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Jyoti: An Intelligent App for Women Security 1

4th International Conference on Inventive Computation and Information Technologies, ICICIT 2022

作者： Pathak, Pooja Choudhary, Parul Department of Mathematics Institute of Applied Sciences GLA University Uttar Pradesh Mathura India Department of Computer Application and Engineering GLA University Uttar Pradesh Mathura India

ISBN: (数字)9789811974021

ISBN: (纸本)9789811974014

In today’s world of rapid urbanization, a new research chapter in social relations and women’s safety has begun. With this wave of urban transformation comes a growing sense of loneliness and helplessness among certain social groups. Women’s safety is a major issue in this situation, not only for the poor but also for civilized women. Weaknesses in the situation women’s safety plans, practices, and guidelines exist to reduce violence against women and their fear of crime. While alone walking or traveling, women should be aware of their surroundings. The advancement of technology may also contribute to women’s safety. Our proposed application is "Jyoti" for women’s safety. This application helps to prevent female homicide. When a female is in a bad situation, the application will send a message to trusted people and the police will go to current location. This application has unique feature that it sends messages to trusted people at regular intervals while shaking the phone. It also calls for the first trusted contact to assist in an emergency. It will help to reduce female crime. © 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Global positioning system

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Safe-Path: A Perspective on Next-Generation Road Safety Recommendations 25th

Safe-Path: A Perspective on Next-Generation Road Safety Rec...

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25th International Conference on Web Information Systems Engineering, WISE 2024

作者： Ibtissem, Khedher Noura, Faci Sami, Faiz National School of Computer Sciences University of Manouba Manouba Tunisia National Institute of Applied Sciences and Technology LTSIRS - LR20ES06 Geomatics Team University of Carthage Ariana Tunis Tunisia Université Claude Bernard Lyon 1 Computer Science Department CNRS LIRIS Villeurbanne France Higher Institute of Multimedia Arts of Manouba Geomatics Team LTSIRS University of Manouba Manouba Tunisia

ISBN: (纸本)9789819605729

Recommending safe routes has become a fundamental necessity due to the increasing number of accidents. This heavily relies on how to evaluate the road severity. Existing recommendation systems are based on user feedback, either positive or negative. However, this type of evaluation overlooks many aspects of road severity like accident history and volunteered geographic information on road conditions. To fill this gap, we elaborate a comprehensive and predictive road risk analysis, relying on objective and subjective data. To recommend safe roads, we propose an algorithm called Safe-Path based on accurate and reliable risk values. To validate our approach, we conduct some experiments to benchmark various machine and deep learning models. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Adversarial machine learning

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LogicAD: Explainable Anomaly Detection via VLM-based Text Feature Extraction

arXiv

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arXiv 2025年

作者： Jin, Er Feng, Qihui Mou, Yongli Decker, Stefan Lakemeyer, Gerhard Simons, Oliver Stegmaier, Johannes Institute of Imaging and Computer Vision RWTH Aachen University Aachen Germany Department of Computer Science RWTH Aachen University Aachen Germany Fraunhofer Institute for Applied Information Technology FIT Sankt Augustin Germany

Logical image understanding involves interpreting and reasoning about the relationships and consistency within an image's visual content. This capability is essential in applications such as industrial inspection, where logical anomaly detection (AD) is critical for maintaining high-quality standards and minimizing costly recalls. Previous research in AD has relied on prior knowledge for designing algorithms, which often requires extensive manual annotation effort, significant computing power, and large amounts of data for training. Autoregressive, multimodal vision Language Models (AVLMs) offer a promising alternative due to their exceptional performance in visual reasoning across various domains. Despite this, their application in logical AD remains unexplored. In this work, we investigate using AVLMs for logical AD and demonstrate that they are well-suited to the task. Combining AVLMs with format embedding and a logic reasoner, we achieve state-of-the-art (SOTA) AD performance on public benchmarks, MVTec LOCO AD, with an AUROC of 86.0% and an F1-max of 83.7% along with explanations of the anomalies. This significantly outperforms the existing SOTA method by 18.1% in AUROC and 4.6% in F1-max score. The dataset, code and supplementary materials are available at https://***/***/. © 2025, CC BY.

关键词： Visual languages

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Drug Repurposing for CoVID-19 Spike Protein through Molecular Docking

Drug Repurposing for CoVID-19 Spike Protein through Molecula...

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2022 International Conference on Recent Advances in Electrical Engineering and computer sciences, RAEE and CS 2022

作者： Aqeel, Imra Zafar, Sadia Bilal, Muhammad Majid, Abdul Pakistan Institute of Engineering and Applied Sciences Pieas Department of Computer and Information Sciences Islamabad Pakistan Comsats Institute of Information Technology Department of Bio Sciences Islamabad Pakistan

ISBN: (数字)9781665491037

ISBN: (纸本)9781665491020

Drug repurposing is the technique of finding new uses for currently used or under-researched drugs. Because this strategy requires less time and money, it is thought to be a particularly effective drug development strategy. Due to current technological breakthroughs, the accessibility of vast and reliable database resources, as well as data accessibility from genomes, proteomics, transcriptomics, etc., there are numerous opportunities to identify drugs by drug repurposing. The recent SARS-COV-2 epidemic, which has so far claimed 6,245,750 lives, has significantly increased the use of bioinformatics techniques in deciphering the characteristics of viral diseases. Using FDA-approved antiviral drugs that target the COVID-19 spike protein, we have used a bioinformatics approach to drug repurposing to find possible effective inhibitors against the Coronavirus (COVID-19). We used a variety of bioinformatics tools in this study, including AutoDock-Vina, PyMol, and Discovery Studio, to identify a promising drug called Cepharanthine (CEP), which demonstrates successful outcomes and efficacy compared to recently used antiviral drug candidates like arbidol, talampicillin, bromhexine, chloroquine, lycorine, bruceine A, reserpine, indinavir, galidesiver, doxycycline, methisazone, flupentixol, trifluoperazine and fluoxetine. The potential therapeutic value of cepharanthine as a drug for treating COVID-19 has been investigated in this study. It is expected that the proposed study will help medical professionals and researchers cure disorders linked to Severe acute respiratory and variations of it. © 2022 IEEE.

关键词： Coronavirus

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A spatiotemporal datamining approach for road profile estimation using low-cost device 26

A spatiotemporal datamining approach for road profile estima...

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26th International Conference on Knowledge-Based and Intelligent Information and Engineering Systems, KES 2022

作者： Taktak, Mariem Triki, Slim Higher Institute of Applied Sciences and Technologies of Sousse Department of Computer Science and Applied Mathematics Sousse Tunisia National Engineering School of Sfax Tunisia

In this paper, we address the problem of the spatiotemporal data mining in the field of the road conditions estimation. We demonstrate that road condition estimation can be defined as a problem of dynamic discovering of different classes of road profile during training phase. We focus on the road profile classification from time series data collected by a low-cost inertial sensor embedded into smartphone devices. Data used in this work are composed of three datasets which treat real asphalt pavement problems. The first, called Asphalt-obstacle, address the identification problem of obstacles in the pavement where data are collected. The second, called Asphalt pavement-type, aims at identifying three types of pavements namely flexible pavement, cobblestone street and dirt roads. The third and final dataset, called Asphalt pavement-regularity, treat the pavement quality effect on the driver comfortability. In order to estimate the road conditions, we conduct an evaluation of four spatiotemporal algorithms which combine concepts from machine learning and data-driven. With uniform parameters setting and on-line implementation property, we find that Markov spatiotemporal dynamic model achieve the best average classification accuracy of 90.5% ± 0.01 in 4-class Asphalt obstacles, 84% ± 0.006 in 3-class Asphalt pavement-type and 94.9%±0.005 in 2-class Asphalt pavement-regularity. © 2022 The Authors. Published by Elsevier B.V.

关键词： Smartphones

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Graph-based deep learning models in the prediction of early-stage Alzheimers 46

Graph-based deep learning models in the prediction of early-...

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46th Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBC 2024

作者： Thapaliya, Bishal Wu, Zundong Sapkota, Ram Ray, Bhaskar Suresh, Pranav Ghimire, Santosh Calhoun, Vince Liu, Jingyu Georgia State University Department of Computer Science Atlanta United States Tri-Institutional Center for Translational Research in Neuroimaging and Data Science United States Georgia Institute of Technology School of Electrical and Computer Engineering Atlanta United States Tribhuvan University Department of Applied Sciences and Chemical Engineering Nepal

ISBN: (纸本)9798350371499

Alzheimer's disease is the most common age-related problem and progresses in different stages, from cognitively normal to early mild cognitive impairment, and severe dementia. This study investigates the predictive potential of resting-state functional magnetic resonance imaging (rs-fMRI) and its derived functional connectivity (FC) in understanding Alzheimer's progression. Leveraging deep learning and graph-based models, we introduce two key contributions: 1) a comparative analysis of rs-fMRI time points and FC for Alzheimer's prediction. 2) an innovative graph transformer variant incorporating self-clustering for enhanced prediction accuracy. Experiments on the Alzheimer's Disease Neuroimaging Initiative dataset with 830 subjects reveal two notable conclusions. Firstly, rs-fMRI time points offer limited utility compared to functional network connectivity for transformer-based models, even when considering temporal information. Secondly, a clustering-based attention module proves effective for classifying brain networks in predicting Alzheimer's disease progression, providing valuable insights for future research and clinical applications. © 2024 IEEE.

关键词： Prediction models

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Updating Top-k Dominate Individuals with Incomplete Data Addition

Updating Top-k Dominate Individuals with Incomplete Data Add...

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2023 IEEE International Symposium on Information Theory, ISIT 2023

作者： Wu, Jimmy Ming-Tai Wang, Ke Yan, Huizhen Chen, Chao-Chun Chen, Pei-Wei Lin, Jerry Chun-Wei Shandong University of Science and Technology College of Computer Science and Engineering Qingdao China National Cheng Kung University Institute of Manufacturing Information and Systems Tainan70101 Taiwan Western Norway University of Applied Sciences Department of Computer Science Electrical Engineering and Mathematical Sciences Bergen Norway

ISBN: (纸本)9781665475549

Top-k dominance (TKD) query is an extended query method of skyline query and top-k query, which reveals the top-k dominant individuals in an incomplete dataset by analyzing the dominance relationships between individuals and is a common decision tool in intelligent recommendation applications. This research proposes two parallel query algorithms based on Spark computing engine to address the shortcomings of the parallel top-k-dominated query algorithms for dynamic incomplete datasets. The designed model achieves good performance in terms of runtime performance compared to previous studies. © 2023 IEEE.

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Invisible Windows for Measurement of Harmonic Components

Invisible Windows for Measurement of Harmonic Components

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Mediterranean Electrotechnical Conference (MELECON)

作者： Gerd Bumiller Institute of Computer Science Hochschule Ruhr West University of Applied Sciences Bottrop Germany

ISBN: (数字)9798350387025

ISBN: (纸本)9798350387032

This work focuses on the measurement of harmonic signal components as they are usually performed for synchrophasors, harmonic phases or power quality measurements in electrical power systems. A class of windows is defined which, in combination with a periodic superposition of the signals, has no influence on the measurement of harmonic components. The combination of special windows and superposition shown here is invisible for harmonic signal components, but can significantly reduce the measurement errors caused by incorrect estimation of the fundamental frequency and inter-harmonic signal *** commonly used rectangular window is a member of this class and is used as a reference for the evaluation. However, Bartlett, Hann and sine ramp base windows can also be designed as members of this class. All windows have a length of 2 timing intervals and are executed with 50% overlap. It is shown that an implementation of these windows is possible without significant additional *** is caused by a difference between the fundamental frequency of the analyzed signal, which often varies over time, and the fundamental frequency assumed by the measurement system. The simulation results for a frequency offset of 100 mHz show time-variant measurement results of the measurand of fundamental wave when using a rectangular window. Other windows show a much better behavior and a higher stability. Of course, the phase error must still be compensated for *** interference can cause the interference to spread over a wide range of measured harmonics. The rectangular window has a wide spread with limited attenuation. The best results with the lowest interference are obtained for the sine ramp base *** sine ramp base window in combination with the superposition with a measurement interval of one line cycle seems to be the best choice for the measurement of harmonic components, whereby the reduced measurement interval reduces the risk of erroneous

关键词： Time-frequency analysis Power measurement Phase measurement Interference Power system harmonics Attenuation measurement Harmonic analysis

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Drug Repurposing for SARS-COV-2 Using Molecular Docking 19

Drug Repurposing for SARS-COV-2 Using Molecular Docking

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19th International Bhurban Conference on applied sciences and Technology, IBCAST 2022

作者： Aqeel, Imra Majid, Abdul Ismail, Muhammad Bashir, Hina Department of Computer and Information Sciences Pakistan Institute of Engineering and Applied Sciences PIEAS Islamabad Pakistan Department of Bio Sciences COMSATS Institute of Information Technology Islamabad Pakistan

ISBN: (数字)9781665460514

ISBN: (纸本)9781665460514

Drug repurposing is an unconventional approach that is used to investigate new therapeutic aids of existing and shelved drugs. Recent advancement in technologies and the availability of the data of genomics, proteomics, transcriptomics, etc., and with the accessibility of large and reliable database resources, there are abundantly of opportunities to discover drugs by drug repurposing in an efficient manner. The recent pandemic of SARS-COV-2, that caused the death of 6,245,750 human beings to date, has tremendously increase the exceptional usage of bioinformatics tools in interpreting the molecular characterizations of viral infections. In this paper, we have employed various bioinformatics tools such as AutoDock-Vina, PyMol etc. We have found a leading drug candidate Cepharanthine (CEP) that has shown better results and effectiveness than recently used antiviral drug candidates such as Favipiravir, IDX184, Remedesivir, Ribavirin and etc. This paper has analyzed CEP's potential therapeutic importance as a drug of choice in managing COVID-19 cases. It is anticipated that proposed study would be beneficial for researchers and medical practitioners in handling SARS-CoV-2 and its variant related diseases. © 2022 IEEE.

关键词： COVID-19

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An AOP-Based Security Verification Environment for KECCAK Hash Algorithm

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computers, Materials & Continua 2022年第11期73卷 4051-4066页

作者： Hassen Mestiri Imen Barraj Mohsen Machhout Department of Computer Engineering College of Computer Engineering and SciencesPrince Sattam bin Abdulaziz UniversityAl-Kharj 11942Saudi Arabia Higher Institute of Applied Sciences and Technology of Sousse University of SousseTunisia Electronics and Micro-Electronics Laboratory Faculty of Sciences of MonastirUniversity of MonastirTunisia Electrical Engineering Department Systems Integration&Emerging Energies(SI2E)National Engineers School of SfaxUniversity of SfaxTunisia Higher Institute of Computer Science and Multimedia of Gabes University of GabesTunisia

Robustness of the electronic cryptographic devices against fault injection attacks is a great concern to ensure *** to significant resource constraints,these devices are limited in their *** increasing complexity of cryptographic devices necessitates the development of a fast simulation environment capable of performing security tests against fault injection *** is a good choice for Electronic System Level(ESL)modeling since it enables models to run at a faster *** enable fault injection and detection inside a SystemC cryptographic model,however,the model’s source code must be *** altering the source code,Aspect-Oriented Programming(AOP)may be used to evaluate the robustness of cryptographic *** might replace conventional cryptanalysis methods in the real *** the ESL,we discuss a unique technique for simulating security fault attacks on cryptographic *** current study presents a fault injection/detection environment for assessing the KECCAK SystemC model’s resistance against fault injection *** approach of injecting faults into KECCAK SystemC model is accomplished via the use of weaving faults in AspectC++based on AOP programming *** confirm our technique by applying it to two scenarios using a SystemC KECCAK hash algorithm case study:The first concerns discuss the effect of the AOP on fault detection capabilities,while the second concerns discuss the effect of the AOP on simulation time and executable file *** simulation results demonstrate that this technique is fully capable of evaluating the fault injection resistance of a KECCAK *** demonstrate that AOP has a negligible effect on simulation time and executable file size.

关键词： Aspect-Oriented Programming SystemC AspectC++ cryptographic circuits KECCAK fault detection fault attacks

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