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10th International Conference on Advanced Computing and Communication Systems, ICACCS 2024

作者： Hemkiran, S. Kartik, R. Aswath, S. Rathan Kabilan, K. Sudha Sadasivam, G. Psg Institute of Technology and Applied Research Dept. of Computer Science and Engineering Coimbatore India Psg College of Technology Dept. of Computer Science and Engineering Coimbatore India

ISBN: (纸本)9798350384369

In response to the urgent need for coronavirus treatments, this research focuses on leveraging bioactivity data collection and processing for efficient drug discovery, employing computational methods to predict potential antiviral compounds. Exploratory data analysis was performed to identify patterns and trends, and various molecular descriptors were computed for the Mpro inhibitors in the descriptor dataset preparation step. A regression model was trained using the developed random forest algorithm to predict the bioactivity of new compounds against the standard value and compared with other regression models. Additionally, a web-based application was created using the random forest model to allow users to obtain predicted bioactivity values against Mpro by providing information about molecular structures. Machine learning-based bioactivity prediction offers an intriguing plan for drug discovery, and the proposed work provides a comprehensive workflow for COVID- 19 drug discovery. The web application provides a user-friendly interface for drug discovery researchers to evaluate the potential of compounds against the Mpro target quickly. © 2024 IEEE.

关键词： COVID-19

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Sensorless Unmanned Bicycle Robot

Sensorless Unmanned Bicycle Robot

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2022 International Conference on Electrical, computer, Communications and Mechatronics Engineering, ICECCME 2022

作者： Efraim, Hanoch Basri, Ronen Weizmann Institute of Science Dept. of Computer Science and Applied Mathematics Rehovot Israel

ISBN: (数字)9781665470957

ISBN: (纸本)9781665470957

In this work, we present a new approach for designing an autonomous bicycle robot. It is well known that bicycles can be laterally stable at certain velocities. This means that if the steering bar remains free to rotate, a bicycle can be designed so that it naturally steers in a way that corrects for deviations, keeping the bicycle upright. However, to the best of our knowledge this desirable property is not taken advantage of in previous works, since connecting a standard actuator to the steering bar, which is required in order to guide the bicycle to a desired direction, does not allow the steerer to be free to rotate. In this work we propose a unique actuation mechanism that allows guidance while keeping the steerer free to rotate, thus preserving the natural stability of the bicycle. Since the bicycle is mechanically stable there is no need for a low level controller, and thus, no need for sensors and sensor fusion algorithms. We demonstrate the concept with experiments performed on a 3D printed bicycle powered by a small electric motor and guided remotely. We provide all the CAD files and software in hope that it will be used by additional researchers in the field, and advance the frontiers of autonomous systems. © 2022 IEEE.

关键词： Bicycles

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IoT Network Security with PCA and Deep Learning for Unmasking Anomalies 13

IoT Network Security with PCA and Deep Learning for Unmaskin...

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13th IEEE International Conference on Communication Systems and Network Technologies, CSNT 2024

作者： Geo Francis, E. Sheeja, S. Antony John, E.F. Joseph, Jismy Karpagam Academy of Higher Education Dept. of Computer Science Tamil Nadu Coimbatore India Dept. of Mathematics Kerala Thrissur India Dept. of Mathematics Kerala Mala India

ISBN: (纸本)9798350305463

The rapid proliferation of Internet of Things (IoT) devices has revolutionized various domains, introducing unprecedented convenience and efficiency. However, this expansion has concurrently intensified the obstacles associated with network security. Detecting and mitigating anomalies in IoT networks are of utmost importance to protect critical systems and sensitive data. This paper presents an innovative approach to IoT network security that combines Principal Component Analysis (PCA) with advanced deep learning approaches, encompassing Generative Adversarial Networks (GANs) and Transformers. In this research, we investigate the potential of PCA for dimensionality reduction and feature selection within IoT network data. By simplifying data complexity, PCA aids in uncovering critical features and patterns contributing to network anomalies. Advanced deep learning models, such as GANs for generating synthetic data and Transformers for sequence modeling, are harnessed to construct anomaly detection systems capable of identifying irregularities in real-time IoT data streams. Our approach is rigorously evaluated on a comprehensive dataset collected from diverse IoT devices and networks. The results underscore the efficacy of the PCA and deep learning-based anomaly detection model, leveraging GANs and Transformers, in accurately and efficiently unmasking anomalies in IoT network traffic. Through extensive experiments and evaluations, we underscore substantial advancements over traditional security methods. This research significantly enhances IoT network security by presenting a sophisticated, data-driven anomaly detection system adaptable to the dynamic and evolving nature of IoT environments. As the IoT landscape continues to expand, our approach offers a crucial solution to safeguard against the ever-evolving threat of network anomalies. © 2024 IEEE.

关键词： Internet of things

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Towards Early Diagnosis and Intervention: An Ensemble Voting Model for Precise Vital Sign Prediction in Respiratory Disease

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IEEE Journal of Biomedical and Health Informatics 2023年 PP卷 1-13页

作者： Ahmed, Usman Lin, Jerry Chun-Wei Srivastava, Gautam Department of Computer Science Electrical Engineering and Mathematical Sciences Western Norway University of Applied Sciences Bergen Norway Dept. of Math and Computer Science Brandon University Canada

Worldwide, cardiovascular and chronic respiratory diseases account for approximately 19 million deaths annually. Evidence indicates that the ongoing COVID-19 pandemic directly contributes to increased blood pressure, cholesterol, as well as blood glucose levels. Timely screening of critical physiological vital signs benefits both healthcare providers and individuals by detecting potential health issues. This study aims to implement a machine learning-based prediction and classification system to forecast vital signs associated with cardiovascular and chronic respiratory diseases. The system predicts patients' health status and notifies caregivers and medical professionals when necessary. Utilizing real-world data, a linear regression model inspired by the Facebook Prophet model was developed to predict vital signs for the upcoming 180 seconds. With 180 seconds of lead time, caregivers can potentially save patients' lives through early diagnosis of their health conditions. For this purpose, a Naïve Bayes classification model, a Support Vector Machine model, a Random Forest model, and genetic programming-based hyper tunning were employed. The proposed model outdoes previous attempts at vital sign prediction. Compared with alternative methods, the Facebook Prophet model has the best mean square in predicting vital signs. A hyperparameter-tuning is utilized to refine the model, yielding improved short- and long-term outcomes for each and every vital sign. Furthermore, the F-measure for the proposed classification model is 0.98 with an increase of 0.21. The incorporation of additional elements, such as momentum indicators, could increase the model's flexibility with calibration. The findings of this study demonstrate that the proposed model is more accurate in predicting vital signs and trends. IEEE

关键词： Decision trees

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Improving QoE in IoV: a review of solutions and challenges for MPEG-DASH 6

Improving QoE in IoV: a review of solutions and challenges f...

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6th International Conference on Pattern Analysis and Intelligent Systems, PAIS 2024

作者： Abdelghani, Guerrad Abdelatif, Sahraoui Derdour, Makhlouf Dept. of Computer Science Tebessa12000 Algeria University of Larbi Ben m'Hidi Liaoa Laboratory Dept. of Mathematics And Computer Science Oum El Bouaghi Algeria

ISBN: (纸本)9798350350265

The deployment of video streaming in an Internet of Vehicles (IoV) context enhances both driving comfort and road safety. Video transmission provides clear and informative perspectives, offering a wide range of entertainment and infotainment, including details on restaurants, parking, and tourist attractions. However, maintaining a satisfactory quality of experience (QoE) in an IoV environment, which is characterized by fluctuating bandwidth and changing topology, presents a significant challenge. Furthermore, traffic parameters such as vehicle speed negatively impact video quality. This paper reviews various solutions proposed in the literature to optimize the MPEG-DASH (Dynamic Adaptive Streaming over HTTP) protocol in an IoV environment. The objective is to dynamically adjust video quality based on available resources to enhance QoE. Additionally, this review identifies key areas that researchers should focus on to improve this protocol. We highlight also the important challenges for our future contributions. © 2024 IEEE.

关键词： HTTP

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Blockchain-Driven Adaptive Streaming for IoT: Redefining Security in Video Delivery 6

Blockchain-Driven Adaptive Streaming for IoT: Redefining Sec...

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6th International Conference on Pattern Analysis and Intelligent Systems, PAIS 2024

作者： Kheraifia, Mohamed El Amine Sahraoui, Abdelatif Derdour, Makhlouf Dept. of Computer Science Tebessa12000 Algeria University of Larbi Ben m'Hidi Liaoa Laboratory Dept. Mathematics And Computer Science Oum El Bouaghi Algeria

ISBN: (纸本)9798350350265

The video surveillance system is a key component of the technologies deployed in smart cities. It serves a variety of applications, including public safety, crime prevention, traffic management, and environmental monitoring. The data captured by these systems includes sensitive information related to privacy, crime and national security, requiring robust protection against data breaches to ensure confidentiality. In this paper, we introduce a video fingerprinting-based method that uses a timestamp and device number, intended to prevent and detect image manipulation or replacement of original images with copies during transmission and of receiving the monitored data. Additionally, we propose the use of a blockchain system with an immutable distributed ledger for traceability and auditing of authentication procedures. © 2024 IEEE.

关键词： Smart contract

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Optimization of Vehicular Congestion Control in Traffic Signals using YOLO algorithm

Optimization of Vehicular Congestion Control in Traffic Sign...

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2024 International Conference on Smart Systems for Electrical, Electronics, Communication and computer Engineering, ICSSEEC 2024

作者： Sivaganesan, D. Kumar, S. Barath Krishnadharani, S. Rashmitha, G. PSG Institute of Technology and Applied Research Dept. of Computer Science and Engg Coimbatore India

ISBN: (纸本)9798350378177

The increase in the volume of vehicles utilizing our roads has highlighted the seriousness of the ongoing problem of traffic congestion. This problem is even worse at intersections, where a line of cars waits patiently for their turn to move forward. The traditional traffic light systems, which were originally intended to handle a less intense traffic load, are now having difficulty keeping up with the increasing number of vehicles, which further complicates the situation. We have adopted cutting-edge technologies, particularly those found in the fields of computer vision and machine learning, to tackle this modern dilemma. Our methodology is based on the application of a state-of-the-art system powered by a deep Convolutional Neural Network, which we call You Only Look Once (YOLO). With the help of this cutting-edge technology, we can quickly and thoroughly examine real-time data at signalized road crossings to gain a sophisticated understanding of the constantly changing traffic environment. Once we have a thorough understanding of the complex traffic dynamics thanks to YOLO's capabilities, we use this knowledge to make the most out of traffic light phases. We focus on closely examining important data elements, like the average wait time for a car and the number of cars in the line. The main goal is to create a coordinated and efficient traffic flow. This reduces the amount of time that drivers must wait in traffic overall and gives priority to safe and effective vehicle movement through intersections, resulting in a more responsive and fluid urban mobility experience. © 2024 IEEE.

关键词： Traffic congestion

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To What Extent Are Existing Volume Mapping Algorithms Practically Useful?

To What Extent Are Existing Volume Mapping Algorithms Practi...

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2024 Eurographics Italian Chapter Conference on Smart Tools and Applications in Graphics, STAG 2024

作者： Meloni, Federico Cherchi, Gianmarco Scateni, Riccardo Livesu, Marco Dept. of Mathematics and Computer Science University of Cagliari Italy CNR IMATI Genoa Italy

ISBN: (纸本)9783038682653

Mappings between geometric domains play a crucial role in many algorithms in geometry processing and are heavily used in various applications. Despite the significant progress made in recent years, the challenge of reliably mapping two volumes still needs to be solved to an extent that is satisfactory for practical applications. This paper offers a review of provably robust volume mapping algorithms, evaluating their performances in terms of time, memory and ability to generate a correct result both with exact and inexact numerical models. We have chosen and evaluated the two most advanced methods currently available, using a state-of-the-art benchmark designed specifically for this type of analysis. We are sharing both the statistical results and specific volume mappings with the community, which can be utilized by future algorithms for direct comparative analysis. We also provide utilities for reading, writing, and validating volume maps encoded with exact rational coordinates, which is the natural form of output for robust algorithms in this class. All in all, this benchmark offers a neat overview of where do we stand in terms of ability to reliably solve the volume mapping problem, also providing practical data and tools that enable the community to compare future algorithmic developments without the need to re-run existing methods. © 2024 The Authors.

关键词： Mapping

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Real-time Semantic Healthcare System: Visual Risks Identification for Elders and Children

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Informatica (Slovenia) 2024年第14期48卷 65-82页

作者： Belkebir, Malak Maarouk, Toufik Messaoud Nini, Brahim 2 University of Oum el Bouaghi Oum el Bouaghi Algeria ICOSI Laboratory Dept. of Mathematics and Computer Science Khenchela University Khenchela Algeria

Deep learning and data-driven approaches are commonly used to avoid accidents involving elders and children. However, existing models are limited by a semantic gap, hindering their ability to infer new risks that have not been previously trained. In this paper, a real-time healthcare system is developed to identify and infer visual risks in surveillance videos for elders and children. The system consists of three main modules:"visual information extraction," which leverages advancements in artificial vision techniques such as GCD and IoU for relationship detection, YOLO for object detection, and ResNet18 for scene recognition;"ontology modeling," where a new high-level ontology named"Risks-Identification-Onto" is constructed based on FOL and DLs;and"risk identification," where the system infers risks by deducing new knowledge through the reasoning of generated formal rules over the data-driven techniques. Additionally, the system generates a high-level semantic description of the risky situation. Four common risk scenarios -"Hurt,""Burn,""Existing-in-dangerous-places," and"Hit"- are selected to evaluate the effectiveness of the proposed system. Evaluation is conducted using the Charades and A2D datasets, each including 9,848 and 3,782 indoor activity videos. It demonstrate the system’s efficiency in identifying and inferring risks in real-time with an accuracy ranging from 97.61% to 99.43% for each scenario. © 2024 Slovene Society Informatika. All rights reserved.

关键词： Ontology

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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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