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检索条件"任意字段=8th IEEE International Conference on Software Engineering and Computer Systems, ICSECS 2023"

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Artificial Intelligence-Assisted Diagnosis of Endobronchial Tuberculosis 8th

Artificial Intelligence-Assisted Diagnosis of Endobronchial ...

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8th international conference on Life System Modeling and Simulation, LSMS 2024 and 8th international conference on Intelligent Computing for Sustainable Energy and Environment, ICSEE 2024

作者： Hu, Lingyan Wang, Bin Xin, Yuqiao Chen, Zhongshu Li, Yanzhe Ruan, Hengkai School of Electronic and Electrical Engineering Shanghai University of Engineering Science Shanghai201620 China School of Information Engineering Nanchang University Nanchang330031 China School of Electronics and Computer Science University of Southampton SouthamptonSO171BJ United Kingdom Jiangxi Chest Hospital Nanchang330006 China

ISBN: (纸本)9789819601875

With the development of artificial intelligence (AI) in the field of diagnosis, AI-assisted diagnosis has attracted much attention. However, hospitals have no AI diagnosis system for endobronchial tuberculosis (EBTB). To build an AI diagnosis system for EBTB, a high-quality EBTB medical image database and an efficient ResNet34-MH network model that can identify and classify EBTB were built. the traditional 3 × 3 convolution of Layer4 in the original ResNet34 model is replaced with a Multi-Head Self-Attention mechanism (MHSA) module, allowing the model to extract local and global image features. the accuracy of the AI diagnosis system for EBTB was 87.5%, and the time of diagnosis was 41ms. © the Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.

关键词： Deep learning

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Examining the Influence of Dependency Injection on software Maintainability

Examining the Influence of Dependency Injection on Software ...

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Big Data, Cloud Computing, Data Science & engineering (BCD)

作者： Chun-Wei Sun Chun-Feng Liao Department of Computer Science National Chengchi University Taipei Taiwan Department of Computer Science and Program in Digital Content and Technologies National Chengchi University Taipei Taiwan

Dependency Injection (DI) is a widely adopted design pattern in the software industry. DI reduces the otherwise necessary coupling between a client and the concrete object providing the service. this decoupling is achieved by delegating the responsibility of object creation to an external DI module, commonly referred to as a DI Container. Nevertheless, DI can introduce certain side effects. To guide the external DI module on the wiring logic, developers must either detail these logics in configuration files or use language constructs specific to the DI implementation in use. In large-scale projects, managing these wiring logics can pose a significant challenge to software maintainability. Currently, the Spring Framework stands as one of the most prominent DI implementations. It provides three methods for describing the wiring logic: XML, Annotation, and Java Class. this paper delves into the effects of these three methods on software maintainability, their influence on software design, and the optimal circumstances for each method’s use in managing wiring logic. Our study examines the issues aforementioned using the well-known web application benchmark, JPetStore. We adapt the benchmark application’s source code through different wiring logic implementations and evaluate the resultant effects on software coupling metrics. Finally, we explore the implications of these modifications on software coupling and maintainability.

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Smart Traffic Light Switching and Traffic Density Calculation Model using computer Vision

Smart Traffic Light Switching and Traffic Density Calculatio...

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international conference for Convergence of Technology (I2CT)

作者： Sarvesh Ransubhe Mohammad Abdul Mughni Chinmay R. Shiralkar Bhakti Ratnaparkhi Pimpri Chinchwad College Of Engineering Pune

Different Traffic control systems have played a crucial part in traffic management around the globe, especially in densely populated major cities, but they are still not as efficient as they could be. Perhaps some changes can be made to better deal with the traffic in this ever-changing traffic density environment. Traffic congestion has consistently been a rage issue in numerous urban cities. the traditional way was to give each lane a specific predefined time with the green light and had to stop for the rest of the time. Even the lanes with no traffic got the same amount of time as the lane with huge traffic jams. these were promoting traffic congestion rather than solving the issue. thus, the need for a better system has emerged for changing the current traffic handling setup to be smarter enough to meet this ever-changing demand. In this paper, the idea of traffic lights controlled by live video feed is explored with an enhanced traffic flow system to optimally benefit from the computer vision technology used.

关键词： computer vision Computational modeling Urban areas Green products Switches Traffic control Feeds

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Attention-Based Knowledge-aware Multi-Interest Intelligent Model for Sequential Recommendation 19

Attention-Based Knowledge-aware Multi-Interest Intelligent M...

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2022 ieee SmartWorld, 19th ieee international conference on Ubiquitous Intelligence and Computing, 2022 ieee international conference on Autonomous and Trusted Vehicles conference, 22nd ieee international conference on Scalable Computing and Communications, 2022 ieee international conference on Digital Twin, 8th ieee international conference on Privacy Computing and 2022 ieee international conference on Metaverse, SmartWorld/UIC/ATC/ScalCom/DigitalTwin/PriComp/Metaverse 2022

作者： Li, Yang Li, Qianmu Meng, Shunmei Hou, Jun Nanjing University of Science and Technology School of Computer Science and Engineering Nanjing China Wuyi University Intelligent Manufacturing Department Jiangmen China Nanjing University State Key Lab. for Novel Software Technology Nanjing China Nanjing Vocational University of Industry Technology School of Social Science Nanjing China

ISBN: (纸本)9798350346558

As a fundamental means of intelligent service, recommender systems have shown a trend of diversification. Sequential recommendation, a class of time-aware methods, aims to predict the user's next click. Multi-interest representation is a hot topic in recommender systems, but the research on how to apply multi-interest into sequences is still in the minority. And many methods will introduce knowledge graph to improve the representation of items, but how to consider both knowledge graph and user's multi-interest in sequential recommendation has become a temporary blank. In this paper, we propose an attentive sequential recommendation with knowledge-aware multi-interest module (AKAMI). Items are embedded from the sequence's structure and user's interests. Specifically, we represent items by stacking self-attention layers to mine the internal correlation of the sequence. Meanwhile, we segment the sequence via the common neighbors in the knowledge graph and cast the user's multi-interest based on the aggregation of subsequences. Multi-interests are integrated into item representation by the gating network adaptively. Comprehensive experiments on two real-world datasets show that the proposed model performs better than various representative methods under different evaluation metrics. Additionally, we also visually explain the model through a case study. © 2022 ieee.

关键词： Recommender systems

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Battery State of Energy Estimation Method Based on Conditional Selection Neural Network Model 8th

Battery State of Energy Estimation Method Based on Condition...

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8th international conference on Life System Modeling and Simulation, LSMS 2024 and 8th international conference on Intelligent Computing for Sustainable Energy and Environment, ICSEE 2024

作者： Liu, Kaituo Li, Yihuan Peng, Qiao School of Control and Computer Engineering North China Electric Power University Beijing102206 China Group of Information Technology Analytics and Operations Queen’s University Belfast BelfastBT9 5EE United Kingdom

ISBN: (纸本)9789819602315

there are differences in the discharge data of batteries at different life stages. Considering these differences, this paper proposes a conditional selection neural network model for estimating the SOE. It can flexibly select the appropriate network for prediction based on a set selection mechanism under different conditions, thereby improving the model’s applicability and generalization ability. Firstly, the concept of Squeeze-and-Excitation is incorporated into the CNN and the resulting SE-CNN model is pretrained. Next, a selection mechanism is designed according to the degree of battery aging, thus the training data is divided into four parts based on the number of discharge cycles to further train the pre-trained network to form a "four-in-one" CS-SE-CNN model. During SOE estimation, the CS-SE-CNN model selects the prediction network based on the number of discharge cycles in the input data. the results demonstrate that the proposed method exhibits high accuracy and robustness in estimating the SOE of lithium-ion batteries. © the Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： State of charge

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Rule-based Self-supervised learning Non-Intrusive Load Monitoring system for residential houses

Rule-based Self-supervised learning Non-Intrusive Load Monit...

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Recent Advances and Innovations in engineering (ICRAIE)

作者： Nimantha Madhushan Rasanjalee Rathnayake Ashmini Udhyani Uditha Wijewardhana Nishan Dharmaweera Dept. of Electrical & Electronic Eng University of Sri Jayewardenepura Nugegoda Sri Lanka Dept. of Computer Eng University of Sri Jayewardenepura Nugegoda Sri Lanka

Real-time non-intrusive load monitoring (NILM) systems have gained significant importance in various applications, including demand-side management, appliance anomaly detection, and ambient assisted living. While numerous research-based solutions have been developed over the past two decades, still it remains a need for an affordable application-level NILM solution. In this paper, we present a novel rule-based self-supervised approach that operates within a low-price range. Our proposed methodology offers automatic identification of unknown appliances within the system, achieving real-time appliance identification with a satisfactory level of accuracy. We contribute to the development of a cost-effective and efficient solution for non-intrusive load monitoring, enabling applications across diverse domains.

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A Low-cost ESP32-driven Wireless Key Generation System Based on Response Mechanism

A Low-cost ESP32-driven Wireless Key Generation System Based...

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international conference on computers, Communications, and systems (ICCCS)

作者： Huaicong Zhang Jiabao Yu Xudong Chen Ye Tian Wangdong Qi Aiqun Hu School of Cyber Science and Engineering Southeast University Nanjing China Purple Mountain Laboratories Nanjing China Department of Security Technology Research Institute of China Mobile Communications Corporation Beijing China School of Information Science and Engineering Southeast University Nanjing China

In recent years, secret key generation method based on the physical layer of wireless channel has been widely studied. Since Channel State Information (CSI) is a fine-grained characterization of channel information, there are currently many studies using CSI for key generation. However, existing CSI extraction tools have the problem that the time interval between uplink and downlink interaction is uncertain, which may result in insufficient reciprocity of CSI. In this paper, we propose a channel measurement method based on the response mechanism of the ieee 802.11 protocol, and develop a low-cost and universal CSI extraction tool using ESP32. We further design and implement a wireless key generation system based on the tool. To evaluate the performance of the proposed method, we have conducted extensive experiments in an indoor environment. Results show that our method can obtain highly reciprocal CSI in both static and mobile scenarios and can achieve a high key generation rate at around 1788.23 bits/s.

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thermal Monitoring of Li-Ion Batteries Using Convolutional Neural Networks and Fibre Bragg Grating Sensors 8th

Thermal Monitoring of Li-Ion Batteries Using Convolutional ...

引用

8th international conference on Life System Modeling and Simulation, LSMS 2024 and 8th international conference on Intelligent Computing for Sustainable Energy and Environment, ICSEE 2024

作者： Zhang, Li Li, Kang Ma, Jingjie Jia, Xingyu Liu, Shiyun School of Electrical and Electronic Engineering Shanghai Institute of Technology Shanghai201418 China School of Electronic and Electrical Engineering University of Leeds LeedsLS2 9JT United Kingdom School of Industrial and Information Engineering Politecnico di Milano Milan20133 Italy

ISBN: (纸本)9789819602315

thermal safety of Li-ion Batteries is an important issue in their applications in electric vehicles and grid-tied energy storage. thermal monitoring of Li-ion Batteries is crucial in preventing thermal failures and prolong lifetime. However, the existing temperature monitoring method are sensitive to corrosion and electromagnetic radiation, without considering the aging factor and non-electric features leading to low accuracy for full life cycle thermal monitoring of batteries under different operating conditions. To tackle the challenges, this paper presents a thermal model using convolutional neural networks incorporating FBG sensors. In order to more accurate, the attribute information fusion of non-electric variables (wavelengths) and the electric variables (voltage, current, state of charge) is developed for battery temperature monitoring in the proposed method. the experimental results based on different cells indicate that the proposed model has desirable accuracy which achieves the root mean squared error around 0.2∘C on the validation data. © the Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Convolutional neural networks

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Conversion of American Sign Language to Text Using Deep Learning for Feature Extraction and Naive Bayes for Classification

Conversion of American Sign Language to Text Using Deep Lear...

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international conference for Convergence of Technology (I2CT)

作者： Nishanth Addepalli Ravi Kanth Pabolu Siva Ganesh Chennuru Vara Lakshmi Vissampalli G L Madhumati Department of ECE Velagapudi Ramakrishna Siddhartha Engineering College Vijayawada A.P

Sign language serves as a bridge between persons who are physically impaired and those who are not. It is necessary to increase the consciousness about sign language in public, cause people with abnormalities loose many opportunities of growth, recognition in life due to lack of communication abilities. Hence, sign language plays a vital role in human life. It also improves Human computer interaction (HCI) . this paper aims to describe a vision-based system of approach for recognition of people’s hand gestures in order to help them communicate more effectively with normal people. this method aims to recognize American Sign Language (ASL) hand gestures and translate them into Text. After a thorough analysis, the best potential technique to converting real-time American Sign Language into text with the approach of Transfer Learning using Alexnet for feature extraction and with classification algorithms like KNN, SVM, Naive Bayes in MATLAB software are used. Among the three Classifiers, the Naive Bayes achieved highest accuracy of 97.92%.

关键词： Support vector machines Human computer interaction Training Analytical models Text recognition Transfer learning Gesture recognition

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Improving Intrusion Detection systems for IoT Devices using Automated Feature Generation based on ToN_IoT dataset

Improving Intrusion Detection Systems for IoT Devices using ...

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international conference on computer Science and engineering (UBMK)

作者： Kazım Kıvanç Eren Kerem Küçük Department of Computer Engineering Kocaeli University Kocaeli Turkey Department of Software Engineering Kocaeli University Kocaeli Turkey

the Internet of things (IoT) has witnessed exponential growth in recent years, leading to a diverse and interconnected ecosystem of devices. However, this rapid expansion has also made IoT vulnerable to various security threats and attacks. the interconnected nature of IoT devices and their extensive integration into everyday life make them enticing targets for malicious actors. Consequently, the development and deployment of effective intrusion detection systems for IoT environments have become crucial. In the literature, it has been observed that feature engineering, feature extraction, and other preprocessing steps are problematic. the general trend has been to develop intrusion detection systems using complex models such as deep learning concepts, while reducing the effort spent on feature engineering. In this study, the importance of feature engineering is addressed, and it is demonstrated that effective results can be achieved with simple models when proper preprocessing and feature generation steps are applied. An intrusion detection system for IoT devices has been implemented in the ToN_IoT dataset by employing appropriate preprocessing steps and, additionally, utilizing mechanisms for automatic feature generation. In the experiments conducted on the ToN-IoT dataset, we propose a simple model that gives comparable results with the state-of-the-art deep learning models. this model utilizes a basic random forest algorithm and benefits f rom a different t raining scheme that take the benefits of grouping, stratification, re sampling, and automated feature generation strategies. We achieved 99.99% ROC-AUC values for both train and independent test sets. the proposed method shows mostly better performances for specifity, precision, recall, and F1-score than deep learning based models.

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