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2023 International Conference on Energy, Power, Environment, Control, and Computing, ICEPECC 2023

作者： Mursal, Muhammad Hassan Ahmed, Usama Nayyer, Muhammad Ziad Government College University Department of Software Engineering Faisalabad Pakistan GIFT University Department of Computer Science Gujranwala Pakistan

ISBN: (纸本)9781665476010

Cloud federation has enabled organizations to adopt collaborative services for sharing data and workloads across various platforms. Induction of federation members may require some verifications and predictions related to capacity and capability of these members for compensating such types of workloads. However, the nature of federated services require stringent methods to keep track of dynamically forming resource clusters for forecasting their behavior. Recent literature has mostly focused on the applicability of forecasting algorithms based on static datasets with little or no applicability to real time scenarios. Proposed research has utilized a real world application of Clouds4Coordination (C4C) federation system. A resource forecasting strategy using two well-known algorithms, namely Autoregressive Integrated Moving Average (ARIMA) and Long Short-Term Memory (LSTM) has been proposed for collaborative clouds used in Architecture, engineering and Construction (AEC) industry. The results have shown that no single algorithm is sufficient enough to deal with dynamic scenarios of cloud federation. Moreover, the selection of algorithm is highly dependent upon the type and duration of prediction required i.e. short term or long term as required by the user. © 2023 IEEE.

关键词： Forecasting

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Classifying Human Emotions through EEG data with Machine Learning

Classifying Human Emotions through EEG data with Machine Lea...

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2024 International Conference Automatics and Informatics, ICAI 2024

作者： Kaur, Gaganjot Gupta, Meenu Kumar, Rakesh Chandigarh University Department of Computer Science and Engineering Punjab India

ISBN: (纸本)9798350353907

The Psychological and neurobiological phenomenon is called an emotion. Nowadays, using emotional engineering for the prediction of human emotions is one of the major attempts of Machine Learning (ML). However, the capability of human beings to present different expressions clashes with the emotional state of mind. Therefore, by observing physical appearance it is challenging to be able to identify a human's real emotional state. In this study, the objective is to examine the usage of machine learning techniques for an efficient emotion prediction that relies on Electroencephalography (EEG) signals. Using the Kaggle dataset that was collected through a Muse headband, five distinct ML methods are applied to classify emotions. To differentiate between different classification techniques performance metrics such as precision, recall, F1 score, support, and accuracy are used, Random Forest turned out to be the best method due to its high accuracy. Three emotions are predicted based on the results of performance metrics. This study is beneficial for mental health monitoring, neurofeedback therapy, stress management, human-computer interaction, etc. This study lays the groundwork for exploration into this field and helps in the development of more sophisticated control systems in the future. © 2024 IEEE.

关键词： Emotion Recognition

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Ensuring Confidentiality and Integrity of Electronic Health Records in Blockchain Through Multi-Attribute Access Control

Ensuring Confidentiality and Integrity of Electronic Health ...

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International Health Informatics Conference, IHIC 2023

作者： Mahapatro, Smita Basha, Syed Muzamil Department of Computer Science and Engineering REVA University Bengaluru560064 India

ISBN: (纸本)9789819771899

The digitalization of traditional healthcare data, such as Electronic Health Records (EHR), presents significant challenges for healthcare organizations. Sharing and storing EHRs can be difficult, and accessing the information can be time consuming and sometimes inaccurate due to limited access. To address these issues, this article suggests a permission-based blockchain-based system for sharing healthcare data. This system uses multiple attribute encryption based on cipher text to protect data privacy and authenticate healthcare information. Additionally, it integrates blockchain technology (also knownas DistributedLedgerTechnology) with an RSAcryptosystem for added security. The proposed system employs a multi-attribute access control mechanism along with an RSA cryptosystem to achieve strong data security. The ultimate goal of this technology is to create a single platform that stores and distributes health information using a Distributed Ledger-based approach. This eliminates the need for printed healthcare reports, streamlining the entire medical business onto one secure platform. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Electronic health record

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A Review on Feature Selection Techniques for Anxiety Classification from Physiological Signals 1

A Review on Feature Selection Techniques for Anxiety Classif...

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1st International Conference on Advances in computer science, Electrical, Electronics, and Communication Technologies, CE2CT 2025

作者： Shikha, Shikha Sethia, Divyashikha Indu, S. Delhi Technological University Department of Computer Science and Engineering New Delhi India Delhi Technological University Department of Software Engineering New Delhi India Delhi Technological University Department of Electronics and Communication New Delhi India

ISBN: (纸本)9798331518578

Anxiety disorders significantly impact individuals' quality of life and are traditionally assessed using subjective methods, which often lack accuracy. Recent advancements have enabled the use of physiological signals for anxiety classification, providing a more objective approach. However, the multidomain nature of these signals results in high-dimensional feature sets, necessitating effective feature selection. Therefore, this paper reviews various feature selection techniques-filter, wrapper, embedded, and hybrid-used in physiological-based anxiety classification, analyzing the advantages and limitations. It also highlights key challenges and suggests future research directions. Furthermore, this review introduces a proposed study utilizing a novel Corr-FGA hybrid feature selection algorithm to develop an efficient anxiety classification framework. The proposed framework achieves the highest accuracy of 88.93% using 8 features. This review aims to guide researchers in developing a robust, efficient anxiety classification model while providing insights that can help streamline feature selection and optimize machine learning model performance in real-world applications. © 2025 IEEE.

关键词： Adversarial machine learning

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Prexion: Probabilistic Matching-based Compression to Mitigate Compression Side Channel Attacks against HTTPS

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IEEE Access 2025年 13卷 86069-86082页

作者： Kim, Hyoungshick Hyun, Sangwon Sungkyunkwan University Department of Computer Science and Engineering Gyeonggi-do Suwon16419 Korea Republic of Myongji University Department of Computer Engineering Gyeonggi-do Yongin17058 Korea Republic of

Web servers typically use data compression and encryption to optimize data transmission and protect user privacy. However, compression can inadvertently introduce vulnerabilities to compression side channel attacks (CSCA), which exploit the relationship between data redundancy and compression size to infer sensitive information. In this paper, we introduce Prexion, a novel compression system designed to counter CSCA while maintaining high compression efficiency. Prexion leverages a probabilistic mechanism to selectively include or exclude overlapping sequences in web page data during compression, making it difficult for attackers to exploit patterns in compressed sizes. This approach preserves the benefits of compression without compromising security. Through extensive experiments with real-world web pages, Prexion demonstrates superior performance, achieving up to 4.9 times better compression ratios than the current state-of-the-art solution, Debreach, for web pages with significant amounts of sensitive data. Furthermore, Prexion ensures robust security, with attack success rates consistently below 5% under recommended configurations. © 2013 IEEE.

关键词： Sensitive data

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A Federated Learning Approach Towards a Privacy-Preserving Technique for Brain Tumor Classification 2nd

A Federated Learning Approach Towards a Privacy-Preserving T...

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2nd International Conference on Data science and Network engineering, ICDSNE 2024

作者： De, Anurag Pal, Gautam Shyam, Karnam Tej, Kalakanti Pawan School of Computer Science and Engineering VIT-AP University Andhra Pradesh Amaravati India Computer Science and Engineering Department TIT Narsingarh Tripura Agartala India

ISBN: (纸本)9789819783359

The brain is one of the main organs in the human body. It controls almost all the actions of a human being. Any problem with the brain can lead to even fatal consequences. One of the most dangerous problems faced by the human brain is a brain tumor. If it is not treated properly at early stages, it may lead to severe conditions. However, finding a brain tumor is a very challenging task as the brain structure is very complex. Also, a lot of image data is required to train the model to detect brain tumors. However, the hospitals are not ready to share patient data to train the model. Hence, an approach to train the model by seeing the actual dataset or simply using decentralized data from multiple clients needs to be followed. So, a federated learning methodology is utilized to train the model. The trained federated learning model has achieved a training accuracy of 91% and a test accuracy of 88%. The federated learning and transfer learning approach stands out in preserving privacy, optimizing model performance, and leading to good collaboration among healthcare entities. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Federated learning

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Advanced Threat Protection for File Storage 4

Advanced Threat Protection for File Storage

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4th IEEE International Conference on Emerging Techniques in Computational Intelligence, ICETCI 2024

作者： Neelakantan, P. Malige, Gangappa Vnr Vjiet Department of Computer Science and Engineering Hyderabad India

ISBN: (纸本)9798350387063

Cloud storage is widely used in various industries, but concerns about data security persist. Traditional techniques like cryptography and steganography have limitations and cannot provide sufficient security on their own. To address this issue, we propose a unique security system that combines multiple cryptographic algorithms and steganography. By using fast and secure symmetric key algorithms, such as AES and DES, along with the benefits of the asymmetric key algorithm RSA, we develop an efficient method to encrypt and decrypt data. This approach takes advantage of the strengths of each algorithm to create a strong encryption system that protects data from unauthorized access. Furthermore, incorporating splitting and merging techniques adds an extra layer of security. The LSB steganography technique is used to securely store essential key information. Implementing this comprehensive approach in the cloud environment not only enhances server security but also builds user confidence, as encrypted data is stored on a single cloud server. Overall, this sophisticated security mechanism offers enhanced data protection, making cloud storage a more reliable and secure option for various applications in different industries. © 2024 IEEE.

关键词： Cloud security

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IoT and Cloud Synergy in Healthcare: A Unified Approach for Remote Monitoring of Vital Health Metric

IoT and Cloud Synergy in Healthcare: A Unified Approach for ...

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2024 International Conference on Computational Intelligence for Green and Sustainable Technologies, ICCIGST 2024

作者： Nailwal, Sagar Raza, Arbaz Chauhan, Kunal Singh, Gurpreet Kaur, Amanpeet Chandigarh University Department of Computer Science and Engineering Punjab India

ISBN: (纸本)9798350381887

The goal of this study is to revolutionise healthcare monitoring systems by introducing a novel method that combines cloud computing and the Internet of Things. The model prioritises the use of an advanced array of sensors to collect and analyse vital health indicators in real-time. The temperature sensor, infrared sensor, sound, pulse oximeter, and CO2 level monitor are among the important sensors (MQ-145). Sophisticated machine learning and artificial intelligence methods can detect and anticipate major health issues like asthma and cardiovascular diseases early on in the process of gathering data, which is then effortlessly transmitted to a cloud platform. In addition to guaranteeing patient comfort and delivering reliable health data, the system sets itself apart by providing continuous, non-invasive monitoring. Cloud technology integration improves data access and storage, giving medical practitioners immediate analytics to help them make better decisions. By facilitating quick medical reactions and lowering the dangers related to delayed diagnosis, this approach promises to greatly improve patient care. All things considered, our system offers a scalable, efficient, and proactive approach to health management in the digital age, marking a significant advancement in remote healthcare monitoring. © 2024 IEEE.

关键词： Electronic health record

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The Psychology Behind Addictive Applications in Technology 5

The Psychology Behind Addictive Applications in Technology

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5th IEEE International Conference on Computing, Power, and Communication Technologies, IC2PCT 2024

作者： Hans, Aarti Singh, Nongmeikapam Thoiba Kumar, Rohit Kumar, Anshul Choubey, Anish Suresh Chandigarh University Department of Computer Science and Engineering Punjab India

ISBN: (纸本)9798350383522

The proliferation of mobile applications in the digital age has introduced a captivating yet disconcerting dimension to human behavior and technology interaction. Mobile applications, ranging from social media platforms to games and productivity tools, have gained notoriety for their potential to foster addiction-like behaviors in users. This research paper delves into the intricate realm of the psychology behind addictive applications in technology, seeking to illuminate the multifaceted dynamics that underlie this contemporary challenge. Drawing on insights from psychology, cognitive science, and human-computer interaction, this study explores the fundamental questions surrounding the nature of addiction within the digital context. It investigates the cognitive and emotional factors that drive users to engage compulsively with specific applications, shedding light on the alluring mechanisms that captivate and hold user attention. Furthermore, this research uncovers the ethically charged design and marketing strategies employed by application developers to perpetuate addictive use patterns. By examining the deliberate design choices that exploit human psychology, we aim to provoke a deeper conversation about the responsible development and marketing of digital technologies. © 2024 IEEE.

关键词： human behavior human-computer interaction retention social comparison social media platforms user engagement

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LSTM-Based Resource Prediction for Disaggregated RAN in 5G Non-Terrestrial Networks 2

LSTM-Based Resource Prediction for Disaggregated RAN in 5G N...

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2nd IEEE Virtual Conference on Communications, VCC 2024

作者： Tsegaye, Henok B. Tshakwanda, Petro M. Worku, Yonatan M. Sacchi, Claudio Christodoulou, Christos Devetsikiotis, Michael University of New Mexico Department of Electrical and Computer Engineering United States University of Trento Department of Information Engineering and Computer Science Italy

ISBN: (纸本)9798331530099

The growing demand for advanced beyond 5G connectivity solutions explores the deployment of end-to-end 5G Non- Terrestrial Networks (NTNs) in cloud-native environments. With the increasing reliance on mobile communications, leveraging next-generation radio access network (NG-RAN) architectures with functional splits has become essential. In the 5G NTN network, some split NG-RAN components can be moved to the satellite node to improve connectivity and resilience. This paper investigates advanced beyond 5G connectivity by deploying end-to-end 5G Non-Terrestrial Networks (NTNs) in cloud-native environments, focusing on Low Earth Orbit (LEO) satellites operating in regenerative mode. Specifically, it explores the implementation of F1 and E1 interface splits within such networks. The first architecture extends the F1 interface over the satellite radio interface (F1 over SRI), linking terrestrial central units (gNB-CU) with satellite-based distributed units (gNB-DU). The second architecture incorporates both F1 and E1 splits, facilitating connections between terrestrial control plane units (gNB-CUCP) and user plane units (gNB-CUUP) on the satellite via the E1 interface over SRI (F1-E1 over SRI). The study's primary goal is to predict the resource utilization-specifically CPU, memory, and bandwidth-of gNB-DU and gNB-CUUP functioning as satellite payloads. Employing Long-Short-Term Memory (LSTM) neural networks, this research aims to enhance network resilience by enabling proactive monitoring and resource allocation decisions, addressing the significant computational and bandwidth demands of payloading gNB-CUUP compared to gNB-DU. © 2024 IEEE.

关键词： 5G mobile communication systems

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