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Network, Multimedia and Information Technology (NMITCON) International Conference on

作者： Vikram Puri Aman Kataria Sita Rani Piyush Kumar Pareek Center of Visualization and Simulation Duy Tan University Da Nang Vietnam Amity Institute of Defence Technology Amity University Noida Uttar Pradesh India Department of Computer Science and Engineering Guru Nanak Dev Engineering College Ludhiana India Dept. of Artificial Intelligence and Machine Learning Nitte Meenakshi Institute of Technology Bengaluru India

The healthcare system has become more reliant on the data collection capabilities of fast evolving IoT devices. Patient healthcare records (PHR) now contain additional information as a result. However, securing identities and guaranteeing data security have emerged as critical challenges in the healthcare system. When it comes to regulating PHR, many of the centralized and decentralized methods now in use struggle difficulties such as single point of failure, high transaction fees, slow throughput, and excessive latency. To address these concerns, a distributed ledger technology (DLT)-based framework named IOTA rather than a blockchain-based platform such as Ethereum or Polygon is proposed. The proposed framework considered the example of a hospital with several patients on the same and various floors. Furthermore, the suggested approach employs a real-time experimental setup for calculating the time and power consumption of the devices.

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Smart Emergency Management System Using Raspberry Pi Pico, GPS and GSM Modules

Smart Emergency Management System Using Raspberry Pi Pico, G...

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Sentiment Analysis and Deep learning (ICSADL), International Conference on

作者： Govinda Rajulu. G L. Sharmila D. Venkatesan E. Soumya Sargu Chandra Prakash Ms. P. Priyanka Department of Computer Science and Engineering Vel Tech Rangarajan Dr. Sagunthala R&D Institute of Science and Technology Avadi Chennai Tamil Nadu India Dept. of Artificial Intelligence and Machine Learning St. Martin's Engineeringollege Dhulapally Secunderabad Telangana India Deptarment of CSE St. Martin's Engineeringollege Dhulapally Secunderabad Telangana India Department of CSE(AI&ML) St. Martin's Engineeringollege Dhulapally Secunderabad Telangana India

ISBN: (数字)9798331523923

ISBN: (纸本)9798331523930

This paper proposes a GSM-based children's rescue system designed for earthquake emergencies, emphasizing cost-effectiveness and simplicity while ensuring rapid child rescue. The system operates in three key stages: data acquisition, classification, and alert notification. Sensors collect temperature, pH, and heart rate data, which are then digitized and classified to assess the child's condition. If abnormal values are detected, an alert notification is sent via GSM to parents and rescue teams, providing real-time location data through GPS. The hardware implementation, based on Raspberry Pi, converts analog sensor signals into digital data for processing. The system's efficacy is validated through various scenarios, demonstrating its ability to detect critical conditions and promptly trigger alerts, enhancing child safety during disasters.

关键词： Temperature sensors GSM Chemical sensors Earthquakes Sensor systems Real-time systems Biomedical monitoring Monitoring Intelligent sensors Global Positioning System

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learning weakly convex sets in metric spaces

arXiv

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arXiv 2021年

作者： Stadtländer, Eike Horváth, Tamás Wrobel, Stefan Dept. of Computer Science University of Bonn Germany Fraunhofer IAIS Schloss Birlinghoven Sankt Augustin Germany Lamarr Institute for Machine Learning and Artificial Intelligence Germany

One of the central problems studied in the theory of machine learning is the question of whether, for a given class of hypotheses, it is possible to efficiently find a consistent hypothesis, i.e., which has zero training error. While problems involving convex hypotheses have been extensively studied, the question of whether efficient learning is possible for non-convex hypotheses composed of possibly several disconnected regions is still less understood. Although it has been shown quite a while ago that efficient learning of weakly convex hypotheses, a parameterized relaxation of convex hypotheses, is possible for the special case of Boolean functions, the question of whether this idea can be developed into a generic paradigm has not been studied yet. In this paper, we provide a positive answer and show that the consistent hypothesis finding problem can indeed be solved in polynomial time for a broad class of weakly convex hypotheses over metric spaces. To this end, we propose a general domain-independent algorithm for finding consistent weakly convex hypotheses and prove sufficient conditions for its efficiency that characterize the corresponding hypothesis classes. To illustrate our general algorithm and its properties, we discuss several non-trivial learning examples to demonstrate how it can be used to efficiently solve the corresponding consistent hypothesis finding problem. Without the weak convexity constraint, these problems are known to be computationally intractable. We then proceed to show that the general idea of our algorithm can even be extended to the case of extensional weakly convex hypotheses, as it naturally arise, e.g., when performing vertex classification in graphs. We prove that using our extended algorithm, the problem can be solved in polynomial time provided the distances in the domain can be computed efficiently. Copyright © 2021, The Authors. All rights reserved.

关键词： Set theory

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Few-shot Class-incremental learning for 3D Point Cloud Objects

arXiv

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arXiv 2022年

作者： Chowdhury, Townim Cheraghian, Ali Ramasinghe, Sameera Ahmadi, Sahar Saberi, Morteza Rahman, Shafin Dept. of Electrical and Computer Engineering North South University Bangladesh School of Engineering Australian National University Australia Data61 Commonwealth Scientific and Industrial Research Organisation Australia Australian Institute for Machine Learning University of Adelaide Australia Business School The University of New South Wales Australia School of Computer Science and DSI University of Technology Sydney Australia

Few-shot class-incremental learning (FSCIL) aims to incrementally fine-tune a model (trained on base classes) for a novel set of classes using a few examples without forgetting the previous training. Recent efforts address this problem primarily on 2D images. However, due to the advancement of camera technology, 3D point cloud data has become more available than ever, which warrants considering FSCIL on 3D data. This paper addresses FSCIL in the 3D domain. In addition to well-known issues of catastrophic forgetting of past knowledge and overfitting of few-shot data, 3D FSCIL can bring newer challenges. For example, base classes may contain many synthetic instances in a realistic scenario. In contrast, only a few real-scanned samples (from RGBD sensors) of novel classes are available in incremental steps. Due to the data variation from synthetic to real, FSCIL endures additional challenges, degrading performance in later incremental steps. We attempt to solve this problem using Microshapes (orthogonal basis vectors) by describing any 3D objects using a pre-defined set of rules. It supports incremental training with few-shot examples minimizing synthetic to real data variation. We propose new test protocols for 3D FSCIL using popular synthetic datasets (ModelNet and ShapeNet) and 3D real-scanned datasets (ScanObjectNN and CO3D). By comparing state-of-the-art methods, we establish the effectiveness of our approach in the 3D domain. Code is available at: https://***/townim-faisal/FSCIL-3D. © 2022, CC BY.

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Wavelength-Selective Distribution of Polarization Entanglement over Deployed Fiber

Wavelength-Selective Distribution of Polarization Entangleme...

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Conference on Lasers and Electro-Optics (CLEO)

作者： Anirudh Ramesh Joaquin Chung Gregory Kanter Cristián Peña Robert K. Plunkett Si Xie Panagiotis Spentzouris Rajkumar Kettimuthu Prem Kumar Data Science and Learning Division Argonne National Laboratory Lemont IL USA Dept. of Electrical and Computer Eng. Center for Photonic Communication and Computing Northwestern University Evanston IL USA NuCrypt LLC Park Ridge IL USA Fermi National Accelerator Laboratory Batavia IL USA Mathematics and Computer Science Division Argonne National Laboratory Lemont IL USA

ISBN: (纸本)9781957171258

We demonstrate distribution of polarization entangled photon-pairs across multiple channels in a 6 km loop of deployed fiber. We use an all-optical networking switch to route Bell-state photons in three different wavelength pairs and verify entanglement distribution in all channel pairs by measuring two-photon interference fringes.

关键词： Entanglement distribution Interference Photon polarization Photons Polarization Quantum communications

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Exploring Advanced Techniques in Computer and Network Forensics for Enhanced Cybersecurity

Exploring Advanced Techniques in Computer and Network Forens...

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Emerging Trends in Industry 4.0 (ETI 4.0)

作者： Amandeep Nagpal S Vinod Kumar Uma Reddy K. Radha Ashwani Kumar Namaat R. Abdulla Lovely Professional University Phagwara Institute of Aeronautical Engineering Dundigal Hyderabad Department of Artificial Intelligence and Machine Learning New Horizon College of Engineering Bangalore AP Dept. of IT St. Martin’s Engineering College Secunderabad Telangana India Knowledge Park II Lloyd Institute of Engineering & Technology Greater Noida Uttar Pradesh College of Health and Medical Technology National University of Science and Technology Dhi Qar Iraq

ISBN: (数字)9798350373783

ISBN: (纸本)9798350373790

Because cybersecurity is continually changing, it’s crucial to have effective, adaptable defenses. This paper demonstrates a novel computer security method. This is done via blockchain technology, enhanced cryptography, threat intelligence integration, machine learning-based strangeness detection, and behavioral analysis. After carefully comparing our solution to others, we feel it is the best way to secure computer and network systems. The machine learning-Based Anomaly Identification approach accurately identifies anomalies using complex mathematical calculations and repetitive procedures to adjust parameters. Blockchain technology unites odd data into a decentralized ledger using encryption for forensics efficiency. Homomorphic encryption and quantum-resistant signatures protect the program against new attacks. The proposed method exhibits superior performance across various metrics, achieving a detection accuracy of 98.5% with a low false positive rate of 1.0%. Visualizations elucidate trade-offs, trends, and classification metrics. This cybersecurity framework offers a promising solution for addressing the dynamic challenges posed by a wide range of cybersecurity threats. Its adaptability, versatility, and collective efficacy position it as a noteworthy advancement in the pursuit of securing digital environments from evolving and sophisticated cyber threats.

关键词： Measurement Technological innovation Accuracy Forensics Current measurement Market research Blockchains Computer security Usability Network systems

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A Provably Secure ECC-based Multi-factor 5G-AKA Authentication Protocol

A Provably Secure ECC-based Multi-factor 5G-AKA Authenticati...

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GLOBECOM 2022 - 2022 IEEE Global Communications Conference

作者： Awaneesh Kumar Yadav Manoj Misra Pradumn Kumar Pandey Kuljeet Kaur Sahil Garg Xi Chen Dept. of Computer Science and Engineering Indian Institute of Technology Roorkee Uttarakhand India Electrical Engineering Department Ecole de Technologie Supérieure (ETS) Montreal QC Canada Resilient Machine Learning Institute (ReMI) Montreal QC Canada School of Computer Science McGill University Montreal QC Canada

ISBN: (纸本)9781665435413

Due to the constant penetration of various security attacks, it is highly important to secure the underlying communication networks between the IoT, Fog and Cloud in the next generation of mobile communication system (5G). Thus, secure authentication and key agreement protocol, namely 5G-AKA, has been proposed in the literature to safely and stably access the 5G mobile services. However, some recent findings reveal that 5G-AKA and its numerous versions based on symmetric or asymmetric encryption are either vulnerable to different attacks such as perfect forward secrecy violation, malicious Serving Network (SN), de-synchronization attack, privacy theft, stolen device, or are computationally intensive. Apart from that, these protocols use single-factor authentication. Considering the above demerits of these protocols and the necessity to provide enhanced security, we propose an Elliptic Curve-Cryptography (ECC)-based multi-factor 5G-AKA authentication protocol. It provides additional security and achieves cost-effectiveness in terms of computational, communication, storage costs and energy consumption. The formal security analysis using Real-Or-Random (ROR) logic has been done to confirm its security. Moreover, we evaluate the performance of the proposed protocol in terms of computational, communication, storage costs and energy consumption. The evaluation results show that the proposed protocol requires less cost than its counterparts, reducing computational cost by up to 57%, communication cost by up to 59%, storage cost by up to 52%, and energy consumption by up to 51%.

关键词： Energy consumption Privacy Protocols Costs Multi-factor authentication 5G mobile communication Robustness

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The unrealized potential of agroforestry for an emissions-intensive agricultural commodity

arXiv

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arXiv 2024年

作者： Becker, Alexander Wegner, Jan D. Dawoe, Evans Schindler, Konrad Thompson, William J. Bunn, Christian Garrett, Rachael D. Castro, Fabio Hart, Simon P. Blaser-Hart, Wilma J. ETH Zurich Switzerland EcoVision Lab Dept. of Mathematical Modeling and Machine Learning University of Zurich Switzerland Dept. of Agroforestry Faculty of Renewable Natural Resources Kwame Nkrumah University of Science and Technology Kumasi Ghana Nature-based Solutions Initiative Department of Biology University of Oxford OxfordOX12JD United Kingdom Alliance of Bioversity International and CIAT Rome00153 Italy Department of Geography Conservation Research Institute University of Cambridge Cambridge United Kingdom School of the Environment The University of Queensland St LuciaQLD4072 Australia

Reconciling agricultural production with climate-change mitigation and adaptation is one of the most formidable problems in sustainability. One proposed strategy for addressing this problem is the judicious retention of trees in agricultural systems. However, the magnitude of the current and future-potential benefit that trees contribute remains uncertain, particularly in the agricultural sector where trees can also limit production. Here we help to resolve these issues across a West African region responsible for producing ≈60% of the world’s cocoa, a crop that contributes one of the highest per unit carbon footprints of all foods. We use machine learning to generate spatially-explicit estimates of shade-tree cover and carbon stocks across the region. We find that existing shade-tree cover is low, and not spatially aligned with climate threat. But we also find enormous unrealized potential for the sector to counterbalance a large proportion of their high carbon footprint annually, without threatening production. Our methods can be applied to other globally significant commodities that can be grown in agroforests, and align with accounting requirements of carbon markets, and emerging legislative requirements for sustainability reporting. © 2024, CC BY.

关键词： Sustainable agriculture

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Brain Cancer Tumor Detection by U-Net Deep learning Algorithm from MRI Images

Brain Cancer Tumor Detection by U-Net Deep Learning Algorith...

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Annual Information Technology, Electromechanical Engineering and Microelectronics Conference (IEMECON)

作者： Utpal Chandra Das Watit Benjapolakul Manoj Gupta Chanyanan Somthawinpongsai Timporn Vitoonpong Pannee Suanpang Sujin Butdisuwan Aziz Nanthaamornphong Department of Electrical Engineering Center of Excellence in Artificial Intelligence Machine Learning and Smart Grid Technology Faculty of Engineering Chulalongkorn University Bangkok Thailand Department of Electrical Engineering Guru Ghasidas Vishwavidyalaya Bilaspur Chhattisgarh India Shinawatra University Pathum Thani Thailand Dept. of Rehabilitation Medicine King Chulalongkorn Memorial Hospital and Dept. of Rehabilitation Medicine Faculty of Medicine Chulalongkorn University Bangkok Thailand Department of Information Technology Faculty of Science and Technology Suan Dusit University Bangkok Thailand College of Computing Prince of Songkla University Phuket Thailand

ISBN: (数字)9798350387315

ISBN: (纸本)9798350387322

This research looks at the genomic subtypes of low-grade glioma tumors and their shape characteristics by deep learning magnetic resonance image (MRI) segmentation. We analyzed preoperative imaging and genetic data from 110 patients with low-grade glioma from the Cancer Genome Atlas. Three shape features were recovered to quantify the two- and three-dimensional aspects of the malignancies. Based on gene expression, DNA copy number, IDH mutation, 1p/19q co-deletion, DNA methylation, and microRNA, previously identified clusters were found in genomic data. We used the exact trait test to investigate the connection between chromosomal clusters and imaging traits. Our findings show a significant correlation between the margin fluctuation-bounding ellipsoid volume ratio and the RNA Seq clusters. Furthermore, a correlation was discovered between RNA-seq clusters and angular standard deviation. The U-net deep learning algorithm demonstrated a test accuracy of 94\% and a mean Dice coefficient of 90\%. These findings suggest that tumor shape characteristics derived from MRI can be projected through genomic subtypes in lower-grade gliomas

关键词： Deep learning Image segmentation Correlation Shape Magnetic resonance imaging Genomics DNA Clustering algorithms Bioinformatics Standards

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Customary Behavior of Sorting Reals with Linear Time Complexity

Customary Behavior of Sorting Reals with Linear Time Complex...

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International Conference on Mathematics and Computers in science and Engineering (MACISE)

作者： Marcel Jiřina Dept. of Machine Learning Institute of Computers Science AS CR Prague Czech Republic

ISBN: (数字)9781728166957

ISBN: (纸本)9781728166964

Sorting with real number keys has time complexity n log n. This holds under the assumption that for all n samples a comparison sort is used. Here we propose to use the counting sort with just n cells for initial placement of samples. We resolve cases of groups of several samples placed into one cell by a comparison sort. Surprisingly, even this part has time complexity proportional to n. Numerical experiments confirm this finding and shows influence of the computing environment such as paging, and reflects a higher speed than the quicksort.

关键词： Sorting Time complexity Exponential distribution Indexes Histograms Distribution functions

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