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检索条件"机构=Dept. System Engineering and Computer Science"

共 36316 条记录，以下是741-750 订阅

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Improving Time Series Forecasting Accuracy with Transformers: A Comprehensive Analysis with Explainability 3

Improving Time Series Forecasting Accuracy with Transformers...

引用

3rd International Conference on Electrical, Electronics, Information and Communication Technologies, ICEEICT 2024

作者： Joseph, Susmera Jo, Ashly Ann Raj, Ebin Deni Indian Institute of Information Technology Dept. of Computer Science Engineering Kottayam India

ISBN: (纸本)9798350369083

Time series forecasting is crucial in numerous sectors, including healthcare, energy, and finance. Transformer models, initially designed for natural language processing applications, demonstrate potential in capturing temporal dependencies within sequential data. In this paper, the application of transformer models in time-series forecasting is explored, and their performance and interpretability are investigated. Transparent models build trust and facilitate decision-making, especially in industries with ethical guidelines. While inherently interpretable time-series Transformers exist, there is a gap in research regarding post hoc methods, which is an area ripe for exploration. Understanding and comparing complex Transformer models for time series forecasting can benefit from advancements in explainability, ensuring accurate and comprehensible predictions. In comparison to five other transformer-based models and classical models, the Temporal Fusion Transformer (TFT) model outperformed them in short-term forecasting. Further enhancement of the model performance was achieved through a meticulous analysis of the Encoder-Decoder's importance, which contributed significantly to refining the forecasting accuracy. The vanilla transformer was explained using permutation feature importance. The model stability was analyzed using perturbation methods. © 2024 IEEE.

关键词： Spatio-temporal data

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An Innovative Ensemble Approach to Detecting Distributed Denial of Service Attacks Using Machine Learning

An Innovative Ensemble Approach to Detecting Distributed Den...

引用

2024 International Conference on Artificial Intelligence and Quantum Computation-Based Sensor Applications, ICAIQSA 2024

作者： Rajput, Deepak Singh Upadhyay, Arvind Kumar Amity University Dept. of Computer Science and Engineering Madhya Pradesh Gwalior India

ISBN: (纸本)9798331517953

In the present era marked by rapid Internet development, there is a growing emphasis on information security. Distributed Denial of Service (DDoS) attacks poses significant threats to servers globally. Various methods and services have emerged to prevent and mitigate damage caused by these attacks. The challenge in implementing defence lies in timely attack detection, requiring a highly accurate mechanism. DDoS attack methods are diverse and evolving, rendering traditional prevention methods less effective. This research explores the application of machine learning, utilizing the CICDDoS2019 dataset to train hybrid models. The proposed ensemble model combines unsupervised K-Means for dataset labelling and supervised classifiers (Random Forest, XGBoost, AdaBoost, SVM) with Artificial Neural Network (ANN). Achieving an overall accuracy of 99.46%, this study underscores the effectiveness of deep learning in DDoS attack detection. © 2024 IEEE.

关键词： Random forests

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Fair and Efficient Congestion Management for Low Voltage Distribution Networks 8

Fair and Efficient Congestion Management for Low Voltage Dis...

引用

8th IEEE International Energy Conference, ENERGYCON 2024

作者： Vadavathi, Aswin Ramesh Hoogsteen, Gerwin Hurink, Johann dept. of Electrical Engineering Mathematics and Computer Science University of Twente Enschede Netherlands

ISBN: (纸本)9798350382150

The widespread increase of photovoltaic panels in the distribution grid often leads to overvoltage. Traditional voltage regulation techniques are unfair for prosumers at the sensitive part of the grid due to larger power curtailment. Consequently, there has been considerable research on implementing fair curtailment strategies for congestion management. However, existing fairness-incorporated curtailment approaches yield varying results based on changes in the grid topology and integration of renewable energy sources. Therefore, this paper introduces proportional voltage fairness that is independent of the above-mentioned network properties and inherently distinguishes regions for fair curtailment. Additionally, there is a need to consider the underutilisation of the grid while adhering to a fair curtailment scheme. Thus this paper proposes a control algorithm incorporating proportional voltage fairness and gives the opportunity to specify the trade-off between fairness and the total power curtailed. Further, a comparative analysis with respect to state-of-the-art notions of fairness is performed. ©2024 IEEE.

关键词： Electric power distribution

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Eva: Python-based Desktop Virtual Assistant for Visually Impaired 7

Eva: Python-based Desktop Virtual Assistant for Visually Imp...

引用

7th International Conference on Circuit Power and Computing Technologies, ICCPCT 2024

作者： Abraham, Anishamol Mathew, Benita Susan Mathew, Dona Lisa Mohammad, Fiaz S Krishnan, Gokul Amal Jyothi College of Engineering Dept. of Computer Science Kottayam686518 India

ISBN: (纸本)9798350372816

Virtual assistants have become essential resources in the digital age, providing users with a wide range of functionalities to expedite work and increase productivity. In this research, we provide a complete examination of Eva, a virtual assistant created using Python, and compare it to other virtual assistants such as Cortana and Copilot. Through a meticulous examination of Eva's features, we demonstrate its superiority in versatility and effectiveness. By embracing Eva's strengths and capitalizing on its competitive advantages, we push for a future in which digital experiences are inclusive and empowering for all users. © 2024 IEEE.

关键词： Python

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Survey on Online Learning Applications in Multi-UAV Networks 15

Survey on Online Learning Applications in Multi-UAV Networks

引用

15th International Conference on Information and Communication Technology Convergence, ICTC 2024

作者： Kim, Jaemin Kim, Hyosu Paek, Jeongyeup Son, Yongseok Lee, Hyung Tae Cho, Sungrae Chung-Ang University Dept. of Computer Science and Engineering Seoul Korea Republic of

ISBN: (纸本)9798350364637

This paper provides a comprehensive review of the application of online learning techniques in multi-UAV systems, highlighting their role in enhancing autonomy and efficiency in dynamic environments. Key areas of focus include adaptive path planning, collision avoidance, and swarm behavior control, along with the associated technical challenges and future research directions. © 2024 IEEE.

关键词： Federated learning

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Transformer-based Self-Supervised Imputation and Attention GANs Oversampling for Medical Data Processing 31

Transformer-based Self-Supervised Imputation and Attention G...

引用

31st Annual IEEE International Conference on High Performance Computing, Data, and Analytics, HiPC 2024

作者： Singh, Aryan Kumar Saikia, Arpit Baro, Pranita Borah, Malaya Dutta Dept. of Computer Science and Engineering National Institute of Technology Silchar Silchar India

ISBN: (纸本)9798331509095

Dealing with missing data and class imbalance is a necessity for the development of a solid predictive model in medical research. This work presents a novel hybrid dynamic method devoted to addressing these dual challenges, specifically focusing on medical datasets. In this work, we investigate the incompleteness and imbalance of the dataset using two sophisticated measures: the Incompleteness Factor and the Imbalance Ratio. These metrics are used to guide customized approaches. A transformer-based imputation method is employed to learn complex data dependencies to impute missing data. Second, the oversampling method of Self-Attention Enhanced Conditional Generative Adversarial Network is implemented. This technique generates better model training results by accommodating self-attention mechanisms that create fake samples that follow the actual data distributions as well as class distributions. This paper introduces one potential solution to obtain model-level accuracy increase for tasks like disease prediction, patient prognosis, etc., over medical datasets. The proposed method aims to remove the measurement biases caused by incompleteness and class imbalance and preprocess the data to enhance the credibility and clinical relevance of the experimental results. © 2024 IEEE.

关键词： Generative adversarial networks

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Empirical Investigation of Adversarial Attacks for Semi-Supervised Object Detection 6

Empirical Investigation of Adversarial Attacks for Semi-Supe...

引用

6th International Conference on Artificial Intelligence in Information and Communication, ICAIIC 2024

作者： Jo, Junhyung Kim, Joongsu Suh, Young-Joo Pohang Korea Republic of Dept. of Computer Science and Engineering Pohang Korea Republic of

ISBN: (纸本)9798350344349

Semi-supervised learning (SSL) techniques have been rapidly developed and adopted in various vision tasks because of their advantage of leveraging unlabeled data. However, existing works have neglected the vulnerability of SSL because most of the existing adversarial attacks are mainly discussed in a supervised learning manner. In this paper, we study the effects of adversarial examples on Semi-Supervised Object Detection (SS-OD) which is the mainstream of SSL techniques. We build our hypothesis that attacks on the supervised learning model are also effective on SSL models. Since the state-of-The-Art SS-OD methods borrow the teacher-student network, we prepared two pseudo-label based SS-OD networks to validate our hypothesis. We attempt to attack the inference model with adversarial examples crafted by using pretrained auxiliary model and found that SS-OD networks are more vulnerable to adversarial attacks. In addition, we found that selecting different loss components of SS-OD networks to generate perturbations determines the effect and performance of the attack such as misclassification or mislocalization. Visual examples are provided for a clearer understanding. To the best of our knowledge, this is the first effort to investigate the vulnerability of SS-OD. © 2024 IEEE.

关键词： Object detection

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A Survey on Machine and Deep Learning Approaches in Sign Language Recognition: Techniques and Future Trends

A Survey on Machine and Deep Learning Approaches in Sign Lan...

引用

2024 International Conference on Signal Processing, Computation, Electronics, Power and Telecommunication, IConSCEPT 2024

作者： Harshitha, C. Sendil Vadivu, D. Rajagopalan, Narendran National Institute of Technology Puducherry Dept. of Computer Science and Engineering Karaikal India

ISBN: (纸本)9798331540685

Sign language is an essential communication medium for individuals with hearing impairments. It enables them to convey messages, disseminate knowledge, and transfer ideas within the deaf community. However, not everyone understands sign language, making it challenging for individuals with hearing impairments to communicate effectively with other common people. Sign Language Recognition (SLR) is vital for bridging the communication gap between persons with hearing loss and non-sign language speakers. This survey paper aims to explore Machine Learning (ML) and Deep Learning (DL) techniques for recognition of sign languages. Investigate further research scope in this field. © 2024 IEEE.

关键词： Adversarial machine learning

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Machine Learning-based Decision Making for Charging/Discharging Cost Optimization of PREV in Smart City 1

Machine Learning-based Decision Making for Charging/Discharg...

引用

1st International Symposium on Parallel Computing and Distributed systems, PCDS 2024

作者： Neelakantam, Gone Ecole Centrale School of Engineering Mahindra University Dept. Computer Science and Engineering Hyderabad India

ISBN: (纸本)9798350349658

Smart cities, equipped with smart grid infrastructure, establish advanced communication networks that facilitate interactions among multiple entities. It is well-known that smart grid technology ensures the reliable transmission of electricity. Additionally, Plug-in Hybrid Electric Vehicles (PHEVs) significantly contribute to the efficient utilization of energy in mobility-aware environments. The Intelligent Transportation system (ITS) enables Vehicle-to-Infrastructure (V2I) communication, which is crucial for providing transportation services and managing PHEV recharging at user-preferred locations. This capability is a key element of smart city infrastructure. To optimize the use of PHEV services in a smart city, addressing the cost minimization of charging and discharging is essential. Therefore, this paper proposes a decision tree machine learning-based algorithm within a fog computing platform aimed at minimizing charging and discharging costs. Performance evaluation demonstrates that the model outperforms existing algorithms in terms of accuracy. These results indicate that our model can accurately predict the costs associated with charging and discharging PHEVs in smart city environments. © 2024 IEEE.

关键词： Adversarial machine learning

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A novel approach incorporating feature extraction followed by YOLOv7 for quality enhancement of mangoes in Bangladesh 14

A novel approach incorporating feature extraction followed b...

引用

14th International Conference on Computing Communication and Networking Technologies, ICCCNT 2023

作者： Kundu, Debashis Rahaman, Md. Mushfiqur Sakib, Md. Nazmus Abdullah Al Sohan, Md. Faruk Fardaush Mithu, Jannatul Hossain, Afzal Aktar, Nishita American International University- Bangladesh Dept. Management Information Systems Dhaka Bangladesh Daffodil Institute of It Dept. of Computer Science and Engineering Dhaka Bangladesh World University of Bangladesh Dept. of Computer Science and Engineering Dhaka Bangladesh American International University- Bangladesh Dept. of Computer Science Dhaka Bangladesh North South University Dept. of Electrical and Computer Engineering Dhaka Bangladesh Savar Dept. of Computer Science and Engineering Dhaka Bangladesh

ISBN: (纸本)9798350335095

Qualitative approach for automated grading and quality assessment of fruits, machine learning techniques are crucial in agricultural applications. Automation enhances a nation's agricultural quality, production, and economic prosperity. Fruit quality grading, particularly the surface fault identification of a fruit, is a crucial indicator in the export market. This is particularly important for mangoes, which are quite well-liked inBangladesh. On the other hand, the physical grading of mangoes is a procedure that is labor-intensive, prone to error, and very subjective. In this paper, we proposed a YOLOv7 integrated Discrete wave transformation computer vision system. The proposed model includes support vector machine (SVM) and decision tree for the classification of high-quality mangoes. The results of the experiments show that the proposed solution obtained 96.25% accuracy when the system was trained and tested using a publicly accessible mango database. © 2023 IEEE.

关键词： Support vector machines

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