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1st International Conference on Innovations in communications, Electrical and computer engineering, ICICEC 2024

作者： Ranganathan, Chitra Sabapathy Rajasekaran, Mothiram Meenakshi, R. Arul Prasath, A. Ganeshbabu, T.R. Sujatha, S. Mphasis Corporation Tamil Nadu Chennai India Pine Candle Way Saint AugustineFL United States Chennai Institute of Technology Department of Computer Science and Engineering Tamil Nadu Chennai India Department of Computer Science and Engineering Vel Tech Rangarajan Dr.Sagunthala R&D Institute of Science and Technology Tamil Nadu Chennai India Department of Electronics and Communication Engineering Tamnil Nadu Namakkal India Saveetha University Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences Department of Biomedical Engineering Tamil Nadu Chennai India

ISBN: (纸本)9798350376517

This research introduces a new approach to retail pricing elasticity analysis that uses the complementary strengths of the Internet of Things (IoT) and machine learning. IoT sensors, smart shelves, and cameras allow for the continuous collection of real-time data on key retail factors, including customer behavior, inventory levels, and foot traffic. The next step is seamlessly combining this data with price and sales history. Applying the robust ensemble learning approach known as Gradient Boosting Machines (GBM) to examine the integrated dataset forms the core of the proposed system. With GBM, complex relationships between price changes and customer reactions may be more easily identified, leading to more accurate price elasticity estimates. By automatically altering pricing in response to real-time market dynamics, competition activities, and consumer preferences, the system enables merchants to make data-driven choices. This flexible price optimization technique enhances revenue generation, consumer happiness, and loyalty. The system also uses a feedback loop to improve real-time pricing techniques by considering new information from current data streams. The proposed methodology has been shown effective via case studies and simulations by optimizing pricing strategies and maximizing profitability in varied retail situations. © 2024 IEEE.

关键词： Sales

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Intelligent System for Early Diagnosis of Skin Cancer using Bilinear CNN and Soft Attention Approach 4

Intelligent System for Early Diagnosis of Skin Cancer using ...

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4th International Conference on Electronics and Sustainable communication Systems, ICESC 2023

作者： Pundir, Aditya Kumar Singh Walid, Md. Abul Ala Adivarekar, Pravin Gopi, Arepalli Malathy, V. Singh, Kamlesh Arya College of Engineering and Information Technology Department of Electronics and Communication Engineering Jaipur India Department of Computer Science and Engineering Khulna9203 Bangladesh A.P. Shah Institute of Technology Maharashtra Thane India Koneru Lakshmaiah Education Foundation Department of CSE Andhra Pradesh Guntur India Sr University School of Engineering Department of Ece Telangana Warangal India Graphic Era Hill University Department of Computer Science and Engineering Dehradun Campus Dehradun India

ISBN: (纸本)9798350300093

Skin cancer is becoming the leading cause of death in the Western world. Although sun exposure is a major risk factor for the development of skin cancer, this malignant neoplasm can appear anywhere in the body's skin. When detected early, most cases of skin cancer can be treated successfully. Skin cancer is fatal if not caught and treated quickly. New tools allow for the first-stage detection of skin cancer. The biopsies are the official method of skin cancer diagnosis. This is accomplished by scraping out a small amount of skin and sending it off to the lab for analysis. It takes a lot of effort and time. The suggested skin cancer detection method makes use of BilinearCNN-SA to identify malignant moles at an early stage. Patients benefit more from it. Preprocessing steps in the diagnostic approach include various elements like noise removal, grayscale conversion, and image enhancement. Once the data has been cleaned up, they apply Otsu segmentation. The ABCD Rule is used for feature extraction. The BilinearCNN-SA Model is then used to make the final classification. When compared to convolutional neural network and support vector machine models, the proposed method fares very well. © 2023 IEEE.

关键词： Convolutional neural networks

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A Review of Data Mining in Personalized Education: Current Trends and Future Prospects

arXiv

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arXiv 2024年

作者： Xiong, Zhang Li, Haoxuan Liu, Zhuang Chen, Zhuofan Zhou, Hao Rong, Wenge Ouyang, Yuanxin School of Computer Science and Engineering Beihang University Beijing100191 China School of Information Technology and Management University of International Business and Economics Beijing100029 China

Personalized education, tailored to individual student needs, leverages educational technology and artificial intelligence (AI) in the digital age to enhance learning effectiveness. The integration of AI in educational platforms provides insights into academic performance, learning preferences, and behaviors, optimizing the personal learning process. Driven by data mining techniques, it not only benefits students but also provides educators and institutions with tools to craft customized learning experiences. To offer a comprehensive review of recent advancements in personalized educational data mining, this paper focuses on four primary scenarios: educational recommendation, cognitive diagnosis, knowledge tracing, and learning analysis. This paper presents a structured taxonomy for each area, compiles commonly used datasets, and identifies future research directions, emphasizing the role of data mining in enhancing personalized education and paving the way for future exploration and innovation. © 2024, CC BY.

关键词： Data mining

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Adaptive region reaching control of fully actuated ocean surface vessels

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Journal of Control and Decision 2021年第3期8卷 251-259页

作者： X.M.Sun S.S.Ge Q.Xu Y.Zhou X.W.Zheng School of Engineering Science and Technology Shanghai Ocean UniversityShanghai 201306People’s Republic of China Robotics Institute and School of Computer Science and EngineeringUniversity of Electronic Science and Technology of ChinaChengduPeople’s Republic of China School of Electrical and Computer Engineering National University of SingaporeSingaporeSingapore School of Electronic and Information Engineering Beihang UniversityBeijingPeople’s Republic of China

In this paper,region reaching controller is designed for fully actuated ocean surface vessels to reach a desired target region instead of a *** are not the requirements for both the pre-specified trajectory outside the desired region and the desired pinpoint position inside the desired *** controller design is based on the potential energy function,backstepping recursive design methodology,and the Lyapnov stability analysis *** the target region is specified arbitrarily small,the target region reduces to a point,and hence the region reaching control can be a generalisation of the setpoint *** results are presented to illustrate the performance of the proposed controller.

关键词： Reaching control potential energy function adaptive control backstepping technique Lyapunov stability

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Multi-Timescale Motion-Decoupled Spiking Transformer for Audio-Visual Zero-Shot Learning

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IEEE Transactions on Circuits and Systems for Video technology 2025年

作者： Li, Wenrui Wang, Penghong Wang, Xingtao Zuo, Wangmeng Fan, Xiaopeng Tian, Yonghong Harbin Institute of Technology Department of Computer Science and Technology Harbin150001 China Harbin Institute of Technology Suzhou Research Institute Suzhou215104 China Peking University School of Electronics and Computer Engineering The School of Computer Science Beijing100871 China Peng Cheng Laboratory Shenzhen518055 China

Audio-visual zero-shot learning (ZSL) has been extensively researched for its capability to classify video data from unseen classes during training. Nevertheless, current methodologies often struggle with background scene biases and inadequate motion detail. This paper proposes a novel dual-stream Multi-Timescale Motion-Decoupled Spiking Transformer (MDST++), which decouples contextual semantic information and sparse dynamic motion information. The recurrent joint learning unit is proposed to extract contextual semantic information and capture joint knowledge across various modalities to understand the environment of actions. By converting RGB images to events, our method captures motion information more accurately and mitigates background scene biases. Moreover, we introduce a discrepancy analysis block to model audio motion information. To enhance the robustness of SNNs in extracting temporal and motion cues, we dynamically adjust the threshold of Leaky Integrate-and-Fire neurons based on global motion and contextual semantic information. Our experiments validate the effectiveness of MDST++, demonstrating their consistent superiority over state-of-the-art methods on mainstream benchmarks. Additionally, incorporating motion and multi-timescale information significantly improves HM and ZSL accuracy by 26.2% and 39.9%. © 1991-2012 IEEE.

关键词： Zero-shot learning

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Quantum-Aided Digital Twin Synchronization for Autonomous Vehicular Ecosystems over 6G-Enabled Edge Networks

Quantum-Aided Digital Twin Synchronization for Autonomous Ve...

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Pervasive Computing and Social Networking (ICPCSN), International Conference on

作者： R.Preethi A.Vasantharaj K Sanjith Badri B.Karthikprabu K.Kodeeswari C.Rohith Bhat Department of Computer and Communication Engineering Sri Eshwar College of Engineering Coimbatore Department of Electronics and Communication Engineering Kalaignar Karunanidhi Institute of Technology Coimbatore Department of Computer Science Vellore Institute of Technology Chennai Department of Electronics and Communication Engineering Excel Engineering College Namakkal Department of Electronics and Communication Engineering Kongu Engineering College Erode Department of Computer Science Engineering Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences (SIMATS) Saveetha University Chennai

ISBN: (数字)9798331535193

ISBN: (纸本)9798331535209

QSync-Twin6G, a novel algorithmic paradigm, is proposed to address the emerging challenges in synchronizing autonomous vehicular ecosystems via quantum-assisted digital twin frameworks over 6G-enabled edge environments. This work conceptualizes and implements a hybrid quantum-twin model, where quantum entanglement principles are leveraged to achieve ultraprecise, low-latency synchronization across distributed vehicular digital replicas. QSync-Twin6G operates through a dual-layer synergy—Quantum State Propagation Module (QSPM) and Edge Twin Synchronization Engine (ETSE)— which collectively orchestrate entangled state coherence and seamless data state transition across vehicular nodes. The entanglement-assisted twin fidelity modeling ensures that vehicular events in physical space are instantaneously mirrored within their digital counterparts, minimizing update drift and eliminating the propagation lags inherent in classical digital twin approaches. Extensive simulations utilizing vehicular trace datasets and QuNetSim quantum communication modules validate the superiority of the proposed method in terms of synchronization precision, convergence rapidity, and energy-latency balance when benchmarked against existing AI-based and edge-driven synchronization frameworks. Performance indicators illustrate up to 81% reduction in update drift, 73% enhancement in convergence speed, and 62% energy savings across diverse vehicular scenarios. The proposed architecture signifies a leap toward cognitively self-aligning vehicular ecosystems, setting a new frontier in quantum-assisted edge computing and vehicular intelligence.

关键词： 6G mobile communication Adaptation models Quantum computing Quantum entanglement Ecosystems computer architecture Coherence Digital twins Synchronization Convergence

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Optimizing Query Performance in Big Data Systems Using Machine Learning Algorithms 2

Optimizing Query Performance in Big Data Systems Using Machi...

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2nd IEEE International Conference on communication, Security and Artificial Intelligence, ICCSAI 2023

作者： Thirupurasundari, D.R. Rajesh Kumar, K. Palani, Hari Kumar Ilangovan, S. Senthilvel, P. Gururama Bharath Institute of Higher Education and Research Department of Computer Science and Engineering Tamil Nadu Chennai India Madha Engineering College Kundrathur Department of Electronics and Communication Engineering TamilNadu Chennai India School of Engineering Ajeenkya D y Patil University India K.L.N. College of Engineering Department of Information Technology Tamilnadu Pottapalayam630612 India Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences Department of Computer Science and Engineering India

ISBN: (纸本)9798350369960

The retrieval of information from extensive datasets in an efficient manner is critical in the era of big data. Conventional optimization methods encounter difficulties in managing the multitude of intricate and varied queries that big data systems confront. To optimize query efficacy in big data systems, this article introduces an innovative strategy that uses machine learning (ML) algorithms. To forecast optimal query execution strategies, our methodology entails training machine learning models using historical query execution data. The methodology consists of two components: First, characteristics are extracted from query plans, encompassing query patterns and statistics pertaining to the underlying data. Furthermore, to forecast the efficacy of a specific strategy, ML models, including decision trees, neural networks, and gradient-boosting machines, are trained using these characteristics. The findings were persuasive. In particular, for intricate queries, our ML-based optimizer enhanced query performance by as much as 60 % when compared to conventional optimizers. Changing duties and data distributions were also accommodated by our system, demonstrating its robustness and adaptability. Our research establishes a foundation for an emerging class of adaptive query optimizers capable of acquiring knowledge and adjusting to the dynamic characteristics of data and queries. By integrating machine learning capabilities with big data systems, we present a novel solution that significantly enhances the speed and efficiency of data retrieval. © 2023 IEEE.

关键词： Adaptive Query Planning Big Data Machine Learning Performance Improvement Query Optimization

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Exploring Kernel Transformations for Implicit Neural Representations

arXiv

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arXiv 2025年

作者： Zheng, Sheng Zhang, Chaoning Han, Dongshen Puspitasari, Fachrina Dewi Hao, Xinhong Yang, Yang Shen, Heng Tao Center for Future Media the School of Computer Science and Engineering University of Electronic Science and Technology of China Chengdu China School of Mechatronical Engineering Beijing Institute of Technology Beijing China School of Computing Kyung Hee University Yongin-si Korea Republic of Institute of Electronic and Information Engineering University of Electronic Science and Technology of China Guangdong China Center for Future Multimedia the School of Computer Science and Engineering University of Electronic Science and Technology of China Chengdu China Peng Cheng Laboratory Shenzhen China

Implicit neural representations (INRs), which leverage neural networks to represent signals by mapping coordinates to their corresponding attributes, have garnered significant attention. They are extensively utilized for image representation, with pixel coordinates as input and pixel values as output. In contrast to prior works focusing on investigating the effect of the model’s inside components (activation function, for instance), this work pioneers the exploration of the effect of kernel transformation of input/output while keeping the model itself unchanged. A byproduct of our findings is a simple yet effective method that combines scale and shift to significantly boost INR with negligible computation overhead. Moreover, we present two perspectives, depth and normalization, to interpret the performance benefits caused by scale and shift transformation. Overall, our work provides a new avenue for future works to understand and improve INR through the lens of kernel transformation. Copyright © 2025, The Authors. All rights reserved.

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A Multi-Modal Behavior Quantitative Analysis Model for Autism Early Screening

A Multi-Modal Behavior Quantitative Analysis Model for Autis...

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2023 IEEE International Conference on Systems, Man, and Cybernetics, SMC 2023

作者： Lei, Jiayi Zhang, E. She, Yingying Wang, Xin Liao, Yuhan Hu, Bin Wu, Hang Yang, Minqiang Tian, Jiajia Wang, Yong School of Informatics Xiamen University China University of Kansas Medical Center Department of Occupational Therapy Education United States School of Film National Institute for Data Science in Health and Medicine Xiamen University China School of Computer Science and Technology Beijing Institute of Technology China Together Education Institute China School of Information Science and Engineering Lanzhou University China Fujian Medical University Union Hospital Pediatric Department China

ISBN: (纸本)9798350337020

Human-computer Interaction (HCI) and Machine Learning (ML) technologies have potential for the behavioral screening of autistic children but how to design a tool and analyse behavior reliably is challenging. Based on psychophysiological computation, this paper proposes an interactive behavior perception analytical model for autism screening. We presented the multi-scenario reactive behavior paradigms that designed based on the atypical characteristics of autistic children. We recorded the eye movement data and facial data of 91 participants, and performed multi-modal feature extraction, used machine learning to train classification model. We conducted comparative experiments, and the experimental results verified the advantages of multi-scenario paradigms and multi-modal feature groups, which indicates that our analysis methods and screening models are effective and reliable and have real research significance. © 2023 IEEE.

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CHRT:Clustering-Based Hybrid Re-Routing System for Traffic Congestion Avoidance

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China communications 2021年第7期18卷 86-102页

作者： Jie Huo Xiangming Wen Luning Liu Luhan Wang Meiling Li Zhaoming Lu Beijing Laboratory of Advanced Information Networks and Beijing Key Laboratory of Network System Architecture and ConvergenceSchool of Information and Communication EngineeringBeijing University of Posts and TelecommunicationsBeijing 100876China Witcomm Open Source Communication Research Institute Beijing 100088China Taiyuan University of Science and Technology Taiyuan 030024China

Re-routing system has become an important technology to improve traffic *** traditional re-routing schemes do not consider the dynamic characteristics of urban traffic,making the planned routes unable to cope with the changing traf-fic *** on real-time traffic information,it is challenging to dynamically re-route connected vehicles to alleviate traffic ***,how to obtain global traffic information while reducing communication costs and improving travel efficiency poses a challenge to the re-routing *** deal with these challenges,this paper proposes CHRT,a clustering-based hybrid re-routing system for traffic congestion *** develops a multi-layer hybrid *** central server accesses the global view of traffic,and the distributed part is composed of vehicles divided into clusters to reduce latency and communication ***,a clustering-based priority mechanism is proposed,which sets priorities for clusters based on realtime traffic information to avoid secondary ***,to plan the optimal routes for vehicles while alleviating global traffic congestion,this paper presents a multi-metric re-routing *** extensive simulations based on the SUMO traffic simulator,CHRT reduces vehicle traveling time,fuel consumption,and CO2 emissions compared to other *** addition,CHRT globally alleviates traffic congestion and improves traffic efficiency.

关键词： traffic congestion dynamic re-routing intelligent transportation system(ITS) real-time traffic information VANET

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