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Communications and Information Technologies (GCCIT), Global Conference on

作者： Parikshit N. Mahalle Manoj Chandra Lohani S. Hemelatha Divya Sharma Chitra S Sujayaraj Samuel Jayakumar Department of Artificial Intelligence & Data Science Vishwakarma Institute of Technology Pune India School of Computing Graphic Era Hill University Haldwani India Department of Computer Science Engineering Karpagam Academy of Higher Education Coimbatore Centre of Research Impact and Outcome Chitkara University Rajpura Punjab India Department of Electronics and Communication Engineering Prince Shri Venkateshwara Padmavathy Engineering College Chennai School of Sciences Forensic Science JAIN (Deemed to be University) Bangalore Karnataka

ISBN: (数字)9798350388916

ISBN: (纸本)9798350388923

Synthetic computed tomography (sCT) based on magnetic resonance imaging (MRI) streamlines the planning of radiation therapy treatments by doing away with CT simulation and error-prone picture registration, which in turn reduces radiation dosage for patients and setup uncertainty. To create sCT images that matched the MRI anatomy, an optimally trained MultiHead Attention was used in conjunction with the reverse diffusion process. We generated sCT using MRI on two datasets—one for the brain and one for the prostate—to test the suggested strategy. The following metrics were used for qualitative evaluation: ground truth CTs and sCTs normalized cross correlation (NCC) indices, multi-scale Structure Similarity index (MS-SSIM), peak signal-to-noise ratio (PSNR), and mean absolute error (MAE) of Hounsfield unit (HU). A new method for creating CT images from standard MRIs utilizing a transformer-based convolutional neural network (CNN) has been designed and verified by us. Synthetic CT (sCT) pictures of excellent quality and robustness may be produced in a few minutes using this model, which successfully depicts the intricate interaction among CT and MRI images. By cutting down on patient time spent on treatment planning, improving accuracy of treatment delivery, and doing away with the necessity for extra CT scans, this method may considerably streamline radiation treatment planning.

关键词： PSNR Uncertainty Magnetic resonance imaging Computed tomography Diffusion processes Streaming media Transformers Planning Convolutional neural networks Standards

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Enhancing Business Intelligence Through A Semi-Distributed Blockchain Framework for Predictive Analytics On Sales Growth

Enhancing Business Intelligence Through A Semi-Distributed B...

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Disruptive Technologies (ICDT), International Conference on

作者： Mullaikodi C Sharath Kumar Jagannathan Jayanthi. N Rakhi Singh Yesha Tomar B Jaison Department of ECE J. N. N Institute of Engineering Tiruvallur Tamilnadu India Data Science Institute Saint Peter's University New Jersery USA School of Management Studies Vels Institute of Science Technology and Advanced Studies (VISTAS) Chennai India Faculty of Commerce and Management SGT University Gurugrm Haryana India School of Management Doon University Dehradun Uttarakhand India Department of Computer Science and Engineering RMK Engineering College Chennai Tamilnadu India

The cutting-edge technologies in the ever-evolving sector of data analytics has been accelerated by the requirement for efficient and secure techniques of acquiring business intelligence. This approach provides a solution to the existing issues. In order to address the issues of data integrity, security, and transparency that are associated with the existing methodologies, there is a need for a paradigm shift that will lead to a solution that is more resilient. The traditional centralised data analytics solutions have their limitations when it comes to guaranteeing the accuracy and dependability of forecasts, and this is the most important aspect of the situation. A semi-distributed blockchain paradigm is proposed as a solution to the problems that are associated with centralised systems. This paradigm spreads the computing burden while maintaining a certain degree of decentralisation in order to enhance security. The approach that is currently being considered is the construction and deployment of a blockchain network that is semi-distributed and contains data analytics techniques. Smart contracts simplify the processes of data transmission and validation among participants, hence ensuring transparency and confidence in the predictive analytics model. These findings reveal an improvement in the system efficiency, data quality, and accuracy of forecasting.

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QoE Maximization for Multiple-UAV-Assisted Multi-Access Edge Computing: An Online Joint Optimization Approach

arXiv

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arXiv 2024年

作者： He, Long Sun, Geng Sun, Zemin Wu, Qingqing Kang, Jiawen Niyato, Dusit Han, Zhu Leung, Victor C.M. The College of Computer Science and Technology Jilin University Changchun130012 China The College of Computing and Data Science Nanyang Technological University Singapore639798 Singapore The Department of Electronic Engineering Shanghai Jiao Tong University Shanghai China The School of Automation Guangdong University of Technology Guangzhou510006 China The University of Houston HoustonTX77004 United States The Department of Computer Science and Engineering Kyung Hee University Seoul446701 Korea Republic of The College of Computer Science and Software Engineering Shenzhen University Shenzhen518060 China The Department of Electrical and Computer Engineering University of British Columbia VancouverBCV6T 1Z4 Canada

In disaster scenarios, conventional terrestrial multi-access edge computing (MEC) paradigms, which rely on fixed infrastructure, may become unavailable due to infrastructure damage. With high-probability line-of-sight (LoS) communication, flexible mobility, and low cost, unmanned aerial vehicle (UAV)-assisted MEC is emerging as a new promising paradigm to provide edge computing services for ground user devices (UDs) in disaster-stricken areas. However, the limited battery capacity, computing resources, and spectrum resources also pose serious challenges for UAV-assisted MEC, which can potentially shorten the service time of UAVs and degrade the quality of experience (QoE) of UDs without an effective control approach. To this end, in this work, we first present a hierarchical architecture of multiple-UAV-assisted MEC networks that enables the coordinated provision of edge computing services by multiple UAVs. Then, we formulate a joint task offloading, resource allocation, and UAV trajectory planning optimization problem (JTRTOP) to maximize the QoE of UDs while considering the energy consumption constraints of UAVs. Since the problem is proven to be a future-dependent and NP-hard problem, we propose a novel online joint task offloading, resource allocation, and UAV trajectory planning approach (OJTRTA) to solve the problem. Specifically, the JTRTOP is first transformed into a per-slot real-time optimization problem (PROP) using the Lyapunov optimization framework. Then, a two-stage optimization method based on game theory and convex optimization is proposed to solve the PROP. Simulation results provide empirical evidence supporting the superior system performance of the proposed OJTRTA in comparison to alternative approaches. Copyright © 2024, The Authors. All rights reserved.

关键词： Game theory

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Towards Sample-Efficiency and Generalization of Transfer and Inverse Reinforcement Learning: A Comprehensive Literature Review

arXiv

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arXiv 2024年

作者： Hassani, Hossein Razavi-Far, Roozbeh Saif, Mehrdad Lin, Liang Department of Electrical and Computer Engineering Western University LondonONN6A 3K7 Canada Faculty of Computer Science Canadian Institute for Cybersecurity University of New Brunswick FrederictonNBE3B 5A3 Canada Department of Electrical and Computer Engineering University of Windsor WindsorONN9B 3P4 Canada School of Data and Computer Science Sun Yat-sen University Guangzhou510006 China

Reinforcement learning (RL) is a sub-domain of machine learning, mainly concerned with solving sequential decision-making problems by a learning agent that interacts with the decision environment to improve its behavior through the reward it receives from the environment. This learning paradigm is, however, well-known for being time-consuming due to the necessity of collecting a large amount of data, making RL suffer from sample inefficiency and difficult generalization. Furthermore, the construction of an explicit reward function that accounts for the trade-off between multiple desiderata of a decision problem is often a laborious task. These challenges have been recently addressed utilizing transfer and inverse reinforcement learning (T-IRL). In this regard, this paper is devoted to a comprehensive review of realizing the sample efficiency and generalization of RL algorithms through T-IRL. Following a brief introduction to RL, the fundamental T-IRL methods are presented and the most recent advancements in each research field have been extensively reviewed. Our findings denote that a majority of recent research works have dealt with the aforementioned challenges by utilizing human-in-the-loop and sim-to-real strategies for the efficient transfer of knowledge from source domains to the target domain under the transfer learning scheme. Under the IRL structure, training schemes that require a low number of experience transitions and extension of such frameworks to multi-agent and multi-intention problems have been the priority of researchers in recent years. © 2024, CC BY.

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Detection and classification of breast lesions using multiple information on contrast-enhanced mammography by a multiprocess deep-learning system: A multicenter study

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Chinese Journal of Cancer Research 2023年第4期35卷 408-423页

作者： Yuqian Chen Zhen Hua Fan Lin Tiantian Zheng Heng Zhou Shijie Zhang Jing Gao Zhongyi Wang Huafei Shao Wenjuan Li Fengjie Liu Simin Wang Yan Zhang Feng Zhao Hao Liu Haizhu Xie Heng Ma Haicheng Zhang Ning Mao School of Information and Electronic Engineering Shandong Technology and Business UniversityYantai 264005China Department of Radiology Yantai Yuhuangding HospitalAffiliated Hospital of Qingdao UniversityYantai 264000China School of Medical Imaging Binzhou Medical UniversityYantai 264003China Department of Radiology Fudan University Cancer CenterShanghai 200433China Department of Radiology Guangdong Maternal and Child Health HospitalGuangzhou 510010China School of Computer Science and Technology Shandong Technology and Business UniversityYantai 264005China Yizhun Medical AI Co.Ltd. Beijing 100080China Big Data and Artificial Intelligence Laboratory Yantai Yuhuangding HospitalAffiliated Hospital of Qingdao UniversityYantai 264000China

Objective: Accurate detection and classification of breast lesions in early stage is crucial to timely formulate effective treatments for patients. We aim to develop a fully automatic system to detect and classify breast lesions using multiple contrast-enhanced mammography(CEM) ***: In this study, a total of 1,903 females who underwent CEM examination from three hospitals were enrolled as the training set, internal testing set, pooled external testing set and prospective testing set. Here we developed a CEM-based multiprocess detection and classification system(MDCS) to perform the task of detection and classification of breast lesions. In this system, we introduced an innovative auxiliary feature fusion(AFF)algorithm that could intelligently incorporates multiple types of information from CEM images. The average freeresponse receiver operating characteristic score(AFROC-Score) was presented to validate system’s detection performance, and the performance of classification was evaluated by area under the receiver operating characteristic curve(AUC). Furthermore, we assessed the diagnostic value of MDCS through visual analysis of disputed cases,comparing its performance and efficiency with that of radiologists and exploring whether it could augment radiologists’ ***: On the pooled external and prospective testing sets, MDCS always maintained a high standalone performance, with AFROC-Scores of 0.953 and 0.963 for detection task, and AUCs for classification were 0.909[95% confidence interval(95% CI): 0.822-0.996] and 0.912(95% CI: 0.840-0.985), respectively. It also achieved higher sensitivity than all senior radiologists and higher specificity than all junior radiologists on pooled external and prospective testing sets. Moreover, MDCS performed superior diagnostic efficiency with an average reading time of 5 seconds, compared to the radiologists’ average reading time of 3.2 min. The average performance of all radiologists was also improved to

关键词： Deep learning contrast-enhanced mammography breast lesions detection classification

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Deepfakes: A Review on Technologies, Applications and Strategies

Deepfakes: A Review on Technologies, Applications and Strate...

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International Conference on Power Energy, Environment and Intelligent Control (PEEIC)

作者： Kuldeep Singh Kaswan Kiran Malik Jagjit Singh Dhatterwal Mahaveer Singh Naruka D Govardhan School of Computing Science and Engineering Galgotias University Greater Noida UP India Department of Computer Science and Engineering MRIEM Rohtak India Department of Artificial Intelligence and Data Science Koneru Lakshmaiah Education Foundation Andhra Pradesh India G L Bajaj Institute of Technology and Management Greater Noida India Department of Aeronautical Engineering Institute of Aeronautical Engineering Hyderabad

Deepfakes are artificial intelligence-generated multimedia, primarily videos or audio, that effectively edit or invent information by superimposing one person's likeness or voice onto that of another. They use deep learning techniques such as GAN s to generate hyper-realistic simulations that blur the line between reality and fiction. Deepfakes encompass everything from political satire to possible concerns in the fields of disinformation, privacy invasion, and cybersecurity. Understanding deepfakes is critical in an age when manipulated media may influence public opinion and undermine confidence. The primary objective of this paper is to provide a holistic overview of deepfake technology, shedding light on both its creative potential and inherent risks. Through a systematic exploration of the evolution, applications, techniques, and detection strategies, we aim to equip readers with an informed perspective on this ever-evolving field. By synthesizing existing knowledge and insights, this paper intends to contribute to the ongoing discourse surrounding deepfakes, enabling stakeholders to make informed decisions, develop effective countermeasures, and anticipate the future trajectory of this technology. Ultimately, our work strives to foster awareness and preparedness in an age where discerning the truth has never been more vital.

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Cross-View Fuzziness and Intra-view Uncertainty-Based Weight Reconstruction for Multi-view Feature Selection 11th

Cross-View Fuzziness and Intra-view Uncertainty-Based Weigh...

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11th International Joint Conference on Rough Sets, IJCRS 2025

作者： Xiang, Biao Chen, Hongmei Mi, Yong Sang, Binbin Horng, Shi-Jinn Li, Tianrui Institute of Artificial Intelligence School of Computing and Artificial Intelligence Southwest Jiaotong University Chengdu611756 China National Engineering Laboratory of Integrated Transportation Big Data Application Technology Southwest Jiaotong University Chengdu611756 China Engineering Research Center of Sustainable Urban Intelligent Transportation Ministry of Education Southwest Jiaotong University Chengdu611756 China Manufacturing Industry Chains Collaboration and Information Support Technology Key Laboratory of Sichuan Province Southwest Jiaotong University Chengdu611756 China College of Computer and Information Science Chongqing Normal University Chongqing401331 China Department of Computer Science and Information Engineering Asia University Taichung41354 Taiwan

ISBN: (纸本)9783031927430

Weight assignment is a universal and indispensable process in multi-view feature selection. However, most existing methods overlook the fuzziness and uncertainty implied in multi-view data. This may result in improper weight assignment, degrading the quality of selected features. A cross-view fuzziness and intra-view uncertainty-based weight reconstruction method (CFIUWR) is proposed for multi-view feature selection. With the fuzzy rough set theory, fuzzy approximation operators recognize cross-view fuzziness and intra-view uncertainty, which are evaluated by classification-related measurement. Cross-view fuzziness and intra-view uncertainty are incorporated to develop a reconstruction mechanism for view and feature weights. An optimization algorithm is derived, guaranteeing rapid convergence. Comparisons with state-of-the-art methods on several real-world datasets demonstrate the superiority of the CFIUWR. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Rough set theory

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Prediction Analysis for Diabetic Disease Using Machine Learning Model

Prediction Analysis for Diabetic Disease Using Machine Learn...

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Information Technology (OCIT), OITS International Conference on

作者： N. Senthamarai Jafar Ali Ibrahim Syed Masood M. Sathya N. S. Kalyan Chakravarthy Bharati Rathore M Mohamed Kalith Oli Department of Network and Communications SRM Institute of Science and Technology Chennai India Department of IoT School of Computer Science and Engineering Vellore Institute of Technology Vellore India Department of IT Nadar Saraswathi College of Engineering and Technology Theni India Center for Data Science AI&ML QIS College of Engineering and Technology Ongole India Faculty of Business and Creative Industries University of South Wales Pontypridd UK School of Bio Sciences and Technology Vellore Institute of Technology Vellore India

ISBN: (数字)9798331510404

ISBN: (纸本)9798331510411

The Diabetes Disease Prediction System is based on predictive modeling, which forecasts the user's disease based on the information that the user inputs to the system, including the number of pregnancies, age, weight, blood pressure, insulin, glucose, and other factors contributing to diabetes. The system evaluates the data supplied by the user as input and outputs the likelihood of the prediction. Nowadays, better production results are key for machine learning activities. Research has been conducted on a dataset from the Kaggle data set where single machine learning algorithms have been applied, and recorded results have been compared with results of multialgorithms of machine learning using neural networks. Some major classification algorithms, Random Forest, SVM, and Hybrid model of SVM and Random Forest, have been broadly utilized to anticipate the disease, where different accuracies were obtained. data preprocessing and feature selection were performed before building the models to increase the model's accuracy. Evaluation of the models in the proposed paper is based on the model's accuracy. The hybrid model performed best with 83% accuracy.

关键词： Support vector machines Accuracy Machine learning algorithms Linear regression Predictive models Prediction algorithms Diabetes Classification algorithms Random forests Diseases

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MCRec: Multi-channel Gated Gifts Recommendation

MCRec: Multi-channel Gated Gifts Recommendation

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IEEE International Conference on data Mining (ICDM)

作者： Ting-Ting Su Wu-Dong Xi Xing-Xing Xing Chang-Dong Wang School of Computer Science and Engineering Sun Yat-sen University Guangzhou China UX Center NetEase Games Guangzhou China Guangdong Provincial Key Laboratory of Intellectual Property and Big Data Guangzhou China

In recent years, various recommendation methods are proposed to capture user preferences more accurately, with the assumption that different types of records reflects the positive intention of users to buy items in different degree. However, the records of different channels may denote positive or negative impacts on users’ willingness to buy items in multi-channel scenario, which is a salient features of games. Making recommendation only with the records of buy channel makes it difficult to capture cross-channel impact of items. To solve the issue, this paper proposes a multi-channel gated gifts recommendation method, named MCRec, which is able to mine the impact of acquisition in different channels on buy channel from multi-channel records and generate personalized gifts for users. The MCRec method extracts channel-aware correlation of items from channel-aware item-item graphs. The contribution of different items in various sessions is distinguished with item gate, and the impact of the context of different channels on buy channel is measured with channel gate. Finally, personalized gifts will be generated hierarchically with different purposes. Extensive experiments are conducted on two datasets that are constructed with the data collected from two massively multiplayer online (MMO) games. The results demonstrate the superiority of our MCRec over state-of-the-art recommendation methods in gifts recommendation. Further ablation studies validate the effectiveness of the design of MCRec in modeling cross-channel impact of items.

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Enhancing Speech Recognition for Hearing-Impaired Individuals Using Deep Learning Models

Enhancing Speech Recognition for Hearing-Impaired Individual...

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International Conference on Circuit, Power and Computing Technologies (ICCPCT)

作者： K. Kalaiarasi Winson Medidhi Niresh Kumar S S. Thenappan R. Sathiyaseelan Mohana Sundaram K Department of ECE Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences Chennai India Department of CSE(Data Science) Prasad V. Potluri Siddhartha Institute of Technology Kanuru Vijayawada Andhra Pradesh India Department of CSE St. Joseph's College of Engineering OMR Chennai Department of Electronics and Communication Engineering Vel Tech Rangarajan Dr.Sagunthala R&D Institute of Science and Technology Chennai Department of Computer Science Engineering Arunai Engineering College Thiruvannamalai Tamil Nadu India Department of Computer Science and Engineering R.M.D. Engineering College Kavaraipettai Thiruvallur India

ISBN: (数字)9798350372816

ISBN: (纸本)9798350372823

This study examines the constraints of current methodologies, specifically in diverse acoustic environments, to surmount the obstacles associated with speech recognition for individuals with hearing impairments. Conventional processes frequently experience reduced accuracy and utility as a result of their incapability to dynamically adjust to evolving circumstances. This study presents an innovative approach that surpasses these constraints through the utilization of a transformer-based model. Through the implementation of sophisticated data augmentation techniques, the proposed model demonstrates exceptional performance across various acoustic environments, notwithstanding obstacles such as speaker adjustments and background noise. One notable characteristic of this methodology is its transformer architecture, which aptly captures subtle speech inflections by employing self-attention processes. Additionally, through independent learning, the model improves its ability to address the unique challenges associated with hearing-impaired speech. Integrating these components, the model achieves superior accuracy and adaptability compared to current technologies.

关键词： Adaptation models Accuracy Speech recognition Auditory system Artificial neural networks Speech enhancement Transformers

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