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2024 8th International Conference on Mechanics, Mathematics and Applied Physics, ICMMAP 2024

作者： Jin, Xinze Tian, Shuangliang School of Mathematics and Computer Science Northwest for Minzu University Lanzhou730030 China Key Laboratory of Streaming Data Computing Technologies and Applications Northwest for Minzu University Lanzhou730030 China

The coloring theory of graphs is a very important direction in graph theory. The graph coloring problem has a strong application background. Many practical problems such as timetabling problems, frequency allocation problems, traffic arrangements, circuit design, and storage problems can be transformed into graph coloring problems. A substantial amount of scholarly work has been dedicated to the exploration of acyclic coloring, resulting in a plethora of significant findings that significantly contribute to the theoretical framework of vertex coloring in graphs. An acyclic coloring of a graph G refers to a proper vertex coloring where the subgraph resulting from any pair of color classes does not encompass any cycles. We analyze acyclic coloring in splitting graphs and Cartesian products of paths and cycles by using the method of structural coloring, reductive proof, and mathematical induction. Moreover, we present the precise acyclic chromatic numbers for splitting graphs of cycles and Cartesian products of paths and cycles. © Published under licence by IOP Publishing Ltd.

关键词： Integrated circuit design

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Injective coloring of edge corona product of graphs 8

Injective coloring of edge corona product of graphs

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2024 8th International Conference on Mechanics, Mathematics and Applied Physics, ICMMAP 2024

作者： Zhao, Wenying Tian, Shuangliang School of Mathematics and Computer Science Northwest for Minzu University Lanzhou730030 China Key Laboratory of Streaming Data Computing Technologies and Applications Northwest for Minzu University Lanzhou730030 China

The injective chromatic number χi(G) of a graph G is the minimum number of colors needed to color the vertices of G such that different vertices with the same neighbor are colored differently. In this here, we get some exact values of the injective chromatic number of the edge corona product of some graphs. © Published under licence by IOP Publishing Ltd.

关键词： Graph theory

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Machine Learning Approach for Onion Leaf Disease Detection: A Case Study in Maharashtra, India 15

Machine Learning Approach for Onion Leaf Disease Detection: ...

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15th International Conference on Advances in Computing, Control, and Telecommunication Technologies, ACT 2024

作者： Amondkar, Madhavi Bhoite, Sachin Vidyalankar School of Information Technology Department of Information Technology & Data Science Mumbai India Dr. Vishwanath Karad MIT World Peace University Department of Computer Science & Applications Pune India

ISBN: (纸本)9798331300579

Maharashtra is a remarkable state in India that is a major producer of onions, accounting for a sizeable portion of the country's total production. An ideal climate for onion growth is provided in areas like Nashik, Ahmednagar, and Pune Despite its importance, growing onions is filled with difficulties, the most significant of which being the frequency of illnesses that damage onion crops. Serious risks to onion production include diseases which are downy mildew, purple blotch, bacterial blight, white rot & basal rot which result in output losses and financial hardship for farmers. Machine learning models have become extremely effective tools for managing and forecasting agricultural crop diseases such as tomato, corn, grape etc. While nearly all crop leaf diseases may be predicted by these algorithms, very little or none has been done for onions in Maharashtra. There is almost no research done focusing on the onion disease found in different parts of Maharashtra. The purpose of the study is to assist farmers in Maharashtra in recognizing various diseases during the cultivation of onions by utilizing a machine learning model that combines CNN with image classification. Using a dataset of plant leaf diseases, this model ran with 95% accuracy. Additionally, given that the suggested model may be trained on any kind of leaf disease picture dataset and assist in disease type prediction, it can be suggested to work on an onion disease dataset as well. © Grenze Scientific Society, 2024.

关键词： basal rot CNN leaf disease machine learning Maharashtra onion disease purple blotch

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A Multimodal Deep Learning Approach for Typhoon Track Forecast by Fusing CNN and Transformer Structures 4

A Multimodal Deep Learning Approach for Typhoon Track Foreca...

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4th International Conference on computer Vision, Image and Deep Learning, CVIDL 2023

作者： Zhan, Ruijie Lu, Jiadong Jiang, Yuchao Li, Tongfei Zhong, Guohao Lv, Wei City University of Macau Falcuty of Data Science 999078 China School of Aliyun Big Data Applications Zhuhai College of Science and Technology Zhuhai519041 China School of Computer Science Zhuhai College of Science and Technology Zhuhai519041 China

ISBN: (纸本)9798350326444

The prediction of typhoon trajectory is essential to protect people and property. However, traditional methods of typhoon track prediction are difficult to guarantee the prediction efficiency and accuracy when dealing with a large amount of complex data. With the improvement of computer computing power and the development of deep learning technology, deep neural networks are gradually applied to the field of meteorology. In this paper, we describe a typhoon trajectory prediction model named TyT (Typhoon technology with Transformer), which effectively uses large-scale multimodal historical data and advanced deep learning techniques to achieve typhoon trajectory forecasting. With the above design, Our TyT can make full use of the implicit correlation of multimodal data to realize the advantages that unimodal data-based methods do not have. Meanwhile, the structure of Transformer enables to establish long-range dependencies by attention mechanism and position encoding. Experiments on historical typhoon track data in the northwest Pacific Ocean show that our TyT has a significant improvement in accuracy compared to previous typhoon track prediction methods. Finally, four different typhoons are randomly selected to plot their tracks to visually demonstrate the prediction performance of our TyT. © 2023 IEEE.

关键词： Forecasting

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RSM-NLP at BLP-2023 Task 2: Bangla Sentiment Analysis using Weighted and Majority Voted Fine-Tuned Transformers 1

RSM-NLP at BLP-2023 Task 2: Bangla Sentiment Analysis using ...

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1st Workshop on Bangla Language Processing, BLP 2023

作者： Seth, Pratinav Goel, Rashi Mathur, Komal Vemulapalli, Swetha Dept. of Data Science & Computer Applications Manipal Institute of Technology Manipal Academy of Higher Education Manipal India Dept. of Computer Science & Engineering Dept. of Information and Communication Technology

ISBN: (纸本)9798891760585

This paper describes our approach to submissions made at Shared Task 2 at BLP Workshop - Sentiment Analysis of Bangla Social Media Posts(Hasan et al., 2023a;Islam et al., 2021;Hasan et al., 2023b). Sentiment Analysis is an action research area in the digital age. With the rapid and constant growth of online social media sites and services and the increasing amount of textual data, the application of automatic Sentiment Analysis is on the rise. However, most of the research in this domain is based on the English language. Despite being the world’s sixth most widely spoken language, little work has been done in Bangla. This task aims to promote work on Bangla Sentiment Analysis while identifying the polarity of social media content by determining whether the sentiment expressed in the text is Positive, Negative, or Neutral. Our approach consists of experimenting and finetuning various multilingual and pre-trained BERT-based models on our downstream tasks and using a Majority Voting and Weighted ensemble model that outperforms individual baseline model scores. Our system scored 0.711 for the multiclass classification task and scored 10th place among the participants on the leaderboard for the shared task. Our code is available at https://***/ptnv-s/RSM-NLP-BLP-Task2. © 2023 Association for Computational Linguistics.

关键词： Sentiment analysis

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An Efficient Machine Learning Based data Auditing Scheme for Cloud Users

An Efficient Machine Learning Based Data Auditing Scheme for...

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2023 International Conference on Computational Intelligence, Networks and Security, ICCINS 2023

作者： Vamshi, A. Eswari, R. Nirmal Joel, X.I. Regan, A. Divine National Institute of Technology Department of Computer Applications Tiruchirappalli India St. Joseph's College Department of Data Science Tiruchirappalli India

ISBN: (纸本)9798350313796

With the wide spread of the digital world, IoT devices generate huge amounts of data and store it in cloud servers as they have less storage space. When data is outsourced to the cloud servers, IoT device users lose control of it, which means the confidentiality and integrity of the data can be compromised. Further, several Third-Party Auditors (TPAs) are available to check the integrity of the data. But most of the TPAs are based on trust. In some cases, TPAs are being hired by Cloud data centers and in some cases, the TPAs may have direct connection with data consumers. In both the cases, there is a bias and the data may be at high risk. Therefore, to overcome the trust issue, in this paper an Efficient Machine Learning based data Auditing Scheme for Cloud Users is proposed using a hybrid recommendation system. A repository is maintained which keeps track of all TPA's history. The user submits auditing request to the repository which will run the recommendation system to select appropriate TPA to check the integrity of the user data in the cloud. It chooses the best TPA and gives the auditing results to the user. The proposed scheme reduces the risk of user data. © 2023 IEEE.

关键词： Recommender systems

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An Overview of Recent Research on IoT-Based Energy Management System in Smart Homes 1

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18th International Conference on Intelligent Information Hiding and Multimedia Signal Processing, IIH-MSP 2022

作者： Sung, Tien-Wen Huang, Zeming Liang, Qiaoxin Fujian Provincial Key Laboratory of Big Data Mining and Applications College of Computer Science and Mathematics Fujian University of Technology Fuzhou350118 China

ISBN: (数字)9789819906055

ISBN: (纸本)9789819906048

Increasing load demand and energy crisis issues are becoming more and more concerned. Nowadays, the demand for electricity in household life is rising sharply and the structure of electricity consumption is gradually becoming complex. With the development of the Internet of Things technology, it provides a better platform for the improvement of the energy management system. The home energy management system (HEMS) based on the Internet of Things comes into being, which can integrate the management of all home power loads and distributed energy, realize the optimal configuration of energy, and help users save electricity. This paper firstly describes the basic concept of IoT smart home energy management system, then describes the framework of HEMS, and finally reviews the current research status in this field from three modules, namely, data collection module, communication module, and management and optimization module, and discusses its technical challenges and future research directions. © 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Energy management systems

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ContTune: Continuous Tuning by Conservative Bayesian Optimization for Distributed Stream data Processing Systems 49th

ContTune: Continuous Tuning by Conservative Bayesian Optimiz...

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49th International Conference on Very Large data Bases, VLDB 2023

作者： Lian, Jinqing Zhang, Xinyi Shao, Yingxia Pu, Zenglin Xiang, Qingfeng Li, Yawen Cui, Bin Beijing University of Posts and Telecommunications China School of Computer Science and National Engineering Laboratory for Big Data Analysis and Applications Peking University China

The past decade has seen rapid growth of distributed stream data processing systems. Under these systems, a stream application is realized as a Directed Acyclic Graph (DAG) of operators, where the level of parallelism of each operator has a substantial impact on its overall performance. However, finding optimal levels of parallelism remains challenging. Most existing methods are heavily coupled with the topological graph of operators, unable to efficiently tune under-provisioned jobs. They either insufficiently use previous tuning experience by treating successively tuning independently, or explore the configuration space aggressively, violating the Service Level Agreements (SLA). To address the above problems, we propose ContTune, a continuous tuning system for stream applications. It is equipped with a novel Big-small algorithm, in which the Big phase decouples the tuning from the topological graph by decomposing the job tuning problem into sub-problems that can be solved concurrently. We propose a conservative Bayesian Optimization (CBO) technique in the Small phase to speed up the tuning process by utilizing the previous observations. It leverages the state-of-the-art (SOTA) tuning method as conservative exploration to avoid SLA violations. Experimental results show that ContTune reduces up to 60.75% number of reconfigurations under synthetic workloads and up to 57.5% number of reconfigurations under real workloads, compared to the SOTA method DS2. © 2023, VLDB Endowment. All rights reserved.

关键词： Directed graphs

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A Multi-Objective Particle Swarm Optimization Algorithm Based on Decomposition and Multi-Selection Strategy

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computers, Materials & Continua 2025年第1期82卷 997-1026页

作者： Li Ma Cai Dai Xingsi Xue Cheng Peng School of Computer Science Shaanxi Normal UniversityXi’an710119China Fujian Provincial Key Laboratory of Big Data Mining and Applications Fujian University of TechnologyFuzhou350118China Information Construction and Management Center and Institute of Artificial Intelligence and Educational New Productivity Ningxia Normal UniversityGuyuan756099China

The multi-objective particle swarm optimization algorithm(MOPSO)is widely used to solve multi-objective optimization *** the article,amulti-objective particle swarm optimization algorithmbased on decomposition and multi-selection strategy is proposed to improve the search ***,two update strategies based on decomposition are used to update the evolving population and external archive,***,a multiselection strategy is *** first strategy is for the subspace without a non-dominated *** the neighbor particles,the particle with the smallest penalty-based boundary intersection value is selected as the global optimal solution and the particle far away fromthe search particle and the global optimal solution is selected as the personal optimal solution to enhance global *** second strategy is for the subspace with a non-dominated *** the neighbor particles,two particles are randomly selected,one as the global optimal solution and the other as the personal optimal solution,to enhance local *** third strategy is for Pareto optimal front(PF)discontinuity,which is identified by the cumulative number of iterations of the subspace without non-dominated *** the subsequent iteration,a new probability distribution is used to select from the remaining subspaces to ***,an adaptive inertia weight update strategy based on the dominated degree is designed to further improve the search ***,the proposed algorithmis compared with fivemulti-objective particle swarm optimization algorithms and five multi-objective evolutionary algorithms on 22 test *** results show that the proposed algorithm has better performance.

关键词： Multi-objective optimization multi-objective particle swarm optimization decomposition multi-selection strategy

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Recognition of data Breach Method from Story Using Advanced Deep Learning 3

Recognition of Data Breach Method from Story Using Advanced ...

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3rd International Conference for Innovation in Technology, INOCON 2024

作者： Jeba, Samiha Maisha Aurpa, Tanjim Taharat Alom, Md Samiul Joy, Md. Wadud Mahmud Ahamed, Asm Shakil Dash, Satya Ranjan Ahmed, Md Shoaib International University of Business Agriculture and Technology Dept. of Computer Science and Engineering Bangladesh Bangabandhu Sheikh Mujibur Rahman Digital University Department of Data Science Bangladesh Kiit University School of Computer Applications Odisha India Boise State University Department of Computer Science BoiseID United States

ISBN: (纸本)9798350381931

We often open our eyes in the morning with the appalling news of data breaches of different popular companies. This is a significant threat not only to giant companies but also to the general people's privacy. Various methods are maneuvered for data breaches, and identifying these methods in a short can help understand the loss and take proper steps against these incidents. Researchers nowadays focus on the data breaches issue to reduce its threats to the tech industry. In this research, we have used advanced deep learning methods(e.g., BERT, XLNet, and Electra) to predict data breach methods using the data breach story. The proposed classifier has provided an accuracy of 92.86%. © 2024 IEEE.

关键词： Deep learning

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