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2024 International Conference on Cybernation and Computation, CYBERCOM 2024

作者： Paramesh, J. Sriram, K.P. Anbalagan, E. Sasikumar, S. Vimal Kumar, M. Guru Saveetha Engineering College Department of Computer Science and Engineering Chennai India Information Technology St.Joseph's Institute of Technology OMR Chennai India Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences Department of Computer Science and Engineering Saveetha University Chennai India Vel Tech Rangarajan Dr.Sagunthala R&D Institute of Science and Technology Department of Computer Science and Engineering Chennai India

ISBN: (纸本)9798331542344

The study is centred on examining an intelligent security system for real-time threat identification and mitigation in cloud-network systems. The more cloud computing environments are being constructed;conventional models of security do not offer optimal solutions to counteract changing cyber threats. This framework incorporates the use of sophisticated algorithms in machine learning and real-time data analysis to achieve a more proactive form of defence measures. It incorporates anomaly detection, classification algorithms and predictive analysis in the improvement of threat detection and response time. The framework also has active response mechanisms for timely deliberation on anything that may be seen to have a detrimental effect on the cloud. Analyzes reveal high levels of detection, timely responses and scalability proving the suitability of the framework to handle new threats and/or blend with existing cloud components. The findings of this study can be considered as valuable contribution to cloud security since the approach proposed in this paper can be effectively employed to enhance protection against new types of threats in specific organizational contexts. © 2024 IEEE.

关键词： Cloud security

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Multi-Criteria Decision-Making Model to Achieve Sustainable Developmental Goals in Industry 4.0 for Smart City Infrastructure

HighTech and Innovation Journal

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HighTech and Innovation Journal 2024年第4期5卷 1135-1153页

作者： Akila, D. Pal, Souvik Sarkar, Bikramjit Jayalaksshmi, S. Muthaiyah, Saravanan Anbananthen, Kalaiarasi Sonai Muthu Department of Computer Applications Saveetha College of Liberal Arts and Sciences Saveetha Institute of Medical and Technical Sciences India Department of Management Information Systems Saveethas College of Liberal Arts and Sciences Saveetha Institute of Medical and Technical Sciences India Department of Computer Science and Engineering JIS College of Engineering Kalyani India Department of Information Technology Vels Institute of Science Technology and Advanced Studies India School of Business and Technology International Medical University Kuala Lumpur57000 Malaysia Faculty of Information Science and Technology Multimedia University Melaka75450 Malaysia

Due to a shortage of funding and other market challenges, Small and Medium-sized Enterprises (SMEs) face difficulties in adopting new technologies. Numerous technological obstacles negatively impact the long-term commercial achievement of SMEs. The deployment of Industry 4.0hopes to resolve these technological challenges. A sustainable city is a complex structure where economic, societal, and ecological components interact and compete. There is a scarcity of l methodologies for measuring interactions in this complex structure. Industry 4.0 aims to obtain higher performance effectiveness, profitability, and automation. The main goal is to develop a reliable method of evaluating small and medium-sized enterprises (SMEs) adopting Industry 4.0 technologies, particularly concerning smart city applications. This paper aims to determine the influence of Industry 4.0 in fostering economic efficiency and sustainability amongst these SMEs. The study introduces a multi-criteria decision-making (SC-MCDM) system designed to test an SME’s achievement of their targeted sustainable developmental goals. A technique for computing the interaction between various standards, i.e., (static interactions and dynamical pattern resemblance), as well as the weightage of variables of every indicator generated by the connection, is included within SC-MCDM. Furthermore, applying the suggested technique is validated by assessing the sustainable development goals of twelve Chinese cities within the Triple Bottom Line (TBL) paradigm. From a geographic-temporal viewpoint, spatial variations in city sustainability reveal regional sustainable inequalities. Indicator scores suggest that the most significant factors for most communities are the lack of research spending, falling financing in stationary assets, shortage of financial development, and inadequate shared transit. Furthermore, the growth of tertiary industries, improvement of energy performance, expansion of green areas, and reduction of poll

关键词： Sustainable city

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Multilevel Chaotic Maps with Differential Encoding-Based Encryption Method for DICOM Images 5th

Multilevel Chaotic Maps with Differential Encoding-Based En...

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5th International Conference on Computing, Communications, and Cyber Security, IC4S 2023

作者： Sharma, Suvita Rani Singh, Birmohan Kaur, Manpreet Department of Computer Science and Engineering Sant Longowal Institute of Engineering and Technology Longowal Punjab Sangrur148106 India Department of Electrical and Instrumentation Engineering Sant Longowal Institute of Engineering and Technology Longowal Punjab Sangrur148106 India

ISBN: (纸本)9789819773701

This paper proposes an encryption method based on multiple chaotic maps and differential encoding method to secure the medical images. The proposed method uses a permutation-based image scrambling method in the confusion phase. In the diffusion phase, Piecewise Linear Chaotic Map (PWLCM), Piecewise Chaotic Map (PWCM), and tent map with differential encoding method have been employed at different levels of the phase to secure the medical images. The model has been tested on Digital Imaging and Communications in Medicine (DICOM) images. The proposed encryption method demonstrates robust security against statistical and entropy attacks, with encrypted images exhibiting a correlation close to zero and achieving a maximum entropy value of 7.9993. The low SSIM value between the original and encrypted images confirms their dissimilarity. Higher Mean Square Error (MSE) values and lower Peak Signal-to-Noise Ratio (PSNR) values for encrypted images prove a negligible presence of original pixel values. Additionally, the encryption approach showcases resilience against differential attacks, with a Number of Pixel Change Rate (NPCR) value of 99.6% and a Unified Averaged Changed Intensity (UACI) value of 33.4%, both within acceptable standard ranges. The effectiveness of the proposed method demonstrates that these encrypted DICOM images are safe against various forms of attacks. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Digital Imaging and Communications in Medicine (DICOM)

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Investigation of operation and degradation mechanisms in ZnTeSe blue quantum-dot light-emitting diodes by identifying recombination zone

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Nano Research 2024年第7期17卷 6527-6533页

作者： Oul Cho Sujin Park Hogeun Chang Jiwhan Kim Jaekwon Kim Sungwoo Kim Taehyung Kim Jeonghun Kwak Department of Electrical and Computer Engineering Inter-university Semiconductor Research Centerand Soft Foundry InstituteSeoul National UniversitySeoul 08826Republic of Korea Material Research Center Samsung Advanced Institute of TechnologySuwonGyeonggi-do 16419Republic of Korea

ZnTeSe quantum dots(QDs),recognized as promising eco-friendly blue electroluminescent emitters,remain under-explored in light-emitting diode(LED)***,to elucidate the operation and degradation mechanisms of ZnTeSe blue QD-LEDs,stacked ZnTeSe QD layers with discernable luminescence are designed by varying Te doping concentrations,and the recombination zones(RZs)of the blue QD-LEDs are *** RZs are identified near the hole-transport layer(HTL),confirmed by angular-dependent electroluminescence measurements and optical *** addition,in order to investigate carrier dynamics in the process of recombination,the transient electroluminescence(tr-EL)signals of the dichromatic QD-LEDs are *** a result,it is inferred that the RZ initially formed near the electron-transport layer(ETL)due to the high injection barriers of ***,due to the fast electron mobility,the RZ shifts toward the HTL as the operating current *** the device lifetime tests,the RZ remains stationary while the photoluminescence(PL)corresponding to the RZ undergoes a substantial decrease,indicating that the degradation is accelerated by the concentrated *** this study contributes to a deeper understanding of the operational mechanisms of ZnTeSe blue QD-LEDs.

关键词： quantum dots light-emitting diodes degradation recombination zones carrier dynamics

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Fair Machine Learning in Healthcare: A Survey

IEEE Transactions on Artificial Intelligence

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IEEE Transactions on Artificial Intelligence 2025年第3期6卷 493-507页

作者： Feng, Qizhang Du, Mengnan Zou, Na Hu, Xia Texas A&M University Department of Computer Science & Engineering College StationTX77843 United States New Jersey Institute of Technology Department of Data Science NewarkNJ07102 United States University of Houston Department of Industrial Engineering HoustonTX77004 United States Rice University Department of Computer Science HoustonTX77251 United States

The digitization of healthcare data coupled with advances in computational capabilities has propelled the adoption of machine learning (ML) in healthcare. However, these methods can perpetuate or even exacerbate existing disparities, leading to fairness concerns such as the unequal distribution of resources and diagnostic inaccuracies among different demographic groups. Addressing these fairness problems is paramount to prevent further entrenchment of social injustices. In this survey, we analyze the intersection of fairness in ML and healthcare disparities. We adopt a framework based on the principles of distributive justice to categorize fairness concerns into two distinct classes: equal allocation and equal performance. We provide a critical review of the associated fairness metrics from a ML standpoint and examine biases and mitigation strategies across the stages of the ML lifecycle, discussing the relationship between biases and their countermeasures. The article concludes with a discussion on the pressing challenges that remain unaddressed in ensuring fairness in healthcare ML and proposes several new research directions that hold promise for developing ethical and equitable ML applications in healthcare. © 2024 IEEE.

关键词： Adversarial machine learning

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Enhancing Bitcoin Price Prediction Using LSTM and GRU Models 12th

Enhancing Bitcoin Price Prediction Using LSTM and GRU Models

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12th International Conference on Recent Trends in Computing, ICRTC 2024

作者： Anitha Rajakumari, P. Karthick, S. Gupta, Shruti Gupta, Shreya Mahapatra, R.P. Vinoth Kumar, S. Department of Computer Science and Engineering Vel Tech Rangarajan Dr. Sagunthala R&D Institute of Science and Technology Tamil Nadu Chennai India Department of Computer Science and Engineering SRM Institute of Science and Technology Delhi NCR Campus Ghaziabad201204 India

ISBN: (纸本)9789819789450

Abstract Bitcoin, as the pioneering cryptocurrency, has witnessed significant fluctuations in its price since its inception. Predicting these fluctuations is of great interest to investors, traders, and researchers alike. This paper presents a comparative study of Long Short-Term Memory (LSTM) and Gated Recurrent Unit (GRU) models for predicting Bitcoin prices. Cryptocurrency markets, particularly Bitcoin, exhibit high volatility and non-linearity, making accurate price prediction challenging. Recurrent Neural Networks (RNNs), including LSTM and GRU, have shown promise in capturing temporal dependencies and patterns in sequential data. In this study, we train and evaluate LSTM and GRU models separately on historical Bitcoin price data to forecast future price movements. Through comprehensive experimentation and analysis, we assess the performance of both models in terms of accuracy and effectiveness in Bitcoin price prediction. The performance of these models is evaluated based on various metrics such as Mean Absolute Error (MAE), Root Mean Square Error (RMSE), and Mean Absolute Percentage Error (MAPE). The results demonstrate the effectiveness of LSTM and GRU in predicting Bitcoin prices and offer insights into their comparative performance. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Long short-term memory

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Deep learning for deepfake detection: Scientific landscape and future directions 4th

Deep learning for deepfake detection: Scientific landscape a...

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4th International Conference on Computational Methods in Science and technology, ICCMST 2024

作者： Dhiman, Pummy Kaur, Amandeep Sharma, Dilip Kumar Chitkara University Institute of Engineering and Technology Chitkara University Punjab India Department of Computer Engineering and Application GLA University Mathura India

ISBN: (纸本)9781032911571

In today’s digital age, technology has advanced to the point where it is difficult to distinguish be-tween genuine and forged media content. It was intended to be used for entertainment, but it is now being used to deceive people and create biasness, which can lead to disastrous situations. The goal of this bibliometric study is to provide scientific mapping on the role of deep learning in deepfake detection published online. Scopus and VOSviewer are used to create a concept map of 128 articles. Additionally, future directions for research are suggested. © 2025 the Author(s).

关键词： Fake detection

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Fully integrated Windows framework for source localization with MNE Python and FreeSurfer

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Current Directions in Biomedical engineering 2023年第1期9卷 371-374页

作者： Oppermann, Hannes Wulf, Simon Komosar, Milana Haueisen, Jens Haueisen, Jens Department of Computer Science and Automation Institute of Biomedical Engineering and Informatics Technische Universität Ilmenau Ilmenau Germany Department of Neurology Biomagnetic Center University Hospital Jena Jena Germany

There is a variety of software packages, toolboxes, or libraries for the analysis and processing of neurophysiological data such as EEG and MEG. Many of these solutions provide algorithms for both, sensor-space analysis and sourcespace analysis. Especially with the solutions that run on Windows machines, it is noticeable that the step of the volume model generation is usually not included, since the state-ofthe- art software for this (FreeSurfer) is a Unix-based software and thus not available forWindows machines. Therefore, our goal was to develop a fully-integrated software solution for Windows machines, accessing all processing steps already implemented in an existing toolbox and using FreeSurfer in the same system. Due to its widespread use, we chose MNE Python as the basis for our fully integrated software solution. We used the Windows Subsystem for Linux to create a virtual Linux kernel for the FreeSurfer installation. To demonstrate the workflow, the libeep, and AutoReject libraries have been added. A 64-channel EEG recording during right-hand movement (ME) and imagination (MI) was used to test the implemented workflow. The developed framework consists of several modules within Python, mainly using existing scripts and functions. The library libeep was integrated to read the EEG data with the '.cnt', eeprope format. AutoReject was used to automatically interpolate detected bad channels or to reject complete epochs. FreeSurfer was successfully integrated and customized Python scripts enabled the communication between MNE Python on a Windows machine and FreeSurfer on a virtual Linux kernel. With the above-mentioned EEG dataset, we performed source reconstruction and were able to show ERD/S patterns for both, ME and MI. Our new, fullyintegrated software framework can be used on Windows machines to perform a complete process of source reconstruction. © 2023 the author(s), published by Walter de Gruyter Berlin/Boston.

关键词： Python

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A Wavelet Transform Algorithm based Detection and Classification of Sleep Apnea for Monitoring of Health

A Wavelet Transform Algorithm based Detection and Classifica...

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2023 IEEE International Conference on Integrated Circuits and Communication Systems, ICICACS 2023

作者： Pillai, Beena G Madhurya, A.J. Babu, V Jeevan Kumar Reddy, A Siva Siddiqua, Ayesha Gitam School of Technology Gitam University Department of Computer Science & Engineering India Hyderabad Institute of Technology and Management Department of Computer Science and Engineering Telangana India K L University Vadeeshwaram Department of Computer Science & Engineering Vijayawada India Nitte Meenakshi Institute of Technology Department of Electronics and Communication Engineering Bengaluru India

ISBN: (纸本)9798350398465

One-third of a person's life is spent sleeping, and this physiological phenomenon serves several purposes, such as restoring and maintaining normal brain metabolism, allowing the cardiovascular system to recharge, and restoring metabolic balance to the body's glucose supply. Sleep has intimate ties to physical restoration, hormone regulation, and immune system maintenance. During sleep, the body's metabolic rates shift from catabolism (the breakdown of tissues) to anabolism (the rebuilding of tissues). This state of mind is not a constant slumber but rather a dynamic internal structure with a periodic temporal progression. It was often believed that sleep was a non-active state, but modern research has shown that our brains are actually quite busy while we sleep. A multivariate Wavelet transform-based feature selection method has been used since different authors have advocated differing usefulness of different features from the time domain, the frequency domain, and nonlinear features in earlier work on sleep (SL) staging. This provided an optimal collection of 21 features that led to significantly better sleep stage classification performance than previous approaches. Once the signals in the time domain have been transformed into the time-frequency domain, the wavelet coherence coefficients can be calculated to determine the instantaneous respiration rate, and the instantaneous power of the individual signals, the common power, and the phase difference can be examined at the corresponding frequency component. The suggested method improves upon earlier approaches in terms of both event detection and categorization, as evidenced by the experiments. © 2023 IEEE.

关键词： Tissue

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Hybrid Approaches to Emotion Recognition: A Comprehensive Survey of Audio-Textual Methods and Their Application 4

Hybrid Approaches to Emotion Recognition: A Comprehensive Su...

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4th International Conference on Advanced Research in Computing, ICARC 2024

作者： Wewelwala, Sahan Hewage Sumanathilaka, T.G.D.K. School of Computing Informatics Institute of Technology 06 Colombo Sri Lanka Swansea University Department of Computer Science Wales Swansea United Kingdom

ISBN: (纸本)9798350384864

This survey study provides a comprehensive analysis of emotion recognition, documenting its progression from conventional techniques to innovative hybrid algorithms that effectively combine textual and audio information. This work distinguishes itself with its thorough examination of the innovative combination of deep learning-based textual sentiment analysis and sophisticated aural signal processing approaches. This unique combination greatly improves the precision of emotion recognition from intricate sources like social media and consumer feedback. The study addresses the difficulties of processing real-time data and reducing bias in varied datasets. It provides new perspectives on the powers and limitations of neural networks and machine learning in this specific scenario. Our research represents a notable advancement in the utilization of these technologies for human-computer interaction, facilitating the creation of digital interfaces that are more empathic and attuned to human emotions. The final statements underscore the pressing requirement for sophisticated multimodal methodologies and the incorporation of developing technology, underscoring our distinctive contribution to the progress of emotion identification systems. This report offers a thorough analysis of the present condition of the subject and outlines a direction for future advancements, highlighting the crucial significance of emotion identification in improving human-computer interaction. © 2024 IEEE.

关键词： Emotion Recognition

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