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4th International Conference on Smart Systems and Inventive Technology, ICSSIT 2022

作者： Shyam, Shilpa Juliet, Sujitha Ezra, Kirubakaran Computer Science and Engineering Department Karunya Institute of Technology and Sciences Coimbatore India

ISBN: (纸本)9781665401180

Industry 4.0 is one of the latest advancement to the human kind. Manufacturing and production using high end digital technologies are its main contributions. The tracking of assets in an industry is often considered as monotonous and tedious also expecting huge manpower and time. Ultra Wide Band Technology (UWB) has already been used in several ways for such purposes but not completely utilized and moreover several gaps such as Non Line of sight (NLOS) error mitigation and advancement of features such as 3D plotting of tag/asset coordinates are still to be investigated on. This paper proposes a system and software architecture for asset tracking for industries using Ultra Wide band Technology with Time difference of Arrival (TDoA) technology along with triangulation method and is a promising feature to fill up the above said gaps. It implements the plotting of tag coordinates in both 2D and 3D. The assets in an industry can be traced and monitored with the help of a mobile application in this proposed architecture. The accuracy and precision is expected to be better than the present available UWB systems using the proposed work. © 2022 IEEE

关键词： Time difference of arrival

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Vehicle Monitoring and Localization using UWB in Complex Parking Lot

Vehicle Monitoring and Localization using UWB in Complex Par...

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2022 International Conference on Sustainable Computing and Data Communication Systems, ICSCDS 2022

作者： Shyam, Shilpa Juliet, Sujitha Ezra, Kirubakaran Karunya Institute of Technology and Sciences Computer Science and Engineering Department Coimbatore India

ISBN: (纸本)9781665478847

Ultra wide band technology has advanced over the years under several localization applications. Its capability of indoor localization has kept several conventional technologies at bay. Complex parking lot such as of a shopping mall is customized to accommodate huge and multiple vehicles at a time. The availability of locating empty parking spaces and tracking along with monitoring of vehicles precisely are a challenge to such complex environments. To tackle such challenges, the authors have put forth a system capable of locating unoccupied parking spaces, monitoring and tracking of vehicles during the user absence and navigation of vehicles in and out of the area. The proposed system makes use of Ultra wide band technology using TDoA (Time difference of arrival) and triangulation technique for localization. The precise localization of vehicle coordinates inside the parking lot both in 2D and 3D are initiated using a data analysis software. The hardware and software architecture is explained in detail along with complete component design. The system is initially tested in a test bed with dimensions of 50m X 66 m and further expanded to a wider complex parking lot. Both the test bed and further implementations will be done in line of sight and non-line of sight scenarios. Effort has been done to eradicate error challenges in the system. © 2022 IEEE.

关键词： Vehicles

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Future Security Issues in Internet of Energy 4

Future Security Issues in Internet of Energy

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4th International Conference on Advanced science and engineering, ICOASE 2022

作者： Benrebbouh, Chahrazed Cherbal, Sarra Mansouri, Houssem Pathan, Al-Sakib Khan Univ Ferhat Abbas Setif 1 Lrsd Laboratory Faculty of Sciences Department of Computer Science Setif Algeria United International University Department of Computer Science and Engineering Dhaka Bangladesh

ISBN: (纸本)9781665472227

One of the most popular new technologies today is the Internet of Energy (IoE), which utilizes the Internet for collecting, organizing, optimizing and managing network energy information from various edge devices. In this way, a distributed smart energy infrastructure is developed. As IoE is essentially linked with the Internet, the cyber security concerns for this environment are also significant. Like any other technology connected with the Internet, IoE also is vulnerable to various kinds of attacks and threats. In this paper, we investigate the security issues in Internet of Energy. We study various security techniques proposed or developed for this environment during the recent years and discuss what to expect in the future. © 2022 IEEE.

关键词： Internet of Energy

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Machine Learning Approach to LULC Forecasting

Machine Learning Approach to LULC Forecasting

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2024 IEEE Mediterranean and Middle-East Geoscience and Remote Sensing Symposium, M2GARSS 2024

作者： Riche, Abdelkader Ricci, Riccardo Melgani, Farid Drias, Ammar Souissi, Boularbah University of Sciences and Technology Houari Boumediene Department of Geography and Territorial Planning Algeria University of Trento Department of Information Engineering and Computer Science Italy University of Sciences and Technology Houari Boumediene Department of Telecommunication Algeria

ISBN: (纸本)9798350358582

This study explores the prediction of changes in Land Use and Land Cover (LULC) over the period of 2000 to 2040 in a region of Algeria. The research combines remote sensing data and machine learning techniques. Landsat image classification is applied to assess LULC changes for the years 2000 to 2020. In addition, a novel approach is employed to account for temporal factors influencing LULC changes in 2025, 2030, and 2040. Promising results are reported and discussed. © 2024 IEEE.

关键词： Remote sensing

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Exploring Gender Dynamics in Motorcycle Traffic Violations Through Gender Detection Technology 6

Exploring Gender Dynamics in Motorcycle Traffic Violations T...

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6th International Conference on Intelligent Computing in Data sciences, ICDS 2024

作者： Charef, Ayoub Jarir, Zahi Quafafou, Mohamed EMSI Moroccan School of Engineering Sciences LAMIGEP Research Laboratory Marrakech Morocco Marrakech Morocco University of Aix-Marseille Department of Computer Science Marseille France

ISBN: (纸本)9798350351200

Urban traffic management faces significant challenges due to non-compliance with traffic rules, particularly among motorcycle riders. This study introduces an innovative approach employing the YOLOv9 object detection framework to monitor and analyze motorcycle violations at busy intersections in Marrakech. By leveraging advanced machine learning techniques, the study aimed to detect helmet usage, traffic light violations, and assess gender-based differences in violation rates. Data were collected using smartphone cameras at strategic locations during peak traffic times across selected days. The captured video footage was analyzed using the YOLOv9 model, which was pretrained on diverse traffic scenes to enhance its accuracy and reliability in real-Time object detection. The findings reveal that the automated system not only aligns closely with manual counting but also offers greater consistency and efficiency. Key results indicated a pronounced gender disparity in violation frequencies and provided insights into the temporal patterns of traffic rule infractions. Moreover, the study highlighted minor discrepancies due to environmental factors, which were systematically addressed to refine the detection process. Contextual factors in interpreting detection results. © 2024 IEEE.

关键词： Motorcycles

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Design of Medical Privacy Protection Circuit Based on Cross Obfuscation 9

Design of Medical Privacy Protection Circuit Based on Cross ...

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9th International Conference on Integrated Circuits and Microsystems, ICICM 2024

作者： Chen, Zhikang Zhang, Yuejun Ren, Mingze Xia, Donghao Wang, Pengjun Zhang, Huihong Wen, Liang Ningbo University Faculty of Electrical Engineering and Computer Science Ningbo China College of Electrical and Electronic Engineering Wenzhou University Wenzhou China China Coast Guard Academy Department of Electronic Technology Ningbo China

ISBN: (纸本)9798331509453

With the rise of the Internet of Things, smart healthcare with remote monitoring and diagnosis enables patients to reduce the number and duration of hospitalizations, thereby reducing the pressure on doctors. However, the transmission data of disease detection chips is susceptible to cache attacks, leading to user health data leakage. Hardware level encryption design has become a research hotspot. Firstly, by adding a Mask channel at the upper level of each convolution layer, the preprocessed data is transmitted simultaneously with the obfuscated data, and the channel exchange during transmission is utilized to protect user privacy;Secondly, there is a redundancy mechanism between the input matrix and weight matrix of the storage module, and invalid values are added to the redundant parts to prevent cache attacks from stealing user information;Finally, add multi temporal obfuscation to the first input data to achieve the different lengths between the first and subsequent data. The protection circuit designed in this article achieves user privacy protection on the basis of only increasing hardware overhead by 2.5%, power consumption overhead by 3.61%, and not affecting disease monitoring accuracy. © 2024 IEEE.

关键词： Information leakage

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Car Crash Detection System using Machine Learning and Deep Learning Algorithm

Car Crash Detection System using Machine Learning and Deep L...

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2022 IEEE International Conference on Data science and Information System, ICDSIS 2022

作者： Supriya, S. Shankar, Sahana P Himanshu Jain, J. Narayana, Lisha L Gumalla, Nikhita Ramaiah University of Applied Sciences Department of Computer Science and Engineering Bengaluru India

ISBN: (数字)9781665498012

ISBN: (纸本)9781665498012

Over 80% of mishaps are caused by a lack of identifying the accident on time, as well as failure to arrive in time to provide emergency care for the victim. The point is to distinguish and utilize machine learning to decide the best means of detecting car crash with light of the live transfer of dash cam data in the vehicle. The thought is to take every pixel and run it with a deep learning model prepared to recognize video outlines into mishap or non-mishap. Essentially, in Artificial Intelligence (AI), informational indexes are collected. Gathered information bases will be refined and given to AI calculations to prepare for image recognition with the help of computer vision. After fruitful preparation, AI calculations will be tried and the outcomes will be recorded for examination purposes. In the proposed vehicle crash location framework, the impact identification is performed utilizing the Convolutional Neural Network (CNN), taking a bunch of pictures as information, the framework distinguishes the crash, the effect of the vehicle and the greatness of the mishap. Based on the performance of machine learning algorithms, comparative analysis is performed and the results will be tabulated. Two machine learning algorithms are considered i.e. Random Forest Classifier and Logistic Regression which enables the output regarding the generated index from the CNN and running through the indices with location impact and severity of the damage. © 2022 IEEE.

关键词： Deep learning

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Bioinformatics exploration into potential mechanisms of Calamus draco for colorectal cancer treatment 9th

Bioinformatics exploration into potential mechanisms of Cala...

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9th International Conference on computer science and Computational Intelligence, ICCSCI 2024

作者： Kenneth, Christian Nirwantono, Rudi Pardamean, Bens Bioinformatics and Data Science Research Center Bina Nusantara University Jakarta11480 Indonesia Biotechnology Department Faculty of Engineering Bina Nusantara University Jakarta11480 Indonesia Computer Science Department BINUS Graduate Program Computer Science Program Bina Nusantara University Jakarta11480 Indonesia

As one of the cancer types with the highest incidence rates, colorectal cancer (CRC) would benefit from treatments with fewer side effects and reduced treatment-resistant potential. One of the options is to harness the anti-CRC potential of natural products. Previous studies have shown that Calamus draco exudate, dragon's blood, has anticancer activity in liver cancer and acute myeloid leukemia, but its bioactivity has not been studied in CRC. Here we conduct a bioinformatics study based on network pharmacology to explore the anti-CRC potential and mechanism of C. draco-derived compounds. The bioinformatics pipeline is composed of compound and target collection, biological network evaluation, and enrichment analysis. We found that there are 43 bioactive compounds from C. draco targeting 91 CRC-related targets, of which most compounds target MEN1, PTGS2, and IDH1. Further analyses show that the targets of C. draco are involved in the cellular response to hypoxia. By inhibiting those targets, C. draco bioactive compounds can potentially hinder angiogenesis and increase treatment response efficacy. © 2024 The Authors.

关键词： Lung cancer

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AUTOMATIC SPEECH RECOGNITION FOR THE IKA LANGUAGE

arXiv

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arXiv 2024年

作者： Nzenwata, Uchenna Ogbuigwe, Daniel Udoka Department of Computer Science School of Computing and Engineering Sciences

We present a cost-effective approach for developing Automatic Speech Recognition (ASR) models for low-resource languages like Ika. We fine-tune the pretrained wav2vec 2.0 Massively Multilingual Speech Models on a high-quality speech dataset compiled from New Testament Bible translations in Ika. Our results show that fine-tuning multilingual pretrained models achieves a Word Error Rate (WER) of 0.5377 and Character Error Rate (CER) of 0.2651 with just over 1 hour of training data. The larger 1 billion parameter model outperforms the smaller 300 million parameter model due to its greater complexity and ability to store richer speech representations. However, we observe overfitting to the small training dataset, reducing generalizability. Our findings demonstrate the potential of leveraging multilingual pretrained models for low-resource languages. Future work should focus on expanding the dataset and exploring techniques to mitigate overfitting. © 2024, CC BY.

关键词： Speech recognition

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Fault Tolerance in the Joint EDF-RMS Algorithm: A Comparative Simulation Study

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computers, Materials & Continua 2022年第9期72卷 5197-5213页

作者： Rashmi Sharma Nitin Nitin Deepak Dahiya University of Petroleum&Energy Studies(UPES) School of Computer ScienceEnergy Acres BuildingBidholiDehradun248007UttarakhandIndia Department of Electrical Engineering and Computer Science College of Engineering and Applied ScienceUniversity of Cincinnati2600 Clifton AveCincinnatiOH 45221United States College of Computer and Information Sciences(CCIS) Majmaah UniversityMajmaah11952Kingdom of Saudi Arabia

Failure is a systemic error that affects overall system performance and may eventually crash across the entire *** Real-Time Systems(RTS),deadline is the key to successful completion of the *** tasks effectively meet the deadline,it means the system is working in pristine ***,missing the deadline means a systemic fault due to which the system can crash(hard RTS)or degrade inclusive performance(soft RTS).To fine-tune the RTS,tolerance is the critical issue and must be handled with extreme *** article explains the context of fault tolerance with improvised Joint EDF-RMS algorithm in *** backup method has been derived to prevent the system from being recursively migrating the same *** any task migrates three times,this migrated task will get shifted to the backup *** backup queue assigns the task to a backup processor and is destined for final *** performance evaluation purposes,a relative graph between fault and failure rates,failure and total processor utilization along with other averages have been ***,these archived results are compared with fault-tolerant Earliest Deadline First(EDF)and Rate Monotonic Scheduling(RMS)algorithms independently in relatively similar *** comparisons show better performance against overloading conditions.

关键词： Fault tolerance joint edf-rms algorithm real-time systems(RTS) distributed systems migration

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