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38th CCF National Conference of computer Applications, CCF NCCA 2023

作者： Zhang, Zhao Shen, Xiaobin Ge, Jing Zha, Yingying Liu, Lisai Liu, Sheng College of Computer Science and Technology Huaibei Normal University Huaibei235000 China Anui Engineering Research Center for Intelligent Computing and Application on Cognitive Behavior Huaibei235000 China

ISBN: (纸本)9789819987634

To assist blind people in travelling and solve the problems of high hardware costs, insufficient portability of blind assistance devices, and vulnerability to environmental impacts, a modified YOLOv5 algorithm (YOLOv5-CM) based on ground plane segmentation and Euclidean clustering algorithms is proposed and applied to a blind guidance system based on RealSense D435. This proposed algorithm adds coordinate attention and uses MobileNetv3 as the backbone network for YOLOv5s to extract the main features. Compared with those of the YOLOv5 model, the mAP was improved by 0.7%, the model size was decreased by 79.1%, and the number of parameters was reduced by 80.3%. In this study, the YOLOv5-CM algorithm was applied to design a blind guidance system. When the system detects a traffic light, the RealSense D435 is used to obtain depth images to detect and measure the distance from the pedestrian traffic light. The system provides warnings based on the measured distance. When the system does not detect a traffic light, it utilizes the ground plane segmentation and Euclidean clustering algorithms to obtain the location and distance of obstacles. This information is conveyed to the blind through voice guidance, which aids navigation. © 2024, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Clustering algorithms

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Beyond p-y method:A review of artificial intelligence approaches for predicting lateral capacity of drilled shafts in clayey soils

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Journal of Rock Mechanics and Geotechnical engineering 2024年第9期16卷 3812-3840页

作者： M.E.Al-Atroush A.E.Aboelela Ezz El-Din Hemdan Civil and Environmental Engineering Program Engineering Management DepartmentCollege of EngineeringPrince Sultan UniversityRiyadhSaudi Arabia Department of Structural Engineering Faculty of EngineeringAin Shams UniversityCairoEgypt Structure and Materials Research Lab Prince Sultan UniversityRiyadhSaudi Arabia Computer Science and Engineering Department Faculty of Electronic EngineeringMenoufia UniversityMenoufiaEgypt

In 2023,pivotal advancements in artificial intelligence(AI)have significantly *** that in mind,traditional methodologies,notably the p-y approach,have struggled to accurately model the complex,nonlinear soil-structure interactions of laterally loaded large-diameter drilled *** study undertakes a rigorous evaluation of machine learning(ML)and deep learning(DL)techniques,offering a comprehensive review of their application in addressing this geotechnical challenge.A thorough review and comparative analysis have been carried out to investigate various AI models such as artificial neural networks(ANNs),relevance vector machines(RVMs),and least squares support vector machines(LSSVMs).It was found that despite ML approaches outperforming classic methods in predicting the lateral behavior of piles,their‘black box'nature and reliance only on a data-driven approach made their results showcase statistical robustness rather than clear geotechnical insights,a fact underscored by the mathematical equations derived from these ***,the research identified a gap in the availability of drilled shaft datasets,limiting the extendibility of current findings to large-diameter *** extensive dataset,compiled from a series of lateral loading tests on free-head drilled shaft with varying properties and geometries,was introduced to bridge this *** paper concluded with a direction for future research,proposes the integration of physics-informed neural networks(PINNs),combining data-driven models with fundamental geotechnical principles to improve both the interpretability and predictive accuracy of AI applications in geotechnical engineering,marking a novel contribution to the field.

关键词： Laterally loaded drilled shaft load transfer and failure mechanisms Physics-informed neural networks(PINNs) P-y curves Artificial intelligence(AI) Dataset

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CIMulator: Evaluating Neuromorphic Accelerators with HZO FefinFET-Based Synaptic Device 7

CIMulator: Evaluating Neuromorphic Accelerators with HZO Fef...

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7th International Conference on Green Technology and Sustainable Development, GTSD 2024

作者： Le, Hoang-Hiep Lu, Hao-Yu Baig, Md. Aftab De, Sourav Lin, Ing-Chao Lu, Darsen D. Minghsin University of Science and Technology Department of Computer Science and Information Engineering Hsinchu Taiwan National ChengKung University Intelligent Computing Industrial Doctorate Program Tainan Taiwan Institute of Microelectronics National ChengKung University Tainan Taiwan National ChengKung University Department of Computer Science and Information Engineering Tainan Taiwan

ISBN: (纸本)9798350386073

In this study, we use the CIMulator platform to evaluate the performance of neuromorphic accelerators with novel Hf0.5Zr0.5O2 (HZO) ferroelectric fin field-effect transistor (FefinFET) as synaptic device. The MNIST handwritten digit dataset is used for training and inference processes in multilayer perceptron (MLP) neural networks. With the highly optimized synaptic device, the results demonstrate high training and inference accuracy, achieving up to 97% for the MLP, approaching the accuracy of software implementations. © 2024 IEEE.

关键词： Multilayer neural networks

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Polarity Classification of Twitter Data Using Machine Learning Approach

Polarity Classification of Twitter Data Using Machine Learni...

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2023 Human-Centered cognitive Systems, HCCS 2023

作者： Bangyal, Waqas Haider Iqbal, Muddesar Bashir, Ahsan Ubakanma, George Kohsar Univeristy Murree Dept. of Computer Science Muree Pakistan London South Bank University School of Engineering School of Engineering Cognitive Systems Research Centre United Kingdom Abasyn University Dept. of Computer Science Islamabad Pakistan

ISBN: (纸本)9798350359183

Sentiment analysis has become very important nowadays because there are lots of social media platforms peoples are using to express their opinion. Twitter is one of the most popular social media platforms which is used for microblogs. People use to express their opinion on current affairs, and there is a challenge for researchers to classify the sentiment accurately. In this research study, we proposed a greatly efficient technique for the detection of fake news on covid-19. The data set of fake news is taken from the corpus and executes the NLP cycle. In this research, we applied five machine learning to predict the sentiment of fake or real news. Support Vector Machine, Logistic Regression, KNN, Decision Trees, and Random Forest are machine learning classifiers used in this research, and results are compared. © 2023 IEEE.

关键词： Sentiment analysis

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A Shoe Shopping System based on Convolutional Neural Network Image Recognition 5

A Shoe Shopping System based on Convolutional Neural Network...

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5th IEEE Eurasia Conference on IOT, Communication and engineering, ECICE 2023

作者： Chang, Chin-Chih Wei, Chi-Hung Chen, Chen-Sheng Chen, Jian-An Yeh, Shih-Hsien Hsiao, Sean Chung Hua University Department of Computer Science and Information Engineering Hsinchu Taiwan Chung Hua University Program in Engineering Science Hsinchu Taiwan Chang Gung University Department of Computer Science and Information Engineering Taoyuan Taiwan Chung Hua University Program in Applied Foreign Language and Smart Commerce Hsinchu Taiwan

ISBN: (纸本)9798350314694

In this article, we present an innovative approach to enhance the online shoe shopping experience. The convolutional neural network (CNN) image recognition technology was used to enhance shoe classification and recommendations. By training the CNN model on an extensive dataset, unique shoe features and styles were learned. Integrated into a user-friendly online platform, the system offers real-time image recognition, allowing users to snap a photo of a desired shoe for instant identification, including brand, price, and availability details. Moreover, the CNN-based recommendation engine provides personalized suggestions based on style, color, and customer preferences, enriching the shopping experience. Evaluation results confirmed the system's feasibility, and user feedback highlighted its effectiveness in simplifying the shopping process and enhancing satisfaction. This innovative system presents a significant leap in merging AI and e-commerce and shows the potential of image recognition to transform online marketplaces, benefiting consumers, offering valuable insights for retailers, and ultimately reshaping the future of online shoe shopping. © 2023 IEEE.

关键词： convolutional neural network (CNN) deep learning image recognition recommender system

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CAWA: Correlation-Based Adaptive Weight Adjustment via Effective Fields for Debiasing 13

CAWA: Correlation-Based Adaptive Weight Adjustment via Effec...

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Joint 13th International Conference on Soft Computing and Intelligent Systems and 25th International Symposium on Advanced Intelligent Systems, SCIS and ISIS 2024

作者： Noh, Kyoungrae Kim, San Kim, Jaekwang Sungkyunkwan University Computer Science and Engineering Suwon Korea Republic of Sungkyunkwan University Convergence Program for Social Innovation Suwon Korea Republic of

ISBN: (纸本)9798350373332

In real-world image classification tasks, neural networks often encounter significant challenges due to unexpected or deceptive correlations and biases inherent in the dataset. These biases can emerge from disproportionate data distributions, causing models to generalize poorly to new, unseen data. Such data distribution issues are particularly problematic compared to more balanced datasets because they lead the model to rely on bias attributes rather than intrinsic attributes. Ideally, the model should classify based on intrinsic attributes, but due to the influence of bias attributes, frequent misclassification occurs. Such biases compromise the fairness and accuracy of the model, especially in critical scenarios such as medical diagnosis, autonomous driving, or criminal justice, where misclassification can have significant consequences. To address these challenges, we propose an innovative two-stage approach aimed at mitigating bias more effectively and efficiently. © 2024 IEEE.

关键词： Image classification

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Cuisine Image Recognition and Recommender System using Convolutional Neural Network 5

Cuisine Image Recognition and Recommender System using Convo...

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5th IEEE Eurasia Conference on IOT, Communication and engineering, ECICE 2023

作者： Chang, Chin-Chih Wei, Chi-Hung Wu, Kuan-Yun Kao, Hao-Xiang Hsiao, Sean Chung Hua University Department of Computer Science and Information Engineering Hsinchu Taiwan Chung Hua University Ph.D. Program in Engineering Science Hsinchu Taiwan National Kaohsiung University of Science and Technology Department of Computer Science and Information Engineering Kaohsiung Taiwan Chung Hua University Program of Applied Foreign Language and Smart Commerce Hsinchu Taiwan

ISBN: (纸本)9798350314694

The fusion of technology and culinary exploration has allowed for the emergence of advanced online customer service systems. We developed a novel approach to enhance the dining experience. We used a Cuisine Image Recognition and Recommender System (CIRRS) powered by Convolutional Neural Network (CNN) to identify and suggest diverse cuisines based on visual inputs. CIRRS swiftly identified diverse cuisines from user-captured images, offering information on origin, ingredients, and variations by crawling websites. The system enhanced dining experiences by suggesting personalized menus based on individual preferences and past selections. Extensively tested across various culinary genres, CIRRS consistently demonstrated accuracy and adaptability. User feedback validated its potential to simplify dining choices and elevate satisfaction. This innovative system enriched dining experiences as a valuable tool for culinary enthusiasts, chefs, and restaurateurs, bridging the gap between technology and gastronomy, It also offers a unique way to explore Taiwanese cuisines. © 2023 IEEE.

关键词： convolutional neural network (CNN) deep learning image recognition recommender system web crawler

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General Optimal Trajectory Planning:Enabling Autonomous Vehicles with the Principle of Least Action

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engineering 2024年第2期33卷 63-76页

作者： Heye Huang Yicong Liu Jinxin Liu Qisong Yang Jianqiang Wang David Abbink Arkady Zgonnikov School of Vehicle and Mobility Tsinghua UniversityBeijing 100084China Department of Cognitive Robotics Faculty of MechanicalMaritime and Materials Engineering(3mE)Delft University of TechnologyDelft 2628Netherlands Department of Software Technology Faculty of Electrical EngineeringMathematics and Computer ScienceDelft University of TechnologyDelft 2628Netherlands Xi’an Institute of High-Technology Xi’an 710051China

This study presents a general optimal trajectory planning(GOTP)framework for autonomous vehicles(AVs)that can effectively avoid obstacles and guide AVs to complete driving tasks safely and ***,we employ the fifth-order Bezier curve to generate and smooth the reference path along the road *** coordinates are then transformed to achieve the curvature continuity of the generated *** the road constraints and vehicle dynamics,limited polynomial candidate trajectories are generated and smoothed in a curvilinear coordinate ***,in selecting the optimal trajectory,we develop a unified and auto-tune objective function based on the principle of least action by employing AVs to simulate drivers’behavior and summarizing their manipulation characteristics of“seeking benefits and avoiding losses.”Finally,by integrating the idea of receding-horizon optimization,the proposed framework is achieved by considering dynamic multi-performance objectives and selecting trajectories that satisfy feasibility,optimality,and *** simulations and experiments are performed,and the results demonstrate the framework’s feasibility and effectiveness,which avoids both dynamic and static obstacles and applies to various scenarios with multi-source interactive traffic ***,we prove that the proposed method can guarantee real-time planning and safety requirements compared to drivers’manipulation.

关键词： Autonomous vehicle Trajectory planning Multi-performance objectives Principle of least action

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Optimal Multi-Keyword Ranked Search Matching in Public Cloud with Enhanced Secure Search Scheme Over Encrypted Data 9

Optimal Multi-Keyword Ranked Search Matching in Public Cloud...

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9th International Conference on science, Technology, engineering and Mathematics, ICONSTEM 2024

作者： Radha, B. Jaya Bharathi, R. Vidhya Muthulakshmi, R. Vidhya, K. Reddy, R. Vijaya Kumar Rondon, Ruchika Sri Krishna Arts And Science College Department of Ict And Cognitive Systems Tamil Nadu Coimbatore641008 India Rmk College of Engineering And Technology Department of Computer Science Engineering Tamil Nadu Puduvoyal601206 India Panimalar Engineering College Department of Artificial Intelligence And Data Science Tamil Nadu Chennai600 123 India Kpr Institute of Engineering And Technology Department of Computer Science Engineering Tamil Nadu Arasur641407 India Lakshmaiah Education Foundation Department of Computer Science Engineering Andhra Pradesh Vaddeswaram522502 India Graphic Era Deemed To Be University Department of Petroleum Engineering Uttarakhand Dehradun248002 India

ISBN: (纸本)9798350365092

In this research work, the authors present an improved secure search algorithm that will allow for optimum multi-keyword ranked search matching in public cloud storage that uses encrypted data. The goal of the plan is to provide a method that is both safe and effective for searching for and retrieving important data stored in cloud settings, all while preserving the data's privacy and confidentiality. Experiments and simulations are carried out in order to assess the viability of the suggested plan, and mathematical expressions are used to depict the algorithms and assessment criteria. According to the findings of the research, the strategy that was presented is capable of achieving a high level of accuracy, recall, and F1-score while simultaneously reducing the number of false positives and false negatives. The research underlines how important it is to address security problems in cloud computing settings, and it gives significant insights that can be used to design cloud computing services that are both safe and efficient. In the future, possible topics of study might include further refining and optimizing of the scheme that was described, as well as the application of the method in real cloud computing systems. © 2024 IEEE.

关键词： Data privacy

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The Utilization of Fuzzy Logic and Bollinger Bands to Enhance Trading Decision-Making During the Bitcoin Halving Phase

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Procedia computer science 2024年 245卷 272-281页

作者： Oscar Adam Darmawan Yaya Heryadi Lukas Lili Ayu Wulandhari Ilvico Sonata Computer Science Department Binus Graduate Program Doctor of Computer Science Bina Nusantara University Jakarta 11480 Indonesia Cognitive Engineering Research Group (CERG) Universitas Katolik Indonesia Atma Jaya Jakarta 12930 Indonesia

The volatile behavior of Bitcoin's price, especially during its halving periods, poses considerable obstacles for forecasting and decision-making in cryptocurrency trading. This paper presents a novel method that combines application fuzzy logic with Bollinger Bands to improve trading decision-making in times of high market volatility. This study conducted an experiment utilizing three fuzzy logic controllers and Bollinger Bands (BB) to determine the strength of buy, hold, and sell signals. This dataset includes the initial and final prices that are used to calculate the BB. The raw and computed values serve as the precise input parameters for the Fuzzy Inference System (FIS). The membership functions were categorized into four levels: very low, low, high, and very high, based on the input default settings utilized by traders. Rulesets were created using fuzzy logic to produce signals that indicate the level of strength of a trading advice. This study evaluate the effectiveness of this hybrid method in comparison to the traditional utilization of the Bollinger Band only indicator and Moving Average Convergence Divergence (MACD) indicator, which is widely favored by traders to identify possible market fluctuations. This methodology involves creating a trading simulation that is based on past Bitcoin halving events. The objective is to assess the efficacy of these strategies in managing heightened volatility. The application of fuzzy logic with the Bollinger Bands model yielded a success rate of 92.47% while analyzing 93 daily data points from the previous Bitcoin halving event on May 11, 2020.

关键词： fuzzy logic crypto price prediction bitcoin bollinger bands macd cryptocurrency

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