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2nd DMIHER International Conference on Artificial Intelligence in Healthcare, Education and Industry, IDICAIEI 2024

作者： Thote, Rushikesh Barhate, Aditya Tale, Abhay Verma, Prateek Datta Meghe Institute of Higher Education and Research Faculty of Engineering and Technology Department of Computer Science and Design Maharashtra Wardha India Datta Meghe Institute of Higher Education and Research Faculty of Engineering and Technology Department of Artificial Intelligence and Machine Learning Maharashtra Wardha India Datta Meghe Institute of Higher Education and Research Faculty of Engineering and Technology Department of Artificial Intelligence and Data Science Maharashtra Wardha India

ISBN: (纸本)9798331528713

The integration of autonomous vehicles (AVs) into modern transportation systems presents both unprecedented opportunities and challenges, particularly in ensuring road safety and reducing accidents. This paper explores the application of Deep Reinforcement learning (DRL) techniques to enhance decision-making processes in AVs, aiming to proactively prevent road accidents. By simulating complex driving environments, DRL algorithms enable AVs to learn optimal driving strategies through continuous interaction and feedback. We introduce a DRL-based framework that incorporates sensor data fusion, real-time environment analysis, and adaptive control mechanisms. Experimental results from simulated urban and highway scenarios demonstrate a significant reduction in collision rates and improved response to unforeseen obstacles compared to traditional rule-based systems. The findings underscore the potential of DRL in fortifying the reliability and safety of autonomous driving technologies. © 2024 IEEE.

关键词： Deep reinforcement learning

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Hierarchical Multiview Top-k Pooling with Deep-Q-Networks

IEEE Transactions on Artificial Intelligence

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IEEE Transactions on Artificial Intelligence 2024年第6期5卷 2985-2996页

作者： Li, Zhi-Peng Su, Hai-Long Wu, Yong- Zhang, Qin-Hu Yuan, Chang-An Gribova, Valeriya Filaretov, Vladimir Fedorovich Huang, De-Shuang Eastern Institute of Technology Zhejiang Ningbo315201 China University of Science and Technology of China School of Life Sciences Anhui Hefei230026 China Tongji University Institute of Machine Learning and Systems Biology School of Electronics and Information Engineering Shanghai201804 China Guangxi Academy of Sciences Institute of Big Data and Intelligent Computing Research Center Nanning530007 China Far Eastern Branch of the Russian Academy of Sciences Institute of Automation and Control Processes Vladivostok690041 Russia

Graph neural networks (GNNs) are extensions of deep neural networks to graph-structured data. It has already attracted widespread attention for various tasks such as node classification and link prediction. Existing research focuses more on graph convolutional neural networks (GCNs). However, it is usually overlooked that graph pooling can obtain graph representations by summarizing and down-sampling node information. Meanwhile, existing graph pooling methods mainly use top-k for node selection, but most of them consider only single view information when scoring nodes, and the k values in top-k are usually selected empirically. This work proposes the hierarchical multiview top-k pooling (HMTPool) with deep-Q-networks, which scores nodes taking into account multiview information (considering graph structure and features) and does not rely on the empirical adaptive selection of the best k value. HMTPool is a two-stage process. It first uses a variant GCN and multilayer perceptron to score the nodes from structural and feature perspectives, respectively and then performs fusion operations on the multiview information scores of the nodes. In addition, to select the optimal pooling ratio of top-k, we propose a deep-Q-network-based top-k (DTop-k) node selection method, which can adaptively select the best pooling ratio without prior knowledge. Experimental results on six TUdatasets and two Benchmarking GNNs datasets demonstrate the effectiveness of our proposed approach. © 2020 IEEE.

关键词： Convolution

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GeoAgriGuard: AI-Driven Pest and Disease Management with Remote Sensing for Global Food Security

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Remote Sensing in Earth Systems Sciences 2025年第2期8卷 409-422页

作者： Sharada, K. Choudhary, Shailee Lohmor Harikrishna, T. Dixit, Rinku Sharma Suman, Sanjay Kumar Ayyappa Chakravarthi, M. Bhagyalakshmi, L. Department of Computer Science and Engineering GITAM (Deemed to Be University) AP Visakhapatnam India Department of Artificial Intelligence and Machine Learning New Delhi Institute of Management Delhi India Department of AIML Annamacharya University Andhra Pradesh Rajampet India Department of ECE St. Martin’s Engineering College Telangana Secunderabad India Departmentt of CSE-Data Science KSR Institute of Technology and Science KKR & AP Guntur India Department of ECE Rajalakshmi Engineering College TN Chennai India

Agricultural productivity is increasingly threatened by the growing frequency and severity of pest and disease outbreaks, exacerbated by climate change and evolving environmental conditions. The GeoAgriGuard AI-Driven Pest and Disease Management System integrates remote sensing technologies with advanced AI models to address pest and disease outbreaks in agriculture. Utilizing multi-spectral and hyper-spectral satellite and drone imagery, the system processes crop health data through comprehensive preprocessing steps, including radiometric, geometric, and atmospheric corrections. Vegetation indices such as NDVI and NDWI further enhance the data quality, ensuring accurate detection of crop stress. A hybrid deep learning ensemble, combining models like ResNet, Transformer, DenseNet, and AutoEncoders, processes this data to predict and classify pest and disease occurrences. The proposed model achieved a remarkable accuracy of 97.81%, significantly outperforming existing models such as SVM (82.45%) and CNN (90.12%). By integrating these AI predictions with GIS-based spatial analysis, the system generates real-time risk maps, enabling farmers to take timely and localized actions. Performance metrics, including precision (96.72%), recall (95.83%), and F1-score (96.27%), demonstrate the system’s reliability in real-world applications. GeoAgriGuard provides a scalable, data-driven solution for enhancing crop protection and improving global food security. © The Author(s), under exclusive licence to Springer Nature Switzerland AG 2025.

关键词： AI-driven agriculture Crop disease Deep learning Food security GIS NDVI Pest detection Precision agriculture Remote sensing Satellite imagery

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An Analysis of Light Weight Symmetric Encryption Algorithms for Secure data Transmission in IoT

An Analysis of Light Weight Symmetric Encryption Algorithms ...

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Intelligent Algorithms for Computational Intelligence Systems (IACIS), International Conference on

作者： Sanjay Kumar C. S. Pillai Department of CSE (Artificial Intelligence & Machine Learning) Don Bosco Institute of Technology Bengaluru India Department of CSE-Data Science ACS College of Engineering Bengaluru India

ISBN: (数字)9798350360660

ISBN: (纸本)9798350360677

The Internet of Things (IoT) consists of a network of resource-constrained devices, sensors, and machines that are interconnected and communicate via the internet. However, these devices often struggle while handling complex attacks during data encryption and after the encrypted data is transmitted. Cipher algorithms are therefore critical to the data security process because they handle the growing complexities in the applications of data environments. This review aims to present the most effective and efficient symmetric cipher algorithms, which use the same components as the ciphers. It demonstrates how the current cipher has developed into a lightweight encryption by lowering the number and impact of each component. Thus, it becomes clearer how the strength of these components varies between symmetric and asymmetric ciphers dependent on their size. Finally, several efficient lightweight symmetric cipher algorithms are selected, and the security, performance, and resource needs of each algorithm's components are reviewed.

关键词： Ciphers Reviews Encryption Sensors Complexity theory Internet of Things data communication Computational intelligence

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HTC-DC Net: Monocular Height Estimation From Single Remote Sensing Images

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IEEE Transactions on Geoscience and Remote Sensing 2023年 61卷

作者： Chen, Sining Shi, Yilei Xiong, Zhitong Zhu, Xiao Xiang Chair of Data Science in Earth Observation Munich80333 Germany School of Engineering and Design Munich80333 Germany Chair of Data Science in Earth Observation The Munich Center for Machine Learning Munich80333 Germany

Three-dimensional geoinformation is of great significance for understanding the living environment;however, 3-D perception from remote sensing data, especially on a large scale, is restricted, mainly due to the high costs of 3-D sensors such as light detection and ranging (LiDAR). To tackle this problem, we propose a method for monocular height estimation from optical imagery, which is currently one of the richest sources of remote sensing data. As an ill-posed problem, monocular height estimation requires well-designed networks for enhanced representations to improve the performance. Moreover, the distribution of height values is long-tailed with the low-height pixels, e.g., the background (BG), as the head, and thus, trained networks are usually biased and tend to underestimate building heights. To solve the problems, instead of formalizing the problem as a regression task, we propose HTC-DC Net following the classification-regression paradigm, with the head-tail cut (HTC) and the distribution-based constraints (DCs) as the main contributions. HTC-DC Net is composed of the backbone network as the feature extractor, the HTC-AdaBins module, and the hybrid regression process. The HTC-AdaBins module serves as the classification phase to determine bins adaptive to each input image. It is equipped with a vision transformer (ViT) encoder to incorporate local context with holistic information and involves an HTC to address the long-tailed problem in monocular height estimation for balancing the performances of foreground (FG) and BG pixels. The hybrid regression process does the regression via the smoothing of bins from the classification phase, which is trained via DCs. The proposed network is tested on three datasets of different resolutions, namely ISPRS Vaihingen (0.09 m), data Fusion Contest 19 (DFC19) (1.3 m), and Global Building Height (GBH) (3 m). The experimental results show the superiority of the proposed network over existing methods by large margins. Extensiv

关键词： Optical radar

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Quantization of Bandlimited Graph Signals

Quantization of Bandlimited Graph Signals

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2023 International Conference on Sampling Theory and Applications, SampTA 2023

作者： Krahmer, Felix Lyu, He Saab, Rayan Veselovska, Anna Wang, Rongrong Technical University of Munich and Munich Center for Machine Learning Department of Mathematics & Munich Data Science Institute Garching/Munich Germany University of California San Diego Department of Mathematics & Halicioglu Data Science Institute San Diego United States Michigan State University Department of Computational Mathematics Science and Engineering Department of Mathematics East Lansing United States

ISBN: (纸本)9798350328851

Graph models and graph-based signals are becoming increasingly important in machine learning, natural sciences, and modern signal processing. In this paper, we address the problem of quantizing bandlimited graph signals. We introduce two classes of noise-shaping algorithms for graph signals that differ in their sampling methodologies. We demonstrate that these algorithms can be efficiently used to construct quantized representatives of bandlimited graph-based signals with bounded amplitude. Moreover, for one of the algorithms, we provide theoretical guarantees on the relative error between the quantized representative and the true signal. © 2023 IEEE.

关键词： Quantization (signal)

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A $4\times 4$ Millimeter-Wave Antenna Based on a Higher Order Mode in a Sub-mode Microwave SIW Strucutre for Recent 5G Applications

A $4\times 4$ Millimeter-Wave Antenna Based on a Higher Orde...

引用

European Conference on Antennas and Propagation, EuCAP

作者： Amjaad T. Altakhaineh Saqer S. Alja'afreh Chaoyun Song Data Science and Machine Learning Dept Pioneers International Academy Amman Jordan Dept of Electrical Engineering Mutah University Alkarak Jordan Department of Engineering King's College London London United Kingdom

ISBN: (数字)9788831299107

ISBN: (纸本)9798350366327

This paper presents a single structure, multi-port antenna for 5G millimeter wave (mmWave) applications. The proposed antenna is constructed by exploiting the higher-order modes of a fraction-mode microwave substrate-integrated waveguide (SIW) cavity at 28 GHz (quadrature mode substrate-integrated waveguide (QMSIW)). Such properties exhibit spatial multiplexing capabilities, giving the single cavity a MIMO capability. Firstly, two perpendicular T-shaped microstrip ports (Port 1 and Port 2) are installed to excite TE205 higher-order mode of the QMSIW cavity at 28 GHz. To extend the MIMO capability, a 4×4 MIMO QMSIW antenna is created by adding two extra coaxial feeding ports (Port 3 and Port 4), the higher-order modes of the cavity are reused by leveraging two orthogonal inverted L-shaped slots, in which each slot is excited using coaxial cables at 28 GHz. The proposed antenna exhibited a wide -10 dB impedance and > 11 dB isolation based on the simulation environment without using decoupling structures. Finally, we believe this work offers a promising contribution to present and future multi-port single antenna, MIMO, and polarization applications.

关键词： Phased arrays Microwave antennas Slot antennas 5G mobile communication Simulation Microwave communication Millimeter wave propagation Space division multiplexing Millimeter wave communication Substrates

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SPEAR: Design and Implementation of an Advanced Virtual Assistant 4

SPEAR: Design and Implementation of an Advanced Virtual Assi...

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4th International Conference on Sustainable Expert Systems, ICSES 2024

作者： Jain, Garima Shukla, Amita Bairwa, Nitesh Kumar Chaudhary, Anamika Patel, Ashish Jain, Ankush Noida Institute of Engineering and Technology Department of Computer Science and Buisness Systems Greater Noida India Noida Institute of Engineering and Technology Department of Artificial Intelligence Greater Noida India Noida Institute of Engineering and Technology Department of Data Science Greater Noida India Dronacharya Group of Institutions Department of Artificial Intelligence and Machine Learning Greater Noida India Netaji Subhash University of Technology Department of Computer Science and Enginnering Delhi India

ISBN: (纸本)9798331540364

This research presents the development and evaluation of SPEAR, an advanced voice-activated personal desktop assistant designed to address challenges in existing virtual assistant technology, such as limited language support, low recognition accuracy, and inadequate task automation. Despite recent advancements in Natural Language Processing (NLP) and machine learning, implementation in current systems remains subpar. SPEAR achieves an 88% overall command execution success rate, with a speech recognition accuracy of 92% and an average response time of 1.8 seconds, demonstrating efficiency in real-time interactions. Developed using Python and machine learning libraries, SPEAR supports 15 voice commands across multiple platforms, excelling in note creation (95% success), web searching (94%), and information retrieval (92%). Its natural language understanding score of 0.86 and context retention rate of 75% highlight its conversational capabilities. While the assistant performs well in messaging functionality (85% success) and currently supports three languages, expanding its multilingual capabilities is essential for broader user engagement. Metrics indicate a satisfaction score of 8.2/10 and an average of 12 daily interactions per user, showcasing its practical utility. Additionally, the system's text-to-speech quality (8.5/10) and error recovery rate (82 %) affirm its robustness. These findings contribute valuable insights for future research, focusing on enhancing multilingual support, broadening task categories, and improving context retention, ultimately positioning SPEAR to rival established industry leaders by offering a comprehensive suite of services. © 2024 IEEE.

关键词： Speech recognition

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Sentiment Analysis Using machine learning Methods

Sentiment Analysis Using Machine Learning Methods

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2024 International Conference on Intelligent and Innovative Practices in engineering and Management, IIPEM 2024

作者： Badholia, Abhishek Diwan, Tarun Dhar Narooka, Preeti Khatkale, Pravin B Vishnoi, Ankit Kaushik, Keshav Shri Shankaracharya Institute of Professional Management and Technology Department of Data Science Raipur India Atal Bihari Vajpayee University Bilaspur India Manipal University Artificial Intelligence and Machine Learning School of Computer and Engineering Jaipur India Sanjivani University Maharashtra Kopargaon India Graphic Era Deemed to Be University Department of Computer Science and Engineering Uttarakhand Dehradun India Amity University Punjab Amity School of Engineering and Technology Mohali India

ISBN: (纸本)9798350390049

machine learning (ML) will be utilized to evaluate sentiment analysis in this project. This study aims to do this. Sentiment analysis is a popular natural language processing area. This is a natural language processing subfield. This section identifies textual senses. Due to the growing availability of digital text across platforms, sentiment analysis is now vital for many applications. This is because sentiment analysis has become essential. This technology might be used for market research, political analysis, and consumer feedback. A few instances. This study analyzes several machine learning methods, to name a few. This category includes SVM, Naive Bayes, Random Forest, and RNNs. To clarify, several datasets are used to assess these methodologies. To get more information. Preprocessing and feature engineering are crucial to sentiment analysis model construction, as this paper shows. The research emphasises these two phases. In addition, it provides insights into how these approaches are used in constantly changing digital situations. © 2024 IEEE.

关键词： Adversarial machine learning

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BIM: Improving Graph Neural Networks with Balanced Influence Maximization 40

BIM: Improving Graph Neural Networks with Balanced Influence...

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40th IEEE International Conference on data engineering, ICDE 2024

作者： Zhang, Wentao Gao, Xinyi Yang, Ling Cao, Meng Huang, Ping Shan, Jiulong Yin, Hongzhi Cui, Bin Peking University Center for Machine Learning Research China Institute of Advanced Algorithms Research Shanghai China National Engineering Labratory for Big Data Analytics and Applications The University of Queensland Australia Peking University Key Lab of High Confidence Software Technologies China Apple Inc. Institute of Computational Social Science Peking University Qingdao China

ISBN: (纸本)9798350317152

The imbalanced data classification problem has aroused lots of concerns from both academia and industry since data imbalance is a widespread phenomenon in many real-world scenarios. Although this problem has been well researched from the view of imbalanced class samples, we further argue that graph neural networks (GNNs) expose a unique source of imbalance from the influenced nodes of different classes of labeled nodes, i.e., labeled nodes are imbalanced in terms of the number of nodes they influenced during the influence propagation in GNNs. To tackle this previously unexplored influence-imbalance issue, we connect social influence maximization with the imbalanced node classification problem and propose balanced influence maximization (BIM). Specifically, BIM greedily assigns the pseudo label to the node which can maximize the number of influenced nodes in GNN training while making the influence of each class more balance. Experimental results on five public datasets demonstrate the effectiveness of our method in relieving the influence-imbalance issue. For example, when training a GCN with an imbalance ratio of 0.1, BIM significantly outperforms the most competitive baseline by 0.6% -9.8% in five public datasets in terms of the F1 score. © 2024 IEEE.

关键词： Graph neural networks

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