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2024 IEEE International Conference on Signal Processing, Informatics, Communication and Energy Systems, SPICES 2024

作者： Bhavani, Samineni Kommanti, Hima Bindu National Institute of Technology Department of Computer Science and Engineering Andhra Pradesh India Rajiv Gandhi University of Knowledge Technologies Andhra Pradesh India

ISBN: (纸本)9798350376135

This paper presents STGTP, which combines graph and text-based techniques for stance detection of social media posts. This model learns from the text in the post and the relationships between the posts due to users conversations and interactions. The proposed model uses the embeddings constructed using the textual content and an interaction graph of the posts. The concatenated embeddings are fed to a CNN module for predicting the stance label. Results over the ConvinceMe and CreateDebate datasets show that our proposed STGTP surpasses existing stance detection models by up to 3% in terms of Macro F1-score. © 2024 IEEE.

关键词： Graph embeddings

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Improved Adaptive Fairness-Indexed Technique for Congestion Control in Underwater Networks 3

Improved Adaptive Fairness-Indexed Technique for Congestion ...

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3rd International Conference on Communication, Control, and Intelligent Systems, CCIS 2024

作者： Balaji Vijayan, V. Manojee, K.S. Mohammed Ibrahim, M. Nivethitha, R. Thamaraimanalan, T. Ranjithkumar, M. Hkbk College of Engineering Department of Information Science and Engineering Karnataka Bengaluru India Kongunadu College of Engineering and Technology Department of Information Technology Tamilnadu Trichy India Sona College of Technology Department of Computer Science and Engineering Tamilnadu Salem India K.S.R. College of Engineering Department of Computer Science and Engineering Tamilnadu Thiruchengode India Sri Eshwar College of Engineering Department of Electronics and Communication Engineering Tamilnadu Coimbatore India Knowledge Institute of Technology Department of Computer Science and Business Systems Tamilnadu Salem India

ISBN: (纸本)9798331528201

In underwater acoustic sensor networks (UASNs), effective congestion control is essential for ensuring reliable data transmission and resource efficiency, especially in the face of challenging environmental factors such as water velocity, pressure, temperature, and salinity. This paper introduces a novel Adaptive Fairness-Indexed Congestion Control (AFICC) mechanism that employs Q-learning to dynamically adjust transmission rates among nodes, ensuring equitable resource distribution and minimizing congestion. Our simulations, conducted with 100 nodes at depths of up to 1000 meters, reveal that AFICC significantly outperforms two existing algorithms: Rate-Based Congestion Control (RBCC) and Energy-Aware Congestion Control (EACC). Specifically, AFICC achieved a Packet Delivery Ratio (PDR) of 93%, compared to 85% for RBCC and 80% for EACC. Additionally, the End-to-End Delay was reduced to an average of 150 milliseconds with AFICC, while RBCC and EACC recorded delays of 220 milliseconds and 210 milliseconds, respectively. AFICC also demonstrated a 25% improvement in Energy Efficiency over EACC and maintained a Jain's Fairness Index of 0.92, surpassing the 0.82 and 0.78 indices achieved by RBCC and EACC, respectively. These results confirm that AFICC offers a robust framework for congestion control in UASNs, effectively adapting to fluctuating underwater conditions while ensuring reliable communication and equitable resource allocation. © 2024 IEEE.

关键词： Energy efficiency

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IKM_Lab at BioLaySumm Task 1: Longformer-based Prompt Tuning for Biomedical Lay Summary Generation 22

IKM_Lab at BioLaySumm Task 1: Longformer-based Prompt Tuning...

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22nd Workshop on Biomedical Natural Language Processing and BioNLP Shared Tasks, BioNLP 2023

作者： Wu, Yu-Hsuan Lin, Ying-Jia Kao, Hung-Yu Intelligent Knowledge Management Lab Department of Computer Science and Information Engineering National Cheng Kung University Tainan Taiwan

ISBN: (纸本)9781959429852

This paper describes the entry by the Intelligent knowledge Management (IKM) Laboratory in the BioLaySumm 2023 task1. We aim to transform lengthy biomedical articles into concise, reader-friendly summaries that can be easily comprehended by the general public. We utilized a long-text abstractive summarization longformer model and experimented with several prompt methods for this task. Our entry placed 10th overall, but we were particularly proud to achieve a 3rd place score in the readability evaluation metric. Our code is available at https://***/IKMLab/ BioLaySumm. © 2023 Association for Computational Linguistics.

关键词： knowledge management

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Unified Security Framework for Cloud Applications with IoT Fusion via Decentralized Blockchain 3

Unified Security Framework for Cloud Applications with IoT F...

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3rd International Conference on Applied Artificial Intelligence and Computing, ICAAIC 2024

作者： Mohanraj, B. Kalpana A, M. Priya, M. Abdul Jaleel, D. Karthikeyan A, N. Velusamy, A. Sona College of Technology Department of Information Technology Salem India Government College of Engineering Department of Computer Science Engineering Salem India Knowledge Institute of Technology Department of Information Technology Salem India Madanapalle Institute of Technology & Science Department of Computer Science & Technology Madanapalle India Sri Venkateshwara College of Engineering Department of Cse - Artificial Intelligence Vidyanagar India

ISBN: (纸本)9798350375190

Recent advancements in blockchain-based cloud computing highlight its potential in providing robust data security, integrity, and confidentiality. While cloud computing is increasingly utilized for remote resource access, it presents significant security challenges that necessitate innovative solutions. This research proposes a novel SecureCloudChain IoT (SCC-IoT) architecture, which integrates blockchain technology into cloud-supportive platforms to enhance security measures. The SCC-IoT architecture includes a distributed blockchain approach to ensure data security while maintaining privacy and integrity. This system is designed to be adaptable and scalable, meeting real-world requirements effectively. By promoting a decentralized and efficient environment, SCC-IoT improves the reliability, stability, and load balancing of cloud services. Performance assessments through various experimental conditions have been conducted to evaluate the proposed system. The results demonstrate SCC-IoT's effectiveness in mitigating potential attacks and ensuring secure and reliable cloud computing. This research highlights the significance of integrating blockchain technology into cloud platforms, paving way for more secure and efficient cloud computing solutions. © 2024 IEEE.

关键词： Blockchain

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Performance Enhancement of IoT LoRa Network for Mobile Devices based on Improved AMILCC Technique 1

Performance Enhancement of IoT LoRa Network for Mobile Devic...

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1st International Conference on Computing, sciences and Communications, ICCSC 2024

作者： Venkatesan, C. Thamaraimanalan, T. Mohammed Ibrahim, M. Vinoly, G. Ramkumar, M. Saranya, K. HKBK College of Engineering Department of Electronics and Communication Engineering Karnataka Bengaluru India Sri Eshwar College of Engineering Department of Electronics and Communication Engineering Tamilnadu Coimbatore India Sona College of Technology Department of Computer Science and Engineering Tamilnadu Salem India Tamilnadu Thiruchengode India Knowledge Institute of Technology Department of Computer Science and Business Systems Tamilnadu Salem India

ISBN: (数字)9798350353648

ISBN: (纸本)9798350353648

In Internet of Things (IoT) networks, long-range (LoRa) technology is well-known for its wide communication range, but using it with mobile devices presents unique difficulties. Frequently occurring channel attenuation, which causes packet loss, increased energy consumption, and longer transmission times, is one of these difficulties. An improved Adaptive Mobility-based IoT LoRa Clustering Communication (AMILCC) strategy addressed to solve these problems. AMILCC partitions the network into optimal Spreading Factor (SF) areas by using a 2D random waypoint mobility model. The Hybrid Adaptive Data Rate (HADR) technique, which combines intra-and inter-SF region modifications to improve resource allocation for mobile IoT LoRa devices, is a key component of AMILCC. The simulation findings demonstrate the advantages of AMILCC over conventional stationary techniques, even under low mobility conditions (up to 5 m/s). In particular, AMILCC decreases energy usage by 56.5%, improves packet success ratio (PSR) by 72 %, and decreases end-to-end delay by 45.32%. These outcomes demonstrate how well AMILCC works to enhance efficiency and performance for mobile LoRa applications. © 2024 IEEE.

关键词： Resource allocation

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Billiards Optimization with Modified Deep Learning for Fault Detection in Wireless Sensor Network

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computer Systems science & engineering 2023年第11期47卷 1651-1664页

作者： Yousif Sufyan Jghef Mohammed Jasim Mohammed Jasim Subhi R.M.Zeebaree Rizgar R.Zebari Department of Computer Engineering College of EngineeringKnowledge UniversityErbil44001Iraq Engineering College Al-Kitab UniversityKirkukIraq Energy Eng.Department Technical College of EngineeringDuhok Polytechnic UniversityDuhokIraq Computer Science Department College of ScienceNawroz UniversityDuhokIraq

Wireless Sensor Networks(WSNs)gather data in physical environments,which is some *** ubiquitous sensors face several challenges responsible for corrupting them(mostly sensor failure and intrusions in external agents).WSNs were disposed to error,and effectual fault detection techniques are utilized for detecting faults from WSNs in a timely *** learning(ML)was extremely utilized for detecting faults in ***,this study proposes a billiards optimization algorithm with modified deep learning for fault detection(BIOMDL-FD)in *** BIOMDLFD technique mainly concentrates on identifying sensor faults to enhance network *** do so,the presented BIOMDL-FD technique uses the attention-based bidirectional long short-term memory(ABLSTM)method for fault *** the ABLSTM model,the attention mechanism enables us to learn the relationships between the inputs and modify the probability to give more attention to essential *** the same time,the BIO algorithm is employed for optimal hyperparameter tuning of the ABLSTM model,which is stimulated by billiard games,showing the novelty of the *** analyses are made to affirm the enhanced fault detection outcomes of the BIOMDL-FD *** simulation results demonstrate the improvement of the BIOMDL-FD technique over other models with a maximum classification accuracy of 99.37%.

关键词： Wireless sensor network fault detection reliability deep learning metaheuristics

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Integrity Estimation of Twitter Based Event Recognition Using Scrutiny of Analyzed Data 2

Integrity Estimation of Twitter Based Event Recognition Usin...

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2nd International Conference on Emerging Trends in Information Technology and engineering, ic-ETITE 2024

作者： Siva Rama Lingham, N. Parameswari, M. Karthick, P. Priya, T. Subha Rathi Rajesh, G. Jagannathan, J. Vel Tech Rangarajan Dr.Sagunthala R&d Institute of Science and Technology Department of Computer Science and Engineering Chennai India Knowledge Institute of Technology Department of Computer Science and Engineering Salem India Vel Tech Multi Tech Dr.Rangarajan Dr.Sakunthala Engineering College Department of Computer Science and Engineering Chennai India School of Engineering and Technology Jeppiaar University Chennai India School of Computer Science Engineering and Information Systems Vellore Institute of Technology Department of Software and Systems Engineering Vellore Katpadi632014 India

ISBN: (纸本)9798350328202

Twitter has been observed to be one of the essential data resources for dependable event accreditation. In any case, Twitter-based event affirmation structures can't guarantee assessment concerning their attestation results. Talk statement has been locked in starting late to enable liberal event Recognition. This problem is not yet evident in light of the assumption that most people tend to think that they have a specific limit to the data on Twitter that have frames based on events based on Twitter. Current appraisals see new pieces by seeing and looking at the significant psychological aspects of hitting the details on Twitter. No matter, the scale of the visual cues can be well established, with the intention that it is essential to design an integrated Twitter experiment with another set of extensible data resources to rectify this problem. The problem is the processes by which you should wholeheartedly investigate all converted data because it has different data structures, transfer times, etc. This issue of address, the paper proposes a framework for evaluating the use of Twitter-based events and examines two types of data resources for the reliability of impact testing. Our framework uses the resultant of the request obtaining by various events and relevant to the articles on it. This paper has improved the cleaning test with the proposed process how the integrity estimation of twitter based event can be recognized with the help of group of analyzed data. The evaluation indicates that the proposed system provides visible high-level testing events and a variety of low-level testing events. © 2024 IEEE.

关键词： Impact testing

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Solar Radiation Prediction Using the Random Forest Regression Algorithm 4th

Solar Radiation Prediction Using the Random Forest Regressio...

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4th EAI International Conference on Big Data Innovation for Sustainable Cognitive Computing, BDCC 2021

作者： Thota, Sandeep Kumar Reddy Mala, C. Chandamuri, Praneetha Nooka, Chandana Department of Computer Science and Engineering Rajiv Gandhi University of Knowledge Technologies- RK Valley Campus Andhra Pradesh Kadapa India Department of Computer Science and Engineering National Institute of Technology Tamil Nadu Tiruchirappalli India

ISBN: (纸本)9783031076534

Solar radiation data is critical for solar energy research and development. It is extremely valuable for design and simulation of solar thermal technologies and solar photovoltaic applications because it offers essential data about the energy that impacts the earth. Because conventional energy sources are rapidly decreasing, sustainable energy sources such as solar, biomass, and wind are growing more prominent. Solar energy is the most generally acknowledged renewable energy source since it is abundant all around the planet. As a result, this project examines the model and methodologies utilized to forecast solar radiation. Random forest algorithm is used in solar radiation prediction;this algorithm combines the benefits of both techniques utilized, resulting in faster calculation and convergence to optimal results. © 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： Forecasting

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Investigation of LSSVM and RBFNN-based Techniques for Intrusion Detection Systems for IoT Networks

Investigation of LSSVM and RBFNN-based Techniques for Intrus...

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2023 International Conference on Self Sustainable Artificial Intelligence Systems, ICSSAS 2023

作者： Jayapal, Medida Subhashree, K. Ashwini, B. Satheesh Kumar, D. Dimri, Sushil Chandra Nishant, Neerav Department of Cse Telangana Hyderabad India Karp Agam College of Engineering Department of Computer Science and Engineering Coimbatore India Knowledge Institute of Technology Department of Cse Tamilnadu Salem India Hindusthan College of Engineering and Technology Department of Computer Science and Engineering Coimbatore India Graphic Era Deemed to Be University Department of Computer Science & Engineering Uttarakhand Dehradun India School of Engineering Babu Banarasi das University Department of Computer Science and Engineering Uttar Pradesh Lucknow India

ISBN: (纸本)9798350300857

As more and more devices become internet-enabled, it's no longer possible to imagine modern life without them. In particular, Internet of Things (IoT) devices have become widespread and indispensable. But there are issues that appear to be worsening and for which there are currently no good solutions. The safety of Internet of Things (IoT) devices and other connected gadgets is a growing concern. There has been a lot of development in the area of safeguarding IoT networks, but there is always room for improvement. The proposed methodology consists of three stages: preprocessing the data, selecting the features to use, and training the model. In data preprocessing, normalization and standardization techniques are utilized. The use of particle swarm optimization helps improve feature selection. Models are trained with SVM-RBFNN after information gain is used to choose relevant features. The proposed method achieves better results than both SVM and LSSVM, two widely usedalternatives. © 2023 IEEE.

关键词： Internet of things

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PNESR-DDI: An Effective Drug-Drug Interaction Prediction Model Based on Pretraining Method and Enhanced Subgraph Reconstruction

PNESR-DDI: An Effective Drug-Drug Interaction Prediction Mod...

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2024 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2024

作者： Chen, Ke Han, Xiaosong Li, Xiaoran Liang, Yanchun Xu, Dong Guan, Renchu Jilin University Key Laboratory for Symbol Computation and Knowledge Engineering of National Education Ministry College of Software Changchun China Jilin University Key Laboratory for Symbol Computation and Knowledge Engineering of National Education Ministry College of Computer Science and Technology Changchun China Zhuhai College of Science and Technology Zhuhai Laboratory of Key Laboratory of Symbol Computation and Knowledge Engineering of Ministry of Education School of Computer Science Zhuhai China University of Missouri Christopher S. Bond Life Sciences Center Department of Electrical Engineering and Computer Science Columbia United States

ISBN: (纸本)9798350386226

Drug-Drug Interaction (DDI) task plays a crucial role in clinical treatment and drug development. Recently, deep learning methods have been successfully applied for DDI prediction. However, training deep learning models always need large amount of data, while known DDIs are scarce. To address this challenge, a graph neural network-based DDI prediction model named PNESR-DDI is proposed, which compensates for the lack of DDIs by enriching drug representations. First, to obtain initial node representations that incorporate rich semantic information from the biomedical knowledge graph (KG), a link prediction pre-training method on external KG is proposed in the node embedding pre-training module. Then, considering the large scale of the KG, subgraph extraction for the target drug pairs is introduced to reduce noise and decrease computational complexity in the subgraph anchoring module. After that, the subgraph is updated, and node similarities are propagated in the subgraph reconstruction module. Based on the node similarity scores, the subgraph is pruned and reconstructed, which adjusts node representations to be more conducive to DDI prediction. Finally, the drug embeddings, subgraph representations, and drug fingerprint features are concatenated to predict DDIs. PNESRDDI is evaluated on two benchmark DDI datasets: DrugBank and TWOSIDES. Experiment results show that PNESR-DDI achieves better performance than baselines. Ablation results validate the effectiveness of the pre-training method and the adaptive subgraph reconstruction strategy. © 2024 IEEE.

关键词： knowledge graph

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