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Dynamic Service Centric Sensitivity-Based Risk Analysis Model in Secure Cloud 1

Dynamic Service Centric Sensitivity-Based Risk Analysis Mode...

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1st IEEE International Conference on Computing and Data Science, ICCDS 2024

作者： Imtiaz, N. Mohamed Subramaniam, M. Uyyala, Ravi Nagarjuna College of Engineering and Technology Dept. of ISE Bengaluru562110 India Chaitanya Bharathi Institute of Technology Dept. of Computer Engineering & Technology Telangana Hyderabad500075 India Chaitanya Bharathi Institute of Technology Dept. of CSE Telangana Hyderabad500075 India

ISBN: (纸本)9798350365337

A substantial amount of research has been done to enforce data security and access restriction, in order to achieve higher privacy preservation. This article focused on identifying service threats and analyzing the risk at service and data level towards the security of cloud, using a new approach, said as Dynamic Service Centric Sensitivity-based Risk Analysis Model (DSSRAM). Meta-data are classified under different sensitivity class. The method first manages the Metadata, towards the risk analysis, and measures its Targeted Score (TS). The same has been used for data indexing. Based on the result of risk analysis, the user profile and taxonomy are generated. On the other side, the access restriction is performed by enforcing CFR model. To measure the Service Centric Trust Measure (SCTM), the Coverage-Feature-Response model examines the user behavior using different parameters. Based on SCTM, access restrictions are performed. The method not just analyses the risk at feature level, but also analyses how the services are targeted to perform different threats. Experimental results, the DSSRAM shows the overall performance analysis with varying users and number of features. Analyzing on varying users compared with Access Restriction, its performance is 95% high, and with Metadata management is 95.33% high. False ratio in access restriction is 6% less, and time complexity in access restriction is 22.66% less. The proposed method enhances performance in access restriction, data security, and Meta data management. © 2024 IEEE.

关键词： Metadata

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Advancements in Flood Mapping Methodologies: A Comprehensive Review 6

Advancements in Flood Mapping Methodologies: A Comprehensive...

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6th IEEE International Conference on Recent Advances in Intelligent Computational Systems, RAICS 2024

作者： Chandran, Divya V Anitha, J. Chandran, Divya V Vijayakumar, Amrutha Raveendran, Anjana Karunya Institute of Technology and Sciences Dept.of Computer Science Engineering Coimbatore India Adi Shankara Institute of Engineering and Technology Dept. of Electronics and Communication Engineering Kalady India

ISBN: (纸本)9798350381689

This review aims to comprehensively assess the recent advancements in flood mapping methodologies and technologies. By analyzing diverse techniques and approaches employed in flood mapping, this review seeks to present an in-dept. overview of the current landscape of this field. The primary focus is on evaluating the strengths and limitations inherent in various flood mapping methods. Special attention will be given to exploring the integration of remote sensing technologies and geographic information systems (GIS) in flood mapping. Additionally, the review will delve into the transformative impact of machine learning and artificial intelligence on enhancing the precision and efficiency of flood mapping processes. Recent developments in flood mapping have significantly elevated the accuracy and reliability of the techniques employed. The integration of AI technologies with remote sensing methods has notably contributed to this advancement. Conventional approaches to flood mapping have traditionally relied on multiple methodologies to comprehend potential flood impacts. However, these methods often prove laborious, resource-intensive, and may lack access to real-time data, posing limitations for precise flood mapping and prediction. These identified limitations underscore the critical need for the development of more robust flood mapping techniques. Addressing these challenges is essential for enhancing flood risk assessment, improving management strategies, and ensuring better preparedness for future flood events. © 2024 IEEE.

关键词： Floods

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A Comparative Study of Machine Learning Methods for Intrusion Detection 10

A Comparative Study of Machine Learning Methods for Intrusio...

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10th International Conference on Electrical and Electronics engineering, ICEEE 2023

作者： Sibai, Fadi N. Asaduzzaman, Abu Sibai, Ahmad Gulf University for Science and Technology Electrical & Computer Engineering Dept. Mishref Kuwait Wichita State University Electrical & Computer Engineering Dept. WichitaKS United States University of South Florida Computer Science Dept. TampaFL United States

ISBN: (纸本)9798350304299

In this work, we applied 8 machine learning (ML) techniques to detect intrusions, namely, neural networks, kNN, SVM, random forest, trees, AdaBoost, naive Bayes, and stochastic gradient descent SGD. Using the NSL-KDD data set, these ML techniques were trained and tested to correctly classify the network and operating system records of this dataset into one of 24 possible attacks. The performances of these ML methods were analyzed and compared, with the random forest method performing at the top. To the best of our knowledge, this is the first work on investigating more than 4 ML classifiers on this data set in one single work and using the same set of tools. © 2023 IEEE.

关键词： Intrusion detection

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Deep Learning-Based Flood Detection System Using Semantic Segmentation 7

Deep Learning-Based Flood Detection System Using Semantic Se...

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7th International Conference on Circuit Power and Computing Technologies, ICCPCT 2024

作者： Chandran, Divya V Anitha, J. Anusree, A.R. Nair, Gowri Karunya Institute of Technology and Sciences Dept.of Computer Science Engineering Coimbatore India Adi Shankara Institute of Engineering and Technology Dept. Electronics and Communication Engineering Ernakulam India

ISBN: (纸本)9798350372816

Floods cause immense damage worldwide, resulting in significant financial losses and loss of life. Existing flood detection methods often face limitations, including delays and errors due to manual data interpretation and limited coverage. In this study, we propose a novel flood detection system that leverages deep learning techniques for semantic segmentation. Our system employs a U-Net architecture to analyze aerial and satellite images, identifying flood-affected areas at a pixel level. Trained on a diverse dataset of labeled flood images from various regions, our model is both robust and adaptable. Notably, our system achieves an Intersection over Union (IoU) score of 0.85, surpassing traditional methods in accuracy. This advanced system offers the potential to provide real-time flood information, enhancing disaster response and management. With real-time flood mapping, emergency services can better understand the disaster's scope and plan rescue operations more effectively. Improved emergency response and resource management become possible with timely, precise data on flooded areas, allowing for informed decisions regarding evacuation, relief distribution, and infrastructure maintenance. Future work will focus on integrating additional data sources, such as weather conditions and water flow rates, to further enhance system performance. © 2024 IEEE.

关键词： Semantic Segmentation

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Risk Assessment and Safety-as-a-Service Provisioning in IoV Networks: A Roadmap 5

Risk Assessment and Safety-as-a-Service Provisioning in IoV ...

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5th IEEE Global Conference for Advancement in technology, GCAT 2024

作者： Pradhan, Patrali Chattopadhyay, Samiran Chowdhury, Subrata Rout, Suchismita Mishra, Manoj Ranjan Patra, Sudhansu Shekhar Haldia Institute of Technology Dept. of Computer Science and Engineering Haldia India Techno India University Kolkata India Sreenivasa Institute of Tech. and Management Studies Dept. of Computer Science and Engineering Andhra Pradesh Chittoor India Kiit Deemed to be University School of Computer Engineering Bhubaneswar India Kiit Deemed to be University School of Computer Applications Bhubaneswar India

ISBN: (纸本)9798350376685

This paper presents a framework for a Safety recommender system that offers Safety-as-a-Service (Safe-aaS) to on-road vehicles in IoT-based transportation systems. The proposed safety recommendation system integrates several phases, including preparing sensed data, self-supervised learning from the data, and predicting personalized safety plans. The customized safety plans, generated by analyzing on-road decision parameters such as dynamic road conditions, weather patterns, and driving assistance parameters, are suggested to registered users in real time through the Safe-aaS platform tailored to their specific *** a Safe-aaS enabled road transportation system, sensor nodes produce large volumes of data in a dynamic environment. Efficient analysis and storage of sensed data in edge devices poses a major challenge for service providers. Conversely, generating safety-related decisions with lower processing time ensures driver safety. The Safe-aaS-based recommender system provides safety-related decisions with low latency and processing delay. In the proposed recommendation system, a safety score is computed from the pre-processed sensed data across various parameters, followed by a user-specific similarity score and a corresponding risk score. We apply a fuzzy rule-based learning technique to predict optimized safety plans. To showcase the real-time scenario of our framework, we develop a user-friendly application interface at the primary stage. Extensive analysis shows that our system effectively detects on-road risks and adapts safety recommendations in dynamic weather and road conditions. © 2024 IEEE.

关键词： Sensor nodes

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On the Quantization of Recurrent Neural Networks for Smiles Generation 48

On the Quantization of Recurrent Neural Networks for Smiles ...

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48th IEEE International Conference on Acoustics, Speech and Signal Processing, ICASSP 2023

作者： Durao, Adriano Arrais, Joel P. Ribeiro, Bernardete Falcao, Gabriel Instituto de Telecomunicações Portugal University of Coimbra Dept. of Electrical and Computer Engineering Portugal Cisuc Portugal University of Coimbra Dept. of Informatics Engineering Portugal

ISBN: (纸本)9781728163277

This paper focuses on the effects of applying quantization during training to Recurrent Neural Networks (RNNs) used in Simplified Molecular-Input Line-Entry System (SMILES) generation, a form of line notation for molecular information used in the development of pharmaceutical drugs, from the PubChem database. It offers the flexibility to choose the precision used by the model, by defining the number of bits at each layer. The RNNs are the focus of the current study, by comparing the performance of three of the most used algorithms, Simple RNN, Long Short-Term Memory (LSTM), and Gated Recurrent Unit (GRU). The models were trained on a selection of SMILES. By exploiting the QK-eras library, quantization performance was compared to their floating-point equivalent for several combinations of parameters. The goal of the testing program developed is to generate a large number of novel SMILES, facilitating the process of Drug Discovery which is traditionally long, and thus very expensive and difficult. By understanding how the behavior of quantized networks deviates from the regular model, in relation to the parameters used, we are able to control the process of choosing whether to quantize a model and to which degree it becomes more or less efficient. In this study, we observed good performance even for 4-bit models making use of LSTM and GRU layers, the same way we concluded that Simple RNN quantization does not compensate the effort. © 2023 IEEE.

关键词： Drug Discovery Gated Recurrent Unit Long Short-Term Memory Quantization Recurrent Neural Networks

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Slotted V Shaped Compact Wideband Antenna with Partial Ground Plane for Microwave Based Head Imaging Applications 6

Slotted V Shaped Compact Wideband Antenna with Partial Groun...

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6th International Conference on Electrical Information and Communication technology, EICT 2023

作者： Moue, Sonia Akter Samsuzzaman, Md. Mahmud, Md Zulfiker Siddiquea, Sefat Talukder, Md Siam Majumder, Sarna Patuakhali Science And Technology University Faculty of Computer Science And Engineering Bangladesh Jagannath University Dept. of Computer Science and Engineering Bangladesh Patuakhali Science and Technology University Faculty of Computer Science and Engineering Dumki Patuakhali Bangladesh

ISBN: (纸本)9798350370836

This study presents a detailed description of a Slotted V Shaped Compact Wideband Antenna designed for the purpose of microwave-based head imaging. The antenna under consideration comprises a radiating patch with a Vshaped slot and a partial ground with slots. Slots have been found to significantly improve the efficiency, dispersion of current, gain, and various other aspects of antennas. The antenna under consideration has a maximum gain of 5.85, operates within the frequency range of 4 to 8.06 GHz, and is deemed suitable for head imaging applications. The dimensions of the antenna under consideration are 0.33λ × 0.667λ × 0.21λ, where λ represents the wavelength, at a lower frequency of 4 GHz. Additionally, the antenna is equipped with a 50 ohm microstrip transmission line. The antenna is fabricated on a cost-effective FR4 substrate with a thickness of 1.5 mm. The antenna under consideration achieves a fractional bandwidth of 67.33% within the frequency range of 4 to 8 GHz. In order to optimize antenna performance, a comprehensive parametric analysis is carried out utilizing the widely recognized software tools HFSS and CST. The experimental testing and analysis of the prototype antenna yielded findings that exhibited a satisfactory level of concurrence with the corresponding calculations. © 2023 IEEE.

关键词： Brain

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Factors Affecting Mental Health of High School Students due to COVID-19: An Exploratory Study

Factors Affecting Mental Health of High School Students due ...

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2023 International Conference on Next-Generation Computing, IoT and Machine Learning, NCIM 2023

作者： Reza, Md. Rokonuzzaman Islam, Muhammad Nazrul Islam, Mohian Mumu, Afnan Alauddin Faisal, Faiz Al The International University of Scholars Dept. of Computer Science and Engineering Dhaka Bangladesh Military Institute of Science and Technology Dept. of Computer Science and Engineering Dhaka Bangladesh LTIMindtree Ltd. Washington United States Green University of Bangladesh Dept. of Computer Science and Engineering Dhaka Bangladesh

ISBN: (纸本)9798350316001

Mental health is one of the most significant factors in the human life span. It also influences how we interact with people, manage stress, and make good decisions. From childhood and youth through maturity, mental health is crucial at every stage of life. In the era of the COVID-19 pandemic, it becomes more vulnerable and affects teenagers as others. In this study, high school students' (10-19 years old) mental health states and the most contributing factors that affect their mental health were assessed through a statistical and machine learning-based approach. The study data were collected through an online survey and received responses from 158 respondents. The data were analyzed statistically with a chi-square test and path analysis considering all the possible factors of anxiety and depression to determine the most contributing factors. Besides, a few Machine Learning (ML) algorithms have been developed such as Logistic Regression(LR), Support Vector Machine(SVM), Naive Bayes(NB), AdaBoost(AB), and Random Forest(RF) for the prediction of anxiety and depression and found that AdaBoost achieved the highest score (86%). As outcomes, the overall analysis revealed that academic concerns such as poor understanding of online classes, daily activities like sleep pattern disruption, and extended uses of electronic devices are the major causes of the deterioration of high school students' mental health. © 2023 IEEE.

关键词： COVID-19

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Enhancing Lymph Node Classification in Rectal Cancer MRI Images Through a Multi-Model Approach

Enhancing Lymph Node Classification in Rectal Cancer MRI Ima...

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2024 IEEE International Conferences of AI, Science, engineering, and technology, AIxSET 2024

作者： Gupta, Rishit Iwahori, Yuji Funahashi, Kenji Bhuyan, M.K. Bhatt, Manish Kijsirikul, Boonserm Wang, Aili Ouchi, Akira Shimizu, Yasuhiro Indian Institute of Technology Guwahati Dept. of Electronics & Electrical Eng. Assam Guwahati India Chubu University Dept. of Computer Science Kasugai Japan Nagoya Institute of Technology Dept. of Computer Science Nagoya Japan Chulalongkorn University Dept. of Computer Engineering Bangkok Thailand Harbin University of Science and Technology Higher Educational Key Laboratory Harbin China Aichi Cancer Center Hospital Dept. of Gastroenterological Surgery Nagoya Japan

ISBN: (纸本)9798350390995

Rectal cancer remains to be a major issue worldwide, necessitating accurate and precise methods for proper classification of lymph nodes in MRI scan images to guide treatment decisions. The use of deep learning based techniques can help with the task of classification. This research focuses on enhancing the process of lymph node classification in rectal cancer MRI images by using a multi-model approach. Firstly, we explore the use of diffusion models in data augmentation, to produce synthetic lymph node images to counter the issue of limited dataset size. Secondly, Real-ESRGAN and Swin2SR super-resolution techniques are examined for their ability to improve image quality and hence enhance the accuracy of lymph node classifications. Lastly, ConvNeXt is employed as an advanced technique used to improve overall performance at classifying lymph nodes for rectal cancer. Through a series of detailed experiments on the dataset consisting of T2-Weighted Axial MRI scans from 54 patients, we demonstrate the effectiveness of our approach in classification of lymph nodes. The metrics like accuracy and precision exhibit significant improvements, highlighting the superiority of our multi-model approach. © 2024 IEEE.

关键词： Magnetic resonance imaging

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Study and Analysis of MultiChain Native Asset 10

Study and Analysis of MultiChain Native Asset

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

作者： Kharjana, Mebanjop Sahana, Subhas Chandra Saha, Goutam North-Eastern Hill University Dept. of Information Technology Meghalaya Shillong India National Institute of Technology Dept. of Computer Science & Engineering West Bengal Durgapur India

ISBN: (纸本)9798350384369

MultiChain native assets were blockchain-based dig-ital assets created, managed, and traded on its network. Such blockchain assets were ensured to be transparent, secured, and trusted. Different applications used MultiChain assets to represent digital and physical objects like images, files, houses, cars, etc. This paper aimed to highlight the MultiChain platform and how it manages its native assets. This was achieved by demonstrating different MultiChain commands to handle and manage assets. MultiChain native assets were then analyzed based on the existing frameworks for blockchain-based digital assets and tokens. They were also analyzed to study their compliance with different token standards established by Ethereum. Both analyses revealed that the MultiChain native asset is more advanced and compatible with most contemporary token standards. The results of this investigation are useful to guide future applications that utilize MultiChain native assets. © 2024 IEEE.

关键词： Asset Management Bitcoin Blockchain Blockchain Asset Ethereum Token Standard MultiChain Token Framework

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