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Women in Innovation, Technology & Entrepreneurship (ICWITE), IEEE International Conference for

作者： Sharon K V Natarajan B P. Madhumitha A Dhanamathi Prabu S S Saranya Dept. of Computer Science and Engineering Amrita School of Computing Amrita Vishwa Vidyapeetham Chennai India School of Computer Science and Engineering VIT University Chennai Tamilnadu India Dept. of Artificial Intell. and Data Science Panimalar Engineering College Chennai India Department of Computer Science and Engineering Roever Engineering College Perambalur Tamilnadu India Dept. of CSE K. Ramakrishnan College of Engineering Tiruchirappalli Tamil Nadu India

One major problem that contributes to many traffic accidents that cause injuries and fatalities all over the world is drowsy driving. This research introduces the novel approach for detecting driver drowsiness using deep networks and computer vision techniques aims to solve this issue and improve road safety. The Proposed system employs an InceptionV3-based Convolutional Neural Network (CNN) model to precisely determine whether the driver’s eyes are open or closed. A sizable dataset of eye images taken in actual driving situations is used to train the CNN model. Whenever the indicators of drowsiness are detected, the proposed system will identify them in real time and promptly notify the driver. The proposed model not only has strong real-time capabilities but also exhibits superior accuracy based on stringent evaluation metrics, which makes a significant contribution to efforts aimed at promoting road safety. The performance and adaptability of the system are improved and further integrates alarm activation for drowsiness detection in a variety of driving conditions.

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Blockchain Integration for Robust Intellectual Property Protection in New Product Development (NPD)

Blockchain Integration for Robust Intellectual Property Prot...

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Electronics and Renewable Systems (ICEARS), International Conference on

作者： P. Chinnasamy Subashini. B Nijanthan Nijanthan Madasamy Raja. G Devulapally Swetha Sreenivasulu Reddy L Department of Computer Science and Engineering School of Computing Kalasalingam Academy of Research and Education Tamilnadu India Department of Data Science and Business Systems School of Computing SRMIST India Computer Science and Engineering Paavai Engineering College Namakkal Tamilnadu India Information Technology Paavai Engineering College Namakkal Tamilnadu India Dept. of Computer Science and Engineering Vaagdevi Engineering College Telangana India Department of Mathematics School of Advanced Sciences Kalasalingam Academy of Research and Education Krishnankoil Tamilnadu India

ISBN: (数字)9798331509675

ISBN: (纸本)9798331509682

The rapid advancement of Blockchain technology has led to its widespread application in several domains of digital activities, such as e-government concerns and monetary security. This article presents a smart contract-based Blockchain solution for protecting intellectual property rights in new product development. The proposed solution effectively tackles the challenges associated with the confidentiality of personally identifiable information, efficiency in storing transaction-related information, as well as cooperation and monitoring in IPR. Initially, we create a mathematical model based on smart contract enabled Blockchain and intelligent contracts. Subsequently, we develop algorithms for Blockchain generation, and intelligent contracts. The system is suitable for protecting IPR in a blockchain network, by implementation of distributed identity authentication and real-time extensible storage. These features enhance the security and expandability of circuit copyright protection based on blockchain technology. The experimental results demonstrate that the suggested algorithm has decreased the cost for communication and enhanced the effectiveness of storing information and administration. Furthermore, it demonstrates resilience against many common attacks such as double-spending assaults, while maintaining low cost and overhead. Additionally, it exhibits a superior level of security in comparison to three other competing algorithms.

关键词： Patents Technological innovation Costs Smart contracts Intellectual property Product development Blockchains Security Protection Resilience

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Autonomous Multi-Objective Optimization Using Large Language Model

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IEEE Transactions on Evolutionary Computation 2025年

作者： Huang, Yuxiao Wu, Shenghao Zhang, Wenjie Wu, Jibin Feng, Liang Tan, Kay Chen Department of Data Science and Artificial Intelligence Hong Kong Polytechnic University Hong Kong Department of Electrical and Elctronics Engineering Hong Kong Polytechnic University Hong Kong Chongqing University College of Computer Science Chongqing Key Laboratory of Big Data Intelligence and Privacy Computing Chongqing400044 China

Multi-objective optimization problems (MOPs) are ubiquitous in real-world applications, presenting a complex challenge of balancing multiple conflicting objectives. Traditional multi-objective evolutionary algorithms (MOEAs), though effective, often rely on domain-specific expertise for improved optimization performance, hindering adaptability to unseen MOPs. In recent years, the Large Language Models (LLMs) has revolutionized software engineering by enabling the autonomous generation and refinement of programs. Leveraging this breakthrough, we propose a new LLM-based framework that autonomously designs MOEAs for solving MOPs. The proposed framework includes a robust testing module to refine the generated MOEA through error-driven dialogue with LLMs, a dynamic selection strategy along with informative prompting-based crossover and mutation to fit textual optimization pipeline. Our approach facilitates the design of MOEA without the extensive demands for expert intervention, thereby speeding up the innovation of MOEA. Empirical studies across various MOP categories validate the robustness and superior performance of our proposed framework. © 1997-2012 IEEE.

关键词： Multiobjective optimization

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Event-driven Robotic Tactile data Learning Using Temporal Spike Sequence Backpropagation Method

Event-driven Robotic Tactile Data Learning Using Temporal Sp...

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2022 Chinese Automation Congress, CAC 2022

作者： Hou, Qing Liu, Tingqing Yang, Jing Ji, Xiaoyang Li, Qinglang Li, Jian Yin, Baofan Guizhou University School of Computer Science and Technology Guiyang China Guizhou University School of Computer Science and Technology Guizhou University State Key Laboratory of Public Big Data Guiyang China Guizhou University State Key Laboratory of Public Big Data Guizhou University School of Mechanical Engineering Guiyang China Guizhou University School of Mechanical Engineering Guiyang China

ISBN: (纸本)9781665465335

Tactile perception is indispensable for intelligent robots to interact intelligently like humans. Therefore, the effective use of deep learning methods to acquire tactile features has become an important focus of tactile perception research. Satisfactory time-driven characteristics and the ability to process spatiotemporal information efficiently of spiking neural networks are advantageous for event-based data. We apply a temporal spike sequence learning backpropagation method that can handle continuous spikes to improve the spike neural network for tactile object recognition based on event-driven data. We prove the effectiveness of the temporal spike sequence error backpropagation method in practical applications to address the problem of losing temporal information of data using approximate derivatives. In practical application, we have proved the validity of the back propagation method of temporal spike sequence error in solving the problem of losing temporal information of data using approximate derivatives © 2022 IEEE.

关键词： Backpropagation

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Assessing a Personalized, Hybrid, and Generic Approach for Glucose Prediction in Type 1 Diabetes

Assessing a Personalized, Hybrid, and Generic Approach for G...

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IEEE International Conference on Bioinformatics and Biomedicine (BIBM)

作者： Pietro Bosoni Morten Hasselstrøm Jensen Riccardo Bellazzi Simon Lebech Cichosz Department of Electrical Computer and Biomedical Engineering University of Pavia Pavia Italy Department of Health Science and Technology Aalborg University Data Science Aalborg Denmark Department of Health Science and Technology Aalborg University Aalborg Denmark

ISBN: (数字)9798350386226

ISBN: (纸本)9798350386233

This study investigates the potential of using generic, hybrid, and personalized neural network models for glucose prediction in individuals with Type 1 Diabetes (T1D). data from 194 participants in the Wireless Innovations for Seniors with Diabetes Mellitus (WISDM) study, totaling over 46 million minutes of Continuous Glucose Monitoring (CGM), were used to develop and evaluate the models. A baseline reference model, Last Observation Carried Forward (LOCF), was also included for comparison. Models were trained using data from 70% of the participants and tested on the remaining 30%, with prediction horizons (PH) set at 30 and 60 minutes. At the 30-minute PH, the generic model achieved a Root Mean Square Error (RMSE) of 19.6 mg/dL and a Mean Absolute Relative Difference (MARD) of 9.2%. These results were slightly worse than those of the hybrid and personalized models, which yielded RMSEs of 19.4 mg/dL and 19.5 mg/dL, respectively, and MARDs of 9.6% for both. However, the differences were not statistically significant. At the 60-minute PH, the generic model showed the best performance, with an RMSE of 34.9 mg/dL and MARD of 16.2%. The hybrid and personalized models exhibited slightly higher RMSEs (35.3 mg/dL and 35.5 mg/dL, respectively) and MARDs (17.8% and 18.1%, respectively). These findings suggest that both generic and individualized models can provide satisfactory glucose forecasting results based solely on CGM data. Nonetheless, the potential benefits of individualized approaches deserve further investigation, particularly when substantial training data are available.

关键词： Wireless communication Training Analytical models Technological innovation Biological system modeling Training data Predictive models data models Diabetes Forecasting

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GarbageNet: A Unified Learning Framework for Robust Garbage Classification

IEEE Transactions on Artificial Intelligence

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IEEE Transactions on Artificial Intelligence 2021年第4期2卷 372-380页

作者： Yang, Jianfei Zeng, Zhaoyang Wang, Kai Zou, Han Xie, Lihua The School of Electrical and Electronics Engineering Nanyang Technological University Singapore639798 Singapore The School of Data and Computer Science Sun Yat-sen University Guangzhou510006 China The Institutes of Data Science School of Computing National University of Singapore Singapore119077 Singapore The Department of Electrical Engineering and Computer Sciences University of California BerkeleyCA94720 United States

The recyclability of domestic waste plays a crucial role in the modern society, which helps reduce multiple types of pollution and brings economic effect. To achieve this goal, garbage classification is one of the most important steps during the recycling process. Prevailing deep learning techniques empower high-performance visual recognition models and can benefit the automation of garbage classification task. Nevertheless, there exist three challenges when directly leveraging deep recognition models for this task, referring to lack of sufficient data, high cost of category increment, and noisy data quality. In this article, we present a novel incremental learning framework, GarbageNet, to address the aforementioned challenges. First, weakly-supervised transfer learning guarantees the capacity of feature extractor. Second, for new categories of garbages, GarbageNet embeds them as anchors for reference and classifies the test samples by finding their nearest neighbors in the latent space. Third, an attentive mixup of training data is utilized for suppressing the negative effect of mislabeled data. We evaluate our method on real-world datasets, and the empirical results demonstrate that GarbageNet achieves the state-of-the-art performance with regard to accuracy, robustness, and extendability. The proposed method won the first place in the HUAWEI Cloud Garbage Classification Challenge in 2019. Impact Statement-This article contributes to a vision-based garbage recognition system for the recyclability of domestic waste. To the best of our knowledge, it is the first article that explores a realistic vision-based solution for the garbage recognition with a real-world dataset and a comprehensive benchmark on current state-of-the-art visual recognition models. The proposed method addresses several challenges and achieves state-of-the-art performance. We believe that this interdisciplinary research contributes to the AI for the environment, which helps promote environmenta

关键词： Convolutional neural networks

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Two Factors that Influence Our Selection of Digital Avatars: Gender Performativity and Historical Culture 5th

Two Factors that Influence Our Selection of Digital Avatars:...

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5th International Conference on Smart Vehicular Technology, Transportation, Communication and Applications, VTCA 2022

作者： Liu, Shutang Li, Yongyu Li, Minggui Guan, Yin Jiang, Hang Jiang, Lei Faculty of Humanities and Arts Macau University of Science and Technology 999078 China Academic Affairs Office Minjiang University Fuzhou China College of Computer and Control Engineering Minjiang University Fuzhou China College of Mathematics and Data Science Minjiang University Fuzhou China Culture and Creation Department Fuzhou Polytechnic Fuzhou China

ISBN: (纸本)9789819908479

In this study, 74 college student volunteers were recruited as experimental participants, and 108 game 3D images of the Arena of Valor game were used as the selected samples. The digital avatar is presented to the subject through AR technology. Then, the participants were asked to choose five digital avatar that they would probably go on to use in the future metaverse. Finally, after excluding the effects of game familiarity and other socio-statistical variables, it was found that the gender of the selected Digital avatar was positively correlated with the actual participants’ gender, and it was further found that females were more gender specific and more aggregated in terms of Gender Expression than males, while males were less gender specific and more dispersed. This study found that although there was certain influence of historical culture on the selection aspect of the digital avatar, it was not significant. Local game characters and Japanese game characters have equally important influence. © 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Students

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Modelling High-Order Social Relations for Item Recommendation (Extended Abstract)

Modelling High-Order Social Relations for Item Recommendatio...

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International Conference on data engineering

作者： Yang Liu Liang Chen Xiangnan He Jiaying Peng Zibin Zheng Jie Tang School of Computer Science and Engineering Sun Yat-sen University Guangzhou China School of Data Science School of Information Science and Technology University of Science and Technology of China Hefei China Department of Computer Science and Technology Tsinghua University Beijing China

Personalized recommendation is becoming increasingly important in online information systems in the current era of information explosion. In real-world scenarios, when a user considers which items to consume, the decision choice may be affected by her friends. For example, she may ask her friends for suggestions or be attracted by products purchased by one friend. As such, to provide satisfactory recommendation service, it is important to account for the evidence in social relations when they are available to use. Several prior efforts have been made to leverage social relations to build the recommender system and verified their utility. However, most existing methods, such as the well-known TrustSVD, leverage only first-order social relations, i.e., the direct neighbors that are connected to the target user. The high-order social relations, e.g., the friends of friends, which are very informative to reveal user preference, have been largely ignored.

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Swarm-LSTM: Condition Monitoring of Gearbox Fault Diagnosis Based on Hybrid LSTM Deep Neural Network Optimized by Swarm Intelligence Algorithms

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computers, Materials & Continua 2021年第2期66卷 2041-2059页

作者： Gopi Krishna Durbhaka Barani Selvaraj Mamta Mittal Tanzila Saba Amjad Rehman Lalit Mohan Goyal School of Electrical and Electronics Sathyabama Institute of Science and TechnologyChennai600119India Department of Computer Science and Engineering G.B.Pant Government Engineering CollegeNew Delhi110020India Artificial Intelligence and Data Analytics(AIDA)Laboratory CCISPrince Sultan UniversityRiyadh11586Saudi Arabia Department of Computer Engineering J.C.Bose University of Science and TechnologyFaridabad121006India

Nowadays,renewable energy has been emerging as the major source of energy and is driven by its aggressive expansion and falling *** of the renewable energy sources involve turbines and their operation and maintenance are vital and a difficult *** monitoring and fault diagnosis have seen remarkable and revolutionary up-gradation in approaches,practices and technology during the last *** mostly do use a rotating type of machinery and analysis of those signals has been challenging to localize the *** paper proposes a new hybrid model wherein multiple swarm intelligence models have been evaluated to optimize the conventional Long Short-Term Memory(LSTM)model in classifying the faults from the vibration signals data acquired from the *** helps to analyze the performance and behavioral patterns of the system more effectively and efficiently which helps to suggest for replacement of the unit with higher *** results have demonstrated that the proposed hybrid modeling approach is effective in classifying the faults of the gearbox from the time series data and achieve higher diagnostic accuracy in comparison to the conventional LSTM methods.

关键词： Gearbox long short term memory fault classification swarm intelligence optimization condition monitoring

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UAV-Enabled Relaying and Edge Computing: Hybrid Network Optimization

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IEEE Transactions on Vehicular Technology 2025年

作者： Ma, Bo Ye, Jiawei Feng, Shaohan Li, Chuanhuang Niyato, Dusit Kim, Dong In Hangzhou310018 China Nanyang Technological University College of Computing and Data Science 639798 Singapore Sungkyunkwan University Department of Electrical and Computer Engineering Suwon16419 Korea Republic of

This paper exploits an Unmanned Aerial Vehicle assisted Mobile Edge Computing (UAV-MEC) system to meet the computation demand of User Equipments (UEs). However, in conventional UAV-MEC architecture, a UAV is dedicated only to one single service, which will inevitably limit overall system performance. To address the above issue of the conventional UAVMEC architecture, we propose a hybrid UAV-MEC model. In this model, a UAV is treated as an agent to provide either relay or computing services. Moreover, we propose an MEC Multiple Agent UAV Trajectory and User Association Policy Gradient (MEC-MAUTUA-PG) algorithm to optimize offloading strategies of the UEs as well as the trajectory design of UAV. We show the superiority of the proposed algorithm in terms of delay and the safety of trajectory design of UAV in the environment. © 1967-2012 IEEE.

关键词： Mobile edge computing

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