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2023 International Conference on Emerging Trends in Networks and computer Communications, ETNCC 2023

作者： Upreti, Kamal Vats, Prashant Borkhade, Gauri Raut, Ranjana Dinkar Hundekari, Sheela Parashar, Jyoti Dept. of Computer Science Delhi NCR Ghaziabad India Scse Manipal University Jaipur Dept. of Cse Rajasthan Jaipur India Bharati Vidyapeeth College of Engineering Dept. of Electronics & Telecommunication Engg Maharashtra Navi Mumbai India Sant Gadge Baba Amravati University Pgtd Applied Electronics Maharashtra Amravati India Mit Adt University Loni Kalbhor Pune Pune India Adgitm Dept. of Computer Science & Engg. New Delhi India

ISBN: (纸本)9798350314878

The Internet of Healthcare Things (IoHT) and blockchain technologies have made it feasible to share data in a secure and effective manner, but it is still challenging to ensure the data's veracity and privacy. This paper presents a blockchain authentication method that utilizes Machine Learning (ML) techniques that use smart contracts to ensure the security and privacy of IoHT data. The process utilizes smart contracts to manage access control and ensure data integrity, and deep learning algorithms to identify and validate the accuracy of user data. Furthermore, the approach improves the resilience and dependability of the authentication process and permits secure data ex-change between multiple IoHT systems. The proposed approach provides a potentially revolutionary solution to enhance the safety and confidentiality of IoHT data. It has the potential to fundamentally change how healthcare is provided in the future. © 2023 IEEE.

关键词： Blockchain

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Evaluating the Benefits of Asymmetry Features for Emotion Recognition

Evaluating the Benefits of Asymmetry Features for Emotion Re...

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Canadian Conference on Electrical and computer Engineering (CCECE)

作者： Camilo E. Valderrama Fatima Islam Mouri Sergio G. Camorlinga Dept. of Applied Computer Science University of Winnipeg Winnipeg MB Canada

Emotion recognition is a topic of interest in Affective Computing (AC). While deep learning architectures have gained popularity for classification tasks, their reliance on large datasets limits their applicability when data availability is scarce. An alternative approach is feature engineering, which involves extracting relevant features to train supervised machine learning models. Neuroscientific theories on emotion processing, such as the lateralization theory, have motivated the introduction of asymmetry features for emotion prediction. However, none of these studies have statistically evaluated whether including asymmetrical features could reduce classification error or computational time. To address that direction, the current work compared two approaches for emotion recognition. The first approach used features extracted from individual EEG channels, while the second used asymmetry features calculated by matching pairs of EEG nodes. The two approaches were compared in terms of performance and fitted computational time. The comparison indicated that the performances of both approaches were not statistically significant. Notably, the asymmetry approach required less computational time for the training stage. This finding implies that incorporating asymmetry features in emotion recognition models is viable when computational resources are limited, without significantly compromising performance.

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DDRM-PR: Fourier Phase Retrieval using Denoising Diffusion Restoration Models

arXiv

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arXiv 2025年

作者： Kaya, Mehmet Onurcan Oktem, Figen S. Dept. of Applied Mathematics and Computer Science DTU Lyngby2800 Denmark Dept. of Electrical and Electronics Eng. METU Ankara06800 Turkey

Diffusion models have demonstrated their utility as learned priors for solving various inverse problems. However, most existing approaches are limited to linear inverse problems. This paper exploits the efficient and unsupervised posterior sampling framework of Denoising Diffusion Restoration Models (DDRM) for the solution of nonlinear phase retrieval problem, which requires reconstructing an image from its noisy intensity-only measurements such as Fourier intensity. The approach combines the model-based alternating-projection methods with the DDRM to utilize pretrained unconditional diffusion priors for phase retrieval. The performance is demonstrated through both simulations and experimental data. Results demonstrate the potential of this approach for improving the alternating-projection methods as well as its limitations. Copyright © 2025, The Authors. All rights reserved.

关键词： Inverse problems

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Generalized paths and cycles in semicomplete multipartite digraphs

arXiv

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arXiv 2024年

作者： Bang-Jensen, Jørgen Wang, Yun Yeo, Anders Dept. of Math. and Computer Science University of Southern Denmark Odense Denmark Dept. of Math. and Computer Science University of Southern Denmark Odense Denmark School of Mathematics Shandong University Jinan250100 China Department of Mathematics and Applied Mathematics University of Johannesburg Auckland Park2006 South Africa

A digraph is semicomplete if it has no pair of non-adjacent vertices. It is complete if every pair of distinct vertices induce a 2-cycle. It is well-known and easy to show that even the following version of the directed travelling salesman problem is NP-complete: Given a strongly connected complete digraph D = (V, A), a cost function w: A → {0, 1} and a natural number K;decide whether D has a directed Hamiltonian cycle of cost at most K. We study the following variant of this problem for {0, 1}-weighted semicomplete digraphs where the set of arcs which have cost 1 form a collection of vertex-disjoint complete digraphs. A digraph is semicomplete multipartite if it can be obtained from a semicomplete digraph D by choosing a collection of vertex-disjoint subsets X1, . . ., Xc of V (D) and then deleting all arcs both of whose end-vertices lie inside some Xi. Let D be a semicomplete digraph with a cost function w as above, where w(a) = 1 precisely when a is an arc inside one of the subsets X1, . . ., Xc and let D∗ be the corresponding semicomplete multipartite digraph that we obtain by deleting all arcs inside the Xi’s. Then every cycle C of D corresponds to a generalized cycle Cg of D∗ which is either the cycle C itself if w(C) = 0 or a collection of two or more paths that we obtain by deleting all arcs of cost 1 on C. Similarly we can define a generalized path P g in a semicomplete multipartite digraph. The purpose of this paper is to study structural and algorithmic properties of generalized paths and cycles in semicomplete multipartite digraphs. This allows us to identify classes of directed {0, 1}-weighted TSP instances that can be solved in polynomial time as well as others for which we can get very close to the optimum in polynomial time. Along with these results we also show that two natural questions about properties of cycles meeting all partite sets in semicomplete multipartite digraphs are *** Codes 05C20, 05c38, 05c45, 05c85 © 2024, CC BY.

关键词： Cost functions

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Predictive Insights Beyond Boundaries: Integrating Clinical Data for Hepatitis C Forecasting Using Machine Learning and Deep Learning

Predictive Insights Beyond Boundaries: Integrating Clinical ...

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International Conference on Electrical, computer and Communication Engineering (ECCE)

作者： Fatema Tuj Jenin Al-Amain Khandaker Mohammad Mohi Uddin Faizul Ahmed Robin Md. Ferdous Bin Hafiz Dept. of Computer Science and Engineering Southeast University Dhaka Bangladesh Dept. of Information and Electrical Engineering Hamburg University of Applied Sciences Hamburg Germany

ISBN: (数字)9798350357509

ISBN: (纸本)9798350357516

A person's well-being and wellness are seriously hampered by the chronic diseases' lasting implications and recurrent signs. A startling 58 million people worldwide suffer from an incurable chronic form of hepatitis C, disease is brought about by a virus called hepatitis C (HCV). Additionally, it results in 290,000 deaths every year from cirrhosis and liver cancer, both of which linked to HCV. The goal of this venture is to reliably identify and anticipate hepatitis C utilizing Machine Learning (ML) and deep Learning (DL) techniques. It does this by utilizing ML and DL algorithms' capacity to scan big datasets and spot patterns and associations that help with diagnosis. Predictive models were created utilizing blood values from 615 patients, including both hepatitis patients and healthy blood donors, using a variety of approaches comprising SVM, Logistic regression (LR), KNN, LSTM and others. The dataset underwent extensive preprocessing, which included managing missing values, scaling, and diminished dimensionality using Principal Component Analysis (PCA). After a variety of ML along with the SVM classifier showed the best result, giving an accuracy concerning 98.33%, recall concerning 94.17%, precision concerning 93.99%, and F1-score concerning 94.16%.

关键词： Support vector machines Deep learning Accuracy Liver diseases Predictive models Reliability Long short term memory Viruses (medical) Medical diagnostic imaging Principal component analysis

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Device Distribution Scheme of Random Access in Space-Air-Ground Integrated Network for Massive IoT 13

Device Distribution Scheme of Random Access in Space-Air-Gro...

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13th International Conference on Information and Communication Technology Convergence, ICTC 2022

作者： Oh, Joohyun Park, Joohan Youn, Jiseung Im, Jeong-Ju Cho, Sunghyun Major in Bio-Artificial Intelligence Hanyang University Dept. of Applied Artificial Intelligence Ansan Korea Republic of Hanyang University Dept. of Computer Science & Engineering Ansan Korea Republic of Major in Bio-Artificial Intelligence Hanyang University Dept. of Computer Science & Engineering Ansan Korea Republic of

ISBN: (数字)9781665499392

ISBN: (纸本)9781665499392

To support massive random access trials of devices with diverse QoS is a major challenge for massive machine-type communications. Space-air-ground integrated network(SAGIN) can be a promising solution for the congestion issues because the SAGIN can extend preamble pool. In this paper, we propose a device distribution scheme to maximize random access success rate while conserving delay of the packet in SAGIN. The proposed scheme consists of two phases. One is an algorithm in which the device selects the appropriate SAGIN infrastructure to maximize the random access success rate. The other is an algorithm in which devices that have failed previous random access select an infrastructure different from the previously selected infrastructure to resolve congestion. We compare the performance of the proposed algorithm with two benchmark algorithms in terms of random access success rate and delay. © 2022 IEEE.

关键词： Benchmarking

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IoT-Enabled Remote-Controlled Raft for Enhanced Water Quality Assessment

IoT-Enabled Remote-Controlled Raft for Enhanced Water Qualit...

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International Colloquium on Signal Processing & Its Applications, CSPA

作者： Thattapon Surasak Kotcharat Kitchat Nattagit Jiteurtragool Dept. of Computer and Information Science Faculty of Applied Science King Mongkut’s University of Technology North Bangkok Bangkok Thailand Dept. of Computer Science and Information Engineering National Central University Taoyuan Taiwan

ISBN: (数字)9798350382310

ISBN: (纸本)9798350382327

Water is vital for all life, serving as the foundation for plants and aquatic creatures and promoting abundance among all living beings. This study introduces an innovative approach by combining the Internet of Things (IoT) with advanced sensing techniques to assess water quality. The IoT-powered water quality testing raft has been developed to significantly enhance the monitoring capabilities. Equipped with sensors measuring key parameters such as turbidity, pH, and temperature, the raft provides a comprehensive understanding of water quality. Its GPS module enables real-time tracking and maintains location history, ensuring precise sampling. The collected data undergoes robust processing by a Raspberry Pi, ensuring integrity before being showcased on the Thingsboard. The system’s uniqueness lies in its capacity to promptly issue alerts via LINE Notify upon detecting water quality abnormalities, ensuring timely intervention. This IoT-powered raft represents a paradigm shift in water quality assessment, characterised by its precision, remote capabilities, and user-focused approach. Practical applications have been demonstrated through real-world testing in a private lake in Cha-choengsao province, Thailand. The seamless integration of IoT into environmental monitoring marks a significant advancement in ensuring water quality and ecosystem health.

关键词： Temperature sensors Temperature measurement Water quality Turbidity Real-time systems Sensor systems Sensors

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Key Exchange in the Quantum Era: Evaluating a Hybrid System of Public-Key Cryptography and Physical-Layer Security 59

Key Exchange in the Quantum Era: Evaluating a Hybrid System ...

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59th Annual Conference on Information sciences and Systems, CISS 2025

作者： Staat, Paul Dorpinghaus, Meik Sheikholeslami, Azadeh Paar, Christof Fcllwcis, Gerhard Goeckel, Dennis Max Planck Institute for Security and Privacy Bochum Germany Technische Universität Dresden Vodafone Chair Mobile Communications Systems Dresden Germany Suffolk University Math and Computer Science Department Boston United States University of Massachusetts Dept. Electrical and Computer Engineering Amherst United States

ISBN: (纸本)9798331513269

Today's information society relies on cryptography to achieve security goals such as confidentiality, integrity, authentication, and non-repudiation for digital communications. Here, public-key cryptosystems play a pivotal role to share encryption keys and create digital signatures. However, quantum computers threaten the security of traditional public-key cryptosystems as they can tame computational problems underlying the schemes, i.e., discrete logarithm and integer factorization. The prospective arrival of capable-enough quantum computers already threatens today's secret communication in terms of their long-term secrecy when stored to be later decrypted. Therefore, researchers strive to develop and deploy alternative *** this work, we evaluate a key exchange protocol based on combining public-key schemes with physical-layer security, anticipating the prospect of quantum attacks: If a powerful quantum attacker cannot immediately obtain a private key, legitimate parties have a window of short-term secrecy to perform a physical-layer jamming key exchange (JKE) to establish a long-term shared secret. Thereby, the protocol constraints the computation time available to the attacker to break the employed public-key cryptography. In this paper, we outline the protocol, discuss its security, and point out challenges to be resolved. © 2025 IEEE.

关键词： Quantum computers

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Evaluating Low-Resource Lane Following Algorithms for Compute-Constrained Automated Vehicles

arXiv

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arXiv 2024年

作者： Froemming-Aldanondo, Beñat Rastoskueva, Tatiana Evans, Michael Machado, Marcial Vadella, Anna Johnson, Rickey Escamilla, Luis Jostes, Milan Butani, Devson Kaddis, Ryan Chung, Chan-Jin Siegel, Josh Dept. of Computer Science & Engineering University of Minnesota Minneapolis United States Dept. of Computer Science The University of Arizona Tucson United States Dept. of Computer Science Old Dominion University Norfolk United States Dept. of Computer Science & Engineering The Ohio State University Columbus United States Dept. of Computer Science SWE Butler University Indianapolis United States Dept. of Computer Science North Carolina A&T State University Greensboro United States Dept. of Computer Science New Mexico State University Las Cruces United States Dept. of Math & Computer Science Lawrence Technological University Southfield United States DeepTech Lab Michigan State University East Lansing United States

Reliable lane-following is essential for automated and assisted driving, yet existing solutions often rely on models that require extensive computational resources, limiting their deployment in compute-constrained vehicles. We evaluate five low-resource lane-following algorithms designed for real-time operation on vehicles with limited computing resources. Performance was assessed through simulation and deployment on real drive-by-wire electric vehicles, with evaluation metrics including reliability, comfort, speed, and adaptability. The top-performing methods used unsupervised learning to detect and separate lane lines with processing time under 10 ms per frame, outperforming compute-intensive and poor generalizing deep learning approaches. These approaches demonstrated robustness across lighting conditions, road textures, and lane geometries. The findings highlight the potential for efficient lane detection approaches to enhance the accessibility and reliability of autonomous vehicle technologies. Reducing computing requirements enables lane keeping to be widely deployed in vehicles as part of lower-level automation, including active safety systems. © 2024, CC BY.

关键词： Unsupervised learning

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Visual Exploratory Data Analysis of the Covid-19 Pandemic in Nigeria: Two Years after the Outbreak 5

Visual Exploratory Data Analysis of the Covid-19 Pandemic in...

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5th International Conference on Information Technology for Education and Development, ITED 2022

作者： Orji, Ugochukwu. E. Ukwandu, Elochukwu Obianuju, Ezugwu. A. Ezema, Modesta. E. Ugwuishiwu, Chikaodili. H. Egbugha, Malachi. C. University of Nigeria Dept. of Computer Science Nsukka Enugu Nigeria Cardiff School of Technologies Cardiff Metropolitan University Dept. of Applied Computing and Engineering United Kingdom Ahmadu Bello University Dept of Computer Engineering Zaria Nigeria

ISBN: (纸本)9781509064229

The outbreak of the coronavirus disease in Nigeria and all over the world in 2019/2020 caused havoc on the world's economy and put a strain on global healthcare facilities and personnel. It also threw up many opportunities to improve processes using artificial intelligence techniques like big data analytics and business intelligence. The need to speedily make decisions that could have far-reaching effects is prompting the boom in data analytics which is achieved via exploratory data analysis (EDA) to see trends, patterns, and relationships in the data. Today, big data analytics is revolutionizing processes and helping improve productivity and decision-making capabilities in all aspects of life. The large amount of heterogeneous and, in most cases, opaque data now available has made it possible for researchers and businesses of all sizes to effectively deploy data analytics to gain action-oriented insights into various problems in real time. In this paper, we deployed Microsoft Excel and Python to perform EDA of the covid-19 pandemic data in Nigeria and presented our results via visualizations and a dashboard using Tableau. The dataset is from the Nigeria Centre for Disease Control (NCDC) recorded between February 28th, 2020, and July 19th, 2022. This paper aims to follow the data and visually show the trends over the past 2 years and also show the powerful capabilities of these data analytics tools and techniques. Furthermore, our findings contribute to the current literature on Covid-19 research by showcasing how the virus has progressed in Nigeria over time and the insights thus far. © 2022 IEEE.

关键词： Data Analytics

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