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2022 IEEE International Conference on Systems, Man, and Cybernetics, SMC 2022

作者： Kumoi, Gendo Yagi, Hideki Kobayashi, Manabu Hirasawa, Shigeichi Waseda University Center for Data Science Tokyo Japan University of Electro-Communications Dept. of Computer & Network Eng. Tokyo Japan

ISBN: (数字)9781665452588

ISBN: (纸本)9781665452588

Error-Correcting Output Coding (ECOC) is a method for constructing a multi-valued classifier using a combination of binary classifiers. The effectiveness of ECOC for multivalued classification problems has been demonstrated by many experimental evaluations. Therefore, classification performance have strongly depended on the data under consideration, and it is not clear what kind of combinations of binary classifiers have good performance. Motivated by this fact, the authors have clarified the best combination of binary classifiers that makes ECOC, assuming a situation in which each binary classifier can estimate the true posterior probability. They also have proposed a total framework for analytical evaluation when a binary classifier outputs an estimated posterior probability that approximates the true posterior probability. These studies established a framework for evaluating the theoretical performance of *** on these findings, this study discusses the theoretical performance of ECOC from the upper bound perspective. The results showed that increasing the Hamming distance between code words can blackuce the error rate. We then evaluate various combinations of binary classifiers based on analytical evaluation. © 2022 IEEE.

关键词： Hamming distance

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Cybernated Watermarking Approach on Image with Cryptographic Encryption 2

Cybernated Watermarking Approach on Image with Cryptographic...

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2nd International Conference on computer, Communication and Control, IC4 2024

作者： Sivasankari, R. Veeramakali, T. Mustafa Nawaz, S.M. Prakash, P. Umadevi, P. Alvin Ancy, A. B.S.Abdur Rahman Crescent Institute of Science and Technology Dept. of Computer Science and Engineering India Srm Institute of Science and Technology School of Computing Dept of Data Science and Business Systems Tamilnadu Kattankulathur India Sri Sai Ram Institute of Technology Dept of Computer Science and Engineering India Sri Sai Ram Engineering College Dept of Electronics and Communication Engineering India Kpr Institute of Engineering and Technology Department of S & H Coimbatore India Anna University Dept of Electronics and Communication Engineering Chennai India

ISBN: (纸本)9798350387933

Digital watermarking of interactive media content has become an active experimentation field in recent years. In addition to reviewing some of the methods that have been developed for various media types;a general structure for watermark engrafting and identification is outlined. We highlight a few of these application-oriented differences, including copyright protection, substantiation, tamper identification, and data hiding, as well as the technological and system conditions for different media types such as digital photos, digital files, digital audio, and text. We describe a general structure for digital watermarking-based image copyright protection in this approach. In this paper, we outline the pre-processing and implementation of tide marking technique where Twofish algorithm and double encryption is described. The image processing toolbox is also specified by comparing five different image processing techniques. In addition, the post implementation is carried out with the wavelet toolbox and matlab coder to analyze signal, image properties and generate the efficient code for embedding the text. © 2024 IEEE.

关键词： Cryptography

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Examination of Phishing Attempts on Web-Based Business Applications and Their Preventions

Examination of Phishing Attempts on Web-Based Business Appli...

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2024 International Conference on Communication, computer sciences and Engineering, IC3SE 2024

作者： Shaukat, Shahbaz Rehman, Faisal Chhabra, Gunjan Kaushik, Keshav Khan, Inam Ullah Sharef, Hanan Ali, Muhammad Zahid Ghaffar, Irfan Arshad, Muhammad Naeem Lahore Leads University Dept. Of Computer Science & Information Technology Lahore Pakistan Dept. Statistics & Data Science University Of Mianwali Mianwali Pakistan National University Of Sciences & Tech. Nust Islamabad Pakistan Graphic Era Hill University Department Of Cse Dehradun India Graphic Era Deemed To Be University Uttarakhand Dehradun India University Of Petroleum And Energy Studies School Of Computer Science Dehradun India Isra University Department Of Electronic Engineering Seas Islamabad Pakistan

ISBN: (纸本)9798350366846

Because of developments in the online sector, the occurrence of data breaches in web-based business systems is rising quickly. The attackers employ a range of different methods, including Distributed-Denial-of-Service attacks, Hacking Current Sessions, Reusing of Usernames and Passwords, Injections of Structured Query Language, and Phishing Attacks. Phishing attacks are widely used in the modern age. Phishing attacks are frauds attempt for tricking users into giving up sensitive information. Based on the intruder's intended target, such digital threats can damage specific people or whole industries. Researchers in this field offer a variety of phishing identification methods to stop such threats. Throughout this review paper, we categorize modern phishing identification methods into five main groups, which includes framework based on deep learning algorithms and artificial intelligence methodology based on search engines, methodology based on scanned URLs, methodology based on Whitelisting-Blacklisting Using Pictorial Resemblance as a Basis. Additionally, a statistical assessment of every individual category, giving its advantages and disadvantages. Additionally, this article offers users advice on how to prevent cyber-attacks. © 2024 IEEE.

关键词： Denial-of-service attack

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Comparing nnU-Net and deepflash2 for Histopathological Tumor Segmentation

Comparing nnU-Net and deepflash2 for Histopathological Tumor...

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34th Medical Informatics Europe Conference, MIE 2024

作者： Hieber, Daniel Haisch, Nico Grambow, Gregor Holl, Felix Liesche-Starnecker, Friederike Pryss, Rüdiger Schlegel, Jürgen Schobel, Johannes DigiHealth Institute Neu-Ulm University of Applied Sciences Germany Pathology Medical Faculty University of Augsburg Germany Institute of Medical Data Science University Hospital Würzburg Germany Dept. of Computer Science Aalen University of Applied Sciences Germany Technical University of Munich Germany

ISBN: (纸本)9781643685335

Machine Learning (ML) has evolved beyond being a specialized technique exclusively used by computer scientists. Besides the general ease of use, automated pipelines allow for training sophisticated ML models with minimal knowledge of computer science. In recent years, Automated ML (AutoML) frameworks have become serious competitors for specialized ML models and have even been able to outperform the latter for specific tasks. Moreover, this success is not limited to simple tasks but also complex ones, like tumor segmentation in histopathological tissue, a very time-consuming task requiring years of expertise by medical professionals. Regarding medical image segmentation, the leading AutoML frameworks are nnU-Net and deepflash2. In this work, we begin to compare those two frameworks in the area of histopathological image segmentation. This use case proves especially challenging, as tumor and healthy tissue are often not clearly distinguishable by hard borders but rather through heterogeneous transitions. A dataset of 103 whole-slide images from 56 glioblastoma patients was used for the evaluation. Training and evaluation were run on a notebook with consumer hardware, determining the suitability of the frameworks for their application in clinical scenarios rather than high-performance scenarios in research labs. © 2024 The Authors.

关键词： Image segmentation

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Integrated Framework for Simulated Business Intelligence and Real-Time Analytics to Enhance Agility in Industry 4.0 Decision-Making

Integrated Framework for Simulated Business Intelligence and...

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Computational Intelligence Approaches and Applications (ICCIAA), International Conference on

作者： Bilal Ibrahim Sowan Ahmad Altarawneh Mohammad Fasha Dept. of Bus. Intell. & Data Analytics University of Petra Amman Jordan Dept. of Computer Science Brunel University of London London UK

ISBN: (数字)9798331523657

ISBN: (纸本)9798331523664

Agility and real-time responsiveness are critical for achieving competitiveness in Industry 4.0. This study introduces an innovative framework that integrates simulation-driven business intelligence and real-time analytics to enhance decision-making processes in dynamic industrial environments. The proposed framework employs advanced multi-agent systems, operational data stores, and machine learning models to process high-velocity data streams, optimize operational workflows, and provide actionable insights. By adopting a layered architecture for data integration, simulation, and decision support, the framework ensures adaptability and scalability. Experimental results demonstrate significant improvements in processing speed, predictive accuracy, and decision latency when compared to traditional approaches. Applications across sectors such as manufacturing and logistics validate its effectiveness, showcasing its ability to transform raw data into valuable insights for strategic and operational decisions. This work establishes a solid foundation for future research, including blockchain integration for secure decision-making and real-time analytics in smart city ecosystems.

关键词： Smart cities Scalability Decision making Transforms Solids Real-time systems Fourth Industrial Revolution Business intelligence Streams Multi-agent systems

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Automated Detection of Poultry Farms from Aerial Images for Actionable AI System toward Biosecurity Applications

Automated Detection of Poultry Farms from Aerial Images for ...

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2023 IEEE Applied Imagery Pattern Recognition Workshop, AIPR 2023

作者： Li, Yu Das, Rana Duong, Cuong Lim, Teng-Teeh Haithcoat, Timothy Ersoy, Ilker Shyu, Chi-Ren University of Missouri Department of Electrical Engineering and Computer Science ColumbiaMO United States University of Missouri Biological Engineering ColumbiaMO United States University of Missouri Plant Science and Technology Extension ColumbiaMO United States University of Missouri Institute for Data Science and Informatics Columbia United States University of Missouri Institute for Data Science and Informatics Electrical Eng. and Computer Science Dept. ColumbiaMO United States

ISBN: (纸本)9798350359527

Poultry productions have shifted towards larger farms and often cluster in certain regions. However, many of the smaller farms with a considerable amount of production are not considered concentrated animal feeding operation under regulation in many regions, and therefore are not required to obtain a permit. As a result, information of these operations is not being recorded and monitored, contributing to an increased risk for biosecurity issues. The development in computer vision and availability of high-resolution remote sensing imagery allows the possibility for automatic localization and operation estimation of the unregistered farms, filling in the missing information needed for biosecurity risk management. The farm information extracted from images can be incorporated with other available geospatial data, such as road networks, watersheds, etc., to build a comprehensive knowledge base for actionable plans. In the current study, we present a framework for automated localization, analysis, and visualization of poultry farms from remote sensing images. A robust machine learning method was developed to computationally link the visual features from remote sensing imagery to Missouri Department of Natural Resources Concentrated Animal Feeding Operation data, to identify the animal farm type, location, and potential capacity in the region. The developed process can be performed automatically on images of different timestamps to analyze temporal and spatial development of poultry production facilities of a region. This research contributes to the effective management and regulation of poultry and potentially other animal farms, providing insights into farm characteristics and capacities while facilitating informed decision-making. © 2023 IEEE.

关键词： Object detection

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Decentralized Network for Cloud Storage 2

Decentralized Network for Cloud Storage

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2nd Edition of IEEE Delhi Section Owned Conference, DELCON 2023

作者： Chirag, N. Kshitij, K. Gupta, Gaurav Sir M Visvesvaraya Institute of Technology Dept. of Information Science Engineering Bangalore India Shoolini University Yogananda School of Artificial Intelligence Computer and Data Sciences H.P Solan India

ISBN: (纸本)9798350322064

The amount of data loss on corporate servers in cloud environments has increased significantly. In the cloud, there are many security compromises and account hijackings, resulting in severe vulnerabilities for the service. Cloud computing is one of the most popular on-demand services. Over the past decade, cloud computing has been incorporated into various industries, including health care and the military. Security has, however, been a major concern with cloud computing. There have been many approaches and security implications implemented. However, when it comes to the security and privacy of data, there are loopholes. By decentralizing the cloud, which is currently a centralized entity, the authors attempt to improve cloud security. It is possible to solve these problems by deploying a decentralized cloud. Client software encrypts, fragments data, and then a client engine distributes the data among a number of hosting nodes (servers) spread throughout the world. As a result, the user has complete ownership of his data. In their study, the authors found that decentralizing cloud solved most of the problems of centralized cloud, since it increases the security of data by fragmenting and encrypting fragments. © 2023 IEEE.

关键词： Cloud security

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Sarcasm Prediction Using Different Learning Approaches on User Behavior and Contextual Evaluation 5

Sarcasm Prediction Using Different Learning Approaches on Us...

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5th International Conference on Information Management and Machine Intelligence, ICIMMI 2023

作者： Ahire, Lomesh Kautik Babar, S.D. Mahalle, P.N. Dept of Computer Engineering Smt. Kashibai Navale College of Engineering Savitribai Phule Pune University Pune India Dept of Computer Engineering Savitribai Phule Pune University Pune India Dept. of Artificial Intelligence and Data Science Vishwakarma Institute of Information Technology Pune India

ISBN: (纸本)9798400709418

A large number of people have been using social media platforms extensively to communicate their thoughts and feelings in the recent era of social networking. Both the user base and data volume on social networks are growing quickly every day. Any time an event or activity occurs nearby, nearby individuals express their thoughts and reactions on social media. When a new product is introduced, users on social media platforms also comment on it. Some people express their views or feelings using informal or complex language which makes it difficult to understand for another user. It is challenging to ascertain the true thoughts because different people express their opinions in complex ways. In this study, the various factors that affect these feelings are briefly discussed. In order to identify sarcasm on Twitter, a generic technique is also necessary in addition to the tweet's content. The proposed approach uses contents of tweet in association with important aspects like user behavior and context of tweet. By users' behavior we can identify its influence on other users and context is required to identify user behavior while detecting sarcasm. Proposed approach uses user behavior pattern and personality features along with contextual data. This all information and the already known sarcasm prediction mechanism will help us to set up the generic approach to detect sarcasm on Twitter. © 2023 ACM.

关键词： Behavioral research

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SELFORMER: MOLECULAR REPRESENTATION LEARNING VIA SELFIES LANGUAGE MODELS

arXiv

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arXiv 2023年

作者： Yüksel, Atakan Ulusoy, Erva Ünlü, Atabey Dogan, Tunca Biological Data Science Lab Dept. of Computer Engineering Hacettepe University Turkey

Automated computational analysis of the vast chemical space is critical for numerous fields of research such as drug discovery and material science. Representation learning techniques have recently been employed with the primary objective of generating compact and informative numerical expressions of complex data, for efficient usage in subsequent prediction tasks. One approach to efficiently learn molecular representations is processing string-based notations of chemicals via natural language processing (NLP) algorithms. Majority of the methods proposed so far utilize SMILES notations for this purpose, which is the most extensively used string-based encoding for molecules. However, SMILES is associated with numerous problems related to validity and robustness, which may prevent the model from effectively uncovering the knowledge hidden in the data. In this study, we propose SELFormer, a transformer architecture-based chemical language model that utilizes a 100% valid, compact and expressive notation, SELFIES, as input, in order to learn flexible and high-quality molecular representations. SELFormer is pre-trained on two million drug-like compounds and fine-tuned for diverse molecular property prediction tasks. Our performance evaluation has revealed that, SELFormer outperforms all competing methods, including graph learning-based approaches and SMILES-based chemical language models, on predicting aqueous solubility of molecules and adverse drug reactions, while producing comparable results for the remaining tasks. We also visualized molecular representations learned by SELFormer via dimensionality reduction, which indicated that even the pretrained model can discriminate molecules with differing structural properties. We shared SELFormer as a programmatic tool, together with its datasets and pre-trained models. Overall, our research demonstrates the benefit of using the SELFIES notations in the context of chemical language modeling and opens up new possibilities fo

关键词： Molecules

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CAPLC: Cellular Automata-Powered Lightweight Cipher-Unveiling Secure Communication in Resource-Constrained Environments Using Cellular Automata and Henon Map

CAPLC: Cellular Automata-Powered Lightweight Cipher-Unveilin...

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Next Generation Communication & Information Processing (INCIP), International Conference on

作者： Biswarup Yogi Satyabrata Roy Umashankar Rawat Shweta Redkar Utsav Upadhyay Dept. of Computational Sciences Brainware University Kolkata West Bengal India Dept. of Computer Science and Engineering Manipal University Jaipur Rajasthan India Dept. of Data Science and Engineering Manipal University Jaipur Rajasthan India Dept. of Computer Science & Engineering Sir Padampat Singhania University Udaipur Rajasthan India

ISBN: (数字)9798331528140

ISBN: (纸本)9798331528157

In today's interconnected world, ensuring secure communication in resource-constrained contexts is still challenging. This study offers CAPLC, a novel technique for lightweight encryption that uses Cellular Automata (CA) and Henon maps. Its goal is to improve security in such situations. The method uses 1D Cellular Automata for initial data transformation and incorporates the Henon map to improve encryption strength. We used conventional metrics to evaluate CAPLC's effectiveness on image encryption, and the findings were promising: NPCR of 99.6154%, UACI of 33.88235, and PSNR of 9.1924. These numbers show the method's capacity to dramatically change image while maintaining quality and anonymity. The implications of CAPLC go beyond image encryption, providing a scalable solution for secure communication in circumstances with limited computational resources. Future studies ought to explore CAPLC's real-time application and performance across various kinds of datasets, with a focus on the potential of merging CA with Henon maps to create efficient encryption methods for improving secure communications technology.

关键词： Measurement Merging Automata Medical services Information processing Parallel processing Real-time systems Encryption Resilience Principal component analysis

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