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Hybrid AI-Electronic Systems for Real-Time Edge Processing in IoT Networks 3

Hybrid AI-Electronic Systems for Real-Time Edge Processing i...

3rd International Conference on Intelligent and Innovative Technologies in Computing, Electrical and Electronics, IITCEE 2025

作者： Sweety Prasanna Kiruba, G. Esakki Madura, E. Kumar, Bura Vijay Kavitha, M. Reddy, T. Srinivas Athiraja, A. Bannari Amman Institute of Technology Department of Computer Technology Tamil Nadu Sathyamangalam India Bannari Amman Institute of Technology Department of Artificial Intelligence and Data Science Tamil Nadu Sathyamangalam India School of Computer Science and Artificial Intelligence Sr University Warangal India Kathir College of Engineering Department of Artificial Intelligence and Data Science Neelambur Coimbatore India Malla Reddy Engineering College Department of Ece Main Campus Maisammaguda Telangana Medchal India Hyderabad India

ISBN: (纸本)9798331515911

Real-time data processing is facing many issues as a result of the Internet of Things' rapid expansion, especially at the network's edge where latency, bandwidth, and compute resources are limited. The creation of hybrid AI-electronic systems for real-time edge processing is investigated as a solution to these problems. To improve the effectiveness and precision of data processing in Internet of Things contexts, the research combines machine learning algorithms with sophisticated electronic circuits. The techniques include creating a brand-new hybrid framework that blends edge computing and AI models and focuses for low latency and low power consumption. When compared to conventional edge processing systems, extensive simulations and real-world research show a 25% reduction in processing latency and a 15% gain in data categorization accuracy. According to the findings, the suggested hybrid AI-electronic system can successfully handle the requirements of contemporary IoT networks and offer a scalable real-time edge processing solution. This strategy has potential for use in smart city applications, industrial automation, and other fields in the future. © 2025 IEEE.

关键词： Adversarial machine learning

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Representation Alignment and Adversarial Networks for Cross-lingual Dependency Parsing

Representation Alignment and Adversarial Networks for Cross-...

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

作者： Li, Ying Liu, Jianjian Yu, Zhengtao Gao, Shengxiang Huang, Yuxin Mao, Cunli Yunnan Provincial Key Laboratory of Artificial Intelligence Faculty of Information Engineering and Automation Kunming University of Science and Technology China

ISBN: (纸本)9798891761681

With the strong representational capabilities of pre-trained language models, dependency parsing in resource-rich languages has seen significant ***, the parsing accuracy drops sharply when the model is transferred to low-resource language due to distribution *** alleviate this issue, we propose a representation alignment and adversarial model to filter out useful knowledge from rich-resource language and ignore useless *** proposed model consists of two components, i.e., an alignment network in the input layer for selecting useful language-specific features and an adversarial network in the encoder layer for augmenting the language-invariant contextualized *** on the benchmark datasets show that our proposed model outperforms RoBERTa-enhanced strong baseline models by 1.37 LAS and 1.34 *** analysis shows that both alignment and adversarial networks are equally important in alleviating the distribution shifts problem and can complement each *** addition, the comparative experiments demonstrate that both the alignment and adversarial networks can substantially facilitate extracting and utilizing relevant target language features, thereby increasing the adaptation capability of our proposed model. © 2024 Association for Computational Linguistics.

关键词： Computational linguistics

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From Segmentation to Generation: the Emerging Trends in Skin Lesion Detection 8

From Segmentation to Generation: the Emerging Trends in Skin...

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8th International Symposium on Computer science and Intelligent Control, ISCSIC 2024

作者： Yu, Junwei Xia, Yuhe Henan University of Technology School of Artificial Intelligence and Big Data Zhengzhou China Henan University of Technology College of Information Science and Engineering Zhengzhou China

ISBN: (纸本)9798350380286

Skin disease detection has undergone significant advancements with the advent of deep learning-based image segmentation techniques. In this paper, we provide a comprehensive overview of the evolution of skin disease detection, from traditional methods to modernapproaches. We discuss the role of U-Net and its variants in medical image segmentation, highlighting their improved performance and robustness through multi-scale features, multi-task learning, and attention mechanisms. Additionally, we explore the recent trend of generation-based methods, including generative adversarial networks and diffusion models, which have shown promising results in generating high-quality skin disease segmentation masks. We also review popular skin disease segmentation datasets and evaluation metrics,and present experimental results comparing the performance of various models. As deep learning continues to advance and skin disease datasets expand, we anticipate the emergence of innovative network architectures and technologies that will further improve the diagnosis and treatment of skin diseases. This paper aims to inspire future research in this direction, exploring the potential of generation-based methods for skin disease detection and paving the way for more accurate and efficient diagnosis and treatment. © 2024 IEEE.

关键词： Generative adversarial networks

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Reinforcement Learning in Cognitive Robots for Autonomous Path Planning 5

Reinforcement Learning in Cognitive Robots for Autonomous Pa...

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5th International Conference on data intelligence and Cognitive Informatics, ICDICI 2024

作者： Das, Archana Poornima, R. Indu Deepa, G. Gokila Sheela, K. Selva Akshya, J. Choudhry, Mani Deepak New Horizon College of Engineering Department of Information Science and Engineering Bengaluru India KGiSL Institute of Technology Department of Information Technology Coimbatore India Ppg Institute of Technology Department of Artificial Intelligence and Data Science Coimbatore India KGiSL Institute of Technology Department of Artificial Intelligence and Data Science Coimbatore India Srm Institute of Science and Technology Department of Computational Intelligence Chennai India Srm Institute of Science and Technology Department of Computing Technologies Chennai India

ISBN: (纸本)9798350389609

Cognitive robots are intelligent systems that learn from their environment, adapt to dynamic changes, and make decisions without a human's direct intervention. This research studies the integration of Reinforcement Learning in cognitive robots with Q-Learning for autonomous path planning. The study addresses the most critical issues in traditional A∗ and Dynamic Programming algorithms by devising path-planning techniques, mainly the lack of adaptability and computational efficiency in real-time and unpredictable environments. Our approach is based on Q-Learning, which is a model-free RL algorithm allowing the robots to find paths autonomously by optimizing their paths and avoiding obstacles. The Q-Learning algorithm provides the possibility of learning optimal policies for the robot through iterative interaction with its environment balanced between exploration and exploitation of the decision process. A reward system is utilized by the proposed model, encouraging the robot to explore shorter, collision-free paths and adjust based on feedback in real time from its surroundings. The model was found to be significantly better than its counterparts. In this context, as indicated, the Q-Learning model runs at 11.5 seconds, faster than A∗ at 18.3 seconds and Dynamic Programming at 16.7 seconds, yet with an accuracy of 94% and was found to have the highest collision avoidance rate at 98%. Additionally, the adaptability of the model about the environment presents a marked difference in terms of path length optimization, having done so with a mean path length of 12.3, compared to approaches or models. The robustness and scalability of the Q-Learning model make it highly applicable in real-world applications. © 2024 IEEE.

关键词： Adversarial machine learning

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A Machine Learning and Deep Learning based Approach to Generate a Speech Emotion Recognition System 18

A Machine Learning and Deep Learning based Approach to Gener...

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18th INDIAcom;11th International Conference on Computing for Sustainable Global Development, INDIACom 2024

作者： Sumera Vaidehi, K. Nisha, Qamar Stanley College of Engineering and Technology for Women Department of Artificial Intelligence & Data Science and Computer Engineering Hyderabad India Stanley College of Engineering and Technology for Women Department. of Computer Science Engineering Hyderabad India

ISBN: (纸本)9789380544519

The most common and general medium via which we humans convey or communicate our thoughts, emotions, feelings or ideas artlessly is by speech or articulation. Blending of this artless way of speech with the technological advancements of AI, has given rise to the importance of building emotion recognition systems from speech today. Even more, the speech/articulation emotion recognition system presented here is also to contribute in and facilitate various emerging applications of today like, in detecting persons' physiological state (as in lie detectors), also be used in forensics, medicine. The proposed work identifies/associates an appropriate label/emotion for the respective emotion from speech presented in the form of an audio file (.wav format). About 4240 audio samples are taken. 1440, 2800 samples from RAVDESS and TESS datasets are considered respectively. After this process of data collection, features are separately extracted for each audio dataset mentioned above. Energy, pitch, ZCR, co-efficient of Mel frequency ceptrum (MFCC) are some of the features considered in this study. Furthermore, clubbing and merging of 2 datasets is performed resulting in a total of 4240 rows and 24 columns (features/characteristics including 1class label) of audio samples. The resulting 4240 samples of feature dataset is split/bifurcated into training and testing set by considering 3 different possibilities/instances viz;60%-40% ratio, 70%-30% ratio, 80%-30% ratio. The models namely CNN, Random forest and Support Vector Machine are trained to classify the dataset into 8 different emotions (neutral, calm, happy, sad, angry, fearful, disgust, surprise). An attempt to implement the models using two very essential disciplines of AI i.e. Machine Learning and Deep Learning is made here. The accuracy or results are depicted by generating confusion matrices on test data for CNN, RF and SVM models (Each model is trained and test across 3 different ratios viz;60%-40%, 70%-30%, 80%-20%). C

关键词： Classification CNN Confusion matrices Extraction of features MFCC RF Speech/articulation Emotion Recognition SVM

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A Review on Machine Learning Assisted Drying Behaviour Prediction of Agricultural Seeds and Fruits 2

A Review on Machine Learning Assisted Drying Behaviour Predi...

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2nd DMIHER International Conference on artificial intelligence in Healthcare, Education and Industry, IDICAIEI 2024

作者： Jawre, Sandip Verma, Prateek Kothare, Chandrakant B. Datta Meghe Institute of Higher Education and Research Faculty of Engineering and Technology Department of Artificial Intelligence and Data Science 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 Shri Shankarprasad Agnihotri College of Engineering Department of Mechanical Engineering Maharashtra Wardha India

ISBN: (纸本)9798331528713

Machine learning approaches have been more popular in recent years across many scientific and engineering fields, with the potential to improve drying behavior and thermal property predictions in terms of accuracy and efficiency. Agricultural seeds and fruits must be handled carefully after harvest because drying has a considerable impact on their quality, shelf life, and overall worth. To optimize drying processes and guarantee product quality, drying kinetics and thermal characteristics must be accurately predicted. This review article outlines the basic principles of drying, emphasizes their significance in determining the ideal drying conditions, and provides a thorough overview of the application of machine learning methods for predicting the drying kinetics and thermal properties of agricultural seeds and fruits. Then, in the context of their applications to drying prediction, a summary of several machine learning approaches, including artificial neural networks, support vector machines, and decision trees have been discussed. © 2024 IEEE.

关键词： Fruits

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Bridging Sentence and Document Contexts: A PEFT-Enhanced Framework for Document-Level Neural Machine Translation 4

Bridging Sentence and Document Contexts: A PEFT-Enhanced Fra...

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4th International Conference on artificial intelligence, Robotics, and Communication, ICAIRC 2024

作者： Liu, Chunjiao Guo, Junjun Kunming University of Science and Technology Yunnan Key Laboratory of Artificial Intelligence Faculty of Information Engineering and Automation Kunming China

ISBN: (纸本)9798331531225

Document-level neural machine translation (DNMT) faces challenges in integrating context and maintaining semantic consistency. This paper proposes a coarse-to-fine semantic transfer approach that captures both fine-grained and coarse-grained document-level context. By employing a two-stage training strategy with Adapter-enhanced modules, the method ensures effective semantic adaptation from sentence to document levels. Experiments on three datasets demonstrate significant improvements in s-BLEU and d-BLEU metrics, outperforming existing approaches. © 2024 IEEE.

关键词： Neural machine translation

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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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An empirical review of Machine Learning models for Energy Optimizations in IoT Networks 14

An empirical review of Machine Learning models for Energy Op...

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14th International Conference on Computing Communication and Networking Technologies, ICCCNT 2023

作者： Charde, Pallavi Lonkar, Bhupesh B. Faculty of Engineering and Technology Department of Artificial Intelligence and Data Science Wardha Sawangi India Datta Meghe Institute of Engineering Technology & Research Department of Information Technology Wardha Sawangi India

ISBN: (纸本)9798350335095

The Internet of Things (IoT) has transformed how users interact with their devices and enabled the accumulation of enormous quantities of data from various sources. There has been an increase in energy consumption due to the expanding use of IoT devices, resulting in environmental concerns and higher operational costs. The use of Machine Learning (ML) algorithms to optimize energy consumption in IoT networks is widespread for different scenarios. This paper empirically analyzes the most widely used machine learning models for energy optimization in IoT networks. The researchers evaluate the efficacy of various ML models regarding energy efficiency, precision, and scalability. The review includes a summary of the most recent research in this field and a comparison of different algorithms, including Decision Trees, Random Forests, Support Vector Machines, Neural Networks, and Deep Learning methods. In addition, this paper discusses the difficulties and limitations of ML-based energy optimization in IoT networks, including data acquisition and processing, model selection, and scalability levels. Finally, this paper presents recommendations for future research and prospective machine-learning applications in energy optimization for IoT networks. This review emphasizes the potential of machine learning (ML) models to reduce energy consumption in IoT networks. It provides researchers and practitioners with insight into this field under real-time scenarios. © 2023 IEEE.

关键词： Internet of things

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HCMHS: High-Order SNP Interactions Detection Based on Hierarchical Clustering and Multi-Task Harmony Search Algorithm

HCMHS: High-Order SNP Interactions Detection Based on Hierar...

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2024 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2024

作者： Tan, Kaiwen Tian, Jin Zhang, Yongbing Shu, Zhenqiu Yu, Zhengtao Kunming University of Science and Technology Faculty of Information Engineering and Automation Yunnan Key Laboratory of Artificial Intelligence Kunming China

ISBN: (纸本)9798350386226

The interaction between SNPs plays a key role in revealing the genetic mechanisms of complex diseases. However, as the order of SNP interactions increases, the number of SNP combinations increases exponentially, presenting a serious combinatorial explosion problem. Although swarm intelligence algorithms can alleviate the combinatorial explosion problem by optimizing search paths and have been widely used in SNP interaction detection, the performance of swarm intelligence algorithms is greatly affected by initialization and search direction. To mitigate these issues, we propose a high-order SNP interaction detection algorithm based on hierarchical clustering and multitask harmony search (abbreviated as HCMHS). In harmony memory initialization, considering that similar SNPs are more likely to form pathogenic combinations, hierarchical clustering is first used to cluster SNPs into different clusters, and then based on these clusters, harmony memory is initialized. In harmony search, considering the correlation between different orders of SNP interactions, the search directions for SNPs of various orders are optimized through a multi-task framework. To verify the performance of HCMHS, we carried out experiments on 58 simulated datasets and one real dataset, and HCMHS achieved the best results compared to nine advanced algorithms. The code of HCMHS is available at https://***/huoluan17-tian/HCMHS. © 2024 IEEE.

关键词： Hierarchical clustering

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