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2nd International Conference on Artificial Intelligence, Blockchain, and Internet of Things, AIBThings 2024

作者： Khan, Andleeb Nand, Parma Bhushan, Bharat Hameed, Alaa Ali Jamil, Akhtar Sharda School of Engineering & Technology Department of Computer Science & Engineering Greater Noida India Istinye University Department of Computer Engineering Istanbul Turkey National University of Computer and Emerging Sciences Department of Computer Science Islamabad Pakistan

ISBN: (纸本)9798331529529

With the advancing technology, it becomes difficult to cope up with novel trends and configurations. Similarly, it is difficult to secure the systems against each emerging threat. With this the loopholes in conventional forms of system security based on centralized format are now visible. Hence there are explorations in various alternatives of centralized system authentication and authorization. Blockchain technology has appeared as a means of securing the systems as it works on decentralization thereby by passing the loopholes that exist in centralized authentication. Hence, this study lays down the recent advancements in the field of blockchain authorization and authentication techniques. It further supplements the study with an explanation of MetaMask extension which connects the user to the blockchain network thereby enabling decentralization in system security. This study further proposes the future potentials of MetaMask and blockchain integration. © 2024 IEEE.

关键词： Authentication Protocol

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DALR: A Dynamically Adjustable Linear Regression Model for Handling Stream Data 4

DALR: A Dynamically Adjustable Linear Regression Model for H...

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4th International Conference on computer, Communication, Control and Information Technology, C3IT 2024

作者： Mondal, Rishita Mandal, Sourab Dutta, Paramartha Pal, Arindrajit Visva-Bharati Department of Computer & System Sciences WB Santiniketan India Academy of Technology Department of Computer Science and Engineering Adisaptagram WB India

ISBN: (纸本)9798350389128

In response to the escalating demand for machine learning techniques capable of handling real-time data streams, particularly in applications like stock markets, this research dives deep into the domain of stream regression. The objective is to adeptly navigate challenges posed by evolving data over time, differentiating it from conventional static regression. Existing models, such as Fast and Incremental Model Trees with Drift Detection (FIMT-DD), k-Nearest Neighbours (KNN), and Adaptive Linear Regression (ALR) and some other studies, though competent, exhibit certain limitations like learning based on certain portions of data, using a significantly large memory and not capable of handling each data in every timestamp it arrives. We propose the Dynamically Adjustable Linear Regression Model (DALR) to address these shortcomings. DALR introduces an adaptive regression model that dynamically evolves based on historical knowledge and newly acquired data, providing an innovative solution to the intricacies of dynamic data streams. This endeavor has also highlighted that the DALR algorithm consistently demonstrates strong performance across various datasets according to the RMSE metric, surpassing other state-of -the-art algorithms in most instances. © 2024 IEEE.

关键词： Financial markets

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Resource Allocation in Edge Computing Environment Using Deterministic Policy Gradient Algorithm 10

Resource Allocation in Edge Computing Environment Using Dete...

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10th International Conference on Advanced Computing and Communication systems, ICACCS 2024

作者： Shabariram, C.P. Shanthi, N. Ponnuswamy, P. Priya Subramaniyaswamy, V. Sathana, V. Psg Institute of Technology and Applied Research Department of Computer Science and Engineering Coimbatore India Kongu Engineering College Department of Computer Science and Engineering Erode India School of Computing Sastra University Department of Computer Science and Engineering Thanjavur India

ISBN: (纸本)9798350384369

In recent years, the edge computing paradigm enables the movement of processing units and storage nearer to the data available locations. The mechanism completes the computation in a short span of time in minimum bandwidth. Edge ecosystem is a type of distributed computing that is sensitive to topology and geography;the Internet of Things is a prime instance of this. Rather than referring to a single technology, Edge computing refers to an architecture. This paper proposes a resource allocation methodology that will enliven the situation between users and edge servers. By creating continuous control at the edge servers to determine resource allocation, edge computing improves reaction time, provides high security with decreased risk, scalability, lowers transmission costs, and versatility (offload targets, migration bandwidth and computing resources). The Deterministic Policy Gradient, Deep learning and Quality Network concepts are combined in the proposed system. The continuous action space is achieved by a deterministic policy gradient. The experience relay includes a quality network. In the proposed system, the actor-critic network produces a single continuous action instead of resulting probability based actions. The critic-part uses Q-value from a quality network based on current status and activity. The goal of the proposed system is to develop a Deep Deterministic Policy Gradient methodology to allocate servers for mobile users with the help of the Edge computing while taking computation resources, offloading goals and migration bandwidth into consideration. The simulation result indicates that deterministic policy gradients integrated deep learning models improve the system performance compared with Game theory. © 2024 IEEE.

关键词： Mobile edge computing

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Enhanced Cyclone Intensity Estimation Through CNN Analysis of INSAT-3D IR Imagery 12th

Enhanced Cyclone Intensity Estimation Through CNN Analysis o...

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12th International Conference on Recent Trends in Computing, ICRTC 2024

作者： Kumar, Divvela Vishnu Sai Arora, Deepak Tiwari, Shivam Department of Computer Science and Engineering Amity School of Engineering and Technology Amity University Lucknow Campus Lucknow India Department of Computer Science and Engineering Sharda University Greater Noida India

ISBN: (纸本)9789819788354

Cyclone forecasting using satellite pictures involves anticipating the cyclone’s intensity in advance of its arrival. The results of this study can inform people’s preparations for the cyclone. In order to save lives and reduce damage, cyclone strength forecast is crucial. As a result of the many dangers they pose, these cyclones are among the most expensive natural disasters that have ever occurred. Five separate CNN (Convolutional Neural Network) models are presented by the authors to assess the cyclone’s severity using INSAT-3D infrared pictures. While many methods exist for making such an assessment, the authors’ top model yields an RMSE of 10.02 kts for intensity. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Convolutional neural networks

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A Trust-Based Hierarchical Consensus Mechanism for Consortium Blockchain in Smart Grid

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Tsinghua science and Technology 2023年第1期28卷 69-81页

作者： Xingguo Jiang Aidong Sun Yan Sun Hong Luo Mohsen Guizani School of Computer Science(National Pilot Software Engineering School) Beijing University of Posts and TelecommunicationsBeijing 100876China Institute of Food safety and Nutrition Jiangsu Academy of Agricultural SciencesNanjing 210000China Computer Science and Engineering Department Qatar UniversityDoha 2713Qatar

As the smart grid develops rapidly,abundant connected devices offer various trading *** raises higher requirements for secure and effective data *** centralized data management does not meet the above ***,smart grid with conventional consortium blockchain can solve the above ***,in the face of a large number of nodes,existing consensus algorithms often perform poorly in terms of efficiency and *** this paper,we propose a trust-based hierarchical consensus mechanism(THCM)to solve this ***,we design a hierarchical mechanism to improve the efficiency and ***,intra-layer nodes use an improved Raft consensus algorithm and inter-layer nodes use the Byzantine Fault Tolerance ***,we propose a trust evaluation method to improve the election process of ***,we implement a prototype system to evaluate the performance of *** results demonstrate that the consensus efficiency is improved by 19.8%,the throughput is improved by 12.34%,and the storage is reduced by 37.9%.

关键词： consortium blockchain consensus algorithm trust evaluation method smart grid Internet of Things(IoT)

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Text-Tuple-Table: Towards Information Integration in Text-to-Table Generation via Global Tuple Extraction

Text-Tuple-Table: Towards Information Integration in Text-to...

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2024 Conference on Empirical Methods in Natural Language Processing, EMNLP 2024

作者： Deng, Zheye Chan, Chunkit Wang, Weiqi Sun, Yuxi Fan, Wei Zheng, Tianshi Yim, Yauwai Song, Yangqiu Department of Computer Science and Engineering HKUST Hong Kong School of Computer Science Fudan University Shanghai China

ISBN: (纸本)9798891761643

The task of condensing large chunks of textual information into concise and structured tables has gained attention recently due to the emergence of Large Language Models (LLMs) and their potential benefit for downstream tasks, such as text summarization and text mining. Previous approaches often generate tables that directly replicate information from the text, limiting their applicability in broader contexts, as text-to-table generation in real-life scenarios necessitates information extraction, reasoning, and integration. However, there is a lack of both datasets and methodologies towards this task. In this paper, we introduce LIVESUM, a new benchmark dataset created for generating summary tables of competitions based on real-time commentary texts. We evaluate the performances of state-of-the-art LLMs on this task in both fine-tuning and zero-shot settings, and additionally propose a novel pipeline called T3(Text-Tuple-Table) to improve their performances. Extensive experimental results demonstrate that LLMs still struggle with this task even after fine-tuning, while our approach can offer substantial performance gains without explicit training. Further analyses demonstrate that our method exhibits strong generalization abilities, surpassing previous approaches on several other text-to-table datasets. Our code and data can be found at https://***/HKUST-KnowComp/LiveSum. © 2024 Association for Computational Linguistics.

关键词： Computational linguistics

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Smart Nutrient Deficiency Prediction system for Groundnut Leaf

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Intelligent Automation & Soft Computing 2023年第5期36卷 1845-1862页

作者： Janani Malaisamy JebakumarRethnaraj Department of Computer Science and Engineering School of ComputingSRM Institute of Science and TechnologyChengalpattuTamil Nadu603203India

Prediction of the nutrient deﬁciency range and control of it through application of an appropriate amount of fertiliser at all growth stages is critical to achieving a qualitative and quantitative *** fertiliser in optimum amounts will protect the environment’s condition and human health *** identiﬁcation also prevents the disease’s occurrence in groundnut crops.A convo-lutional neural network is a computer vision algorithm that can be replaced in the place of human experts and laboratory methods to predict groundnut crop nitro-gen nutrient deﬁciency through image *** chlorophyll and nitrogen are proportionate to one another,the Smart Nutrient Deﬁciency Prediction system(SNDP)is proposed to detect and categorise the chlorophyll concentration range via which nitrogen concentration can be *** model’sﬁrst part is to per-form preprocessing using Groundnut Leaf Image Preprocessing(GLIP).Then,in the second part,feature extraction using a convolution process with Non-negative ReLU(CNNR)is done,and then,in the third part,the extracted features areﬂat-tened and given to the dense layer(DL)***,the Maximum Margin clas-siﬁer(MMC)is deployed and takes the input from DL for the classiﬁcation process toﬁnd *** dataset used in this work has no visible symptoms of a deﬁciency with three categories:low level(LL),beginning stage of low level(BSLL),and appropriate level(AL).This model could help to predict nitrogen deﬁciency before perceivable *** performance of the implemented model is analysed and compared with ImageNet pre-trained *** result shows that the CNNR-MMC model obtained the highest training and validation accuracy of 99%and 95%,respectively,compared to existing pre-trained models.

关键词： CNN chlorophyll groundnut crop deep learning nitrogen deﬁciency

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A novel workload forecasting model for cloud computing using ALAA-DBN algorithm

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Multimedia Tools and Applications 2025年第13期84卷 11383-11407页

作者： Karimunnisa, Syed Gopu, Arunkumar Rao, T. Prabhakara Ayyadurai, M. Kumar, ESaravana Department of Computer Science and Engineering Koneru Lakshmaiah Education Foundation AP VaddeswaramGuntur-522501 India School of Computer Science and Engineering VIT - AP University Amaravati522237 India Department of CSE Godavari Institute Of Engineering And Technology AP Rajahmundry533296 India Department of ECE SRM Institute of Science and Technology Ramapuram Campus Chennai India Department of CSE The Oxford College of Engineering Bengaluru India

Cloud Computing (CC) generally exhibits varying workload patterns. This autoscaling feature of CC has been extensively managed through predictive cloud resource management approaches. For this reason, a solitary forecasting model is not sufficient to forecast various workload patterns of CC web applications. With the intention of developing a proficient CC model, this paper implements a novel cloud workload forecasting model with the efficiency of DL algorithms like Deep Belief Network (DBN) and Lion Algorithm (LA). Here, the previous workload models are assessed and trained via DBN, and the prediction efficacy is enhanced by optimizing the DBN's hidden neurons through LA. The proposed workload forecast model for the cloud is accomplished by tuning the significant parameters via the Adaptive Lion Algorithm Assisted-DBN (ALAA-DBN)model. Besides, a well-organized resource provisioning pattern is followed to manage the under/over provisioning issues. The proposed CC model also considers providing better Quality of Service (QoS) to the users without violating the Service Level Agreement (SLA). Eventually, the significance of the proposed ALAA-DBN is verified through a comparative analysis with state-of-the-art models. © The Author(s), under exclusive licence to Springer science+Business Media, LLC, part of Springer Nature 2024.

关键词： Quality of service

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Novel Vegetation Mapping Through Remote Sensing Images Using Deep Meta Fusion Model

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Intelligent Automation & Soft Computing 2023年第6期36卷 2915-2931页

作者： S.Vijayalakshmi S.Magesh Kumar Department of Computer Science and Engineering Saveetha School of EngineeringSaveetha Institute of Medical and Technical SciencesChennaiTamilnaduIndia

Preserving biodiversity and maintaining ecological balance is essential in current environmental *** is challenging to determine vegetation using traditional map classification *** primary issue in detecting vegetation pattern is that it appears with complex spatial structures and similar spectral *** is more demandable to determine the multiple spectral ana-lyses for improving the accuracy of vegetation mapping through remotely sensed *** proposed framework is developed with the idea of ensembling three effective strategies to produce a robust architecture for vegetation *** architecture comprises three approaches,feature-based approach,region-based approach,and texture-based approach for classifying the vegetation *** novel Deep Meta fusion model(DMFM)is created with a unique fusion frame-work of residual stacking of convolution layers with Unique covariate features(UCF),Intensity features(IF),and Colour features(CF).The overhead issues in GPU utilization during Convolution neural network(CNN)models are reduced here with a lightweight *** system considers detailing feature areas to improve classification accuracy and reduce processing *** proposed DMFM model achieved 99%accuracy,with a maximum processing time of 130 *** training,testing,and validation losses are degraded to a significant level that shows the performance quality with the DMFM *** system acts as a standard analysis platform for dynamic datasets since all three different fea-tures,such as Unique covariate features(UCF),Intensity features(IF),and Colour features(CF),are considered very well.

关键词： Vegetation mapping deep learning machine learning remote sensing data image processing

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Parameterized Algorithms and Hardness for the Maximum Edge q-Coloring Problem 44

Parameterized Algorithms and Hardness for the Maximum Edge q...

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44th IARCS Annual Conference on Foundations of Software Technology and Theoretical computer science, FSTTCS 2024

作者： Mathew, Rogers Panolan, Fahad Seshikanth Department of Computer Science and Engineering IIT Hyderabad India School of Computer Science University of Leeds United Kingdom

ISBN: (纸本)9783959773553

An edge q-coloring of a graph G is a coloring of its edges such that every vertex sees at most q colors on the edges incident on it. The size of an edge q-coloring is the total number of colors used in the coloring. Given a graph G and a positive integer t, the Maximum Edge q-Coloring problem is about whether G has an edge q-coloring of size t. Studies on this coloring problem were motivated by its application in the channel assignment problem in wireless networks. Goyal, Kamat, and Misra (MFCS 2013) studied Maximum Edge 2-Coloring from the perspective of parameterized complexity. Given a graph on n vertices, they considered the standard parameter t, the number of colors in an optimal edge 2-coloring, and the dual parameter , where n - is the number of colors in an optimal edge 2-coloring. They designed FPT algorithms for Maximum Edge 2-Coloring parameterized by t and . In this paper, we revisit and study Maximum Edge 2-Coloring from the perspective of parameterized complexity and show the following results. 1. Let γ(G) denote the maximum matching size in a given graph G. It is easy to see that a maximum edge 2-coloring of G is of size at least γ(G). Goyal, Kamat, and Misra (MFCS 2013) had asked if there exists an FPT algorithm for Maximum Edge 2-Coloring parameterized by k, where k := (size of a maximum edge 2-coloring of G) - γ(G). We show that Maximum Edge 2-Coloring parameterized by k is W[1] hard. 2. On the positive side, we show that there is an algorithm that, given a graph G on n vertices and a tree decomposition of width tw, runs in time 2O(qtw log qtw)n and outputs a maximum edge q-coloring of G. © 2024 Rogers Mathew, Fahad Panolan, and Seshikanth.

关键词： Color

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