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21st International Multi-Conference on Systems, Signals and Devices, SSD 2024

作者： Rafa, Sara Abbas Al-Qfail, Zohaa Mubarak Adil Nafea, Ahmed Abd-Hood, Samia F. Al-Ani, Mohammed M Alameri, Saeed Amer College of Law and Political Science University of Anbar Dept. Law Ramadi Iraq College of Computer Science and Information Technology University of Anbar Dept. Artificail Inteligence Ramadi Iraq Selangor Bangi Malaysia Seiyun University Dept. Computer Science Faculty of Applied Sciences Hadhramout Yemen Hadhramout University Faculty of Computers and Information Almukalla Hadhramout Yemen Seiyun University Dept. Department of Information Technology Hadhramout Yemen

ISBN: (纸本)9798350374131

The aim of this study is to improve the performance of detection Birds Species by proposed a hybrid model using a combination of MobileNetV2 for feature extraction, an Autoencoder for dimensionality reduction, and Support Vector Machines (SVM) for classification. The methodology includes preprocessing, augmentation, feature extraction, and classification phases. The proposed model is evaluated using the Bird Species dataset, consisting of 525 bird species the dataset is carefully curated to ensure cleanliness and absence of leakage between sets. The evaluation metrics include precision, recall, accuracy, and F-measure. The results have shown the efficiency of the proposed model in bird species classification. © 2024 IEEE.

关键词： Support vector machines

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Site Connectivity with Towalink - Implementing an Open Source "SD-WAN Light" 30

Site Connectivity with Towalink - Implementing an Open Sourc...

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30th Telecommunications Forum, TELFOR 2022

作者： Henrici, Dirk Wischhof, Lars University of Applied Sciences Dept. of Computer Science and Mathematics Munich Germany

ISBN: (数字)9781665472739

ISBN: (纸本)9781665472739

Towalink is an open source solution for interconnecting multiple sites securely and flexibly. It is based on WireGuard and BGP. With central management of the site routers, it can be considered an "SD-WAN light". We explain the rationale and design decisions of the implementation and share the experiences of using the solution in a real-world setup with a complex topology. © 2022 IEEE.

关键词： Wide area networks

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A New Algorithm to Find the KthSmallest Element in an Unordered List (Efficient for Big Data) 2

A New Algorithm to Find the KthSmallest Element in an Unorde...

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2nd International Conference on Computing and Information Technology, ICCIT 2022

作者： Ryalat, Mohammad Hashem Al-Balqa Applied University Dept. of Computer Science As-Salt Jordan

ISBN: (纸本)9781665436052

Finding the kth smallest element in an unsorted list of values is of vital importance in several disciplines and applications such as image and signal processing, machine learning, feature extraction, statistics, trend analysis, genome sequencing and many others. Different algorithms that find the kth smallest element in an unsorted list of values were presented in literature and used in industry. The speed and utilization of memory (time and space) are considered as the main factors on which researcher depend to select one of the algorithms instead of the other. This paper presents a proposed algorithm that is developed to find the kth smallest element in an unsorted array of elements. The proposed algorithm is compared with other five well-known efficient algorithms (quick sort, merge sort, radix sort, counting sort, and quick-select algorithms). The outcome indicates that the proposed algorithm exceeds the other algorithms in terms of speed and memory utilization. The proposed algorithm achieves more superiority when it is applied over large sets of data which makes this algorithm competent for big data. © 2022 IEEE.

关键词： Big data

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The 2-MAXSAT Problem Can Be Solved in Polynomial Time

The 2-MAXSAT Problem Can Be Solved in Polynomial Time

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2022 International Conference on Computational science and Computational Intelligence, CSCI 2022

作者： Chen, Yangjun The University of Winnipeg Dept. Applied Computer Science WinnipegMB Canada

ISBN: (纸本)9798350320282

By the MAXSAT problem, we are given a set V of m variables and a collection C of C clauses over V. We will seek a truth assignment to maximize the number of satisfied clauses. This problem is NP-hard even for its restricted version, the 2-MAXSAT problem by which every clause contains at most 2 literals. In this paper, we discuss a polynomial time algorithm to solve this problem. Its time complexity is bounded by O(n2m3). Hence, we provide a proof of P=NP. © 2022 IEEE.

关键词： Polynomial approximation

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Investigation on Relative Pose Regression for Multi-Camera Setups with Neural Radiance Fields 8

Investigation on Relative Pose Regression for Multi-Camera S...

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8th International ISPRS Workshop LowCost 3D - Sensors, Algorithms, Applications

作者： Kullmann, Timo Hübner, Patrick Wirth, Tristan Kuijper, Arjan Iwaszczuk, Dorota Technical University of Darmstadt Dept. of Civil and Environmental Engineering Sciences Remote Sensing and Image Analysis Darmstadt Germany Technical University of Darmstadt Dept. of Computer Science Mathematical and Applied Visual Computing Darmstadt Germany Fraunhofer IGD Darmstadt Germany Technical University of Darmstadt Dept. of Computer Science Interactive Graphics Systems Darmstadt Germany

Neural Radiance Fields (NeRFs) are a novel approach that is being intensively investigated in 3D scene reconstruction and similar fields to overcome challenges of conventional methods. In this paper, we address the problem of estimating missing camera poses in a six degrees of freedom setting, pushing the capabilities of NeRFs to address scenarios where only the primary camera's pose is known. Specifically, we focus on dual-camera setups with this constraint. Our core contribution is a novel pose correction model that operates alongside an unmodified NeRF model, for which we have chosen Nerfacto in the Nerfstudio framework. The pose correction model learns the necessary relative translation and rotation adjustments for the secondary camera solely through the NeRF loss. This allows us to integrate our correction model directly into the Nerfacto training pipeline without altering the core functionality. Through extensive experiments on different camera configurations in a synthetic scene, we rigorously evaluate our model's performance across diverse scenarios, pushing it to its limits. Our findings reveal that our model can effectively learn pose correction parameters within a constrained range, with increased sensitivity to larger translations and particular challenges in rotation corrections. This research highlights the potential of NeRFs for machine learning-driven 3D reconstruction on dual- and multi-camera platforms, expanding the applicability of NeRFs to more complex, real-world setups despite the inherent challenges. Copyright: © 2024 Timo Kullmann et al.

关键词： 3D reconstruction

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An Efficient Framework for classifying Cancer diseases using Ensemble machine learning over Cancer Gene Expression and Sequence Based Protein Interactions. 2

An Efficient Framework for classifying Cancer diseases using...

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2nd International Conference for Innovation in Technology, INOCON 2023

作者： Metipatil, Prabhuraj Bhuvaneshwari, P. Basha, Syed Muzamil Patil, Siddanagouda S REVA University School of CSE Bengaluru India Dept. of Computer Science & Applied Mathematics UAS Bengaluru Bengaluru India

ISBN: (纸本)9798350320923

In recent years, a significant number of deaths worldwide have been due to cancer diseases. Analysis of Microarray gene expressions and protein interaction data facilitates early cancer identification. The accurate prediction of information for thousands of genes is made possible by using DNA microarray technology. Protein-Protein Interactions (PPIs) are the crucial protein activities involved in the cell cycle that replicates the DNA and cellular signaling. Determining whether a pair of proteins interacts is crucial for diagnosing an illness in molecular biology is therefore important. In existing machine learning classifiers have two-class problem that is limited and only be used to solve binary class problems, additionally, they can be prone to overfitting, as the classification framework may also become too specialized to the training data and not generalized to the varied data. To overcome this problem, this paper proposes an ensemble machine learning technique;ensembling combines the strengths of both classifiers that allow more robust and accurate framework. The better combination of both Support Vector machine and Naïve Bayes ensemble provides better performance in terms of various performance parameters. The proposed SVM-NB Ensemble classifier outperforms the existing classifiers by 15-20% over various performance parameters like classification accuracy, time taken for classification, precision, recall, and F-measure. The results were drawn by comparing the proposed ensemble (SVM+NB) classifier with the existing most applied classifiers like Logistic Regression (LR), Support Vector Machine and Naive Bayes techniques. © 2023 IEEE.

关键词： Support vector machines

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Prediction of Typhoid Using Machine Learning and ANN Prior to Clinical Test

Prediction of Typhoid Using Machine Learning and ANN Prior t...

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2023 International Conference on computer Communication and Informatics, ICCCI 2023

作者： Bhuiyan, Md. Atik Rad, Sharaf Shahariare Johora, Fatema Tuj Islam, Abdullah Hossain, Md Ismail Khan, Aliza Ahmed Daffodil International University Dept. of Computer Science and Engineering Dhaka Bangladesh Rhine-Waal University of Applied Sciences Dept. of Communication and Environment Kamp-Lintfort Germany

ISBN: (纸本)9798350348217

One of the most prevalent illnesses, typhoid causes a large number of fatalities each year, primarily in Africa. A quick and accurate diagnosis is essential in the medical sector. Self-medication, delayed diagnosis, a lack of medical expertise, and inadequate healthcare facilities all contribute to the high incidence of typhoid fever mortality. Machine learning as well as deep learning has worked wonders for extrapolative analysis in the health industry, and as a result, more health industries are utilizing machine learning techniques. This is the earliest evaluation where a typhoid fever prediction model is being developed which predicts prior to a clinical trial. In this paper, deep learning and machine learning have been employed to develop the model. Ten algorithms have been utilized here, and the XGBoost classifier is the best performer with 97.87% accuracy. © 2023 IEEE.

关键词： Neural networks

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Multi-Tenancy Architecture for Augmented Security in Cloud Computing 5

Multi-Tenancy Architecture for Augmented Security in Cloud C...

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5th International Conference on Inventive Research in Computing Applications, ICIRCA 2023

作者： Koley, Santanu Majumdar, Jayeeta Bera, Mihir Baran Acharjya, Pinaki Pratim Haldia Institute of Technology Dept. of Computer Science & Engineering Haldia India School of Applied Science and Humanities Haldia Institute of Technology Haldia India

ISBN: (纸本)9798350321425

Several Virtual Machines (VMs) belonging to various users transparently share physical resources in multi-tenancy cloud milieu. In these situations, implementing an effective access control system can stop unwanted access to cloud resources. In the context of multi-tenancy cloud systems, a right of entry in charge of technique is suggested in this article that offers scalable and safe access management to the cloud. By implementing such a method, malevolent tenants will be prevented from creating and transmitting unlawful traffic to the Cloud network. Although both cloud providers and customers can greatly benefit from multi-tenancy, the main barriers to use are security and privacy issues. Since resource sharing is so important to Multi-Tenancy, numerous experts have proposed several strategies to protect Multi-Tenancy. © 2023 IEEE.

关键词： Access control

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Block-Hash Signature (BHS) for Transaction Validation in Smart Contracts for Security and Privacy using Blockchain

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Journal of Mobile Multimedia 2023年第4期19卷 935-962页

作者： Bhatnagar, Sonika Dayal, Mohit Singh, Deepti Upreti, Shitiz Upreti, Kamal Kumar, Jitender Dept. of Computer Science & Engineering Government Polytechnic Baheri Bareilly India Dept. of Applied Science Bharati Vidyapeeth’s College of Engineering New Delhi India Dept. of Information Technology ABES Institute of Technology Ghaziabad India Dept.of Information Technology Asian Education Group (AEG) Noida India Dept. of Computer Science & Engineering Dr. Akhilesh Das Gupta Institute of Technology and Management New Delhi India Dept. of Computer Science & Engineering J.C Bose University of Science & Technology YMCA Haryana Faridabad India

Some of the well-known signature techniques like Winternitz and Lamport are not considered to be very appropriate for the usage of hashing or smart contracts in Blockchains security because of their size O(n²), which is prominently too high. Although in Blockchain, the security concern is on the top priority because of its distributed P2P design still, the security enhancement is required to sign and verify the documents forwarded to the peers, especially in Hyperledger Fabric. Here, this paper presents a new signature technique “Block-Hash” to enhance Blockchain security by using it in smart contracts as well as hashing with size 3Xn bits (n = 256, generally for SHA-256 Hashing) and which can score 112 bits security. The proposed signature can be used appropriately for signing a smart contract by the endorser or committer node. Also, it can be used with a hash algorithm in forming a Merkle tree. Apart from the description and implementation of Block-Hash Signature, this paper has covered the analysis of its security and correctness measures with a table for result comparison. © 2023 River Publishers.

关键词： blockchain security Digital signature hyperledger fabric smart contract

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Faster R-CNN in Healthcare and Disease Detection: A Comprehensive Review

Faster R-CNN in Healthcare and Disease Detection: A Comprehe...

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2025 International Conference on Electronics, Information, and Communication, ICEIC 2025

作者： Tian, Jiawei Lee, Seungho Kang, Kyungtae Major in Bio Artificial Intelligence Hanyang University Dept. of Computer Science and Engineering Ansan-si Korea Republic of Hanyang University Dept. of Applied Artificial Intelligence Ansan-si Korea Republic of Hanyang University Dept. of Artificial Intelligence Ansan-si Korea Republic of

ISBN: (纸本)9798331510756

This review examines the applications, challenges, and prospects of Faster Region-based Convolutional Neural Networks (Faster R-CNN) in healthcare and disease detection. Through a meta-analysis of Web of science literature from 2017 to 2024, we provide insights into the evolving landscape of Faster R-CNN in medical contexts. The algorithm can be applied to medical image analysis across various modalities, including radiography, computed tomography, magnetic resonance imaging, ultrasound, microscopy, and endoscopy. Its applications extend to optical RGB images for dermatological and surgical uses, as well as broader healthcare areas such as posture detection, medication recognition, and assistive technologies. Despite its success, Faster R-CNN faces challenges in handling subtle abnormalities, addressing class imbalance in medical datasets, ensuring result interpretability, and managing patient privacy. Our analysis reveals dominant application areas, technological advancements, and integration trends with other artificial intelligence technologies. The review highlights Faster R-CNN's significant impact on improving diagnostic accuracy and healthcare delivery while acknowledging the need for continued research to address limitations. Emerging directions include real-time disease detection and advancements in personalized medicine, underscoring Faster R-CNN's potential to further transform healthcare practices and patient outcomes. © 2025 IEEE.

关键词： computerized tomography

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