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检索条件"机构=Computer Science and Engineering Amity School of Engineering and Technology"

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A Classification Method for Depression Based on MRI 20

A Classification Method for Depression Based on MRI

引用

20th International computer Conference on Wavelet Active Media technology and Information Processing, ICCWAMTIP 2023

作者： Zhou, Chunwen Zeng, Feng Xing, Jianchuan School of Computer Science and Engineering University of Electronic Science and Technology of China Chengdu611731 China

ISBN: (纸本)9798350318982

Depression is a serious threat to human health, and the difficulty of diagnosis and stigmatized by society make it difficult to theat. More than 350 million people worldwide suffer from depression. Therefore, earlier and more accurate diagnosis can save patients' lives. Most depression detection methods such as deep learning methods and manual questionnaires, perform poor interpretability or need long detection time. Therefore, our designed a depression diagnosis method based on Magnetic resonance imaging data. Firstly, we preprocessed the image to obtain data with high signal-to-noise ratio. Secondly, we extracted structural brain networks and functional brain networks from the data as input features. Finally, we used a classification method base on support vector machines and light gradient boosting machine algorithm to obtain classification results. © 2023 IEEE.

关键词： Magnetic resonance imaging

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Delayed-choice quantum erasure using Poisson-distributed photons 14

Delayed-choice quantum erasure using Poisson-distributed pho...

引用

14th International Conference on Information and Communication technology Convergence, ICTC 2023

作者： Kim, Sangbae Ham, Byoung S. Gwangju Institute of Science and Technology School of Electrical Engineering and Computer Science Gwangju Korea Republic of

ISBN: (纸本)9798350313277

Quantum superposition lies at the core of quantum mechanics and has been applied to various quantum technologies. Over the past few decades, Wheeler's delayed-choice thought experiment has been extensively examined to explore the wave-particle duality of the complementary nature of quantum mechanics. The essence of the delayed-choice quantum eraser lies in the contradictory quantum property that violates the cause-effect relationship in classical physics. Our experiments present empirical evidence of the quantum eraser using coherent photon pairs provided in a noninteracting Mach-Zehnder interferometer for the polarization-basis control of output photons. The observed interference fringe is the direct proof of the quantum eraser, where the violation of the cause-effect relationship is due to the selective choices of the measurement events. © 2023 IEEE.

关键词： attenuated coherent photons complementarity theory delayed-choice quantum eraser self-interference wave-particle duality

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Proactive Congestion Management in Next-Generation Wireless Networks: Evaluating Traditional Approaches Against Machine Learning Models 2

Proactive Congestion Management in Next-Generation Wireless ...

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2nd International Conference Computational and Characterization Techniques in engineering and sciences, IC3TES 2024

作者： Thangaraj, Johnjustin Mohanraj, T. Caleb, S. Srija, J. Latha, R. Rajendran, Pushpalatha Saveetha Institute of Medical and Technical Sciences Saveetha School of Engineering Department of Computer Science and Engineering Chennai India Karpagam Academy of Higher Education Coimbatore Department of Computer Science Engineering India Karunya University Karunya Institute of Technology and Sciences Department of Computer Science and Engineering India Saveetha University Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences Department of Electronics and Communications Engineering Chennai India

ISBN: (纸本)9798350364699

sensible congestion control approaches continue to be imperative in attaining the highest capability of the next-generation facilities for wireless networks. To this end, the objective of the present research is to establish the effectiveness of the study conducted with classical congestion management techniques to that of the machine models. The proposed work environments a novel and controlled setup of the next-generation wireless network and applies, assesses, and optimizes the traditional QoS and traffic-shaping methodologies, and generic AI forms of the supervised learning and reinforcement learning. Other standards that were utilized and incorporated in the efficient aspect included the latency, through put and packet loss with the view of evaluating efficiency levels of the employed methods. This simply measures that the use of ML is more superior to the conventional methods because it relieves latency and packet loss while at times enhancing through put. The analysis of the two states' comparison proof that congestion recovery problems are more fit for the application of ML algorithms in modern transport systems due to better flexibility and online decision-making chances. Nevertheless, it discusses some problems that exist and they include;The major challenge of how to integrate the ML model in the system and the need to obtain massive training data. Therefore, one can state that the approach of applying more preventive ML models in controlling congestion also appears to be wiser when it comes to deploying lesions over wireless networks from the viewpoints of efficiency and scalability. Future work includes: working with real data;further work on the Advanced ML section;Security and Compliance. © 2024 IEEE.

关键词： Efficiency

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A Real-Time Road Anamoly Detection Using Smart Phone Sensors

A Real-Time Road Anamoly Detection Using Smart Phone Sensors

引用

International Conference on Contemporary Computing and Informatics (IC3I)

作者： Prabhat Singh Shilpi Sharma Department of Computer Science Amity School of Engineering and Technology Noida Uttar Pradesh India

ISBN: (数字)9798350350067

ISBN: (纸本)9798350350074

The quality of the conventional transport system has been significantly impacted recently by the swift advancement of emerging technologies and the rapid growth of the economy. ITS which seeks to enhance the transportation network, is gaining popularity. Additionally, one of the main goals of ITS is to enhance traffic safety, and potholes seriously compromise the safety of vehicles. Therefore, by establishing a real-time pothole-detecting system and disseminating pothole information, drivers’ safety may be enhanced. For assessing road conditions, we propose to analyze various supervised machine learning approaches employing mobile sensors and GPS data obtained from cellphones. Our research focuses on classifying three primary class labels: deep transverse cracks, potholes, and smooth roads. We test our hypothesis that employing information from the three sensors’ axes yields more accurate results than using data from just one. We also study how well deep neural networks do in classifying different types of traffic situations. Our findings demonstrate that smartphone sensor axes outperform models using only one axis. This demonstrates that vibration signals for road abnormalities contain information throughout all three axes. We also note that substantially more accurate data classification is obtained when using neural networks. The techniques discussed here can be applied more broadly to monitor road conditions that could endanger people’s safety. Commuters in addition to giving pertinent authorities maintenance information.

关键词： Vibrations Accuracy Roads Transportation Machine learning Real-time systems Safety Maintenance Smart phones Monitoring

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Flexible and Effective Cellular Traffic Data Synthesis with Large Language Model

Flexible and Effective Cellular Traffic Data Synthesis with ...

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2024 IEEE Global Communications Conference, GLOBECOM 2024

作者： Duan, Sijing Lyu, Feng Cen, Jinfeng Ren, Ju Yang, Peng Zhang, Yaoxue Tsinghua University Department of Computer Science and Technology Beijing China Central South University School of Computer Science and Engineering Changsha China Huazhong University of Science and Technology School of Electronic Information and Communications Wuhan China

ISBN: (纸本)9798350351255

Cellular traffic data hold significant potential for applications such as network planning, traffic prediction, mobility modeling, and personalized recommendations. However, limited data accessibility hinders more open data-driven research. Previous studies have explored data synthesis, while exhibiting flexible limitations in supporting conditional traffic synthesis, and are vulnerable to multidimensional data modeling. In this paper, we present LLMCell, a flexible and effective framework that leverages the arbitrary conditioning and contextual understanding capabilities of the large language model (LLM) to generate high-quality synthetic cellular traffic data. The LLMCell comprises three key components: i) a textual encoder for converting raw cellular traffic data into textual representations, ii) a generative model learner to fine-tune pre-trained LLM based on encoded textual representation for cellular traffic generation, and iii) a synthetic data sampling module for final synthetic data sampling and textual-to-data transformation. Experiments conducted on a large-scale dataset demonstrate the superior fidelity and utility of LLMCell over state-of-the-art baselines, and the synthetic data can effectively preserve user privacy. We release our synthetic dataset to the public to benefit future research in the wireless network community1 © 2024 IEEE.

关键词： Cellular Traffic Flexible Data Synthesis Large Language Model

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Air Pollutant (CO) Forecasting: A Hybrid Approach Using Gooseneck Barnacle Optimization and Artificial Neural Networks 4

Air Pollutant (CO) Forecasting: A Hybrid Approach Using Goos...

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4th IEEE International Conference on Artificial Intelligence in Cybersecurity, ICAIC 2025

作者： Ahmed, Marzia Mohamad, Ahmad Johari Hassan, Md Maruf Bhuiyan, Touhid Department of Software Engineering Dhaka Bangladesh Faculty of Electrical & Electronics Engineering Technology Malaysia Southeast University Department of Computer Science and Engineering Dhaka Bangladesh School of IT VA United States

ISBN: (纸本)9798331518882

Air quality forecasting is critical for environmental monitoring and public health, and in this study, we propose a hybrid approach utilizing Gooseneck Barnacle Optimization (GBO) and Artificial Neural Networks (ANN) to enhance predictive accuracy. The air quality dataset, comprising pollutant concentrations (CO, NOx, benzene) and meteorological factors (temperature, humidity), is sourced from the UCI Machine Learning Repository. GBO is employed to fine-tune the ANN's hyperparameters (especially the weight and bias), as well as the number of layers, neurons, and activation functions, to improve regression-based forecasting. The GBOANN model achieves a Mean Squared Error (MSE) of 0.562 and a Root Mean Squared Percentage Error (RMSPE) of 5.63%, outperforming other metaheuristic methods like BMO, SSA, DA, MVO, and EMA. This study demonstrates the GBO-ANN model's potential for accurate real-time air quality forecasting, offering a robust, sustainable solution. © 2025 IEEE.

关键词： Air Quality GBO Metaheuristic Neural Network Time series forecasting

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nmODE-MVC: Neural Memory ODE-Based Multi-View Clustering 7

nmODE-MVC: Neural Memory ODE-Based Multi-View Clustering

引用

7th Asian Conference on Artificial Intelligence technology, ACAIT 2023

作者： Zheng, Yan Wu, Song Ren, Yazhou Pu, Xiaorong Xu, Zenglin He, Lifang Yi, Zhang University of Electronic Science And Technology of China School of Computer Science And Engineering Chengdu China University of Electronic Science And Technology of China School of Computer Science And Engineering Shenzhen Institute For Advanced Study China Harbin Institute of Technology Shenzhen School of Computer Science And Technology Shenzhen China Lehigh University Department of Computer Science And Engineering Bethlehem United States Sichuan University Intelligent Interdisciplinary Research Center College of Computer Science Chengdu China

ISBN: (纸本)9798350359145

Deep multi-view clustering (MVC) has recently gained significant interest for its capability to harness complementary information across multiple views through deep neural networks, enhancing clustering performance. However, existing deep MVC methods still have two issues: (1) They separate the feature learning objective and the clustering objective, potentially leading to suboptimal embedding features due to the neglect of the global clustering structure. (2) Most works utilize the target distribution obtained by the clustering objective to supervise feature learning. However, they simply sharpen the soft clustering distribution to obtain the target distribution, lacking the dynamics of the clustering process. To address these issues, in this paper we propose a novel end-to-end self-supervised multi-view clustering approach based on NeuralODEs, namely nmODE-MVC. Our approach operates in two stages: In the first stage, all view-embedded features are extracted, and then unified them into a global feature, facilitating global clustering assignments by exploiting multi-view data's complementarity. The second stage leverages the dynamic attributes of NeuralODEs to dynamically refine the data features and generate a target distribution that actively drives the clustering decisions. We formulate these learning processes as a unified optimization problem, facilitating iterative training and refinement. The effectiveness of nmODE-MVC is validated through extensive experiments on multiple real-world multi-view datasets. © 2023 IEEE.

关键词： Ordinary differential equations

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Landform Segmentation in Terrain Images Using Image Translation Neural Network Architectures 23th

Landform Segmentation in Terrain Images Using Image Translat...

引用

23rd International Conference on Intelligent Systems Design and Applications, ISDA 2023

作者： Bajaj, Anu Bhardwaj, Ankit Tuteja, Yessica Abraham, Ajith Computer Science and Engineering Department Thapar Institute of Engineering and Technology Patiala India School of Artificial Intelligence Bennett University Greater Noida India

ISBN: (纸本)9783031648496

Satellites have made it easier for scientists to study various aspects of Earth and however, it is still difficult to interpret the complexities of Earth’s terrains accurately and efficiently. One of the key endeavors of satellite imagery is landform classification and terrain analysis which can be performed by the recent developments in areas of machine learning, especially deep learning architectures. Therefore, in this work, we proposed variants of three most widely used Neural Network Architectures: Convolutional Neural Networks (CNN), Autoencoders, and U-Net for mapping terrain maps to landform segmentation maps. The output is validated by using popular metrics like accuracy and structural similarity index (SSIM). A comprehensive comparative analysis of each of these models’ training patterns and how these parameters change over repeated training iterations is also presented. Observations state that the proposed U Net performed well in decreasing the loss and increasing the structural similarity. On the other hand, Convolutional Autoencoder has better accuracy and lesser computational cost compared to U Net and CNN. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2024.

关键词： Landforms

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Network Abnormal Traffic Detection Approach Based on Multi-Scaled Deep-CapsNet 5

Network Abnormal Traffic Detection Approach Based on Multi-S...

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5th International Conference on Inventive Research in Computing Applications, ICIRCA 2023

作者： Tanwar, Shashi Thokala, Rishi Reddy Thapak, Sonika Maranan, Ramya Kumar, Sudheer Sravanthi, S. Aravali College of Engineering and Management Department of Computer Science and Engineering Haryana Faridabad India Srm Institute of Science and Technology Computer Science Engineering India Ies Collage of Technology Computer Science and Engineering India Saveetha School of Engineering Simats Department of Research and Innovation Tamil Nadu Chennai602105 India Piet Parul University Computer Science & Engineering Gujrat Vadodara India St. Martin's Engineering College Department of Computer Science Engineering secunderabad India

ISBN: (纸本)9798350321425

It is crucial for network security to be able to detect a variety of threats and unusual network traffic. In order to accomplish aberrant traffic detection, existing detection methods required enormous volumes of data. However, there are rare circumstances in which even a fewshot attack sample can be stopped. The shift in feature attitude will also have an impact on traffic attribute discrimination. However, the standard neural network model is unable to recognize this shift in perspective. As a result, few-shot sample anomalous traffic detection has poor accuracy and efficiency. This research has proposed a strategy for detecting unusual network traffic with a small number of samples. Multi-scale Deep-CapsNet and adversarial-reconstruction (MDAR) made up the system. To begin, the Deep-CapsNet's EM vector clustering is revamped. The prediction is finalized by transforming low-level to high-level features using the attitude transformation matrix. Second, the Deep-CapsNet was improved with the help of a multi-scale convolutional capsule. Finally, a reconstruction classification network (ARCN) that is vulnerable to adversarial input is proposed. Classification of the supervised source data and reconstruction of the unsupervised target data are both completed. In addition, an adversarial training technique that reduced the impact of noise on the reconstruction process is suggested. Using a mixture of MDAR, classification is obtained from a small number of samples (point 4). The results are tested on the ICSX2012 and CICIDS2017 datasets. The experimental outcomes validate the superior training performance of the approach. Additionally, its accuracy is unparalleled in both two- and multi-classification tasks. It is especially useful for real-time few-shot aberrant network traffic detection because to its good anti-noise performance and quick operating time. © 2023 IEEE.

关键词： Network security

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Fortifying Cloud Data Security with the Maritime Search and Rescue Algorithm 2

Fortifying Cloud Data Security with the Maritime Search and ...

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2nd International Conference on Data science and Information System, ICDSIS 2024

作者： Monica, K.M. Parameswaran, T. Swapna, J. Prabu, S. Kaliappan, S. Vit University School Of Computer Science And Engineering Chennai India Cmr University Lakeside Campus School Of Engineering And Technology Department Of Computer Science And Engineering Bangalore India Veltech Rangarajan Dr.Sagunthala R&d Institute Of Science And Technology Department Of Computer Science And Engineering Tamilnadu Chennai India Mahendra Institute Of Technology Department Of Ece Namakkal India Lovely Professional University Division Of Research And Development Punjab Phagwara India

ISBN: (纸本)9798350381665

Cloud computing has fundamentally transformed the accessibility and payment models of services within distributed system environments. In response to heightened security concerns, cloud service providers offer diverse resources spanning Infrastructure, Software, Platform, and Database services, supported by security measures such as Service Level Agreements (SLAs), access control rules, and cryptographic methods. This paper introduces a novel data encryption technique, termed Data Security in Cloud Computing using Maritime Search and Rescue Algorithm (DSCC-MSRA). By integrating an enhanced cypher block chaining algorithm with logical substitution and the Maritime Search and Rescue Algorithm (MSRA) for key generation, this method provides a unique approach to encryption. Through rigorous optimization and analysis, it has undergone significant refinement, with comparisons against industry-standard algorithms showcasing its efficacy. The primary objective is to elevate encryption procedures and introduce complexity to cryptanalysis, ultimately enhancing data security in cloud computing. © 2024 IEEE.

关键词： Cryptography

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