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International Conference on Communication, computing and Internet of Things (IC3IoT)

作者： Cuddapah Anitha Upendra Singh Aswal M H Chaithra Jyoti Dange Amit Barve Ramya Maranan Department of Computer Science and Engineering School of Computing Mohan Babu University Erstwhile Sree Vidyanikethan Engineering College Tirupati Andhra Pradesh Department of Computer Science & Engineering Graphic Era Deemed to be University Dehradun Uttarakhand India Department of Computer Science and Engineering REVA University Bangalore Karnataka India Department of Information Technology Atharva College of Engineering Malad West Mumbai Maharashtra India Department of Computer Science and Engineering Parul Institute of Technology Faculty of Engineering and Technology Parul University P.O.Limda Gujarat India Department of Research and Innovation Saveetha School of Engineering SIMATS Chennai Tamil Nadu India

ISBN: (数字)9798350352689

ISBN: (纸本)9798350352696

With the increasing complexity and sophistication of cyber threats, the demand for effective anomaly detection mechanisms in network security has grown substantially. This research focuses on the utilization of deep learning (DL) to detect anomalies in networks, implementing and analyzing three distinct models of deep learning-based classifiers. The classifiers include a binary classification model, a multiclass classification model with proportionality classification, and a multiclass classification model with exclusion strategies. The efficacy of these models is evaluated through cross-validation runs and testing on data from a database parallel to that of its training. The binary classification model serves as the baseline, distinguishing between normal and anomalous network behavior. The multiclass classification model with proportionality classification extends the binary approach by categorizing anomalies into multiple classes with consideration for their proportional representation. On the other hand, the multiclass classification model with exclusion strategies refines anomaly detection by explicitly excluding certain patterns from classification, enhancing the model’s specificity. The research methodology involves the development of deep learning-based classifiers, the collection of network data for training, and rigorous evaluation through cross-validation techniques. Additionally, the models are tested on a separate dataset to assess their generalization capabilities in real-world scenarios. The outcomes of this research contribute to the understanding of DL applications in network anomaly detection, providing insights into the strengths and limitations of different classifier strategies.

关键词： Training Analytical models Databases Computational modeling Network analyzers Network security Data models

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A Brief Analysis on Efficient Machine Learning Techniques for Intrusion Detection Model to Provide Network Security

A Brief Analysis on Efficient Machine Learning Techniques fo...

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Sustainable computing and Data Communication Systems (ICSCDS), International Conference on

作者： Aswadhati. Sirisha Bulla Premamayudu Department of IT and computer Applications school of computing and informatics Vignan’s Foundation for science Technology & Research Deemed to be university vadlamudi Guntur Andhra pradesh India Department of IT Vignan’s Institute of Information And Technology (Autonomous) Vishakapatnam Andhra Pradesh

An Intrusion Detection System (IDS) is a software used to monitor a single computer or a network of computers to combat network attacks, block access to certain websites, or otherwise the attack on network protocols. The dynamic and complicated nature of cyber attacks on computer systems is beyond the capabilities of the majority of current IDS solutions. By combating intrusion attempts, network security professionals ensure that the services can be accessed at all times. One of the tools at disposal to detect and categorize suspicious behaviour is IDS. For almost 40 years, researchers in both academia and industry have been proposing strategies for detecting and avoiding such security breaches, and even constructing systems to do so. Intrusion detection systems that rely on attack signatures examine network traffic for suspicious activity. Anomaly-based intrusion detection systems, on the other hand, build a model to tell good users apart from bad ones, allowing them to spot previously unseen attacks. Machine learning (ML) techniques are increasingly being employed as a means of categorizing normal and abnormal activities. Many other ML-based intrusion detection systems have been proposed in the past few years. The purpose of this work is to provide a critical overview of the state of the art in machine learning (ML)-based intrusion detection methods. Researchers focusing on ML-based systems that detect intrusions could use this survey as a supplement to previous generic surveys on intrusion detection and a source to recent work done in the area.

关键词： Industries Analytical models computer hacking Computational modeling Intrusion detection Machine learning Telecommunication traffic

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Multi-Modal Data Analysis Based Game Player Experience Modeling Using LSTM-DNN

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computers, Materials & Continua 2021年第9期68卷 4087-4108页

作者： Sehar Shahzad Farooq Mustansar Fiaz Irfan Mehmood Ali Kashif Bashir Raheel Nawaz KyungJoong Kim Soon Ki Jung School of Computer Science and Engineering Kyungpook National UniversityDaeguKorea Faculty of Engineering&Informatics School of MediaDesign and TechnologyUniversity of BradfordBradfordBD71DPUK Department of Computing and Mathematics Manchester Metropolitan UniversityManchesterUK Department of Operations TechnologyEvents and Hospitality ManagementManchester Metropolitan UniversityUK Institute of Integrated Technology Gwangju Institute of Science and TechnologyKorea

Game player modeling is a paradigm of computational models to exploit players’behavior and experience using game and player *** modeling refers to descriptions of players based on frameworks of data derived from the interaction of a player’s behavior within the game as well as the player’s experience with the *** behavior focuses on dynamic and static information gathered at the time of *** experience concerns the association of the human player during gameplay,which is based on cognitive and affective physiological measurements collected from sensors mounted on the player’s body or in the player’s *** this paper,player experience modeling is studied based on the board puzzle game“Candy Crush Saga”using cognitive data of players accessed by physiological and peripheral *** Short-Term Memory-based Deep Neural Network(LSTM-DNN)is used to predict players’effective states in terms of valence,arousal,dominance,and liking by employing the concept of transfer *** learning focuses on gaining knowledge while solving one problem and using the same knowledge to solve different but related *** homogeneous transfer learning approach has not been implemented in the game domain before,and this novel study opens a new research area for the game industry where the main challenge is predicting the significance of innovative games for entertainment and players’*** not only from a player’s point of view,it is also a benchmark study for game developers who have been facing problems of“cold start”for innovative games that strengthen the game industrial economy.

关键词： Game player modeling experience modeling player analytics deep learning LSTM game play data Candy Crush Saga

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IBATree: A Novel Method for Interpretable Cancer Cell Diagnosis Using Information Bottleneck Attribution

IBATree: A Novel Method for Interpretable Cancer Cell Diagno...

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IEEE International Conference on Bioinformatics and Biomedicine (BIBM)

作者： Muhammad Umair Raza Jie Chen Adil Nawaz Faisal Saeed Victor C.M. Leung Jianqiang Li Zhaoxia Wang College of Computer Science and Software Engineering Shenzhen University China National Engineering Laboratory for Big Data System Computing Technology Shenzhen University China Department of Gastroenterology Shenzhen Children’s Hospital China

ISBN: (数字)9798350386226

ISBN: (纸本)9798350386233

Deep Neural Networks (DNNs) have demonstrated remarkable performance in classification and regression tasks on RGB-based pathological inputs. The network’s prediction mechanism must be interpretable to establish trust in the clinical routine. One principal approach to interpretation is feature attribution. Feature attribution methods identify the importance of input features for the output prediction. Building on the Information Bottleneck Attribution (IBA) method, we recognize the RGB’s input regions with high mutual information with the network’s output for each prediction. IBA identifies input regions that have sufficient predictive information. In this paper, we introduce "IBATree", a novel approach that combines IBA with decision trees to enhance both the interpretability and accuracy of DNNs in cancer cell classification. Our method leverages the information bottleneck framework to inject noise into feature maps and then isolates the most informative features for model predictions while maintaining high classification performance. We evaluated our proposed approach on three datasets—CNMC, ISBI2016, and BreaKHis—demonstrating competitive accuracy and producing clear interpretations. This makes IBATree particularly suitable for clinical applications, where understanding the rationale behind predictions is crucial. Our results show that IBATree provides reliable predictions and valuable insights into feature importance, paving the way for its application in various biomedical domains.

关键词： Pathology Accuracy Biological system modeling Noise Predictive models Reliability Decision trees Biological cells Cancer Biomedical imaging

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Investigation of DSR protocol performance in Wireless MANET using Correlation Method 1

Investigation of DSR protocol performance in Wireless MANET ...

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1st International Conference on Advances in Computational science and Engineering, ICACSE 2020

作者： Rastogi, Umang Pattanaik, Balachandra Barani Sundaram, B. Mishra, Manish Kumar Thirumoorthy, Dhanabal Department of Computer Science and Engineering Meerut Institute of Engineering and Technology Meerut India Department of Electronics and Communication Engineering Bule Hora University Bule Hora Ethiopia Department of Computer Science Bule Hora University Bule Hora Ethiopia Department of Computer Science University of Gondar Ethiopia College of Informatics Bule Hora University Bule Hora Ethiopia

As we know already MANET means Mobile ad-hoc network that works made by two words Mobile and Ad-hoc Network collection of moving nodes shows mobile and kind of temporarily network which do not have any fixed point or centralised administration. Here assortment of nodes forms a *** can be adjust mobility to bandwidth in high or low level. In this paper, we evaluate performance of DSR protocol. In this unique design shows the results in terms of how protocol changes quickly according to nodes movement along with compare by correlation method. The result of this paper is coming after analysing of different behaviour of DSR can be applied in other new protocols that are using DSR framework. In this paper I showed results in term of break time, PDF, delay, throughput etc by simulation. These simulation results are applied on correlation method and finding out in which point DSR protocol parameters are performing good performance. In this result shows when pause time is increases along with no of nodes then correlation will be high. © Published under licence by IOP Publishing Ltd.

关键词： Correlation methods

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Crowd Safety Sensing (CroSS) for the Post Pandemic Era

Crowd Safety Sensing (CroSS) for the Post Pandemic Era

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2021 IEEE Globecom Workshops, GC Wkshps 2021

作者： Almalki, Khalid J Mohzary, Muhammad Choi, Baek-Young Song, Sejun Chen, Yu School of Computing and Engineering University of Missouri - Kansas City MO United States Dept. of Electrical and Computer Engineering Binghamton University BinghamtonNY United States College of Computing and Informatics Saudi Electronic University Riyadh Saudi Arabia Dept. of Computer Science Jazan University Jazan Saudi Arabia

ISBN: (纸本)9781665423908

Due to its long incubation period, aggressive asymptomatic transmission, and new mutations of the virus, COVID-19 is causing multiple pandemic waves worldwide. Despite recent vaccination, social distancing, and social restriction efforts, false negatives, and dormant positives can make pandemics challenging to restrain. In addition to rapid vaccination, effective contact tracing, mask-wearing, and social distancing are critical for out-break containment and for achieving herd immunity. However, the existing technology solutions, such as contact tracing apps and social-distance sensing, have been met with suspicion due to privacy and accuracy concerns and have not been widely adopted. Without achieving a critical mass of individual users, these personal technologies have been rendered useless. On the other hand, large-scale policy efforts have been complicated, requiring the coordination of federal, state, and local governments and regulation enforcement logistics. However, local communities balance these approaches and are an unrealized, powerful resource to prevent future *** paper proposes a novel Crowd Safety Sensing (CroSS) for building a sustainable safe community cluster against COVID-19 and beyond using affordable Internet of Things (IoT) technologies. CroSS monitors social distancing policies to small, focused communities for accommodating efficient technology penetration, greater accuracy, effective practices, and privacy policy assistance. We implemented a social distancing method and integrated it into an edge-based IoT system. The experimental results show that CroSS detects false-positive social distancing cases. © 2021 IEEE.

关键词： COVID-19

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Deep Learning vs. Feature Extraction Based Multi-Objective Approach for Sentiment Classification

Deep Learning vs. Feature Extraction Based Multi-Objective A...

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International Conference on computing for Sustainable Global Development (INDIACom)

作者： Jitendra Kumar Chaudhary Muddada Murali Krishna Rohit Kumar Singhal Rajesh Singh R. Suganthi Himanshu Rai Goyal School of Computing Graphic Era Hill University Uttrakhand Computer Science & Engineering Department (SOSO) Sanskriti College of Engineering Puttaparthi Andhra Pradesh Department of Computer Science & Engineering Chandigarh University (CU) Mohali Punjab INDIA Uttaranchal Institute of Technology Uttaranchal University India Dr. NGP Arts and Science College Coimbatore India Department of Computer Science & Engineering Graphic Era Deemed to be University Dehradun Uttarakhand India

Sentiment analysis is used for classification and analyzing of text through data and text mining. The expected output is generated using the identification and extraction of the information from the various sources provided in the Internet that helps in various kind of business and collection of data for researchers. In recent days deep learning and machine learning technologies are used for sentiment analysis for accurate prediction and analysis. Sentiment analysis can be done in 3 different ways like document level aspect level and sentence level. Not only the web sources text messages blogs articles emails are analyzed for extraction of text related to our searches. Sentiment analysis has significant role in in natural language processing and emotional tone identification. Sentiment analysis can be used for numerous applications for market research, reputation of a specific brand, consumer reception of products, success evaluation of marketing campaign and much more. It can be applied in different types of fields for collection of data using text extraction from various sources. The main objective of the study is the development of multi objective approach and comparison of deep learning versus feature extraction for sentiment classification. The proposed multi-objective sentiment classification model give the output of 99% accuracy using multi objective approach.

关键词： Deep learning Text mining Sentiment analysis Analytical models Solid modeling Computational modeling Text categorization

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Factors Affecting Information Assurance for Big Data

Factors Affecting Information Assurance for Big Data

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Software Engineering and Information Technology (ICoSEIT), International Conference on

作者： Rio Guntur Utomo Farashazillah Yahya Fahdah Almarshad Gary B Wills School of Computing Telkom University Bandung Indonesia Faculty of Computing and Informatics Universiti Malaysia Sabah Kota Kinabalu Sabah Malaysia College of Computer Science and Engineering Prince Sattam University Alkharj KSA School of Electronics and Computer Science University of Southampton Southampton United Kingdom

ISBN: (纸本)9781665473040

Big Data is a concept used in various sectors today, including the government sector in the Smart Government initiative. With a large amount of structured and unstructured data being managed, information assurance becomes important in adopting Big Data. However, so far, no research has focused on information assurance for Big Data. This paper identified information assurance factors for Big Data. This research used the systematic snapshot mapping approach to examine factors relating to information assurance from the literature related to Big Data from 2011 through 2021. The data extraction process in gathering 15 relevant papers. The findings revealed ten factors influencing the information assurance implementation for Big Data, with the security factor becoming the most concentrated factor with 18 sub-factors. The findings are expected to serve as a foundation for adopting information assurance for Big Data to develop an information assurance framework for Smart Government.

关键词： Systematics Government Big Data Security Data mining Information technology Software engineering

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SurgicalGaussian: Deformable 3D Gaussians for High-Fidelity Surgical Scene Reconstruction

arXiv

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arXiv 2024年

作者： Xie, Weixing Yao, Junfeng Cao, Xianpeng Lin, Qiqin Tang, Zerui Dong, Xiao Guo, Xiaohu Center for Digital Media Computing School of Film School of Informatics Xiamen University Xiamen China National Institute for Data Science in Health and Medicine Xiamen University China Key Laboratory of Digital Protection and Intelligent Processing of Intangible Cultural Heritage of Fujian and Taiwan Ministry of Culture and Tourism China Guangdong Provincial Key Laboratory IRADS Department of Computer Science BNU-HKBU United International College Zhuhai China Department of Computer Science The University of Texas at Dallas Dallas United States

Dynamic reconstruction of deformable tissues in endoscopic video is a key technology for robot-assisted surgery. Recent reconstruction methods based on neural radiance fields (NeRFs) have achieved remarkable results in the reconstruction of surgical scenes. However, based on implicit representation, NeRFs struggle to capture the intricate details of objects in the scene and cannot achieve real-time rendering. In addition, restricted single view perception and occluded instruments also propose special challenges in surgical scene reconstruction. To address these issues, we develop SurgicalGaussian, a deformable 3D Gaussian Splatting method to model dynamic surgical scenes. Our approach models the spatio-temporal features of soft tissues at each time stamp via a forward-mapping deformation MLP and regularization to constrain local 3D Gaussians to comply with consistent movement. With the depth initialization strategy and tool mask-guided training, our method can remove surgical instruments and reconstruct high-fidelity surgical scenes. Through experiments on various surgical videos, our network outperforms existing method on many aspects, including rendering quality, rendering speed and GPU usage. The project page can be found at https://***. © 2024, CC BY-SA.

关键词： Tissue

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Blockchain Technology for Secure Shared Medical Data 23

Blockchain Technology for Secure Shared Medical Data

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23rd International Arab Conference on Information Technology, ACIT 2022

作者： Mancer, Mhamed Akram, Khelili Mohamed Barka, Ezedin Okba, Kazar Sihem, Slatnia Harous, Saad Athamena, Belkacem Houhamdi, Zina Smart Computer Science Laboratory Department of Computer Science Biskra Algeria University of Mohamed Khider Biskra Algeria Numidia Institute of Technology Algeria Algeria Algeria UAE University Department of Information Systems and Security Al Ain United Arab Emirates Department of Information Systems and Security College of Information Technology United Arab Emirate University United Arab Emirates Department of Computer Science College of Computing and Informat Ics AL Ain United Arab Emirates University of Sharjah AL Ain United Arab Emirates College of Business Al Ain University Al Ain United Arab Emirates University of Al Ain Al Ain United Arab Emirates

ISBN: (纸本)9798350320244

A blockchain is a shared database allowing trust to be created between individuals without using intermediaries. The architecture here is decentralized, the data is distributed among the users, and therefore the information can never be erased. Today, many fields are interested in the development of products and technical solutions based on Blockchain technology. In this paper, we investigate blockchain in developing a Secure Shared Medical Record system (SSMR) for health data management. The proposed system aims to collect, store and share electronic medical records securely. It also makes it possible to provide doctors, for example, with medical information from other healthcare professionals, by defining a medical profile for each patient. © 2022 IEEE.

关键词： Blockchain

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