. With the help of effective pre-processing techniques such as SMOTE and FastICA on the noisy and imbalanced dataset, we could achieve improved performance for the binary classification of stroke prediction by employing a deep dense neural network. The architectural hyper-parameters are critically designed with the help of Keras Tuner and consisting of just ten layers in the deep dense neural network. With the help of this light weight deep dense model, we report an accuracy of 95%, AUC Score of 100% and F1-Score of 95% on a testing set. Moreover, we also present comparative illustration of various machine learning models for the aforesaid task along with the comparative illustration of various state of the arts with our proposed model for the stroke prediction. © The Author(s), under exclusive licence to Springer science+Business Media, LLC, part of Springer Nature 2024.

关键词： Forecasting

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A privacy and compliance in regulated anonymous payment system based on blockchain

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Journal of Ambient Intelligence and Humanized Computing 2024年第8期15卷 3141-3157页

作者： Elfadul, Issameldeen Wu, Lijun Elhabob, Rashad Elkhalil, Ahmed School of Computer Science and Engineering University of Electronic Science and Technology of China Chengdu611731 China School of Information and Software Engineering University of Electronic Science and Technology of China Chengdu610054 China School of Computer and Information Engineering Qilu Institute of Technology Shandong Jinan250200 China Faculty of Computer Science and Information Technology Karary University Khartoum Sudan

Decentralized Anonymous Payment Systems (DAP), often known as cryptocurrencies, stand out as some of the most innovative and successful applications on the blockchain. These systems have garnered significant attention in the financial industry due to their highly secure and reliable features. Regrettably, the DAP system can be exploited to fund illegal activities such as drug dealing and terrorism. Therefore, governments are increasingly worried about the illicit use of DAP systems, which poses a critical threat to their security. This paper proposes Privacy and Compliance in Regulated Anonymous Payment System Based on Blockchain (PCRAP), which provides government supervision and enforces regulations over transactions without sacrificing the essential idea of the blockchain, that is, without surrendering transaction privacy or anonymity of the participants. The key characteristic of the proposed scheme is using a ring signature and stealth address to ensure the anonymity of both the sender and receiver of the transaction. Moreover, a Merkle Tree is used to guarantee government supervision and enforce regulations. Our proposed scheme satisfies most of the stringent security requirements and complies with the standards of secure payment systems. Additionally, while our work supports government regulations and supervision, it guarantees unconditional anonymity for users. Furthermore, the performance analysis demonstrates that our suggested scheme still remains applicable and effective even when achieving complete anonymity. © The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature 2024.

关键词： Blockchain

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