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4th International Conference on computer Vision, High-Performance Computing, Smart Devices, and Networks, CHSN 2023

作者： Kanaujia, Vishal Kumar Sah, Chitranjan Prasad Computer Science and Engineering-Data Science Department ABES Engineering College Uttar Pradesh Ghaziabad India School of Engineering and Technology Noida International University Uttar Pradesh Greater Noida India

ISBN: (纸本)9789819777938

Agriculture stands as a linchpin for national prosperity, especially in regions blessed with abundant water and fertile soil, capable of yielding a diverse range of food products crucial for human sustenance. However, escalating global population and diminishing freshwater reserves pose significant challenges to sustainable agriculture. Approximately, 96% of Earth's water is saline, leaving a mere 3% as freshwater resources, intensifying the need for advanced techniques to conserve water in farming practices. This research addresses the pressing issue by proposing a solution rooted in smart agriculture, leveraging modern technologies like the Internet of Things (IoT) to revolutionize conventional farming methods. The integration of automation and IoT technologies, such as the ESP8266 IoT automatic irrigation system, is presented as a means to enhance crop productivity. This system utilizes cutting-edge technologies like Arduino and wireless sensor networks, offering a comprehensive solution for monitoring environmental conditions crucial for maximizing crop yield. The paper underscores the paramount importance of connecting agriculture to the Internet, emphasizing the role of IoT in facilitating applications like crop monitoring and automated irrigation decisions. The proposed smart agriculture system, featuring energy autonomy and cost-effectiveness, aims to address persistent agricultural challenges that impede national development. The research introduces a novel approach to agriculture, offering a tangible pathway toward sustainable farming practices in the face of water scarcity and environmental uncertainties. Through the integration of sensors and live data feeds, accessible via mobile devices, this system represents a promising step toward a technologically advanced and efficient agricultural landscape, especially beneficial for water-scarce and geographically isolated regions. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 20

关键词： Smart agriculture

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Systematic Literature Review on Statistics and Machine Learning Predictive Models for Rice Phenotypes 8

Systematic Literature Review on Statistics and Machine Learn...

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8th International Conference on computer science and Computational Intelligence, ICCSCI 2023

作者： Dominic, Nicholas Cenggoro, Tjeng Wawan Pardamean, Bens Bioinformatics and Data Science Research Center Bina Nusantara University Jakarta11480 Indonesia Computer Science Department School of Computer Science Bina Nusantara University Jakarta11480 Indonesia Computer Science Department BINUS Graduate Program - Master of Computer Science Bina Nusantara University Jakarta11480 Indonesia

Predicting the best-quality of rice phenotypes is the priority among agricultural researchers to fulfill worldwide food security. Trend development of predictive models from statistics to machine learning is the subject of this review. Gathered from the Google Scholar database, 14 appropriate papers (2016-2020) related to the rice phenotypes prediction were selected through title and abstract content filtering. The outputs show that Support Vector Machine, Multi-layer Perceptron, and regression are the most used models, while yield is the priority prediction point besides tiller, panicle, and 1000-grain weight of rice. However, finding the accurate predictor is invariably challenging due to distinct rice varieties in the world and high confounding factors. Thus, developing an advanced deep learning model that accommodates these needs is worth considering further. © 2023 The Authors. Published by Elsevier B.V.

关键词： machine learning rice phenotypes prediction statistics

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Silicon Electro-optic Modulator for Photonic Ring Network On-Chip Based on Dual ITO Layer Directional Coupler 11th

Silicon Electro-optic Modulator for Photonic Ring Network On...

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11th International Conference on computer Engineering and Networks, CENet2021

作者： Zhixun, Liang Yunfei, Yi Fang, Lin Yuanyuan, Fan School of Data and Computer Science Hechi University Guangxi Yizhou546300 China

ISBN: (纸本)9789811665530

As a key component of the Photonic network on chip (PNoC). The electro-optic modulator converts the electrical signal of the processor into optical signal, which is transmitted in the PNoC. The performance of the PNoC is directly affected by the property of the electro-optic converter. A novel electro-optic (EO) modulator adopt dual ITO layers directional coupler is proposed in this paper. Utilizing double layer ITO activation materials Designed a photonic electro-absorption (EA) ITO modulator, it has a double-waveguide structure on both sides, and electro-optic modulation is realized by the EA effect of the ITO surface plasma in the middle island. Due to the double waveguide structure, this EO modulator is extremely suitable for on-chip optical network processor of exceptional architecture, for example, the PRNoC (Photonic Ring Network-on-Chip). We simulated the component adopting FDTD (finite time domain difference method) software. Simulation results and analysis show that the coupling ratio at the input of the modulator is up to 52%, and the extinction ratio (ER) of the EO modulator is −14.2 dB, and the insertion loss (IL) is less than 1.0 dB. The modulator is compact footprint only 9 μm × 1.4 μm, the bandwidth up to 0.4963T bit/S, and the power consumption per bit is 4.354 fJ/bit. © 2022, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Silicon

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Multicomponent Similarity Graphs for Cross-Network Node Classification

IEEE Transactions on Artificial Intelligence

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IEEE Transactions on Artificial Intelligence 2024年第3期5卷 1411-1424页

作者： Zhang, Yuhong Shi, Congmei Li, Xinzheng Zhang, Zan Hu, Xuegang School of Computer Science and Information Engineering Hefei University of Technology Hefei230601 China Institute of Artificial Intelligence Hefei Comprehensive National Science Center Hefei230088 China Key Laboratory of Knowledge Engineering with Big Data Hefei University of Technology Ministry of Education Hefei230009 China

Cross-network node classification aims to train a classifier for an unlabeled target network using a source network with rich labels. In applications, the degree of nodes mostly conforms to the long-tail distribution, i.e., most nodes in the network are tail nodes with sparse neighborhoods. The established methods focus on either the discrepancy cross network or the long tail in a single network. As for the cross-network node classification under long tail, the coexistence of sparsity of tail nodes and the discrepancy cross-network challenges existing methods for long tail or methods for the cross-network node classification. To this end, a multicomponent similarity graphs for cross-network node classification (MS-CNC) is proposed in this article. Specifically, in order to address the sparsity of the tail nodes, multiple component similarity graphs, including attribute and structure similarity graphs, are constructed for each network to enrich the neighborhoods of the tail nodes and alleviate the long-tail phenomenon. Then, multiple representations are learned from the multiple similarity graphs separately. Based on the multicomponent representations, a two-level adversarial model is designed to address the distribution difference across networks. One level is used to learn the invariant representations cross network in view of structure and attribute components separately, and the other level is used to learn the invariant representations in view of the fused structure and attribute graphs. Extensive experimental results show that the MS-CNC outperforms the state-of-the-art methods. Impact Statement-Node classification is an important task in graph mining. With the unavailability of labels, some researchers propose cross-network node classification, using one labeled network to assist the node classification of another unlabeled network. However, the long-tail of nodes leads to unsatisfactory performance and challenges the recent cross-network node classification m

关键词： Graphic methods

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Exploring the Efficiency of Text-Similarity Measures in Automated Resume Screening for Recruitment 10

Exploring the Efficiency of Text-Similarity Measures in Auto...

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17th INDIACom;2023 10th International Conference on Computing for Sustainable Global Development, INDIACom 2023

作者： Alsharef, Ahmad Sonia Nassour, Hasan Sharma, Jitender Yogananda School of AI Computer and Data Science Shoolini University Solan India School of Management Sciences and Liberal Arts Shoolini University Solan India

ISBN: (纸本)9789380544472

With many online recruitment portals requesting job applicants to upload their resumes, the automated process of screening and shortlisting candidates can accelerate selection and decision-making. This study explores the use of text similarity measures as an alternative to experienced human hiring managers in processing resumes. Three text similarity measures: Cosine, Sqrt-Cosine, and Improved Sqrt-Cosine (ISC) similarity were utilized as computer programs in scanning resumes for the recruitment of a business development manager and a software engineer. The decisions of the algorithms were compared to those of an expert hiring manager within the same scenarios. The findings indicate that ISC and Sqrt-Cosine were closer to the expert-human decision than Cosine similarity. These specific text-similarity algorithms can also make acceptable decisions even when recruiting for high-level positions and can do so in seconds when executed as a program on a normal CPU processor. This study suggests that these algorithms can efficiently facilitate the process of decision-making in recruitment and shortlisting candidates and can be an effective alternative to expert hiring managers. © 2023 Bharati Vidyapeeth, New Delhi.

关键词： Decision making

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Global and Local Mixer for Micro-Expression Recognition 6

Global and Local Mixer for Micro-Expression Recognition

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6th IEEE International Conference on Pattern Recognition and Artificial Intelligence, PRAI 2023

作者： Tang, Huaying Zhang, Xiaorong Mao, Xinglong Liu, Shifeng Zhao, Sirui Chen, Enhong University of Science and Technology of China and State Key Laboratory of Cognitive Intelligence School of Computer Science and Technology Hefei China Southwest University of Science and Technology School of Computer Science and Technology Mianyang China University of Science and Technology of China and State Key Laboratory of Cognitive Intelligence School of Data Science Hefei China University of Science and Technology of China & Southwest University of Science and Technology School of Computer Science and Technology Hefei Mianyang China

ISBN: (纸本)9798350325485

Micro-expressions (MEs) refer to spontaneous, subtle, and fleeting facial movements that usually only occur in local facial areas, which can effectively reveal genuine emotions that humans try to hide. However, micro-expression recognition (MER) is still a difficult task due to its low intensity and local movements, and existing datasets are usually small-scale with class imbalance problem. To address these problems, we propose a novel MER framework named Global and Local Mixer (GL-Mixer) based on MLP-Mixer, which can focus on local regions where MEs appear and reduce the influence of ME-irrelevant regions. Specifically, the GL-Mixer mainly consists of three parts: Global Feature Learning (GFL) module, Local Feature Learning (LFL) module, and Feature Refinement (FR) module. The GFL module is designed to learn the global ME features from the whole face region. Meanwhile, the LFL module is composed of multiple parallel local mixers, and each local mixer extracts fine-grained features in a specific facial region. Then, Feature Refinement (FR) module aims to refine the fin e-grained ME features by focusing more on the local features of the effective ME regions and suppressing the features of the irrelevant region under the guidance of the global feature. Last, the model fuses the global and fine-grained ME features for classification. In addition, to solve the problem of insufficient data and class imbalance in ME datasets, we introduce class-balanced implicit semantic data augmentation. Extensive experiments are conducted on CASMEII, SAMM, and SMIC datasets, and the experimental results demonstrate the effectiveness of our method. © 2023 IEEE.

关键词： Semantics

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Receding-Horizon Trajectory Planning for Under-Actuated Autonomous Vehicles Based on Collaborative Neurodynamic Optimization

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IEEE/CAA Journal of Automatica Sinica 2022年第11期9卷 1909-1923页

作者： Jiasen Wang Jun Wang Qing-Long Han IEEE the Future Network Research Center Purple Mountain LaboratoriesNanjing 211111China the Department of Computer Science the School of Data ScienceCity University of Hong KongHong KongChina the School of Science Computing and Engineering TechnologiesSwinburne University of TechnologyMelbourne VIC 3122Australia

This paper addresses a major issue in planning the trajectories of under-actuated autonomous vehicles based on neurodynamic optimization.A receding-horizon vehicle trajectory planning task is formulated as a sequential global optimization problem with weighted quadratic navigation functions and obstacle avoidance constraints based on given vehicle goal *** feasibility of the formulated optimization problem is guaranteed under derived *** optimization problem is sequentially solved via collaborative neurodynamic optimization in a neurodynamics-driven trajectory planning method/*** results with under-actuated unmanned wheeled vehicles and autonomous surface vehicles are elaborated to substantiate the efficacy of the neurodynamics-driven trajectory planning method.

关键词： Collaborative neurodynamic optimization receding-horizon planning trajectory planning under-actuated vehicles

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Improvement of semantic segmentation algorithm based on ResNeSt network

Improvement of semantic segmentation algorithm based on ResN...

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2023 IEEE International Conference on Control, Electronics and computer Technology, ICCECT 2023

作者： Mian, Jia Yaping, Wan Wei, Yan Jianbin, Xie Hua, Zhong Yue, Wang University of South China School of Computer Hengyang China National University of Defense Technology School of Electronic Science Changsha China North University of China School of Data Science and Technology Taiyuan China

ISBN: (纸本)9798350399028

This paper proposes an improved semantic segmentation algorithm based on the split attention network resnest. The original icnet semantic segmentation model has some defects in the segmentation of foreground objects. In view of these problems, we optimized the icnet model with resnest network and CBAM convolution attention module. On the one hand, we replace the backbone network in the original model with resnest network, which enhances the feature extraction capability of the model. On the other hand, we introduce the CBAM convolution attention module to improve the segmentation ability of the model to the target in the foreground region. Without affecting the inference rate of the original model, the training effect of the improved idea proposed in this paper on the cityscapes open data set is improved separately from 71.0% to 71.3% and 71.9%. Respectively compared with the MIoU of the baseline model, the MIoU of the icnet model combined with the resnest backbone network and the CBAM convolution attention module on the cityscapes open data set is increased from 71.0% to 72.1%, and the segmentation effect of the foreground area target is significantly optimized in the visualization images, which has certain positive significance for the application of the semantic segmentation algorithm in the field of unmanned driving. © 2023 IEEE.

关键词： Semantic Segmentation

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Decentralized Metaverse: Towards a Secure, Autonomous, and Inclusive Virtual World

Decentralized Metaverse: Towards a Secure, Autonomous, and I...

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2023 IEEE International Conference on Electrical, computer and Energy Technologies, ICECET 2023

作者： Alsamhi, Saeed Hamood Hawbani, Ammar Kumar, Santosh Porwol, Lukasz Curry, Edward Insight Centre for Data Analytics University of Galway Galway Ireland School of Computer Science Shenyang Aerospace University Shenyang China Computer Science and Engineering Iiit Naya Raipur Chhattisgarh India

ISBN: (纸本)9798350327816

The metaverse is a universal and immersive virtual world, which are components of cyber-physical-social systems (CPSS). The traditional centralized approach to building a metaverse poses risks to user privacy, security, and autonomy. A growing movement towards a decentralized metaverse, built on blockchain and distributed networks, has emerged as a response. This paper presents a conceptual framework for building a de-centralized metaverse emphasising security, user autonomy, and inclusivity within situation-aware CPSS. Drawing on literature from blockchain, virtual worlds, and decentralized systems, we explore the technical and regulatory challenges that must be addressed to create a more secure, autonomous, and inclusive virtual world. Our framework highlights the importance of community-driven governance and transparent data management for building a decentralized metaverse that empowers users and promotes collaboration. © 2023 IEEE.

关键词： Blockchain Cyber-Physical-Social Systems Decentralization Metaverse Situation Awareness

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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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