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Robust graph‐based localization for industrial Internet of things in the presence of flipping ambiguities

CAAI Transactions on Intelligence Technology 2023年第4期8卷 1140-1149页

作者： Mian Imtiaz ul Haq Ruhul Amin Khalil Muhannad Almutiry Ahmad Sawalmeh Tanveer Ahmad Nasir Saeed Faculty of Electrical Engineering Swedish College of Engineering and TechnologyWah CanttPakistan Department of Electrical Engineering Faculty of Electrical and Computer EngineeringUniversity of Engineering and TechnologyPeshawarPakistan Department of Electrical Engineering Remote Sensing UnitNorthern Border UniversityArarSaudi Arabia Data Science and Artificial Intelligence Department‐College of Science and Information Technology Irbid National UniversityIrbadJordan Innovation Education and Research Center for On‐Device AI Software(Bk21) Department of Computer Science and EngineeringChungnam National UniversityDaejeonRepublic of Korea Department of Electrical and Communication Engineering United Arab Emirates University(UAEU)Al AinUnited Arab Emirates

Localisation of machines in harsh Industrial Internet of Things(IIoT)environment is necessary for various ***,a novel localisation algorithm is proposed for noisy range measurements in IIoT *** position of an unknown machine device in the network is estimated using the relative distances between blind machines(BMs)and anchor machines(AMs).Moreover,a more practical and challenging scenario with the erroneous position of AM is considered,which brings additional uncertainty to the final position ***,the AMs selection algorithm for the localisation of BMs in the IIoT network is *** those AMs will participate in the localisation process,which increases the accuracy of the final location ***,the closed‐form expression of the proposed greedy successive anchorization process is derived,which prevents possible local convergence,reduces computation,and achieves Cramér‐Rao lower bound accuracy for white Gaussian measurement *** results are compared with the state‐of‐the‐art and verified through numerous simulations.

关键词： Cramér‐Rao lower bound greedy successive anchorization industrial internet of things localization

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A Novel Machine Learning Approach to Delay Efficient Offloading Strategy for Mobile Edge Computing 1st

A Novel Machine Learning Approach to Delay Efficient Offload...

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1st International Conference on Microelectronics, Communication Systems, Machine Learning, and Internet of Things, MCMI 2020

作者： Bharti, Piyush Chaudhary, Srishti Snigdh, Itu Department of Computer Science and Engineering Birla Institute of Technology Mesra India Software Development Microland Limited Bengaluru India

ISBN: (纸本)9789811919053

There have been studies on the problems experienced in offloading. But, how to leverage machine learning for resource-efficient computation offloading is not completely explored yet. We present this paper with the motivation to leverage machine learning and backward induction to suggest a method for the resource-efficient computation offloading in IoT devices. In particular, we use machine learning to predict the most efficient device to which a task should be offloaded through the use of a two-model machine learning system deployed in the edge cloud itself, since most high power IoT devices are fixed in their positions and display a certain pattern in their CPU utilization data. This is followed by using backward induction to determine the optimal execution location for each task component. © 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： computation offloading

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Money Laundering Detection and Mitigation Using Decision Tree Ensembles

Money Laundering Detection and Mitigation Using Decision Tre...

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2024 IEEE SmartBlock4Africa, SmartBlock4Africa 2024

作者： Ebenezer, Adeoti Babajide Olutayo, Boyinbode Olumide O, Obe Akintola, K.G. Emmanuel, Mgbeahuruike Oluwayemisi Boye, Fatade Babcock University Software Engineering Department Ogun state Ilisan Remo Nigeria Federal University of Technology Akure Department of Computer Science Ondo state Akure Nigeria

ISBN: (纸本)9798350376838

Money laundering is an unlawful activity that has caused underdevelopment to Africa, it's a ravaging vices that cripples economic growth, increases criminality and leads to economic sabotage. Many works previously done on money laundering detection, it was observed that it takes times before fraudulent transactions can be detected, it is also less effective, meaning that is can easily be circumvents and the data sampling methods used in previous researches makes the model to suffer from overfitting and loss of information, however this research adopts SMOTE (Synthetic Minority Oversampling Techniques) algorithm method to balance the dataset so as to have a fair representation of the two classes in order for machine learning model to accurately evaluate the models. The Objective of this research is to adapt and implement decision tree ensemble learning algorithms to detect money laundering activities, also design and implement an Anti Money Laundering (AML) query system to mitigate money laundering activities based on detection results and finally to carry out the performance evaluation of decision tree ensemble algorithms using standard performance metrics. © 2024 IEEE.

关键词： Laundering

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General Optimal Trajectory Planning:Enabling Autonomous Vehicles with the Principle of Least Action

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engineering 2024年第2期33卷 63-76页

作者： Heye Huang Yicong Liu Jinxin Liu Qisong Yang Jianqiang Wang David Abbink Arkady Zgonnikov School of Vehicle and Mobility Tsinghua UniversityBeijing 100084China Department of Cognitive Robotics Faculty of MechanicalMaritime and Materials Engineering(3mE)Delft University of TechnologyDelft 2628Netherlands Department of Software Technology Faculty of Electrical EngineeringMathematics and Computer ScienceDelft University of TechnologyDelft 2628Netherlands Xi’an Institute of High-Technology Xi’an 710051China

This study presents a general optimal trajectory planning(GOTP)framework for autonomous vehicles(AVs)that can effectively avoid obstacles and guide AVs to complete driving tasks safely and ***,we employ the fifth-order Bezier curve to generate and smooth the reference path along the road *** coordinates are then transformed to achieve the curvature continuity of the generated *** the road constraints and vehicle dynamics,limited polynomial candidate trajectories are generated and smoothed in a curvilinear coordinate ***,in selecting the optimal trajectory,we develop a unified and auto-tune objective function based on the principle of least action by employing AVs to simulate drivers’behavior and summarizing their manipulation characteristics of“seeking benefits and avoiding losses.”Finally,by integrating the idea of receding-horizon optimization,the proposed framework is achieved by considering dynamic multi-performance objectives and selecting trajectories that satisfy feasibility,optimality,and *** simulations and experiments are performed,and the results demonstrate the framework’s feasibility and effectiveness,which avoids both dynamic and static obstacles and applies to various scenarios with multi-source interactive traffic ***,we prove that the proposed method can guarantee real-time planning and safety requirements compared to drivers’manipulation.

关键词： Autonomous vehicle Trajectory planning Multi-performance objectives Principle of least action

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Direction Estimation for Collaborative Search in MOEA/D

Direction Estimation for Collaborative Search in MOEA/D

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2024 International Annual Conference on Complex Systems and Intelligent science, CSIS-IAC 2024

作者： Chen, Jingjing Li, Genghui Lin, Xi Zhang, Qingfu City University of Hong Kong Department of Computer Science Hong Kong The City University of Hong Kong Shenzhen Research Institute Shenzhen China Shenzhen University College of Computer Science and Software Engineering Shenzhen China

ISBN: (纸本)9798331504755

The decomposition-based multi-objective evolutionary algorithm (MOEA/D) is an efficient framework for solving multi-objective optimization problems. It decomposes a multi-objective optimization problem into a set of single-objective optimization subproblems and solves them collaboratively. However, most MOEA/D variants use heuristic operators to generate new solutions, which might lead to inefficient search performance. Direction estimation is a powerful derivative-free technique for tackling black-box optimization problems. By iteratively estimating valid search directions, this method can efficiently search for the optimal solution in a principled way;yet it suffers from high computational cost since it requires a number of function evaluations to approximate the true gradient. In this work, we leverage the neighborhood structure of MOEA/D and interactions among different subproblems to estimate a promising descent direction. We demonstrate that sharing and exploiting valuable information among neighboring subproblems can significantly reduce the overall computational budget for direction estimation in MOEA/D (MOEA/D-DirE). Experimental results demonstrate that the proposed MOEAD-DirE algorithm outperforms many multi-objective evolutionary algorithms on various multi-objective optimization problems. © 2024 IEEE.

关键词： Optimization algorithms

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Accuracy and Interpretability: Developing a computer-Aided Diagnosis System for Pneumonia Detection in Chest X-Ray Images 2

Accuracy and Interpretability: Developing a Computer-Aided D...

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2nd IEEE International Conference on Distributed Computing and Electrical Circuits and Electronics, ICDCECE 2023

作者： Mridha, Krishna Barman, Apu Chandra Biswas, Shekhar Sarkar, Shakil Biswas, Sunanda Priyok, Masrur Ahsan Marwadi University Computer Engineering Rajkot India Marwadi University Department of Pharmacy Rajkot India Brac University Computer Science and Engineering Bangladesh Tianjin University Software Engineering China

ISBN: (纸本)9798350347456

People of all ages can contract pneumonia, a deadly respiratory illness that is more common in underdeveloped countries. For successful treatment and higher survival rates, pneumonia must be accurately and quickly diagnosed. While chest X-ray imaging is a frequently used diagnostic technique for detecting pneumonia, its interpretation can be arbitrary and error-prone. In this study, we used a convolutional neural network (CNN) architecture to construct a computer-aided diagnostic system for pneumonia identification in chest X-ray images. Our dataset comprises 5,863 anterior-posterior view X-ray pictures that have been divided into two categories: normal and pneumonia. The photos were chosen from the Guangzhou Women and Children's Medical Center's pediatric patients between the ages of one and five, and they were first checked for quality control before being judged by two highly qualified doctors. A third expert also reviewed the assessment set to verify correctness. For the test set, our CNN model performed well, with 99% accuracy, 99% precision, 99.25% f1-score, and 98% specificity. To improve the interpretability of our model, we also included explainable AI techniques like Grad-CAM and Grad-CAM++. These approaches offer insights into CNN's decision-making process. Our computer-aided diagnosis system offers a trustworthy and unbiased method for detecting pneumonia in chest X-ray pictures, which can help with the quick identification and treatment of this potentially fatal condition. © 2023 IEEE.

关键词： Convolutional neural networks

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Insider Attack Detection Using Deep Belief Neural Network in Cloud Computing

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computer Systems science & engineering 2022年第5期41卷 479-492页

作者： A.S.Anakath R.Kannadasan Niju P.Joseph P.Boominathan G.R.Sreekanth School of Computing E.G.S.Pillay Engineering CollegeNagapattinam611002TamilnaduIndia Department of Software Systems School of Computer Science and Engineering(SCOPE)Vellore Institute of Technology UniversityVellore632014TamilnaduIndia Department of Computer Science CHRIST(Deemed to be University)Bengaluru560029India Department of Information Security School of Computer Science and Engineering(SCOPE)Vellore Institute of Technology UniversityVellore632014TamilnaduIndia Department of Computer Science and Engineering Kongu Engineering CollegeErode638060TamilnaduIndia

Cloud computing is a high network infrastructure where users,owners,third users,authorized users,and customers can access and store their information *** use of cloud computing has realized the rapid increase of information in every field and the need for a centralized location for processing *** cloud is nowadays highly affected by internal threats of the *** applications such as banking,hospital,and business are more likely affected by real user *** intruder is presented as a user and set as a member of the *** becoming an insider in the network,they will try to attack or steal sensitive data during information sharing or *** major issue in today's technological development is identifying the insider threat in the cloud *** data are lost,compromising cloud users is *** and security are not ensured,and then,the usage of the cloud is not *** solutions are available for the external security of the cloud ***,insider or internal threats need to be *** this research work,we focus on a solution for identifying an insider attack using the artificial intelligence *** insider attack is possible by using nodes of weak users’*** will log in using a weak user id,connect to a network,and pretend to be a trusted ***,they can easily attack and hack information as an insider,and identifying them is very *** types of attacks need intelligent solutions.A machine learning approach is widely used for security *** date,the existing lags can classify the attackers *** information hijacking process is very absurd,which motivates young researchers to provide a solution for internal *** our proposed work,we track the attackers using a user interaction behavior pattern and deep learning *** usage of mouse movements and clicks and keystrokes of the real user is stored in a *** deep belief neural

关键词： Cloud computing security insider attack network security privacy user interaction behavior deep belief neural network

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A Review on Feature Selection Techniques for Anxiety Classification from Physiological Signals 1

A Review on Feature Selection Techniques for Anxiety Classif...

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1st International Conference on Advances in computer science, Electrical, Electronics, and Communication Technologies, CE2CT 2025

作者： Shikha, Shikha Sethia, Divyashikha Indu, S. Delhi Technological University Department of Computer Science and Engineering New Delhi India Delhi Technological University Department of Software Engineering New Delhi India Delhi Technological University Department of Electronics and Communication New Delhi India

ISBN: (纸本)9798331518578

Anxiety disorders significantly impact individuals' quality of life and are traditionally assessed using subjective methods, which often lack accuracy. Recent advancements have enabled the use of physiological signals for anxiety classification, providing a more objective approach. However, the multidomain nature of these signals results in high-dimensional feature sets, necessitating effective feature selection. Therefore, this paper reviews various feature selection techniques-filter, wrapper, embedded, and hybrid-used in physiological-based anxiety classification, analyzing the advantages and limitations. It also highlights key challenges and suggests future research directions. Furthermore, this review introduces a proposed study utilizing a novel Corr-FGA hybrid feature selection algorithm to develop an efficient anxiety classification framework. The proposed framework achieves the highest accuracy of 88.93% using 8 features. This review aims to guide researchers in developing a robust, efficient anxiety classification model while providing insights that can help streamline feature selection and optimize machine learning model performance in real-world applications. © 2025 IEEE.

关键词： Adversarial machine learning

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EEG-EMG FAConformer: Frequency Aware Conv-Transformer for the fusion of EEG and EMG

EEG-EMG FAConformer: Frequency Aware Conv-Transformer for th...

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2024 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2024

作者： He, ZhengXiao Cai, Minghong Li, Letian Tian, Siyuan Dai, Ren-Jie Tongji University College of Electronic and Information Engineering Shanghai China Tongji University School of Software Engineering Shanghai China Shanghai Jiao Tong University Department of Computer Science and Engineering China

ISBN: (纸本)9798350386226

Motor pattern recognition paradigms are the main forms of Brain-computer Interfaces(BCI) aimed at motor function rehabilitation and are the most easily promoted applications. In recent years, many researchers have suggested encouraging patients to perform real motor control execution simultaneously in MI-based BCI rehabilitation training systems. Electromyography (EMG) signals are the most direct physiological signals that can assess the execution of movements. Multimodal signal fusion is practically significant for decoding motor patterns. Therefore, we introduce a multimodal motion pattern recognition algorithm for EEG and EMG signals: EEG-EMG FAConformer, a method with several attention modules correlated with temporal and frequency information for motor pattern recognition. We especially devise a frequency band attention module to encode EEG information accurately and efficiently. What's more, modules like Multi-Scale Fusion Module, Independent ChannelSpecific Convolution Module(ICSCM), and Fuse Module which can effectively eliminate irrelevant information in EEG and EMG signals and fully exploit hidden dynamics are developed and show great effects. Extensive experiments show that EEGEMG FAConformer surpasses existing methods on Jeong2020 dataset, showcasing outstanding performance, high robustness and impressive stability. © 2024 IEEE.

关键词： Electromyography

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Product Recommendation System using Deep Learning based Recurrent Neural Network

Product Recommendation System using Deep Learning based Recu...

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2023 International Conference on computer Communication and Informatics, ICCCI 2023

作者： Shanthi, S. Nirmaladevi, K. Mathiarasi, N. Tharani, As. Krithika Monisha, A. Kuppuchamy, R. Krishnamoorthy, N. Kongu Engineering College Department of Computer Science & Engineering Perundurai India PSNA College of Engineering and Technology Department of Computer Applications Tamilnadu Dindigul India Vellore Institute of Technology Department of Software Systems and Engineering Vellore Campus India

ISBN: (纸本)9798350348217

Today's marketing strategies place a high priority on comprehending customer sentiments. It will not only give businesses a better understanding of how their clients view their goods and/or services, but it will also give them suggestions on how to enhance their offerings. We make an effort to comprehend the relationship between various factors in customer reviews on an online store for women's clothing. The proposed system also classifies each review, according to, whether it recommends the product in question or not, and whether it has a positive, negative, or neutral sentiment. With the exception of review titles and review texts, we used univariate and multivariate analyses on datasets features to accomplish these goals. Additionally, we have implemented a bidirectional recurrent neural network (RNN) with a long-short term memory unit (LSTM) for sentiment and recommendation. © 2023 IEEE.

关键词： Long short-term memory

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