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DNN-STACK: a stacking technique based on deep neural network for detecting copy-move forgery

Neural Computing and Applications 2025年第6期37卷 4989-5004页

作者： Krishnalal, G. Jagathy Raj, V.P. Madhu, G. Arun, K.S. School of Engineering Cochin University of Science and Technology Kerala Cochin682022 India School of Management Studies Cochin University of Science and Technology Kerala Cochin682022 India Department of Computer Applications Cochin University of Science and Technology Kerala Cochin682022 India

In recent years, detecting image forgery has become an important topic because of the availability of efficient and sophisticated image-editing software. Copy-move forgery, which involves copying a section of an image and pasting it to a different location inside the same image, is one of the most popular tampering techniques that alter the identity of a given image. This paper presents DNN-STACK, a deep neural network-based stacking scheme to identify copy-move forgery by effectively combining the predictions yielded by different base-level models. To extract hierarchical representations from the given images and generate base-level predictions, the proposed approach uses five distinct but complementary deep learning-based base-level models. After that, a consensus prediction is generated using the proposed DNN-STACK model, which can infer the nonlinear relationship between the base-level predictions. Experimental evaluation on three publicly available datasets, such as MICC-F600, MICC-F2000, and FAU, reveals the fact that the proposed DNN-STACK model outperforms the existing forgery detection techniques, significantly improving detection accuracy across varying image resolutions and attack types, including rotation, scaling, noise, and compression levels. © The Author(s), under exclusive licence to Springer-Verlag London Ltd., part of Springer Nature 2024.

关键词： Deep neural networks

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SynchroLearn: Master Deadlock and Synchronization Through Interactive Learning 15

SynchroLearn: Master Deadlock and Synchronization Through In...

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

作者： Bhavya, G. Bhanot, Shreya Srinidhi, M. Sidhu, Samreen Krishnan, Sajitha Amrita Vishwa Vidyapeetham Amrita School of Computing Department of Computer Science and Engineering Bengaluru India Amrita Vishwa Vidyapeetham Amrita School of Engineering Department of Computer Science & Engineering Bengaluru India

ISBN: (纸本)9798350370249

Today, educational tools have become necessary for learners to learn well and improve their knowledge. These tools complement knowledge as they can make it more vivid and add some simulation or game into the learning process. It is self-learning since the students can learn from any place and it also enables the students to go further on content that may interest them, and which may help them in the understanding of the concept. This paper takes the idea of developing SynchroLearn, which is an educational visualization tool developed with the help of HTML, CSS, and JavaScript that enables the teaching of deadlock handling techniques effectively for both the developers and the students. To expound on techniques such as the Producer-Consumer problem, Peterson's method, Test-and-Set Lock, and the Dining Philosophers problem, an online tool known as SynchroLearn helps in providing the learner an actual time visualization. These strategies present several ways of handling resources, and with that, the management of their utilization process, so that the processes could be controlled in terms of what resources they require, which in turn, helps to solve and prevent such cases of deadlocks. This approach helps to learn complex relationships as well as to understand the interactions between the different processes, identify the deadlock situations, and use the essential methods for their resolution. They bring out the idea linking theoretical proficiencies and practical application by demonstrating them as simple-to-follow graphics which are therefore easier to implement. © 2024 IEEE.

关键词： Students

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Computing Random r-Orthogonal Latin Squares 16th

Computing Random r-Orthogonal Latin Squares

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16th Annual International Conference on Combinatorial Optimization and Applications, COCOA 2023

作者： Bereg, Sergey Department of Computer Science Erik Jonsson School of Engineering and Computer Science University of Texas at Dallas Richardson United States

ISBN: (纸本)9783031496134

Two Latin squares of order n are r-orthogonal if, when superimposed, there are exactly r distinct ordered pairs. The spectrum of all values of r for Latin squares of order n is known. A Latin square A of order n is r-self-orthogonal if A and its transpose are r-orthogonal. The spectrum of all values of r is known for all orders n≠ 14. We develop randomized algorithms for computing pairs of r-orthogonal Latin squares of order n and algorithms for computing r-self-orthogonal Latin squares of order n. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2024.

关键词： Latin rectangles Latin squares r-orthogonal Latin squares

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Efficient and Secure IoT Based Smart Home Automation Using Multi-Model Learning and Blockchain Technology

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computer Modeling in engineering & sciences 2024年第6期139卷 3387-3415页

作者： Nazik Alturki Raed Alharthi Muhammad Umer Oumaima Saidani Amal Alshardan Reemah M.Alhebshi Shtwai Alsubai Ali Kashif Bashir Department of Information Systems College of Computer and Information SciencesPrincess Nourah bint Abdulrahman UniversityRiyadhSaudi Arabia Department of Computer Science and Engineering University of Hafr Al-BatinHafar Al-BatinSaudi Arabia Department of Computer Science&Information Technology The Islamia University of BahawalpurBahawalpurPakistan Department of Computer Science Faculty of Computing and Information TechnologyKing Abdulaziz UniversityJeddahSaudi Arabia Department of Computer Science College of Computer Engineering and SciencesPrince Sattam bin Abdulaziz UniversityAl-KharjSaudi Arabia Department of Computing and Mathematics Manchester Metropolitan UniversityManchesterUK Woxsen School of Business Woxsen UniversityHyderabadIndia Department of Computer Science and Mathematics Lebanese American UniversityBeirutLebanon

The concept of smart houses has grown in prominence in recent *** challenges linked to smart homes are identification theft,data safety,automated decision-making for IoT-based devices,and the security of the device *** home automation systems try to address these issues but there is still an urgent need for a dependable and secure smart home solution that includes automatic decision-making systems and methodical *** paper proposes a smart home system based on ensemble learning of random forest(RF)and convolutional neural networks(CNN)for programmed decision-making tasks,such as categorizing gadgets as“OFF”or“ON”based on their normal routine in *** have integrated emerging blockchain technology to provide secure,decentralized,and trustworthy authentication and recognition of IoT *** system consists of a 5V relay circuit,various sensors,and a Raspberry Pi server and database for managing *** have also developed an Android app that communicates with the server interface through an HTTP web interface and an Apache *** feasibility and efficacy of the proposed smart home automation system have been evaluated in both laboratory and real-time *** is essential to use inexpensive,scalable,and readily available components and technologies in smart home automation ***,we must incorporate a comprehensive security and privacy-centric design that emphasizes risk assessments,such as cyberattacks,hardware security,and other cyber *** trial results support the proposed system and demonstrate its potential for use in everyday life.

关键词： Blockchain Internet of Things(IoT) smart home automation cybersecurity

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Harnessing LLMs for Medical Insights:NER Extraction from Summarized Medical Text 15

Harnessing LLMs for Medical Insights:NER Extraction from Sum...

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

作者： Sasikala, D. Sudarshan, R. Sivasathya, S. Amrita Vishwa Vidyapeetham Amrita School of Computing Department of Computer Science and Engineering Chennai India Pondicherry University School of Engineering & Technology Department of Computer Science Pondicherry India

ISBN: (纸本)9798350370249

The rapid growth of medical text presents a significant challenge for healthcare professionals to stay updated with current research and clinical guidelines. Effective text summarization is essential to facilitate access to knowledge, improve clinical decision-making, and ultimately enhance patient outcomes. This study aims to fine-tune and assess a medical text summarization system utilizing large language models (LLMs). The objective is to produce concise, coherent summaries of medical literature, prioritizing clinical relevance. Additionally, automated named entity recognition (NER) is employed to extract vital entities from the summaries generated by GPT3, T5, PEGASUS LLMs. These LLMs are trained on benchmark PubMed dataset which are evaluated under standard metrics such as ROUGE (1, 2, L) and BERTScore metrics. The model pipeline demonstrates summarization and NER performance over precision of 6 5% on medical text data. The resulting entity-annotated summaries provide condensed representations, enabling efficient information extraction and knowledge discovery. This automated approach accelerates biomedical research by large-scale medical text analysis. © 2024 IEEE.

关键词： GPT3 large language model medical text summarization Named Entity Recognition PEGASUS T5

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ConU: Conformal Uncertainty in Large Language Models with Correctness Coverage Guarantees

ConU: Conformal Uncertainty in Large Language Models with Co...

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2024 Findings of the Association for Computational Linguistics, EMNLP 2024

作者： Wang, Zhiyuan Duan, Jinhao Cheng, Lu Zhang, Yue Wang, Qingni Shi, Xiaoshuang Xu, Kaidi Shen, Hengtao Zhu, Xiaofeng School of Computer Science and Engineering University of Electronic Science and Technology of China China Department of Computer Science Drexel University United States Department of Computer Science University of Illinois Chicago United States

ISBN: (纸本)9798891761681

Uncertainty quantification (UQ) in natural language generation (NLG) tasks remains an open challenge, exacerbated by the closed-source nature of the latest large language models (LLMs). This study investigates applying conformal prediction (CP), which can transform any heuristic uncertainty notion into rigorous prediction sets, to black-box LLMs in open-ended NLG tasks. We introduce a novel uncertainty measure based on self-consistency theory, and then develop a conformal uncertainty criterion by integrating the uncertainty condition aligned with correctness into the CP algorithm. Empirical evaluations indicate that our uncertainty measure outperforms prior state-of-the-art methods. Furthermore, we achieve strict control over the correctness coverage rate utilizing 7 popular LLMs on 4 free-form NLG datasets, spanning general-purpose and medical scenarios. Additionally, the calibrated prediction sets with small size further highlights the efficiency of our method in providing trustworthy guarantees for practical open-ended NLG applications. © 2024 Association for Computational Linguistics.

关键词： Computational linguistics

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Identification and Classification of Medicinal Plants using Machine Learning Algorithms 3

Identification and Classification of Medicinal Plants using ...

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3rd International Conference on Electronics and Renewable Systems, ICEARS 2025

作者： Bhargavi, G. Jayaraman, Venkatesh Jabez, J. Deepa, R. Surendran, R. Srm Institute of Science and Technology Department of Computing Technologies Chennai India Chennai Institute of Technology Department of Computer Science and Engineering Chennai600069 India Sathyabama Institute of Science and Technology School of Computing Department of Computer Science and Engineering Chennai India Srm Institute of Science and Technology School of Computing Department of Computing Technologies Kattankulathur Tamilnadu Chennai603203 India Saveetha Institute of Medical and Technical Sciences Saveetha School of Engineering Department of Computer Science and Engineering Chennai India

ISBN: (纸本)9798331509675

The increaing significance of plant life and botanical expertise extends beyond mere visual appreciation. With the growing interest in sustainable living and alternative remedies, there is a pressing demand for easily accessible and credible information on plant categorization, particularly focusing on medicinal plants. This innovative project synergizes machine learning capabilities, botanical proficiency, and user-friendly technology to give users the insights required to harness the benefits of medicinal plants responsibly and sustainably. The platform facilitates precise categorization and a comprehensive understanding of the therapeutic attributes of plants. © 2025 IEEE.

关键词： Medicinal chemistry

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Edge-Enabled Metaverse:The Convergence of Metaverseand Mobile Edge Computing

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Tsinghua science and Technology 2024年第3期29卷 795-805页

作者： Nyothiri Aung Sahraoui Dhelim Liming Chen Huansheng Ning Luigi Atzori Tahar Kechadi Information Engineering College Xinjiang Institute of EngineeringUrumqi 830023Chinaand also with School of Computer ScienceUniversity College DublinDublinD04 V1W8Ireland School of Computing Ulster UniversityBelfastBT151EDUK School of Computer and Communication Engineering University of Science and Technology BeijingBeijing 100083China Department of Electrical and Electronic Engineering University of CagliariSardinia 09124Italy School of Computer Science University College DublinDublinD04 V1W8Ireland

Metaverse is a virtual environment where users are represented by their avatars to navigate a virtual world having strong links with its physical *** state-of-the-art Metaverse architectures rely on a cloud-based approach for avatar physics emulation and graphics rendering *** current centralized architecture of such systems is unfavorable as it suffers from several drawbacks caused by the long latency of cloud access,such as low-quality *** this end,we propose a Fog-Edge hybrid computing architecture for Metaverse applications that leverage an edge-enabled distributed computing *** applications leverage edge devices’computing power to perform the required computations for heavy tasks,such as collision detection in the virtual universe and high-computational 3D physics in virtual *** computational costs of a Metaverse entity,such as collision detection or physics emulation,are performed at the device of the associated physical *** validate the effectiveness of the proposed architecture,we simulate a distributed social Metaverse *** simulation results show that the proposed architecture can reduce the latency by 50%when compared with cloud-based Metaverse applications.

关键词： industrial Metaverse edge computing Blockchain virtual environment task offloading fog computing

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Label correlation preserving visual-semantic joint embedding for multi-label zero-shot learning

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Multimedia Tools and Applications 2024年 1-18页

作者： Ma, Zhongchen Ma, Runze Wang, Guangchen Mao, Qirong Dong, Ming School of Computer Science and Communications Engineering Jiangsu University Xuefu Road Jiangsu Zhenjiang212000 China Department of Computer Science Wayne State University Woodward Ave DetroitMI48202 United States

Multi-label zero-shot learning is a branch of the classification problem more in line with practical applications, because a real image does not usually contain only one category, and the labels to be classified are likely to never appear in the training set. The key to solving this problem is to effectively transfer the knowledge learned in seen classes to unseen classes. To this end, the commonly used approach is to embed visual features and semantic attributes into a joint visual-semantic space and align them by a transfer matrix W. Previous works tacitly assuming that the relation between the visual embeddings and the semantic embeddings of the seen classes can be generalized to the unseen classes. However, in this paper, we argue that this default is only an idealized hypothesis. In practice, the knowledge generalization of seen classes to unseen classes may be weakened by the disruption of semantic label correlation. This study delves into the impact of semantic correlation on the generalization capabilities of models in multi-label zero-shot learning and verifys that through preserving the semantic correlation of seen classes in the visual-semantic joint space, the model can generalize better to the unseen classes. Subsequently, we prove that by keeping the transfer matrix W orthogonal, we can preserve the semantic correlation in the joint space. Then, we explore a method to use self-orthogonality regularization to keep the W orthogonal. Extensive experiments show that our proposed semantic correlation preserving method improves the mAP of the state-of-the-art model BiAM by 3.4% on NUS-WIDE and 1.6% on the large-scaled Open Images (V4) datasets, respectively. © The Author(s), under exclusive licence to Springer science+Business Media, LLC, part of Springer Nature 2024.

关键词： Semantics

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A GMM-CGABC Based Energy Replenishment Scheme for On-Demand Wireless Rechargeable Sensor Networks 1

A GMM-CGABC Based Energy Replenishment Scheme for On-Demand ...

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1st International Symposium on Parallel Computing and Distributed Systems, PCDS 2024

作者： Mouli, S.Sudhamsu Veeraiah, T. Mahesh, K. Singh, M.P. Ecole Centrale School of Engineering Mahindra University Department of Computer Science and Engineering Hyderabad India National Institute of Technology Department of Computer Science and Engineering Patna India

ISBN: (纸本)9798350349658

Wireless Sensor Networks (WSNs) are constrained by the limited energy capacity of Sensor Nodes (SNs), which hinders their perpetual operation. The advent of Wireless Energy Transfer (WET) technology has emerged as a promising solution to this challenge, enabling the creation of Wireless Rechargeable Sensor Networks (WRSNs). Recent research has shown growing interest in integrating Wireless Mobile Chargers (WMCs) into WRSNs to replenish the energy reserves of critical SNs through WET. A key aspect of WET involves using one or more WMCs to recharge SNs, with optimizing the charging schedule (i.e., the sequence in which SNs are charged) crucial in on-demand WRSNs. In addition, spatiotemporal constraints imposed by the requesting SNs must be considered when devising the WMC's charging schedule. To address these challenges, this work proposes the integration of a Gaussian Mixture Model with a Chaotic Gaussian Artificial Bee Colony (GMM-CGABC) to develop an efficient recharge schedule that revitalizes SNs. This model is designed to optimize SN deployment, maximize network coverage, minimize energy consumption, and extend network longevity. The proposed method determines the optimal charging path for WMCs within each cluster to minimize Average Charging Latency (ACL) while incorporating a novel strategy for fitness function evaluation. Extensive simulations demonstrate that the proposed system outperforms state-of-the-art algorithms, including First Come, First Served (FCFS), Nearest Job Next with Preemption (NJNP), and the Gravitational Search Algorithm (GSA), in terms of charging latency. © 2024 IEEE.

关键词： Inductive power transmission

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