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12th IEEE International Conference on Advanced computing, ICoAC 2023

作者： Ezhilmathi, S. Selvakumara Samy, S. Srm Institute of Science and Technology School of Computing Department of Computer Science and Engineering Tamilnadu Kattankulathur603203 India

ISBN: (纸本)9798350318210

Digital currencies have emerged as a new platform for money laundering as the first blockchain platform to support smart contracts, and the number of transaction records for Bitcoin has expanded substantially in recent years. Due to privacy services such as tumblers, which conceal the identities of payers and payees, pseudonymous digital currencies are frequently referred to as anonymous address and untraceable. A large proportion of accounts participating in Bitcoin transactions are engaging in illegal activity. According to the digital ledger, a transaction was transferred from one of many potential payers to one of many possible payees. As a consequence, it is vital to have a dependable system for classifying accounts and keeping track of the transactions linked with each account. We utilized machine learning techniques to apply multi-class classification methods to a Bitcoin transaction record obtained from *** in order to improve the model's accuracy. Experiment results show that ML algorithms like logistic regression, random forests, and multilayer perceptron's with the macro-average F1 score for bitcoin tumblers outperform state-of-the-art approaches for identifying unknown digital currency accounts, with the RF model outperforming LR and MLP by 97% accuracy. © 2023 IEEE.

关键词： Blockchain

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Multi-granularity feature enhancement network for maritime ship detection

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CAAI Transactions on Intelligence Technology 2024年第3期9卷 649-664页

作者： Li Ying Duoqian Miao Zhifei Zhang Hongyun Zhang Witold Pedrycz Department of Computer Science and Technology Tongji UniversityShanghaiChina Project Management Office of China National Scientific Seafloor Observatory Tongji UniversityShanghaiChina Department of Electrical and Computer Engineering AlbertaEdmontonCanada System Research Institute Polish Academy of SciencesWarsawPoland

Due to the characteristics of high resolution and rich texture information,visible light images are widely used for maritime ship ***,these images are suscep-tible to sea fog and ships of different sizes,which can result in missed detections and false alarms,ultimately resulting in lower detection *** address these issues,a novel multi-granularity feature enhancement network,MFENet,which includes a three-way dehazing module(3WDM)and a multi-granularity feature enhancement module(MFEM)is *** 3WDM eliminates sea fog interference by using an image clarity automatic classification algorithm based on three-way decisions and FFA-Net to obtain clear image ***,the MFEM improves the accuracy of detecting ships of different sizes by utilising an improved super-resolution reconstruction con-volutional neural network to enhance the resolution and semantic representation capa-bility of the feature maps from *** results demonstrate that MFENet surpasses the other 15 competing models in terms of the mean Average Pre-cision metric on two benchmark datasets,achieving 96.28%on the McShips dataset and 97.71%on the SeaShips dataset.

关键词： object classification object recognition rough sets rough set theory

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Application of Machine Learning and Deep Learning Techniques for COVID-19 Screening Using Radiological Imaging: A Comprehensive Review

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SN computer science 2023年第1期4卷 65页

作者： Lasker, Asifuzzaman Obaidullah, Sk Md Chakraborty, Chandan Roy, Kaushik Department of Computer Science & Engineering Aliah University Kolkata India Department of Computer Science & Engineering National Institute of Technical Teachers’ Training & Research Kolkata Kolkata India Department of Computer Science West Bengal State University Barasat India

Lung, being one of the most important organs in human body, is often affected by various SARS diseases, among which COVID-19 has been found to be the most fatal disease in recent times. In fact, SARS-COVID 19 led to pandemic that spreads fast among the community causing respiratory problems. Under such situation, radiological imaging-based screening [mostly chest X-ray and computer tomography (CT) modalities] has been performed for rapid screening of the disease as it is a non-invasive approach. Due to scarcity of physician/chest specialist/expert doctors, technology-enabled disease screening techniques have been developed by several researchers with the help of artificial intelligence and machine learning (AI/ML). It can be remarkably observed that the researchers have introduced several AI/ML/DL (deep learning) algorithms for computer-assisted detection of COVID-19 using chest X-ray and CT images. In this paper, a comprehensive review has been conducted to summarize the works related to applications of AI/ML/DL for diagnostic prediction of COVID-19, mainly using X-ray and CT images. Following the PRISMA guidelines, total 265 articles have been selected out of 1715 published articles till the third quarter of 2021. Furthermore, this review summarizes and compares varieties of ML/DL techniques, various datasets, and their results using X-ray and CT imaging. A detailed discussion has been made on the novelty of the published works, along with advantages and limitations. © 2022, The Author(s), under exclusive licence to Springer Nature Singapore Pte Ltd.

关键词： COVID-19 CT Deep learning Machine learning Radiological imaging X-ray

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Random Separating Hyperplane Theorem and Learning Polytopes 51

Random Separating Hyperplane Theorem and Learning Polytopes

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51st International Colloquium on Automata, Languages, and Programming, ICALP 2024

作者： Bhattacharyya, Chiranjib Kannan, Ravindran Kumar, Amit Department of Computer Science and Automation Indian Institute of Science Bangalore India Department of Operations Research Carnegie Mellon University Pittsburgh United States Department of Computer Science and Engineering Indian Institute of Technology Delhi India

ISBN: (纸本)9783959773225

The Separating Hyperplane theorem is a fundamental result in Convex Geometry with myriad applications. The theorem asserts that for a point a not in a closed convex set K, there is a hyperplane with K on one side and a strictly on the other side. Our first result, Random Separating Hyperplane Theorem (RSH), is a strengthening of this for polytopes. RSH asserts that if the distance between a and a polytope K with k vertices and unit diameter in ℜd is at least δ, where δ is a fixed constant in (0,1), then a randomly chosen hyperplane separates a and K with probability at least 1/poly(k) and margin at least Ω(δ/√d). RSH has algorithmic applications in learning polytopes. We consider a fundamental problem, denoted the "Hausdorff problem", of learning a unit diameter polytope K within Hausdorff distance δ, given an optimization oracle for K. Using RSH, we show that with polynomially many random queries to the optimization oracle, K can be approximated within error O(δ). To our knowledge, this is the first provable algorithm for the Hausdorff Problem in this setting. Building on this result, we show that if the vertices of K are well-separated, then an optimization oracle can be used to generate a list of points, each within distance O(δ) of K, with the property that the list contains a point close to each vertex of K. Further, we show how to prune this list to generate a (unique) approximation to each vertex of the polytope. We prove that in many latent variable settings, e.g., topic modeling, LDA, optimization oracles do exist provided we project to a suitable SVD subspace. Thus, our work yields the first efficient algorithm for finding approximations to the vertices of the latent polytope under the well-separatedness assumption. This assumption states that each vertex of K is far from the convex hull of the remaining vertices of K, and is much weaker than other assumptions behind algorithms in the literature which find vertices of the latent polytope. © Chiranjib Bhatt

关键词： Set theory

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Enhancing Dimensional Image Emotion Detection with a Low-resource Dataset via Two-stage Training

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IEEE Transactions on Cognitive and Developmental Systems 2024年第3期17卷 455-464页

作者： Lee, SangEun Kim, Seoyun Lee, Yubeen Yang, Jufeng Park, Eunil Electronics and Telecommunications Research Institute Gyeonggi13488 Korea Republic of Korea Aerospace Industries Ltd. Seoul06151 Korea Republic of Sungkyunkwan University Department of Applied Artificial Intelligence Seoul03063 Korea Republic of Nankai University College of Computer Science Tianjin300350 China Jaume I University Department of Computer Science and Engineering Castellon12006 Spain

Image emotion analysis has gained notable attention owing to the growing importance of computationally modeling human emotions. Most previous studies have focused on classifying the feelings evoked by an image into pre-defined emotion categories. Compared with these categorical approaches which cannot address the ambiguity and complexity of human emotions, recent studies have taken dimensional approaches to address these problems. However, there is still a limitation in that the number of dimensional datasets is significantly smaller for model training, compared with many available categorical datasets. We propose four types of frameworks that use categorical datasets to predict emotion values for a given image in the valence-arousal (VA) space. Specifically, our proposed framework is trained to predict continuous emotion values under the supervision of categorical labels. Extensive experiments demonstrate that our approach showed a positive correlation with the actual VA values of the dimensional dataset. In addition, our framework improves further when a small number of dimensional datasets are available for the fine-tuning process. © 2016 IEEE.

关键词： Multitasking Predictive models Training Social networking (online) Sentiment analysis Psychology Emotion recognition

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Detecting Double Mixed Compressed Images Based on Quaternion Convolutional Neural Network

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Chinese Journal of Electronics 2024年第3期33卷 657-671页

作者： Hao WANG Jinwei WANG Xuelong HU Bingtao HU Qilin YIN Xiangyang LUO Bin MA Jinsheng SUN Department of Automation Nanjing University of Science and Technology Engineering Research Center of Digital Forensics Ministry of Education Department of Computer and Software Nanjing University of Information Science and Technology School of Management Nanjing University of Posts and Telecommunications Department of Computer Science and Engineering Guangdong Province Key Laboratory of Information Security Technology Sun Yat-sen University State Key Laboratory of Mathematical Engineering and Advanced Computing Qilu University of Technology

Detection of color images that have undergone double compression is a critical aspect of digital image *** the existence of various methods capable of detecting double Joint Photographic Experts Group(JPEG) compression,they are unable to address the issue of mixed double compression resulting from the use of different compression *** particular,the implementation of Joint Photographic Experts Group 2000(JPEG2000)as the secondary compression standard can result in a decline or complete loss of performance in existing *** tackle this challenge of JPEG+JPEG2000 compression,a detection method based on quaternion convolutional neural networks(QCNN) is *** QCNN processes the data as a quaternion,transforming the components of a traditional convolutional neural network(CNN) into a quaternion *** relationships between the color channels of the image are preserved,and the utilization of color information is ***,the method includes a feature conversion module that converts the extracted features into quaternion statistical features,thereby amplifying the evidence of double *** results indicate that the proposed QCNN-based method improves,on average,by 27% compared to existing methods in the detection of JPEG+JPEG2000 compression.

关键词： Color image forensics JPEG JPEG2000 Mixed double compression Quaternion convolutional neural network

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Utilising SkyScript for Open-Vocabulary Categorization, Extraction, and Captioning to Enhance Multi-Modal Tasks in Remote Sensing

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Remote Sensing in Earth Systems sciences 2024年第3期7卷 149-158页

作者： Nagaraj, Saranya Sivakumar, Shanmuga Priya Annabel, Lawrence Sherly Puspha Joshi, Vilas Ramrao Patil, Mithun Baswaraj Patil, Vishal Ratansing SRM Institute of Science and Technology School of Computing SRM Institute of Science and Technology Department of Artificial Intelligence and Machine Learning St. Joseph’s College of Engineering Department of Computer Engineering ISBM College of Engineering Department of Computer Science and Engg N K Orchid College of Engg and Technology Solapur Department of CSE (AIML) Pimpri Chinchwad College of Engineering Nigdi

The SkyScript dataset was developed by integrating large-scale remote sensing images from Google Earth Engine with geo-tagged semantic data from OpenStreetMap. This open-access dataset, consisting of 2.6 million image-text pairs covering 29,000 unique tags, facilitates various remote sensing tasks such as cross-modal retrieval, image captioning, and classification. The dataset ensures global representation and semantic diversity, although it exhibits a higher concentration of high-resolution images from the USA and Europe due to licencing constraints. The images, sourced from multiple collections with varying ground sampling distances, are paired with captions generated using a combination of rule-based methods and logistic regression models for tag classification. Experiments demonstrate that models pre-trained on SkyScript outperform those trained on other datasets in zero-shot classification and fine-grained attribute recognition, highlighting its potential for advancing vision-language models in remote sensing applications. Future improvements could involve enhancing geographic coverage and refining caption quality using advanced language models. © The Author(s), under exclusive licence to Springer Nature Switzerland AG 2024.

关键词： Image captioning Remote sensing Sampling distance Vision language

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Artificial intelligence in physiological characteristics recognition for internet of things authentication

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Digital Communications and Networks 2024年第3期10卷 740-755页

作者： Zhimin Zhang Huansheng Ning Fadi Farha Jianguo Ding Kim-Kwang Raymond Choo School of Computer and Communication Engineering University of Science and Technology BeijingBeijing100083China Beijing Engineering Research Center for Cyberspace Data Analysis and Applications Beijing100083China Faculty of Informatics Engineering Aleppo UniversityAleppoSyria Department of Computer Science Blekinge Institute of Technology37179KarlskronaSweden Department of Information Systems and Cyber Security University of Texas at San AntonioSan AntonioTX78249-0631USA

Effective user authentication is key to ensuring equipment security,data privacy,and personalized services in Internet of Things(IoT)***,conventional mode-based authentication methods(e.g.,passwords and smart cards)may be vulnerable to a broad range of attacks(e.g.,eavesdropping and side-channel attacks).Hence,there have been attempts to design biometric-based authentication solutions,which rely on physiological and behavioral *** characteristics need continuous monitoring and specific environmental settings,which can be challenging to implement in ***,we can also leverage Artificial Intelligence(AI)in the extraction and classification of physiological characteristics from IoT devices processing to facilitate ***,we review the literature on the use of AI in physiological characteristics recognition pub-lished after *** use the three-layer architecture of the IoT(i.e.,sensing layer,feature layer,and algorithm layer)to guide the discussion of existing approaches and their *** also identify a number of future research opportunities,which will hopefully guide the design of next generation solutions.

关键词： Physiological characteristics recognition Artificial intelligence Internet of things Biological-driven authentication

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Efficient Optimal Routing Algorithm Based on Reward and Penalty for Mobile Adhoc Networks

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computers, Materials & Continua 2023年第4期75卷 1331-1351页

作者： Anubha Ravneet Preet Singh Bedi Arfat Ahmad Khan Mohd Anul Haq Ahmad Alhussen Zamil S.Alzamil Department of Computer Applications I.K.G.Punjab Technical UniversityKapurthala144601PunjabIndia Research and Development I.K.G.Punjab Technical UniversityKapurthala144604PunjabIndia Department of Computer Science College of ComputingKhon Kaen UniversityKhon Kaen40002Thailand Department of Computer Science College of Computer and Information SciencesMajmaah UniversityAl-Majmaah11952Saudi Arabia Department of Computer Engineering College of Computer and Information SciencesMajmaah UniversityAl-Majmaah11952Saudi Arabia

Mobile adhoc networks have grown in prominence in recent years,and they are now utilized in a broader range of *** main challenges are related to routing techniques that are generally employed in *** Adhoc system management,on the other hand,requires further testing and improvements in terms of *** routing protocols,such as Adhoc On-Demand Distance Vector(AODV)and Dynamic Source Routing(DSR),employ the hop count to calculate the distance between two *** main aim of this research work is to determine the optimum method for sending packets while also extending life time of the *** is achieved by changing the residual energy of each network ***,in this paper,various algorithms for optimal routing based on parameters like energy,distance,mobility,and the pheromone value are ***,an approach based on a reward and penalty system is given in this paper to evaluate the efficiency of the proposed algorithms under the impact of *** simulation results unveil that the reward penalty-based approach is quite effective for the selection of an optimal path for routing when the algorithms are implemented under the parameters of interest,which helps in achieving less packet drop and energy consumption of the nodes along with enhancing the network efficiency.

关键词： Routing optimization reward penalty mobility energy throughout pheromone

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A Novel Approach for Photo-Realistic High Quality Sign Language Video Synthesis 3

A Novel Approach for Photo-Realistic High Quality Sign Langu...

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3rd IEEE International Conference on Intelligent Techniques in Control, Optimization and Signal Processing, INCOS 2024

作者： Pravine Mukesh, R. Natarajan, B. Elakkiya, R. Vaidhya, G.K. Murali, P. Geetha, K. Amrita Vishwa Vidyapeetham Chennai601103 India School of Computer Science and Engineering Vit University Tamilnadu Chennai India Birla Institute of Technology and Science Department of Computer Science Dubai United Arab Emirates Srm Institute of Science and Technology Vadapalani Department of Computer Science and Engineering Chennai India Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences Department of Cse Tamilnadu Chennai India Amrita School of Computing Department of Computer Science and Engineering Amrita Vishwa Vidyapeetham Chennai601103 India

ISBN: (纸本)9798350361186

This research offers a brand-new framework for producing high-quality, photo-realistic sign language videos using sign language corpora. The framework consists of three main components such as Neural Machine Translation (NMT) using transformer, an open pose estimation approach, and a Generative Adversarial Network (GAN) with VGG-19 classifier. The NMT transformer translates the sign language corpus into a sequence of sign glosses, pose estimation model performs mapping of equivalent signer images and further assists to generate sign images. The GAN with VGG-19 classifier then generates high- quality sign language videos from the sign language images. The proposed method performs at the cutting edge in terms of evaluation on three benchmark dataset using metrics such as PSNR, SSIM, FID, and Inception Score. In addition, the framework offers better translation outcomes with less processing overhead and produces high-quality, photo-realistic sign language videos without the need for animation or avatar techniques. The proposed framework has the potential to offer the uninterrupted communication medium between common people with deaf-mute society. The evaluation scores highlights the improved perfor- mance of the proposed model compared with earlier approaches in terms of visual quality. © 2024 IEEE.

关键词： Generative adversarial networks

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