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2nd IEEE International Conference on Integrated Circuits and Communication Systems, ICICACS 2024

作者： Parvathi, r. Sarkar, Swagata Supraja, M. Swagatha, J.P. Kanagajothi, d. Srimathi, S. SRM Arts and Science College Computer Science Kattankulathur India Sri Sairam Engineering College Department of Artificial Intelligence and Data Science Chennai India Rajarajeshwari College of Engineering Electronics and Communication Engineering Bangalore India Panimalar Engineering College Department of IT Poonamallee India Vel Tech Rangarajan Dr. Sagunthala R&D Institute of Science and Technology Department of Mathematics India SIMATS Technology Saveetha School of Engineering Department of Bio Chennai India

ISBN: (纸本)9798350317558

Safe traffic management requires citywide flow projections. Transportation, public safety, and municipal planning are substantially affected. It forecasts city intake and outflow using flow data. Traditional methods are inaccurate and less scalable in complex, dynamic metropolitan settings. This study uses deep learning to predict crow traffic. deep learning can handle complicated nonlinear circumstances, making neural network crowd flow prediction more popular. However, deep learning has solved this problem, but it requires plenty of data. data shortages may develop when a city's service or infrastructure is installed. This research introduces a deep spatial temporal transfer learning system that predicts crowd movement in a data-sparse target city using data from a data-rich source city. Geographic and temporal patterns in crow traffic data may be captured by trained algorithms, enhancing prediction accuracy. We found that our deep learning-based solution is more accurate and robust than prior methods. Crow migration patterns are properly predicted by this research, improving urban planning and management. © 2024 IEEE.

关键词： Learning systems

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Auditing citation polarization during the early COVId-19 pandemic

arXiv

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arXiv 2023年

作者： You, Taekho Lee, June Young Park, Jinseo Yun, Jinhyuk Institute for Social Data Science Pohang University of Science and Technology Korea Republic of Center for Global R&D Data Analysis Korea Institute of Science and Technology Information Korea Republic of School of AI Convergence Soongsil University Korea Republic of

The recent pandemic stimulated scientists to publish a significant amount of research that created a surge of citations of COVId-19-related publications in a short time, leading to an abrupt inflation of the journal impact factor (IF). By auditing the complete set of COVId-19-related publications in the Web of Science, we reveal here that COVId-19-related research worsened the polarization of academic journals: the IF before the pandemic was proportional to the increment of IF, which had the effect of increasing inequality while retaining the journal rankings. We also found that the most highly cited studies related to COVId-19 were published in prestigious journals at the onset of the epidemic. Through the present quantitative investigation, our findings caution against the belief that quantitative metrics, particularly IF, can indicate the significance of individual papers. rather, such metrics reflect the social attention given to a particular study. © 2023, CC BY.

关键词： COVId-19

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Effect of silicon carbide-based iron catalyst on reactor optimization for non-oxidative direct conversion of methane

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Journal of Energy Chemistry 2023年第6期81卷 519-532,I0012页

作者： Eun-hae Sim Sung Woo Lee Jin Ju Lee Seung Ju Han Jung Ho Shin Gracia Lee Sungrok Ko Kwan-Young Lee Yong Tae Kim C1 Gas&Carbon Convergent Research Center Korea Research Institute of Chemical TechnologyDaejeon 34114Republic of Korea Department of Chemical and Biological Engineering Korea University145 Anam-roSeoul 02841Republic of Korea Department of Chemical and Biomolecular Engineering Yonsei University50 Yonsei-roSeoul 03722Republic of Korea Chemical Data-Driven Research Center Korea Research Institute of Chemical TechnologyDaejeon 34114Republic of Korea Prospective Tech.Exploring Section Basic Chemical R&D centerLottechemical Corp.Daejeon 34059Republic of Korea Department of Advanced Materials and Chemical Engineering University of Science and Technology(UST)Gajeong-dongYuseongDaejeon 34113Republic of Korea

The conversion of methane to olefins,aromatics,and hydrogen(MTOAH)can be used to stably obtain hydrocarbons when the effect of the catalytic surface is optimized from the reaction engineering *** this study,Fe/Si C catalysts were packed into a quartz tube *** catalytic surfaces of Si C and the impregnated Fe species decreased the apparent activation energies(E_a)of methane consumption in the blank reactor between 965 and 1020℃.Consequently,the hydrocarbon yield increased by 2.4times at 1020℃.Based on the model reactions of ethane,ethylene,and acetylene mixed with hydrogen in the range of 500-1020℃,an excess amount of Fe in the reactor favored the C-C coupling reaction over the selective hydrogenation of acetylene;consequently,coke formation was favored over the hydrogenation *** gas-phase reactions and catalyst properties were optimized to increase hydrocarbon yields while reducing coke *** 0.2Fe catalyst-packed reactor(0.26 wt%Fe)resulted in a hydrocarbon yield of 7.1%and a coke selectivity of<2%when the ratio of the void space of the postcatalyst zone to the catalyst space was adjusted to be≥*** on these findings,the facile approach of decoupling the reaction zone between the catalyst surface and the gas-phase reaction can provide insights into catalytic reactor design,thereby facilitating the scale-up from the laboratory to the commercial scale.

关键词： Non-oxidative methane conversion Ethylene Aromatic Methane pyrolysis Fe/SiC Coke resistance Catalytic reactor

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Spectrum Aware Unequal Clustering with route Selection Protocol for Energy Efficient Cognitive radio Wireless Sensor Networks 2

Spectrum Aware Unequal Clustering with Route Selection Proto...

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2nd International Conference on Augmented Intelligence and Sustainable Systems, ICAISS 2023

作者： Arthi, A. Nayak, Chinmaya Kumar Subbaiyan, Arunmurugan Muthalakshmi, M. Srimathi, S. Venkatesh, P. Rajalakshmi Institute of Technology Department of Artificial Intelligence and Data Science Chennai India Sri Sri University Sri Sri Vihar Faculty of Emerging Technologies Odisha 754006 India Periyar Maniammai Institute of Science & Technology Ece Tamilnadu Thanjavur India Vel Tech Rangarajan Dr. Sagunthala R&d Institute of Science and Technology India Saveetha School of Engineering Simats Department of Bio Technology Tamilnadu Chennai India Ramco Institute of Technology Department of Ece Rajapalayam India

ISBN: (纸本)9798350325799

Spectrum awareness with energy conservation is a main prerequisite in Cognitive radio Sensor Networks (CrSN) as the sensor nodes (SNs) operate in physically unattended areas and are energy constrained. Cluster based routing combined with channel allocation accomplishes energy effective path selection as a countermeasure to preserve energy. It is required to perform reliable routing with cluster based channel distribution contributing to development of network efficiency. Therefore, this study introduces a new Spectrum Aware metaheuristic Unequal Clustering with route Selection Protocol (SAMUC-rSP) technique for Energy Efficient CrSN. The focus of the SAMUC-rSP technique lies in the effective selection of CHs and routes in the CrSN. For accomplishing this, the SAMUC-rSP technique designs fruit fly optimization algorithm based unequal clustering (FFOA-UC) technique to produce unequal clusters in the CrSN. Besides, moth search optimization based routing (MSOr) approach is used to produce an optimal set of routes in the CrSN. The simulation results of the SAMUC-rSP technique are examined in terms of different measures in python environment. Extensive comparison analysis reported the superior results of the SAMUC-rSP method over other existing techniques in the CrSN. © 2023 IEEE.

关键词： Cognitive radio

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The role of Edge Computing in Enhancing Cloud Performance and reducing Latency 7

The Role of Edge Computing in Enhancing Cloud Performance an...

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7th International Conference on Contemporary Computing and Informatics, IC3I 2024

作者： Sharma, Puneet Shyamala devi, J. Manikandan, K. Ani Minisha, r. Harini, r. Karthik, S. Sr University School of Cs & Ai Telangana Warangal India Rajalakshmi Institute of Technology Chennai India Tamilnadu Vellore632014 India R.M. K. Engineering College Department of Artificial Intelligence and Data Science India Veltech Multitech Dr. Rangarajan Dr. Sakunthala Engineering College Department of Computer Science and Engineering India Vel Tech Rangarajan Dr. Sagunthala R&d Institute of Science and Technology Department of Mathematics Avadi Tamilnadu Chennai600062 India

ISBN: (纸本)9798350350067

The flexibility of implementing edge computing over cloud infrastructures has become essential for responding to the need for reducing latency and improving data processing in a distributed environment. This research aims to determine the use of edge computing in enhancing the effectiveness of cloud utilization through conducting machine learning investigations utilizing random Forest in the determination of the optimum contribution of resources. The work also set up and applied real-world datasets from Kaggle to illustrate that edge computing can alleviate the latency issue mainly by offloading computationally intensive tasks to edge nodes away from the central cloud. The results show that edge computing with the help of Machine learning is the best solution for cutting-edge cloud environments as it is fast and does not aggravate the problems of excessive use of resources. Therefore, this research will be a great addition to the existing literature on the integration of cloud and edge computing and is hoped to offer useful information for future developments in the field of cloud computing. © 2024 IEEE.

关键词： Cloud platforms

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Leveraging digital Twins and AI for Enhanced Clinical decision Support in Endometrial Cancer Treatment 5

Leveraging Digital Twins and AI for Enhanced Clinical Decisi...

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5th International Conference on IoT Based Control Networks and Intelligent Systems, ICICNIS 2024

作者： Palarimath, Suresh Maran, Pyingkodi Wilfred Blessing, N.r. Cibi, A. Kavitha, S.J. Steffi, S. College of Computing and Information Sciences University of Technology and Applied Sciences Salalah Oman Kongu Engineering College Department of Computer Application Erode India College of Computing and Information Sciences University of Technology and Applied Sciences Ibri Oman Virtusa Consulting Service Private Limited Data & Analytics Visualizaation - Delivery Chennai India Vels Institute of Science Technology and Advanced Studies Department of Computer Science and Engineering Chennai India Vel Tech Rangarajan Dr. Sagunthala R&D Institute of Science and Technology Computer Science and Engineering Chennai India

ISBN: (纸本)9798331518097

digital twins are a relatively new concept that have lately garnered interest in the industrial sector. digital twins are virtual models that mirror actual products. In this research, we suggest using the technology of digital twins to the field of healthcare in order to enhance clinical decision support and to provide more individualized treatment for patients. When artificial intelligence (AI) is combined with digital twins, healthcare professionals are able to more effectively analyze large volumes of varied data and increase their ability to make diagnostic and therapeutic decisions. In this research, we provide a conceptual framework for leveraging digital twins and AI to solve existing limits in cancer care, notably endometrial cancer therapy. This research study mainly focuses on cancer care, and more specifically on endometrial cancer treatment. In addition, we analyze the possible challenges and opportunities that may arise throughout the process of integrating this technology in healthcare settings. Our overarching objective is to improve the standard of treatment as well as the clinical results for people who have cancer. © 2024 IEEE.

关键词： Lung cancer

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Exploring Meibomian Gland dysfunction Grading: A Comparison of Machine Learning and XAI Approaches 2

Exploring Meibomian Gland Dysfunction Grading: A Comparison ...

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2nd IEEE International Conference on Image Processing and Computer Applications, ICIPCA 2024

作者： Liang, Congxiao Wang, Mini Han Liu, Haoyang Chong, Kelvin K.L. Beijing Institute of Technology Center for Sport Science and Physical Education Zhuhai519000 China The Department of Ophthalmology and Visual Sciences Chinese University of Hong Kong 999077 Hong Kong The Faculty of Data Science City University of Macau Macau999078 China The Center of Big Data and Ai Zhuhai Institute of Advanced Technology Chinese Academy of Sciences Zhuhai519000 China The R&d Center of Ai and Digital Medicine Guangdong Digital Medicine Promotion Science and Technology Co. Ltd Guangdong 519000 China Guangdong Medical University Dongguan China Zhuhai Institute of Advanced Technology Chinese Academy of Sciences Zhuhai China Perspective Technology Group Zhuhai China The Faculty of Medicine Chinese University of Hong Kong Hong Kong

ISBN: (纸本)9798350360240

This research investigates the effectiveness of employing a machine learning methodology and an Explainable Artificial Intelligence (XAI) tool for the assessment of Meibomian Gland dysfunction (MGd). Utilizing resNet34, a convolutional neural network (CNN), trained on a diverse dataset comprising images depicting varying degrees of MGd severity, the machine learning approach endeavors to classify MGd severity into four distinct grades (0, 1, 2, 3) by leveraging extracted characteristic features. Findings reveal comparable accuracy between resNet34 and the XAI tool, with resNet34 achieving a slightly lower accuracy of 99.1% in contrast to the XAI tool's 99.4%. However, resNet34 demonstrates marginally higher precision and F1-score at 98.2% and 98.45%, respectively, suggesting a nuanced advantage in precision-recall equilibrium. Conversely, the XAI tool maintains a commendable precision of 97.2% and an Fl-score of 97.94%, alongside a recall of 98.7%, underscoring its effectiveness in discerning positive instances. Noteworthy is that resNet34 operates as a black-box model, providing limited interpretability, while the XAI tool emphasizes transparency by furnishing clinicians with understandable MGd grading elucidations derived from meibomian gland segmentation and atrophy analysis. This transparency enhances trust and comprehension, which are integral for clinical acceptance and decision-making. Furthermore, the utilization of XAI heatmap generation facilitates data quality enhancement, as evidenced by instances of erroneous artifact identification, emphasizing the imperative nature of artifact elimination for the refinement of MGd grading procedures. © 2024 IEEE.

关键词： Convolutional neural networks

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Hybrid Machine Learning Approach of "KNN – LSTM" for detection of Brain Tumour in Mr images 15

Hybrid Machine Learning Approach of "KNN – LSTM" for detect...

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15th International Conference on Computing Communication and Networking Technologies, ICCCNT 2024

作者： Uganya, G. Gunale, Anuja Govindrao Kulkarni, Shruthi V. rajalakshmi, d. Valsaraj, Vani Srividhya, B. Department of ECE Vel Tech Rangarajan Dr.Sagunthala R&D Institute of Science and Technology Chennai India Department of Computer Engineering UPL University of Sustainable Technology Block no 402 Ankleshwar-Valia Road Gujarat Vataria India Department of AIML HKBK College of Engineering opposite manyata tech park Nagawara Karnataka Bengaluru India Department of Data Science and Business Systems SRM Institute of Science and Technology Kattankulathur Chennai India Department of Computer Application Faculty of Humanities and Sciences SRM Institute Of Science and Technology Tamil Nadu Chengalpattu India

ISBN: (纸本)9798350370249

Now a days, more existing techniques are available to found tumours on brain from Mr images. But still the researchers are concentrated on to detect the brain tumour accurately with less time. So, this work proposed the hybrid machine learning algorithm of "K-Nearest Neighbour – Long Short-Term Memory" ("KNN-LSTM") with increased ‘accuracy’, ‘sensitivity’, ‘specificity’, with less ‘time consumption’. In this work, the brain tumour is identified by predicting the Mr images from huge dataset with usage of machine learning approach of "KNN-LSTM". Brain tumour detection is difficult when to compare the abnormal and typical tissues of large capacity. In order to enable a speedy recovery following therapy, brain tumours should be identified as soon as feasible. If the brain tumour is discovered early on, patients can benefit from improved care. due to the irregular spreading of the abrasions, brain tumours are still difficult to identify even with intensive study. Sometimes the tiny tumours look like normal, but it is challenging to find it. So, the techniques are need to increase the classification accuracy, and selecting the useful features. This paper proposes a hybrid machine learning model "KNN-LSTM" for categorizing the tumours with predicting through Mr images. MrI brain imaging collection is used for our experimentation. The "KNN" algorithm usage is to extract the important characteristics from pictures once the data has been pre-processed effectively. The proposed work predicts the brain tumour with 99.15% of ‘accuracy’, 98% of ‘sensitivity’, 97.12% of ‘specificity’ and testing time consumption of 0.5 minutes. © 2024 IEEE.

关键词： Image segmentation

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dTrAN: A Special Use Case of rAN Optimization using digital Twin

arXiv

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arXiv 2024年

作者： Tunc, Caglar duran, Kubra Bilgin, Buse Kalem, Gökhan Canberk, Berk Next-Gen R&D/6GEN Lab Turkcell Istanbul Turkey School of Computing Engineering and The Built Environment Edinburgh Napier University United Kingdom Next-Gen R&D Turkcell Technology Istanbul Turkey BTS Group Istanbul Turkey Department of Artificial Intelligence and Data Engineering Istanbul Technical University Turkey

The emergence of beyond 5G (B5G) and 6G networks underscores the critical role of advanced computer-aided tools, such as network digital twins (dTs), in fostering autonomous networks and ubiquitous intelligence. Existing solutions in the dT domain primarily aim to model and automate specific tasks within the network lifecycle, which lack flexibility and adaptability for fully autonomous design and management. Unlike the existing dT approaches, we propose rAN optimization using the digital Twin (dTrAN) framework that follows a holistic approach from core to edge networks. The proposed dTrAN framework enables real-time data management and communication with the physical network, which provides a more accurate and detailed digital replica than the existing approaches. We outline the main building blocks of the dTrAN and describe the details of our specific use case, which is rAN configuration optimization, to demonstrate the applicability of the proposed framework for a real-world scenario. Copyright © 2024, The Authors. All rights reserved.

关键词： 5G mobile communication systems

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Certain Investigation of Optimization Methods of Sensor Nodes in Biomedical recording Systems 9

Certain Investigation of Optimization Methods of Sensor Node...

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9th International Conference on Advanced Computing and Communication Systems, ICACCS 2023

作者： Ashokkumar, N. Pappa, C. Kanmani Siva Kumar, A. Srinivasan, dhamodharan Vijay, M.M. Mohan Babu University Department of ECE Tirupati India Vel Tech Rangarajan Dr.Sagunthala R&D Institute of Science and Technology Department of ECE Tamilnadu Chennai India SRM Institute of Science and Technology Department of Data Science and Business Systems Chennai India Sri Eshwar College of Engineering Department of Computer and Communication Engineering Tamilnadu Coimbatore India SCAD College of Engineering and Technology Department of ECE Tirunelveli India

ISBN: (纸本)9798350397376

Biosensors will monitor a patient's physiological signals (ECG, EEG, etc.) and send an alarm as soon as irregularities are discovered. downsized biomedical sensors are the focus of this article, which explores innovative circuit approaches for miniaturised sensors. In order to demonstrate the proposed biomedical compression engine, ECG compression was applied and assessed. A MIP (mixed-integer programming model) is used to represent and solve the clustering issue based on the power consumption analysis. It follows that an architectural model that may be used to design ECG monitoring systems has been provided, as well as an in-depth study of the ECG monitoring system's value chain and a complete evaluation of relevant literature based on an expert classification. Time synchronisation and signal synthesis are the two stages of the distributed cooperative sensing algorithm's execution of ECG signal gathering. This method uses the mutation probability of each solution to prevent local convergence. © 2023 IEEE.

关键词： Electrocardiograms

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