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Advancement in Image Plagiarism Detection using Feature Extraction and Deep Learning Approaches 5

Advancement in Image Plagiarism Detection using Feature Extr...

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5th International Conference on Image Processing and Capsule Networks, ICIPCN 2024

作者： Bhandari, Sonam Chaudhary, Sumit Malhotra, Sonal Joshi, Kapil Kirola, Madhu Rajkumar Uttaranchal Institute Of Technology Uttaranchal University Dept. of Computer Science and Engineering Dehradun India Graphic Era Hill University Graphic Era Deemed to be University Dept. of Computer Science and Engineering Dehradun India Dev Bhoomi Uttarakhand University Dept. of Computer Science and Engineering Dehradun India

ISBN: (纸本)9798350367171

With the widespread use of digital media, amount of visual content on the internet and the incidence of image plagiarism has also increased. In various domains including academia, journalism and creative industries, unauthorized use or reproduction of images without proper attribution has become significant concern. Scaling, rotation, cropping, filtering due to these diverse range of transformations detecting image plagiarism is a challenging task now. This study investigates the use of deep learning approach to image plagiarism detection that combines methods for similarity measurement, clustering and feature extraction. A comprehensive solution is being proposed by combining Scale Invariant Feature Transform (SIFT) for extracting features, Bag of Visual Words (BoVW) model and Convolutional Neural Networks (CNNs) for image analysis. The effectiveness of the proposed approach through extensive experiments is demonstrated, outperforming the baseline methods and showcasing superior performance metrics. © 2024 IEEE.

关键词： Contrastive Learning

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Ensemble Model for Prostate Cancer Detection Using MRI Images 16

Ensemble Model for Prostate Cancer Detection Using MRI Image...

引用

16th International Conference on Developments in eSystems Engineering, DeSE 2023

作者： Kadhim, Omar Jawad Nafea, Ahmed Adil Aliesawi, Salah A. S. Al-Ani, Mohammed M Modern Uuniversity for Business & Science Dept. Computer Science Beirut Lebanon University of Anbar College of Computer Science and Information Technology Dept. Artificail Inteligence Ramadi Iraq University of Anbar College of Computer Science and Information Technology Dept. Computer Science Ramadi Iraq Selangor Bangi Malaysia

ISBN: (纸本)9798350381344

Prostate cancer is a prevalent form of malignancy impacting a substantial male population and ranks among the primary contributors to cancer-related fatalities globally. The utilization of magnetic resonance imaging (MRI) scans for prostate cancer detection has presented significant difficulties. This proposed, explores the use of machine learning algorithms for prostate cancer detection using MRI scans and addresses the challenge mentioned above. The proposed ensemble models employ a combination of Machine Learning (ML) Algorithms, including Support Vector Machine (SVM), AdaBoost, Decision Tree (DT), and Random Forest (RF) to improve accuracy detection. The results of our rigorous evaluation process revealed that the ensemble model achieved an outstanding accuracy rate of 96% in classifying prostate cancer into Significant and Non-Significant. By comparing our results to existing studies, we have demonstrated that the ensemble model-based method is on par with or even surpasses various techniques used in previous research efforts. © 2023 IEEE.

关键词： Magnetic resonance imaging

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WormGPT: A Large Language Model Chatbot for Criminals 24

WormGPT: A Large Language Model Chatbot for Criminals

引用

24th International Arab Conference on Information technology, ACIT 2023

作者： Mohamed Firdhous, Mohamed Fazil Elbreiki, Walid Abdullahi, Ibrahim Sudantha, B.H. Budiarto, Rahmat University of Moratuwa Dept. of Information Technology Moratuwa10400 Sri Lanka College of Computer Technology Dept. of Computer Science Benghazi Libya Ibrahim Badamasi Babangida University Dept. of Computer Science Niger State Lapai Nigeria College of Computer Science and Information Technology AI-Baha University Al Baha Saudi Arabia

ISBN: (纸本)9798350384307

In recent years, AI has made many advances in multiple fronts. One such recent advance is the development of Large Language Models(LLMs), a deep learning model trained with a large set of data on billions of parameters. Many Generative Pretrained Transformer (GPT) models have been developed on these LLMs. ChatGPT has become the most popular GPT used by many people. Recently, a blackhat GPT named WormGPT has been launched for cybercriminals. WormGPT is capable of launching many types of attacks resulting in irreparable damage. Hence, Users are required to be familiar with these tools, if they are to protect themselves. This article is possibly the very first attempt to initiate a discussion on this topic within the formal research circles. This article starts with an introduction to LLMs and GPTs and then presents the details of WormGPT, its capabilities and the damage it can cause. Finally, a set of safeguards has been presented that can help to protect users from possible attacks. © 2023 IEEE.

关键词： Artificial Intelligence ChatGPT GPT Large Language Models LLM WormGPT

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Brain-computer Interfaces: A Key to Neural Communication's Limitless Possibilities 1

Brain-Computer Interfaces: A Key to Neural Communication's L...

引用

1st International Conference on Cognitive, Green and Ubiquitous Computing, IC-CGU 2024

作者： Saxena, Shilpi Ranjan, Mritunjay Kr. Sattar, Arif Md. School of Computer Science and Engineering Sandip University Nashik India A.M. College Dept. of Computer Science & Information Technology Gaya India

ISBN: (纸本)9798350361780

Brain-computer Interfaces (BCIs) have revolutionized human-machine interaction by establishing a direct communication pathway between the brain and an external machine. These systems rely on neural signals, acquired invasively through implanted electrodes or non-invasively using scalp sensors. Signal processing algorithms extract meaningful information from these signals, such as motor intentions or cognitive states. BCIs have applications in various domains, including healthcare, neurorehabilitation, gaming, virtual reality, and augmented reality. However, BCI development faces challenges such as improving signal acquisition and decoding techniques, enhancing reliability and robustness, and ensuring user safety and privacy. The translation of BCI technology from the lab to real-world applications requires addressing ethical, legal, and societal considerations. Standardized evaluation metrics and protocols are needed to compare and validate different BCI approaches effectively. In conclusion, BCIs have the potential to revolutionize human-machine interaction across various domains, but challenges remain in technology refinement, user acceptance, and ethical considerations. Future research directions include enhancing spatial and temporal resolution of neural signals, exploring novel electrode materials, and investigating long-term implantation methods. © 2024 IEEE.

关键词： Augmented reality

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Performance Evaluation of Deep Learning Models for Leaf Disease Detection: A Comparative Study 4

Performance Evaluation of Deep Learning Models for Leaf Dise...

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4th International Conference on Computing, Mathematics and Engineering Technologies, iCoMET 2023

作者： Akbar, Wajahat Soomro, Abdullah Ullah, Mohib Inam Ul Haq, Muhammad Ullah Khan, Sana Ali Shah, Tahir Khushal Khan Khattak University Dept. of Computer Science & Bi Karak Pakistan The Islamia University of Bahawalpur Dept. of Computer Science Bahawalpur Pakistan Kohat University of Science and Technology Dept. of Computer Science Kohat Pakistan Shaanxi Normal University Dept. of Computer Science Shaanxi China

ISBN: (纸本)9798350335316

Early detection of plant diseases is crucial before plant growth is affected. Plant diseases have been detected and classified using a variety of machine learning (ML) models in the past. Deep Learning (DL) appears to have great potential in terms of increased accuracy;however, in agricultural applications of Convolutional Neural Networks (CNN) has widely been utilised by researchers. CNNs are so effective at identifying plant species, managing yields, detecting weeds, managing soil, and water, counting fruits, detecting diseases and pests, and evaluating plant nutrient status. A farmer can diagnose plant diseases quickly and accurately with an automated disease detection system. To speed up crop diagnosis, plant leaf disease detection systems must be automated. In this paper, we evaluated twelve different models on a new plant diseases dataset and demonstrated that the most accurate model was Densenet169. In training and validation, the accuracy was 97.2% and 97.8%, respectively. © 2023 IEEE.

关键词： Convolutional neural networks

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Designing Blockchain Based Consensus Mechanism for Smart Healthcare IoT

Designing Blockchain Based Consensus Mechanism for Smart Hea...

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2023 IEEE International Conference on Intelligent and Innovative Technologies in Computing, Electrical and Electronics, ICIITCEE 2023

作者： Kumari, Trishla Kumar, Rakesh Dwivedi, Rajendra Kumar Madan Mohan Malviya University of Technology Dept. of Computer Science and Engineering Gorakhpur India Madan Mohan Malviya University of Technology Dept. of Information Technology and Computer Application Gorakhpur India

ISBN: (纸本)9781665462631

In today's world, healthcare system is improving day by day by using smart IoT based system. Blockchain based system is totally a decentralized system due to which attackers are unable to access the confidential records easily. Blockchain play an important role in controlling and management of sensitive information in healthcare system and insurance sector. Consensus mechanisms used in blockchain technology to validate whichever block is going to be added next in the blockchain network. In this paper we proposed a hybrid based system which solves trust issues arises in the blockchain. Hybrid consensus mechanism provides reputation according to the activities of blocks in a blockchain network. Four layer architecture is designed in Hybrid model. Five algorithms are used to create, validate, fork handling, reward and punishment scheme and Markle tree is constructed respectively. This model is implemented on Ethereum based platform which shows that HCM model is better than a PoXR model as it consume less memory (50 percent) and consume less CPU(below 2 percent). © 2023 IEEE.

关键词： Blockchain

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Cybersecurity: Threats, Requirements, Deployments, Techniques, System Architecture, and Future Research

Cybersecurity: Threats, Requirements, Deployments, Technique...

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2024 International Conference on Smart Devices, ICSD 2024

作者： Yadav, Ajay Karnatak, Vijay Mishra, Amit Kumar Verma, Shalini Aswal, Pallaw Singh Tripathi, Neha University Institute of Computing Chandigarh University Punjab Mohali India THDC Institute of Hydropower Engineering and Technology Dept. of Computer Science & Engineering Tehri India Graphic Era Hill University Dept. of Computer Science & Engineering Dehradun India Doon Group Of Institution Dept. of Computer Science & Engineering Rishikesh India THDC Institute of Hydropower Engineering and Technology Computer Science and Engineering Tehri India Department of Computer Science & Engineering Dehradun India

ISBN: (纸本)9798350394412

Cybersecurity safeguards computer systems, net- works, and digital information from a wide variety of cyber-attacks. Its major purpose is to protect the privacy of users' data and resources while maintaining their integrity and making them accessible in the digital world. Cybersecurity is an area that receives investment from businesses, individuals, and governments all around the world in order to secure sensitive information, key infrastructure, and digital assets. Protection of Sensitive Information Both individuals and businesses store huge amounts of sensitive information digitally, including personal data, financial records, intellectual property, and trade secrets. This information must be protected in order to prevent unauthorized access or disclosure. The protection provided by cybersecurity prevents this information from being stolen, exposed, or accessed without authorization. Cyberattacks can result in severe financial losses, including theft of funds, fraud, and the expenditures associated with recovering from a cyber incident. It is possible to prevent these types of losses by taking preventative measures. Implementing these strategies helps prevent losses of this nature. In this paper, we propose a system architecture of a generic secure system, which can be deployed for various applications, i.e., smart healthcare, Intelligent Transport Systems (ITS), smart city, etc. We discuss the threats and attacks in cyberspace. Then, we explain the requirements of cybersecurity. Further, we discuss the devastation in the absence of cyber security. We also explain the cybersecurity deployments and techniques under cybersecurity. Then, in the end, some future research directions for cybersecurity are provided. © 2024 IEEE.

关键词： Cyber attacks

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A Self-Supervised Semantic Segmentation Method for Identifying Barrett' s Esophagus in Endoscopic Images 24

A Self-Supervised Semantic Segmentation Method for Identifyi...

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6th Asia Pacific Information technology Conference, APIT 2024

作者： Lin, Guo-Shiang Chang, Ku-Yaw Hsu, Wen-Teng Yang, Sheng-Chih Yan, Shen-Lei Dept. of Artificial Intelligence and Computer Engineering National Chin-Yi University of Technology Taiwan Dept. of Computer Science and Information Engineering National Chin-Yi University of Technology Taiwan Dept. of Internal Medicine Chang Bing Show Chwan Memorial Hospital Taiwan

ISBN: (纸本)9798400716218

Barrett's esophagus is considered a precancerous condition that may lead to esophageal cancer. The condition is usually diagnosed through an endoscopy with biopsy. This careful imaging examination is quite labor-intensive and usually lacks of diagnostic consistency. In this paper, a computer-aided diagnosis (CAD) method was proposed to assist pathologist in Barrett's esophagus diagnosis from endoscopic images. The proposed semantic segmentation was built based on the U-Net architecture, which features capturing detailed spatial information and context to produce accurate pixel-wise predictions. An autoencoder, which is quite capable of learning a compact and meaningful representation of the input data, is incorporated to achieve self-supervised learning. A two-stage pretraining for the encoder and the decoder is adopted for better performance and reduced data requirements. The proposed method can extract features and patterns from unlabeled data without requiring human annotation or labels, which is very suitable in many biomedical image analyses. The experimental results show that the segmentation accuracy with mean pixel accuracy, mean dice coefficient, and mIoU reaches 98.20%, 87.96%, and 83.18% respectively, indicating that the proposed method has a good performance on the identification of Barrett's esophagus in endoscopic images. © 2024 Owner/Author.

关键词： Semantic Segmentation

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Device Identification using Network Traces in a Smart Home IoT Network 17

Device Identification using Network Traces in a Smart Home I...

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17th IEEE International Conference on Advanced Networks and Telecommunications Systems, ANTS 2023

作者： Brahma, Jwngfu Matam, Rakesh Choudhury, Nikumani Indian Institute of Information Technology Dept. of Computer Science & Engineering Guwahati India Birla Institute of Technology & Science Dept. of Computer Science & Information Systems Hyderabad India

ISBN: (纸本)9798350307672

Device identification is a crucial aspect of securing networks, particularly in the context of the Internet of Things (IoT), where a vast variety of devices are interconnected. Recently, there has been significant research on developing techniques for identifying IoT devices based on their unique characteristics, called as device fingerprints. These techniques use machine learning algorithms, which can effectively learn and classify devices based on their features. However, device identification remains a challenging task due to the diversity of IoT devices and the constant evolution of their characteristics. This paper presents our attempt at device identification by analyzing distinct device characteristics observed during network communication and using different machine learning techniques. © 2023 IEEE.

关键词： Internet of things

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Post Quantum Code-Based Cryptosystems with Dual Inverse Matrix 13

Post Quantum Code-Based Cryptosystems with Dual Inverse Matr...

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13th International Conference on Information technology in Asia, CITA 2023

作者： Makoui, Farshid Haidary Gulliver, T. Aaron Dakhilalian, Mohammad University of Victoria Dept. of Electrical & Computer Engineering VictoriaBC Canada Isfahan University of Technology Dept. of Electrical & Computer Engineering Isfahan Iran

ISBN: (纸本)9798350326208

The Shor algorithm demonstrates the significant risk that quantum attacks pose to the security of widely used cryptographic primitives. However, code-based cryptography has been shown to be resistant to these attacks. To date, no polynomial-time attack exists that can break code-based cryptosystems such as the McEliece cryptosystem. Despite this, these cryptosystems are not employed in practical applications in domains such as online banking, blockchains, and e-commerce platforms. The primary reason is the large sizes of the public and private keys associated with code-based cryptosystems. In this paper, a new code-based cryptosystem is introduced which employs a dual matrix based on the transpose and inverse of the parity check matrix to reduce the key size compared to the McEliece cryptosystem. © 2023 IEEE.

关键词： Public key cryptography

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