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Diving into Brain Complexity: Exploring Functional and Effective Connectivity Networks

Diving into Brain Complexity: Exploring Functional and Effec...

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International Conference on Emerging Technologies, ICET

作者： Abu Bakar Siddique Hassan Bin Khalid Raja Hashim Ali Faculty of Computer Science & Engineering GIK Institute of Engg. Sciences & Technology Topi Pakistan Dept. of Technology & Software Engineering University of Europe for Applied Sciences Berlin Germany

The human brain's intricate functions are under-pinned by a vast network of synapses that enable chemical impulses between neurons. Neuroscientists employ two key approaches, functional and effective connectivity, to understand the brain's complexity, focusing on its operations, cognition, and behavior. While both methods utilize graph theory for network analysis, functional connectivity, which investigates associated brain activity, has seen more substantial research efforts than effective connectivity, which examines information processing's inter-regional impacts. In this research paper, we aim to present an extensive examination of the emerging discipline that combines graph theory with the analysis of brain characteristics, shedding light on how these characteristics arise from the interactions among different groups of neurons. Our primary focus lies in exploring the diverse cognitive and neurological applications that leverage functional Magnetic Resonance Imaging (fMRI) as a tool. Additionally, we offer a comprehensive overview of the methods employed to construct brain networks based on functional and efficient connections. Throughout the discussion, we emphasize the advantages and limitations associated with these approaches.

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Quantitative Trends Analysis on Russian Literature Research in Korean Academia: Insights from KCI Data

Quantitative Trends Analysis on Russian Literature Research ...

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International Conference on Platform Technology and Service (PlatCon)

作者： Ten Alexander Iosifovich Sang-Soo Yeo Young-Ae Jung Dept. of Computer Science Sun Moon University Asan South Korea Dept. of Computer Engineering Mokwon University Daejeon South Kora Dept. of AI Software Technology Sun Moon University Asan South Korea

ISBN: (数字)9798350367874

ISBN: (纸本)9798350367881

This paper presents a quantitative analysis of trends in Russian literature and Korean-Russian translated literature within Korean academia from 1996 to 2024. By analyzing frequency themes and contexts, the study explores evolving research trends, revealing how sociocultural, political, and artistic exchanges between Korea and Russia have influenced each other. Future research using text mining promises to uncover more specific patterns, offering deeper insights into literary and cultural exchanges. This study contributes to understanding the role of literature in expressing and understanding sociopolitical and cultural values in both countries.

关键词： Text mining Translation Statistical analysis Data visualization Curriculum development Market research Cultural differences Indexes

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IoT Based Smart Automated Agriculture and Real Time Monitoring System 2

IoT Based Smart Automated Agriculture and Real Time Monitori...

引用

2nd International Conference on Smart Electronics and Communication, ICOSEC 2021

作者： Shamrat, F. M. Javed Mehedi Hossain, Alamin Roy, Tonmoy Adeeb Khan, Md Ahasanul Khater, Ankit Rahman, Md Tareq Daffodil International University Dept. of Software Engineering Dhaka Bangladesh Daffodil International University Dept. of Computer Science and Engineering Dhaka Bangladesh Dhaka Bangladesh Dalhousie University MEng In Internetworking Halifax Canada Jadavpur University Dept. of Computer Science and Engineering India

ISBN: (纸本)9781665433686

In Bangladesh, agriculture is the primary source of income. It has a significant impact on the country's economy. However, agriculture is being hampered these days due to citizens shifting from rural to urban areas. Monitoring environmental factors is not a natural remedy for increasing crop production. Several causes have a significant impact on efficiency. Consequently, to address these issues, agriculture must incorporate automation. A farmer can save time, cost, resources, and energy by using an automated irrigation device. Traditional agricultural irrigation methods necessitate human interference. Human interference can be reduced with automated irrigation technology. This research work has designed and developed a reliable smart farming system (IoT) to reduce farmers' time costs and resources. Our proposed system can detect temperature, detect the moisture level and water level of the agricultural land, and remotely monitor the land crops. The proposed model sort out into four modules: Water Le vel Detection Module (WLDM) always detect the water level to avoid drops destruction;Soil Moisture Detection Module (SMDM) calculate the soil moisture level from the land, if the level goes down its start the water pump;Temperature Detection Module (TDM) is always counting the temperature and humidity of the air if its high then it will start the fan;Cloud and Notification Module (CNM) is handled the user notification through message and remotely monitoring the data of first three modules to take the necessary steps. The result shows the system successfully performed and it can be noted that our proposed can be implemented with any type of environment and agricultural land. © 2021 IEEE.

关键词： Soil moisture

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Revolutionizing Campus Exploration with GikiLenS: A Deep Learning-Powered Object Detection App

Revolutionizing Campus Exploration with GikiLenS: A Deep Lea...

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International Conference on Emerging Technologies, ICET

作者： Iqra Mueed Usama Arshad Raja Hashim Ali Faculty of Computer Science & Engineering GIK Institute of Engg. Sciences & Technology Topi Pakistan Dept. of Technology & Software Engineering University of Europe for Applied Sciences Berlin Germany

For newcomers and tourists, navigating university campuses can be difficult, resulting in aggravation and lost time. We respond by introducing “GikiLenS”, an object identification application driven by deep learning that revolutionizes campus exploration by accurately identifying buildings and landmarks while enhancing user experience. GikiLenS is a comprehensive and user-friendly smartphone application that precisely caters to the demands of newcomers and visitors, unlike previous studies in campus navigation. Our study intends to close this gap and develop a reliable method for identifying campus buildings, optimizing navigation, and enhancing user experience. Through rigorous testing, GikiLenS has proven to have remarkable real-time building detection accuracy, highlighting its potential as an important tool for campus exploration. The app's outcomes and conclusions demonstrate how well it works to give users specific building information, promoting a more knowledgeable and enjoyable campus experience. The importance of our findings lies in the development of a unique, user-centered app that offers a cutting-edge method of campus mobility. Our contribution consists of a game-changing method for building detection that improves campus exploration and user pleasure.

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Discrete Shuffled Frog Leading Algorithm for Multiple-product Human-robot Collaborative Disassembly Line Balancing Problem

Discrete Shuffled Frog Leading Algorithm for Multiple-produc...

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2022 IEEE International Conference on Systems, Man, and Cybernetics, SMC 2022

作者： Fan, ChenYang Wang, JiaCun Guo, XiWang Qin, ShuJin Zhou, MengChu Qi, Liang Engineering of Liaoning Petrochemical University School of Information and Control Fushun China Engineering of Monmouth University Dept. Computer Science and Software West Long Branch United States Liaoning Petrochemical University Fushun China Monmouth University West Long Branch United States College of Economics and Management of Shangqiu Normal University Shangqiu China Dept. Electrical and Computer Engineering of New Jersey Institute of Technology Newark United States Tech of Shandong University of Science and Technology Dept. Computer Intelligent Sci. & Qingdao China

ISBN: (数字)9781665452588

ISBN: (纸本)9781665452588

With the rapid development of recycling and remanufacturing technologies, disassembly line balancing problems (DLBP) have drawn great attention. Considering the limitation of disassembly by humans or robots alone, this paper focuses on human-robot collaborative disassembly lines. Specifically, this work proposes a multi-product human-robot collaborative disassembly line balancing model to tackle the inflexibility of single product disassembly and inconsistency in recovery values of different product components. Its objective is to maximize disassembly profit. As a commercially available solver, IBM's CPLEX is used to obtain the exact solution of DLBP and verify the correctness of the proposed mathematical model. A discrete shuffled frog leading algorithm is newly designed to solve the sizable problems. Experimental results show that the proposed algorithm has a fast convergence rate and can find solutions consistent with those with CPLEX but requires much less time than the latter, thus advancing the field of disassembly automation. © 2022 IEEE.

关键词： Robots

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Energy Management for Solar PV Generation with Contactless Power Transfer

Energy Management for Solar PV Generation with Contactless P...

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IEEE Electronic Power Grid (eGrid)

作者： Aratrika Ghosh Abhisek Ukil Aiguo Patrick Hu Dept. of Electrical Computer and Software Engineering The University of Auckland Auckland New Zealand

ISBN: (数字)9798350331592

ISBN: (纸本)9798350331608

In the future, solar photovoltaics(PV) and battery energy storage will be widely used for distributed stand-alone generation. Therefore, in this paper, the energy management scheme is proposed when PV and battery energy storage are integrated with contactless power transfer to supply the household loads for emergency electrification. The proposed system consists of a 215W PV module, 24V li-ion battery and 48V DC bus, with LCL resonant circuit for Inductive Power Transfer(IPT). This system can maintain constant load voltage at 48V when there is a change in solar irradiance or load with contactless power transfer at 93% efficiency. The voltage and current characteristics of the model are analysed when the PV module is at STC(Standard Test Conditions) using MATLAB/Simulink software.

关键词： Photovoltaic systems Lithium-ion batteries RLC circuits Voltage software Mathematical models Energy management

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Flexibility and Privacy: A Multi-Head Federated Continual Learning Framework for Dynamic Edge Environments

Flexibility and Privacy: A Multi-Head Federated Continual Le...

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International Symposium on Computing and Networking (CANDAR)

作者： Chunlu Chen Kevin I-Kai Wang Peng Li Kouichi Sakurai Graduate School of Information Science and Electrical Engineering Kyushu University Fukuoka Japan dept. of Electrical Computer and Software Engineering The University of Auckland Auckland New Zealand School of Computer Science and Engineering The University of Aizu Aizuwakamatsu Japan Faculty of Information Science and Electrical Engineering Kyushu University Fukuoka Japan

Federated Learning (FL) and the Internet of Things (IoT) have revolutionized data processing and analysis, overcoming the traditional limitations of cloud computing. However, traditional machine learning strategies lead to increased costs and catastrophic forgetting due to model retraining with new datasets. Continual learning has been proposed to counter this, enabling models to adapt to new data while preserving previous knowledge, which is beneficial for dynamic edge environments. Despite these advantages, the retention of previous knowledge during the continual learning process may lead to the information leakage. To address the inherent challenges of multitask scenarios, we present a Federated Continual Learning (FCL) framework that integrates the privacy-preserving benefits of Federated Learning (FL) into a continual learning system, ensuring both continual learning and privacy preservation in edge computing data processing and analysis. Specifically, our architecture introduces dedicated fully-connected layers for each task. This architecture ensures that distinctive features pertinent to each task are not only captured but also preserved throughout the model’s lifespan. Within our framework, data is processed via task-specific layers. Subsequently, the final label is determined by associating it with the paramount prediction value, thus capitalizing on the model’s comprehensive knowledge reservoir to bolster prediction accuracy. We subjected our FCL framework to rigorous validation using two benchmark datasets: MNIST and CFAR-10. Experimental outcomes unequivocally substantiate the efficacy of our proposed methodology.

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Gru-Based Anti-Jamming Dynamic Frequency Hopping Strategy Optimization

Gru-Based Anti-Jamming Dynamic Frequency Hopping Strategy Op...

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IEEE International Conference on Cloud Computing and Big Data Analysis (ICCCBDA)

作者： Zhihang He Xianhua Niu Jianhong Zhou Shiyang Zhou Jianwei Duan Caimei Wu Xinhua Feng Dept. of Computer and Software Engineering Xihua University Chengdu China Chengdu Aerospace Communication Equipment Co. Chengdu China Sichuan Institute of Atomic Energy Chengdu China

ISBN: (数字)9798331530808

ISBN: (纸本)9798331530815

In complex and dynamically changing environments, intelligent reconnaissance and jamming techniques employed by frequency hopping jamming systems pose significant challenges for maintaining stable communications. To address the challenge of dynamic anti-jamming, this paper proposes an adaptive dynamic frequency hopping strategy optimization capable of adjusting the frequency hopping in response to jamming. A GRU-based prediction model is utilized by the strategy to predict jamming frequency points. Jamming frequency intervals are effectively avoided, and the Lempel-Greenberger bound is satisfied by the dynamic frequency hopping strategy, thereby achieving dynamic anti-jamming performance. Simulation results show that our proposed GRU-based prediction model achieves higher prediction accuracy compared to existing approaches.

关键词： Cloud computing Accuracy Simulation Computational modeling Spread spectrum communication Predictive models Reconnaissance Stability analysis Jamming Optimization

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Establishing a Correlation of Clinical Characteristics with the Level of Tumor Mutation Burden in Urothelial Bladder Carcinoma

Establishing a Correlation of Clinical Characteristics with ...

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IEEE International Conference on Bioinformatics and Biomedicine (BIBM)

作者： Lujain Alshomali Rania Khorma Abdullah Al-Refai Abedalrhman Alkhateeb Software Engineering Dept. Princess Sumaya University for Technology Amman Jordan Computer Science Dept. Lakehead University Thunder Bay Canada

Bladder cancer is a complex disease and one of the most lethal types of cancer. Recently, some malignancies, including bladder carcinomas, have shown better results with immunotherapy using immune checkpoint inhibitors. Tumor mutational burden (TMB) is a potential biomarker for predicting tumor behavior and immunotherapy response as an outcome. Publicly available clinical data from the bladder cancer of TCGA project is used to analyze correlations of clinical variables with an increased tumor mutation burden (TMB) number compared with those with a lower number of mutations. The threshold for the high mutation burden in the analysis was set at 10 mutations (Mut) per Megabase (Mb). The Chi-Square test (χ 2 ) was used to compare categorical data. The Chi-Square "Best first" method was used to find a correlation between clinical variables and TMB, then compared with the p-value of significance (p<0.05). A significant correlation was found between TMB and Race, Neoplasm Histologic Grade, and gender when applying the Best First/Chi-Square method to clinical variables and level of TMB. This enables further investigation and application of the prediction models of the level of TMB, responsiveness to immunotherapy, and prognosis based on the clinical features of the patients.

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Language Models for Code-switch Detection of te reo Maori and English in a Low-resource Setting

Language Models for Code-switch Detection of te reo Maori an...

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2022 Findings of the Association for Computational Linguistics: NAACL 2022

作者： James, Jesin Yogarajan, Vithya Shields, Isabella Watson, Catherine Keegan, Peter J. Jones, Peter-Lucas Mahelona, Keoni Dept. of Electrical Computer and Software Engineering The University of Auckland New Zealand Strong AI Lab School of Computer Science The University of Auckland New Zealand Te Puna Wananga The University of Auckland New Zealand Te Hiku Media New Zealand

ISBN: (纸本)9781955917766

Te reo Maori, New Zealand's only indigenous language, is code-switched with English. Maori speakers are atleast bilingual, and the use of Maori is increasing in New Zealand English. Unfortunately, due to the minimal availability of resources, including digital data, Maori is under-represented in technological advances. Cloud-based multilingual systems such as Google and Microsoft Azure support Maori language detection. However, we provide experimental evidence to show that the accuracy of such systems is low when detecting Maori. Hence, with the support of Maori community, we collect Maori and bilingual data to use natural language processing (NLP) to improve Maori language detection. We train bilingual sub-word embeddings and provide evidence to show that our bilingual embeddings improve overall accuracy compared to the publiclyavailable monolingual embeddings. This improvement has been verified for various NLP tasks using three bilingual databases containing formal transcripts and informal social media data. We also show that BiLSTM with pretrained Maori-English sub-word embeddings outperforms large-scale contextual language models such as BERT on down streaming tasks of detecting Maori language. However, this research uses large models 'as is' for transfer learning, where no further training was done on Maori-English data. The best accuracy of 87% was obtained using BiLSTM with bilingual embeddings to detect Maori-English codeswitching points. © Findings of the Association for Computational Linguistics: NAACL 2022 - Findings.

关键词： Embeddings

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