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2nd IEEE Global Conference on Communications and Information Technologies, GCCIT 2024

作者： Anuprathibha, T. Praveen, R.V.S. Jayanth, H. Sukumar, P. Suganthi, G. Ravichandran, T. V.S.B. Engineering College Department of Information Technology Tamilnadu Karur India Department of Digital Engineering and Assurance Skyview Building 20 Sarelingampally Telangana Hyderabad India St. Claret College Autonomous Department of Commerce and Management Karnataka Bengaluru India Department of Computer Science and Engineering Tamilnadu Erode India Sona College of Technology Department of Mathematics Tamil Nadu Salem India Akshaya College of Engineering and Technology Department of Artificial Intelligence and Data Science Coimbatore India

ISBN: (纸本)9798350388916

The establishment of trusting connections between buyers and sellers is essential in electronic commerce (EC) since it paves the way for mutual benefit. Users can research a business's reputation before deciding to do business with them. There are some aspects of the reputation system that are unflattering to both vendors and clients. For instance, as a consequence of their dishonesty or lack of effort while posting reviews, some users may create phantom feedback, which seems as fabricated assessments meant to boost their *** proposed approach incorporates stage preprocessing, feature extraction, and model training. In order to prepare the data for the SA job, the pre-processing phase incorporates initial procedures that aid in data transformation. One way to improve the model's accuracy is to use features extraction to remove irrelevant data points. We kept using the HGAN model throughout training. This state-of-the-art method outperforms SVM and LSTM with an average accuracy of 91.26%. © 2024 IEEE.

关键词： data accuracy

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Comparing graph transformers via positional encodings 24

Comparing graph transformers via positional encodings

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Proceedings of the 41st International Conference on Machine Learning

作者： Mitchell Black Zhengchao Wan Gal Mishne Amir Nayyeri Yusu Wang School of Electrical Engineering and Computer Science Oregon State University Corvallis Oregon Halicioǧlu Data Science Institute University of California San Diego San Diego California

The distinguishing power of graph transformers is tied to the choice of positional encoding: features used to augment the base transformer with information about the graph. There are two primary types of positional encoding: absolute positional encodings (APEs) and relative positional encodings (RPEs). APEs assign features to each node and are given as input to the transformer. RPEs instead assign a feature to each pair of nodes, e.g., shortest-path distance, and are used to augment the attention block. A priori, it is unclear which method is better for maximizing the power of the resulting graph transformer. In this paper, we aim to understand the relationship between these different types of positional encodings. Interestingly, we show that graph transformers using APEs and RPEs are equivalent in their ability to distinguish non-isomorphic graphs. In particular, we demonstrate how to interchange APEs and RPEs while maintaining their distinguishing power in terms of graph transformers. However, in the case of graphs with node features, we show that RPEs may have an advantage over APEs. Based on our theoretical results, we provide a study of different APEs and RPEs--including the shortest-path and resistance distance and the recently introduced stable and expressive positional encoding (SPE)-- and compare their distinguishing power in terms of transformers. We believe our work will help navigate the vast number of positional encoding choices and provide guidance on the future design of positional encodings for graph transformers.

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A Comparative Analysis of Artificial Intelligence and Business Intelligence Using Big data Analytics 1

A Comparative Analysis of Artificial Intelligence and Busine...

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1st International Conference on Advances in computer science, Electrical, Electronics, and Communication Technologies, CE2CT 2025

作者： Marella, Bhagath Chandra Chowdari Vegineni, Gopi Chand Addanki, Sireesha Ellahi, Ehsan Arun Kumar, K. Mandal, Rishabh Insights & Data Department of Financial Services Capgemini India Enrollment and Eligibility Team India System soft Technologies LLC India Cleveland State University Department of Information Systems OH United States Tula's Institute Department of Computer Science and Engineering UK Dehradun India India

ISBN: (纸本)9798331518578

Business intelligence (BI) encompasses the tools and uses to gather, combine, examine, and display data about a specific firm. Using historical and current efficiency comparisons, these structures assist in formulating plans and choices to achieve better outcomes in the future. Additionally, it helps firms determine which business issues and industry trends require attention. It has several benefits, such as increasing responsibility and efficiency, but it carries some disadvantages, such as being expensive, complicated, and time-consuming to adopt. Using big data-driven approaches to optimize talent recruitment is essential for improved decisions in the cutthroat world of BI. The efficacy of many machine learning algorithms to increase recruiting performance and accuracy is compared in this investigation. For optimizing, cross-validating, and hyperparameter modification were employed. The Random Forest (RF) algorithm has the greatest accuracy (92.9%), trailed by the Gradient Boosting Classifying system (92.6%) and the neural network (NNs) models (92.7%). These findings imply that sophisticated ML algorithms, in particular RF and NN, can greatly improve BI platforms' hiring procedures. Recruiters can benefit from this research's insightful recommendations for incorporating advanced ML methods into talent recruitment plans. © 2025 IEEE.

关键词： data accuracy

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Robust Optical Quantum Imaging Framework with Entangled Photons in Oceanic Turbulent Environments

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IEEE Transactions on Communications 2025年

作者： Cao, Jingyang Zhou, Mu Zhang, Ruichen Niyato, Dusit Nie, Wei Han, Zhu Chongqing University of Posts and Telecommunications School of Communications and Information Engineering Chongqing China Nanyang Technological University College of Computing and Data Science Nanyang China University of Houston Department of Electrical and Computer Engineering HoustonTX77004 United States Kyung Hee University Department of Computer Science and Engineering Seoul446-701 Korea Republic of

As an important part of underwater optical technology, underwater imaging plays a crucial role in accurately capturing underwater targets and environmental features. Facing the challenges of complex ocean environments and severe photon attenuation, quantum imaging breaks through the limitations of traditional optical imaging technology by utilizing the characteristics of two-photon entanglement and time-space correlation, thus offering a new perspective on ocean turbulence. To this end, we propose a new underwater entangled-photon quantum imaging system. Specifically, we first construct an entangled photon quantum imaging physical model through ocean long-exposure turbulence and then exploit an entangled light co-incidence imaging reconstruction method to image the target object. Furthermore, in response to the problem that ocean environment has a great impact on photons, we develop a photon capture probability method based on entangled photon pairs to reduce the impact of the ocean turbulence noise on imaging and improve the underwater target imaging resolution. We experimentally demonstrate the effectiveness of our method by showing that even in harsh ocean environments, quantum imaging performs superior resolution capabilities over traditional light source imaging techniques. © 1972-2012 IEEE.

关键词： Quantum entanglement

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arXiv

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arXiv 2025年

作者： Li, Yangning Kang, Hui Li, Jiahui Sun, Geng Sun, Zemin Wang, Jiacheng Zhao, Changyuan Niyato, Dusit College of Computer Science and Technology Key Laboratory of Symbolic Computation and Knowledge Engineering of Ministry of Education Jilin University Changchun130012 China College of Computer Science and Technology Jilin University Key Laboratory of Symbolic Computation and Knowledge Engineering of Ministry of Education Jilin University Changchun130012 China College of Computing and Data Science Nanyang Technological University 639798 Singapore College of Computing and Data Science Nanyang Technological University Singapore

An expansion of Internet of Things (IoTs) has led to significant challenges in wireless data harvesting, dissemination, and energy management due to the massive volumes of data generated by IoT devices. These challenges are exacerbated by data redundancy arising from spatial and temporal correlations. To address these issues, this paper proposes a novel data-driven collaborative beamforming (CB)-based communication framework for IoT networks. Specifically, the framework integrates CB with an overlap-based multi-hop routing protocol (OMRP) to enhance data transmission efficiency while mitigating energy consumption and addressing hot spot issues in remotely deployed IoT networks. Based on the data aggregation to a specific node by OMRP, we formulate a node selection problem for the CB stage, with the objective of optimizing uplink transmission energy consumption. Given the complexity of the problem, we introduce a softmax-based proximal policy optimization with long-short-term memory (SoftPPO-LSTM) algorithm to intelligently select CB nodes for improving transmission efficiency. Simulation results show that the proposed OMRP improves network lifetime by 17% compared to benchmark routing protocols, while the SoftPPO-LSTM method for CB node selection achieves an 8.3% increase in throughput over benchmark algorithms. The results also reveal that the combined OMRP with the SoftPPO- Copyright © 2025, The Authors. All rights reserved.

关键词： Deep reinforcement learning

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An Empirical Analysis on Spatial Reasoning Capabilities of Large Multimodal Models

arXiv

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arXiv 2024年

作者： Shiri, Fatemeh Guo, Xiao-Yu Far, Mona Golestan Yu, Xin Haffari, Gholamreza Li, Yuan-Fang Department of Data Science & AI Monash University Australia Australian Institute for Machine Learning University of Adelaide Australia School of Electrical Engineering and Computer Science University of Queensland Australia

Large Multimodal Models (LMMs) have achieved strong performance across a range of vision and language tasks. However, their spatial reasoning capabilities are under-investigated. In this paper, we construct a novel VQA dataset, Spatial-MM, to comprehensively study LMMs’ spatial understanding and reasoning capabilities. Our analyses on object-relationship and multi-hop reasoning reveal several important findings. Firstly, bounding boxes and scene graphs, even synthetic ones, can significantly enhance LMMs’ spatial reasoning. Secondly, LMMs struggle more with questions posed from the human perspective than the camera perspective about the image. Thirdly, chain of thought (CoT) prompting does not improve model performance on complex multi-hop questions involving spatial relations. Lastly, our perturbation analysis on GQA-spatial reveals that LMMs are much stronger at basic object detection than complex spatial reasoning. We believe our benchmark dataset and in-depth analyses can spark further research on LMMs spatial reasoning. © 2024, CC BY.

关键词： Visual languages

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Model learning:a survey of foundations,tools and applications

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Frontiers of computer science 2021年第5期15卷 71-92页

作者： Shahbaz ALI Hailong SUN Yongwang ZHAO Beijing Advanced Innovation Center for Big Data and Brain Computing Beihang University Beijing 100191China SKLSDE School of Computer Science and EngineeringBeihang UniversityBeijing 100191China School of Software Beihang UniversityBeijing 100191China School of Cyber Science and Technology College of Computer ScienceZhejiang UniversityHangzhou 310058China

Software systems are present all around us and playing their vital roles in our daily *** correct functioning of these systems is of prime *** addition to classical testing techniques,formal techniques like model checking are used to reinforce the quality and reliability of software ***,obtaining of behavior model,which is essential for model-based techniques,of unknown software systems is a challenging *** mitigate this problem,an emerging black-box analysis technique,called Model Learning,can be *** complements existing model-based testing and verification approaches by providing behavior models of blackbox systems fully *** paper surveys the model learning technique,which recently has attracted much attention from researchers,especially from the domains of testing and ***,we review the background and foundations of model learning,which form the basis of subsequent ***,we present some well-known model learning tools and provide their merits and shortcomings in the form of a comparison ***,we describe the successful applications of model learning in multidisciplinary fields,current challenges along with possible future works,and concluding remarks.

关键词： model learning active automata learning automata learning libraries/tools inferring behavior models testing and formal verification

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JH-Ranker: Enhancing Keigo Recognition in Japanese Sentences through Multi-Task Learning

JH-Ranker: Enhancing Keigo Recognition in Japanese Sentences...

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International Joint Conference on Neural Networks (IJCNN)

作者： Shih-Wei Guo Ya-Chun Wen Jui-Ling Yu Yao-Chung Fan Department of Computer Science and Engineering National Chung Hsing University Taichang Taiwan Department of Japanese Language and Literature Providence University Taichang Taiwan Department of Data Science and Big Data Analytics Providence University Taichang Taiwan

ISBN: (数字)9798350359312

ISBN: (纸本)9798350359329

In the realm of Japanese language processing, the correct use and understanding of Keigo (honorific language) presents unique challenges due to its complex nature and context-dependent usage. This study introduces JH-Ranker, a novel multitask learning framework designed to enhance the recognition and understanding of Keigo in Japanese sentences. We are employing a multi-task learning approach by integrating Keigo Level, Contextual Field, and the Classification of Keigo Forms. This integration aims to foster a comprehensive understanding of Keigo usage from small data, leveraging the synergy between multiple tasks to achieve a deeper understanding with limited resources. Utilizing the BERT-base-japanese pre-trained model, JH-Ranker takes into account various Keigo Forms and contextual scenarios, going beyond mere syntactic analysis. Our methodology also emphasizes the importance of considering both the form and context of Keigo, addressing challenges such as label imbalance and overfitting in multi-label classification tasks. The experimental results demonstrate a significant improvement in Keigo recognition capabilities over existing baseline models, particularly in discerning the subtle nuances of Keigo Levels and Forms. This study contributes to the realm of natural language processing by presenting a refined and contextually sensitive method for Japanese Keigo. It demonstrates the language model’s adaptability and understanding in dealing with intricate honorific language scenarios.

关键词： Adaptation models Analytical models Neural networks Syntactics Multitasking Natural language processing Context modeling

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MFANEDH: Multimodal Fusion of MRI, PET, CT, and Ultrasound Using Advanced Neural Architectures for Enhanced Early Detection of Heart Cancer

MFANEDH: Multimodal Fusion of MRI, PET, CT, and Ultrasound U...

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Emerging Trends in engineering and Medical sciences (ICETEMS), International Conference on

作者： Kiran Khandare Gaurav Divtelwar Charvi Suri Aparna Bondade Department of Computer Technology Yeshwantrao Chavan College of Engineering Nagpur India Department of Artificial Intelligence & Data Science Priyadarshini College of Engineering Nagpur India

ISBN: (数字)9798331529352

ISBN: (纸本)9798331529390

Heart cancer poses unique diagnostic challenges due to the complexities of the cardiac environment and the subtle nature of tumor manifestations. Timely detection is crucial given the increasing incidence of heart cancer cases. Current imaging technologies, while providing significant advancements, often operate independently, limiting the utilization of diagnostic information. Integrating sense modality such as MRI, PET scans, CT imaging, and ultrasound can overcome these limitations. In response to the fragmented diagnostic viewpoints observed in clinical practice, a multi-modal fusion framework was developed. This framework utilizes deep learning models tailored to each modality's strengths: 3D CNNs for MRI, U-Net combined with ResNet for PET scans, DenseNet for CT imaging, and BiLSTM for ultrasound. By amalgamating these models, the framework effectively leverages the unique features of each modality, resulting in substantial improvements across key diagnostic metrics. The context achieved a 3.5% increase in precision, 2.9% in accuracy, 2.5% in recall, 4.5% in AUC, and 4.9% in specificity. Additionally, it led to an 8.3% reduction in diagnostic delay, thereby enhancing diagnostic heftiness and ensuring timely detection. This research represents a paradigm shift in heart cancer diagnostics, laying the groundwork for integrated imaging platforms and refining patient outcomes and modified treatment strategies.

关键词： Heart Solid modeling Ultrasonic imaging Three-dimensional displays Magnetic resonance imaging Computed tomography Refining Positron emission tomography Cancer Tumors

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Particle Swarm Optimization based Detection of Diabetic Retinopathy using a Novel Deep CNN

Particle Swarm Optimization based Detection of Diabetic Reti...

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Artificial Intelligence and Smart Systems (ICAIS), International Conference on

作者： B. Revathi S.K. Kezial Elizabeth P. Nagaraj S. Selva Birunda D. Nithya Department of Artificial Intelligence and Data Science Ramco Institute of Technology Virudhunagar India Department of Computer Science and Engineering Mangayarkarasi College of Engineering Madurai India Department of Computer Science and Engineering Kalasalingan Academy of Research and Education Virudhunagar India Department of Computer Science and Engineering Theni Kammavar Sangam College of Technology Theni India

Diabetic Retinopathy (DR) is a disease. Diabetic patients are mostly affected by this disease. This causes damage to the eyes. Most probably the disease affects both the back of the eye and the blood vessels of the light-sensitive. Initially, this DR disease causes only minor problems with vision. But eventually, the eyesight will go away. This condition may occur in any diabetic patient. When the disease is more affected, the blood sugar level is high. DR detection is the application of image processing to medical examinations. To diagnose DR the retinal images should be evaluated. However, it takes a lot of time and resources to physically assess the images so that the severity of the DR can be removed. The blood vessels in the retina are damaged due to this problem can be noticed. When it starts to bleed the small vessels, then it forms a way on the retina. To overcome the challenges of DR, CNN with PSO is introduced. There are different stages implicated in the diabetic retinopathy detection technique. They are pre-processing, optimization and classification. The main objective of the proposed work is to use the CNN algorithm to analyze the disease that seems to be most affected and classify and report only that area from the given input. PSO is used to optimize the given classified images, and then finally PSO with CNN technique will produce accurate results.

关键词： Retinopathy Image processing Neural networks Optimization methods Blood vessels Retina Diabetes

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