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15th International Conference on Computing Communication and Networking Technologies, ICCCNT 2024

作者： Mishra, Medha Tiwari, Sadhana Chandra, Ritesh Agarwal, Sonali Sonbhadra, Sanjay Kumar Iiit Allahabad Department of Information Technology Prayagraj India University Department of Computer Science and Engineering Odisha Bhubaneswar India

ISBN: (纸本)9798350370249

Geolocated Event Detection is one of the most prevalent and novel research topic in social media event detection but presently very few researchers have addressed this problem. Thus, the aim of this research is to tackle the problem of natural disaster event detection with the help of real-time social media data in order to successfully incorporate the sentiments of the affected people so that the developed model must be able to train better with the aid of real-life scenarios. Currently, the focused natural disasters for this work are, earthquakes, floods and cyclones keeping the Indian Geography in mind, as Indian subcontinent is mostly affected by these calamities. Due to this, the real-time data required for training of the model was sufficiently available. Therefore, the prime aim of this work is to detect these events by mining real-time twitter data in the form of user tweets and images. Thus, these collected data is then linked to form graphs by finding patterns or relationships among them. This research is basically considering two different modalities of data i.e., text and image data in order to attain realistic results. This study will help in detecting natural disasters, such as earthquakes, floods etc. along with their location coordinates with the help of real-time user tweets and alerting the users in close proximity to the deducted location. This will benefit not only the people but also alert the concerned authorities so that help and other necessities can be provided well in time to the affected people and evacuations can be made before being too late, and thus the damages thus involved in any such event are kept to minimum. © 2024 IEEE.

关键词： Tweets

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Word-Diffusion: Diffusion-Based Handwritten Text Word Image Generation 27th

Word-Diffusion: Diffusion-Based Handwritten Text Word Image ...

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27th International Conference on Pattern Recognition, ICPR 2024

作者： Gurav, Aniket Krishnan, Narayanan C. Chanda, Sukalpa Department of Computer Science and Communication Østfold University College Halden1757 Norway Department of Data Science Indian Institute of Technology Palakkad Kerala India

ISBN: (纸本)9783031784941

Generating realistic handwritten word images that closely resemble a target style remains a challenging task in document image analysis. In recent years, deep learning techniques, such as Latent Diffusion Models (LDM), have shown promise in generating styled handwritten text. However, these models face significant challenges when creating images for ‘Out of Vocabulary’ (OOV) words, impacting their overall effectiveness. In this paper, we introduce an extended diffusion-based Handwritten generation method that incorporates a novel conditioning mechanism. It is based on the Pyramidal Histogram of Shapes (PHOS) representation, which takes into account the spatial and structural characteristics of the target handwriting style. By conditioning the diffusion model on input text, PHOS vector, and writer ID, our approach enables the generation of handwritten word images. Notably, our approach outperforms the original diffusion model, which only uses text and writer ID as conditions, in generating both in-sample and out-of-sample. Furthermore, we have developed a faster inference method that significantly reduces the number of steps required for generating the output. Through qualitative and quantitative evaluations, we demonstrate the effectiveness of our proposed method. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Deep learning

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Two-stage IDS for IoT using layered machine- and deep-learning models

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Cyber-Physical Systems 2024年第1期10卷 60-83页

作者： van der Walt, André Quazi, Tahmid van Niekerk, Brett School of Engineering University of KwaZulu-Natal Durban South Africa School of Mathematics Statistics and Computer Science University of KwaZuluNatal South Africa Department of Information Technology Durban University of Technology South Africa

The ever-growing integration of Internet-of-Things (IoT) devices into our daily lives provides us with a level of convenience never before seen. However, the effects of attacks on these devices can be devastating. The discrete, low-powered nature of IoT devices makes their security a difficult problem to solve. To provide a solution, this work proposes a two-stage Intrusion Detection Systems (IDS) using layered machine and deep learning models. The potential benefits of the system are examined and the results presented show a reduction of threat detection/identification time of 0.51 s on average and an increase of threat classification F1-Score by 0.05. . © 2022 Informa UK Limited, trading as Taylor & Francis Group.

关键词： Internet of things

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Leveraging Computational Techniques for Accurate Diagnosis of Early-Onset Alzheimer’s Disease 8th

Leveraging Computational Techniques for Accurate Diagnosis ...

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8th International Conference on Intelligent Technologies, ICIT 2023

作者： Okasha Anjum, Mohammad Anjali Arya, K.V. Department of Information Technology ABV-Indian Institute of Information Technology and Management Gwalior474015 India Department of Computer Science and Engineering ABV-Indian Institute of Information Technology and Management Gwalior474015 India

ISBN: (纸本)9789819738588

Alzheimer’s disease (AD) poses a growing global health concern, demanding innovative approaches for accurate diagnosis and early prediction. investigates the potential of machine learning algorithms in predicting the early onset of AD. Through the analysis of a comprehensive dataset containing biomarkers and clinical information, we explore various machine-learning models to identify individuals at risk of developing AD in its initial stages. This research underscores the significance of leveraging computational techniques to provide early warnings for timely interventions and personalized patient care. We focus on enhancing the accuracy of AD classification through ensemble techniques. By aggregating predictions from multiple machine learning models, we introduce a voting ensemble method to improve the overall classification performance of AD cases. This approach demonstrates the potential of combining diverse models to mitigate individual weaknesses and achieve more reliable diagnostic outcomes, contributing to refined clinical decision-making processes. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Adversarial machine learning

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On the Rique Number of Series-Parallel Graphs and Planar Bipartite Graphs 2nd

On the Rique Number of Series-Parallel Graphs and Planar ...

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2nd International Conference on Applied Algorithms, ICAA 2025

作者： Azgor, Sk Ruhul Rahman, Md. Saidur Graph Drawing and Information Visualization Laboratory Department of Computer Science and Engineering Bangladesh University of Engineering and Technology Dhaka Bangladesh

ISBN: (纸本)9783031845420

The restricted input queue (rique) is a data structure which is recently introduced to study linear layout of graphs. The rique data structure is a special queue where insertions occur only at the head and removals occur at both the head and the tail. Considering a rique data structure the goal of a linear layout of a graph is to find a linear order of the vertices of the graph and a partition of its edges into pages such that the edges in each page follow the restriction of rique in the underlying order. The rique number of a graph is the smallest number of pages required for linear layout of the graph with rique data structure. A characterization of graphs for admitting a single page rique layout and some bounds on the rique number of complete and complete bipartite graphs are known. In this paper, we show that the rique number of series-parallel graphs as well as planar bipartite graphs is 2. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Directed graphs

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Secure Communication in Resource-Constrained IoT Environments using MQTT Protocol: A Review 3

Secure Communication in Resource-Constrained IoT Environment...

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3rd International Conference on Automation, Computing and Renewable Systems, ICACRS 2024

作者： Kaganurmath, Sharadadevi Cholli, Nagaraj G. Department of Computer Science Engineering Global Academy of Technology Bengaluru Visvesvaraya Technological University India Department of Information Science Engineering RV College of Engineering Bengaluru Visvesvaraya Technological University India

ISBN: (纸本)9798331532420

The Internet of Things (IoT) has revolutionized our lives by connecting a vast array of devices. The Message Queuing Telemetry Transport (MQTT) protocol plays a crucial role in enabling efficient communication between these devices. However, the resource-constrained nature of many IoT devices presents significant security challenges. This research review comprehensively addresses the security management of resource-constrained IoT devices utilizing the MQTT protocol. It explores the unique security threats faced by IoT devices, including those related to computation, memory, and energy constraints. The paper examines various security measures applicable to MQTT, such as authentication, encryption, and access control mechanisms, tailored to the specific needs of resource-constrained devices. This comprehensive review provides valuable insights into the security challenges and solutions for IoT devices utilizing the MQTT protocol, guiding researchers, developers, and stakeholders in enhancing the security and reliability of IoT ecosystems. © 2024 IEEE.

关键词： Telemetering

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A Random Forest Based Approach to Classify Spam URLs Data

A Random Forest Based Approach to Classify Spam URLs Data

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2022 ASU International Conference in Emerging Technologies for Sustainability and Intelligent Systems, ICETSIS 2022

作者： Jilani, Abdul Khader Sultana, Jabeen University of Technology Department of Computer Science Bahrain Majmaah University College of Computer and Information Sciences Department of Computer Science Riyadh Saudi Arabia

ISBN: (数字)9781665469197

ISBN: (纸本)9781665469197

Networking plays leading role to meet day-to-day activities. Huge usage of networking in education systems, financial transactions, businesses, and social gatherings across the globe peaked to the level of spam URL's. In addition to this, the quench for finding most relevant and up-to-date data in any sector lead to severe threat of spam URL's. It's quite difficult to identify the real website from fake one because of spam URL's and thus leads the users to fall under hacker's trick which further threatens the system safety. Therefore, it is worth to classify spam URL's to identify the true websites from fake ones. This led to the emergence of using machine learning techniques to classify Spam URL'S data. In this research, spam URL's data was taken from Kaggle database and machine learning classifiers were used to classify the URL's into spam or non-spam URL's. 10-fold cross validation and hold out method was used. It was observed that random forest yielded high classification accuracy of 97% in classifying the URL data when 10-fold cross validation was performed when compared to hold-out method. Support vector machine yielded 92% classification accuracy followed by naive bayes with a classification accuracy of 91%. The outcomes were assessed on few parameters like accuracy, true positive rate, false positive rate, precision and recall. © 2022 IEEE.

关键词： Websites

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Fuzzy Logic Based Brake Failure Detection & Collision Avoidance Model for Automated Driving System 6

Fuzzy Logic Based Brake Failure Detection & Collision Avoida...

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6th International Conference on Robotics and Automation in Industry, ICRAI 2024

作者： Dar, Amil Roohani Riaz, Faisal Department of CSandIT AJandK Kotli Pakistan HEC Islamabad Pakistan Department of Computer Science and Information Technology Mirpur Pakistan

ISBN: (纸本)9798350355703

The manual and automated braking system is intended to prevent rear-end collisions. However, unforeseen malfunctions within the vehicle's system during braking could potentially cause accidents. Collision Warning (CW) systems are safety technologies in vehicles that aid drivers in preventing collisions. This paper introduces a fuzzy-assisted pre-warning system for brake testing that considers multiple factors contributing to brake failure, along with a post-failure CW algorithm tailored for heavy commercial vehicles. Simulations conducted using NetLogo demonstrate that accidents can be prevented during brake failure through appropriate adjustments in vehicle speed. The proposed collision avoidance system has been validated for its effectiveness, and the results are encouraging and enhance road user safety. © 2024 IEEE.

关键词： Brakes

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Broadening Effective Assessment with Bloom's for Computing: A Call to Translate Computing-related Verbs into World Languages 2023

Broadening Effective Assessment with Bloom's for Computing: ...

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28th Annual Conference on Innovation and technology in computer science Education, ITiCSE 2023

作者： Geissler, Markus Servin, Christian Tang, Cara Department of Computer Information Science Cosumnes River College SacramentoCA United States Department of Computer Science El Paso Community College El PasoTX United States Department of Computer Information Systems Portland Community College PortlandOR United States

ISBN: (纸本)9798400701399

The ACM Education Board recently endorsed the ACM CCECC's Bloom's for Computing publication [2] which enhances the traditional verb list of Bloom's Revised Taxonomy [3] with computing-related verbs to facilitate the development of learning outcomes and competencies which enable more effective assessment of student achievement in the computing disciplines. The specific goals of this poster are a) to inform the global computing community about Bloom's for Computing and various opportunities it provides, and b) to invite members to contribute to the community by translating the Bloom's for Computing verbs into their native language and publicizing them throughout their language area. While terminology originating from the English language dominates many areas of study, including the computing disciplines, creating detailed learning outcomes consisting of native language elements can facilitate more effective communication between faculty, students, and industry partners about academic expectations and achievement. Several learning taxonomies have been translated into different languages to broaden their impact [1,5]. By creating Bloom's for Computing, the ACM CCECC worked to remove the layer of abstraction between cross-disciplinary Bloom's verbs and more effective learning outcomes which implement verbs used in various computing disciplines. Having the computing-related verbs available in different languages would make them more easily accessible for non-native English speakers. © 2023 Owner/Author.

关键词： Taxonomies

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Efficient Congestion Control Through Energy and Traffic-Aware Clustering In Future Wireless Sensor Networks 2

Efficient Congestion Control Through Energy and Traffic-Awar...

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2nd IEEE International Conference on Futuristic Technologies, INCOFT 2023

作者： Vennila, N.L. Saju Raj, T. Junnarkar, Aparna Atul Shalini, K.B.Devi Geetha, P. Muslim Arts College Department of Computer Science Kanyakumari Thiruvithancode India Vel Tech Rangarajan Dr. Sagunthala R&d Institute of Science and Technology Department of Computer Science and Engineering Avadi Chennai India Department of Information Technology Pune India Department of Computer Science and Engineering Thandalam Chennai India

ISBN: (纸本)9798350308846

When the aggregated capacity of a channel is surpassed by the amount of data flowing across it, a condition known as congestion occurs in wireless sensor networks (WSNs). Because retransmission of every unacknowledged packet is not an optimal solution in terms of energy for resource-restricted sensor nodes, the result is overflowing of the buffer at each receiving sensor node. This eventually leads to a reduction in the packet delivery ratio, the dropping of the packets, and a degradation of the network throughput. Since the routing problem has been shown to be NP-hard and it has been revealed that a heuristic-based method outperforms their traditional counterparts, routing is one of the most common strategies for lowering node energy consumption and boosting throughput in WSNs. In addition, routing is one of the most popular ways for raising throughput. As a result, there is a need for optimum routing solutions for the WSNs of the future, taking into consideration both the energy and traffic factors. This research will only focus on wireless sensor networks (WSNs for short). The suggested protocol will be referred to as Energy and Traffic-aware Clustering for Congestion Control (ETCCC) throughout this article. In graphs compared ETCCC performances with three recent protocol names. © 2023 IEEE.

关键词： clustering Congestion Energy and traffic aware WSN

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