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3D Object Detection with Attention:Shell-Based Modeling

computer Systems Science & engineering 2023年第7期46卷 537-550页

作者： Xiaorui Zhang Ziquan Zhao Wei Sun Qi Cui School of Computer and Software Nanjing University of Information Science&TechnologyNanjing210044China Wuxi Research Institute Nanjing University of Information Science&TechnologyWuxi214100China Engineering Research Center of Digital Forensics Ministry of EducationJiangsu Engineering Center of Network MonitoringNanjing210044China Jiangsu Collaborative Innovation Center of Atmospheric Environment and Equipment Technology(CICAEET) Nanjing University of Information Science&TechnologyNanjing210044China School of Automation Nanjing University of Information Science&TechnologyNanjing210044China Department of Electrical and Computer Engineering University of WindsorWindsorN9B 3P4Canada

LIDAR point cloud-based 3D object detection aims to sense the surrounding environment by anchoring objects with the Bounding Box(BBox).However,under the three-dimensional space of autonomous driving scenes,the previous object detection methods,due to the pre-processing of the original LIDAR point cloud into voxels or pillars,lose the coordinate information of the original point cloud,slow detection speed,and gain inaccurate bounding box *** address the issues above,this study proposes a new two-stage network structure to extract point cloud features directly by PointNet++,which effectively preserves the original point cloud coordinate *** improve the detection accuracy,a shell-based modeling method is *** roughly determines which spherical shell the coordinates belong ***,the results are refined to ground truth,thereby narrowing the localization range and improving the detection *** improve the recall of 3D object detection with bounding boxes,this paper designs a self-attention module for 3D object detection with a skip connection *** of these features are highlighted by weighting them on the feature *** training,it makes the feature weights that are favorable for object detection get ***,the extracted features are more adapted to the object detection *** comparison experiments and ablation experiments conducted on the KITTI dataset verify the effectiveness of our proposed method in improving recall and precision.

关键词： 3D object detection autonomous driving point cloud shell-based modeling self-attention mechanism

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Identification of Fish and Shrimp Species using computer Vision echniques and Convolutional Neural Networks

Identification of Fish and Shrimp Species using Computer Vis...

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Mobile Computing and Sustainable informatics (ICMCSI), International Conference on

作者： Levina Tukaram Swati Shalet Benvin Swapnil Parikh Harshal Patil Ramya Maranan Department of Computer Science & Engineering K N S Institute of Technology Bengaluru Karnataka India Department of Computer Science & Engineering Graphic Era Deemed to be University Dehradun Uttarakhand India Department of Computer Science and Engineering BGS Institute of Technology Adichunchanagiri University Mandya Karnataka India Department of Computer Science and Engineering Parul Institute of Engineering and Technology Parul University Waghodia Gujarat India Department of Computer Science and Engineering Symbiosis Institute of Technology Symbiosis International (Deemed University) Pune Maharashtra India Department of Research and Innovation Saveetha School of Engineering SIMATS Chennai Tamil Nadu India

The objective of this work is to identify fish and shrimp species using Artificial Intelligence (AI) and computer vision techniques. Artificial Intelligence (AI) techniques include Artificial Neural Networks (ANN). Therefore, neural networks can be used as a way to improve the industry, aiming to reduce costs and improve processes. This work proposes a transform network model for fish identification. Eight species of fish and one species of shrimp, were used. The database has already been consolidated and made available by the company, with a substantial amount of data, which contributed to the selection. The results regarding the number of hits were in the order of 99%, which demonstrates promising results that will contribute to the reliability of seafood species identification. The 99% accuracy of the proposed model indicates good performance for identifying fish species. Of all the classes used, only five were not fully utilized, including the black sea sprat (I), Gilt Head Bream (II), Hourse Makerel (III), red mullet (IV), red sea bream (V), sea bass (VI), shrimp (VII), striped red mullet (VIII), and trout (IX). However, the identification errors did not exceed 2%.

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Real Time Transformer Health Monitoring System Using IoT in R 12

Real Time Transformer Health Monitoring System Using IoT in ...

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12th International Conference on computer Communication and informatics, ICCCI 2022

作者： Perumal, B. Nagarai, P. Venkatesh, R. Muneeswaran, V. Gopishankar, Y. Saikumar, A. Koushik, A. Anil, B. Kalasalingam Academy of Research and Education Department of Electronics and Communication Engineering Virudhunagar Krishnankoil India Kalasalingam Academy of Research and Education Department of Computer Science and Engineering Virudhunagar Krishnankoil India Sri Shakthi Institute of Engineering and Technology Department of Computer Science and Engineering Chinniyampalayam Coimbatore India Kalasalingam Acadamy of Research and Education Department of Electronics and Communication Engineering Krishnan koil India

ISBN: (纸本)9781665480352

Observing of electrical gear utilizing IOT might assist with supplanting the hardware before disappointment and progression of the force won't be upset. This paper presents an execution of this idea which gains the continuous state of the conveyance transformer distantly with the utilization of web carrying out IOT. The proposed wellbeing checking framework works continuously and utilizes temperature-sensor, possible transformer and flow transformer for observing temperature, voltage and flow of the conveyance transformer and send these data to a distant worker where it tends to be checked and essential move might be made to keep away from the blackout of the power supply. The proposed structure has been finished in Laboratory and endeavored. The information gathered are approved and it has been tracked down that the proposed framework is working acceptably. © 2022 IEEE.

关键词： Internet of things

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Designing Quantum-Safe Lattice-Based Multi-Authority CP-ABE Scheme for Blockchain-Enabled IoT-Based Consumer Healthcare Electronics

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IEEE Transactions on Consumer Electronics 2025年

作者： Bagchi, Prithwi Bisht, Abhishek Das, Ashok Kumar Saxena, Neetesh Hossain, M. Shamim International Institute of Information Technology Center for Security Theory and Algorithmic Research Hyderabad500 032 India Department of Computer Science and Engineering College of Informatics Korea University 145 Anam-ro Seongbuk-gu Seoul02841 Korea Republic of Cardiff University School of Computer Science and Informatics CardiffCF24 4AG United Kingdom King Saud University College of Computer and Information Sciences Department of Software Engineering Riyadh13273 Saudi Arabia

Despite edge computing reducing communication delays associated with cloud computing, privacy concerns remain a significant challenge when sharing data from edge-based consumer electronics (CE) or Internet-of-Things (IoT) devices. Ciphertext policy attribute-based encryption (CP-ABE) is a cryptographic tool that facilitates intricate and refined access control. It can deliver more flexible, secure, compact, and effective access control policies for the cloud data. In the case of a conventional CP-ABE scheme, the keys can only be issued and disseminated by a trusted central authority (CA). However, if the CA is compromised, the entire system becomes more susceptible to assaults or failures, which leads to a single-point failure. In this article, we propose a Ring-Learning with Errors (Ring-LWE)-based MA-CP-ABE scheme that is effectively resolved by the multi-authority CP-ABE (MA-CP-ABE), and it ensures the security against quantum attacks. The threshold secret sharing by Shamir and the Lagrange interpolation formula are applied in the key-generation and decryption procedures of the proposed scheme, which make it easier to segment and restore the private keys. The proposed scheme is implemented in the CE-enabled IoT-based smart healthcare applications using the blockchain technology as a secure storage. A detailed comparative study, security analysis and experimental results with the existing relevant schemes shows that the proposed scheme exhibits superior security and better efficiency as compared to other schemes, demonstrating its feasibility in practical IoT-based healthcare applications. © 2025 IEEE.

关键词： Quantum cryptography

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ML_BrainDetection: An Intelligent Model for Brain Tumor Identification Using Machine Learning 3

ML_BrainDetection: An Intelligent Model for Brain Tumor Iden...

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3rd International Mobile, Intelligent, and Ubiquitous Computing Conference, MIUCC 2023

作者： Abdelminaam, Diaa Salama Madbouly, Magda M. Farag, Mohamed S Gomaa, Ibrahim A Abd-Elghany Zeid, Magdy Abualigah, Laith Misr International University Faculty of Computer Science Cairo Egypt Benha University Faculty of Computers and Artificial Intelligence Benha Egypt Alexandria University Faculty of Computer and Data Science Alexandria Egypt Alobour High Institute for Computer and Informatics Computer Science Department Cairo Egypt Lebanese American University Department of Electrical and Computer Engineering Byblos13-5053 Lebanon Al-Ahliyya Amman University Hourani Center for Applied Scientific Research Amman19328 Jordan Middle East University Meu Research Unit Amman11831 Jordan School of Computer Sciences Universiti Sains Malaysia Pulau Pinang11800 Malaysia

ISBN: (纸本)9798350306231

Brain tumors are one of the greatest causes of death worldwide. Due to that, early diagnosis and classification of the tumor would surely help increase the chance of survival for patients worldwide. However, classifying and identifying brain tumors requires certain tools, including using Magnetic Resonance Imaging (MRI) to detect brain tumors. Moreover, many researchers in the past have also built up computer-Aided Diagnosis (CAD) systems to aid radiologists in their purpose of detecting brain abnormalities and tumors effectively. In this research, extracted multi-modal features like texture, morphological, entropy-based, Scale Invariant Features are proposed, and consequently, certain robust machine learning techniques, which are k-nearest neighbors (KNN), Decision Tree (DT), Support Vector Machine (SVM), Naive Bayes, as well as the Logistic Regression classifier were used to analyze the two data-sets included in the paper, detect tumors found in the brain, not to mention, examining their results, which have then shown the highest accuracy when using the Logistic regression classifier in both data sets, with the Support Vector Machine (SVM) classifier following it, which further support the argument made in the paper. © 2023 IEEE.

关键词： Support vector machines

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MLCardiovascularPrediction: Machine Learning-Based Cardiovascular Disease Prediction 3

MLCardiovascularPrediction: Machine Learning-Based Cardiovas...

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3rd International Mobile, Intelligent, and Ubiquitous Computing Conference, MIUCC 2023

作者： Abdelminaam, Diaa Salama Madbouly, Magda M. Zeid, Magdy Abd-Elghany Elatif Gomaa, Ibrahim Abd Abualigah, Laith Misr International University Faculty of Computer Science Cairo Egypt Benha University Faculty of Computers and Artificial Intelligence Benha Egypt Alexandria University Faculty of Computer and Data Science Alexandria Egypt Alobour High Institute for Computer and Informatics Computer Science Department Cairo Egypt Lebanese American University Department of Electrical and Computer Engineering Byblos13-5053 Lebanon Al-Ahliyya Amman University Hourani Center for Applied Scientific Research Amman19328 Jordan Middle East University Meu Research Unit Amman11831 Jordan School of Computer Sciences Universiti Sains Malaysia Pinang Pulau11800 Malaysia

ISBN: (纸本)9798350306231

Like most diseases that affect a human's life, car-diovascular disease is dangerous and unwanted for anyone who has heard of it as it brings a high risk of death. In today's world, most deaths are caused by heart disease as it could come from age, heritage, smoking, and many factors that could seem normal, and it doesn't matter what part of the world you are in. There must be a way to know or predict if this person is at high risk of cardiovascular disease. This paper and project talk about six algorithms: K-Nearest-Neighbour (KNN), Decision Tree (DT), Naive Bayes, Neural Network, Logistic Regression, and Support Vector Machine (SVM). The results came between 60% to 70% in one dataset and from 80 % to 90 % in the second. Still, they mostly favor the Neural Network algorithm as it can learn and model around complex relationships. The SVM showed different outputs between the two datasets as one is significantly larger than the other, possibly due to the outliers in both datasets being different and more complex in the larger one. Cardiovascular disease can be very dangerous, but finding and discovering it soon can be a lifesaver. These algorithms also can help find and predict it, as it was shown with not just decent, but good and respectable success rates for each one. © 2023 IEEE.

关键词： Decision trees

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Fast Mapping of Flexibility Regions at TSO-DSO Interfaces under Uncertainty

Fast Mapping of Flexibility Regions at TSO-DSO Interfaces un...

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2022 IEEE PES Innovative Smart Grid Technologies Conference Europe, ISGT-Europe 2022

作者： Patig, Alice Stanojev, Ognjen Aristidou, Petros Kiprakis, Aristides Hug, Gabriela Eth Eeh - Power Systems Laboratory Zurich Switzerland University of Edinburgh School of Engineering Institute for Energy Systems United Kingdom Cyprus University of Technology Computer Engineering and Informatics Department of Electrical Engineering Cyprus

ISBN: (数字)9781665480321

ISBN: (纸本)9781665480321

The gradual decommissioning of fossil fuel-driven power plants, that traditionally provide most operational flexibility in power systems, has led to more frequent grid stability issues. To compensate for the lack of flexible resources, Distributed Energy Resources (DERs) in distribution networks can be employed. To facilitate the use of DERs, the aggregated flexibility in a distribution grid is commonly represented on a PQ-plane displaying the feasible active and reactive power exchange with the upstream grid. This paper proposes a fast feasible operating region mapping mechanism that utilizes a linear power flow approximation in combination with linearized generator, current, and voltage constraints to construct a high-dimensional polyhedral feasible set of DER power injections. The obtained polytope is projected onto the PQ-plane using Fourier-Motzkin Elimination to represent the aggregate network flexibility. Additionally, uncertainty in DER generation is addressed using chance-constraints. Our analysis of a modified 33-bus IEEE test system demonstrates that the proposed method obtains more accurate approximations than former geometric methods and is ten times faster than the tested optimization-based method. © 2022 IEEE.

关键词： Electric power distribution

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Exploring Network Dynamics: Community Detection and Influencer Analysis in Multidimensional Social Networks

Exploring Network Dynamics: Community Detection and Influenc...

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IEEE International Conference on Big Data

作者： Andreas Kanavos Gerasimos Vonitsanos Ioannis Karamitsos Khalil Al-Hussaeni Department of Informatics Ionian University Corfu Greece Computer Engineering and Informatics Department University of Patras Patras Greece Rochester Institution of Technology Dubai UAE Computing Sciences Department Rochester Institute of Technology Dubai UAE

ISBN: (数字)9798350362480

ISBN: (纸本)9798350362497

In the digital era, multidimensional social networks have become integral to daily communication, catering to diverse relational needs, from interpersonal to professional and commercial. This study utilizes two comprehensive datasets from Twitter to explore and visualize user interactions within these networks. Focusing on advanced community detection algorithms, we apply the Louvain and Label Propagation methods to delineate the structure of these communities and identify influential users effectively. Through systematic analysis, our research reveals significant insights into the dynamics of network clusters and the pivotal role of influencers. We demonstrate that community structures significantly influence in formation dissemination and user engagement, providing key data to optimize digital communication strategies in complex environments. The findings underscore the importance of strategic influencer engagement and tailored community management in enhancing interaction within multidimensional social networks. Additionally, our results suggest that understanding the network’s structural nuances can aid in developing targeted interventions that leverage influencer capabilities to maximize communication impact, illustrating potential applications across various sectors, including marketing, politics, and public health.

关键词： Systematics Social networking (online) Heuristic algorithms Blogs Focusing Media Big Data Digital communication Public healthcare Detection algorithms

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Retraction Note: Exploring subcellular location anomalies: a novel quantum bioimaging paradigm

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Optical and Quantum Electronics 2024年第11期56卷 1-2页

作者： Kumar, Kailash Abbas, Syed Hauider Gupta, Manish Waris, Saiyed Faiayaz Bordoloi, Dibyahash Kirubakaran, M. K. College of Computing and Informatics Saudi Electronic University Riyadh Kingdom of Saudi Arabia Department of Computer Science and Engineering Integral University Lucknow India Department of Electronics and Communication Engineering GLA University Mathura India Department of Computer Science and Engineering Vignan’s Foundation for Science Technology and Research Vadlamudi Guntur India Department of Computer Science and Engineering Graphic Era Hill University Dehradun India Department of Artificial Intelligence and Data Science St. Joseph’s Institute of Technology Chennai India

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Analogous Particle Swarm Optimization for Choosing Optimal Selection of Virtual Machine in Cloud 12

Analogous Particle Swarm Optimization for Choosing Optimal S...

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12th International Conference on computer Communication and informatics, ICCCI 2022

作者： Kumar, R. Prasanna Vignesh, T. Venkateswarulu, Balaji Vijayan Sivakumar, R. Amrita School of Engineering Amrita Vishwa Vidyapeetham Department of Computer Science and Engineering Vidyapeetham Chennai India Srm Institute of Science and Technology Department of Master of Computer Application Chennai Ramapuram India Vignan's Foundation for Science Technology and Research Department of Computer Science and Engineering Vadlamudi AP Guntur India Easwari Engineering College Dept of Physics Chennai Ramapuram India

ISBN: (纸本)9781665480352

Cloud computing is one of the chief elements in Medical Care Administrations. It tends to be utilized to recover data about patients, their records identified with diagnosing, therapy taken and other clinical data in less measure of time. In this field picking of an ideal VM is a difficult and more intriguing in medical care administrations. The way to deal with pick the best ideal VM helps in expanding the exhibition of medical care benefits consequently decreasing the running opportunity to execute the solicitation. This paper proposes a novel and upgraded rendition of PSO called Similar to Molecule Multitude Enhancement (APSO). The presentation is contrasted and Hereditary Calculation and PSO. The assessment is finished by considering three boundaries like holding up time, use of computer chip and turnaround time. The outcomes unmistakably express that the APSO approach performs well when contrasted with different models. The general execution time for every molecule is registered as 1s and effectiveness apparently is improved by 5.6%. © 2022 IEEE.

关键词： Genetic algorithms

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