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Traffic surveillance from a safety perspective: an ITS data application

Traffic surveillance from a safety perspective: an ITS data ...

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international conference on intelligent Transportation

作者： M. Abdel-Aty A. Pande N. Uddin Department of Civil and Environmental Engineering University of Central Florida Orlando FL USA Department of Statistics and Actuarial Sciences University of Central Florida Orlando FL USA

Reactive traffic management strategies such as incident detection are becoming less relevant with the advancement of mobile phone usage. Freeway management in the 21st century needs to shift focus toward proactive strategies that include anticipating incidents such as the crashes. "Predicting" crash occurrences would also be the key to traffic safety. A two-step approach to identify freeway locations with high probability of crashes through real-time traffic surveillance data is presented here. For this study historical crash and corresponding traffic data from loop detectors were gathered from a 58-km (36-mile) corridor of Interstate-4. Following an exploratory analysis two types of logistic regression models, i.e., simple and multivariate, were developed. The simple models were used to deduce time-space patterns of variation in crash risk while the multivariate model was chosen for final classification of traffic patterns. As a suggested application for the simple models, their output may be used for preliminary assessment of the crash risk. If there is an indication of high crash risk then the multivariate model may be employed to explicitly classify the data patterns as leading or not-leading to crash occurrence. A demonstration of this two-stage real-time application strategy is also provided in the paper.

关键词： Surveillance Computer crashes Traffic control Detectors Predictive models Vehicle crash testing Road safety Logistics Frequency estimation Mobile handsets

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Enhancing Flood Forecasting with BiLstM Networks

Enhancing Flood Forecasting with BiLSTM Networks

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ACS/IEEE international conference on Computer Systems and Applications

作者： Rahma Boughale Amira Zrelli Tahar Ezzedine Syscom ENIT University of Tunis-ElManar Syscom National Engineering School of Tunis University of Tunis-EIManar

ISBN: (数字)9798331518240

ISBN: (纸本)9798331518257

Accurate flood forecasting is essential for effective disaster management and mitigation. Traditional models often struggle with the complex temporal dependencies present in flood-related time-series data. This study investigates the use of Bidirectional Long Short-Term Memory (BiLstM) networks for flood forecasting, leveraging their ability to capture dependencies and context from both past and future data points. We utilize the Historical Daily Weather dataset from the Australian Commonwealth Office of Meteorology, focusing on variables such as precipitation and river discharge to test our model. The BiLstM model is compared against traditional models and other deep learning algorithms, including simple LstM, GRU, and RNN networks. Our findings reveal that the BiLstM model consistently achieves lower root mean square error (RMSE) and mean absolute error (MAE) compared to the other models. Specifically, the BiLstM model's bidirectional architecture allows it to capture intricate temporal dynamics, resulting in more accurate and reliable flood forecasts. These results underscore the potential of BiLstM networks as a powerful tool for enhancing flood forecasting and improving disaster preparedness and response.

关键词： Deep learning Accuracy Prevention and mitigation Time series analysis Bidirectional long short term memory Rivers Floods Reliability Forecasting Root mean square

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Deep Information Retrieval-Oriented Smart System for Construction Project Quota standards

Deep Information Retrieval-Oriented Smart System for Constru...

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Expert Clouds and Applications (ICOECA), international conference on

作者： GUIMEI HE Chen Huang Wuhan Huaxia University of Technology School of Civil Engineering Wuhan China WenHua College Wuhan Hubei China

ISBN: (数字)9798350385793

ISBN: (纸本)9798350385809

Since the 21st century, with the rapid development of computer technology, information technology and network technology, the level of international engineering construction has continued to improve, but the project cost budgets have become more and more complex, and the work intensity has also increased. In this research study, the novel deep information retrieval-oriented intelligent system for construction project quota standards is studied. For the traditional system, the information is always collected by the target users and the model, while missing the latest information from the real-time website, in this research study, the novel website information collection model is first designed. This processing usually includes data cleansing, de-duplication, integration, analysis and visualization to better meet user needs and provide more accurate, reliable and useful information, and the novel steps are discussed. Then, the Continuous Bag of Words combined with Text-Rank word graph construction is considered for efficient information retrieval analysis. Furthermore, the fuzzy analytic hierarchy process is applied to the project quota standards analysis. In the experiment, the focus is on the retrieval model performance, the proposed model is compared with the methods of traditional neural network and Support Vector Machine (SVM), the recall value is selected to evaluate the performance. From the result, it is proved that the proposed model has the better recall performance.

关键词： Uniform resource locators Support vector machines Neural networks Web pages Analytic hierarchy process Real-time systems Reliability

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Elevating Cybersecurity in Defence: A Collaborative and Technologically Driven Approach

Elevating Cybersecurity in Defence: A Collaborative and Tech...

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international conference on Contemporary Computing and Informatics (IC3I)

作者： Syed Anas Ansar Shruti Aggarwal Swati Arya Mohd Asim Sayeed Nupur Soni Prabhash Chandra Pathak School of Computer Application Babu Banarasi Das University Lucknow India Department of Computer Science and Engineering Thapar Institute of Engineering and Technology Punjab India

As the world enters the 21st century, the imperative of safeguarding cybersecurity has intensified. Considering the expanding dependence on information technology (IT) systems for a broad spectrum of military operations, the stakes have never been higher for thwarting successful cyberattacks. Conventional security protocols have become increasingly precarious in the face of evolving cyber threats. This necessitates the adoption of avant-garde and nascent technologies to bolster the military's cybersecurity stance. The paper delves into an array of incipient technologies that proffers the prospect of enhancing cybersecurity in the realm of defence. These include artificial intelligence (AI), machine learning (ML), blockchain, and quantum computing. AI can be leveraged to automate numerous cybersecurity functions, such as threat detection and response. ML can augment the precision of threat detection and response systems. Blockchain, a distributed ledger technology, can safeguard data and transactions. Quantum computing, a nascent technology, harbours the potential to revolutionize computation. It could be utilized to breach present-day encryption standards, thereby rendering data vulnerable to attackers. Moreover, the paper appraises the exigencies and possibilities in defence cyber security and scrutinizes the policy and practice ramifications of these emergent technologies and best practices.

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Investigating the Effectiveness of Artificial Intelligence in Watermarking and steganography for Digital Media Security

Investigating the Effectiveness of Artificial Intelligence i...

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IEEE SSD international Multi-conference on Systems, Signals and Devices

作者： Shadan M.J.a Abdalwahid Wassan Adnan Hashim Mohammed Ganim Saeed Sarmad A. Altaie Shahab Wahhab Kareem Information System Engineering Dept. Engineering College Erbil Polytechnic University Erbil Iraq Medical Instruments techniques Dept AIQAlam University College Kirkuk Iraq Department of Information Technology Dohuk Polytechnic University Dohuk Iraq Computer Engineering Department University of Technology Baghdad Iraq Information System Engineering Dept. Erbil Technical Engineering College Erbil Polytechnic University Erbil Iraq

ISBN: (数字)9798350374131

ISBN: (纸本)9798350374148

Watermarking and steganography are methods of embedding digital information within images or other media, such as text or audio, for the purpose of Copyright Protection or covert communication. This field of study is not recent and has been ongoing for several years, culminating in its current advanced stage. The utilization of Artificial Intelligence algorithms has played a pivotal role in revolutionizing various aspects, including security concerns and the precision of outcomes, as compared to traditional methods. This paper focuses on doing an in-depth analysis of cutting-edge research, techniques, and methodologies employed in the domain of Watermarking and steganography, specifically in conjunction with Artificial Intelligence. By thoroughly examining and evaluating a collection of recent studies in this domain, we have scrutinized the outcomes of each study with respect to its research goal, the acquired results, the employed algorithm, and the research's robustness in terms of susceptibility to various forms of attacks and the technique of data embedding. Our findings indicate that the use of Artificial Intelligence algorithms has a substantial influence on enhancing result precision, system resilience, and establishing data ownership. Deep Neural Networks (DNN) are essential in Watermarking and steganography due to their robustness, effectiveness, and accuracy. Novel methodologies and systems improve security, incorporation rates, precision of detection, and speed of convergence. Deep learning is being investigated in techniques such as data concealment, information hiding, and steganography to improve security.

关键词： Deep learning steganography Fault tolerance Reviews Measurement standards Watermarking Artificial neural networks

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A Pervasive Community Detection Method Through Modular Decomposition of Markov Chain for Social Media Analysis

A Pervasive Community Detection Method Through Modular Decom...

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international Symposium on Soft Computing and intelligent Systems (SCIS)

作者： Takeshi Matsui Antonio O. N. Rene Faculty of Social and Information Studies Gunma University Maebashi Japan Faculty of Engineering Toyama Prefectural University Imizu Japan

ISBN: (数字)9781728197326

ISBN: (纸本)9781728197333

In a network structure, a subset with a dense link is called a community. Many networks in the real world are composed of loosely linked multiple communities. Individual communities are compatible with a functional unit constituting a complex system represented by the network. Therefore, if the community structure of the network is clarified, it can be seen how the entire complex system is composed of individual functional units. Clarifying the community structure is a fundamental and essential task for understanding complex systems expressed in networks. Many researchers have been studying to detect communities from networks effectively and efficiently. Meanwhile, the connection density of water pipes affects the failure rate. Therefore, the connection of water pipes is regarded as a network, it is possible to estimate the trouble spot through an application of community detection. Clarifying a community of members is to demonstrate a link between members. However, autonomously formed communities in the real world have a ubiquitous (pervasive) structure. If a community has a ubiquitous structure, it implies pervasive overlapping between communities. Though studies on the community detection method (modularity maximization and infomap method divide communities into the partition that do not overlap with each other. It follows that each member belongs to only one community. Under these circumstances, in this research, we propose a new community detection method through the modular decomposition of Markov chain method for social network activity.

关键词： Social networking (online) Markov processes Sociology Physics Graph theory data visualization data mining

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Retinal Blood Vessel Segmentation Using Twin U-Net based Deep learning Architecture

Retinal Blood Vessel Segmentation Using Twin U-Net based Dee...

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IEEE India conference (INDICON)

作者： Aditya Bhongade Yogita Dubey Nita Nimbarte Punit Fulzele Department of Electronics and Telecommunication Yeshwantrao Chavan College of Engineering Nagpur India Directorate of Research and Innovation SPDC Datta Meghe Institute of Higher Education and Research Sawangi (Meghe) Wardha India

ISBN: (数字)9798350391282

ISBN: (纸本)9798350391299

Reliable and faithful diagnosis of diabetic retinopathy (DR) relies on segmented fundus eye images. This paper proposes the Twin U-Net architecture, along with a robust framework, to segment retinal blood vessels. The framework includes preprocessing the inverted green channel of retinal images using contrast-limited adaptive histogram equalization (CLAHE) and power-law transformation to enhance vessel visibility. data augmentation techniques, such as flipping, deformation, and distortion, are applied to the acquired dataset to generate additional samples for training the Twin U-Net model. To demonstrate the efficacy of the proposed framework, a comparative study using quantitative metrics: accuracy, sensitivity, precision, F1 score, and Jaccard score, was conducted. The proposed model achieved accuracy, sensitivity, F1 score, and specificity values of 96.19%, 78.92%, 80.82%, and 97.79%, respectively, on the DRIVE dataset.

关键词： Deep learning Training Image segmentation Sensitivity Accuracy statistical analysis Manuals Blood vessels Retina Biomedical imaging

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Real-Time VoIP Classification in Encrypted Traffic Using Deep learning

Real-Time VoIP Classification in Encrypted Traffic Using Dee...

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international Bhurban conference on Applied Sciences & Technology, IBCAst

作者： Fahad Naeem Rabia Irfan School of Electrical Engineering and Computer Science (SEECS) National University of Sciences and Technology (NUST) Islamabad Pakistan

ISBN: (数字)9798331516680

ISBN: (纸本)9798331516697

With over 95% of internet traffic now encrypted, traditional methods like Deep Packet Inspection (DPI) and payload-based classification have been rendered ineffective. This study investigates the application of deep learning for real-time Voice over IP (VoIP) classification in encrypted network traffic. The ineffectiveness of traditional methods motivates the exploration of deep learning models, including Long Short-Term Memory (LstM), Multi-Layer Perceptron (MLP), and 1D Convolutional Neural Network (1D-CNN). This work leverages the pattern recognition power of deep learning to classify internet traffic as either VoIP or non-VOIP. The study includes improving data diversification by collecting and integrating network traces of VoIP and non-VoIP traffic from different origins. Furthermore, the efficacy of manual and automatic feature extraction techniques is comparatively evaluated. The findings demonstrate that manually extracted features yield superior classification accuracy. Manual feature extraction resulted in LstM, MLP, and ID-CNN accuracy rates of 94.7%, 94.8%, and 67.5%, respectively. Automatic feature extraction yielded 73.1%, 71.6%, and 41.5%. Notably, the MLP model achieves the highest accuracy (94.8%) while maintaining significantly faster classification times. This efficiency makes it well-suited for real-time VoIP classification.

关键词： Deep learning Accuracy Computational modeling Manuals Telecommunication traffic Feature extraction Real-time systems Cryptography Convolutional neural networks Long short term memory

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Unveiling Hidden Patterns Using Association Rule Mining and Machine learning for intelligent Transportation Systems

Unveiling Hidden Patterns Using Association Rule Mining and ...

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IEEE India conference (INDICON)

作者： Moumita Mishra Krishnakali Banerjee Soumya K Ghosh Bhargab Maitra Indian Institute of Technology (IIT) Kharagpur India Kalinga Institute of Industrial Technology (KIIT) Bhubaneswar India

ISBN: (数字)9798350391282

ISBN: (纸本)9798350391299

Road safety is a paramount concern in urban areas, particularly in populated cities like Kolkata, India, where the frequency and severity of traffic accidents pose serious difficulties. Every day huge number of accident occurs that ought to be reduced. The association rule mining (ARM) technique is used for extracting hidden patterns from the crash data. This paper explores the application of the algorithm in the transportation domain, focusing on identifying hidden patterns, correlations, and relationships aiming to enhance decision-making in traffic management, road safety, route optimization, and resource allocation. We utilize a comprehensive dataset of 2017–2020 and 2021–2023. We then employ the Apriori algorithm to identify the frequent itemsets in the dataset. We then took only the non-redundant association rules. After that, partition the non-redundant association rule into two different class sets. Finally, we apply some classification algorithms like logistic regression, random forest, Support vector machine, Naive Bayes, K neighrest neighbour, and XgBoost. Among these classification algorithms, XgBoost achieves the highest accuracy. The findings of this research not only shed light on the patterns and predictors of road accidents but also contribute significantly to the development of targeted data-driven interventions aimed at enhancing road safety measures in Kolkata.

关键词： Temperature measurement Temperature Road accidents Accuracy Urban areas Predictive models Road safety Real-time systems Association rule learning Classification algorithms

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An intelligent Digital System For Foot-Weight Distribution Analysis

An Intelligent Digital System For Foot-Weight Distribution A...

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international Bhurban conference on Applied Sciences & Technology, IBCAst

作者： Tassadaq Hussain Amna Haider Uman Ahmad Muhammad Haris Muhammad Zeerak Awan Eduard Ayguade Department of Electrical Engineering Centre for AI and Big Data Mianwali Pakistan UCERD Pakistan Supercomputing Foot Analytic Islamabad Pakistan Centre for AI and Big Data Namal University Mianwali Pakistan Computer Sciences Department Barcelona Supercomputing Centre Spain

ISBN: (数字)9798331516680

ISBN: (纸本)9798331516697

Maintaining proper weight distribution on the feet during daily activities can reduce postural and ambulatory issues. Therefore, accurate foot weight distribution analysis is crucial for medical science, occupational rehabilitation, physical therapy, sports, military training, and daily life postural training, influencing diagnostic and preventive healthcare. Despite the growing significance of body weight analysis, existing research in this domain is limited. The complexity of this issue arises from inherent variations in factors such as weight, height, Body Mass Index (BMI), physiological parameters, and accidentally conceived abnormalities. This paper introduces a novel approach to address this challenge by developing a smart digital system designed to analyze human body weight distribution across different parts of the foot. The system uses a data acquisition module connected with Force Sensing Resistor (FSR)-based matrix sensors placed under the plantar surface of the foot. The collected data is processed by an embedded system that uses analytic techniques to identify foot patterns and diagnose defects in the body. The proposed foot weight distribution system can be a helpful diagnostic tool for analyzing foot pressure distribution to identify and suggest potential medical problems (such as osteoporosis and prostheses). To validate the effectiveness of the system, a study involving 200 participants (comprising 100 males and 100 females) was conducted. The results show a significant level of accuracy in identifying abnormalities in osteoporosis and detecting prosthetic misalignment.

关键词： Training Osteoporosis Accuracy Digital systems data acquisition Sensor systems Medical diagnostic imaging intelligent sensors Surface treatment Foot

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