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Advancement in Renewable Energy and Intelligent Systems (AREIS), International Conference on

作者： M. A. Mukunthan Jose Anand A. Stalin Kesavan Malini Mutyala R. Kesavan R. Geetha Department of CSE Vel Tech Rangarajan Dr. Sagunthala R&D Institute of Science and Technology Chennai Tamil Nadu India Department of ECE KCG College of Technology Chennai Tamil Nadu India Department of Marine Engineering Academy of Maritime Education and Training Chennai Tamil Nadu Department of CSBS Vignana Bharathi Institute of Technology Hyderabad Telangana India Department of Computer Science and Engineering Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences (SIMATS) Chennai Tamilnadu Department of Computing Technologies SRM Institute of Science and Technology Chennai Tamilnadu

ISBN: (数字)9798350387230

ISBN: (纸本)9798350387247

This work focuses on the implementation of a Machine Learning (ML) algorithm for detecting physical anomalies in Internet of Things (IoT) networks. A Wireless Sensor Network (WSN) was deployed, collecting data under two scenarios: normal operation and an altered state exhibiting physical anomalies. The WSN, composed of humidity, Hall effect, barometer, and temperature sensors, communicated using the MQTT protocol with an Edge node for data capture. Data profiling and preprocessing were conducted to clean and optimize the dataset for ML. The K-Nearest Neighbors (KNN), Support Vector Machine (SVM), and Random Forest (RF) algorithm were employed to classify network traffic, with hyperparameters tuned using Randomized Search CV. The dataset was split into training (75%) and testing (25%) sets, with performance metrics such as accuracy, precision, recall, F1 score, and ROC curve analyzed. The final model achieved high accuracy (99.53 %) and showed excellent precision (1) with a classification time of 0.0012 seconds per packet. These results demonstrate the RF model's effectiveness in detecting physical anomalies in IoT networks, although further optimization may be needed for real-world scalability.

关键词： Support vector machines Radio frequency Performance evaluation Wireless sensor networks Accuracy Computational modeling Scalability Nearest neighbor methods Reliability Internet of Things

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Design and implementation of A Bio-Inspired Robot Arm: Machine learning, Robot vision

Design and implementation of A Bio-Inspired Robot Arm: Machi...

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New Frontiers in Communication, Automation, Management and Security (ICCAMS), International Conference on

作者： A Basi Reddy K Michael Mahesh M. Prabha R.Senthamil Selvan Department of Computer Science and Engineering School of Computing Mohan Babu University Tirupati India Department of ECE SRM Institute of Science and Technology Ramapuram Campus Chennai India Department of ECE Vel Tech Rangarajan Dr.Sagunthala R&D Institute of Science and Technology Chennai India Department of ECE Annamacharya Institute of Technology and Sciences Tirupati India

ISBN: (数字)9798350317060

ISBN: (纸本)9798350317077

This research highlights a method for remotely controlling a highly complex humanoid robot that relies on the operator’s bio-signals The framework combines machine learning, robot vision, and collision avoidance to ensure safe interaction with the environment. This occurs even with rather noisy control signals, such as the acceleration and electromyography (EMG) data from the operator’s hands. Similar actions, such as reaching for and grasping objects, may be performed with the 5-DOF arm with the use of bio-signals.

关键词： Hazardous materials Weapons Disasters Robot vision systems Humanoid robots Machine learning Electromyography

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ChrSNet: Chromosome Straightening using Self-attention Guided Networks

arXiv

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arXiv 2022年

作者： Zheng, Sunyi Li, Jingxiong Shui, Zhongyi Zhu, Chenglu Zhang, Yunlong Chen, Pingyi Yang, Lin Artificial Intelligence and Biomedical Image Analysis Lab School of Engineering Westlake University China Institute of Advanced Technology Westlake Institute for Advanced Study China College of Computer Science and Technology Zhejiang University China

Karyotyping is an important procedure to assess the possible existence of chromosomal abnormalities. However, because of the non-rigid nature, chromosomes are usually heavily curved in microscopic images and such deformed shapes hinder the chromosome analysis for cytogeneticists. In this paper, we present a self-attention guided framework to erase the curvature of chromosomes. The proposed framework extracts spatial information and local textures to preserve banding patterns in a regression module. With complementary information from the bent chromosome, a refinement module is designed to further improve fine details. In addition, we propose two dedicated geometric constraints to maintain the length and restore the distortion of chromosomes. To train our framework, we create a synthetic dataset where curved chromosomes are generated from the real-world straight chromosomes by grid-deformation. Quantitative and qualitative experiments are conducted on synthetic and real-world data. Experimental results show that our proposed method can effectively straighten bent chromosomes while keeping banding details and length. Copyright © 2022, The Authors. All rights reserved.

关键词： Chromosomes

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Federated Graph Learning via Constructing and Sharing Feature Spaces for Cross-Domain IoT

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IEEE Internet of Things Journal 2025年

作者： Chen, Jiale Zhuo, Shengda He, Jinchun Qiu, Wangjie Zhang, Qinnan Xiong, Zehui Zheng, Zhiming Tang, Yin Chen, Min Wang, Changdong Huang, Shuqiang Jinan University College of Cyber Security Guangzhou China Beihang University Institute of Artificial Intelligence Beijing Advanced Innovation Center for Future Blockchain and Privacy Computing Beijing100191 China Zhongguancun Laboratory Beijing China Singapore University of Technology and Design Information Systems Technology and Design 487372 Singapore Jinan University School of Management Guangzhou China South China University of Technology School of Computer Science and Engineering Guangzhou China Sun Yat-sen University School of Computer Guangzhou China Jinan University College of Cyber Security of Jinan University China Guangdong Key Laboratory of Data Security and Privacy Preserving Guangzhou China

The Internet of Things (IoT) collects large volumes of diverse data, with graph data as a critical component, and extensively utilizes Federated Graph Learning (FGL) to process this data while preserving data security. However, the graph data collected by different IoT institutions are relatively independent due to various factors (e.g., data collection methods, geographical locations), and data access is restricted to local environments due to privacy constraints, IoT institutions typically possess heterogeneous feature spaces. Aggregating under these conditions could potentially contaminate local graph representations. Unfortunately, most existing FGL methods tend to overlook this issue. To address this challenge, we propose Federated Graph Learning via Constructing and Sharing Features (FedCSF), a novel FGL framework designed to build a globally consistent feature space and share it among IoT institutions. To construct and extract the feature space, we employ an uniform feature initialization across IoT institutions and design an encoder to extract both global and local feature relationships, thereby facilitating effective collaboration across data from different IoT institutions. Furthermore, we introduce an independent adaptive aggregation strategy to eliminate the integration of harmful knowledge, ensuring that the contributions from each IoT institution are effectively integrated into the global model. We theoretically analyze the convergence of FedCSF. To validate the effectiveness of FedCSF, we conducted extensive experiments under various settings (i.e., cross-datasets, and cross-domains), demonstrating its significant advantages of FedCSF in terms of performance, convergence speed, and practical adaptability © 2014 IEEE.

关键词： Federated learning

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A Multi-Module Spatial Semantic Network with Gradient Boosting for Efficient Crop Classification

A Multi-Module Spatial Semantic Network with Gradient Boosti...

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Distributed Systems, computer Networks and Cybersecurity (ICDSCNC), International Conference on

作者： Saif O. Husain Meghana A Rajireddy Soujanya G Sunil Vijaya Bhaskar Reddy Muvva Department of Computers Techniques Engineering College of Technical Engineering The Islamic University Najaf Iraq Department of EEE Nitte Meenakshi Institute of Technology Bengaluru India Department of CSE Gokaraju Rangaraju Institute of Engineering and Technology Hyderabad India School of Computer Science & Artificial Intelligence SR University Warangal India Department of Information Technology College of Computing and Information Sciences University of Technology and Applied Sciences –Shinas Shinas Sultanate of Oman

ISBN: (数字)9798350375442

ISBN: (纸本)9798350375459

For the past few years, accurate crop classification is critical for efficient agricultural management and monitoring. While traditional classification techniques have leveraged spectral indices, they often struggle to fully capture the complex and diverse multispectral features present in agricultural landscapes. This challenge is particularly evident when differentiating crops with similar spectral signatures, leading to misclassification and reduced accuracy in traditional models. To address this problem, a hybrid approach that combines a deep learning model namely Multi-module Spatial Semantic Network (MSSNet) with the Gradient Boosting Classifier (GBC) is proposed. The dataset utilized in this research is sentinel-2, where the median filter is used to remove noise at the preprocessing stage. Then, MSSNet architecture is applied in feature extraction where pre-trained residual network (ResNet50), embedded receptive field module (RFB) and skip connection are incorporated to extract multi-spectral and spatial features. Finally, the extracted features are fed into a GBC, which is suitable for the robust performance in pixel mapping. This MSSNet+GBC combination leverages the strengths and offers improved accuracy in distinguishing between crop types and conditions. The experimental results demonstrate that MSSNet+GBC outperforms conventional methods such as 3D-Convolutional Neural Network (CNN) with higher accuracy of 98.67% using a sentinel-2 dataset for precise crop classification.

关键词： Deep learning Accuracy Semantics Noise Transfer learning Soil moisture Crops Feature extraction Boosting Residual neural networks

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Width Helps and Hinders Splitting Flows

arXiv

引用

arXiv 2022年

作者： Cáceres, Manuel Cairo, Massimo Grigorjew, Andreas Khan, Shahbaz Mumey, Brendan Rizzi, Romeo Tomescu, Alexandru I. Williams, Lucia Department of Computer Science University of Helsinki Finland Department of Computer Science and Engineering Indian Institute of Technology Roorkee India School of Computing Montana State University United States Department of Computer Science University of Verona Italy

Minimum flow decomposition (MFD) is the NP-hard problem of finding a smallest decomposition of a network flow/circulation X on a directed graph G into weighted source-to-sink paths whose superposition equals X. We show that, for acyclic graphs, considering the width of the graph (the minimum number of paths needed to cover all of its edges) yields advances in our understanding of its approximability. For the version of the problem that uses only non-negative weights, we identify and characterise a new class of width-stable graphs, for which a popular heuristic is a O(log |X|)approximation (|X| being the total flow of X), and strengthen its worst-case approximation ratio from Ω(√m) to Ω(m/ log m) for sparse graphs, where m is the number of edges in the graph. We also study a new problem on graphs with cycles, Minimum Cost Circulation Decomposition (MCCD), and show that it generalises MFD through a simple reduction. For the version allowing also negative weights, we give a (dlog kXke + 1)-approximation (kXk being the maximum absolute value of X on any edge) using a power-of-two approach, combined with parity fixing arguments and a decomposition of unitary circulations (kXk ≤ 1), using a generalised notion of width for this problem. Finally, we disprove a conjecture about the linear independence of minimum (non-negative) flow decompositions posed by Kloster et al. [17], but show that its useful implication (polynomial-time assignments of weights to a given set of paths to decompose a flow) holds for the negative version. © 2022, CC BY.

关键词： Approximation algorithms

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Small sets of genuinely nonlocal GHZ states in multipartite systems

arXiv

引用

arXiv 2023年

作者： Xiong, Zong-Xing Zhang, Yongli Li, Mao-Sheng Li, Lvzhou Institute of Quantum Computing and Software School of Computer Science and Engineering Sun Yat-Sen University Guangzhou510006 China School of Mathematics and Systems Science Guangdong Polytechnic Normal University Guangzhou510665 China School of Mathematics South China University of Technology Guangzhou510641 China

A set of orthogonal multipartite quantum states are called (distinguishability-based) genuinely nonlocal if they are locally indistinguishable across any bipartition of the subsystems. In this work, we consider the problem of constructing small genuinely nonlocal sets consisting of generalized GHZ states in multipartite systems. For system (C2) ⊗ N where N is large, using the language of group theory, we show that a tiny proportion Θ(1/√2N) of states among the N-qubit GHZ basis suffice to exhibit genuine nonlocality. Similar arguments also hold for the canonical generalized GHZ bases in systems (Cd) ⊗ N, wherever d is even and N is large. What is more, moving to the condition that any fixed N is given, we show that d + 1 genuinely nonlocal generalized GHZ states exist in (Cd) ⊗ N, provided the local dimension d is sufficiently large. As an additional merit, within and beyond an asymptotic sense, the latter result also indicates some evident limitations of the "trivial othogonality-preserving local measurements" (TOPLM) technique that has been utilized frequently for detecting genuine nonlocality. © 2023, CC BY.

关键词： Group theory

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Momentum Decoding: Open-ended Text Generation As Graph Exploration

arXiv

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arXiv 2022年

作者： Lan, Tian Su, Yixuan Liu, Shuhang Huang, Heyan Mao, Xian-Ling School of Computer Science and Technology Beijing Institute of Technology China Beijing Engineering Research Center of High Volume Language Information Processing and Cloud Computing Applications China

Open-ended text generation with autoregressive language models (LMs) is one of the core tasks in natural language processing. However, maximization-based decoding methods (e.g., greedy/beam search) often lead to the degeneration problem, i.e., the generated text is unnatural and contains undesirable repetitions. Existing solutions to this problem either introduce randomness prone to incoherence or require a look-ahead mechanism that demands extra computational overhead. In this study, we formulate open-ended text generation from a new perspective, i.e., we view it as an exploration process within a directed graph. Thereby, we understand the phenomenon of degeneration as circular loops within the directed graph. Based on our formulation, we propose a novel decoding method—momentum decoding—which encourages the LM to greedily explore new nodes outside the current graph. Meanwhile, it also allows the LM to return to the existing nodes with a momentum downgraded by a pre-defined resistance function. We extensively test our approach on three benchmarks from different domains through automatic and human evaluations. The results show that momentum decoding performs comparably with the current state of the art while enjoying notably improved inference speed and computation FLOPs. Furthermore, we conduct a detailed analysis to reveal the merits and inner workings of our approach. © 2022, CC BY.

关键词： Momentum

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Efficient Diabetes Detection using Hybrid Machine Learning Model

Efficient Diabetes Detection using Hybrid Machine Learning M...

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Distributed Systems, computer Networks and Cybersecurity (ICDSCNC), International Conference on

作者： A S Gowri P Jose Karthik S M S Berlin Shaheema K M Karuppasway R. Balamurugan Department of Networking and Communications School of Computing SRM Institute of Science and Technology Kattankulathur India Department of Computer Science and Engineering Vel Tech Rangarajan Dr. Sagunthala R&D Institute of Science and Technology Chennai India Department for Applied Research The Gandhigram Rural Institute (Deemed to be University) Gandhigram India Department of Artificial Intelligence and Data Science Stella Mary’s College of Engineering Azhikal India Department of Mathematics and Statistics Bharath Institute of Higher Education and Research Chennai India Department of Information Technology DMI College of Engineering Chennai India

ISBN: (数字)9798350375442

ISBN: (纸本)9798350375459

Diabetes is a metabolic disease that affects a large number of the global population and is incurable. The primary causes of death symptoms are kidney failure, heart attacks, strokes, and blindness. In this paper Principal component analysis used for pre-processing the data to extract features and The Efficient Hybrid Machine Learning Model Convolution Neural Network-Support Vector Machine is used to categorize the Pima Indian dataset. CNN and non-linear SVM used in a hybrid model to classify the diabetes data from the Pima Indians. To identify positive classes of pima Indians diabetes, the HCNN-SVM extract the features from the original data expression and learns diabetes data through a novel convolution network environment to the SVM classifier with RBF function and achieve the accuracy as 98,04%.

关键词： Support vector machines Accuracy Convolution Machine learning Feature extraction Data models Diabetes Data mining Convolutional neural networks Principal component analysis

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Repairing with Zero Skip Cost

Repairing with Zero Skip Cost

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IEEE International Symposium on Information Theory

作者： Yeow Meng Chee Son Hoang Dau Tuvi Etzion Han Mao Kiah Yuan Luo Wenqin Zhang Dept. of Industrial Systems Engineering and Management National University of Singapore School of Computing Technologies RMIT University Melbourne VIC Australia Computer Science Faculty Technion Israel Institute of Technology School of Physical and Mathematical Sciences Nanyang Technological University Singapore School of Electronic Information and Electrical Engineering Shanghai Jiao Tong University Shanghai China

To measure repair latency at helper nodes, we introduce a new metric called skip cost that quantifies the number of contiguous sections accessed on a disk. We provide explicit constructions of zigzag codes and fractio... 详细信息

ISBN: (数字)9798350382846

ISBN: (纸本)9798350382853

关键词： Measurement Costs Codes Maintenance engineering

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